Predicting 30-Day Hospital Readmissions in Acute Myocardial Infarction: The AMI "READMITS" (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus, Nonmale Sex, Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score.

PubMed

Nguyen, Oanh Kieu; Makam, Anil N; Clark, Christopher; Zhang, Song; Das, Sandeep R; Halm, Ethan A

2018-04-17

Readmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor-to-modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high-risk patients as early as possible during hospitalization. We used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all-cause nonelective 30-day readmissions, using stepwise backward selection and 5-fold cross-validation. Of 826 patients hospitalized with AMI, 13% had a 30-day readmission. The first-day AMI model (the AMI "READMITS" score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism-corrected C-statistic of 0.73 (95% confidence interval, 0.71-0.74) and was well calibrated. The full-stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism-corrected C-statistic of 0.75 (95% confidence interval, 0.74-0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models. The parsimonious AMI READMITS score enables early prospective identification of high-risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full-stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did not meaningfully improve reclassification. © 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Longitudinal predictive ability of mapping models: examining post-intervention EQ-5D utilities derived from baseline MHAQ data in rheumatoid arthritis patients.

PubMed

Kontodimopoulos, Nick; Bozios, Panagiotis; Yfantopoulos, John; Niakas, Dimitris

2013-04-01

The purpose of this methodological study was to to provide insight into the under-addressed issue of the longitudinal predictive ability of mapping models. Post-intervention predicted and reported utilities were compared, and the effect of disease severity on the observed differences was examined. A cohort of 120 rheumatoid arthritis (RA) patients (60.0% female, mean age 59.0) embarking on therapy with biological agents completed the Modified Health Assessment Questionnaire (MHAQ) and the EQ-5D at baseline, and at 3, 6 and 12 months post-intervention. OLS regression produced a mapping equation to estimate post-intervention EQ-5D utilities from baseline MHAQ data. Predicted and reported utilities were compared with t test, and the prediction error was modeled, using fixed effects, in terms of covariates such as age, gender, time, disease duration, treatment, RF, DAS28 score, predicted and reported EQ-5D. The OLS model (RMSE = 0.207, R(2) = 45.2%) consistently underestimated future utilities, with a mean prediction error of 6.5%. Mean absolute differences between reported and predicted EQ-5D utilities at 3, 6 and 12 months exceeded the typically reported MID of the EQ-5D (0.03). According to the fixed-effects model, time, lower predicted EQ-5D and higher DAS28 scores had a significant impact on prediction errors, which appeared increasingly negative for lower reported EQ-5D scores, i.e., predicted utilities tended to be lower than reported ones in more severe health states. This study builds upon existing research having demonstrated the potential usefulness of mapping disease-specific instruments onto utility measures. The specific issue of longitudinal validity is addressed, as mapping models derived from baseline patients need to be validated on post-therapy samples. The underestimation of post-treatment utilities in the present study, at least in more severe patients, warrants further research before it is prudent to conduct cost-utility analyses in the context of RA by means of the MHAQ alone.

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia.

PubMed

Vallat, Laurent; Kemper, Corey A; Jung, Nicolas; Maumy-Bertrand, Myriam; Bertrand, Frédéric; Meyer, Nicolas; Pocheville, Arnaud; Fisher, John W; Gribben, John G; Bahram, Seiamak

2013-01-08

Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions--notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverse-engineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method integrates the temporal dimension of biological rewiring of genetic programs and enables the prediction of the effect of targeted gene disruption at the system level. We tested the performance accuracy of this model on synthetic data before reverse-engineering the response of primary cancer cells to a proliferative (protumorigenic) stimulation in a multistate leukemia biological model (i.e., chronic lymphocytic leukemia). To validate the ability of our method to predict the effects of gene modulation on the global program, we performed an intervention experiment on a targeted gene. Comparison of the predicted and observed gene expression changes demonstrates the possibility of predicting the effects of a perturbation in a gene regulatory network, a first step toward an orientated intervention in a cancer cell genetic program.

Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions.

PubMed

Dong, Yuwen; Deshpande, Sunil; Rivera, Daniel E; Downs, Danielle S; Savage, Jennifer S

2014-06-01

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs

PubMed Central

McCarthy, John F.; Katz, Ira R.; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M.; Nielson, Christopher; Schoenbaum, Michael

2015-01-01

Objectives. The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Methods. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Results. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Conclusions. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions. PMID:26066914

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs.

PubMed

McCarthy, John F; Bossarte, Robert M; Katz, Ira R; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M; Nielson, Christopher; Schoenbaum, Michael

2015-09-01

The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions.

Predicting dental attendance from dental hygienists' autonomy support and patients' autonomous motivation: A randomised clinical trial.

PubMed

Halvari, Anne E Münster; Halvari, Hallgeir; Williams, Geoffrey C; Deci, Edward L

2017-02-01

To test the hypothesis that a Self-Determination Theory (SDT) intervention designed to promote oral health care competence in an autonomy-supportive way would predict change in caries competence relative to standard care. Further, to test the SDT process path-model hypotheses with: (1) the intervention and individual differences in relative autonomous locus of causality (RALOC) predicting increases in caries competence, which in turn would positively predict dental attendance; (2) RALOC negatively predicting dental anxiety, which would negatively predict dental attendance; (3) RALOC and caries disease referred to the dentist after an autonomy-supportive clinical exam directly positively predicting dental attendance; and (4) the intervention moderating the link between RALOC and dental attendance. A randomised two-group experiment was conducted at a dental clinic with 138 patients (M age  = 23.31 yr., SD = 3.5), with pre- and post-measures in a period of 5.5 months. The experimental model was supported. The SDT path model fit the data well and supported the hypotheses explaining 63% of the variance in dental attendance. Patients personality (RALOC) and hygienists promoting oral health care competence in an autonomy-supportive way, performance of autonomy-supportive clinical exams and reductions of anxiety for dental treatment have important practical implications for patients' dental attendance.

Interventions for the prediction and management of chronic postsurgical pain after total knee replacement: systematic review of randomised controlled trials

PubMed Central

Beswick, Andrew D; Wylde, Vikki; Gooberman-Hill, Rachael

2015-01-01

Objectives Total knee replacement can be a successful operation for pain relief. However, 10–34% of patients experience chronic postsurgical pain. Our aim was to synthesise evidence on the effectiveness of applying predictive models to guide preventive treatment, and for interventions in the management of chronic pain after total knee replacement. Setting We conducted a systematic review of randomised controlled trials using appropriate search strategies in the Cochrane Library, MEDLINE and EMBASE from inception to October 2014. No language restrictions were applied. Participants Adult patients receiving total knee replacement. Interventions Predictive models to guide treatment for prevention of chronic pain. Interventions for management of chronic pain. Primary and secondary outcome measures Reporting of specific outcomes was not an eligibility criterion but we sought outcomes relating to pain severity. Results No studies evaluated the effectiveness of predictive models in guiding treatment and improving outcomes after total knee replacement. One study evaluated an intervention for the management of chronic pain. The trial evaluated the use of a botulinum toxin A injection with antinociceptive and anticholinergic activity in 49 patients with chronic postsurgical pain after knee replacement. A single injection provided meaningful pain relief for about 40 days and the authors acknowledged the need for a large trial with repeated injections. No trials of multidisciplinary interventions or individualised treatments were identified. Conclusions Our systematic review highlights a lack of evidence about the effectiveness of prediction and management strategies for chronic postsurgical pain after total knee replacement. As a large number of people are affected by chronic pain after total knee replacement, development of an evidence base about care for these patients should be a research priority. PMID:25967998

Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers

PubMed Central

Street, Amy E.; Rosellini, Anthony J.; Ursano, Robert J.; Heeringa, Steven G.; Hill, Eric D.; Monahan, John; Naifeh, James A.; Petukhova, Maria V.; Reis, Ben Y.; Sampson, Nancy A.; Bliese, Paul D.; Stein, Murray B.; Zaslavsky, Alan M.; Kessler, Ronald C.

2016-01-01

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence, but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically-guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively-recorded (in the population) and self-reported (in a representative survey) victimization. Capture-recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the Receiver Operating Characteristic curve was .83−.88. 33.7-63.2% of victimizations occurred among soldiers in the highest-risk ventile (5%). This high concentration of risk suggests that the models could be useful in targeting preventive interventions, although final determination would require careful weighing of intervention costs, effectiveness, and competing risks. PMID:28154788

Risk prediction models for major adverse cardiac event (MACE) following percutaneous coronary intervention (PCI): A review

NASA Astrophysics Data System (ADS)

Manan, Norhafizah A.; Abidin, Basir

2015-02-01

Five percent of patients who went through Percutaneous Coronary Intervention (PCI) experienced Major Adverse Cardiac Events (MACE) after PCI procedure. Risk prediction of MACE following a PCI procedure therefore is helpful. This work describes a review of such prediction models currently in use. Literature search was done on PubMed and SCOPUS database. Thirty literatures were found but only 4 studies were chosen based on the data used, design, and outcome of the study. Particular emphasis was given and commented on the study design, population, sample size, modeling method, predictors, outcomes, discrimination and calibration of the model. All the models had acceptable discrimination ability (C-statistics >0.7) and good calibration (Hosmer-Lameshow P-value >0.05). Most common model used was multivariate logistic regression and most popular predictor was age.

Modeling health impact of global health programs implemented by Population Services International

PubMed Central

2013-01-01

Background Global health implementing organizations benefit most from health impact estimation models that isolate the individual effects of distributed products and services - a feature not typically found in intervention impact models, but which allow comparisons across interventions and intervention settings. Population Services International (PSI), a social marketing organization, has developed a set of impact models covering seven health program areas, which translate product/service distribution data into impact estimates. Each model's primary output is the number of disability-adjusted life-years (DALYs) averted by an intervention within a specific country and population context. This paper aims to describe the structure and inputs for two types of DALYs averted models, considering the benefits and limitations of this methodology. Methods PSI employs two modeling approaches for estimating health impact: a macro approach for most interventions and a micro approach for HIV, tuberculosis (TB), and behavior change communication (BCC) interventions. Within each intervention country context, the macro approach determines the coverage that one product/service unit provides a population in person-years, whereas the micro approach estimates an individual's risk of infection with and without the product/service unit. The models use these estimations to generate per unit DALYs averted coefficients for each intervention. When multiplied by program output data, these coefficients predict the total number of DALYs averted by an intervention in a country. Results Model outputs are presented by country for two examples: Water Chlorination DALYs Averted Model, a macro model, and the HIV Condom DALYs Averted Model for heterosexual transmission, a micro model. Health impact estimates measured in DALYs averted for PSI interventions on a global level are also presented. Conclusions The DALYs averted models offer implementing organizations practical measurement solutions for understanding an intervention's contribution to improving health. These models calculate health impact estimates that reflect the scale and diversity of program operations and intervention settings, and that enable comparisons across health areas and countries. Challenges remain in accounting for intervention synergies, attributing impact to a single organization, and sourcing and updating model inputs. Nevertheless, these models demonstrate how DALYs averted can be viably used by the global health community as a metric for predicting intervention impact using standard program output data. PMID:23902668

Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models: A Case Study in Type 1 Diabetes.

PubMed

Kruger, Jen; Pollard, Daniel; Basarir, Hasan; Thokala, Praveen; Cooke, Debbie; Clark, Marie; Bond, Rod; Heller, Simon; Brennan, Alan

2015-10-01

. Health economic modeling has paid limited attention to the effects that patients' psychological characteristics have on the effectiveness of treatments. This case study tests 1) the feasibility of incorporating psychological prediction models of treatment response within an economic model of type 1 diabetes, 2) the potential value of providing treatment to a subgroup of patients, and 3) the cost-effectiveness of providing treatment to a subgroup of responders defined using 5 different algorithms. . Multiple linear regressions were used to investigate relationships between patients' psychological characteristics and treatment effectiveness. Two psychological prediction models were integrated with a patient-level simulation model of type 1 diabetes. Expected value of individualized care analysis was undertaken. Five different algorithms were used to provide treatment to a subgroup of predicted responders. A cost-effectiveness analysis compared using the algorithms to providing treatment to all patients. . The psychological prediction models had low predictive power for treatment effectiveness. Expected value of individualized care results suggested that targeting education at responders could be of value. The cost-effectiveness analysis suggested, for all 5 algorithms, that providing structured education to a subgroup of predicted responders would not be cost-effective. . The psychological prediction models tested did not have sufficient predictive power to make targeting treatment cost-effective. The psychological prediction models are simple linear models of psychological behavior. Collection of data on additional covariates could potentially increase statistical power. . By collecting data on psychological variables before an intervention, we can construct predictive models of treatment response to interventions. These predictive models can be incorporated into health economic models to investigate more complex service delivery and reimbursement strategies. © The Author(s) 2015.

Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework.

PubMed

Smith, Morgan E; Singh, Brajendra K; Irvine, Michael A; Stolk, Wilma A; Subramanian, Swaminathan; Hollingsworth, T Déirdre; Michael, Edwin

2017-03-01

Mathematical models of parasite transmission provide powerful tools for assessing the impacts of interventions. Owing to complexity and uncertainty, no single model may capture all features of transmission and elimination dynamics. Multi-model ensemble modelling offers a framework to help overcome biases of single models. We report on the development of a first multi-model ensemble of three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and evaluate its predictive performance in comparison with that of the constituents using calibration and validation data from three case study sites, one each from the three major LF endemic regions: Africa, Southeast Asia and Papua New Guinea (PNG). We assessed the performance of the respective models for predicting the outcomes of annual MDA strategies for various baseline scenarios thought to exemplify the current endemic conditions in the three regions. The results show that the constructed multi-model ensemble outperformed the single models when evaluated across all sites. Single models that best fitted calibration data tended to do less well in simulating the out-of-sample, or validation, intervention data. Scenario modelling results demonstrate that the multi-model ensemble is able to compensate for variance between single models in order to produce more plausible predictions of intervention impacts. Our results highlight the value of an ensemble approach to modelling parasite control dynamics. However, its optimal use will require further methodological improvements as well as consideration of the organizational mechanisms required to ensure that modelling results and data are shared effectively between all stakeholders. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Predicting intention to attend and actual attendance at a universal parent-training programme: a comparison of social cognition models.

PubMed

Thornton, Sarah; Calam, Rachel

2011-07-01

The predictive validity of the Health Belief Model (HBM) and the Theory of Planned Behaviour (TPB) were examined in relation to 'intention to attend' and 'actual attendance' at a universal parent-training intervention for parents of children with behavioural difficulties. A validation and reliability study was conducted to develop two questionnaires (N = 108 parents of children aged 4-7).These questionnaires were then used to investigate the predictive validity of the two models in relation to 'intention to attend' and 'actual attendance' at a parent-training intervention ( N = 53 parents of children aged 4-7). Both models significantly predicted 'intention to attend a parent-training group'; however, the TPB accounted for more variance in the outcome variable compared to the HBM. Preliminary investigations highlighted that attendees were more likely to intend to attend the groups, have positive attitudes towards the groups, perceive important others as having positive attitudes towards the groups, and report elevated child problem behaviour scores. These findings provide useful information regarding the belief-based factors that affect attendance at universal parent-training groups. Possible interventions aimed at increasing 'intention to attend' and 'actual attendance' at parent-training groups are discussed.

Using kaizen to improve employee well-being: Results from two organizational intervention studies.

PubMed

von Thiele Schwarz, Ulrica; Nielsen, Karina M; Stenfors-Hayes, Terese; Hasson, Henna

2017-08-01

Participatory intervention approaches that are embedded in existing organizational structures may improve the efficiency and effectiveness of organizational interventions, but concrete tools are lacking. In the present article, we use a realist evaluation approach to explore the role of kaizen, a lean tool for participatory continuous improvement, in improving employee well-being in two cluster-randomized, controlled participatory intervention studies. Case 1 is from the Danish Postal Service, where kaizen boards were used to implement action plans. The results of multi-group structural equation modeling showed that kaizen served as a mechanism that increased the level of awareness of and capacity to manage psychosocial issues, which, in turn, predicted increased job satisfaction and mental health. Case 2 is from a regional hospital in Sweden that integrated occupational health processes with a pre-existing kaizen system. Multi-group structural equation modeling revealed that, in the intervention group, kaizen work predicted better integration of organizational and employee objectives after 12 months, which, in turn, predicted increased job satisfaction and decreased discomfort at 24 months. The findings suggest that participatory and structured problem-solving approaches that are familiar and visual to employees can facilitate organizational interventions.

Using kaizen to improve employee well-being: Results from two organizational intervention studies

PubMed Central

von Thiele Schwarz, Ulrica; Nielsen, Karina M; Stenfors-Hayes, Terese; Hasson, Henna

2016-01-01

Participatory intervention approaches that are embedded in existing organizational structures may improve the efficiency and effectiveness of organizational interventions, but concrete tools are lacking. In the present article, we use a realist evaluation approach to explore the role of kaizen, a lean tool for participatory continuous improvement, in improving employee well-being in two cluster-randomized, controlled participatory intervention studies. Case 1 is from the Danish Postal Service, where kaizen boards were used to implement action plans. The results of multi-group structural equation modeling showed that kaizen served as a mechanism that increased the level of awareness of and capacity to manage psychosocial issues, which, in turn, predicted increased job satisfaction and mental health. Case 2 is from a regional hospital in Sweden that integrated occupational health processes with a pre-existing kaizen system. Multi-group structural equation modeling revealed that, in the intervention group, kaizen work predicted better integration of organizational and employee objectives after 12 months, which, in turn, predicted increased job satisfaction and decreased discomfort at 24 months. The findings suggest that participatory and structured problem-solving approaches that are familiar and visual to employees can facilitate organizational interventions. PMID:28736455

Understanding Proximal-Distal Economic Projections of the Benefits of Childhood Preventive Interventions

PubMed Central

Slade, Eric P.; Becker, Kimberly D.

2014-01-01

This paper discusses the steps and decisions involved in proximal-distal economic modeling, in which social, behavioral, and academic outcomes data for children may be used to inform projections of the economic consequences of interventions. Economic projections based on proximal-distal modeling techniques may be used in cost-benefit analyses when information is unavailable for certain long term outcomes data in adulthood or to build entire cost-benefit analyses. Although examples of proximal-distal economic analyses of preventive interventions exist in policy reports prepared for governmental agencies, such analyses have rarely been completed in conjunction with research trials. The modeling decisions on which these prediction models are based are often opaque to policymakers and other end-users. This paper aims to illuminate some of the key steps and considerations involved in constructing proximal-distal prediction models and to provide examples and suggestions that may help guide future proximal-distal analyses. PMID:24337979

Additional challenges for uncertainty analysis in river engineering

NASA Astrophysics Data System (ADS)

Berends, Koen; Warmink, Jord; Hulscher, Suzanne

2016-04-01

The management of rivers for improving safety, shipping and environment requires conscious effort on the part of river managers. River engineers design hydraulic works to tackle various challenges, from increasing flow conveyance to ensuring minimal water depths for environmental flow and inland shipping. Last year saw the completion of such large scale river engineering in the 'Room for the River' programme for the Dutch Rhine River system, in which several dozen of human interventions were built to increase flood safety. Engineering works in rivers are not completed in isolation from society. Rather, their benefits - increased safety, landscaping beauty - and their disadvantages - expropriation, hindrance - directly affect inhabitants. Therefore river managers are required to carefully defend their plans. The effect of engineering works on river dynamics is being evaluated using hydraulic river models. Two-dimensional numerical models based on the shallow water equations provide the predictions necessary to make decisions on designs and future plans. However, like all environmental models, these predictions are subject to uncertainty. In recent years progress has been made in the identification of the main sources of uncertainty for hydraulic river models. Two of the most important sources are boundary conditions and hydraulic roughness (Warmink et al. 2013). The result of these sources of uncertainty is that the identification of single, deterministic prediction model is a non-trivial task. This is this is a well-understood problem in other fields as well - most notably hydrology - and known as equifinality. However, the particular case of human intervention modelling with hydraulic river models compounds the equifinality case. The model that provides the reference baseline situation is usually identified through calibration and afterwards modified for the engineering intervention. This results in two distinct models, the evaluation of which yields the effect of the proposed intervention. The implicit assumption underlying such analysis is that both models are commensurable. We hypothesize that they are commensurable only to a certain extent. In an idealised study we have demonstrated that prediction performance loss should be expected with increasingly large engineering works. When accounting for parametric uncertainty of floodplain roughness in model identification, we see uncertainty bounds for predicted effects of interventions increase with increasing intervention scale. Calibration of these types of models therefore seems to have a shelf-life, beyond which calibration does not longer improves prediction. Therefore a qualification scheme for model use is required that can be linked to model validity. In this study, we characterize model use along three dimensions: extrapolation (using the model with different external drivers), extension (using the model for different output or indicators) and modification (using modified models). Such use of models is expected to have implications for the applicability of surrogating modelling for efficient uncertainty analysis as well, which is recommended for future research. Warmink, J. J.; Straatsma, M. W.; Huthoff, F.; Booij, M. J. & Hulscher, S. J. M. H. 2013. Uncertainty of design water levels due to combined bed form and vegetation roughness in the Dutch river Waal. Journal of Flood Risk Management 6, 302-318 . DOI: 10.1111/jfr3.12014

A Computer Simulation Approach to Assessing Therapeutic Intervention Points for the Prevention of Cytokine-Induced Cartilage Breakdown

PubMed Central

Proctor, CJ; Macdonald, C; Milner, JM; Rowan, AD; Cawston, TE

2014-01-01

Objective To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions for reducing collagen release. Methods We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language, a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein levels and activity and initiate cartilage collagen breakdown. Simulations were performed using the COPASI software package. Results The model predicted that simulated inhibition of JNK or p38 MAPK, and overexpression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Overexpression of TIMP-1 was much less effective than that of TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of JAK-1, the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets that are downstream, such as the JNK pathway, rather than those that are close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion Our study shows the value of computer modeling as a tool for examining possible interventions by which to reduce cartilage collagen breakdown. The model predicts that interventions that either prevent transcription or inhibit the activity of collagenases are promising strategies and should be investigated further in an experimental setting. PMID:24757149

ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour.

PubMed

Evans, Stephanie; Alden, Kieran; Cucurull-Sanchez, Lourdes; Larminie, Christopher; Coles, Mark C; Kullberg, Marika C; Timmis, Jon

2017-02-01

A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model's sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor RORγt, is sufficient to drive switching of Th17 cells towards an IFN-γ-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from http://www.york.ac.uk/ycil/software.

A dynamical model for describing behavioural interventions for weight loss and body composition change

PubMed Central

Navarro-Barrientos, J.-Emeterio; Rivera, Daniel E.; Collins, Linda M.

2011-01-01

We present a dynamical model incorporating both physiological and psychological factors that predicts changes in body mass and composition during the course of a behavioral intervention for weight loss. The model consists of a three-compartment energy balance integrated with a mechanistic psychological model inspired by the Theory of Planned Behavior (TPB). The latter describes how important variables in a behavioural intervention can influence healthy eating habits and increased physical activity over time. The novelty of the approach lies in representing the behavioural intervention as a dynamical system, and the integration of the psychological and energy balance models. Two simulation scenarios are presented that illustrate how the model can improve the understanding of how changes in intervention components and participant differences affect outcomes. Consequently, the model can be used to inform behavioural scientists in the design of optimised interventions for weight loss and body composition change. PMID:21673826

Using Multiple Outcomes of Sexual Behavior to Provide Insights Into Chlamydia Transmission and the Effectiveness of Prevention Interventions in Adolescents.

PubMed

Enns, Eva Andrea; Kao, Szu-Yu; Kozhimannil, Katy Backes; Kahn, Judith; Farris, Jill; Kulasingam, Shalini L

2017-10-01

Mathematical models are important tools for assessing prevention and management strategies for sexually transmitted infections. These models are usually developed for a single infection and require calibration to observed epidemiological trends in the infection of interest. Incorporating other outcomes of sexual behavior into the model, such as pregnancy, may better inform the calibration process. We developed a mathematical model of chlamydia transmission and pregnancy in Minnesota adolescents aged 15 to 19 years. We calibrated the model to statewide rates of reported chlamydia cases alone (chlamydia calibration) and in combination with pregnancy rates (dual calibration). We evaluated the impact of calibrating to different outcomes of sexual behavior on estimated input parameter values, predicted epidemiological outcomes, and predicted impact of chlamydia prevention interventions. The two calibration scenarios produced different estimates of the probability of condom use, the probability of chlamydia transmission per sex act, the proportion of asymptomatic infections, and the screening rate among men. These differences resulted in the dual calibration scenario predicting lower prevalence and incidence of chlamydia compared with calibrating to chlamydia cases alone. When evaluating the impact of a 10% increase in condom use, the dual calibration scenario predicted fewer infections averted over 5 years compared with chlamydia calibration alone [111 (6.8%) vs 158 (8.5%)]. While pregnancy and chlamydia in adolescents are often considered separately, both are outcomes of unprotected sexual activity. Incorporating both as calibration targets in a model of chlamydia transmission resulted in different parameter estimates, potentially impacting the intervention effectiveness predicted by the model.

Predicting sugar-sweetened behaviours with theory of planned behaviour constructs: Outcome and process results from the SIPsmartER behavioural intervention.

PubMed

Zoellner, Jamie M; Porter, Kathleen J; Chen, Yvonnes; Hedrick, Valisa E; You, Wen; Hickman, Maja; Estabrooks, Paul A

2017-05-01

Guided by the theory of planned behaviour (TPB) and health literacy concepts, SIPsmartER is a six-month multicomponent intervention effective at improving SSB behaviours. Using SIPsmartER data, this study explores prediction of SSB behavioural intention (BI) and behaviour from TPB constructs using: (1) cross-sectional and prospective models and (2) 11 single-item assessments from interactive voice response (IVR) technology. Quasi-experimental design, including pre- and post-outcome data and repeated-measures process data of 155 intervention participants. Validated multi-item TPB measures, single-item TPB measures, and self-reported SSB behaviours. Hypothesised relationships were investigated using correlation and multiple regression models. TPB constructs explained 32% of the variance cross sectionally and 20% prospectively in BI; and explained 13-20% of variance cross sectionally and 6% prospectively. Single-item scale models were significant, yet explained less variance. All IVR models predicting BI (average 21%, range 6-38%) and behaviour (average 30%, range 6-55%) were significant. Findings are interpreted in the context of other cross-sectional, prospective and experimental TPB health and dietary studies. Findings advance experimental application of the TPB, including understanding constructs at outcome and process time points and applying theory in all intervention development, implementation and evaluation phases.

Effects of compassion meditation on a psychological model of charitable donation.

PubMed

Ashar, Yoni K; Andrews-Hanna, Jessica R; Yarkoni, Tal; Sills, Jenifer; Halifax, Joan; Dimidjian, Sona; Wager, Tor D

2016-08-01

Compassion is critical for societal wellbeing. Yet, it remains unclear how specific thoughts and feelings motivate compassionate behavior, and we lack a scientific understanding of how to effectively cultivate compassion. Here, we conducted 2 studies designed to a) develop a psychological model predicting compassionate behavior, and b) test this model as a mediator of a Compassion Meditation (CM) intervention and identify the "active ingredients" of CM. In Study 1, we developed a model predicting compassionate behavior, operationalized as real-money charitable donation, from a linear combination of self-reported tenderness, personal distress, perceived blamelessness, and perceived instrumental value of helping with high cross-validated accuracy, r = .67, p < .0001. Perceived similarity to suffering others did not predict charitable donation when controlling for other feelings and attributions. In Study 2, a randomized controlled trial, we tested the Study 1 model as a mediator of CM and investigated active ingredients. We compared a smartphone-based CM program to 2 conditions-placebo oxytocin and a Familiarity intervention-to control for expectancy effects, demand characteristics, and familiarity effects. Relative to control conditions, CM increased charitable donations, and changes in the Study 1 model of feelings and attributions mediated this effect (pab = .002). The Familiarity intervention led to decreases in primary outcomes, while placebo oxytocin had no significant effects on primary outcomes. Overall, this work contributes a quantitative model of compassionate behavior, and informs our understanding of the change processes and intervention components of CM. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Do causal concentration-response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality.

PubMed

Cox, Louis Anthony Tony

2017-08-01

Concentration-response (C-R) functions relating concentrations of pollutants in ambient air to mortality risks or other adverse health effects provide the basis for many public health risk assessments, benefits estimates for clean air regulations, and recommendations for revisions to existing air quality standards. The assumption that C-R functions relating levels of exposure and levels of response estimated from historical data usefully predict how future changes in concentrations would change risks has seldom been carefully tested. This paper critically reviews literature on C-R functions for fine particulate matter (PM2.5) and mortality risks. We find that most of them describe historical associations rather than valid causal models for predicting effects of interventions that change concentrations. The few papers that explicitly attempt to model causality rely on unverified modeling assumptions, casting doubt on their predictions about effects of interventions. A large literature on modern causal inference algorithms for observational data has been little used in C-R modeling. Applying these methods to publicly available data from Boston and the South Coast Air Quality Management District around Los Angeles shows that C-R functions estimated for one do not hold for the other. Changes in month-specific PM2.5 concentrations from one year to the next do not help to predict corresponding changes in average elderly mortality rates in either location. Thus, the assumption that estimated C-R relations predict effects of pollution-reducing interventions may not be true. Better causal modeling methods are needed to better predict how reducing air pollution would affect public health.

Factors Predicting Sustainability of the Schoolwide Positive Behavior Intervention Support Model

ERIC Educational Resources Information Center

Chitiyo, Jonathan; May, Michael E.

2018-01-01

The Schoolwide Positive Behavior Intervention Support model (SWPBIS) continues to gain widespread use across schools in the United States and abroad. Despite its widespread implementation, little research has examined factors that influence its sustainability. Informed by Rogers's diffusion theory, this study examined school personnel's…

Playing Active Video Games may not develop movement skills: An intervention trial.

PubMed

Barnett, Lisa M; Ridgers, Nicola D; Reynolds, John; Hanna, Lisa; Salmon, Jo

2015-01-01

To investigate the impact of playing sports Active Video Games on children's actual and perceived object control skills. Intervention children played Active Video Games for 6 weeks (1 h/week) in 2012. The Test of Gross Motor Development-2 assessed object control skill. The Pictorial Scale of Perceived Movement Skill Competence assessed perceived object control skill. Repeated measurements of object control and perceived object control were analysed for the whole sample, using linear mixed models, which included fixed effects for group (intervention or control) and time (pre and post) and their interaction. The first model adjusted for sex only and the second model also adjusted for age, and prior ball sports experience (yes/no). Seven mixed-gender focus discussions were conducted with intervention children after programme completion. Ninety-five Australian children (55% girls; 43% intervention group) aged 4 to 8 years (M 6.2, SD 0.95) participated. Object control skill improved over time (p = 0.006) but there was no significant difference (p = 0.913) between groups in improvement (predicted means: control 31.80 to 33.53, SED = 0.748; intervention 30.33 to 31.83, SED = 0.835). A similar result held for the second model. Similarly the intervention did not change perceived object control in Model 1 (predicted means: control: 19.08 to 18.68, SED = 0.362; intervention 18.67 to 18.88, SED = 0.406) or Model 2. Children found the intervention enjoyable, but most did not perceive direct equivalence between Active Video Games and 'real life' activities. Whilst Active Video Game play may help introduce children to sport, this amount of time playing is unlikely to build skill.

Playing Active Video Games may not develop movement skills: An intervention trial

PubMed Central

Barnett, Lisa M.; Ridgers, Nicola D.; Reynolds, John; Hanna, Lisa; Salmon, Jo

2015-01-01

Background: To investigate the impact of playing sports Active Video Games on children's actual and perceived object control skills. Methods: Intervention children played Active Video Games for 6 weeks (1 h/week) in 2012. The Test of Gross Motor Development-2 assessed object control skill. The Pictorial Scale of Perceived Movement Skill Competence assessed perceived object control skill. Repeated measurements of object control and perceived object control were analysed for the whole sample, using linear mixed models, which included fixed effects for group (intervention or control) and time (pre and post) and their interaction. The first model adjusted for sex only and the second model also adjusted for age, and prior ball sports experience (yes/no). Seven mixed-gender focus discussions were conducted with intervention children after programme completion. Results: Ninety-five Australian children (55% girls; 43% intervention group) aged 4 to 8 years (M 6.2, SD 0.95) participated. Object control skill improved over time (p = 0.006) but there was no significant difference (p = 0.913) between groups in improvement (predicted means: control 31.80 to 33.53, SED = 0.748; intervention 30.33 to 31.83, SED = 0.835). A similar result held for the second model. Similarly the intervention did not change perceived object control in Model 1 (predicted means: control: 19.08 to 18.68, SED = 0.362; intervention 18.67 to 18.88, SED = 0.406) or Model 2. Children found the intervention enjoyable, but most did not perceive direct equivalence between Active Video Games and ‘real life’ activities. Conclusions: Whilst Active Video Game play may help introduce children to sport, this amount of time playing is unlikely to build skill. PMID:26844136

Validation of Models Used to Inform Colorectal Cancer Screening Guidelines: Accuracy and Implications.

PubMed

Rutter, Carolyn M; Knudsen, Amy B; Marsh, Tracey L; Doria-Rose, V Paul; Johnson, Eric; Pabiniak, Chester; Kuntz, Karen M; van Ballegooijen, Marjolein; Zauber, Ann G; Lansdorp-Vogelaar, Iris

2016-07-01

Microsimulation models synthesize evidence about disease processes and interventions, providing a method for predicting long-term benefits and harms of prevention, screening, and treatment strategies. Because models often require assumptions about unobservable processes, assessing a model's predictive accuracy is important. We validated 3 colorectal cancer (CRC) microsimulation models against outcomes from the United Kingdom Flexible Sigmoidoscopy Screening (UKFSS) Trial, a randomized controlled trial that examined the effectiveness of one-time flexible sigmoidoscopy screening to reduce CRC mortality. The models incorporate different assumptions about the time from adenoma initiation to development of preclinical and symptomatic CRC. Analyses compare model predictions to study estimates across a range of outcomes to provide insight into the accuracy of model assumptions. All 3 models accurately predicted the relative reduction in CRC mortality 10 years after screening (predicted hazard ratios, with 95% percentile intervals: 0.56 [0.44, 0.71], 0.63 [0.51, 0.75], 0.68 [0.53, 0.83]; estimated with 95% confidence interval: 0.56 [0.45, 0.69]). Two models with longer average preclinical duration accurately predicted the relative reduction in 10-year CRC incidence. Two models with longer mean sojourn time accurately predicted the number of screen-detected cancers. All 3 models predicted too many proximal adenomas among patients referred to colonoscopy. Model accuracy can only be established through external validation. Analyses such as these are therefore essential for any decision model. Results supported the assumptions that the average time from adenoma initiation to development of preclinical cancer is long (up to 25 years), and mean sojourn time is close to 4 years, suggesting the window for early detection and intervention by screening is relatively long. Variation in dwell time remains uncertain and could have important clinical and policy implications. © The Author(s) 2016.

Analysis of Variables to Predict First Year Persistence Using Logistic Regression Analysis at the University of South Florida: Model v2.0

ERIC Educational Resources Information Center

Herreid, Charlene H.; Miller, Thomas E.

2009-01-01

This article is the fourth in a series of articles describing an attrition prediction and intervention project at the University of South Florida (USF) in Tampa. In this article, the researchers describe the updated version of the prediction model. The original model was developed from a sample of about 900 First Time in College (FTIC) students…

Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

PubMed Central

Brown, Marshall D.; Zhu, Kehao; Janes, Holly

2016-01-01

The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man’s risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics. PMID:27247223

Parameterising User Uptake in Economic Evaluations: The role of discrete choice experiments.

PubMed

Terris-Prestholt, Fern; Quaife, Matthew; Vickerman, Peter

2016-02-01

Model-based economic evaluations of new interventions have shown that user behaviour (uptake) is a critical driver of overall impact achieved. However, early economic evaluations, prior to introduction, often rely on assumed levels of uptake based on expert opinion or uptake of similar interventions. In addition to the likely uncertainty surrounding these uptake assumptions, they also do not allow for uptake to be a function of product, intervention, or user characteristics. This letter proposes using uptake projections from discrete choice experiments (DCE) to better parameterize uptake and substitution in cost-effectiveness models. A simple impact model is developed and illustrated using an example from the HIV prevention field in South Africa. Comparison between the conventional approach and the DCE-based approach shows that, in our example, DCE-based impact predictions varied by up to 50% from conventional estimates and provided far more nuanced projections. In the absence of observed uptake data and to model the effect of variations in intervention characteristics, DCE-based uptake predictions are likely to greatly improve models parameterizing uptake solely based on expert opinion. This is particularly important for global and national level decision making around introducing new and probably more expensive interventions, particularly where resources are most constrained. © 2016 The Authors. Health Economics published by John Wiley & Sons Ltd.

Technology Adoption Applied to Educational Settings: Predicting Interventionists' Use of Video-Self Modeling

ERIC Educational Resources Information Center

Heckman, Andrew R.

2010-01-01

Technology provides educators with a significant advantage in working with today's students. One particular application of technology for the purposes of academic and behavioral interventions is the use of video self-modeling (VSM). Although VSM is an evidence-based intervention, it is rarely used in educational settings. The present research…

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.

PubMed

Brown, Jeremiah R; MacKenzie, Todd A; Maddox, Thomas M; Fly, James; Tsai, Thomas T; Plomondon, Mary E; Nielson, Christopher D; Siew, Edward D; Resnic, Frederic S; Baker, Clifton R; Rumsfeld, John S; Matheny, Michael E

2015-12-11

Acute kidney injury (AKI) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do existing models account for risk factors prior to the index admission. We aimed to develop such a model for use in prospective automated surveillance programs in the Veterans Health Administration. We collected data on all patients undergoing cardiac catheterization or percutaneous coronary intervention in the Veterans Health Administration from January 01, 2009 to September 30, 2013, excluding patients with chronic dialysis, end-stage renal disease, renal transplant, and missing pre- and postprocedural creatinine measurement. We used 4 AKI definitions in model development and included risk factors from up to 1 year prior to the procedure and at presentation. We developed our prediction models for postprocedural AKI using the least absolute shrinkage and selection operator (LASSO) and internally validated using bootstrapping. We developed models using 115 633 angiogram procedures and externally validated using 27 905 procedures from a New England cohort. Models had cross-validated C-statistics of 0.74 (95% CI: 0.74-0.75) for AKI, 0.83 (95% CI: 0.82-0.84) for AKIN2, 0.74 (95% CI: 0.74-0.75) for contrast-induced nephropathy, and 0.89 (95% CI: 0.87-0.90) for dialysis. We developed a robust, externally validated clinical prediction model for AKI following cardiac catheterization or percutaneous coronary intervention to automatically identify high-risk patients before and immediately after a procedure in the Veterans Health Administration. Work is ongoing to incorporate these models into routine clinical practice. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Development of a dynamic computational model of social cognitive theory.

PubMed

Riley, William T; Martin, Cesar A; Rivera, Daniel E; Hekler, Eric B; Adams, Marc A; Buman, Matthew P; Pavel, Misha; King, Abby C

2016-12-01

Social cognitive theory (SCT) is among the most influential theories of behavior change and has been used as the conceptual basis of health behavior interventions for smoking cessation, weight management, and other health behaviors. SCT and other behavior theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly needed as new technologies allow for intensive longitudinal measures and interventions adapted from these inputs. These within-person explanatory theoretical applications can be modeled as dynamical systems. SCT constructs, such as reciprocal determinism, are inherently dynamical in nature, but SCT has not been modeled as a dynamical system. This paper describes the development of a dynamical system model of SCT using fluid analogies and control systems principles drawn from engineering. Simulations of this model were performed to assess if the model performed as predicted based on theory and empirical studies of SCT. This initial model generates precise and testable quantitative predictions for future intensive longitudinal research. Dynamic modeling approaches provide a rigorous method for advancing health behavior theory development and refinement and for guiding the development of more potent and efficient interventions.

Predicting sugar-sweetened behaviours with theory of planned behaviour constructs: Outcome and process results from the SIPsmartER behavioural intervention

PubMed Central

Zoellner, Jamie M.; Porter, Kathleen J.; Chen, Yvonnes; Hedrick, Valisa E.; You, Wen; Hickman, Maja; Estabrooks, Paul A.

2017-01-01

Objective Guided by the theory of planned behaviour (TPB) and health literacy concepts, SIPsmartER is a six-month multicomponent intervention effective at improving SSB behaviours. Using SIPsmartER data, this study explores prediction of SSB behavioural intention (BI) and behaviour from TPB constructs using: (1) cross-sectional and prospective models and (2) 11 single-item assessments from interactive voice response (IVR) technology. Design Quasi-experimental design, including pre- and post-outcome data and repeated-measures process data of 155 intervention participants. Main Outcome Measures Validated multi-item TPB measures, single-item TPB measures, and self-reported SSB behaviours. Hypothesised relationships were investigated using correlation and multiple regression models. Results TPB constructs explained 32% of the variance cross sectionally and 20% prospectively in BI; and explained 13–20% of variance cross sectionally and 6% prospectively. Single-item scale models were significant, yet explained less variance. All IVR models predicting BI (average 21%, range 6–38%) and behaviour (average 30%, range 6–55%) were significant. Conclusion Findings are interpreted in the context of other cross-sectional, prospective and experimental TPB health and dietary studies. Findings advance experimental application of the TPB, including understanding constructs at outcome and process time points and applying theory in all intervention development, implementation and evaluation phases. PMID:28165771

Towards an Effective Health Interventions Design: An Extension of the Health Belief Model

PubMed Central

Orji, Rita; Vassileva, Julita; Mandryk, Regan

2012-01-01

Introduction The recent years have witnessed a continuous increase in lifestyle related health challenges around the world. As a result, researchers and health practitioners have focused on promoting healthy behavior using various behavior change interventions. The designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. Several health behavior theories have been used to inform health intervention designs, such as the Theory of Planned Behavior, the Transtheoretical Model, and the Health Belief Model (HBM). However, the Health Belief Model (HBM), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories of health behavior. However, the effectiveness of this model is limited. The first limitation is the low predictive capacity (R2 < 0.21 on average) of existing HBM’s variables coupled with the small effect size of individual variables. The second is lack of clear rules of combination and relationship between the individual variables. In this paper, we propose a solution that aims at addressing these limitations as follows: (1) we extended the Health Belief Model by introducing four new variables: Self-identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance as possible determinants of healthy behavior. (2) We exhaustively explored the relationships/interactions between the HBM variables and their effect size. (3) We tested the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model. Methods: To achieve the objective of this paper, we conducted a quantitative study of 576 participants’ eating behavior. Data for this study were collected over a period of one year (from August 2011 to August 2012). The questionnaire consisted of validated scales assessing the HBM determinants – perceived benefit, barrier, susceptibility, severity, cue to action, and self-efficacy – using 7-point Likert scale. We also assessed other health determinants such as consideration of future consequences, self-identity, concern for appearance and perceived importance. To analyses our data, we employed factor analysis and Partial Least Square Structural Equation Model (PLS-SEM) to exhaustively explore the interaction/relationship between the determinants and healthy eating behavior. We tested for the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model and investigated possible mediating effects. Results: The results show that the three newly added determinants are better predictors of healthy behavior. Our extended HBM model lead to approximately 78% increase (from 40 to 71%) in predictive capacity compared to the old model. This shows the suitability of our extended HBM for use in predicting healthy behavior and in informing health intervention design. The results from examining possible relationships between the determinants in our model lead to an interesting discovery of some mediating relationships between the HBM’s determinants, therefore, shedding light on some possible combinations of determinants that could be employed by intervention designers to increase the effectiveness of their design. Conclusion: Consideration of future consequences, self-identity, concern for appearance, perceived importance, self-efficacy, perceived susceptibility are significant determinants of healthy eating behavior that can be manipulated by healthy eating intervention design. Most importantly, the result from our model established the existence of some mediating relationships among the determinants. The knowledge of both the direct and indirect relationships sheds some light on the possible combination rules. PMID:23569653

Causal Rasch models.

PubMed

Stenner, A Jackson; Fisher, William P; Stone, Mark H; Burdick, Donald S

2013-01-01

Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.

Causal Rasch models

PubMed Central

Stenner, A. Jackson; Fisher, William P.; Stone, Mark H.; Burdick, Donald S.

2013-01-01

Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained. PMID:23986726

A multilevel modeling approach to examining the implementation-effectiveness relationship of a behavior change intervention for health care professional trainees.

PubMed

Tomasone, Jennifer R; Sweet, Shane N; McReynolds, Stuart; Martin Ginis, Kathleen A

2017-09-01

Changing Minds, Changing Lives, a seminar-mediated behavior change intervention, aims to enhance health care professionals' (HCPs') social cognitions for discussing leisure-time physical activity (LTPA) with patients with physical disabilities. This study examines which seminar implementation variables (presenter characteristics, delivery components) predict effectiveness using multilevel modeling. HCP trainees (n = 564) attended 24 seminars and completed Theory of Planned Behavior-based measures for discussing LTPA at pre-, post-, 1-month post-, and 6-months post-seminar. Implementation variables were extracted from presenter-completed questionnaires/checklists. Seminars presented by a HCP predicted positive changes in all cognitions pre-post but negative changes in attitudes and perceived behavioral control (PBC) over follow-up (ps < .05). The number of seminars the presenter had delivered predicted negative changes in attitudes and PBC during follow-up (ps < .001). Inclusion of audiovisual components predicted positive changes in attitudes pre-post (p < .001). Presenter characteristics may be "key ingredients" to educational interventions for HCPs; however, future studies should examine additional implementation variables.

Examining the Predictive Validity of a Dynamic Assessment of Decoding to Forecast Response to Tier 2 Intervention

ERIC Educational Resources Information Center

Cho, Eunsoo; Compton, Donald L.; Fuchs, Douglas; Fuchs, Lynn S.; Bouton, Bobette

2014-01-01

The purpose of this study was to examine the role of a dynamic assessment (DA) of decoding in predicting responsiveness to Tier 2 small-group tutoring in a response-to-intervention model. First grade students (n = 134) who did not show adequate progress in Tier 1 based on 6 weeks of progress monitoring received Tier 2 small-group tutoring in…

Perspective-Taking, Position Power, and Third Party Intervention Style: A Classroom Application.

ERIC Educational Resources Information Center

Schneider, David E.

In order to understand how power affects other relationships, to offer an exploratory methodology for operationalizing an intervention typology, and to eventually develop a theoretical model that predicts affective influence on third party intervention modes in given conflict situations, a pilot study hypothesized that the frequency of preferred…

Applying a health action model to predict and improve healthy behaviors in coal miners.

PubMed

Vahedian-Shahroodi, Mohammad; Tehrani, Hadi; Mohammadi, Faeze; Gholian-Aval, Mahdi; Peyman, Nooshin

2018-05-01

One of the most important ways to prevent work-related diseases in occupations such as mining is to promote healthy behaviors among miners. This study aimed to predict and promote healthy behaviors among coal miners by using a health action model (HAM). The study was conducted on 200 coal miners in Iran in two steps. In the first step, a descriptive study was implemented to determine predictive constructs and effectiveness of HAM on behavioral intention. The second step involved a quasi-experimental study to determine the effect of an HAM-based education intervention. This intervention was implemented by the researcher and the head of the safety unit based on the predictive construct specified in the first step over 12 sessions of 60 min. The data was collected using an HAM questionnaire and a checklist of healthy behavior. The results of the first step of the study showed that attitude, belief, and normative constructs were meaningful predictors of behavioral intention. Also, the results of the second step revealed that the mean score of attitude and behavioral intention increased significantly after conducting the intervention in the experimental group, while the mean score of these constructs decreased significantly in the control group. The findings of this study showed that HAM-based educational intervention could improve the healthy behaviors of mine workers. Therefore, it is recommended to extend the application of this model to other working groups to improve healthy behaviors.

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020.

PubMed

Michael, Edwin; Singh, Brajendra K; Mayala, Benjamin K; Smith, Morgan E; Hampton, Scott; Nabrzyski, Jaroslaw

2017-09-27

There are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies. METHODS AND RESULTS: We show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries. We have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.

Social network influences on initiation and maintenance of reduced drinking among college students.

PubMed

Reid, Allecia E; Carey, Kate B; Merrill, Jennifer E; Carey, Michael P

2015-02-01

To determine whether (a) social networks influence the extent to which college students initiate and/or maintain reductions in drinking following an alcohol intervention and (b) students with riskier networks respond better to a counselor-delivered, vs. a computer-delivered, intervention. Mandated students (N = 316; 63% male) provided their perceptions of peer network members' drinking statuses (e.g., heavy drinker) and how accepting each friend would be if the participant reduced his or her drinking. Next, they were randomized to receive a brief motivational intervention (BMI) or Alcohol Edu for Sanctions (EDU). In latent growth models controlling for baseline levels on outcomes, influences of social networks on 2 phases of intervention response were examined: initiation of reductions in drinks per heaviest week, peak blood alcohol content (BAC), and consequences at 1 month (model intercepts) and maintenance of reductions between 1 and 12 months (model slopes). Peer drinking status predicted initiation of reductions in drinks per heaviest week and peak BAC; peer acceptability predicted initial reductions in consequences. Peer Acceptability × Condition interactions were significant or marginal for all outcomes in the maintenance phase. In networks with higher perceived acceptability of decreasing use, BMI and EDU exhibited similar growth rates. In less accepting networks, growth rates were significantly steeper among EDU than BMI participants. For consumption outcomes, lower perceived peer acceptability predicted steeper rates of growth in drinking among EDU but not BMI participants. Understanding how social networks influence behavior change and how interventions mitigate their influence is important for optimizing efficacy of alcohol interventions. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

A research model--forecasting incident rates from optimized safety program intervention strategies.

PubMed

Iyer, P S; Haight, J M; Del Castillo, E; Tink, B W; Hawkins, P W

2005-01-01

INTRODUCTION/PROBLEM: Property damage incidents, workplace injuries, and safety programs designed to prevent them, are expensive aspects of doing business in contemporary industry. The National Safety Council (2002) estimated that workplace injuries cost $146.6 billion per year. Because companies are resource limited, optimizing intervention strategies to decrease incidents with less costly programs can contribute to improved productivity. Systematic data collection methods were employed and the forecasting ability of a time-lag relationship between interventions and incident rates was studied using various statistical methods (an intervention is not expected to have an immediate nor infinitely lasting effect on the incident rate). As a follow up to the initial work, researchers developed two models designed to forecast incident rates. One is based on past incident rate performance and the other on the configuration and level of effort applied to the safety and health program. Researchers compared actual incident performance to the prediction capability of each model over 18 months in the forestry operations at an electricity distribution company and found the models to allow accurate prediction of incident rates. These models potentially have powerful implications as a business-planning tool for human resource allocation and for designing an optimized safety and health intervention program to minimize incidents. Depending on the mathematical relationship, one can determine what interventions, where and how much to apply them, and when to increase or reduce human resource input as determined by the forecasted performance.

Risk Prediction Models in Psychiatry: Toward a New Frontier for the Prevention of Mental Illnesses.

PubMed

Bernardini, Francesco; Attademo, Luigi; Cleary, Sean D; Luther, Charles; Shim, Ruth S; Quartesan, Roberto; Compton, Michael T

2017-05-01

We conducted a systematic, qualitative review of risk prediction models designed and tested for depression, bipolar disorder, generalized anxiety disorder, posttraumatic stress disorder, and psychotic disorders. Our aim was to understand the current state of research on risk prediction models for these 5 disorders and thus future directions as our field moves toward embracing prediction and prevention. Systematic searches of the entire MEDLINE electronic database were conducted independently by 2 of the authors (from 1960 through 2013) in July 2014 using defined search criteria. Search terms included risk prediction, predictive model, or prediction model combined with depression, bipolar, manic depressive, generalized anxiety, posttraumatic, PTSD, schizophrenia, or psychosis. We identified 268 articles based on the search terms and 3 criteria: published in English, provided empirical data (as opposed to review articles), and presented results pertaining to developing or validating a risk prediction model in which the outcome was the diagnosis of 1 of the 5 aforementioned mental illnesses. We selected 43 original research reports as a final set of articles to be qualitatively reviewed. The 2 independent reviewers abstracted 3 types of data (sample characteristics, variables included in the model, and reported model statistics) and reached consensus regarding any discrepant abstracted information. Twelve reports described models developed for prediction of major depressive disorder, 1 for bipolar disorder, 2 for generalized anxiety disorder, 4 for posttraumatic stress disorder, and 24 for psychotic disorders. Most studies reported on sensitivity, specificity, positive predictive value, negative predictive value, and area under the (receiver operating characteristic) curve. Recent studies demonstrate the feasibility of developing risk prediction models for psychiatric disorders (especially psychotic disorders). The field must now advance by (1) conducting more large-scale, longitudinal studies pertaining to depression, bipolar disorder, anxiety disorders, and other psychiatric illnesses; (2) replicating and carrying out external validations of proposed models; (3) further testing potential selective and indicated preventive interventions; and (4) evaluating effectiveness of such interventions in the context of risk stratification using risk prediction models. © Copyright 2017 Physicians Postgraduate Press, Inc.

A Structural Evaluation of a Large-Scale Quasi-Experimental Microfinance Initiative.

PubMed

Kaboski, Joseph P; Townsend, Robert M

2011-09-01

This paper uses a structural model to understand, predict, and evaluate the impact of an exogenous microcredit intervention program, the Thai Million Baht Village Fund program. We model household decisions in the face of borrowing constraints, income uncertainty, and high-yield indivisible investment opportunities. After estimation of parameters using pre-program data, we evaluate the model's ability to predict and interpret the impact of the village fund intervention. Simulations from the model mirror the data in yielding a greater increase in consumption than credit, which is interpreted as evidence of credit constraints. A cost-benefit analysis using the model indicates that some households value the program much more than its per household cost, but overall the program costs 20 percent more than the sum of these benefits.

Implementation of brief alcohol interventions by nurses in primary care: do non-clinical factors influence practice?

PubMed

Lock, Catherine A; Kaner, Eileen F S

2004-06-01

In the UK, GPs and practice nurses selectively provide brief alcohol interventions to risk drinkers. GPs' provision of a brief alcohol intervention can be predicted by patient characteristics, practitioner characteristics and structural factors such as the features of the practice and how it is organized. However, much less is known about possible modifiers of nurse practice. Our aim was to investigate if patient characteristics, nurse characteristics and practice factors influence provision of a brief alcohol intervention by practice nurses in primary health care. One hundred and twenty-eight practice nurses who had implemented a brief alcohol intervention programme in a previous trial based in the North of England were requested to screen adults presenting to their surgery and follow a structured protocol to give a brief intervention (5 min of advice plus an information booklet) to all 'risk' drinkers. Anonymized carbon copies of 5541 completed Alcohol Use Disorders Identification Test (AUDIT) screening questionnaires were collected after a 3-month implementation period and analysed by logistic regression analysis. Although AUDIT identified 1500 'risk' drinkers, only 926 (62%) received a brief intervention. Logistic regression modelling showed that patients' risk status as measured by AUDIT score was the most influential predictor of a brief intervention by practice nurses. However, risk drinkers who were most likely to receive a brief intervention were male. Patients' age or social class did not independently predict a brief intervention. The multilevel model was unable to identify any independent nurse characteristics that could predict a brief intervention, but indicated significant variation between nurses in their tendency to offer the intervention to patients. No structural factors were found to be positively associated with selective provision. Patient and nurse factors contributed to the selective provision of a brief intervention in primary care. If patients are to experience the beneficial effects of a brief alcohol intervention, then there is a need to improve the accuracy of delivery.

A Risk-based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

PubMed Central

Zafra-Cabeza, Ascensión; Rivera, Daniel E.; Collins, Linda M.; Ridao, Miguel A.; Camacho, Eduardo F.

2010-01-01

This paper examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based Model Predictive Control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm. PMID:21643450

Healthy work revisited: do changes in time strain predict well-being?

PubMed

Moen, Phyllis; Kelly, Erin L; Lam, Jack

2013-04-01

Building on Karasek and Theorell (R. Karasek & T. Theorell, 1990, Healthy work: Stress, productivity, and the reconstruction of working life, New York, NY: Basic Books), we theorized and tested the relationship between time strain (work-time demands and control) and seven self-reported health outcomes. We drew on survey data from 550 employees fielded before and 6 months after the implementation of an organizational intervention, the results only work environment (ROWE) in a white-collar organization. Cross-sectional (wave 1) models showed psychological time demands and time control measures were related to health outcomes in expected directions. The ROWE intervention did not predict changes in psychological time demands by wave 2, but did predict increased time control (a sense of time adequacy and schedule control). Statistical models revealed increases in psychological time demands and time adequacy predicted changes in positive (energy, mastery, psychological well-being, self-assessed health) and negative (emotional exhaustion, somatic symptoms, psychological distress) outcomes in expected directions, net of job and home demands and covariates. This study demonstrates the value of including time strain in investigations of the health effects of job conditions. Results encourage longitudinal models of change in psychological time demands as well as time control, along with the development and testing of interventions aimed at reducing time strain in different populations of workers.

Predicting performance in a first engineering calculus course: implications for interventions

NASA Astrophysics Data System (ADS)

Hieb, Jeffrey L.; Lyle, Keith B.; Ralston, Patricia A. S.; Chariker, Julia

2015-01-01

At the University of Louisville, a large, urban institution in the south-east United States, undergraduate engineering students take their mathematics courses from the school of engineering. In the fall of their freshman year, engineering students take Engineering Analysis I, a calculus-based engineering analysis course. After the first two weeks of the semester, many students end up leaving Engineering Analysis I and moving to a mathematics intervention course. In an effort to retain more students in Engineering Analysis I, the department collaborated with university academic support services to create a summer intervention programme. Students were targeted for the summer programme based on their score on an algebra readiness exam (ARE). In a previous study, the ARE scores were found to be a significant predictor of retention and performance in Engineering Analysis I. This study continues that work, analysing data from students who entered the engineering school in the fall of 2012. The predictive validity of the ARE was verified, and a hierarchical linear regression model was created using math American College Testing (ACT) scores, ARE scores, summer intervention participation, and several metacognitive and motivational factors as measured by subscales of the Motivated Strategies for Learning Questionnaire. In the regression model, ARE score explained an additional 5.1% of the variation in exam performance in Engineering Analysis I beyond math ACT score. Students took the ARE before and after the summer interventions and scores were significantly higher following the intervention. However, intervention participants nonetheless had lower exam scores in Engineering Analysis I. The following factors related to motivation and learning strategies were found to significantly predict exam scores in Engineering Analysis I: time and study environment management, internal goal orientation, and test anxiety. The adjusted R2 for the full model was 0.42, meaning that the model could explain 42% of the variation in Engineering Analysis I exam scores.

Predicting healthcare trajectories from medical records: A deep learning approach.

PubMed

Pham, Trang; Tran, Truyen; Phung, Dinh; Venkatesh, Svetha

2017-05-01

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules.

PubMed

Zhang, Man; Zhuo, Na; Guo, Zhanlin; Zhang, Xingguang; Liang, Wenhua; Zhao, Sheng; He, Jianxing

2015-10-01

The aim of this study was to establish a model for predicting the probability of malignancy in solitary pulmonary nodules (SPNs) and provide guidance for the diagnosis and follow-up intervention of SPNs. We retrospectively analyzed the clinical data and computed tomography (CT) images of 294 patients with a clear pathological diagnosis of SPN. Multivariate logistic regression analysis was used to screen independent predictors of the probability of malignancy in the SPN and to establish a model for predicting malignancy in SPNs. Then, another 120 SPN patients who did not participate in the model establishment were chosen as group B and used to verify the accuracy of the prediction model. Multivariate logistic regression analysis showed that there were significant differences in age, smoking history, maximum diameter of nodules, spiculation, clear borders, and Cyfra21-1 levels between subgroups with benign and malignant SPNs (P<0.05). These factors were identified as independent predictors of malignancy in SPNs. The area under the curve (AUC) was 0.910 [95% confidence interval (CI), 0.857-0.963] in model with Cyfra21-1 significantly better than 0.812 (95% CI, 0.763-0.861) in model without Cyfra21-1 (P=0.008). The area under receiver operating characteristic (ROC) curve of our model is significantly higher than the Mayo model, VA model and Peking University People's (PKUPH) model. Our model (AUC =0.910) compared with Brock model (AUC =0.878, P=0.350), the difference was not statistically significant. The model added Cyfra21-1 could improve prediction. The prediction model established in this study can be used to assess the probability of malignancy in SPNs, thereby providing help for the diagnosis of SPNs and the selection of follow-up interventions.

Derivation and Evaluation of a Risk-Scoring Tool to Predict Participant Attrition in a Lifestyle Intervention Project.

PubMed

Jiang, Luohua; Yang, Jing; Huang, Haixiao; Johnson, Ann; Dill, Edward J; Beals, Janette; Manson, Spero M; Roubideaux, Yvette

2016-05-01

Participant attrition in clinical trials and community-based interventions is a serious, common, and costly problem. In order to develop a simple predictive scoring system that can quantify the risk of participant attrition in a lifestyle intervention project, we analyzed data from the Special Diabetes Program for Indians Diabetes Prevention Program (SDPI-DP), an evidence-based lifestyle intervention to prevent diabetes in 36 American Indian and Alaska Native communities. SDPI-DP participants were randomly divided into a derivation cohort (n = 1600) and a validation cohort (n = 801). Logistic regressions were used to develop a scoring system from the derivation cohort. The discriminatory power and calibration properties of the system were assessed using the validation cohort. Seven independent factors predicted program attrition: gender, age, household income, comorbidity, chronic pain, site's user population size, and average age of site staff. Six factors predicted long-term attrition: gender, age, marital status, chronic pain, site's user population size, and average age of site staff. Each model exhibited moderate to fair discriminatory power (C statistic in the validation set: 0.70 for program attrition, and 0.66 for long-term attrition) and excellent calibration. The resulting scoring system offers a low-technology approach to identify participants at elevated risk for attrition in future similar behavioral modification intervention projects, which may inform appropriate allocation of retention resources. This approach also serves as a model for other efforts to prevent participant attrition.

Interventions for the prediction and management of chronic postsurgical pain after total knee replacement: systematic review of randomised controlled trials.

PubMed

Beswick, Andrew D; Wylde, Vikki; Gooberman-Hill, Rachael

2015-05-12

Total knee replacement can be a successful operation for pain relief. However, 10-34% of patients experience chronic postsurgical pain. Our aim was to synthesise evidence on the effectiveness of applying predictive models to guide preventive treatment, and for interventions in the management of chronic pain after total knee replacement. We conducted a systematic review of randomised controlled trials using appropriate search strategies in the Cochrane Library, MEDLINE and EMBASE from inception to October 2014. No language restrictions were applied. Adult patients receiving total knee replacement. Predictive models to guide treatment for prevention of chronic pain. Interventions for management of chronic pain. Reporting of specific outcomes was not an eligibility criterion but we sought outcomes relating to pain severity. No studies evaluated the effectiveness of predictive models in guiding treatment and improving outcomes after total knee replacement. One study evaluated an intervention for the management of chronic pain. The trial evaluated the use of a botulinum toxin A injection with antinociceptive and anticholinergic activity in 49 patients with chronic postsurgical pain after knee replacement. A single injection provided meaningful pain relief for about 40 days and the authors acknowledged the need for a large trial with repeated injections. No trials of multidisciplinary interventions or individualised treatments were identified. Our systematic review highlights a lack of evidence about the effectiveness of prediction and management strategies for chronic postsurgical pain after total knee replacement. As a large number of people are affected by chronic pain after total knee replacement, development of an evidence base about care for these patients should be a research priority. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Predicting individual affect of health interventions to reduce HPV prevalence.

PubMed

Corley, Courtney D; Mihalcea, Rada; Mikler, Armin R; Sanfilippo, Antonio P

2011-01-01

Recently, human papilloma virus (HPV) has been implicated to cause several throat and oral cancers and HPV is established to cause most cervical cancers. A human papilloma virus vaccine has been proven successful to reduce infection incidence in FDA clinical trials, and it is currently available in the USA. Current intervention policy targets adolescent females for vaccination; however, the expansion of suggested guidelines may extend to other age groups and males as well. This research takes a first step toward automatically predicting personal beliefs, regarding health intervention, on the spread of disease. Using linguistic or statistical approaches, sentiment analysis determines a text's affective content. Self-reported HPV vaccination beliefs published in web and social media are analyzed for affect polarity and leveraged as knowledge inputs to epidemic models. With this in mind, we have developed a discrete-time model to facilitate predicting impact on the reduction of HPV prevalence due to arbitrary age- and gender-targeted vaccination schemes.

Predicting Individual Affect of Health Interventions to Reduce HPV Prevalence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corley, Courtney D.; Mihalcea, Rada; Mikler, Armin R.

Recently, human papilloma virus has been implicated to cause several throat and oral cancers and hpv is established to cause most cervical cancers. A human papilloma virus vaccine has been proven successful to reduce infection incidence in FDA clinical trials and it is currently available in the United States. Current intervention policy targets adolescent females for vaccination; however, the expansion of suggested guidelines may extend to other age groups and males as well. This research takes a first step towards automatically predicting personal beliefs, regarding health intervention, on the spread of disease. Using linguistic or statistical approaches, sentiment analysis determinesmore » a texts affective content. Self-reported HPV vaccination beliefs published in web and social media are analyzed for affect polarity and leveraged as knowledge inputs to epidemic models. With this in mind, we have developed a discrete-time model to facilitate predicting impact on the reduction of HPV prevalence due to arbitrary age and gender targeted vaccination schemes.« less

Mathematical Modeling of the Transmission Dynamics of Clostridium difficile Infection and Colonization in Healthcare Settings: A Systematic Review

PubMed Central

Gingras, Guillaume; Guertin, Marie-Hélène; Laprise, Jean-François; Drolet, Mélanie; Brisson, Marc

2016-01-01

Background We conducted a systematic review of mathematical models of transmission dynamic of Clostridium difficile infection (CDI) in healthcare settings, to provide an overview of existing models and their assessment of different CDI control strategies. Methods We searched MEDLINE, EMBASE and Web of Science up to February 3, 2016 for transmission-dynamic models of Clostridium difficile in healthcare settings. The models were compared based on their natural history representation of Clostridium difficile, which could include health states (S-E-A-I-R-D: Susceptible-Exposed-Asymptomatic-Infectious-Resistant-Deceased) and the possibility to include healthcare workers and visitors (vectors of transmission). Effectiveness of interventions was compared using the relative reduction (compared to no intervention or current practice) in outcomes such as incidence of colonization, CDI, CDI recurrence, CDI mortality, and length of stay. Results Nine studies describing six different models met the inclusion criteria. Over time, the models have generally increased in complexity in terms of natural history and transmission dynamics and number/complexity of interventions/bundles of interventions examined. The models were categorized into four groups with respect to their natural history representation: S-A-I-R, S-E-A-I, S-A-I, and S-E-A-I-R-D. Seven studies examined the impact of CDI control strategies. Interventions aimed at controlling the transmission, lowering CDI vulnerability and reducing the risk of recurrence/mortality were predicted to reduce CDI incidence by 3–49%, 5–43% and 5–29%, respectively. Bundles of interventions were predicted to reduce CDI incidence by 14–84%. Conclusions Although CDI is a major public health problem, there are very few published transmission-dynamic models of Clostridium difficile. Published models vary substantially in the interventions examined, the outcome measures used and the representation of the natural history of Clostridium difficile, which make it difficult to synthesize results and provide a clear picture of optimal intervention strategies. Future modeling efforts should pay specific attention to calibration, structural uncertainties, and transparent reporting practices. PMID:27690247

Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.

PubMed

Miovský, Michal; Vonkova, Hana; Čablová, Lenka; Gabrhelík, Roman

2015-11-01

To study the effect of a universal prevention intervention targeting cannabis use in individual children with different risk profiles. A school-based randomized controlled prevention trial was conducted over a period of 33 months (n=1874 sixth-graders, baseline mean age 11.82). We used a two-level random intercept logistic model for panel data to predict the probabilities of cannabis use for each child. Specifically, we used eight risk/protective factors to characterize each child and then predicted two probabilities of cannabis use for each child if the child had the intervention or not. Using the two probabilities, we calculated the absolute and relative effect of the intervention for each child. According to the two probabilities, we also divided the sample into a low-risk group (the quarter of the children with the lowest probabilities), a moderate-risk group, and a high-risk group (the quarter of the children with the highest probabilities) and showed the average effect of the intervention on these groups. The differences between the intervention group and the control group were statistically significant in each risk group. The average predicted probabilities of cannabis use for a child from the low-risk group were 4.3% if the child had the intervention and 6.53% if no intervention was provided. The corresponding probabilities for a child from the moderate-risk group were 10.91% and 15.34% and for a child from the high-risk group 25.51% and 32.61%. School grades, thoughts of hurting oneself, and breaking the rules were the three most important factors distinguishing high-risk and low-risk children. We predicted the effect of the intervention on individual children, characterized by their risk/protective factors. The predicted absolute effect and relative effect of any intervention for any selected risk/protective profile of a given child may be utilized in both prevention practice and research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Designing a Minimal Intervention Strategy to Control Taenia solium.

PubMed

Lightowlers, Marshall W; Donadeu, Meritxell

2017-06-01

Neurocysticercosis is an important cause of epilepsy in many developing countries. The disease is a zoonosis caused by the cestode parasite Taenia solium. Many potential intervention strategies are available, however none has been able to be implemented and sustained. Here we predict the impact of some T. solium interventions that could be applied to prevent transmission through pigs, the parasite's natural animal intermediate host. These include minimal intervention strategies that are predicted to be effective and likely to be feasible. Logical models are presented which reflect changes in the risk that age cohorts of animals have for their potential to transmit T. solium. Interventions that include a combined application of vaccination, plus chemotherapy in young animals, are the most effective. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention

NASA Astrophysics Data System (ADS)

Winder, Anthony J.; Siemonsen, Susanne; Flottmann, Fabian; Fiehler, Jens; Forkert, Nils D.

2017-03-01

Voxel-based tissue outcome prediction in acute ischemic stroke patients is highly relevant for both clinical routine and research. Previous research has shown that features extracted from baseline multi-parametric MRI datasets have a high predictive value and can be used for the training of classifiers, which can generate tissue outcome predictions for both intravenous and conservative treatments. However, with the recent advent and popularization of intra-arterial thrombectomy treatment, novel research specifically addressing the utility of predictive classi- fiers for thrombectomy intervention is necessary for a holistic understanding of current stroke treatment options. The aim of this work was to develop three clinically viable tissue outcome prediction models using approximate nearest-neighbor, generalized linear model, and random decision forest approaches and to evaluate the accuracy of predicting tissue outcome after intra-arterial treatment. Therefore, the three machine learning models were trained, evaluated, and compared using datasets of 42 acute ischemic stroke patients treated with intra-arterial thrombectomy. Classifier training utilized eight voxel-based features extracted from baseline MRI datasets and five global features. Evaluation of classifier-based predictions was performed via comparison to the known tissue outcome, which was determined in follow-up imaging, using the Dice coefficient and leave-on-patient-out cross validation. The random decision forest prediction model led to the best tissue outcome predictions with a mean Dice coefficient of 0.37. The approximate nearest-neighbor and generalized linear model performed equally suboptimally with average Dice coefficients of 0.28 and 0.27 respectively, suggesting that both non-linearity and machine learning are desirable properties of a classifier well-suited to the intra-arterial tissue outcome prediction problem.

Cost effectiveness of nutrition support in the prevention of pressure ulcer in hospitals.

PubMed

Banks, M D; Graves, N; Bauer, J D; Ash, S

2013-01-01

This study estimates the economic outcomes of a nutrition intervention to at-risk patients compared with standard care in the prevention of pressure ulcer. Statistical models were developed to predict 'cases of pressure ulcer avoided', 'number of bed days gained' and 'change to economic costs' in public hospitals in 2002-2003 in Queensland, Australia. Input parameters were specified and appropriate probability distributions fitted for: number of discharges per annum; incidence rate for pressure ulcer; independent effect of pressure ulcer on length of stay; cost of a bed day; change in risk in developing a pressure ulcer associated with nutrition support; annual cost of the provision of a nutrition support intervention for at-risk patients. A total of 1000 random re-samples were made and the results expressed as output probability distributions. The model predicts a mean 2896 (s.d. 632) cases of pressure ulcer avoided; 12, 397 (s.d. 4491) bed days released and corresponding mean economic cost saving of euros 2 869 526 (s.d. 2 078 715) with a nutrition support intervention, compared with standard care. Nutrition intervention is predicted to be a cost-effective approach in the prevention of pressure ulcer in at-risk patients.

Quantitative Microbial Risk Assessment for Escherichia coli O157:H7 in Fresh-Cut Lettuce.

PubMed

Pang, Hao; Lambertini, Elisabetta; Buchanan, Robert L; Schaffner, Donald W; Pradhan, Abani K

2017-02-01

Leafy green vegetables, including lettuce, are recognized as potential vehicles for foodborne pathogens such as Escherichia coli O157:H7. Fresh-cut lettuce is potentially at high risk of causing foodborne illnesses, as it is generally consumed without cooking. Quantitative microbial risk assessments (QMRAs) are gaining more attention as an effective tool to assess and control potential risks associated with foodborne pathogens. This study developed a QMRA model for E. coli O157:H7 in fresh-cut lettuce and evaluated the effects of different potential intervention strategies on the reduction of public health risks. The fresh-cut lettuce production and supply chain was modeled from field production, with both irrigation water and soil as initial contamination sources, to consumption at home. The baseline model (with no interventions) predicted a mean probability of 1 illness per 10 million servings and a mean of 2,160 illness cases per year in the United States. All intervention strategies evaluated (chlorine, ultrasound and organic acid, irradiation, bacteriophage, and consumer washing) significantly reduced the estimated mean number of illness cases when compared with the baseline model prediction (from 11.4- to 17.9-fold reduction). Sensitivity analyses indicated that retail and home storage temperature were the most important factors affecting the predicted number of illness cases. The developed QMRA model provided a framework for estimating risk associated with consumption of E. coli O157:H7-contaminated fresh-cut lettuce and can guide the evaluation and development of intervention strategies aimed at reducing such risk.

A Structural Evaluation of a Large-Scale Quasi-Experimental Microfinance Initiative

PubMed Central

Kaboski, Joseph P.; Townsend, Robert M.

2010-01-01

This paper uses a structural model to understand, predict, and evaluate the impact of an exogenous microcredit intervention program, the Thai Million Baht Village Fund program. We model household decisions in the face of borrowing constraints, income uncertainty, and high-yield indivisible investment opportunities. After estimation of parameters using pre-program data, we evaluate the model’s ability to predict and interpret the impact of the village fund intervention. Simulations from the model mirror the data in yielding a greater increase in consumption than credit, which is interpreted as evidence of credit constraints. A cost-benefit analysis using the model indicates that some households value the program much more than its per household cost, but overall the program costs 20 percent more than the sum of these benefits. PMID:22162594

Return to Work After Lumbar Microdiscectomy - Personalizing Approach Through Predictive Modeling.

PubMed

Papić, Monika; Brdar, Sanja; Papić, Vladimir; Lončar-Turukalo, Tatjana

2016-01-01

Lumbar disc herniation (LDH) is the most common disease among working population requiring surgical intervention. This study aims to predict the return to work after operative treatment of LDH based on the observational study including 153 patients. The classification problem was approached using decision trees (DT), support vector machines (SVM) and multilayer perception (MLP) combined with RELIEF algorithm for feature selection. MLP provided best recall of 0.86 for the class of patients not returning to work, which combined with the selected features enables early identification and personalized targeted interventions towards subjects at risk of prolonged disability. The predictive modeling indicated at the most decisive risk factors in prolongation of work absence: psychosocial factors, mobility of the spine and structural changes of facet joints and professional factors including standing, sitting and microclimate.

Contact tracing of tuberculosis: a systematic review of transmission modelling studies.

PubMed

Begun, Matt; Newall, Anthony T; Marks, Guy B; Wood, James G

2013-01-01

The WHO recommended intervention of Directly Observed Treatment, Short-course (DOTS) appears to have been less successful than expected in reducing the burden of TB in some high prevalence settings. One strategy for enhancing DOTS is incorporating active case-finding through screening contacts of TB patients as widely used in low-prevalence settings. Predictive models that incorporate population-level effects on transmission provide one means of predicting impacts of such interventions. We aim to identify all TB transmission modelling studies addressing contact tracing and to describe and critically assess their modelling assumptions, parameter choices and relevance to policy. We searched MEDLINE, SCOPUS, COMPENDEX, Google Scholar and Web of Science databases for relevant English language publications up to February 2012. Of the 1285 studies identified, only 5 studies met our inclusion criteria of models of TB transmission dynamics in human populations designed to incorporate contact tracing as an intervention. Detailed implementation of contact processes was only present in two studies, while only one study presented a model for a high prevalence, developing world setting. Some use of relevant data for parameter estimation was made in each study however validation of the predicted impact of interventions was not attempted in any of the studies. Despite a large body of literature on TB transmission modelling, few published studies incorporate contact tracing. There is considerable scope for future analyses to make better use of data and to apply individual based models to facilitate more realistic patterns of infectious contact. Combined with a focus on high burden settings this would greatly increase the potential for models to inform the use of contract tracing as a TB control policy. Our findings highlight the potential for collaborative work between clinicians, epidemiologists and modellers to gather data required to enhance model development and validation and hence better inform future public health policy.

Contact Tracing of Tuberculosis: A Systematic Review of Transmission Modelling Studies

PubMed Central

Begun, Matt; Newall, Anthony T.; Marks, Guy B.; Wood, James G.

2013-01-01

The WHO recommended intervention of Directly Observed Treatment, Short-course (DOTS) appears to have been less successful than expected in reducing the burden of TB in some high prevalence settings. One strategy for enhancing DOTS is incorporating active case-finding through screening contacts of TB patients as widely used in low-prevalence settings. Predictive models that incorporate population-level effects on transmission provide one means of predicting impacts of such interventions. We aim to identify all TB transmission modelling studies addressing contact tracing and to describe and critically assess their modelling assumptions, parameter choices and relevance to policy. We searched MEDLINE, SCOPUS, COMPENDEX, Google Scholar and Web of Science databases for relevant English language publications up to February 2012. Of the 1285 studies identified, only 5 studies met our inclusion criteria of models of TB transmission dynamics in human populations designed to incorporate contact tracing as an intervention. Detailed implementation of contact processes was only present in two studies, while only one study presented a model for a high prevalence, developing world setting. Some use of relevant data for parameter estimation was made in each study however validation of the predicted impact of interventions was not attempted in any of the studies. Despite a large body of literature on TB transmission modelling, few published studies incorporate contact tracing. There is considerable scope for future analyses to make better use of data and to apply individual based models to facilitate more realistic patterns of infectious contact. Combined with a focus on high burden settings this would greatly increase the potential for models to inform the use of contract tracing as a TB control policy. Our findings highlight the potential for collaborative work between clinicians, epidemiologists and modellers to gather data required to enhance model development and validation and hence better inform future public health policy. PMID:24023742

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-08-03

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Stages of Change or Changes of Stage? Predicting Transitions in Transtheoretical Model Stages in Relation to Healthy Food Choice

ERIC Educational Resources Information Center

Armitage, Christopher J.; Sheeran, Paschal; Conner, Mark; Arden, Madelynne A.

2004-01-01

Relatively little research has examined factors that account for transitions between transtheoretical model (TTM) stages of change. The present study (N=787) used sociodemographic, TTM, and theory of planned behavior (TPB) variables, as well as theory-driven interventions to predict changes in stage. Longitudinal analyses revealed that…

Epidemiology of Mild Traumatic Brain Injury with Intracranial Hemorrhage: Focusing Predictive Models for Neurosurgical Intervention.

PubMed

Orlando, Alessandro; Levy, A Stewart; Carrick, Matthew M; Tanner, Allen; Mains, Charles W; Bar-Or, David

2017-11-01

To outline differences in neurosurgical intervention (NI) rates between intracranial hemorrhage (ICH) types in mild traumatic brain injuries and help identify which ICH types are most likely to benefit from creation of predictive models for NI. A multicenter retrospective study of adult patients spanning 3 years at 4 U.S. trauma centers was performed. Patients were included if they presented with mild traumatic brain injury (Glasgow Coma Scale score 13-15) with head CT scan positive for ICH. Patients were excluded for skull fractures, "unspecified hemorrhage," or coagulopathy. Primary outcome was NI. Stepwise multivariable logistic regression models were built to analyze the independent association between ICH variables and outcome measures. The study comprised 1876 patients. NI rate was 6.7%. There was a significant difference in rate of NI by ICH type. Subdural hematomas had the highest rate of NI (15.5%) and accounted for 78% of all NIs. Isolated subarachnoid hemorrhages had the lowest, nonzero, NI rate (0.19%). Logistic regression models identified ICH type as the most influential independent variable when examining NI. A model predicting NI for isolated subarachnoid hemorrhages would require 26,928 patients, but a model predicting NI for isolated subdural hematomas would require only 328 patients. This study highlighted disparate NI rates among ICH types in patients with mild traumatic brain injury and identified mild, isolated subdural hematomas as most appropriate for construction of predictive NI models. Increased health care efficiency will be driven by accurate understanding of risk, which can come only from accurate predictive models. Copyright © 2017 Elsevier Inc. All rights reserved.

Predicting sugar consumption: Application of an integrated dual-process, dual-phase model.

PubMed

Hagger, Martin S; Trost, Nadine; Keech, Jacob J; Chan, Derwin K C; Hamilton, Kyra

2017-09-01

Excess consumption of added dietary sugars is related to multiple metabolic problems and adverse health conditions. Identifying the modifiable social cognitive and motivational constructs that predict sugar consumption is important to inform behavioral interventions aimed at reducing sugar intake. We tested the efficacy of an integrated dual-process, dual-phase model derived from multiple theories to predict sugar consumption. Using a prospective design, university students (N = 90) completed initial measures of the reflective (autonomous and controlled motivation, intentions, attitudes, subjective norm, perceived behavioral control), impulsive (implicit attitudes), volitional (action and coping planning), and behavioral (past sugar consumption) components of the proposed model. Self-reported sugar consumption was measured two weeks later. A structural equation model revealed that intentions, implicit attitudes, and, indirectly, autonomous motivation to reduce sugar consumption had small, significant effects on sugar consumption. Attitudes, subjective norm, and, indirectly, autonomous motivation to reduce sugar consumption predicted intentions. There were no effects of the planning constructs. Model effects were independent of the effects of past sugar consumption. The model identified the relative contribution of reflective and impulsive components in predicting sugar consumption. Given the prominent role of the impulsive component, interventions that assist individuals in managing cues-to-action and behavioral monitoring are likely to be effective in regulating sugar consumption. Copyright © 2017 Elsevier Ltd. All rights reserved.

Barriers to Mindfulness: a Path Analytic Model Exploring the Role of Rumination and Worry in Predicting Psychological and Physical Engagement in an Online Mindfulness-Based Intervention.

PubMed

Banerjee, Moitree; Cavanagh, Kate; Strauss, Clara

2018-01-01

Little is known about the factors associated with engagement in mindfulness-based interventions (MBIs). Moreover, engagement in MBIs is usually defined in terms of class attendance ('physical engagement') only. However, in the psychotherapy literature, there is increasing emphasis on measuring participants' involvement with interventions ('psychological engagement'). This study tests a model that rumination and worry act as barriers to physical and psychological engagement in MBIs and that this in turn impedes learning mindfulness. One hundred and twenty-four participants were given access to a 2-week online mindfulness-based self-help (MBSH) intervention. Self-report measures of mindfulness, rumination, worry, positive beliefs about rumination, positive beliefs about worry and physical and psychological engagement were administered. A path analysis was used to test the linear relationships between the variables. Physical and psychological engagement were identified as two distinct constructs. Findings were that rumination and worry both predicted psychological disengagement in MBSH. Psychological engagement predicted change in the describe, act with awareness, non-judge and non-react facets of mindfulness while physical engagement only predicted changes in the non-react facet of mindfulness. Thus, rumination and worry may increase risk of psychological disengagement from MBSH which may in turn hinder cultivating mindfulness. Future suggestions for practice are discussed.

Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance.

PubMed

Brubaker, Douglas; Difeo, Analisa; Chen, Yanwen; Pearl, Taylor; Zhai, Kaide; Bebek, Gurkan; Chance, Mark; Barnholtz-Sloan, Jill

2014-01-01

The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.

Do Motivational Interviewing Behaviors Predict Reductions in Partner Aggression for Men and Women?

PubMed Central

Woodin, Erica M.; Sotskova, Alina; O’Leary, K. Daniel

2011-01-01

Motivational interviewing is a directive, non-confrontational intervention to promote behavior change. The current study examined therapist behaviors during a successful brief motivational interviewing intervention for physically aggressive college dating couples (Woodin & O’Leary, 2010). Forty-five minute motivational interviews with each partner were videotaped and coded using the Motivational Interviewing Treatment Integrity scale (MITI; Moyers, Martin, Manuel, & Miller, 2003). Hierarchical modeling analyses demonstrated that therapist behaviors consistent with motivational interviewing competency predicted significantly greater reductions in physical aggression perpetration following the intervention. Specifically, greater reflection to question ratios by the therapists predicted reductions in aggression for both men and women, greater percentages of open versus closed questions predicted aggression reductions for women, and there was a trend for greater levels of global therapist empathy to predict aggression reductions for women. These findings provide evidence that motivational interviewing seems to have an effect on behavior change through therapist behaviors consistent with the theoretical underpinnings of motivational interviewing. PMID:22119133

Predicting adult weight change in the real world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure.

PubMed

Dhurandhar, E J; Kaiser, K A; Dawson, J A; Alcorn, A S; Keating, K D; Allison, D B

2015-08-01

Public health and clinical interventions for obesity in free-living adults may be diminished by individual compensation for the intervention. Approaches to predict weight outcomes do not account for all mechanisms of compensation, so they are not well suited to predict outcomes in free-living adults. Our objective was to quantify the range of compensation in energy intake or expenditure observed in human randomized controlled trials (RCTs). We searched multiple databases (PubMed, CINAHL, SCOPUS, Cochrane, ProQuest, PsycInfo) up to 1 August 2012 for RCTs evaluating the effect of dietary and/or physical activity interventions on body weight/composition. subjects per treatment arm ≥5; ≥1 week intervention; a reported outcome of body weight/body composition; the intervention was either a prescribed amount of over- or underfeeding and/or supervised or monitored physical activity was prescribed; ≥80% compliance; and an objective method was used to verify compliance with the intervention (for example, observation and electronic monitoring). Data were independently extracted and analyzed by multiple reviewers with consensus reached by discussion. We compared observed weight change with predicted weight change using two models that predict weight change accounting only for metabolic compensation. Twenty-eight studies met inclusion criteria. Overfeeding studies indicate 96% less weight gain than expected if no compensation occurred. Dietary restriction and exercise studies may result in up to 12-44% and 55-64% less weight loss than expected, respectively, under an assumption of no behavioral compensation. Compensation is substantial even in high-compliance conditions, resulting in far less weight change than would be expected. The simple algorithm we report allows for more realistic predictions of intervention effects in free-living populations by accounting for the significant compensation that occurs.

Human grief: a model for prediction and intervention.

PubMed

Bugen, L A

1977-04-01

The prevalent approach to understanding of and clinical intervention in the process of mourning employs a model based on stages of bereavement. This paper suggests a theoretical conception that is not tied to a fixed order of emotional states. Two dimensions--closeness of relationship and mourner's perception of preventability of the death--are identified as prime predictors of the intensity and duration of bereavement.

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses

ERIC Educational Resources Information Center

Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A.

2015-01-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test…

Analyzing transmission dynamics of cholera with public health interventions.

PubMed

Posny, Drew; Wang, Jin; Mukandavire, Zindoga; Modnak, Chairat

2015-06-01

Cholera continues to be a serious public health concern in developing countries and the global increase in the number of reported outbreaks suggests that activities to control the diseases and surveillance programs to identify or predict the occurrence of the next outbreaks are not adequate. These outbreaks have increased in frequency, severity, duration and endemicity in recent years. Mathematical models for infectious diseases play a critical role in predicting and understanding disease mechanisms, and have long provided basic insights in the possible ways to control infectious diseases. In this paper, we present a new deterministic cholera epidemiological model with three types of control measures incorporated into a cholera epidemic setting: treatment, vaccination and sanitation. Essential dynamical properties of the model with constant intervention controls which include local and global stabilities for the equilibria are carefully analyzed. Further, using optimal control techniques, we perform a study to investigate cost-effective solutions for time-dependent public health interventions in order to curb disease transmission in epidemic settings. Our results show that the basic reproductive number (R0) remains the model's epidemic threshold despite the inclusion of a package of cholera interventions. For time-dependent controls, the results suggest that these interventions closely interplay with each other, and the costs of controls directly affect the length and strength of each control in an optimal strategy. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015.

PubMed

Mehmandar, Mohammadreza; Soori, Hamid; Mehrabi, Yadolah

2016-01-01

Predicting the trend in traffic accidents deaths and its analysis can be a useful tool for planning and policy-making, conducting interventions appropriate with death trend, and taking the necessary actions required for controlling and preventing future occurrences. Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. It was a cross-sectional study. All the information related to fatal traffic accidents available in the database of Iran Legal Medicine Organization from 2004 to the end of 2013 were used to determine the change points (multi-variable time series analysis). Using autoregressive integrated moving average (ARIMA) model, traffic accidents death rates were predicted for 2014 and 2015, and a comparison was made between this rate and the predicted value in order to determine the efficiency of the model. From the results, the actual death rate in 2014 was almost similar to that recorded for this year, while in 2015 there was a decrease compared with the previous year (2014) for all the months. A maximum value of 41% was also predicted for the months of January and February, 2015. From the prediction and analysis of the death trends, proper application and continuous use of the intervention conducted in the previous years for road safety improvement, motor vehicle safety improvement, particularly training and culture-fostering interventions, as well as approval and execution of deterrent regulations for changing the organizational behaviors, can significantly decrease the loss caused by traffic accidents.

Usefulness of cardiovascular magnetic resonance imaging to predict the need for intervention in patients with coarctation of the aorta.

PubMed

Muzzarelli, Stefano; Meadows, Alison Knauth; Ordovas, Karen Gomes; Higgins, Charles Bernard; Meadows, Jeffery Joshua

2012-03-15

Cardiovascular magnetic resonance (CMR) imaging can predict hemodynamically significant coarctation of the aorta (CoA) with a high degree of discrimination. However, the ability of CMR to predict important clinical outcomes in this patient population is unknown. Therefore, we sought to define the ability of CMR to predict the need for surgical or transcatheter intervention in patients with CoA. We retrospectively reviewed the data from 133 consecutive patients who had undergone CMR for the evaluation of known or suspected CoA. The characteristics of the CMR-derived variables predicting the need for surgical or transcatheter intervention for CoA within 1 year were determined through logistic regression analysis. Therapeutic aortic intervention was performed in 41 (31%) of the 133 patients during the study period. The indexed minimum aortic cross-sectional area was the strongest predictor of subsequent intervention (area under the receiver operating characteristic curve 0.975) followed by heart rate-corrected deceleration time in the descending aorta (area under the receiver operating characteristic curve 0.951), and the percentage of flow increase (area under the receiver operating characteristic curve 0.867). The combination of the indexed minimum aortic cross-sectional area and rate-corrected deceleration time in the descending aorta provided the best predictive model (area under the receiver operating characteristic curve 0.986). In conclusion, CMR findings can predict the need for subsequent intervention in CoA. These findings reinforce the "gate-keeper role" of CMR to cardiac catheterization by providing valuable diagnostic and powerful prognostic information and could guide additional treatment of patients with CoA with the final intent of reducing the number of diagnostic catheterizations in such patients. Copyright © 2012 Elsevier Inc. All rights reserved.

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention.

PubMed

Deshpande, Sunil; Rivera, Daniel E; Younger, Jarred W; Nandola, Naresh N

2014-09-01

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations.

Applying psychological frameworks of behaviour change to improve healthcare worker hand hygiene: a systematic review.

PubMed

Srigley, J A; Corace, K; Hargadon, D P; Yu, D; MacDonald, T; Fabrigar, L; Garber, G

2015-11-01

Despite the importance of hand hygiene in preventing transmission of healthcare-associated infections, compliance rates are suboptimal. Hand hygiene is a complex behaviour and psychological frameworks are promising tools to influence healthcare worker (HCW) behaviour. (i) To review the effectiveness of interventions based on psychological theories of behaviour change to improve HCW hand hygiene compliance; (ii) to determine which frameworks have been used to predict HCW hand hygiene compliance. Multiple databases and reference lists of included studies were searched for studies that applied psychological theories to improve and/or predict HCW hand hygiene. All steps in selection, data extraction, and quality assessment were performed independently by two reviewers. The search yielded 918 citations; seven met eligibility criteria. Four studies evaluated hand hygiene interventions based on psychological frameworks. Interventions were informed by goal setting, control theory, operant learning, positive reinforcement, change theory, the theory of planned behaviour, and the transtheoretical model. Three predictive studies employed the theory of planned behaviour, the transtheoretical model, and the theoretical domains framework. Interventions to improve hand hygiene adherence demonstrated efficacy but studies were at moderate to high risk of bias. For many studies, it was unclear how theories of behaviour change were used to inform the interventions. Predictive studies had mixed results. Behaviour change theory is a promising tool for improving hand hygiene; however, these theories have not been extensively examined. Our review reveals a significant gap in the literature and indicates possible avenues for novel research. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

InMAP: a new model for air pollution interventions

NASA Astrophysics Data System (ADS)

Tessum, C. W.; Hill, J. D.; Marshall, J. D.

2015-10-01

Mechanistic air pollution models are essential tools in air quality management. Widespread use of such models is hindered, however, by the extensive expertise or computational resources needed to run most models. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations - the air pollution outcome generally causing the largest monetized health damages - attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model (WRF-Chem) within an Eulerian modeling framework, to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. InMAP uses a variable resolution grid that focuses on human exposures by employing higher spatial resolution in urban areas and lower spatial resolution in rural and remote locations and in the upper atmosphere; and by directly calculating steady-state, annual average concentrations. In comparisons run here, InMAP recreates WRF-Chem predictions of changes in total PM2.5 concentrations with population-weighted mean fractional error (MFE) and bias (MFB) < 10 % and population-weighted R2 ~ 0.99. Among individual PM2.5 species, the best predictive performance is for primary PM2.5 (MFE: 16 %; MFB: 13 %) and the worst predictive performance is for particulate nitrate (MFE: 119 %; MFB: 106 %). Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. Features planned for future model releases include a larger spatial domain, more temporal information, and the ability to predict ground-level ozone (O3) concentrations. The InMAP model source code and input data are freely available online.

How health leaders can benefit from predictive analytics.

PubMed

Giga, Aliyah

2017-11-01

Predictive analytics can support a better integrated health system providing continuous, coordinated, and comprehensive person-centred care to those who could benefit most. In addition to dollars saved, using a predictive model in healthcare can generate opportunities for meaningful improvements in efficiency, productivity, costs, and better population health with targeted interventions toward patients at risk.

Developing symptom-based predictive models of endometriosis as a clinical screening tool: results from a multicenter study

PubMed Central

Nnoaham, Kelechi E.; Hummelshoj, Lone; Kennedy, Stephen H.; Jenkinson, Crispin; Zondervan, Krina T.

2012-01-01

Objective To generate and validate symptom-based models to predict endometriosis among symptomatic women prior to undergoing their first laparoscopy. Design Prospective, observational, two-phase study, in which women completed a 25-item questionnaire prior to surgery. Setting Nineteen hospitals in 13 countries. Patient(s) Symptomatic women (n = 1,396) scheduled for laparoscopy without a previous surgical diagnosis of endometriosis. Intervention(s) None. Main Outcome Measure(s) Sensitivity and specificity of endometriosis diagnosis predicted by symptoms and patient characteristics from optimal models developed using multiple logistic regression analyses in one data set (phase I), and independently validated in a second data set (phase II) by receiver operating characteristic (ROC) curve analysis. Result(s) Three hundred sixty (46.7%) women in phase I and 364 (58.2%) in phase II were diagnosed with endometriosis at laparoscopy. Menstrual dyschezia (pain on opening bowels) and a history of benign ovarian cysts most strongly predicted both any and stage III and IV endometriosis in both phases. Prediction of any-stage endometriosis, although improved by ultrasound scan evidence of cyst/nodules, was relatively poor (area under the curve [AUC] = 68.3). Stage III and IV disease was predicted with good accuracy (AUC = 84.9, sensitivity of 82.3% and specificity 75.8% at an optimal cut-off of 0.24). Conclusion(s) Our symptom-based models predict any-stage endometriosis relatively poorly and stage III and IV disease with good accuracy. Predictive tools based on such models could help to prioritize women for surgical investigation in clinical practice and thus contribute to reducing time to diagnosis. We invite other researchers to validate the key models in additional populations. PMID:22657249

XplOit: An Ontology-Based Data Integration Platform Supporting the Development of Predictive Models for Personalized Medicine.

PubMed

Weiler, Gabriele; Schwarz, Ulf; Rauch, Jochen; Rohm, Kerstin; Lehr, Thorsten; Theobald, Stefan; Kiefer, Stephan; Götz, Katharina; Och, Katharina; Pfeifer, Nico; Handl, Lisa; Smola, Sigrun; Ihle, Matthias; Turki, Amin T; Beelen, Dietrich W; Rissland, Jürgen; Bittenbring, Jörg; Graf, Norbert

2018-01-01

Predictive models can support physicians to tailor interventions and treatments to their individual patients based on their predicted response and risk of disease and help in this way to put personalized medicine into practice. In allogeneic stem cell transplantation risk assessment is to be enhanced in order to respond to emerging viral infections and transplantation reactions. However, to develop predictive models it is necessary to harmonize and integrate high amounts of heterogeneous medical data that is stored in different health information systems. Driven by the demand for predictive instruments in allogeneic stem cell transplantation we present in this paper an ontology-based platform that supports data owners and model developers to share and harmonize their data for model development respecting data privacy.

Healthy Work Revisited: Do Changes in Time Strain Predict Well-Being?

PubMed Central

Moen, Phyllis; Kelly, Erin L.; Lam, Jack

2013-01-01

Building on Karasek and Theorell (R. Karasek & T. Theorell, 1990, Healthy work: Stress, productivity, and the reconstruction of working life, New York, NY: Basic Books), we theorized and tested the relationship between time strain (work-time demands and control) and seven self-reported health outcomes. We drew on survey data from 550 employees fielded before and 6 months after the implementation of an organizational intervention, the Results Only Work Environment (ROWE) in a white-collar organization. Cross-sectional (Wave 1) models showed psychological time demands and time control measures were related to health outcomes in expected directions. The ROWE intervention did not predict changes in psychological time demands by Wave 2, but did predict increased time control (a sense of time adequacy and schedule control). Statistical models revealed increases in psychological time demands and time adequacy predicted changes in positive (energy, mastery, psychological well-being, self-assessed health) and negative (emotional exhaustion, somatic symptoms, psychological distress) outcomes in expected directions, net of job and home demands and covariates. This study demonstrates the value of including time strain in investigations of the health effects of job conditions. Results encourage longitudinal models of change in psychological time demands as well as time control, along with the development and testing of interventions aimed at reducing time strain in different populations of workers. PMID:23506547

The effect of a multi-component smoking cessation intervention in African American women residing in public housing.

PubMed

Andrews, Jeannette O; Felton, Gwen; Ellen Wewers, Mary; Waller, Jennifer; Tingen, Martha

2007-02-01

The purpose of this study was to test the effectiveness of a multi-component smoking cessation intervention in African American women residing in public housing. The intervention consisted of: (a) nurse led behavioral/empowerment counseling; (b) nicotine replacement therapy; and, (c) community health workers to enhance smoking self-efficacy, social support, and spiritual well-being. The results showed a 6-month continuous smoking abstinence of 27.5% and 5.7% in the intervention and comparison groups. Changes in social support and smoking self-efficacy over time predicted smoking abstinence, and self-efficacy mediated 6-month smoking abstinence outcomes. Spiritual well-being did not predict or mediate smoking abstinence outcomes. These findings support the use of a nurse/community health worker model to deliver culturally tailored behavioral interventions with marginalized communities.

Moderating effects of parental well-being on parenting efficacy outcomes by intervention delivery model of the early risers conduct problems prevention program.

PubMed

Piehler, Timothy F; Lee, Susanne S; Bloomquist, Michael L; August, Gerald J

2014-10-01

Parent-focused preventive interventions for youth conduct problems are efficacious when offered in different models of delivery (e.g., individual in-home, group center-based). However, we know little about the characteristics of parents associated with a positive response to a particular model of delivery. We randomly assigned the parents of an ethnically diverse sample of kindergarten through second grade students (n = 246) displaying elevated levels of aggression to parent-focused program delivery models emphasizing receiving services in a community center largely with groups (Center; n = 121) or receiving services via an individualized in-home strategy (Outreach; n = 125). In both delivery models, parents received parent skills training and goal setting/case management/referrals over an average of 16 months. Structural equation modeling revealed a significant interaction between parental well-being at baseline and intervention delivery model in predicting parenting efficacy at year 2, while controlling for baseline levels of parenting efficacy. Within the Outreach model, parents with lower levels of well-being as reported at baseline appeared to show greater improvements in parenting efficacy than parents with higher levels of well-being. Within the Center model, parental well-being did not predict parenting efficacy outcomes. The strong response of low well-being parents within the Outreach model suggests that this may be the preferred model for these parents. These findings provide support for further investigation into tailoring delivery model of parent-focused preventive interventions using parental well-being in order to improve parenting outcomes.

Moderating Effects of Parental Well-Being on Parenting Efficacy Outcomes by Intervention Delivery Model of the Early Risers Conduct Problems Prevention Program

PubMed Central

Piehler, Timothy F.; Lee, Susanne S.; Bloomquist, Michael L.; August, Gerald J.

2014-01-01

Parent-focused preventive interventions for youth conduct problems are efficacious when offered in different models of delivery (e.g., individual in-home, group center-based). However, we know little about the characteristics of parents associated with a positive response to a particular model of delivery. We randomly assigned the parents of an ethnically diverse sample of kindergarten through second grade students (n = 246) displaying elevated levels of aggression to parent-focused program delivery models emphasizing receiving services in a community center largely with groups (Center; n = 121) or receiving services via an individualized in-home strategy (Outreach; n = 125). In both delivery models, parents received parent skills training and goal setting/case management/referrals over an average of 16 months. Structural equation modeling revealed a significant interaction between parental well-being at baseline and intervention delivery model in predicting parenting efficacy at year two, while controlling for baseline levels of parenting efficacy. Within the Outreach model, parents with lower levels of well-being as reported at baseline appeared to show greater improvements in parenting efficacy than parents with higher levels of well-being. Within the Center model, parental well-being did not predict parenting efficacy outcomes. The strong response of low well-being parents within the Outreach model suggests that this may be the preferred model for these parents. These findings provide support for further investigation into tailoring delivery model of parent-focused preventive interventions using parental well-being in order to improve parenting outcomes. PMID:25037843

Biomarker-based risk prediction in the community.

PubMed

AbouEzzeddine, Omar F; McKie, Paul M; Scott, Christopher G; Rodeheffer, Richard J; Chen, Horng H; Michael Felker, G; Jaffe, Allan S; Burnett, John C; Redfield, Margaret M

2016-11-01

Guided by predictive characteristics of cardiovascular biomarkers, we explored the clinical implications of a simulated biomarker-guided heart failure (HF) and major adverse cardiovascular events (MACE) prevention strategy in the community. In a community cohort (n = 1824), the predictive characteristics for HF and MACE of galectin-3 (Gal-3), ST2, high-sensitivity cardiac troponin I (hscTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-brain natriuretic peptide (NT-proBNP) and B-type natriuretic peptide (BNP) were established. We performed number needed to screen (NNS) and treat (NNT) with the intervention analyses according to biomarker screening strategy and intervention efficacy in persons with at least one cardiovascular risk factor. In the entire cohort, for both HF and MACE, the predictive characteristics of NT-proBNP and hscTnI were superior to other biomarkers; alone, in a multimarker model, and adjusting for clinical risk factors. An NT-proBNP-guided preventative intervention with an intervention effect size (4-year hazard ratio for intervention in biomarker positive cohort) of ≤0.7 would reduce the global burden of HF by ≥20% and MACE by ≥15%. From this simulation, the NNS to prevent one HF event or MACE in 4 years would be ≤100 with a NNT to prevent one HF event of ≤20 and one MACE of ≤10. The predictive characteristics of NT-proBNP and hscTnI for HF or MACE in the community are superior to other biomarkers. Biomarker-guided preventative interventions with reasonable efficacy would compare favourably to established preventative interventions. This data provides a framework for biomarker selection which may inform design of biomarker-guided preventative intervention trials. © 2016 The Authors. European Journal of Heart Failure © 2016 European Society of Cardiology.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

PubMed Central

Kessler, R.C.; van Loo, H.M.; Wardenaar, K.J.; Bossarte, R.M.; Brenner, L.A.; Ebert, D.D; de Jonge, P.; Nierenberg, A.A.; Rosellini, A.J.; Sampson, N.A.; Schoevers, R.A.; Wilcox, M.A.; Zaslavsky, A.M.

2016-01-01

Aims Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. Methods We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalized) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Results Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention versus control) or differential treatment outcomes (i.e., intervention A versus intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalized treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Conclusions Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists. PMID:26810628

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

PubMed

Kessler, R C; van Loo, H M; Wardenaar, K J; Bossarte, R M; Brenner, L A; Ebert, D D; de Jonge, P; Nierenberg, A A; Rosellini, A J; Sampson, N A; Schoevers, R A; Wilcox, M A; Zaslavsky, A M

2017-02-01

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Prediction of fruit and vegetable intake from biomarkers using individual participant data of diet-controlled intervention studies.

PubMed

Souverein, Olga W; de Vries, Jeanne H M; Freese, Riitta; Watzl, Bernhard; Bub, Achim; Miller, Edgar R; Castenmiller, Jacqueline J M; Pasman, Wilrike J; van Het Hof, Karin; Chopra, Mridula; Karlsen, Anette; Dragsted, Lars O; Winkels, Renate; Itsiopoulos, Catherine; Brazionis, Laima; O'Dea, Kerin; van Loo-Bouwman, Carolien A; Naber, Ton H J; van der Voet, Hilko; Boshuizen, Hendriek C

2015-05-14

Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose-response curve between fruit and vegetable consumption and carotenoid (α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258.0 g, the correlation between observed and predicted intake was 0.78 and the mean difference between observed and predicted intake was - 1.7 g (limits of agreement: - 466.3, 462.8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201.1 g, the correlation was 0.65 and the mean bias was 2.4 g (limits of agreement: -368.2, 373.0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake.

Prediction using patient comparison vs. modeling: a case study for mortality prediction.

PubMed

Hoogendoorn, Mark; El Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter

2016-08-01

Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty

PubMed Central

2012-01-01

Background Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. Aims We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Methods Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. Results We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. Conclusions The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals. PMID:23249291

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty.

PubMed

Ben-Haim, Yakov; Dacso, Clifford C; Zetola, Nicola M

2012-12-19

Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.

Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (the POUNDS Lost study)123

PubMed Central

Ivanescu, Andrada E; Martin, Corby K; Heymsfield, Steven B; Marshall, Kaitlyn; Bodrato, Victoria E; Williamson, Donald A; Anton, Stephen D; Sacks, Frank M; Ryan, Donna; Bray, George A

2015-01-01

Background: Currently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds. Objective: The objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1. Design: Data consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing <5% of body weight in 1 y. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds were calculated for each model. The AUC statistic quantified the ROC curve’s capacity to classify participants likely to lose <5% of their body weight at the end of 1 y. The models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated. Results: Optimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50). Conclusions: The newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention. The POUNDS Lost study was registered at clinicaltrials.gov as NCT00072995. PMID:25733628

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.

PubMed

Flassig, Robert J; Migal, Iryna; der Zalm, Esther van; Rihko-Struckmann, Liisa; Sundmacher, Kai

2015-01-16

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort. In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts. Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

PubMed

Buczak, Anna L; Baugher, Benjamin; Guven, Erhan; Ramac-Thomas, Liane C; Elbert, Yevgeniy; Babin, Steven M; Lewis, Sheri H

2015-06-18

Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as Low, Medium or High, where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the Medium class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict Low, Medium or High cases 7-8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

Application and comparison of the FADES, MADIT, and SHFM-D risk models for risk stratification of prophylactic implantable cardioverter-defibrillator treatment

PubMed Central

van der Heijden, Aafke C.; van Rees, Johannes B.; Levy, Wayne C.; van der Bom, Johanna G.; Cannegieter, Suzanne C.; de Bie, Mihàly K.; van Erven, Lieselot; Schalij, Martin J.; Borleffs, C. Jan  Willem

2017-01-01

Aims Implantable cardioverter-defibrillator (ICD) treatment is beneficial in selected patients. However, it remains difficult to accurately predict which patients benefit most from ICD implantation. For this purpose, different risk models have been developed. The aim was to validate and compare the FADES, MADIT, and SHFM-D models. Methods and results All patients receiving a prophylactic ICD at the Leiden University Medical Center were evaluated. Individual model performance was evaluated by C-statistics. Model performances were compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI). The primary endpoint was non-benefit of ICD treatment, defined as mortality without prior ventricular arrhythmias requiring ICD intervention. A total of 1969 patients were included (age 63 ± 11 years; 79% male). During a median follow-up of 4.5 ± 3.9 years, 318 (16%) patients died without prior ICD intervention. All three risk models were predictive for event-free mortality (all: P < 0.001). The C-statistics were 0.66, 0.69, and 0.75, respectively, for FADES, MADIT, and SHFM-D (all: P < 0.001). Application of the SHFM-D resulted in an improved IDI of 4% and NRI of 26% compared with MADIT; IDI improved 11% with the use of SHFM-D instead of FADES (all: P < 0.001), but NRI remained unchanged (P = 0.71). Patients in the highest-risk category of the MADIT and SHFM-D models had 1.7 times higher risk to experience ICD non-benefit than receive appropriate ICD interventions [MADIT: mean difference (MD) 20% (95% CI: 7–33%), P = 0.001; SHFM-D: MD 16% (95% CI: 5–27%), P = 0.005]. Patients in the highest-risk category of FADES were as likely to experience ICD intervention as ICD non-benefit [MD 3% (95% CI: –8 to 14%), P = 0.60]. Conclusion The predictive and discriminatory value of SHFM-D to predict non-benefit of ICD treatment is superior to FADES and MADIT in patients receiving prophylactic ICD treatment. PMID:28130376

Using the health action process approach to predict and improve health outcomes in individuals with type 2 diabetes mellitus

PubMed Central

MacPhail, Mariana; Mullan, Barbara; Sharpe, Louise; MacCann, Carolyn; Todd, Jemma

2014-01-01

Background The purpose of this study was to explore the predictive utility of the Health Action Process Approach (HAPA) and test a HAPA-based healthy eating intervention, in adults with type 2 diabetes mellitus. Materials and methods The study employed a prospective, randomized, controlled trial design. The 4-month intervention consisted of self-guided HAPA-based workbooks in addition to two telephone calls to assist participants with the program implementation, and was compared to “treatment as usual”. Participants (n=87) completed health measures (diet, body mass index [BMI], waist circumference, blood pressure, blood glucose levels, lipid levels, and diabetes distress) and HAPA measures prior to the intervention and again upon completion 4 months later. Results The overall HAPA model predicted BMI, although only risk awareness and recovery self-efficacy were significant independent contributors. Risk awareness, intentions, and self-efficacy were also independent predictors of health outcomes; however, the HAPA did not predict healthy eating. No significant time × condition interaction effects were found for diet or any HAPA outcome measures. Conclusion Despite the success of HAPA in predicting health outcomes for those with type 2 diabetes mellitus, the intervention was unsuccessful in changing healthy eating or any of the other measured variables, and alternative low-cost health interventions for those with type 2 diabetes mellitus should be explored. PMID:25342914

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems.

PubMed

Nandola, Naresh N; Rivera, Daniel E

2013-01-01

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty.

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2013-01-01

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. PMID:24348004

Identifying At-Risk Students for Early Reading Intervention: Challenges and Possible Solutions

ERIC Educational Resources Information Center

McAlenney, Athena Lentini; Coyne, Michael D.

2011-01-01

Accurate identification of at-risk kindergarten and 1st-grade students through early reading screening is an essential element of responsiveness to intervention models of reading instruction. The authors consider predictive validity and classification accuracy of early reading screening assessments with attention to sensitivity and specificity.…

An Experimental Test of Parenting Practices as a Mediator of Early Childhood Physical Aggression

ERIC Educational Resources Information Center

Brotman, Laurie Miller; O'Neal, Colleen R.; Huang, Keng-Yen; Gouley, Kathleen Kiely; Rosenfelt, Amanda; Shrout, Patrick E.

2009-01-01

Background: Parenting practices predict early childhood physical aggression. Preventive interventions that alter parenting practices and aggression during early childhood provide the opportunity to test causal models of early childhood psychopathology. Although there have been several informative preventive intervention studies that test mediation…

Using administrative data to identify U.S. Army soldiers at high-risk of perpetrating minor violent crimes

PubMed Central

Rosellini, Anthony J.; Monahan, John; Street, Amy E.; Hill, Eric D.; Petukhova, Maria; Reis, Ben Y.; Sampson, Nancy A.; Benedek, David M.; Bliese, Paul; Stein, Murray B.; Ursano, Robert J.; Kessler, Ronald C.

2016-01-01

Growing concerns exist about violent crimes perpetrated by U.S. military personnel. Although interventions exist to reduce violent crimes in high-risk populations, optimal implementation requires evidence-based targeting. The goal of the current study was to use machine learning methods (stepwise and penalized regression; random forests) to develop models to predict minor violent crime perpetration among U.S. Army soldiers. Predictors were abstracted from administrative data available for all 975,057 soldiers in the U.S. Army 2004–2009, among whom 25,966 men and 2,728 women committed a first founded minor violent crime (simple assault, blackmail-extortion-intimidation, rioting, harassment). Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build separate male and female prediction models that were then tested in an independent 2011–2013 sample. Final model predictors included young age, low education, early career stage, prior crime involvement, and outpatient treatment for diverse emotional and substance use problems. Area under the receiver operating characteristic curve was 0.79 (for men and women) in the 2004–2009 training sample and 0.74–0.82 (men-women) in the 2011–2013 test sample. 30.5–28.9% (men-women) of all administratively-recorded crimes in 2004–2009 were committed by the 5% of soldiers having highest predicted risk, with similar proportions (28.5–29.0%) when the 2004–2009 coefficients were applied to the 2011–2013 test sample. These results suggest that it may be possible to target soldiers at high-risk of violence perpetration for preventive interventions, although final decisions about such interventions would require weighing predicted effectiveness against intervention costs and competing risks. PMID:27741501

Using administrative data to identify U.S. Army soldiers at high-risk of perpetrating minor violent crimes.

PubMed

Rosellini, Anthony J; Monahan, John; Street, Amy E; Hill, Eric D; Petukhova, Maria; Reis, Ben Y; Sampson, Nancy A; Benedek, David M; Bliese, Paul; Stein, Murray B; Ursano, Robert J; Kessler, Ronald C

2017-01-01

Growing concerns exist about violent crimes perpetrated by U.S. military personnel. Although interventions exist to reduce violent crimes in high-risk populations, optimal implementation requires evidence-based targeting. The goal of the current study was to use machine learning methods (stepwise and penalized regression; random forests) to develop models to predict minor violent crime perpetration among U.S. Army soldiers. Predictors were abstracted from administrative data available for all 975,057 soldiers in the U.S. Army 2004-2009, among whom 25,966 men and 2728 women committed a first founded minor violent crime (simple assault, blackmail-extortion-intimidation, rioting, harassment). Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build separate male and female prediction models that were then tested in an independent 2011-2013 sample. Final model predictors included young age, low education, early career stage, prior crime involvement, and outpatient treatment for diverse emotional and substance use problems. Area under the receiver operating characteristic curve was 0.79 (for men and women) in the 2004-2009 training sample and 0.74-0.82 (men-women) in the 2011-2013 test sample. 30.5-28.9% (men-women) of all administratively-recorded crimes in 2004-2009 were committed by the 5% of soldiers having highest predicted risk, with similar proportions (28.5-29.0%) when the 2004-2009 coefficients were applied to the 2011-2013 test sample. These results suggest that it may be possible to target soldiers at high-risk of violence perpetration for preventive interventions, although final decisions about such interventions would require weighing predicted effectiveness against intervention costs and competing risks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Persuasive system design does matter: a systematic review of adherence to web-based interventions.

PubMed

Kelders, Saskia M; Kok, Robin N; Ossebaard, Hans C; Van Gemert-Pijnen, Julia E W C

2012-11-14

Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p=.004), setup (p<.001), updates (p<.001), frequency of interaction with a counselor (p<.001), the system (p=.003) and peers (p=.017), duration (F=6.068, p=.004), adherence (F=4.833, p=.010) and the number of primary task support elements (F=5.631, p=.005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.

Ensemble ecosystem modeling for predicting ecosystem response to predator reintroduction.

PubMed

Baker, Christopher M; Gordon, Ascelin; Bode, Michael

2017-04-01

Introducing a new or extirpated species to an ecosystem is risky, and managers need quantitative methods that can predict the consequences for the recipient ecosystem. Proponents of keystone predator reintroductions commonly argue that the presence of the predator will restore ecosystem function, but this has not always been the case, and mathematical modeling has an important role to play in predicting how reintroductions will likely play out. We devised an ensemble modeling method that integrates species interaction networks and dynamic community simulations and used it to describe the range of plausible consequences of 2 keystone-predator reintroductions: wolves (Canis lupus) to Yellowstone National Park and dingoes (Canis dingo) to a national park in Australia. Although previous methods for predicting ecosystem responses to such interventions focused on predicting changes around a given equilibrium, we used Lotka-Volterra equations to predict changing abundances through time. We applied our method to interaction networks for wolves in Yellowstone National Park and for dingoes in Australia. Our model replicated the observed dynamics in Yellowstone National Park and produced a larger range of potential outcomes for the dingo network. However, we also found that changes in small vertebrates or invertebrates gave a good indication about the potential future state of the system. Our method allowed us to predict when the systems were far from equilibrium. Our results showed that the method can also be used to predict which species may increase or decrease following a reintroduction and can identify species that are important to monitor (i.e., species whose changes in abundance give extra insight into broad changes in the system). Ensemble ecosystem modeling can also be applied to assess the ecosystem-wide implications of other types of interventions including assisted migration, biocontrol, and invasive species eradication. © 2016 Society for Conservation Biology.

Spatial model for risk prediction and sub-national prioritization to aid poliovirus eradication in Pakistan.

PubMed

Mercer, Laina D; Safdar, Rana M; Ahmed, Jamal; Mahamud, Abdirahman; Khan, M Muzaffar; Gerber, Sue; O'Leary, Aiden; Ryan, Mike; Salet, Frank; Kroiss, Steve J; Lyons, Hil; Upfill-Brown, Alexander; Chabot-Couture, Guillaume

2017-10-11

Pakistan is one of only three countries where poliovirus circulation remains endemic. For the Pakistan Polio Eradication Program, identifying high risk districts is essential to target interventions and allocate limited resources. Using a hierarchical Bayesian framework we developed a spatial Poisson hurdle model to jointly model the probability of one or more paralytic polio cases, and the number of cases that would be detected in the event of an outbreak. Rates of underimmunization, routine immunization, and population immunity, as well as seasonality and a history of cases were used to project future risk of cases. The expected number of cases in each district in a 6-month period was predicted using indicators from the previous 6-months and the estimated coefficients from the model. The model achieves an average of 90% predictive accuracy as measured by area under the receiver operating characteristic (ROC) curve, for the past 3 years of cases. The risk of poliovirus has decreased dramatically in many of the key reservoir areas in Pakistan. The results of this model have been used to prioritize sub-national areas in Pakistan to receive additional immunization activities, additional monitoring, or other special interventions.

Differences in motivation and adherence to a prescribed assignment after face-to-face and online psychoeducation: an experimental study.

PubMed

Alfonsson, Sven; Johansson, Karin; Uddling, Jonas; Hursti, Timo

2017-01-26

Adherence to treatment homework is associated with positive outcomes in behavioral psychotherapy but compliance to assignments is still often moderate. Whether adherence can be predicted by different types of motivation for the task and whether motivation plays different roles in face-to-face compared to online psychotherapy is unknown. If models of motivation, such as Self-determination theory, can be used to predict patients' behavior, it may facilitate further research into homework promotion. The aims of this study were, therefore, to investigate whether motivation variables could predict adherence to a prescribed assignment in face-to-face and online interventions using a psychotherapy analog model. A total of 100 participants were included in this study and randomized to either a face-to-face or online intervention. Participants in both groups received a psychoeducation session and were given an assignment for the subsequent week. The main outcome measurements were self-reported motivation and adherence to the assignment. Participant in the face-to-face condition reported significantly higher levels of motivation and showed higher levels of adherence compared to participants in the online condition. Adherence to the assignment was positively associated with intrinsic motivation and intervention credibility in the whole sample and especially in the online group. This study shows that intrinsic motivation and intervention credibility are strong predictors of adherence to assignments, especially in online interventions. The results indicate that intrinsic motivation may be partly substituted with face-to-face contact with a therapist. It may also be possible to identify patients with low motivation in online interventions who are at risk of dropping out. Methods for making online interventions more intrinsically motivating without increasing external pressure are needed. clinicaltrials.gov NCT02895308 . Retrospectively registered 30 August 2016.

Examining Predictive Validity of Oral Reading Fluency Slope in Upper Elementary Grades Using Quantile Regression.

PubMed

Cho, Eunsoo; Capin, Philip; Roberts, Greg; Vaughn, Sharon

2017-07-01

Within multitiered instructional delivery models, progress monitoring is a key mechanism for determining whether a child demonstrates an adequate response to instruction. One measure commonly used to monitor the reading progress of students is oral reading fluency (ORF). This study examined the extent to which ORF slope predicts reading comprehension outcomes for fifth-grade struggling readers ( n = 102) participating in an intensive reading intervention. Quantile regression models showed that ORF slope significantly predicted performance on a sentence-level fluency and comprehension assessment, regardless of the students' reading skills, controlling for initial ORF performance. However, ORF slope was differentially predictive of a passage-level comprehension assessment based on students' reading skills when controlling for initial ORF status. Results showed that ORF explained unique variance for struggling readers whose posttest performance was at the upper quantiles at the end of the reading intervention, but slope was not a significant predictor of passage-level comprehension for students whose reading problems were the most difficult to remediate.

Predicting reading outcomes with progress monitoring slopes among middle grade students

PubMed Central

Tolar, Tammy D.; Barth, Amy E.; Fletcher, Jack M.; Francis, David J.; Vaughn, Sharon

2013-01-01

Effective implementation of response-to-intervention (RTI) frameworks depends on efficient tools for monitoring progress. Evaluations of growth (i.e., slope) may be less efficient than evaluations of status at a single time point, especially if slopes do not add to predictions of outcomes over status. We examined progress monitoring slope validity for predicting reading outcomes among middle school students by evaluating latent growth models for different progress monitoring measure-outcome combinations. We used multi-group modeling to evaluate the effects of reading ability, reading intervention, and progress monitoring administration condition on slope validity. Slope validity was greatest when progress monitoring was aligned with the outcome (i.e., word reading fluency slope was used to predict fluency outcomes in contrast to comprehension outcomes), but effects varied across administration conditions (viz., repeated reading of familiar vs. novel passages). Unless the progress monitoring measure is highly aligned with outcome, slope may be an inefficient method for evaluating progress in an RTI context. PMID:24659899

Model for Estimating Acute Health Impacts from Consumption of Contaminated Drinking Water

EPA Science Inventory

This journal article discusses disease transmission models used to predict the spread of disease over time through susceptible, infected and recoverred populations, commonly used to design public intervention strategies. Amodified disease model is linked to flow and transport mod...

A comprehensive subaxial cervical spine injury severity assessment model using numeric scores and its predictive value for surgical intervention.

PubMed

Tsou, Paul M; Daffner, Scott D; Holly, Langston T; Shamie, A Nick; Wang, Jeffrey C

2012-02-10

Multiple factors contribute to the determination for surgical intervention in the setting of cervical spinal injury, yet to date no unified classification system exists that predicts this need. The goals of this study were twofold: to create a comprehensive subaxial cervical spine injury severity numeric scoring model, and to determine the predictive value of this model for the probability of surgical intervention. In a retrospective cohort study of 333 patients, neural impairment, patho-morphology, and available spinal canal sagittal diameter post-injury were selected as injury severity determinants. A common numeric scoring trend was created; smaller values indicated less favorable clinical conditions. Neural impairment was graded from 2-10, patho-morphology scoring ranged from 2-15, and post-injury available canal sagittal diameter (SD) was measured in millimeters at the narrowest point of injury. Logistic regression analysis was performed using the numeric scores to predict the probability for surgical intervention. Complete neurologic deficit was found in 39 patients, partial deficits in 108, root injuries in 19, and 167 were neurologically intact. The pre-injury mean canal SD was 14.6 mm; the post-injury measurement mean was 12.3 mm. The mean patho-morphology score for all patients was 10.9 and the mean neurologic function score was 7.6. There was a statistically significant difference in mean scores for neural impairment, canal SD, and patho-morphology for surgical compared to nonsurgical patients. At the lowest clinical score for each determinant, the probability for surgery was 0.949 for neural impairment, 0.989 for post-injury available canal SD, and 0.971 for patho-morphology. The unit odds ratio for each determinant was 1.73, 1.61, and 1.45, for neural impairment, patho-morphology, and canal SD scores, respectively. The subaxial cervical spine injury severity determinants of neural impairment, patho-morphology, and post-injury available canal SD have well defined probability for surgical intervention when scored separately. Our data showed that each determinant alone could act as a primary predictor for surgical intervention.

Absenteeism screening questionnaire (ASQ): a new tool for predicting long-term absenteeism among workers with low back pain.

PubMed

Truchon, Manon; Schmouth, Marie-Ève; Côté, Denis; Fillion, Lise; Rossignol, Michel; Durand, Marie-José

2012-03-01

Over the last decades, psychosocial factors were identified by many studies as significant predictive variables in the development of disability related to common low back disorders, which thus contributed to the development of biopsychosocial prevention interventions. Biopsychosocial interventions were supposed to be more effective than usual interventions in improving different outcomes. Unfortunately, most of these interventions show inconclusive results. The use of screening questionnaires was proposed as a solution to improve their efficacy. The aim of this study was to validate a new screening questionnaire to identify workers at risk of being absent from work for more than 182 cumulative days and who are more susceptible to benefit from prevention interventions. Injured workers receiving income replacement benefits from the Quebec Compensation Board (n = 535) completed a 67-item questionnaire in the sub-acute stage of pain and provided information about work-related events 6 and 12 months later. Reliability and validity of the 67-item questionnaire were determined respectively by test-retest reliability and internal consistency analysis, as well as by construct validity analyses. The Cox regression model and the maximum likelihood method were used to fix a model allowing calculation of a probability of absence of more than 182 days. Criterion validity and discriminative capacity of this model were calculated. Sub-sections from the 67-item questionnaire were moderately to highly correlated 2 weeks later (r = 0.52-0.80) and showed moderate to good internal consistency (0.70-0.94). Among the 67-item questionnaire, six sub-sections and variables (22 items) were predictive of long-term absence from work: fear-avoidance beliefs related to work, return to work expectations, annual family income before-taxes, last level of education attained, work schedule and work concerns. The area under the ROC curve was 73%. The significant predictive variables of long-term absence from work were dominated by workplace conditions and individual perceptions about work. In association with individual psychosocial variables, these variables could contribute to identify potentially useful prevention interventions and to reduce the significant costs associated with LBP long-term absenteeism.

A predictive model of hospitalization risk among disabled medicaid enrollees.

PubMed

McAna, John F; Crawford, Albert G; Novinger, Benjamin W; Sidorov, Jaan; Din, Franklin M; Maio, Vittorio; Louis, Daniel Z; Goldfarb, Neil I

2013-05-01

To identify Medicaid patients, based on 1 year of administrative data, who were at high risk of admission to a hospital in the next year, and who were most likely to benefit from outreach and targeted interventions. Observational cohort study for predictive modeling. Claims, enrollment, and eligibility data for 2007 from a state Medicaid program were used to provide the independent variables for a logistic regression model to predict inpatient stays in 2008 for fully covered, continuously enrolled, disabled members. The model was developed using a 50% random sample from the state and was validated against the other 50%. Further validation was carried out by applying the parameters from the model to data from a second state's disabled Medicaid population. The strongest predictors in the model developed from the first 50% sample were over age 65 years, inpatient stay(s) in 2007, and higher Charlson Comorbidity Index scores. The areas under the receiver operating characteristic curve for the model based on the 50% state sample and its application to the 2 other samples ranged from 0.79 to 0.81. Models developed independently for all 3 samples were as high as 0.86. The results show a consistent trend of more accurate prediction of hospitalization with increasing risk score. This is a fairly robust method for targeting Medicaid members with a high probability of future avoidable hospitalizations for possible case management or other interventions. Comparison with a second state's Medicaid program provides additional evidence for the usefulness of the model.

Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015

PubMed Central

Mehmandar, Mohammadreza; Soori, Hamid; Mehrabi, Yadolah

2016-01-01

Background: Predicting the trend in traffic accidents deaths and its analysis can be a useful tool for planning and policy-making, conducting interventions appropriate with death trend, and taking the necessary actions required for controlling and preventing future occurrences. Objective: Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. Settings and Design: It was a cross-sectional study. Materials and Methods: All the information related to fatal traffic accidents available in the database of Iran Legal Medicine Organization from 2004 to the end of 2013 were used to determine the change points (multi-variable time series analysis). Using autoregressive integrated moving average (ARIMA) model, traffic accidents death rates were predicted for 2014 and 2015, and a comparison was made between this rate and the predicted value in order to determine the efficiency of the model. Results: From the results, the actual death rate in 2014 was almost similar to that recorded for this year, while in 2015 there was a decrease compared with the previous year (2014) for all the months. A maximum value of 41% was also predicted for the months of January and February, 2015. Conclusion: From the prediction and analysis of the death trends, proper application and continuous use of the intervention conducted in the previous years for road safety improvement, motor vehicle safety improvement, particularly training and culture-fostering interventions, as well as approval and execution of deterrent regulations for changing the organizational behaviors, can significantly decrease the loss caused by traffic accidents. PMID:27308255

Impact of a comprehensive law on the prevalence of tobacco consumption in Spain: evaluation of different scenarios.

PubMed

Raña, P; Pérez-Ríos, M; Santiago-Pérez, M I; Crujeiras, R M

2016-09-01

Since 2011, smoking legislation was hardened in Spain, banning tobacco consumption in all hospitality venues. Law 42/2010 was the first comprehensive tobacco control policy enacted in Spain. The aim of this paper is to evaluate the effect that this intervention has had in reducing the prevalence of tobacco consumption, setting up three scenarios on the basis of different theoretical levels of effect of the law. A predictive model based on Markov Chains was developed to distinguish the effect of tobacco control policies in different scenarios. The model developed uses population, smoking rates and smoking characteristics from a non-transmissible disease surveillance system developed in Galicia (namely SICRI). Results show that tobacco control policies hardly affect the predicted trend in a temporal frame of 10 years, with relative reduction in the predicted male smoking prevalence of 20.4% with no intervention, reaching a reduction of 26.1% under the maximum effect of the policies. In the global population the effects of the law in the predicted prevalence have been barely perceived. For people under 25 years of age, interventions have had an important and positive effect, which proves that policies affecting this age group should be hardened. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Testing an idealized dynamic cascade model of the development of serious violence in adolescence.

PubMed

Dodge, Kenneth A; Greenberg, Mark T; Malone, Patrick S

2008-01-01

A dynamic cascade model of development of serious adolescent violence was proposed and tested through prospective inquiry with 754 children (50% male; 43% African American) from 27 schools at 4 geographic sites followed annually from kindergarten through Grade 11 (ages 5-18). Self, parent, teacher, peer, observer, and administrative reports provided data. Partial least squares analyses revealed a cascade of prediction and mediation: An early social context of disadvantage predicts harsh-inconsistent parenting, which predicts social and cognitive deficits, which predicts conduct problem behavior, which predicts elementary school social and academic failure, which predicts parental withdrawal from supervision and monitoring, which predicts deviant peer associations, which ultimately predicts adolescent violence. Findings suggest targets for in-depth inquiry and preventive intervention.

Gut Microbiota Signatures Predict Host and Microbiota Responses to Dietary Interventions in Obese Individuals

PubMed Central

Korpela, Katri; Flint, Harry J.; Johnstone, Alexandra M.; Lappi, Jenni; Poutanen, Kaisa; Dewulf, Evelyne; Delzenne, Nathalie; de Vos, Willem M.; Salonen, Anne

2014-01-01

Background Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC  =  0.77–1; predicted vs. observed correlation  =  0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of microbiota signatures for personalized nutrition. PMID:24603757

A network model for Ebola spreading.

PubMed

Rizzo, Alessandro; Pedalino, Biagio; Porfiri, Maurizio

2016-04-07

The availability of accurate models for the spreading of infectious diseases has opened a new era in management and containment of epidemics. Models are extensively used to plan for and execute vaccination campaigns, to evaluate the risk of international spreadings and the feasibility of travel bans, and to inform prophylaxis campaigns. Even when no specific therapeutical protocol is available, as for the Ebola Virus Disease (EVD), models of epidemic spreading can provide useful insight to steer interventions in the field and to forecast the trend of the epidemic. Here, we propose a novel mathematical model to describe EVD spreading based on activity driven networks (ADNs). Our approach overcomes the simplifying assumption of homogeneous mixing, which is central to most of the mathematically tractable models of EVD spreading. In our ADN-based model, each individual is not bound to contact every other, and its network of contacts varies in time as a function of an activity potential. Our model contemplates the possibility of non-ideal and time-varying intervention policies, which are critical to accurately describe EVD spreading in afflicted countries. The model is calibrated from field data of the 2014 April-to-December spreading in Liberia. We use the model as a predictive tool, to emulate the dynamics of EVD in Liberia and offer a one-year projection, until December 2015. Our predictions agree with the current vision expressed by professionals in the field, who consider EVD in Liberia at its final stage. The model is also used to perform a what-if analysis to assess the efficacy of timely intervention policies. In particular, we show that an earlier application of the same intervention policy would have greatly reduced the number of EVD cases, the duration of the outbreak, and the infrastructures needed for the implementation of the intervention. Copyright © 2016 Elsevier Ltd. All rights reserved.

A controlled evaluation of an eating disorders primary prevention videotape using the Elaboration Likelihood Model of Persuasion.

PubMed

Withers, Giselle F; Twigg, Kylie; Wertheim, Eleanor H; Paxton, Susan J

2002-11-01

The aim was to extend findings related to a previously reported eating disorders prevention program by comparing treatment and control groups, adding a follow-up, and examining whether receiver characteristics, personal relevance and need for cognition (NFC), could predict attitude change in early adolescent girls. Grade 7 girls were either shown a brief prevention videotape on dieting and body image (n = 104) or given no intervention (n = 114). All girls completed pre-, post- and 1-month follow-up questionnaires. The intervention group resulted in significantly more positive changes in attitude and knowledge at post-intervention, but only in knowledge at follow-up. There was no strong evidence that pre-intervention characteristics of recipients predicted responses to the videotape intervention when changes were compared to the control group. This prevention videotape appeared to have positive immediate effects, but additional intervention (e.g., booster sessions) may be required for longer-term change. Copyright 2002 Elsevier Science Inc.

Inferring interventional predictions from observational learning data.

PubMed

Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

2008-02-01

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.

Predictors of medication adherence in high risk youth of color living with HIV.

PubMed

Macdonell, Karen E; Naar-King, Sylvie; Murphy, Debra A; Parsons, Jeffrey T; Harper, Gary W

2010-07-01

To test predictors of medication adherence in high-risk racial or ethnic minority youth living with HIV (YLH) using a conceptual model of social cognitive predictors including a continuous measure of motivational readiness. Youth were participants in a multi-site clinical trial examining the efficacy of a motivational intervention. Racial-minority YLH (primarily African American) who were prescribed antiretroviral medication were included (N = 104). Data were collected using computer-assisted personal interviewing method via an Internet-based application and questionnaires. Using path analysis with bootstrapping, most youth reported suboptimal adherence, which predicted higher viral load. Higher motivational readiness predicted optimal adherence, and higher social support predicted readiness. Decisional balance was indirectly related to adherence. The model provided a plausible framework for understanding adherence in this population. Culturally competent interventions focused on readiness and social support may be helpful for improving adherence in YLH.

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.

PubMed

Wu, Mike; Ghassemi, Marzyeh; Feng, Mengling; Celi, Leo A; Szolovits, Peter; Doshi-Velez, Finale

2017-05-01

The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations. We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes. We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning. The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean. Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior

PubMed Central

Isma’eel, Hussain A.; Sakr, George E.; Almedawar, Mohamad M.; Fathallah, Jihan; Garabedian, Torkom; Eddine, Savo Bou Zein

2015-01-01

Background High dietary salt intake is directly linked to hypertension and cardiovascular diseases (CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their effectiveness. We aim to compare the accuracy of an artificial neural network (ANN) based tool that predicts behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative to the least square models (LSM) method. Methods We collected knowledge, attitude and behavior data on 115 patients. A behavior score was calculated to classify patients’ behavior towards reducing salt intake. Accuracy comparison between ANN and regression analysis was calculated using the bootstrap technique with 200 iterations. Results Starting from a 69-item questionnaire, a reduced model was developed and included eight knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and reduced models is 82% and 102%, respectively. Conclusions Using ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The statistical model has been implemented in an online calculator and can be used in clinics to estimate the patient’s behavior. This will help implementation in future research to further prove clinical utility of this tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals. PMID:26090333

Attitudes and exercise adherence: test of the Theories of Reasoned Action and Planned Behaviour.

PubMed

Smith, R A; Biddle, S J

1999-04-01

Three studies of exercise adherence and attitudes are reported that tested the Theory of Reasoned Action and the Theory of Planned Behaviour. In a prospective study of adherence to a private fitness club, structural equation modelling path analysis showed that attitudinal and social normative components of the Theory of Reasoned Action accounted for 13.1% of the variance in adherence 4 months later, although only social norm significantly predicted intention. In a second study, the Theory of Planned Behaviour was used to predict both physical activity and sedentary behaviour. Path analyses showed that attitude and perceived control, but not social norm, predicted total physical activity. Physical activity was predicted from intentions and control over sedentary behaviour. Finally, an intervention study with previously sedentary adults showed that intentions to be active measured at the start and end of a 10-week intervention were associated with the planned behaviour variables. A multivariate analysis of variance revealed no significant multivariate effects for time on the planned behaviour variables measured before and after intervention. Qualitative data provided evidence that participants had a positive experience on the intervention programme and supported the role of social normative factors in the adherence process.

Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding

PubMed Central

Billings, John; Georghiou, Theo; Blunt, Ian; Bardsley, Martin

2013-01-01

Objectives To test the performance of new variants of models to identify people at risk of an emergency hospital admission. We compared (1) the impact of using alternative data sources (hospital inpatient, A&E, outpatient and general practitioner (GP) electronic medical records) (2) the effects of local calibration on the performance of the models and (3) the choice of population denominators. Design Multivariate logistic regressions using person-level data adding each data set sequentially to test value of additional variables and denominators. Setting 5 Primary Care Trusts within England. Participants 1 836 099 people aged 18–95 registered with GPs on 31 July 2009. Main outcome measures Models to predict hospital admission and readmission were compared in terms of the positive predictive value and sensitivity for various risk strata and with the receiver operating curve C statistic. Results The addition of each data set showed moderate improvement in the number of patients identified with little or no loss of positive predictive value. However, even with inclusion of GP electronic medical record information, the algorithms identified only a small number of patients with no emergency hospital admissions in the previous 2 years. The model pooled across all sites performed almost as well as the models calibrated to local data from just one site. Using population denominators from GP registers led to better case finding. Conclusions These models provide a basis for wider application in the National Health Service. Each of the models examined produces reasonably robust performance and offers some predictive value. The addition of more complex data adds some value, but we were unable to conclude that pooled models performed less well than those in individual sites. Choices about model should be linked to the intervention design. Characteristics of patients identified by the algorithms provide useful information in the design/costing of intervention strategies to improve care coordination/outcomes for these patients. PMID:23980068

The Empowerment of Low-Income Parents Engaged in a Childhood Obesity Intervention

PubMed Central

Jurkowski, Janine M.; Lawson, Hal A.; Green Mills, Lisa L.; Wilner, Paul G.; Davison, Kirsten K.

2017-01-01

Parents influence children’s obesity risk factors but are infrequently targeted for interventions. This study targeting low-income parents integrated a community-based participatory research approach with the Family Ecological Model and Empowerment Theory to develop a childhood obesity intervention. This article (1) examines pre- to postintervention changes in parents’ empowerment; (2) determines the effects of intervention dose on empowerment, and (3) determines whether changes in parent empowerment mediate previous changes identified in food-, physical activity–, and screen-related parenting. The pre-post quasi-experimental design evaluation demonstrated positive changes in parent empowerment and empowerment predicted improvement in parenting practices. The integrated model applied in this study provides a means to enhance intervention relevance and guide translation to other childhood obesity and health disparities studies. PMID:24569157

Predictors of the physical impact of Multiple Sclerosis following community-based, exercise trial.

PubMed

Kehoe, M; Saunders, J; Jakeman, P; Coote, S

2015-04-01

Studies evaluating exercise interventions in people with multiple sclerosis (PwMS) demonstrate small to medium positive effects and large variability on a number of outcome measures. No study to date has tried to explain this variability. This paper presents a novel exploration of data examining the predictors of outcome for PwMS with minimal gait impairment following a randomised, controlled trial evaluating community-based exercise interventions (N = 242). The primary variable was the physical component of the Multiple Sclerosis Impact Scale-29, version 2 (MSIS-29, v2) after a 10-week, controlled intervention period. Predictors were identified a priori and were measured at baseline. Multiple linear regression was conducted. Four models are presented lower MSIS-29, v2 scores after the intervention period were best predicted by a lower baseline MSIS-29,v2, a lower baseline Modified Fatigue Impact Score (physical subscale), randomisation to an exercise intervention, a longer baseline walking distance measured by the Six Minute Walk Test and female gender. This model explained 57.4% of the variance (F (5, 211) = 59.24, p < 0.01). These results suggest that fatigue and walking distance at baseline contribute significantly to predicting MSIS-29, v29 (physical component) after intervention, and thus should be the focus of intervention and assessment. Exercise is an important contributor to minimising the physical impact of MS, and gender-specific interventions may be warranted. © The Author(s), 2014.

Development and applications of the Veterans Health Administration's Stratification Tool for Opioid Risk Mitigation (STORM) to improve opioid safety and prevent overdose and suicide.

PubMed

Oliva, Elizabeth M; Bowe, Thomas; Tavakoli, Sara; Martins, Susana; Lewis, Eleanor T; Paik, Meenah; Wiechers, Ilse; Henderson, Patricia; Harvey, Michael; Avoundjian, Tigran; Medhanie, Amanuel; Trafton, Jodie A

2017-02-01

Concerns about opioid-related adverse events, including overdose, prompted the Veterans Health Administration (VHA) to launch an Opioid Safety Initiative and Overdose Education and Naloxone Distribution program. To mitigate risks associated with opioid prescribing, a holistic approach that takes into consideration both risk factors (e.g., dose, substance use disorders) and risk mitigation interventions (e.g., urine drug screening, psychosocial treatment) is needed. This article describes the Stratification Tool for Opioid Risk Mitigation (STORM), a tool developed in VHA that reflects this holistic approach and facilitates patient identification and monitoring. STORM prioritizes patients for review and intervention according to their modeled risk for overdose/suicide-related events and displays risk factors and risk mitigation interventions obtained from VHA electronic medical record (EMR)-data extracts. Patients' estimated risk is based on a predictive risk model developed using fiscal year 2010 (FY2010: 10/1/2009-9/30/2010) EMR-data extracts and mortality data among 1,135,601 VHA patients prescribed opioid analgesics to predict risk for an overdose/suicide-related event in FY2011 (2.1% experienced an event). Cross-validation was used to validate the model, with receiver operating characteristic curves for the training and test data sets performing well (>.80 area under the curve). The predictive risk model distinguished patients based on risk for overdose/suicide-related adverse events, allowing for identification of high-risk patients and enrichment of target populations of patients with greater safety concerns for proactive monitoring and application of risk mitigation interventions. Results suggest that clinical informatics can leverage EMR-extracted data to identify patients at-risk for overdose/suicide-related events and provide clinicians with actionable information to mitigate risk. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A novel risk score model for prediction of contrast-induced nephropathy after emergent percutaneous coronary intervention.

PubMed

Lin, Kai-Yang; Zheng, Wei-Ping; Bei, Wei-Jie; Chen, Shi-Qun; Islam, Sheikh Mohammed Shariful; Liu, Yong; Xue, Lin; Tan, Ning; Chen, Ji-Yan

2017-03-01

A few studies developed simple risk model for predicting CIN with poor prognosis after emergent PCI. The study aimed to develop and validate a novel tool for predicting the risk of contrast-induced nephropathy (CIN) in patients undergoing emergent percutaneous coronary intervention (PCI). 692 consecutive patients undergoing emergent PCI between January 2010 and December 2013 were randomly (2:1) assigned to a development dataset (n=461) and a validation dataset (n=231). Multivariate logistic regression was applied to identify independent predictors of CIN, and established CIN predicting model, whose prognostic accuracy was assessed using the c-statistic for discrimination and the Hosmere Lemeshow test for calibration. The overall incidence of CIN was 55(7.9%). A total of 11 variables were analyzed, including age >75years old, baseline serum creatinine (SCr)>1.5mg/dl, hypotension and the use of intra-aortic balloon pump(IABP), which were identified to enter risk score model (Chen). The incidence of CIN was 32(6.9%) in the development dataset (in low risk (score=0), 1.0%, moderate risk (score:1-2), 13.4%, high risk (score≥3), 90.0%). Compared to the classical Mehran's and ACEF CIN risk score models, the risk score (Chen) across the subgroup of the study population exhibited similar discrimination and predictive ability on CIN (c-statistic:0.828, 0.776, 0.853, respectively), in-hospital mortality, 2, 3-years mortality (c-statistic:0.738.0.750, 0.845, respectively) in the validation population. Our data showed that this simple risk model exhibited good discrimination and predictive ability on CIN, similar to Mehran's and ACEF score, and even on long-term mortality after emergent PCI. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

PubMed

Zou, Kelly H; Resnic, Frederic S; Talos, Ion-Florin; Goldberg-Zimring, Daniel; Bhagwat, Jui G; Haker, Steven J; Kikinis, Ron; Jolesz, Ferenc A; Ohno-Machado, Lucila

2005-10-01

Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

Developing the content of two behavioural interventions: Using theory-based interventions to promote GP management of upper respiratory tract infection without prescribing antibiotics #1

PubMed Central

Hrisos, Susan; Eccles, Martin; Johnston, Marie; Francis, Jill; Kaner, Eileen FS; Steen, Nick; Grimshaw, Jeremy

2008-01-01

Background Evidence shows that antibiotics have limited effectiveness in the management of upper respiratory tract infection (URTI) yet GPs continue to prescribe antibiotics. Implementation research does not currently provide a strong evidence base to guide the choice of interventions to promote the uptake of such evidence-based practice by health professionals. While systematic reviews demonstrate that interventions to change clinical practice can be effective, heterogeneity between studies hinders generalisation to routine practice. Psychological models of behaviour change that have been used successfully to predict variation in behaviour in the general population can also predict the clinical behaviour of healthcare professionals. The purpose of this study was to design two theoretically-based interventions to promote the management of upper respiratory tract infection (URTI) without prescribing antibiotics. Method Interventions were developed using a systematic, empirically informed approach in which we: selected theoretical frameworks; identified modifiable behavioural antecedents that predicted GPs intended and actual management of URTI; mapped these target antecedents on to evidence-based behaviour change techniques; and operationalised intervention components in a format suitable for delivery by postal questionnaire. Results We identified two psychological constructs that predicted GP management of URTI: "Self-efficacy," representing belief in one's capabilities, and "Anticipated consequences," representing beliefs about the consequences of one's actions. Behavioural techniques known to be effective in changing these beliefs were used in the design of two paper-based, interactive interventions. Intervention 1 targeted self-efficacy and required GPs to consider progressively more difficult situations in a "graded task" and to develop an "action plan" of what to do when next presented with one of these situations. Intervention 2 targeted anticipated consequences and required GPs to respond to a "persuasive communication" containing a series of pictures representing the consequences of managing URTI with and without antibiotics. Conclusion It is feasible to systematically develop theoretically-based interventions to change professional practice. Two interventions were designed that differentially target generalisable constructs predictive of GP management of URTI. Our detailed and scientific rationale for the choice and design of our interventions will provide a basis for understanding any effects identified in their evaluation. Trial registration Clinicaltrials.gov NCT00376142 PMID:18194527

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

PubMed

Jamei, Mehdi; Nisnevich, Aleksandr; Wetchler, Everett; Sudat, Sylvia; Liu, Eric

2017-01-01

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks

PubMed Central

2017-01-01

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health’s EHR system, we built and tested an artificial neural network (NN) model based on Google’s TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions. PMID:28708848

Preferences for Early Intervention Mental Health Services: A Discrete-Choice Conjoint Experiment.

PubMed

Becker, Mackenzie P E; Christensen, Bruce K; Cunningham, Charles E; Furimsky, Ivana; Rimas, Heather; Wilson, Fiona; Jeffs, Lisa; Bieling, Peter J; Madsen, Victoria; Chen, Yvonne Y S; Mielko, Stephanie; Zipursky, Robert B

2016-02-01

Early intervention services (EISs) for mental illness may improve outcomes, although treatment engagement is often a problem. Incorporating patients' preferences in the design of interventions improves engagement. A discrete-choice conjoint experiment was conducted in Canada to identify EIS attributes that encourage treatment initiation. Sixteen four-level attributes were formalized into a conjoint survey, completed by patients, family members, and mental health professionals (N=562). Participants were asked which EIS option people with mental illness would contact. Latent-class analysis identified respondent classes characterized by shared preferences. Randomized first-choice simulations predicted which hypothetical options, based on attributes, would result in maximum utilization. Participants in the conventional-service class (N=241, 43%) predicted that individuals would contact traditional services (for example, hospital location and staffed by psychologists or psychiatrists). Membership was associated with being a patient or family member and being male. Participants in the convenient-service class (N=321, 57%) predicted that people would contact services promoting easy access (for example, self-referral and access from home). Membership was associated with being a professional. Both classes predicted that people would contact services that included short wait times, direct contact with professionals, patient autonomy, and psychological treatment information. The convenient-service class predicted that people would use an e-health model, whereas the conventional-service class predicted that people would use a primary care or clinic-hospital model. Provision of a range of services may maximize EIS use. Professionals may be more apt to adopt EISs in line with their beliefs regarding patient preferences. Considering several perspectives is important for service design.

The effectiveness of asking behaviors among 9-11 year-old children in increasing home availability and children's intake of fruit and vegetables: results from the Squire's Quest II self-regulation game intervention.

PubMed

DeSmet, Ann; Liu, Yan; De Bourdeaudhuij, Ilse; Baranowski, Tom; Thompson, Debbe

2017-04-21

Home environment has an important influence on children's fruit and vegetable (FV) consumption, but children may in turn also impact their home FV environment, e.g. by asking for FV. The Squire's Quest II serious game intervention aimed to increase asking behaviors to improve home FV availability and children's FV intake. This study's aims were to assess: 1) did asking behaviors at baseline predict home FV availability at baseline (T0) (RQ1); 2) were asking behaviors and home FV availability influenced by the intervention (RQ2); 3) did increases in asking behaviors predict increased home FV availability (RQ3); and 4) did increases in asking behaviors and increases in home FV availability mediate increases in FV intake among children (RQ4)? This is a secondary analysis of a study using a randomized controlled trial, with 4 groups (each n = 100 child-parent dyads). All groups were analyzed together for this paper since groups did not vary on components relevant to our analysis. All children and parents (n = 400 dyads) received a self-regulation serious game intervention and parent material. The intervention ran for three months. Measurements were taken at baseline, immediately after intervention and at 3-month follow-up. Asking behavior and home FV availability were measured using questionnaires; child FV intake was measured using 24-h dietary recalls. ANCOVA methods (research question 1), linear mixed-effect models (research question 2), and Structural Equation Modeling (research questions 3 and 4) were used. Baseline child asking behaviors predicted baseline home FV availability. The intervention increased child asking behaviors and home FV availability. Increases in child asking behaviors, however, did not predict increased home FV availability. Increased child asking behaviors and home FV availability also did not mediate the increases in child FV intake. Children influence their home FV environment through their asking behaviors, which can be enhanced via a serious game intervention. The obtained increases in asking behavior were, however, insufficient to affect home FV availability or intake. Other factors, such as child preferences, sample characteristics, intervention duration and parental direct involvement may play a role and warrant examination in future research. ClinicalTrials.gov NCT01004094 . Date registered 10/28/2009.

Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions

PubMed Central

Kok, Robin N; Ossebaard, Hans C; Van Gemert-Pijnen, Julia EWC

2012-01-01

Background Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. Objective This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. Methods We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. Results We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Conclusions Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere. PMID:23151820

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

PubMed Central

2017-01-01

This paper summarises key advances and priorities since the 2011 presentation of the Malaria Eradication Research Agenda (malERA), with a focus on the combinations of intervention tools and strategies for elimination and their evaluation using modelling approaches. With an increasing number of countries embarking on malaria elimination programmes, national and local decisions to select combinations of tools and deployment strategies directed at malaria elimination must address rapidly changing transmission patterns across diverse geographic areas. However, not all of these approaches can be systematically evaluated in the field. Thus, there is potential for modelling to investigate appropriate ‘packages’ of combined interventions that include various forms of vector control, case management, surveillance, and population-based approaches for different settings, particularly at lower transmission levels. Modelling can help prioritise which intervention packages should be tested in field studies, suggest which intervention package should be used at a particular level or stratum of transmission intensity, estimate the risk of resurgence when scaling down specific interventions after local transmission is interrupted, and evaluate the risk and impact of parasite drug resistance and vector insecticide resistance. However, modelling intervention package deployment against a heterogeneous transmission background is a challenge. Further validation of malaria models should be pursued through an iterative process, whereby field data collected with the deployment of intervention packages is used to refine models and make them progressively more relevant for assessing and predicting elimination outcomes. PMID:29190295

Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression

PubMed Central

Arndt, Alice; Rubel, Julian; Berger, Thomas; Schröder, Johanna; Späth, Christina; Meyer, Björn; Greiner, Wolfgang; Gräfe, Viola; Hautzinger, Martin; Fuhr, Kristina; Rose, Matthias; Nolte, Sandra; Löwe, Bernd; Hohagen, Fritz; Klein, Jan Philipp; Moritz, Steffen

2017-01-01

Background Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective adjunct to face-to-face psychotherapy or pharmacological treatment. Objective The aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects. Methods We applied piecewise growth mixture modeling (PGMM) to identify different latent classes of early change in individuals with mild-to-moderate depression (n=409) who underwent a CBT-based web intervention for depression. Results Overall, three latent classes were identified (N=409): Two early response classes (n=158, n=185) and one early deterioration class (n=66). Latent classes differed in terms of outcome (P<.001) and adherence (P=.03) in regard to the number of modules (number of modules with a duration of at least 10 minutes) and the number of assessments (P<.001), but not in regard to the overall amount of time using the system. Class membership significantly improved outcome prediction by 24.8% over patient intake characteristics (P<.001) and significantly added to the prediction of adherence (P=.04). Conclusions These findings suggest that in Web-based interventions outcome and adherence can be predicted by patterns of early change, which can inform treatment decisions and potentially help optimize the allocation of scarce clinical resources. PMID:28600278

Predicting non-familial major physical violent crime perpetration in the US Army from administrative data.

PubMed

Rosellini, A J; Monahan, J; Street, A E; Heeringa, S G; Hill, E D; Petukhova, M; Reis, B Y; Sampson, N A; Bliese, P; Schoenbaum, M; Stein, M B; Ursano, R J; Kessler, R C

2016-01-01

Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers. A consolidated administrative database for all 975 057 soldiers in the US Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011-2013 sample. Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80-0.82 in 2004-2009 and 0.77 in the 2011-2013 validation sample. Of all administratively recorded crimes, 36.2-33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks.

Predicting non-familial major physical violent crime perpetration in the U.S. Army from administrative data

PubMed Central

Rosellini, Anthony J.; Monahan, John; Street, Amy E.; Heeringa, Steven G.; Hill, Eric D.; Petukhova, Maria; Reis, Ben Y.; Sampson, Nancy A.; Bliese, Paul; Schoenbaum, Michael; Stein, Murray B.; Ursano, Robert; Kessler, Ronald C.

2016-01-01

BACKGROUND Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among U.S. Army soldiers. METHODS A consolidated administrative database for all 975,057 soldiers in the U.S. Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). 5,771 of these soldiers committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression; random forests; penalized regressions). The model was then validated in an independent 2011-2013 sample. RESULTS Key predictors were indicators of disadvantaged social/socio-economic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver operating characteristic curve was .80-.82 in 2004-2009 and .77 in a 2011-2013 validation sample. 36.2-33.1% (male-female) of all administratively-recorded crimes were committed by the 5% of soldiers having highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. CONCLUSIONS Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks. PMID:26436603

An Intervention-Based Model of Student Retention in Adult Learners: Factors Predicting Intention to Consider Leaving or Staying

ERIC Educational Resources Information Center

Mooshegian, Stephanie E.

2010-01-01

The current study merges theory and research in higher education and organizational psychology in order to investigate student retention in adult learners. Factors that are associated with student retention were examined and points of intervention are recommended. Specifically, this study focuses on the role of campus environment, classroom…

Changing the Culture of Silence: The Potential of an Online Educational Sexual Health and Female Cancer Prevention Intervention in Pakistan

ERIC Educational Resources Information Center

Vahe, Mariliis

2013-01-01

This dissertation evaluates the effectiveness of a customized educational health intervention on sexual health and female cancer prevention among young women in Pakistan and evaluates the applicability of the integrated model of behavior prevention (IM) when predicting three health behaviors among this population. The study used randomized…

Exploring the Relationship between Cognitive Characteristics and Responsiveness to a Tier 3 Reading Fluency Intervention

ERIC Educational Resources Information Center

Field, Stacey Allyson

2015-01-01

Current research suggests that certain cognitive functions predict the likelihood of intervention response for students who receive Tier 2 instruction through an RTI-framework. However, less is known about cognitive predictors of responder status at a theoretically more critical point of divergence within the RTI model: Tier 3. Moreover, no…

Predictive models of alcohol use based on attitudes and individual values.

PubMed

García del Castillo Rodríguez, José A; López-Sánchez, Carmen; Quiles Soler, M Carmen; García del Castillo-López, Alvaro; Gázquez Pertusa, Mónica; Marzo Campos, Juan Carlos; Inglés, Candido J

2013-01-01

Two predictive models are developed in this article: the first is designed to predict people's attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The questionnaire used obtained information on participants' alcohol use, attitudes and personal values. The results show that the attitudes model correctly classifies 76.3% of cases. Likewise, the model for level of alcohol use correctly classifies 82% of cases. According to our results, we can conclude that there are a series of individual values that influence drinking and attitudes to alcohol use, which therefore provides us with a potentially powerful instrument for developing preventive intervention programs.

Beyond the usual suspects: target group- and behavior-specific factors add to a theory-based sun protection intervention for teenagers.

PubMed

Schüz, Natalie; Eid, Michael

2013-10-01

Sun protection standards among teenagers are low while sun exposure peaks in this age group. Study 1 explores predictors of adolescent protection intentions and exposure behavior. Study 2 tests the effectiveness of an intervention based on these predictors. Study 1(cross-sectional, N = 207, ages 15-18) and Study 2 (RCT, N = 253, ages 13-19) were conducted in schools. Path models were used to analyze data. Self-efficacy (β = .26, p < .001) and time perspective (β = .17, p = .014) were the strongest predictors of intentions; appearance motivation (β = .54, p < .001) and intention (β = -.18, p = .015) predicted behavior. The intervention effected changes in all predictors except self-efficacy. Changes in outcome expectancies (β = .19, p < .001) and time perspective (β = .09, p = .039) predicted changes in intention, while changes in intention (β = -.17, p = .002) and appearance motivation (β = .29, p < .001) predicted behavior changes. Target group- and behavior-specific intervention components are as important for changes in intentions and behavior as components derived from common health behavior theories.

Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

PubMed

Mokhtari, Fatemeh; Rejeski, W Jack; Zhu, Yingying; Wu, Guorong; Simpson, Sean L; Burdette, Jonathan H; Laurienti, Paul J

2018-06-01

More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to examine whether these models have clinical utility in tailoring treatment. Copyright © 2018 Elsevier Inc. All rights reserved.

Using behavior change frameworks to improve healthcare worker influenza vaccination rates: A systematic review.

PubMed

Corace, Kimberly M; Srigley, Jocelyn A; Hargadon, Daniel P; Yu, Dorothy; MacDonald, Tara K; Fabrigar, Leandre R; Garber, Gary E

2016-06-14

Influenza vaccination of healthcare workers (HCW) is important for protecting staff and patients, yet vaccine coverage among HCW remains below recommended targets. Psychological theories of behavior change may help guide interventions to improve vaccine uptake. Our objectives were to: (1) review the effectiveness of interventions based on psychological theories of behavior change to improve HCW influenza vaccination rates, and (2) determine which psychological theories have been used to predict HCW influenza vaccination uptake. MEDLINE, EMBASE, CINAHL, PsycINFO, The Joanna Briggs Institute, SocINDEX, and Cochrane Database of Systematic Reviews were searched for studies that applied psychological theories of behavior change to improve and/or predict influenza vaccination uptake among HCW. The literature search yielded a total of 1810 publications; 10 articles met eligibility criteria. All studies used behavior change theories to predict HCW vaccination behavior; none evaluated interventions based on these theories. The Health Belief Model was the most frequently employed theory to predict influenza vaccination uptake among HCW. The remaining predictive studies employed the Theory of Planned Behavior, the Risk Perception Attitude, and the Triandis Model of Interpersonal Behavior. The behavior change framework constructs were successful in differentiating between vaccinated and non-vaccinated HCW. Key constructs identified included: attitudes regarding the efficacy and safety of influenza vaccination, perceptions of risk and benefit to self and others, self-efficacy, cues to action, and social-professional norms. The behavior change frameworks, along with sociodemographic variables, successfully predicted 85-95% of HCW influenza vaccination uptake. Vaccination is a complex behavior. Our results suggest that psychological theories of behavior change are promising tools to increase HCW influenza vaccination uptake. Future studies are needed to develop and evaluate novel interventions based on behavior change theories, which may help achieve recommended HCW vaccination targets. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Estimating the Need for Medical Intervention due to Sleep Disruption on the International Space Station

NASA Technical Reports Server (NTRS)

Myers, Jerry G.; Lewandowski, Beth E.; Brooker, John E.; Hurst, S. R.; Mallis, Melissa M.; Caldwell, J. Lynn

2008-01-01

During ISS and shuttle missions, difficulties with sleep affect more than half of all US crews. Mitigation strategies to help astronauts cope with the challenges of disrupted sleep patterns can negatively impact both mission planning and vehicle design. The methods for addressing known detrimental impacts for some mission scenarios may have a substantial impact on vehicle specific consumable mass or volume or on the mission timeline. As part of the Integrated Medical Model (IMM) task, NASA Glenn Research Center is leading the development of a Monte Carlo based forecasting tool designed to determine the consumables required to address risks related to sleep disruption. The model currently focuses on the International Space Station and uses an algorithm that assembles representative mission schedules and feeds this into a well validated model that predicts relative levels of performance, and need for sleep (SAFTE Model, IBR Inc). Correlation of the resulting output to self-diagnosed needs for hypnotics, stimulants, and other pharmaceutical countermeasures, allows prediction of pharmaceutical use and the uncertainty of the specified prediction. This paper outlines a conceptual model for determining a rate of pharmaceutical utilization that can be used in the IMM model for comparison and optimization of mitigation methods with respect to all other significant medical needs and interventions.

A behavior change model for internet interventions.

PubMed

Ritterband, Lee M; Thorndike, Frances P; Cox, Daniel J; Kovatchev, Boris P; Gonder-Frederick, Linda A

2009-08-01

The Internet has become a major component to health care and has important implications for the future of the health care system. One of the most notable aspects of the Web is its ability to provide efficient, interactive, and tailored content to the user. Given the wide reach and extensive capabilities of the Internet, researchers in behavioral medicine have been using it to develop and deliver interactive and comprehensive treatment programs with the ultimate goal of impacting patient behavior and reducing unwanted symptoms. To date, however, many of these interventions have not been grounded in theory or developed from behavior change models, and no overarching model to explain behavior change in Internet interventions has yet been published. The purpose of this article is to propose a model to help guide future Internet intervention development and predict and explain behavior changes and symptom improvement produced by Internet interventions. The model purports that effective Internet interventions produce (and maintain) behavior change and symptom improvement via nine nonlinear steps: the user, influenced by environmental factors, affects website use and adherence, which is influenced by support and website characteristics. Website use leads to behavior change and symptom improvement through various mechanisms of change. The improvements are sustained via treatment maintenance. By grounding Internet intervention research within a scientific framework, developers can plan feasible, informed, and testable Internet interventions, and this form of treatment will become more firmly established.

Patient-specific simulation of the intrastromal ring segment implantation in corneas with keratoconus.

PubMed

Lago, M A; Rupérez, M J; Monserrat, C; Martínez-Martínez, F; Martínez-Sanchis, S; Larra, E; Díez-Ajenjo, M A; Peris-Martínez, C

2015-11-01

The purpose of this study was the simulation of the implantation of intrastromal corneal-ring segments for patients with keratoconus. The aim of the study was the prediction of the corneal curvature recovery after this intervention. Seven patients with keratoconus diagnosed and treated by implantation of intrastromal corneal-ring segments were enrolled in the study. The 3D geometry of the cornea of each patient was obtained from its specific topography and a hyperelastic model was assumed to characterize its mechanical behavior. To simulate the intervention, the intrastromal corneal-ring segments were modeled and placed at the same location at which they were placed in the surgery. The finite element method was then used to obtain a simulation of the deformation of the cornea after the ring segment insertion. Finally, the predicted curvature was compared with the real curvature after the intervention. The simulation of the ring segment insertion was validated comparing the curvature change with the data after the surgery. Results showed a flattening of the cornea which was in consonance with the real improvement of the corneal curvature. The mean difference obtained was of 0.74 mm using properties of healthy corneas. For the first time, a patient-specific model of the cornea has been used to predict the outcomes of the surgery after the intrastromal corneal-ring segments implantation in real patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

Computational Models Used to Assess US Tobacco Control Policies.

PubMed

Feirman, Shari P; Glasser, Allison M; Rose, Shyanika; Niaura, Ray; Abrams, David B; Teplitskaya, Lyubov; Villanti, Andrea C

2017-11-01

Simulation models can be used to evaluate existing and potential tobacco control interventions, including policies. The purpose of this systematic review was to synthesize evidence from computational models used to project population-level effects of tobacco control interventions. We provide recommendations to strengthen simulation models that evaluate tobacco control interventions. Studies were eligible for review if they employed a computational model to predict the expected effects of a non-clinical US-based tobacco control intervention. We searched five electronic databases on July 1, 2013 with no date restrictions and synthesized studies qualitatively. Six primary non-clinical intervention types were examined across the 40 studies: taxation, youth prevention, smoke-free policies, mass media campaigns, marketing/advertising restrictions, and product regulation. Simulation models demonstrated the independent and combined effects of these interventions on decreasing projected future smoking prevalence. Taxation effects were the most robust, as studies examining other interventions exhibited substantial heterogeneity with regard to the outcomes and specific policies examined across models. Models should project the impact of interventions on overall tobacco use, including nicotine delivery product use, to estimate preventable health and cost-saving outcomes. Model validation, transparency, more sophisticated models, and modeling policy interactions are also needed to inform policymakers to make decisions that will minimize harm and maximize health. In this systematic review, evidence from multiple studies demonstrated the independent effect of taxation on decreasing future smoking prevalence, and models for other tobacco control interventions showed that these strategies are expected to decrease smoking, benefit population health, and are reasonable to implement from a cost perspective. Our recommendations aim to help policymakers and researchers minimize harm and maximize overall population-level health benefits by considering the real-world context in which tobacco control interventions are implemented. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A Protective Factors Model for Alcohol Abuse and Suicide Prevention among Alaska Native Youth

PubMed Central

Allen, James; Mohatt, Gerald V.; Fok, Carlotta Ching Ting; Henry, David; Burkett, Rebekah

2014-01-01

This study provides an empirical test of a culturally grounded theoretical model for prevention of alcohol abuse and suicide risk with Alaska Native youth, using a promising set of culturally appropriate measures for the study of the process of change and outcome. This model is derived from qualitative work that generated an heuristic model of protective factors from alcohol (Allen at al., 2006; Mohatt, Hazel et al., 2004; Mohatt, Rasmus et al., 2004). Participants included 413 rural Alaska Native youth ages 12-18 who assisted in testing a predictive model of Reasons for Life and Reflective Processes about alcohol abuse consequences as co-occurring outcomes. Specific individual, family, peer, and community level protective factor variables predicted these outcomes. Results suggest prominent roles for these predictor variables as intermediate prevention strategy target variables in a theoretical model for a multilevel intervention. The model guides understanding of underlying change processes in an intervention to increase the ultimate outcome variables of Reasons for Life and Reflective Processes regarding the consequences of alcohol abuse. PMID:24952249

Transactions Between Substance Use Intervention, the Oxytocin Receptor (OXTR) Gene, and Peer Substance Use Predicting Youth Alcohol Use.

PubMed

Cleveland, H Harrington; Griffin, Amanda M; Wolf, Pedro S A; Wiebe, Richard P; Schlomer, Gabriel L; Feinberg, Mark E; Greenberg, Mark T; Spoth, Richard L; Redmond, Cleve; Vandenbergh, David J

2018-01-01

This study investigated the oxytocin receptor (OXTR) gene's moderation of associations between exposure to a substance misuse intervention, average peer substance use, and adolescents' own alcohol use during the 9th-grade. OXTR genetic risk was measured using five single nucleotide polymorphisms (SNPs), and peer substance use was based on youths' nominated closest friends' own reports of alcohol, cigarette, and marijuana use, based on data from the PROSPER project. Regression models revealed several findings. First, low OXTR risk was linked to affiliating with friends who reported less substance use in the intervention condition but not the control condition. Second, affiliating with high substance-using friends predicted youth alcohol risk regardless of OXTR risk or intervention condition. Third, although high OXTR risk youth in the intervention condition who associated with low substance-using friends reported somewhat higher alcohol use than comparable youth in the control group, the absolute level of alcohol use among these youth was still among the lowest in the sample.

Longitudinal predictors of high school completion.

PubMed

Barry, Melissa; Reschly, Amy L

2012-06-01

This longitudinal study examined predictors of dropout assessed in elementary school. Student demographic data, achievement, attendance, and ratings of behavior from the Behavior Assessment System for Children were used to predict dropout and completion. Two models, which varied on student sex and race, predicted dropout at rates ranging from 75% to 88%. Model A, which included the Behavioral Symptoms Index, School Problems composite, Iowa Tests of Basic Skills battery, and teacher ratings of student work habits, best predicted female and African American dropouts. Model B, which comprised the Adaptive Skills composite, the Externalizing composite, the School Problems composite, referral for a student support team meeting, and sex, was more accurate for predicting Caucasian dropouts. Both models demonstrated the same hit rates for predicting male dropouts. Recommendations for early warning indicators and linking predictors with interventions are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Academic motivation, self-concept, engagement, and performance in high school: key processes from a longitudinal perspective.

PubMed

Green, Jasmine; Liem, Gregory Arief D; Martin, Andrew J; Colmar, Susan; Marsh, Herbert W; McInerney, Dennis

2012-10-01

The study tested three theoretically/conceptually hypothesized longitudinal models of academic processes leading to academic performance. Based on a longitudinal sample of 1866 high-school students across two consecutive years of high school (Time 1 and Time 2), the model with the most superior heuristic value demonstrated: (a) academic motivation and self-concept positively predicted attitudes toward school; (b) attitudes toward school positively predicted class participation and homework completion and negatively predicted absenteeism; and (c) class participation and homework completion positively predicted test performance whilst absenteeism negatively predicted test performance. Taken together, these findings provide support for the relevance of the self-system model and, particularly, the importance of examining the dynamic relationships amongst engagement factors of the model. The study highlights implications for educational and psychological theory, measurement, and intervention. Copyright © 2012 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Using predictive analytics and big data to optimize pharmaceutical outcomes.

PubMed

Hernandez, Inmaculada; Zhang, Yuting

2017-09-15

The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics in implementing population health management interventions that target medication-related patient outcomes. In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data as well as data collected from smartphone applications, wearable devices, social media, and personal genomics services; predictive analytics refers to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging from predictive modeling to machine learning to data mining. Predictive analytics using big data have been applied successfully in several areas of medication management, such as in the identification of complex patients or those at highest risk for medication noncompliance or adverse effects. Because predictive analytics can be used in predicting different outcomes, they can provide pharmacists with a better understanding of the risks for specific medication-related problems that each patient faces. This information will enable pharmacists to deliver interventions tailored to patients' needs. In order to take full advantage of these benefits, however, clinicians will have to understand the basics of big data and predictive analytics. Predictive analytics that leverage big data will become an indispensable tool for clinicians in mapping interventions and improving patient outcomes. Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Processes of behavior change and weight loss in a theory-based weight loss intervention program: a test of the process model for lifestyle behavior change.

PubMed

Gillison, Fiona; Stathi, Afroditi; Reddy, Prasuna; Perry, Rachel; Taylor, Gordon; Bennett, Paul; Dunbar, James; Greaves, Colin

2015-01-16

Process evaluation is important for improving theories of behavior change and behavioral intervention methods. The present study reports on the process outcomes of a pilot test of the theoretical model (the Process Model for Lifestyle Behavior Change; PMLBC) underpinning an evidence-informed, theory-driven, group-based intervention designed to promote healthy eating and physical activity for people with high cardiovascular risk. 108 people at high risk of diabetes or heart disease were randomized to a group-based weight management intervention targeting diet and physical activity plus usual care, or to usual care. The intervention comprised nine group based sessions designed to promote motivation, social support, self-regulation and understanding of the behavior change process. Weight loss, diet, physical activity and theoretically defined mediators of change were measured pre-intervention, and after four and 12 months. The intervention resulted in significant improvements in fiber intake (M between-group difference = 5.7 g/day, p < .001) but not fat consumption (-2.3 g/day, p = 0.13), that were predictive of weight loss at both four months (M between-group difference = -1.98 kg, p < .01; R(2) = 0.2, p < 0.005), and 12 months (M difference = -1.85 kg, p = 0.1; R(2) = 0.1, p < 0.01). The intervention was successful in improving the majority of specified mediators of behavior change, and the predicted mechanisms of change specified in the PMBLC were largely supported. Improvements in self-efficacy and understanding of the behavior change process were associated with engagement in coping planning and self-monitoring activities, and successful dietary change at four and 12 months. While participants reported improvements in motivational and social support variables, there was no effect of these, or of the intervention overall, on physical activity. The data broadly support the theoretical model for supporting some dietary changes, but not for physical activity. Systematic intervention design allowed us to identify where improvements to the intervention may be implemented to promote change in all proposed mediators. More work is needed to explore effective mechanisms within interventions to promote physical activity behavior.

Self-Regulation and Recall: Growth Curve Modeling of Intervention Outcomes for Older Adults

PubMed Central

West, Robin L.; Hastings, Erin C.

2013-01-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills. PMID:21604891

Self-regulation and recall: growth curve modeling of intervention outcomes for older adults.

PubMed

West, Robin L; Hastings, Erin C

2011-12-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills.

Predicting Successful Treatment Outcome of Web-Based Self-help for Problem Drinkers: Secondary Analysis From a Randomized Controlled Trial

PubMed Central

Kramer, Jeannet; Keuken, Max; Smit, Filip; Schippers, Gerard; Cuijpers, Pim

2008-01-01

Background Web-based self-help interventions for problem drinking are coming of age. They have shown promising results in terms of cost-effectiveness, and they offer opportunities to reach out on a broad scale to problem drinkers. The question now is whether certain groups of problem drinkers benefit more from such Web-based interventions than others. Objective We sought to identify baseline, client-related predictors of the effectiveness of Drinking Less, a 24/7, free-access, interactive, Web-based self-help intervention without therapist guidance for problem drinkers who want to reduce their alcohol consumption. The intervention is based on cognitive-behavioral and self-control principles. Methods We conducted secondary analysis of data from a pragmatic randomized trial with follow-up at 6 and 12 months. Participants (N = 261) were adult problem drinkers in the Dutch general population with a weekly alcohol consumption above 210 g of ethanol for men or 140 g for women, or consumption of at least 60 g (men) or 40 g (women) one or more days a week over the past 3 months. Six baseline participant characteristics were designated as putative predictors of treatment response: (1) gender, (2) education, (3) Internet use competence (sociodemographics), (4) mean weekly alcohol consumption, (5) prior professional help for alcohol problems (level of problem drinking), and (6) participants’ expectancies of Web-based interventions for problem drinking. Intention-to-treat (ITT) analyses, using last-observation-carried-forward (LOCF) data, and regression imputation (RI) were performed to deal with loss to follow-up. Statistical tests for interaction terms were conducted and linear regression analysis was performed to investigate whether the participants’ characteristics as measured at baseline predicted positive treatment responses at 6- and 12-month follow-ups. Results At 6 months, prior help for alcohol problems predicted a small, marginally significant positive treatment outcome in the RI model only (beta = .18, P = .05, R2 = .11). At 12 months, females displayed modest predictive power in both imputation models (LOCF: beta = .22, P = .045, R2 = .02; regression: beta = .27, P = .01, R2 = .03). Those with higher levels of education exhibited modest predictive power in the LOCF model only (beta = .33, P = .01, R2 = .03). Conclusions Although female and more highly educated users appeared slightly more likely to derive benefit from the Drinking Less intervention, none of the baseline characteristics we studied persuasively predicted a favorable treatment outcome. The Web-based intervention therefore seems well suited for a heterogeneous group of problem drinkers and could hence be offered as a first-step treatment in a stepped-care approach directed at problem drinkers in the general population. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN): 47285230; http://www.controlled-trials.com/isrctn47285230 (Archived by WebCite at http://www.webcitation.org/5cSR2sMkp). PMID:19033150

Predicting successful treatment outcome of web-based self-help for problem drinkers: secondary analysis from a randomized controlled trial.

PubMed

Riper, Heleen; Kramer, Jeannet; Keuken, Max; Smit, Filip; Schippers, Gerard; Cuijpers, Pim

2008-11-22

Web-based self-help interventions for problem drinking are coming of age. They have shown promising results in terms of cost-effectiveness, and they offer opportunities to reach out on a broad scale to problem drinkers. The question now is whether certain groups of problem drinkers benefit more from such Web-based interventions than others. We sought to identify baseline, client-related predictors of the effectiveness of Drinking Less, a 24/7, free-access, interactive, Web-based self-help intervention without therapist guidance for problem drinkers who want to reduce their alcohol consumption. The intervention is based on cognitive-behavioral and self-control principles. We conducted secondary analysis of data from a pragmatic randomized trial with follow-up at 6 and 12 months. Participants (N = 261) were adult problem drinkers in the Dutch general population with a weekly alcohol consumption above 210 g of ethanol for men or 140 g for women, or consumption of at least 60 g (men) or 40 g (women) one or more days a week over the past 3 months. Six baseline participant characteristics were designated as putative predictors of treatment response: (1) gender, (2) education, (3) Internet use competence (sociodemographics), (4) mean weekly alcohol consumption, (5) prior professional help for alcohol problems (level of problem drinking), and (6) participants' expectancies of Web-based interventions for problem drinking. Intention-to-treat (ITT) analyses, using last-observation-carried-forward (LOCF) data, and regression imputation (RI) were performed to deal with loss to follow-up. Statistical tests for interaction terms were conducted and linear regression analysis was performed to investigate whether the participants' characteristics as measured at baseline predicted positive treatment responses at 6- and 12-month follow-ups. At 6 months, prior help for alcohol problems predicted a small, marginally significant positive treatment outcome in the RI model only (beta = .18, P = .05, R(2) = .11). At 12 months, females displayed modest predictive power in both imputation models (LOCF: beta = .22, P = .045, R(2) = .02; regression: beta = .27, P = .01, R(2) = .03). Those with higher levels of education exhibited modest predictive power in the LOCF model only (beta = .33, P = .01, R(2) = .03). Although female and more highly educated users appeared slightly more likely to derive benefit from the Drinking Less intervention, none of the baseline characteristics we studied persuasively predicted a favorable treatment outcome. The Web-based intervention therefore seems well suited for a heterogeneous group of problem drinkers and could hence be offered as a first-step treatment in a stepped-care approach directed at problem drinkers in the general population. International Standard Randomized Controlled Trial Number (ISRCTN): 47285230; http://www.controlled-trials.com/isrctn47285230 (Archived by WebCite at http://www.webcitation.org/5cSR2sMkp).

Patient and practitioner characteristics predict brief alcohol intervention in primary care.

PubMed

Kaner, E F; Heather, N; Brodie, J; Lock, C A; McAvoy, B R

2001-10-01

The effectiveness of an evidence-based health care intervention depends on it being delivered consistently to appropriate patients. Brief alcohol intervention is known to be effective at reducing excessive drinking and its concomitant health and social problems. However, a recent implementation trial reported partial delivery of brief alcohol intervention by general practitioners (GPs) which is likely to have reduced its impact. To investigate patient-practitioner characteristics influencing brief alcohol intervention in primary care. Cross-sectional analysis of 12,814 completed Alcohol Use Disorders Identification Test (AUDIT) screening questionnaires. Eighty-four GPs who had implemented a brief alcohol intervention programme in a previous trial based in the Northeast of England. GPs were requested to screen all adults (aged over 16 years) presenting to their surgery and follow a structured protocol to give a brief intervention (five minutes of advice plus an information booklet) to all 'risk' drinkers. Anonymized carbon copies of the screening questionnaire were collected from all practices after a three-month implementation period. Although AUDIT identified 4080 'risk' drinkers, only 2043 (50%) received brief intervention. Risk drinkers that were most likely to receive brief intervention were males (58%), unemployed (61%), and technically-trained patients (55%). Risk drinkers that were least likely to receive brief intervention were females (44%), students (38%), and university educated patients (46%). Logistic regression modelling showed that patients' risk status was the most influential predictor of brief intervention. Also, GPs' experience of relevant training and longer average practice consultations predicted brief intervention. However, personal characteristics relating to patients and GPs also predicted brief intervention in routine practice. Interpersonal factors relating to patients and practitioners contributed to the selective provision of brief alcohol intervention in primary care. Ways should be found to remedy this situation or the impact of this evidence-based intervention may be reduced when implemented in routine practice.

Prediction complements explanation in understanding the developing brain.

PubMed

Rosenberg, Monica D; Casey, B J; Holmes, Avram J

2018-02-21

A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.

Theory-Based Cartographic Risk Model Development and Application for Home Fire Safety.

PubMed

Furmanek, Stephen; Lehna, Carlee; Hanchette, Carol

There is a gap in the use of predictive risk models to identify areas at risk for home fires and burn injury. The purpose of this study was to describe the creation, validation, and application of such a model using a sample from an intervention study with parents of newborns in Jefferson County, KY, as an example. Performed was a literature search to identify risk factors for home fires and burn injury in the target population. Obtained from the American Community Survey at the census tract level and synthesized to create a predictive cartographic risk model was risk factor data. Model validation was performed through correlation, regression, and Moran's I with fire incidence data from open records. Independent samples t-tests were used to examine the model in relation to geocoded participant addresses. Participant risk level for fire rate was determined and proximity to fire station service areas and hospitals. The model showed high and severe risk clustering in the northwest section of the county. Strongly correlated with fire rate was modeled risk; the best predictive model for fire risk contained home value (low), race (black), and non high school graduates. Applying the model to the intervention sample, the majority of participants were at lower risk and mostly within service areas closest to a fire department and hospital. Cartographic risk models were useful in identifying areas at risk and analyzing participant risk level. The methods outlined in this study are generalizable to other public health issues.

The Theory of Planned Behavior as a Model of Heavy Episodic Drinking Among College Students

PubMed Central

Collins, Susan E.; Carey, Kate B.

2008-01-01

This study provided a simultaneous, confirmatory test of the theory of planned behavior (TPB) in predicting heavy episodic drinking (HED) among college students. It was hypothesized that past HED, drinking attitudes, subjective norms and drinking refusal self-efficacy would predict intention, which would in turn predict future HED. Participants consisted of 131 college drinkers (63% female) who reported having engaged in HED in the previous two weeks. Participants were recruited and completed questionnaires within the context of a larger intervention study (see Collins & Carey, 2005). Latent factor structural equation modeling was used to test the ability of the TPB to predict HED. Chi-square tests and fit indices indicated good fit for the final structural models. Self-efficacy and attitudes but not subjective norms significantly predicted baseline intention, and intention and past HED predicted future HED. Contrary to hypotheses, however, a structural model excluding past HED provided a better fit than a model including it. Although further studies must be conducted before a definitive conclusion is reached, a TPB model excluding past behavior, which is arguably more parsimonious and theory driven, may provide better prediction of HED among college drinkers than a model including past behavior. PMID:18072832

Increasing Male Involvement in Family Planning Decision Making: Trial of a Social-Cognitive Intervention in Rural Vietnam

ERIC Educational Resources Information Center

Ha, Bui Thi Thu; Jayasuriya, Rohan; Owen, Neville

2005-01-01

We tested a social-cognitive intervention to influence contraceptive practices among men living in rural communes in Vietnam. It was predicted that participants who received a stage-targeted program based on the Transtheoretical Model (TTM) would report positive movement in their stage of motivational readiness for their wife to use an…

Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission.

PubMed

Biswas, Santanu; Subramanian, Abhishek; ELMojtaba, Ibrahim M; Chattopadhyay, Joydev; Sarkar, Ram Rup

2017-01-01

Visceral leishmaniasis (VL) is a deadly neglected tropical disease that poses a serious problem in various countries all over the world. Implementation of various intervention strategies fail in controlling the spread of this disease due to issues of parasite drug resistance and resistance of sandfly vectors to insecticide sprays. Due to this, policy makers need to develop novel strategies or resort to a combination of multiple intervention strategies to control the spread of the disease. To address this issue, we propose an extensive SIR-type model for anthroponotic visceral leishmaniasis transmission with seasonal fluctuations modeled in the form of periodic sandfly biting rate. Fitting the model for real data reported in South Sudan, we estimate the model parameters and compare the model predictions with known VL cases. Using optimal control theory, we study the effects of popular control strategies namely, drug-based treatment of symptomatic and PKDL-infected individuals, insecticide treated bednets and spray of insecticides on the dynamics of infected human and vector populations. We propose that the strategies remain ineffective in curbing the disease individually, as opposed to the use of optimal combinations of the mentioned strategies. Testing the model for different optimal combinations while considering periodic seasonal fluctuations, we find that the optimal combination of treatment of individuals and insecticide sprays perform well in controlling the disease for the time period of intervention introduced. Performing a cost-effective analysis we identify that the same strategy also proves to be efficacious and cost-effective. Finally, we suggest that our model would be helpful for policy makers to predict the best intervention strategies for specific time periods and their appropriate implementation for elimination of visceral leishmaniasis.

A theory and model of conflict detection in air traffic control: incorporating environmental constraints.

PubMed

Loft, Shayne; Bolland, Scott; Humphreys, Michael S; Neal, Andrew

2009-06-01

A performance theory for conflict detection in air traffic control is presented that specifies how controllers adapt decisions to compensate for environmental constraints. This theory is then used as a framework for a model that can fit controller intervention decisions. The performance theory proposes that controllers apply safety margins to ensure separation between aircraft. These safety margins are formed through experience and reflect the biasing of decisions to favor safety over accuracy, as well as expectations regarding uncertainty in aircraft trajectory. In 2 experiments, controllers indicated whether they would intervene to ensure separation between pairs of aircraft. The model closely predicted the probability of controller intervention across the geometry of problems and as a function of controller experience. When controller safety margins were manipulated via task instructions, the parameters of the model changed in the predicted direction. The strength of the model over existing and alternative models is that it better captures the uncertainty and decision biases involved in the process of conflict detection. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management.

PubMed

Erraguntla, Madhav; Zapletal, Josef; Lawley, Mark

2017-12-01

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army.

PubMed

Rosellini, A J; Stein, M B; Benedek, D M; Bliese, P D; Chiu, W T; Hwang, I; Monahan, J; Nock, M K; Petukhova, M V; Sampson, N A; Street, A E; Zaslavsky, A M; Ursano, R J; Kessler, R C

2017-10-01

The U.S. Army uses universal preventives interventions for several negative outcomes (e.g. suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service. 21 832 new soldiers completed a self-administered questionnaire (SAQ) in 2011-2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition. The best-performing models were for TBI (AUC = 0.80), major physical violence perpetration (AUC = 0.78), sexual assault perpetration (AUC = 0.78), and suicide attempt (AUC = 0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk. Data collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.

Executive functioning as a mediator of conduct problems prevention in children of homeless families residing in temporary supportive housing: a parallel process latent growth modeling approach.

PubMed

Piehler, Timothy F; Bloomquist, Michael L; August, Gerald J; Gewirtz, Abigail H; Lee, Susanne S; Lee, Wendy S C

2014-01-01

A culturally diverse sample of formerly homeless youth (ages 6-12) and their families (n = 223) participated in a cluster randomized controlled trial of the Early Risers conduct problems prevention program in a supportive housing setting. Parents provided 4 annual behaviorally-based ratings of executive functioning (EF) and conduct problems, including at baseline, over 2 years of intervention programming, and at a 1-year follow-up assessment. Using intent-to-treat analyses, a multilevel latent growth model revealed that the intervention group demonstrated reduced growth in conduct problems over the 4 assessment points. In order to examine mediation, a multilevel parallel process latent growth model was used to simultaneously model growth in EF and growth in conduct problems along with intervention status as a covariate. A significant mediational process emerged, with participation in the intervention promoting growth in EF, which predicted negative growth in conduct problems. The model was consistent with changes in EF fully mediating intervention-related changes in youth conduct problems over the course of the study. These findings highlight the critical role that EF plays in behavioral change and lends further support to its importance as a target in preventive interventions with populations at risk for conduct problems.

The New York State risk score for predicting in-hospital/30-day mortality following percutaneous coronary intervention.

PubMed

Hannan, Edward L; Farrell, Louise Szypulski; Walford, Gary; Jacobs, Alice K; Berger, Peter B; Holmes, David R; Stamato, Nicholas J; Sharma, Samin; King, Spencer B

2013-06-01

This study sought to develop a percutaneous coronary intervention (PCI) risk score for in-hospital/30-day mortality. Risk scores are simplified linear scores that provide clinicians with quick estimates of patients' short-term mortality rates for informed consent and to determine the appropriate intervention. Earlier PCI risk scores were based on in-hospital mortality. However, for PCI, a substantial percentage of patients die within 30 days of the procedure after discharge. New York's Percutaneous Coronary Interventions Reporting System was used to develop an in-hospital/30-day logistic regression model for patients undergoing PCI in 2010, and this model was converted into a simple linear risk score that estimates mortality rates. The score was validated by applying it to 2009 New York PCI data. Subsequent analyses evaluated the ability of the score to predict complications and length of stay. A total of 54,223 patients were used to develop the risk score. There are 11 risk factors that make up the score, with risk factor scores ranging from 1 to 9, and the highest total score is 34. The score was validated based on patients undergoing PCI in the previous year, and accurately predicted mortality for all patients as well as patients who recently suffered a myocardial infarction (MI). The PCI risk score developed here enables clinicians to estimate in-hospital/30-day mortality very quickly and quite accurately. It accurately predicts mortality for patients undergoing PCI in the previous year and for MI patients, and is also moderately related to perioperative complications and length of stay. Copyright © 2013 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Weight Shame, Social Connection, and Depressive Symptoms in Late Adolescence

PubMed Central

Brewis, Alexandra A.; Bruening, Meg

2018-01-01

Child and adolescent obesity is increasingly the focus of interventions, because it predicts serious disease morbidity later in life. However, social environments that permit weight-related stigma and body shame may make weight control and loss more difficult. Rarely do youth obesity interventions address these complexities. Drawing on repeated measures in a large sample (N = 1443) of first-year (freshman), campus-resident university students across a nine-month period, we model how weight-related shame predicts depressive symptom levels, how being overweight (assessed by anthropometric measures) shapes that risk, and how social connection (openness to friendship) might mediate/moderate. Body shame directly, clearly, and repeatedly predicts depression symptom levels across the whole school year for all students, but overweight youth have significantly elevated risk. Social connections mediate earlier in the school year, and in all phases moderate, body shame effects on depression. Youth obesity interventions would be well-served recognizing and incorporating the influential roles of social-environmental factors like weight stigma and friendship in program design. PMID:29723962

Weight Shame, Social Connection, and Depressive Symptoms in Late Adolescence.

PubMed

Brewis, Alexandra A; Bruening, Meg

2018-05-01

Child and adolescent obesity is increasingly the focus of interventions, because it predicts serious disease morbidity later in life. However, social environments that permit weight-related stigma and body shame may make weight control and loss more difficult. Rarely do youth obesity interventions address these complexities. Drawing on repeated measures in a large sample ( N = 1443) of first-year (freshman), campus-resident university students across a nine-month period, we model how weight-related shame predicts depressive symptom levels, how being overweight (assessed by anthropometric measures) shapes that risk, and how social connection (openness to friendship) might mediate/moderate. Body shame directly, clearly, and repeatedly predicts depression symptom levels across the whole school year for all students, but overweight youth have significantly elevated risk. Social connections mediate earlier in the school year, and in all phases moderate, body shame effects on depression. Youth obesity interventions would be well-served recognizing and incorporating the influential roles of social-environmental factors like weight stigma and friendship in program design.

Integrated Social- and Neurocognitive Model of Physical Activity Behavior in Older Adults with Metabolic Disease.

PubMed

Olson, Erin A; Mullen, Sean P; Raine, Lauren B; Kramer, Arthur F; Hillman, Charles H; McAuley, Edward

2017-04-01

Despite the proven benefits of physical activity to treat and prevent metabolic diseases, such as diabetes (T2D) and metabolic syndrome (MetS), most individuals with metabolic disease do not meet physical activity (PA) recommendations. PA is a complex behavior requiring substantial motivational and cognitive resources. The purpose of this study was to examine social cognitive and neuropsychological determinants of PA behavior in older adults with T2D and MetS. The hypothesized model theorized that baseline self-regulatory strategy use and cognitive function would indirectly influence PA through self-efficacy. Older adults with T2D or MetS (M age  = 61.8 ± 6.4) completed either an 8-week physical activity intervention (n = 58) or an online metabolic health education course (n = 58) and a follow-up at 6 months. Measures included cognitive function, self-efficacy, self-regulatory strategy use, and PA. The data partially supported the hypothesized model (χ 2  = 158.535(131), p > .05, comparative fit index = .96, root mean square error of approximation = .04, standardized root mean square residual = .06) with self-regulatory strategy use directly predicting self-efficacy (β = .33, p < .05), which in turn predicted PA (β = .21, p < .05). Performance on various cognitive function tasks predicted PA directly and indirectly via self-efficacy. Baseline physical activity (β = .62, p < .01) and intervention group assignment via self-efficacy (β = -.20, p < .05) predicted follow-up PA. The model accounted for 54.4 % of the variance in PA at month 6. Findings partially support the hypothesized model and indicate that select cognitive functions (i.e., working memory, inhibition, attention, and task-switching) predicted PA behavior 6 months later. Future research warrants the development of interventions targeting cognitive function, self-regulatory skill development, and self-efficacy enhancement. The trial was registered with the clinical trial number NCT01790724.

Empirical validation of the information-motivation-behavioral skills model of diabetes medication adherence: a framework for intervention.

PubMed

Mayberry, Lindsay S; Osborn, Chandra Y

2014-01-01

Suboptimal adherence to diabetes medications is prevalent and associated with unfavorable health outcomes, but it remains unclear what intervention content is necessary to effectively promote medication adherence in diabetes. In other disease contexts, the Information-Motivation-Behavioral skills (IMB) model has effectively explained and promoted medication adherence and thus may have utility in explaining and promoting adherence to diabetes medications. We tested the IMB model's hypotheses in a sample of adults with type 2 diabetes. Participants (N = 314) completed an interviewer-administered survey and A1C test. Structural equation models tested the effects of diabetes medication adherence-related information, motivation, and behavioral skills on medication adherence and the effect of medication adherence on A1C. The IMB elements explained 41% of the variance in adherence, and adherence explained 9% of the variance in A1C. As predicted, behavioral skills had a direct effect on adherence (β = 0.59; P < 0.001) and mediated the effects of information (indirect effect 0.08 [0.01-0.15]) and motivation (indirect effect 0.12 [0.05-0.20]) on adherence. Medication adherence significantly predicted glycemic control (β = -0.30; P < 0.001). Neither insulin status nor regimen complexity was associated with adherence, and neither moderated associations between the IMB constructs and adherence. The results support the IMB model's predictions and identify modifiable and intervenable determinants of diabetes medication adherence. Medication adherence promotion interventions may benefit from content targeting patients' medication adherence-related information, motivation, and behavioral skills and assessing the degree to which change in these determinants leads to changes in medication adherence behavior.

Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data.

PubMed

Bernecker, Samantha L; Rosellini, Anthony J; Nock, Matthew K; Chiu, Wai Tat; Gutierrez, Peter M; Hwang, Irving; Joiner, Thomas E; Naifeh, James A; Sampson, Nancy A; Zaslavsky, Alan M; Stein, Murray B; Ursano, Robert J; Kessler, Ronald C

2018-04-03

High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median = 26.0%). Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.

Sex and death: the effects of innate immune factors on the sexual reproduction of malaria parasites.

PubMed

Ramiro, Ricardo S; Alpedrinha, João; Carter, Lucy; Gardner, Andy; Reece, Sarah E

2011-03-01

Malaria parasites must undergo a round of sexual reproduction in the blood meal of a mosquito vector to be transmitted between hosts. Developing a transmission-blocking intervention to prevent parasites from mating is a major goal of biomedicine, but its effectiveness could be compromised if parasites can compensate by simply adjusting their sex allocation strategies. Recently, the application of evolutionary theory for sex allocation has been supported by experiments demonstrating that malaria parasites adjust their sex ratios in response to infection genetic diversity, precisely as predicted. Theory also predicts that parasites should adjust sex allocation in response to host immunity. Whilst data are supportive, the assumptions underlying this prediction - that host immune responses have differential effects on the mating ability of males and females - have not yet been tested. Here, we combine experimental work with theoretical models in order to investigate whether the development and fertility of male and female parasites is affected by innate immune factors and develop new theory to predict how parasites' sex allocation strategies should evolve in response to the observed effects. Specifically, we demonstrate that reactive nitrogen species impair gametogenesis of males only, but reduce the fertility of both male and female gametes. In contrast, tumour necrosis factor-α does not influence gametogenesis in either sex but impairs zygote development. Therefore, our experiments demonstrate that immune factors have complex effects on each sex, ranging from reducing the ability of gametocytes to develop into gametes, to affecting the viability of offspring. We incorporate these results into theory to predict how the evolutionary trajectories of parasite sex ratio strategies are shaped by sex differences in gamete production, fertility and offspring development. We show that medical interventions targeting offspring development are more likely to be 'evolution-proof' than interventions directed at killing males or females. Given the drive to develop medical interventions that interfere with parasite mating, our data and theoretical models have important implications.

Sex and Death: The Effects of Innate Immune Factors on the Sexual Reproduction of Malaria Parasites

PubMed Central

Ramiro, Ricardo S.; Alpedrinha, João; Carter, Lucy; Gardner, Andy; Reece, Sarah E.

2011-01-01

Malaria parasites must undergo a round of sexual reproduction in the blood meal of a mosquito vector to be transmitted between hosts. Developing a transmission-blocking intervention to prevent parasites from mating is a major goal of biomedicine, but its effectiveness could be compromised if parasites can compensate by simply adjusting their sex allocation strategies. Recently, the application of evolutionary theory for sex allocation has been supported by experiments demonstrating that malaria parasites adjust their sex ratios in response to infection genetic diversity, precisely as predicted. Theory also predicts that parasites should adjust sex allocation in response to host immunity. Whilst data are supportive, the assumptions underlying this prediction – that host immune responses have differential effects on the mating ability of males and females – have not yet been tested. Here, we combine experimental work with theoretical models in order to investigate whether the development and fertility of male and female parasites is affected by innate immune factors and develop new theory to predict how parasites' sex allocation strategies should evolve in response to the observed effects. Specifically, we demonstrate that reactive nitrogen species impair gametogenesis of males only, but reduce the fertility of both male and female gametes. In contrast, tumour necrosis factor-α does not influence gametogenesis in either sex but impairs zygote development. Therefore, our experiments demonstrate that immune factors have complex effects on each sex, ranging from reducing the ability of gametocytes to develop into gametes, to affecting the viability of offspring. We incorporate these results into theory to predict how the evolutionary trajectories of parasite sex ratio strategies are shaped by sex differences in gamete production, fertility and offspring development. We show that medical interventions targeting offspring development are more likely to be ‘evolution-proof’ than interventions directed at killing males or females. Given the drive to develop medical interventions that interfere with parasite mating, our data and theoretical models have important implications. PMID:21408620

Selecting At-Risk Readers in First Grade for Early Intervention: A Two-Year Longitudinal Study of Decision Rules and Procedures

ERIC Educational Resources Information Center

Compton, Donald L.; Fuchs, Douglas; Fuchs, Lynn S.; Bryant, Joan D.

2006-01-01

Response to intervention (RTI) models for identifying learning disabilities rely on the accurate identification of children who, without Tier 2 tutoring, would develop reading disability (RD). This study examined 2 questions concerning the use of 1st-grade data to predict future RD: (1) Does adding initial word identification fluency (WIF) and 5…

Predicting Sexual Assault Perpetration in the US Army Using Administrative Data

PubMed Central

Rosellini, Anthony J.; Monahan, John; Street, Amy E.; Petukhova, Maria V.; Sampson, Nancy A.; Benedek, David M.; Bliese, Paul; Stein, Murray B.; Ursano, Robert J.; Kessler, Ronald C.

2017-01-01

Introduction The Department of Defense uses a universal prevention framework for sexual assault prevention, with each branch implementing their own branch-wide programs. Intensive interventions exist, but would be cost-effective only if targeted at high-risk personnel. This study developed actuarial models to identify male U.S. Army soldiers at high risk of administratively-recorded sexual assault perpetration. Methods This study investigated administratively-recorded sexual assault perpetration among the 821,807 male Army soldiers serving 2004–2009. Other temporally prior administrative data were used as predictors. Penalized discrete-time (person-month) survival analysis (conducted in 2016) was used to select the smallest possible number of stable predictors to maximize number of sexual assaults among the 5% of soldiers with highest predicted risk of perpetration (top-ventile concentration of risk [COR]). Separate models were developed for assaults against non-family and intra-family adults and minors. Results 4,640 male soldiers were found to be perpetrators against non-family adults, 1,384 against non-family minors, 380 against intra-family adults, and 335 against intra-family minors. Top-ventile COR was 16.2–20.2% predicting perpetration against non-family adults and minors and 34.2–65.1% against intra-family adults and minors. Final predictors consisted largely of measures of prior crime involvement and the presence-treatment of mental disorders. Conclusions Administrative data can be used to develop actuarial models that identify a high proportion of sexual assault perpetrators. If a system is developed to routinely consolidate administrative predictors, predictions could be generated periodically to identify those in need of preventive intervention. Whether this would be cost-effective, though, would depend on intervention costs, effectiveness, and competing risks. PMID:28818420

Gambling and the Reasoned Action Model: Predicting Past Behavior, Intentions, and Future Behavior.

PubMed

Dahl, Ethan; Tagler, Michael J; Hohman, Zachary P

2018-03-01

Gambling is a serious concern for society because it is highly addictive and is associated with a myriad of negative outcomes. The current study applied the Reasoned Action Model (RAM) to understand and predict gambling intentions and behavior. Although prior studies have taken a reasoned action approach to understand gambling, no prior study has fully applied the RAM or used the RAM to predict future gambling. Across two studies the RAM was used to predict intentions to gamble, past gambling behavior, and future gambling behavior. In study 1 the model significantly predicted intentions and past behavior in both a college student and Amazon Mechanical Turk sample. In study 2 the model predicted future gambling behavior, measured 2 weeks after initial measurement of the RAM constructs. This study stands as the first to show the utility of the RAM in predicting future gambling behavior. Across both studies, attitudes and perceived normative pressure were the strongest predictors of intentions to gamble. These findings provide increased understanding of gambling and inform the development of gambling interventions based on the RAM.

Autonomy and Authority in the Resolution of Sibling Disputes.

ERIC Educational Resources Information Center

Ross, Hildy; And Others

1996-01-01

Investigates parental intervention in sibling disputes to reveal how different developmental models inform us about the role of social conflict in early development. Examines predictions made by Piagetian, socialization, and conflict-mediation models regarding the role of adults in children's conflicts, as they are applied to a series of studies…

A Communication Intervention to Reduce Resistiveness in Dementia Care: A Cluster Randomized Controlled Trial.

PubMed

Williams, Kristine N; Perkhounkova, Yelena; Herman, Ruth; Bossen, Ann

2017-08-01

Nursing home (NH) residents with dementia exhibit challenging behaviors or resistiveness to care (RTC) that increase staff time, stress, and NH costs. RTC is linked to elderspeak communication. Communication training (Changing Talk [CHAT]) was provided to staff to reduce their use of elderspeak. We hypothesized that CHAT would improve staff communication and subsequently reduce RTC. Thirteen NHs were randomized to intervention and control groups. Dyads (n = 42) including 29 staff and 27 persons with dementia were videorecorded during care before and/or after the intervention and at a 3-month follow-up. Videos were behaviorally coded for (a) staff communication (normal, elderspeak, or silence) and (b) resident behaviors (cooperative or RTC). Linear mixed modeling was used to evaluate training effects. On average, elderspeak declined from 34.6% (SD = 18.7) at baseline by 13.6% points (SD = 20.00) post intervention and 12.2% points (SD = 22.0) at 3-month follow-up. RTC declined from 35.7% (SD = 23.2) by 15.3% points (SD = 32.4) post intervention and 13.4% points (SD = 33.7) at 3 months. Linear mixed modeling determined that change in elderspeak was predicted by the intervention (b = -12.20, p = .028) and baseline elderspeak (b = -0.65, p < .001), whereas RTC change was predicted by elderspeak change (b = 0.43, p < .001); baseline RTC (b = -0.58, p < .001); and covariates. A brief intervention can improve communication and reduce RTC, providing an effective nonpharmacological intervention to manage behavior and improve the quality of dementia care. No adverse events occurred. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

In Search of Black Swans: Identifying Students at Risk of Failing Licensing Examinations.

PubMed

Barber, Cassandra; Hammond, Robert; Gula, Lorne; Tithecott, Gary; Chahine, Saad

2018-03-01

To determine which admissions variables and curricular outcomes are predictive of being at risk of failing the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1), how quickly student risk of failure can be predicted, and to what extent predictive modeling is possible and accurate in estimating future student risk. Data from five graduating cohorts (2011-2015), Schulich School of Medicine & Dentistry, Western University, were collected and analyzed using hierarchical generalized linear models (HGLMs). Area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of predictive models and determine whether they could be used to predict future risk, using the 2016 graduating cohort. Four predictive models were developed to predict student risk of failure at admissions, year 1, year 2, and pre-MCCQE1. The HGLM analyses identified gender, MCAT verbal reasoning score, two preclerkship course mean grades, and the year 4 summative objective structured clinical examination score as significant predictors of student risk. The predictive accuracy of the models varied. The pre-MCCQE1 model was the most accurate at predicting a student's risk of failing (AUC 0.66-0.93), while the admissions model was not predictive (AUC 0.25-0.47). Key variables predictive of students at risk were found. The predictive models developed suggest, while it is not possible to identify student risk at admission, we can begin to identify and monitor students within the first year. Using such models, programs may be able to identify and monitor students at risk quantitatively and develop tailored intervention strategies.

Active Redesign of a Medicaid Care Management Strategy for Greater Return on Investment: Predicting Impactability.

PubMed

DuBard, C Annette; Jackson, Carlos T

2018-04-01

Care management of high-cost/high-needs patients is an increasingly common strategy to reduce health care costs. A variety of targeting methodologies have emerged to identify patients with high historical or predicted health care utilization, but the more pertinent question for program planners is how to identify those who are most likely to benefit from care management intervention. This paper describes the evolution of complex care management targeting strategies in Community Care of North Carolina's (CCNC) work with the statewide non-dual Medicaid population, culminating in the development of an "Impactability Score" that uses administrative data to predict achievable savings. It describes CCNC's pragmatic approach for estimating intervention effects in a historical cohort of 23,455 individuals, using a control population of 14,839 to determine expected spending at an individual level, against which actual spending could be compared. The actual-to-expected spending difference was then used as the dependent variable in a multivariate model to determine the predictive contribution of a multitude of demographic, clinical, and utilization characteristics. The coefficients from this model yielded the information required to build predictive models for prospective use. Model variables related to medication adherence and historical utilization unexplained by disease burden proved to be more important predictors of impactability than any given diagnosis or event, disease profile, or overall costs of care. Comparison of this approach to alternative targeting strategies (emergency department super-utilizers, inpatient super-utilizers, or patients with highest Hierarchical Condition Category risk scores) suggests a 2- to 3-fold higher return on investment using impactability-based targeting.

A protective factors model for alcohol abuse and suicide prevention among Alaska Native youth.

PubMed

Allen, James; Mohatt, Gerald V; Fok, Carlotta Ching Ting; Henry, David; Burkett, Rebekah

2014-09-01

This study provides an empirical test of a culturally grounded theoretical model for prevention of alcohol abuse and suicide risk with Alaska Native youth, using a promising set of culturally appropriate measures for the study of the process of change and outcome. This model is derived from qualitative work that generated an heuristic model of protective factors from alcohol (Allen et al. in J Prev Interv Commun 32:41-59, 2006; Mohatt et al. in Am J Commun Psychol 33:263-273, 2004a; Harm Reduct 1, 2004b). Participants included 413 rural Alaska Native youth ages 12-18 who assisted in testing a predictive model of Reasons for Life and Reflective Processes about alcohol abuse consequences as co-occurring outcomes. Specific individual, family, peer, and community level protective factor variables predicted these outcomes. Results suggest prominent roles for these predictor variables as intermediate prevention strategy target variables in a theoretical model for a multilevel intervention. The model guides understanding of underlying change processes in an intervention to increase the ultimate outcome variables of Reasons for Life and Reflective Processes regarding the consequences of alcohol abuse.

Pharmaceutical interventions for mitigating an influenza pandemic: modeling the risks and health-economic impacts.

PubMed

Postma, Maarten J; Milne, George; Nelson, E Anthony S; Pyenson, Bruce; Basili, Marcello; Coker, Richard; Oxford, John; Garrison, Louis P

2010-12-01

Model-based analyses built on burden-of-disease and cost-effectiveness theory predict that pharmaceutical interventions may efficiently mitigate both the epidemiologic and economic impact of an influenza pandemic. Pharmaceutical interventions typically encompass the application of (pre)pandemic influenza vaccines, other vaccines (notably pneumococcal), antiviral treatments and other drug treatment (e.g., antibiotics to target potential complications of influenza). However, these models may be too limited to capture the full macro-economic impact of pandemic influenza. The aim of this article is to summarize current health-economic modeling approaches to recognize the strengths and weaknesses of these approaches, and to compare these with more recently proposed alternative methods. We conclude that it is useful, particularly for policy and planning purposes, to extend modeling concepts through the application of alternative approaches, including insurers' risk theories, human capital approaches and sectoral and full macro-economic modeling. This article builds on a roundtable meeting of the Pandemic Influenza Economic Impact Group that was held in Boston, MA, USA, in December 2008.

Identification of factors that affect the adoption of an ergonomic intervention among Emergency Medical Service workers.

PubMed

Weiler, Monica R; Lavender, Steven A; Crawford, J Mac; Reichelt, Paul A; Conrad, Karen M; Browne, Michael W

2012-01-01

This study explored factors contributing to intervention adoption decisions among Emergency Medical Service (EMS) workers. Emergency Medical Service workers (n = 190), from six different organisations, participated in a two-month longitudinal study following the introduction of a patient transfer-board (also known as slide-board) designed to ease lateral transfers of patients to and from ambulance cots. Surveys administered at baseline, after one month and after two months sampled factors potentially influencing the EMS providers' decision process. 'Ergonomics Advantage' and 'Patient Advantage' entered into a stepwise regression model predicting 'intention to use' at the end of month one (R (2 )= 0.78). After the second month, the stepwise regression indicated only two factors were predictive of intention to use: 'Ergonomics Advantage,' and 'Endorsed by Champions' (R (2 )= 0.58). Actual use was predicted by: 'Ergonomics Advantage' and 'Previous Tool Experience.' These results relate to key concepts identified in the diffusion of innovation literature and have the potential to further ergonomics intervention adoption efforts. Practitioner Summary. This study explored factors that potentially facilitate the adoption of voluntarily used ergonomics interventions. EMS workers were provided with foldable transfer-boards (slideboards) designed to reduce the physical demands when laterally transferring patients. Factors predictive of adoption measures included perceived ergonomics advantage, the endorsement by champions, and prior tool experience.

Attacking the mosquito on multiple fronts: Insights from the Vector Control Optimization Model (VCOM) for malaria elimination.

PubMed

Kiware, Samson S; Chitnis, Nakul; Tatarsky, Allison; Wu, Sean; Castellanos, Héctor Manuel Sánchez; Gosling, Roly; Smith, David; Marshall, John M

2017-01-01

Despite great achievements by insecticide-treated nets (ITNs) and indoor residual spraying (IRS) in reducing malaria transmission, it is unlikely these tools will be sufficient to eliminate malaria transmission on their own in many settings today. Fortunately, field experiments indicate that there are many promising vector control interventions that can be used to complement ITNs and/or IRS by targeting a wide range of biological and environmental mosquito resources. The majority of these experiments were performed to test a single vector control intervention in isolation; however, there is growing evidence and consensus that effective vector control with the goal of malaria elimination will require a combination of interventions. We have developed a model of mosquito population dynamic to describe the mosquito life and feeding cycles and to optimize the impact of vector control intervention combinations at suppressing mosquito populations. The model simulations were performed for the main three malaria vectors in sub-Saharan Africa, Anopheles gambiae s.s, An. arabiensis and An. funestus. We considered areas having low, moderate and high malaria transmission, corresponding to entomological inoculation rates of 10, 50 and 100 infective bites per person per year, respectively. In all settings, we considered baseline ITN coverage of 50% or 80% in addition to a range of other vector control tools to interrupt malaria transmission. The model was used to sweep through parameters space to select the best optimal intervention packages. Sample model simulations indicate that, starting with ITNs at a coverage of 50% (An. gambiae s.s. and An. funestus) or 80% (An. arabiensis) and adding interventions that do not require human participation (e.g. larviciding at 80% coverage, endectocide treated cattle at 50% coverage and attractive toxic sugar baits at 50% coverage) may be sufficient to suppress all the three species to an extent required to achieve local malaria elimination. The Vector Control Optimization Model (VCOM) is a computational tool to predict the impact of combined vector control interventions at the mosquito population level in a range of eco-epidemiological settings. The model predicts specific combinations of vector control tools to achieve local malaria elimination in a range of eco-epidemiological settings and can assist researchers and program decision-makers on the design of experimental or operational research to test vector control interventions. A corresponding graphical user interface is available for national malaria control programs and other end users.

Psychological distress and in vitro fertilization outcome

PubMed Central

Pasch, Lauri A.; Gregorich, Steven E.; Katz, Patricia K.; Millstein, Susan G.; Nachtigall, Robert D.; Bleil, Maria E.; Adler, Nancy E.

2016-01-01

Objective To examine whether psychological distress predicts IVF treatment outcome as well as whether IVF treatment outcome predicts subsequent psychological distress. Design Prospective cohort study over an 18-month period. Setting Five community and academic fertility practices. Patients Two hundred and two women who initiated their first IVF cycle. Interventions Women completed interviews and questionnaires at baseline and at 4, 10, and 18 months follow-up. Main Outcome Measures IVF cycle outcome and psychological distress. Results Using a binary logistic model including covariates (woman’s age, ethnicity, income, education, parity, duration of infertility, and time interval), pre-treatment depression and anxiety were not significant predictors of the outcome of the first IVF cycle. Using linear regression models including covariates (woman’s age, income, education, parity, duration of infertility, assessment point, time since last treatment cycle, and pre-IVF depression or anxiety), experiencing failed IVF was associated with higher post-IVF depression and anxiety. Conclusions IVF failure predicts subsequent psychological distress, but pre-IVF psychological distress does not predict IVF failure. Instead of focusing efforts on psychological interventions specifically aimed at improving the chance of pregnancy, these findings suggest that attention be paid to helping patients prepare for and cope with treatment and treatment failure. PMID:22698636

Social Competence in Infants and Toddlers with Special Health Care Needs: The Roles of Parental Knowledge, Expectations, Attunement, and Attitudes toward Child Independence

PubMed Central

Zand, Debra; Pierce, Katherine; Thomson, Nicole; Baig, M. Waseem; Teodorescu, Cristiana; Nibras, Sohail; Maxim, Rolanda

2014-01-01

Little research has empirically addressed the relationships among parental knowledge of child development, parental attunement, parental expectations, and child independence in predicting the social competence of infants and toddlers with special health care needs. We used baseline data from the Strengthening Families Project, a prevention intervention study that tested Bavolek’s Nurturing Program for Parents and Their Children with Health Challenges to explore the roles of these variables in predicting social competence in infants and toddlers with special health care needs. Bivariate relationships among the study variables were explored and used to develop and test a model for predicting social competence among these children. Study findings pointed to a combination of indirect and direct influences of parent variables in predicting social competence. Results indicated that parents who encouraged healthy behaviors for developing a sense of power/independence were more likely to have children with social competence developing on schedule. Elements related to parental expectations, however, did not have the hypothesized relationships to social competence. The present study provides preliminary data to support the development of knowledge based interventions. Within medical settings, such interventions may indeed maximize benefit while minimizing cost. PMID:27417463

Cholera epidemic in Haiti, 2010: using a transmission model to explain spatial spread of disease and identify optimal control interventions.

PubMed

Tuite, Ashleigh R; Tien, Joseph; Eisenberg, Marisa; Earn, David J D; Ma, Junling; Fisman, David N

2011-05-03

Haiti is in the midst of a cholera epidemic. Surveillance data for formulating models of the epidemic are limited, but such models can aid understanding of epidemic processes and help define control strategies. To predict, by using a mathematical model, the sequence and timing of regional cholera epidemics in Haiti and explore the potential effects of disease-control strategies. Compartmental mathematical model allowing person-to-person and waterborne transmission of cholera. Within- and between-region epidemic spread was modeled, with the latter dependent on population sizes and distance between regional centroids (a "gravity" model). Haiti, 2010 to 2011. Haitian hospitalization data, 2009 census data, literature-derived parameter values, and model calibration. Dates of epidemic onset and hospitalizations. The plausible range for cholera's basic reproductive number (R(0), defined as the number of secondary cases per primary case in a susceptible population without intervention) was 2.06 to 2.78. The order and timing of regional cholera outbreaks predicted by the gravity model were closely correlated with empirical observations. Analysis of changes in disease dynamics over time suggests that public health interventions have substantially affected this epidemic. A limited vaccine supply provided late in the epidemic was projected to have a modest effect. Assumptions were simplified, which was necessary for modeling. Projections are based on the initial dynamics of the epidemic, which may change. Despite limited surveillance data from the cholera epidemic in Haiti, a model simulating between-region disease transmission according to population and distance closely reproduces reported disease patterns. This model is a tool that planners, policymakers, and medical personnel seeking to manage the epidemic could use immediately.

Spatiotemporal Bayesian networks for malaria prediction.

PubMed

Haddawy, Peter; Hasan, A H M Imrul; Kasantikul, Rangwan; Lawpoolsri, Saranath; Sa-Angchai, Patiwat; Kaewkungwal, Jaranit; Singhasivanon, Pratap

2018-01-01

Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating village level models with weekly temporal resolution for Tha Song Yang district in northern Thailand. The networks are learned using data on cases and environmental covariates. Three types of networks are explored: networks for numeric prediction, networks for outbreak prediction, and networks that incorporate spatial autocorrelation. Evaluation of the numeric prediction network shows that the Bayes net has prediction accuracy in terms of mean absolute error of about 1.4 cases for 1 week prediction and 1.7 cases for 6 week prediction. The network for outbreak prediction has an ROC AUC above 0.9 for all prediction horizons. Comparison of prediction accuracy of both Bayes nets against several traditional modeling approaches shows the Bayes nets to outperform the other models for longer time horizon prediction of high incidence transmission. To model spread of malaria over space, we elaborate the models with links between the village networks. This results in some very large models which would be far too laborious to build by hand. So we represent the models as collections of probability logic rules and automatically generate the networks. Evaluation of the models shows that the autocorrelation links significantly improve prediction accuracy for some villages in regions of high incidence. We conclude that spatiotemporal Bayesian networks are a highly promising modeling alternative for prediction of malaria and other vector-borne diseases. Copyright © 2017 Elsevier B.V. All rights reserved.

Toward a better understanding of what makes positive psychology interventions work: predicting happiness and depression from the person × intervention fit in a follow-up after 3.5 years.

PubMed

Proyer, René T; Wellenzohn, Sara; Gander, Fabian; Ruch, Willibald

2015-03-01

Robust evidence exists that positive psychology interventions are effective in enhancing well-being and ameliorating depression. Comparatively little is known about the conditions under which they work best. Models describing characteristics that impact the effectiveness of positive interventions typically contain features of the person, of the activity, and the fit between the two. This study focuses on indicators of the person × intervention fit in predicting happiness and depressive symptoms 3.5 years after completion of the intervention. A sample of 165 women completed measures for happiness and depressive symptoms before and about 3.5 years after completion of a positive intervention (random assignment to one out of nine interventions, which were aggregated for the analyses). Four fit indicators were assessed: Preference; continued practice; effort; and early reactivity. Three out of four person × intervention fit indicators were positively related to happiness or negatively related to depression when controlled for the pretest scores. Together, they explained 6 per cent of the variance in happiness, and 10 per cent of the variance of depressive symptoms. Most tested indicators of a person × intervention fit are robust predictors of happiness and depressive symptoms-even after 3.5 years. They might serve for an early estimation of the effectiveness of a positive intervention. © 2014 The International Association of Applied Psychology.

Assessing the Availability of Users to Engage in Just-in-Time Intervention in the Natural Environment.

PubMed

Sarker, Hillol; Sharmin, Moushumi; Ali, Amin Ahsan; Rahman, Md Mahbubur; Bari, Rummana; Hossain, Syed Monowar; Kumar, Santosh

Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. We take a first step in modeling users' availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. We use delay in responding to a prompt to objectively measure availability. We compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. We find that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. We find that users are least available at work and during driving, and most available when walking outside. Our model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out.

Assessing the Availability of Users to Engage in Just-in-Time Intervention in the Natural Environment

PubMed Central

Sarker, Hillol; Sharmin, Moushumi; Ali, Amin Ahsan; Rahman, Md. Mahbubur; Bari, Rummana; Hossain, Syed Monowar; Kumar, Santosh

2015-01-01

Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. We take a first step in modeling users’ availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. We use delay in responding to a prompt to objectively measure availability. We compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. We find that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. We find that users are least available at work and during driving, and most available when walking outside. Our model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out. PMID:25798455

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours.

PubMed

Mariappan, Panchatcharam; Weir, Phil; Flanagan, Ronan; Voglreiter, Philip; Alhonnoro, Tuomas; Pollari, Mika; Moche, Michael; Busse, Harald; Futterer, Jurgen; Portugaller, Horst Rupert; Sequeiros, Roberto Blanco; Kolesnik, Marina

2017-01-01

Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction. Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne's bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion. A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm. A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment.

The future burden of obesity-related diseases in the 53 WHO European-Region countries and the impact of effective interventions: a modelling study

PubMed Central

Webber, Laura; Divajeva, Diana; Marsh, Tim; McPherson, Klim; Brown, Martin; Galea, Gauden; Breda, Joao

2014-01-01

Objective Non-communicable diseases (NCDs) are the biggest cause of death in Europe putting an unsustainable burden on already struggling health systems. Increases in obesity are a major cause of NCDs. This paper projects the future burden of coronary heart disease (CHD), stroke, type 2 diabetes and seven cancers by 2030 in 53 WHO European Region countries based on current and past body mass index (BMI) trends. It also tests the impact of obesity interventions on the future disease burden. Setting and participants Secondary data analysis of country-specific epidemiological data using a microsimulation modelling process. Interventions The effect of three hypothetical scenarios on the future burden of disease in 2030 was tested: baseline scenario, BMI trends go unchecked; intervention 1, population BMI decreases by 1%; intervention 2, BMI decreases by 5%. Primary and secondary outcome measures Quantifying the future burden of major NCDs and the impact of interventions on this future disease burden. Results By 2030 in the whole of the European region, the prevalence of diabetes, CHD and stroke and cancers was projected to reach an average of 3990, 4672 and 2046 cases/100 000, respectively. The highest prevalence of diabetes was predicted in Slovakia (10 870), CHD and stroke—in Greece (11 292) and cancers—in Finland (5615 cases/100 000). A 5% fall in population BMI was projected to significantly reduce cumulative incidence of diseases. The largest reduction in diabetes and CHD and stroke was observed in Slovakia (3054 and 3369 cases/100 000, respectively), and in cancers was predicted in Germany (331/100 000). Conclusions Modelling future disease trends is a useful tool for policymakers so that they can allocate resources effectively and implement policies to prevent NCDs. Future research will allow real policy interventions to be tested; however, better surveillance data on NCDs and their risk factors are essential for research and policy. PMID:25063459

Cost-effectiveness model for prevention of early childhood caries.

PubMed

Ramos-Gomez, F J; Shepard, D S

1999-07-01

This study presents and illustrates a model that determines the cost-effectiveness of three successively more complete levels of preventive intervention (minimal, intermediate, and comprehensive) in treating dental caries in disadvantaged children up to 6 years of age. Using existing data on the costs of early childhood caries (ECC), the authors estimated the probable cost-effectiveness of each of the three preventive intervention levels by comparing treatment costs to prevention costs as applied to a typical low-income California child for five years. They found that, in general, prevention becomes cost-saving if at least 59 percent of carious lesions receive restorative treatment. Assuming an average restoration cost of $112 per surface, the model predicts cost savings of $66 to $73 in preventing a one-surface, carious lesion. Thus, all three levels of preventive intervention should be relatively cost-effective. Comprehensive intervention would provide the greatest oral health benefit; however, because more children would receive reparative care, overall program costs would rise even as per-child treatment costs decline.

Colorectal cancer health services research study protocol: the CCR-CARESS observational prospective cohort project.

PubMed

Quintana, José M; Gonzalez, Nerea; Anton-Ladislao, Ane; Redondo, Maximino; Bare, Marisa; Fernandez de Larrea, Nerea; Briones, Eduardo; Escobar, Antonio; Sarasqueta, Cristina; Garcia-Gutierrez, Susana; Aguirre, Urko

2016-07-08

Colorectal cancers are one of the most common forms of malignancy worldwide. But two significant areas of research less studied deserve attention: health services use and development of patient stratification risk tools for these patients. a prospective multicenter cohort study with a follow up period of up to 5 years after surgical intervention. Participant centers: 22 hospitals representing six autonomous communities of Spain. Participants/Study population: Patients diagnosed with colorectal cancer that have undergone surgical intervention and have consented to participate in the study between June 2010 and December 2012. Variables collected include pre-intervention background, sociodemographic parameters, hospital admission records, biological and clinical parameters, treatment information, and outcomes up to 5 years after surgical intervention. Patients completed the following questionnaires prior to surgery and in the follow up period: EuroQol-5D, EORTC QLQ-C30 (The European Organization for Research and Treatment of Cancer quality of life questionnaire) and QLQ-CR29 (module for colorectal cancer), the Duke Functional Social Support Questionnaire, the Hospital Anxiety and Depression Scale, and the Barthel Index. The main endpoints of the study are mortality, tumor recurrence, major complications, readmissions, and changes in health-related quality of life at 30 days and at 1, 2, 3 and 5 years after surgical intervention. In relation to the different endpoints, predictive models will be used by means of multivariate logistic models, Cox or linear mixed-effects regression models. Simulation models for the prediction of discrete events in the long term will also be used, and an economic evaluation of different treatment strategies will be performed through the use of generalized linear models. The identification of potential risk factors for adverse events may help clinicians in the clinical decision making process. Also, the follow up by 5 years of this large cohort of patients may provide useful information to answer different health services research questions. ClinicalTrials.gov Identifier: NCT02488161 . Registration date: June 16, 2015.

Reciprocal longitudinal relations between weight/shape concern and comorbid pathology among women at very high risk for eating disorder onset.

PubMed

Fitzsimmons-Craft, Ellen E; Eichen, Dawn M; Kass, Andrea E; Trockel, Mickey; Crosby, Ross D; Taylor, C Barr; Wilfley, Denise E

2017-12-28

Understanding how known eating disorder (ED) risk factors change in relating to one another over time may inform efficient intervention targets. We examined short-term (i.e., 1 month) reciprocal longitudinal relations between weight/shape concern and comorbid symptoms (i.e., depressed mood, anxiety) and behaviors (i.e., binge drinking) over the course of 24 months using cross-lagged panel models. Participants were 185 women aged 18-25 years at very high risk for ED onset, randomized to an online ED preventive intervention or waitlist control. We also tested whether relations differed based on intervention receipt. Weight/shape concern in 1 month significantly predicted depressed mood the following month; depressed mood in 1 month also predicted weight/shape concern the following month, but the effect size was smaller. Likewise, weight/shape concern in 1 month significantly predicted anxiety the following month, but the reverse was not true. Results showed no temporal relations between weight/shape concern and binge drinking in either direction. Relations between weight/shape concern, and comorbid symptoms and behaviors did not differ based on intervention receipt. Results support focusing intervention on reducing weight/shape concern over reducing comorbid constructs for efficient short-term change. Level I, evidence obtained from a properly designed randomized controlled trial.

Optimization of Control Strategies for Non-Domiciliated Triatoma dimidiata, Chagas Disease Vector in the Yucatán Peninsula, Mexico

PubMed Central

Barbu, Corentin; Dumonteil, Eric; Gourbière, Sébastien

2009-01-01

Background Chagas disease is the most important vector-borne disease in Latin America. Regional initiatives based on residual insecticide spraying have successfully controlled domiciliated vectors in many regions. Non-domiciliated vectors remain responsible for a significant transmission risk, and their control is now a key challenge for disease control. Methodology/Principal Findings A mathematical model was developed to predict the temporal variations in abundance of non-domiciliated vectors inside houses. Demographic parameters were estimated by fitting the model to two years of field data from the Yucatan peninsula, Mexico. The predictive value of the model was tested on an independent data set before simulations examined the efficacy of control strategies based on residual insecticide spraying, insect screens, and bednets. The model accurately fitted and predicted field data in the absence and presence of insecticide spraying. Pyrethroid spraying was found effective when 50 mg/m2 were applied yearly within a two-month period matching the immigration season. The >80% reduction in bug abundance was not improved by larger doses or more frequent interventions, and it decreased drastically for different timing and lower frequencies of intervention. Alternatively, the use of insect screens consistently reduced bug abundance proportionally to the reduction of the vector immigration rate. Conclusion/Significance Control of non-domiciliated vectors can hardly be achieved by insecticide spraying, because it would require yearly application and an accurate understanding of the temporal pattern of immigration. Insect screens appear to offer an effective and sustainable alternative, which may be part of multi-disease interventions for the integrated control of neglected vector-borne diseases. PMID:19365542

Effects of a Brief Psychoeducational Intervention for Family Conflict: Constructive Conflict, Emotional Insecurity and Child Adjustment.

PubMed

Miller-Graff, Laura E; Cummings, E Mark; Bergman, Kathleen N

2016-10-01

The role of emotional security in promoting positive adjustment following exposure to marital conflict has been identified in a large number of empirical investigations, yet to date, no interventions have explicitly addressed the processes that predict child adjustment after marital conflict. The current study evaluated a randomized controlled trial of a family intervention program aimed at promoting constructive marital conflict behaviors thereby increasing adolescent emotional security and adjustment. Families (n = 225) were randomized into 1 of 4 conditions: Parent-Adolescent (n = 75), Parent-Only (n = 75), Self-Study (n = 38) and No Treatment (n = 37). Multi-informant and multi-method assessments were conducted at baseline, post-treatment and 6-month follow-up. Effects of treatment on destructive and constructive conflict behaviors were evaluated using multilevel models where observations were nested within individuals over time. Process models assessing the impact of constructive and destructive conflict behaviors on emotional insecurity and adolescent adjustment were evaluated using path modeling. Results indicated that the treatment was effective in increasing constructive conflict behaviors (d = 0.89) and decreasing destructive conflict behaviors (d = -0.30). For the Parent-Only Group, post-test constructive conflict behaviors directly predicted lower levels of adolescent externalizing behaviors at 6-month follow-up. Post-test constructive conflict skills also indirectly affected adolescent internalizing behaviors through adolescent emotional security. These findings support the use of a brief psychoeducational intervention in improving post-treatment conflict and emotional security about interparental relationships.

Sex role segregation and mixing among men who have sex with men: implications for biomedical HIV prevention interventions.

PubMed

Armbruster, Benjamin; Roy, Sourya; Kapur, Abhinav; Schneider, John A

2013-01-01

Men who have sex with men (MSM) practice role segregation - insertive or receptive only sex positions instead of a versatile role - in several international settings where candidate biomedical HIV prevention interventions (e.g., circumcision, anal microbicide) will be tested. The effects of these position-specific interventions on HIV incidence are modeled. We developed a deterministic compartmental model to predict HIV incidence among Indian MSM using data from 2003-2010. The model's sex mixing matrix was derived from network data of Indian MSM (n=4604). Our model captures changing distribution of sex roles over time. We modeled microbicide and circumcision efficacy on trials with heterosexuals. Increasing numbers of versatile MSM resulted in little change in HIV incidence over 20 years. Anal microbicides and circumcision would decrease the HIV prevalence at 10 years from 15.6% to 12.9% and 12.7% respectively. Anal microbicides would provide similar protection to circumcision at the population level despite lower modeled efficacy (54% and 60% risk reduction, respectively). Combination of the interventions were additive: in 5 years, the reduction in HIV prevalence of the combination (-3.2%) is almost the sum of their individual reductions in HIV prevalence (-1.8% and -1.7%). MSM sex role segregation and mixing, unlike changes in the sex role distribution, may be important for evaluating HIV prevention interventions in international settings. Synergies between some position-specific prevention interventions such as circumcision and anal microbicides warrant further study.

Vaccine effects on heterogeneity in susceptibility and implications for population health management

USGS Publications Warehouse

Langwig, Kate E.; Wargo, Andrew R.; Jones, Darbi R.; Viss, Jessie R.; Rutan, Barbara J.; Egan, Nicholas A.; Sá-Guimarães, Pedro; Min Sun Kim,; Kurath, Gael; Gomes, M. Gabriela M.; Lipsitch, Marc; Bansal, Shweta; Pettigrew, Melinda M.

2017-01-01

Heterogeneity in host susceptibility is a key determinant of infectious disease dynamics but is rarely accounted for in assessment of disease control measures. Understanding how susceptibility is distributed in populations, and how control measures change this distribution, is integral to predicting the course of epidemics with and without interventions. Using multiple experimental and modeling approaches, we show that rainbow trout have relatively homogeneous susceptibility to infection with infectious hematopoietic necrosis virus and that vaccination increases heterogeneity in susceptibility in a nearly all-or-nothing fashion. In a simple transmission model with an R0 of 2, the highly heterogeneous vaccine protection would cause a 35 percentage-point reduction in outbreak size over an intervention inducing homogenous protection at the same mean level. More broadly, these findings provide validation of methodology that can help to reduce biases in predictions of vaccine impact in natural settings and provide insight into how vaccination shapes population susceptibility.

Therapist adherence to a motivational-interviewing intervention improves treatment entry for substance-misusing adolescents with low problem perception.

PubMed

Smith, Douglas C; Hall, James A; Jang, Mijin; Arndt, Stephan

2009-01-01

This study evaluated whether adherence to the Strengths-Oriented Referral for Teens (SORT) model, a motivational interviewing (MI)-consistent intervention addressing ambivalence about attending treatment, positively predicted adolescents' initial-session attendance. Therapist adherence was rated in 54 audiotaped SORT sessions by coders who were blind to treatment-entry status. Higher adherence scores reflected greater use of MI and solution focused language, discussion of client strengths, and dialogue with families on treatment need and options. Therapist adherence during adolescent segments interacted with adolescent problem perception. Predicted probabilities of attending initial sessions increased for low-problem-perception adolescents at increasingly higher therapist adherence. Although replication studies are needed, the SORT model of providing MI-consistent debriefing following initial assessments appears to be a promising approach for increasing treatment entry. Initial support for the treatment-matching hypothesis was found for substance-misusing adolescents contemplating treatment entry.

A contemporary risk model for predicting 30-day mortality following percutaneous coronary intervention in England and Wales.

PubMed

McAllister, Katherine S L; Ludman, Peter F; Hulme, William; de Belder, Mark A; Stables, Rodney; Chowdhary, Saqib; Mamas, Mamas A; Sperrin, Matthew; Buchan, Iain E

2016-05-01

The current risk model for percutaneous coronary intervention (PCI) in the UK is based on outcomes of patients treated in a different era of interventional cardiology. This study aimed to create a new model, based on a contemporary cohort of PCI treated patients, which would: predict 30 day mortality; provide good discrimination; and be well calibrated across a broad risk-spectrum. The model was derived from a training dataset of 336,433 PCI cases carried out between 2007 and 2011 in England and Wales, with 30 day mortality provided by record linkage. Candidate variables were selected on the basis of clinical consensus and data quality. Procedures in 2012 were used to perform temporal validation of the model. The strongest predictors of 30-day mortality were: cardiogenic shock; dialysis; and the indication for PCI and the degree of urgency with which it was performed. The model had an area under the receiver operator characteristic curve of 0.85 on the training data and 0.86 on validation. Calibration plots indicated a good model fit on development which was maintained on validation. We have created a contemporary model for PCI that encompasses a range of clinical risk, from stable elective PCI to emergency primary PCI and cardiogenic shock. The model is easy to apply and based on data reported in national registries. It has a high degree of discrimination and is well calibrated across the risk spectrum. The examination of key outcomes in PCI audit can be improved with this risk-adjusted model. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Early identification of patients requiring massive transfusion, embolization, or hemostatic surgery for traumatic hemorrhage: a systematic review protocol.

PubMed

Tran, Alexandre; Matar, Maher; Steyerberg, Ewout W; Lampron, Jacinthe; Taljaard, Monica; Vaillancourt, Christian

2017-04-13

Hemorrhage is a major cause of early mortality following a traumatic injury. The progression and consequences of significant blood loss occur quickly as death from hemorrhagic shock or exsanguination often occurs within the first few hours. The mainstay of treatment therefore involves early identification of patients at risk for hemorrhagic shock in order to provide blood products and control of the bleeding source if necessary. The intended scope of this review is to identify and assess combinations of predictors informing therapeutic decision-making for clinicians during the initial trauma assessment. The primary objective of this systematic review is to identify and critically assess any existing multivariable models predicting significant traumatic hemorrhage that requires intervention, defined as a composite outcome comprising massive transfusion, surgery for hemostasis, or angiography with embolization for the purpose of external validation or updating in other study populations. If no suitable existing multivariable models are identified, the secondary objective is to identify candidate predictors to inform the development of a new prediction rule. We will search the EMBASE and MEDLINE databases for all randomized controlled trials and prospective and retrospective cohort studies developing or validating predictors of intervention for traumatic hemorrhage in adult patients 16 years of age or older. Eligible predictors must be available to the clinician during the first hour of trauma resuscitation and may be clinical, lab-based, or imaging-based. Outcomes of interest include the need for surgical intervention, angiographic embolization, or massive transfusion within the first 24 h. Data extraction will be performed independently by two reviewers. Items for extraction will be based on the CHARMS checklist. We will evaluate any existing models for relevance, quality, and the potential for external validation and updating in other populations. Relevance will be described in terms of appropriateness of outcomes and predictors. Quality criteria will include variable selection strategies, adequacy of sample size, handling of missing data, validation techniques, and measures of model performance. This systematic review will describe the availability of multivariable prediction models and summarize evidence regarding predictors that can be used to identify the need for intervention in patients with traumatic hemorrhage. PROSPERO CRD42017054589.

The "ick" Factor Matters: Disgust Prospectively Predicts Avoidance in Chemotherapy Patients.

PubMed

Reynolds, Lisa M; Bissett, Ian P; Porter, David; Consedine, Nathan S

2016-12-01

Chemotherapy can be physically and psychologically demanding. Avoidance and withdrawal are common among patients coping with these demands. This report compares established emotional predictors of avoidance during chemotherapy (embarrassment; distress) with an emotion (disgust) that has been unstudied in this context. This report outlines secondary analyses of an RCT where 68 cancer patients undergoing chemotherapy were randomized to mindfulness or relaxation interventions. Self-reported baseline disgust (DS-R), embarrassment (SES-SF), and distress (Distress Thermometer) were used to prospectively predict multiple classes of avoidance post-intervention and at 3 months follow-up. Measures assessed social avoidance, cognitive and emotional avoidance (IES Avoidance), as well as information seeking and treatment adherence (General Adherence Scale). Repeated-measures ANOVAs evaluated possible longitudinal changes in disgust and forward entry regression models contrasted the ability of the affective variables to predict avoidance. Although disgust did not change over time or vary between groups, greater disgust predicted greater social, cognitive, and emotional avoidance, as well as greater information seeking. Social avoidance was predicted by trait embarrassment and distress predicted non-adherence. This report represents the first investigation of disgust's ability to prospectively predict avoidance in people undergoing chemotherapy. Compared to embarrassment and distress, disgust was a more consistent predictor across avoidance domains and its predictive ability was evident across a longer period of time. Findings highlight disgust's role as an indicator of likely avoidance in this health context. Early identification of cancer patients at risk of deleterious avoidance may enable timely interventions and has important clinical implications (ACTRN12613000238774).

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

PubMed

Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh

2016-05-01

Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for Cancer Research.

Recent development of risk-prediction models for incident hypertension: An updated systematic review

PubMed Central

Xiao, Lei; Liu, Ya; Wang, Zuoguang; Li, Chuang; Jin, Yongxin; Zhao, Qiong

2017-01-01

Background Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative. Methods Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc. Results From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%. Conclusions The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment. PMID:29084293

The role of emotions in UV protection intentions and behaviors.

PubMed

Mahler, Heike I M

2014-01-01

Two studies examined the role of emotions, relative to cognitions, in predicting sun protection intentions and practices. In Study 1, 106 females were assessed for baseline sun protection, ultraviolet (UV) radiation exposure-related cognitions (perceived susceptibility to skin damage, self-efficacy for regular sunscreen use, perceived costs of sun protection use, perceived rewards of tanning), anticipated negative mood following future risky UV behavior, and future sun protection intentions. Self-reported sun protection behavior was then assessed in the same participants five weeks later. The results of Study 1 demonstrated that the extent to which participants' expected to experience negative feelings if they engaged in future risky UV behavior predicted their intentions to sun protect and their subsequent sun protection behaviors independent of their UV radiation exposure-related cognitions. In Study 2, in addition to the assessments collected in Study 1, participants were exposed to an appearance-based intervention that included visual images of their existing skin damage and were then assessed for their emotional reactions to the intervention. The results replicated those of Study 1 and, in addition, showed that negative emotional reactions to the intervention predicted future sun protection intentions and self-reported behaviors at follow-up, independent of the various cognitive factors that are central to prominent models of health behavior. These studies provide preliminary support for the development of expanded health behavior models that incorporate anticipated and experienced emotions.

Multivariate modelling of faecal bacterial profiles of patients with IBS predicts responsiveness to a diet low in FODMAPs.

PubMed

Bennet, Sean M P; Böhn, Lena; Störsrud, Stine; Liljebo, Therese; Collin, Lena; Lindfors, Perjohan; Törnblom, Hans; Öhman, Lena; Simrén, Magnus

2018-05-01

The effects of dietary interventions on gut bacteria are ambiguous. Following a previous intervention study, we aimed to determine how differing diets impact gut bacteria and if bacterial profiles predict intervention response. Sixty-seven patients with IBS were randomised to traditional IBS (n=34) or low fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAPs) (n=33) diets for 4 weeks. Food intake was recorded for 4 days during screening and intervention. Faecal samples and IBS Symptom Severity Score (IBS-SSS) reports were collected before (baseline) and after intervention. A faecal microbiota dysbiosis test (GA-map Dysbiosis Test) evaluated bacterial composition. Per protocol analysis was performed on 61 patients from whom microbiome data were available. Responders (reduced IBS-SSS by ≥50) to low FODMAP, but not traditional, dietary intervention were discriminated from non-responders before and after intervention based on faecal bacterial profiles. Bacterial abundance tended to be higher in non-responders to a low FODMAP diet compared with responders before and after intervention. A low FODMAP intervention was associated with an increase in Dysbiosis Index (DI) scores in 42% of patients; while decreased DI scores were recorded in 33% of patients following a traditional IBS diet. Non-responders to a low FODMAP diet, but not a traditional IBS diet had higher DI scores than responders at baseline. Finally, while a traditional IBS diet was not associated with significant reduction of investigated bacteria, a low FODMAP diet was associated with reduced Bifidobacterium and Actinobacteria in patients, correlating with lactose consumption. A low FODMAP, but not a traditional IBS diet may have significant impact on faecal bacteria. Responsiveness to a low FODMAP diet intervention may be predicted by faecal bacterial profiles. NCT02107625. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

PubMed Central

Yang, Pei-Ching; Chang, Chia-Chi; Chiang, Jung-Hsien; Chen, Ying-Yeh

2016-01-01

Background Prompt recognition and intervention of negative emotions is crucial for patients with depression. Mobile phones and mobile apps are suitable technologies that can be used to recognize negative emotions and intervene if necessary. Objective Mobile phone usage patterns can be associated with concurrent emotional states. The objective of this study is to adapt machine-learning methods to analyze such patterns for the prediction of negative emotion. Methods We developed an Android-based app to capture emotional states and mobile phone usage patterns, which included call logs (and use of apps). Visual analog scales (VASs) were used to report negative emotions in dimensions of depression, anxiety, and stress. In the system-training phase, participants were requested to tag their emotions for 14 consecutive days. Five feature-selection methods were used to determine individual usage patterns and four machine-learning methods were tested. Finally, rank product scoring was used to select the best combination to construct the prediction model. In the system evaluation phase, participants were then requested to verify the predicted negative emotions for at least 5 days. Results Out of 40 enrolled healthy participants, we analyzed data from 28 participants, including 30% (9/28) women with a mean (SD) age of 29.2 (5.1) years with sufficient emotion tags. The combination of time slots of 2 hours, greedy forward selection, and Naïve Bayes method was chosen for the prediction model. We further validated the personalized models in 18 participants who performed at least 5 days of model evaluation. Overall, the predictive accuracy for negative emotions was 86.17%. Conclusion We developed a system capable of predicting negative emotions based on mobile phone usage patterns. This system has potential for ecological momentary intervention (EMI) for depressive disorders by automatically recognizing negative emotions and providing people with preventive treatments before it escalates to clinical depression. PMID:27511748

Clinical and Vitamin Response to a Short-Term Multi-Micronutrient Intervention in Brazilian Children and Teens: From Population Data to Interindividual Responses.

PubMed

Mathias, Mariana Giaretta; Coelho-Landell, Carolina de Almeida; Scott-Boyer, Marie-Pier; Lacroix, Sébastien; Morine, Melissa J; Salomão, Roberta Garcia; Toffano, Roseli Borges Donegá; Almada, Maria Olímpia Ribeiro do Vale; Camarneiro, Joyce Moraes; Hillesheim, Elaine; de Barros, Tamiris Trevisan; Camelo-Junior, José Simon; Campos Giménez, Esther; Redeuil, Karine; Goyon, Alexandre; Bertschy, Emmanuelle; Lévêques, Antoine; Oberson, Jean-Marie; Giménez, Catherine; Carayol, Jerome; Kussmann, Martin; Descombes, Patrick; Métairon, Slyviane; Draper, Colleen Fogarty; Conus, Nelly; Mottaz, Sara Colombo; Corsini, Giovanna Zambianchi; Myoshi, Stephanie Kazu Brandão; Muniz, Mariana Mendes; Hernandes, Lívia Cristina; Venâncio, Vinícius Paula; Antunes, Lusania Maria Greggi; da Silva, Rosana Queiroz; Laurito, Taís Fontellas; Rossi, Isabela Ribeiro; Ricci, Raquel; Jorge, Jéssica Ré; Fagá, Mayara Leite; Quinhoneiro, Driele Cristina Gomes; Reche, Mariana Chinarelli; Silva, Paula Vitória Sozza; Falquetti, Letícia Lima; da Cunha, Thaís Helena Alves; Deminice, Thalia Manfrin Martins; Tambellini, Tâmara Hambúrguer; de Souza, Gabriela Cristina Arces; de Oliveira, Mariana Moraes; Nogueira-Pileggi, Vicky; Matsumoto, Marina Takemoto; Priami, Corrado; Kaput, Jim; Monteiro, Jacqueline Pontes

2018-03-01

Micronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals. A 6-week multivitamin/mineral intervention is conducted in 9-13 year olds. Participants (136) are (i) their own control (n-of-1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6-week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures. The study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Development of assessment tools to measure organizational support for employee health.

PubMed

Golaszewski, Thomas; Barr, Donald; Pronk, Nico

2003-01-01

To develop systems that measure and effect organizational support for employee health. Multiple studies and developmental projects were reviewed that show the process of instrument development, metric quality testing, utilization within intervention studies, and prediction modeling efforts. Demographic patterns indicate high support levels and relationships of subsections to various employee health risks. Successes with the initial version have given rise to 2 additional evaluation tools. The availability of these systems illustrates how ecological models can be practically applied. Such efforts contribute to the paradigm shift in worksite health promotion that focuses on the organization as the target of intervention.

Memory self-efficacy predicts responsiveness to inductive reasoning training in older adults.

PubMed

Payne, Brennan R; Jackson, Joshua J; Hill, Patrick L; Gao, Xuefei; Roberts, Brent W; Stine-Morrow, Elizabeth A L

2012-01-01

In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood.

Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya.

PubMed

Sewe, Maquins Odhiambo; Tozan, Yesim; Ahlm, Clas; Rocklöv, Joacim

2017-06-01

Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.

Kicking Back Cognitive Ageing: Leg Power Predicts Cognitive Ageing after Ten Years in Older Female Twins

PubMed Central

Steves, Claire J.; Mehta, Mitul M.; Jackson, Stephen H.D.; Spector, Tim D.

2016-01-01

Background Many observational studies have shown a protective effect of physical activity on cognitive ageing, but interventional studies have been less convincing. This may be due to short time scales of interventions, suboptimal interventional regimes or lack of lasting effect. Confounding through common genetic and developmental causes is also possible. Objectives We aimed to test whether muscle fitness (measured by leg power) could predict cognitive change in a healthy older population over a 10-year time interval, how this performed alongside other predictors of cognitive ageing, and whether this effect was confounded by factors shared by twins. In addition, we investigated whether differences in leg power were predictive of differences in brain structure and function after 12 years of follow-up in identical twin pairs. Methods A total of 324 healthy female twins (average age at baseline 55, range 43-73) performed the Cambridge Neuropsychological Test Automated Battery (CANTAB) at two time points 10 years apart. Linear regression modelling was used to assess the relationships between baseline leg power, physical activity and subsequent cognitive change, adjusting comprehensively for baseline covariates (including heart disease, diabetes, blood pressure, fasting blood glucose, lipids, diet, body habitus, smoking and alcohol habits, reading IQ, socioeconomic status and birthweight). A discordant twin approach was used to adjust for factors shared by twins. A subset of monozygotic pairs then underwent magnetic resonance imaging. The relationship between muscle fitness and brain structure and function was assessed using linear regression modelling and paired t tests. Results A striking protective relationship was found between muscle fitness (leg power) and both 10-year cognitive change [fully adjusted model standardised β-coefficient (Stdβ) = 0.174, p = 0.002] and subsequent total grey matter (Stdβ = 0.362, p = 0.005). These effects were robust in discordant twin analyses, where within-pair difference in physical fitness was also predictive of within-pair difference in lateral ventricle size. There was a weak independent effect of self-reported physical activity. Conclusion Leg power predicts both cognitive ageing and global brain structure, despite controlling for common genetics and early life environment shared by twins. Interventions targeted to improve leg power in the long term may help reach a universal goal of healthy cognitive ageing. PMID:26551663

Kicking Back Cognitive Ageing: Leg Power Predicts Cognitive Ageing after Ten Years in Older Female Twins.

PubMed

Steves, Claire J; Mehta, Mitul M; Jackson, Stephen H D; Spector, Tim D

2016-01-01

Many observational studies have shown a protective effect of physical activity on cognitive ageing, but interventional studies have been less convincing. This may be due to short time scales of interventions, suboptimal interventional regimes or lack of lasting effect. Confounding through common genetic and developmental causes is also possible. We aimed to test whether muscle fitness (measured by leg power) could predict cognitive change in a healthy older population over a 10-year time interval, how this performed alongside other predictors of cognitive ageing, and whether this effect was confounded by factors shared by twins. In addition, we investigated whether differences in leg power were predictive of differences in brain structure and function after 12 years of follow-up in identical twin pairs. A total of 324 healthy female twins (average age at baseline 55, range 43-73) performed the Cambridge Neuropsychological Test Automated Battery (CANTAB) at two time points 10 years apart. Linear regression modelling was used to assess the relationships between baseline leg power, physical activity and subsequent cognitive change, adjusting comprehensively for baseline covariates (including heart disease, diabetes, blood pressure, fasting blood glucose, lipids, diet, body habitus, smoking and alcohol habits, reading IQ, socioeconomic status and birthweight). A discordant twin approach was used to adjust for factors shared by twins. A subset of monozygotic pairs then underwent magnetic resonance imaging. The relationship between muscle fitness and brain structure and function was assessed using linear regression modelling and paired t tests. A striking protective relationship was found between muscle fitness (leg power) and both 10-year cognitive change [fully adjusted model standardised β-coefficient (Stdβ) = 0.174, p = 0.002] and subsequent total grey matter (Stdβ = 0.362, p = 0.005). These effects were robust in discordant twin analyses, where within-pair difference in physical fitness was also predictive of within-pair difference in lateral ventricle size. There was a weak independent effect of self-reported physical activity. Leg power predicts both cognitive ageing and global brain structure, despite controlling for common genetics and early life environment shared by twins. Interventions targeted to improve leg power in the long term may help reach a universal goal of healthy cognitive ageing. © 2015 The Author(s) Published by S. Karger AG, Basel.

Estimating community health needs against a Triple Aim background: What can we learn from current predictive risk models?

PubMed

Elissen, Arianne M J; Struijs, Jeroen N; Baan, Caroline A; Ruwaard, Dirk

2015-05-01

To support providers and commissioners in accurately assessing their local populations' health needs, this study produces an overview of Dutch predictive risk models for health care, focusing specifically on the type, combination and relevance of included determinants for achieving the Triple Aim (improved health, better care experience, and lower costs). We conducted a mixed-methods study combining document analyses, interviews and a Delphi study. Predictive risk models were identified based on a web search and expert input. Participating in the study were Dutch experts in predictive risk modelling (interviews; n=11) and experts in healthcare delivery, insurance and/or funding methodology (Delphi panel; n=15). Ten predictive risk models were analysed, comprising 17 unique determinants. Twelve were considered relevant by experts for estimating community health needs. Although some compositional similarities were identified between models, the combination and operationalisation of determinants varied considerably. Existing predictive risk models provide a good starting point, but optimally balancing resources and targeting interventions on the community level will likely require a more holistic approach to health needs assessment. Development of additional determinants, such as measures of people's lifestyle and social network, may require policies pushing the integration of routine data from different (healthcare) sources. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Computational modeling to predict nitrogen balance during acute metabolic decompensation in patients with urea cycle disorders

PubMed Central

MacLeod, Erin L.; Hall, Kevin D.; McGuire, Peter J.

2015-01-01

SUMMARY Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. A validated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection. PMID:26260782

Computational modeling to predict nitrogen balance during acute metabolic decompensation in patients with urea cycle disorders.

PubMed

MacLeod, Erin L; Hall, Kevin D; McGuire, Peter J

2016-01-01

Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. A validated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection.

Modeling Determinants of Medication Attitudes and Poor Adherence in Early Nonaffective Psychosis: Implications for Intervention

PubMed Central

Drake, Richard J.; Nordentoft, Merete; Haddock, Gillian; Arango, Celso; Fleischhacker, W. Wolfgang; Glenthøj, Birte; Leboyer, Marion; Leucht, Stefan; Leweke, Markus; McGuire, Phillip; Meyer-Lindenberg, Andreas; Rujescu, Dan; Sommer, Iris E.; Kahn, René S.; Lewis, Shon W.

2015-01-01

We aimed to design a multimodal intervention to improve adherence following first episode psychosis, consistent with current evidence. Existing literature identified medication attitudes, insight, and characteristics of support as important determinants of adherence to medication: we examined medication attitudes, self-esteem, and insight in an early psychosis cohort better to understand their relationships. Existing longitudinal data from 309 patients with early Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, nonaffective psychosis (83% first episode) were analyzed to test the hypothesis that medication attitudes, while meaningfully different from “insight,” correlated with insight and self-esteem, and change in each influenced the others. Rosenberg Self-Esteem Scale, Birchwood Insight Scale, and Positive and Negative Syndrome Scale insight were assessed at presentation, after 6 weeks and 3 and 18 months. Drug Attitudes Inventory (DAI) and treatment satisfaction were rated from 6 weeks onward. Structural equation models of their relationships were compared. Insight measures’ and DAI’s predictive validity were compared against relapse, readmission, and remission. Analysis found five latent constructs best fitted the data: medication attitudes, self-esteem, accepting need for treatment, self-rated insight, and objective insight. All were related and each affected the others as it changed, except self-esteem and medication attitudes. Low self-reported insight at presentation predicted readmission. Good 6-week insight (unlike drug attitudes) predicted remission. Literature review and data modeling indicated that a multimodal intervention using motivational interviewing, online psychoeducation, and SMS text medication reminders to enhance adherence without damaging self-concept was feasible and appropriate. PMID:25750247

The Readmission Risk Flag: Using the Electronic Health Record to Automatically Identify Patients at Risk for 30-day Readmission

PubMed Central

Baillie, Charles A.; VanZandbergen, Christine; Tait, Gordon; Hanish, Asaf; Leas, Brian; French, Benjamin; Hanson, C. William; Behta, Maryam; Umscheid, Craig A.

2015-01-01

Background Identification of patients at high risk for readmission is a crucial step toward improving care and reducing readmissions. The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions. Objective To develop and implement an automated prediction model integrated into our health system’s EHR that identifies on admission patients at high risk for readmission within 30 days of discharge. Design Retrospective and prospective cohort. Setting Healthcare system consisting of three hospitals. Patients All adult patients admitted from August 2009 to September 2012. Interventions An automated readmission risk flag integrated into the EHR. Measures Thirty-day all-cause and 7-day unplanned healthcare system readmissions. Results Using retrospective data, a single risk factor, ≥2 inpatient admissions in the past 12 months, was found to have the best balance of sensitivity (40%), positive predictive value (31%), and proportion of patients flagged (18%), with a c-statistic of 0.62. Sensitivity (39%), positive predictive value (30%), proportion of patients flagged (18%) and c-statistic (0.61) during the 12-month period after implementation of the risk flag were similar. There was no evidence for an effect of the intervention on 30-day all-cause and 7-day unplanned readmission rates in the 12-month period after implementation. Conclusions An automated prediction model was effectively integrated into an existing EHR and identified patients on admission who were at risk for readmission within 30 days of discharge. PMID:24227707

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

PubMed Central

Choudhry, Shahid A.; Li, Jing; Davis, Darcy; Erdmann, Cole; Sikka, Rishi; Sutariya, Bharat

2013-01-01

Introduction: Preventing the occurrence of hospital readmissions is needed to improve quality of care and foster population health across the care continuum. Hospitals are being held accountable for improving transitions of care to avert unnecessary readmissions. Advocate Health Care in Chicago and Cerner (ACC) collaborated to develop all-cause, 30-day hospital readmission risk prediction models to identify patients that need interventional resources. Ideally, prediction models should encompass several qualities: they should have high predictive ability; use reliable and clinically relevant data; use vigorous performance metrics to assess the models; be validated in populations where they are applied; and be scalable in heterogeneous populations. However, a systematic review of prediction models for hospital readmission risk determined that most performed poorly (average C-statistic of 0.66) and efforts to improve their performance are needed for widespread usage. Methods: The ACC team incorporated electronic health record data, utilized a mixed-method approach to evaluate risk factors, and externally validated their prediction models for generalizability. Inclusion and exclusion criteria were applied on the patient cohort and then split for derivation and internal validation. Stepwise logistic regression was performed to develop two predictive models: one for admission and one for discharge. The prediction models were assessed for discrimination ability, calibration, overall performance, and then externally validated. Results: The ACC Admission and Discharge Models demonstrated modest discrimination ability during derivation, internal and external validation post-recalibration (C-statistic of 0.76 and 0.78, respectively), and reasonable model fit during external validation for utility in heterogeneous populations. Conclusions: The ACC Admission and Discharge Models embody the design qualities of ideal prediction models. The ACC plans to continue its partnership to further improve and develop valuable clinical models. PMID:24224068

The effectiveness of a Web-based personalized feedback and social norms alcohol intervention on United Kingdom university students: randomized controlled trial.

PubMed

Bewick, Bridgette M; West, Robert M; Barkham, Michael; Mulhern, Brendan; Marlow, Robert; Traviss, Gemma; Hill, Andrew J

2013-07-24

Alcohol consumption in the student population continues to be cause for concern. Building on the established evidence base for traditional brief interventions, interventions using the Internet as a mode of delivery are being developed. Published evidence of replication of initial findings and ongoing development and modification of Web-based personalized feedback interventions for student alcohol use is relatively rare. The current paper reports on the replication of the initial Unitcheck feasibility trial. To evaluate the effectiveness of Unitcheck, a Web-based intervention that provides instant personalized feedback on alcohol consumption. It was hypothesized that use of Unitcheck would be associated with a reduction in alcohol consumption. A randomized control trial with two arms (control=assessment only; intervention=fully automated personalized feedback delivered using a Web-based intervention). The intervention was available week 1 through to week 15. Students at a UK university who were completing a university-wide annual student union electronic survey were invited to participate in the current study. Participants (n=1618) were stratified by sex, age group, year of study, self-reported alcohol consumption, then randomly assigned to one of the two arms, and invited to participate in the current trial. Participants were not blind to allocation. In total, n=1478 (n=723 intervention, n=755 control) participants accepted the invitation. Of these, 70% were female, the age ranged from 17-50 years old, and 88% were white/white British. Data were collected electronically via two websites: one for each treatment arm. Participants completed assessments at weeks 1, 16, and 34. Assessment included CAGE, a 7-day retrospective drinking diary, and drinks consumed per drinking occasion. The regression model predicted a monitoring effect, with participants who completed assessments reducing alcohol consumption over the final week. Further reductions were predicted for those allocated to receive the intervention, and additional reductions were predicted as the number of visits to the intervention website increased. Unitcheck can reduce the amount of alcohol consumed, and the reduction can be sustained in the medium term (ie, 19 weeks after intervention was withdrawn). The findings suggest self-monitoring is an active ingredient to Web-based personalized feedback.

RAN as a predictor of reading skills, and vice versa: results from a randomised reading intervention.

PubMed

Wolff, Ulrika

2014-07-01

Although phonemic awareness is a well-known factor predicting early reading development, there is also evidence that Rapid Automatized Naming (RAN) is an independent factor that contributes to early reading. The aim of this study is to examine phonemic awareness and RAN as predictors of reading speed, reading comprehension and spelling for children with reading difficulties. It also investigates a possible reciprocal relationship between RAN and reading skills, and the possibility of enhancing RAN by intervention. These issues are addressed by examining longitudinal data from a randomised reading intervention study carried out in Sweden for 9-year-old children with reading difficulties (N = 112). The intervention comprised three main elements: training of phonics, reading comprehension strategies and reading speed. The analysis of the data was carried out using structural equation modelling. The results demonstrated that after controlling for autoregressive effects and non-verbal IQ, RAN predicts reading speed whereas phonemic awareness predicts reading comprehension and spelling. RAN was significantly enhanced by training and a reciprocal relationship between reading speed and RAN was found. These findings contribute to support the view that both phonemic awareness and RAN independently influence early phases of reading, and that both are possible to enhance by training.

Why equal treatment is not always equitable: the impact of existing ethnic health inequalities in cost-effectiveness modeling.

PubMed

McLeod, Melissa; Blakely, Tony; Kvizhinadze, Giorgi; Harris, Ricci

2014-01-01

A critical first step toward incorporating equity into cost-effectiveness analyses is to appropriately model interventions by population subgroups. In this paper we use a standardized treatment intervention to examine the impact of using ethnic-specific (Māori and non-Māori) data in cost-utility analyses for three cancers. We estimate gains in health-adjusted life years (HALYs) for a simple intervention (20% reduction in excess cancer mortality) for lung, female breast, and colon cancers, using Markov modeling. Base models include ethnic-specific cancer incidence with other parameters either turned off or set to non-Māori levels for both groups. Subsequent models add ethnic-specific cancer survival, morbidity, and life expectancy. Costs include intervention and downstream health system costs. For the three cancers, including existing inequalities in background parameters (population mortality and comorbidities) for Māori attributes less value to a year of life saved compared to non-Māori and lowers the relative health gains for Māori. In contrast, ethnic inequalities in cancer parameters have less predictable effects. Despite Māori having higher excess mortality from all three cancers, modeled health gains for Māori were less from the lung cancer intervention than for non-Māori but higher for the breast and colon interventions. Cost-effectiveness modeling is a useful tool in the prioritization of health services. But there are important (and sometimes counterintuitive) implications of including ethnic-specific background and disease parameters. In order to avoid perpetuating existing ethnic inequalities in health, such analyses should be undertaken with care.

Why equal treatment is not always equitable: the impact of existing ethnic health inequalities in cost-effectiveness modeling

PubMed Central

2014-01-01

Background A critical first step toward incorporating equity into cost-effectiveness analyses is to appropriately model interventions by population subgroups. In this paper we use a standardized treatment intervention to examine the impact of using ethnic-specific (Māori and non-Māori) data in cost-utility analyses for three cancers. Methods We estimate gains in health-adjusted life years (HALYs) for a simple intervention (20% reduction in excess cancer mortality) for lung, female breast, and colon cancers, using Markov modeling. Base models include ethnic-specific cancer incidence with other parameters either turned off or set to non-Māori levels for both groups. Subsequent models add ethnic-specific cancer survival, morbidity, and life expectancy. Costs include intervention and downstream health system costs. Results For the three cancers, including existing inequalities in background parameters (population mortality and comorbidities) for Māori attributes less value to a year of life saved compared to non-Māori and lowers the relative health gains for Māori. In contrast, ethnic inequalities in cancer parameters have less predictable effects. Despite Māori having higher excess mortality from all three cancers, modeled health gains for Māori were less from the lung cancer intervention than for non-Māori but higher for the breast and colon interventions. Conclusions Cost-effectiveness modeling is a useful tool in the prioritization of health services. But there are important (and sometimes counterintuitive) implications of including ethnic-specific background and disease parameters. In order to avoid perpetuating existing ethnic inequalities in health, such analyses should be undertaken with care. PMID:24910540

A Random Forest Based Risk Model for Reliable and Accurate Prediction of Receipt of Transfusion in Patients Undergoing Percutaneous Coronary Intervention

PubMed Central

Gurm, Hitinder S.; Kooiman, Judith; LaLonde, Thomas; Grines, Cindy; Share, David; Seth, Milan

2014-01-01

Background Transfusion is a common complication of Percutaneous Coronary Intervention (PCI) and is associated with adverse short and long term outcomes. There is no risk model for identifying patients most likely to receive transfusion after PCI. The objective of our study was to develop and validate a tool for predicting receipt of blood transfusion in patients undergoing contemporary PCI. Methods Random forest models were developed utilizing 45 pre-procedural clinical and laboratory variables to estimate the receipt of transfusion in patients undergoing PCI. The most influential variables were selected for inclusion in an abbreviated model. Model performance estimating transfusion was evaluated in an independent validation dataset using area under the ROC curve (AUC), with net reclassification improvement (NRI) used to compare full and reduced model prediction after grouping in low, intermediate, and high risk categories. The impact of procedural anticoagulation on observed versus predicted transfusion rates were assessed for the different risk categories. Results Our study cohort was comprised of 103,294 PCI procedures performed at 46 hospitals between July 2009 through December 2012 in Michigan of which 72,328 (70%) were randomly selected for training the models, and 30,966 (30%) for validation. The models demonstrated excellent calibration and discrimination (AUC: full model  = 0.888 (95% CI 0.877–0.899), reduced model AUC = 0.880 (95% CI, 0.868–0.892), p for difference 0.003, NRI = 2.77%, p = 0.007). Procedural anticoagulation and radial access significantly influenced transfusion rates in the intermediate and high risk patients but no clinically relevant impact was noted in low risk patients, who made up 70% of the total cohort. Conclusions The risk of transfusion among patients undergoing PCI can be reliably calculated using a novel easy to use computational tool (https://bmc2.org/calculators/transfusion). This risk prediction algorithm may prove useful for both bed side clinical decision making and risk adjustment for assessment of quality. PMID:24816645

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis.

PubMed

Madrasi, Kumpal; Chaturvedula, Ayyappa; Haberer, Jessica E; Sale, Mark; Fossler, Michael J; Bangsberg, David; Baeten, Jared M; Celum, Connie; Hendrix, Craig W

2017-05-01

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM ® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors. © 2016, The American College of Clinical Pharmacology.

Computational Nosology and Precision Psychiatry

PubMed Central

Redish, A. David; Gordon, Joshua A.

2017-01-01

This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present—and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations. PMID:29400354

Individualized Prediction of Heat Stress in Firefighters: A Data-Driven Approach Using Classification and Regression Trees.

PubMed

Mani, Ashutosh; Rao, Marepalli; James, Kelley; Bhattacharya, Amit

2015-01-01

The purpose of this study was to explore data-driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38°C) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data-driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters.

Patient survival and surgical re-intervention predictors for intracapsular hip fractures.

PubMed

González Quevedo, David; Mariño, Iskandar Tamimi; Sánchez Siles, Juan Manuel; Escribano, Esther Romero; Granero Molina, Esther Judith; Enrique, David Bautista; Smoljanović, Tomislav; Pareja, Francisco Villanueva

2017-08-01

Choosing between total hip replacement (THR) and partial hip replacement (PHR) for patients with intracapsular hip fractures is often based on subjective factors. Predicting the survival of these patients and risk of surgical re-intervention is essential to select the most adequate implant. We conducted a retrospective cohort study on mortality of patients over 70 years with intracapsular hip fractures who were treated between January 2010 and December 2013, with either PHR or THR. Patients' information was withdrawn from our local computerized database. The age-adjusted Charlson comorbidity index (ACCI) and American Society of Anesthesiologists (ASA) score were calculated for all patients. The patients were followed for 2 years after surgery. Survival and surgical re-intervention rates were compared between the two groups using a Multivariate Cox proportional hazard model. A total of 356 individuals were included in this study. At 2 years of follow-up, 221 (74.4%) of the patients with ACCI score≤7 were still alive, in contrast to only 20 (29.0%) of those with ACCI score>7. In addition, 201 (76.2%) of the patients with ASA score≤3 were still alive after 2 years, compared to 30 (32.6%) of individuals with ASA >3. Patients with the ACCI score>7, and ASA score>3 had a significant increase in all-cause 2-year mortality (adjusted hazard ratio of 3.2, 95% CI 2.2-4.6; and 3.12, 95% CI 2.2-4.5, respectively). Patients with an ASA score>3 had a quasi-significant increase in the re-intervention risk (adjusted hazard ratio 2.2, 95% CI 1.0-5.1). The sensitivity, specificity, positive predictive value and negative predictive values of ACCI in predicting 2-year mortality were 39.2%, 91.1%, 71%, and 74.4%, respectively. On the other hand, the sensitivity, specificity, positive predictive value and negative predictive values of ASA score in predicting 2-year mortality were 49.6%, 79.1%, 67.4%, and 76.1%, respectively. Both ACCI and ASA scales were able to predict the 2-year survival of patients with intracapsular hip fractures. The ASA scale was also able to predict the risk of re-intervention in these patients. Copyright © 2017 Elsevier Ltd. All rights reserved.

The relationship between formative and summative examinations and PANCE scores; can the past predict the future?

PubMed

Massey, Scott; Stallman, John; Lee, Louise; Klingaman, Kathy; Holmerud, David

2011-01-01

This paper describes how a systematic analysis of students at risk for failing the Physician Assistant National Certifying Examination (PANCE) may be used to identify which students may benefit from intervention prior to taking the PANCE and thus increase the likelihood of successful completion of the PANCE. The intervention developed and implemented uses various formative and summative examinations to predict students' PANCE scores with a high degree of accuracy. Eight end-of-rotation exams (EOREs) based upon discipline-specific diseases and averaging 100 questions each, a 360-question PANCE simulation (SUMM I), the PACKRAT, and a 700-question summative cognitive examination based upon the NCCPA blueprint (SUMM II) were administered to all students enrolled in the program during the clinical year starting in January 2010 and concluding in December 2010. When the PACKRAT, SUMM I, SUMM II, and the surgery, women's health, and pediatrics EOREs were combined in a regression model, an Rvalue of 0.87 and an R2 of 0.75 were obtained. A predicted score was generated for the class of 2009. The predicted PANCE score based upon this model had a final correlation of 0.790 with the actual PANCE score. This pilot study demonstrated that valid predicted scores could be generated from formative and summative examinations to provide valuable feedback and to identify students at risk of failing the PANCE.

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data

PubMed Central

2014-01-01

Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed. PMID:24885128

Can theory predict the process of suicide on entry to prison? Predicting dynamic risk factors for suicide ideation in a high-risk prison population.

PubMed

Slade, Karen; Edelman, Robert

2014-01-01

Each year approximately 110,000 people are imprisoned in England and Wales and new prisoners remain one of the highest risk groups for suicide across the world. The reduction of suicide in prisoners remains difficult as assessments and interventions tend to rely on static risk factors with few theoretical or integrated models yet evaluated. To identify the dynamic factors that contribute to suicide ideation in this population based on Williams and Pollock's (2001) Cry of Pain (CoP) model. New arrivals (N = 198) into prison were asked to complete measures derived from the CoP model plus clinical and prison-specific factors. It was hypothesized that the factors of the CoP model would be predictive of suicide ideation. Support was provided for the defeat and entrapment aspects of the CoP model with previous self-harm, repeated times in prison, and suicide-permissive cognitions also key in predicting suicide ideation for prisoners on entry to prison. An integrated and dynamic model was developed that has utility in predicting suicide in early-stage prisoners. Implications for both theory and practice are discussed along with recommendations for future research.

Cognitive Impairment in Acquired Brain Injury: A Predictor of Rehabilitation Outcomes and an Opportunity for Novel Interventions

PubMed Central

Whyte, Ellen; Skidmore, Elizabeth; Aizenstein, Howard; Ricker, Joseph; Butters, Meryl

2015-01-01

Cognitive impairment is a common sequela in acquired brain injury and one that predicts rehabilitation outcomes. There is emerging evidence that impairments in cognitive functions can be manipulated by both pharmacologic and nonpharmacologic interventions to improve rehabilitation outcomes. By using stroke as a model for acquired brain injury, we review the evidence that links cognitive impairment to poor rehabilitation outcomes and discuss possible mechanisms to explain this association. Furthermore, we examine nascent promising research that suggests that interventions that target cognitive impairments can lead to better rehabilitation outcomes. PMID:21703580

Correlates of motivation to change in pathological gamblers completing cognitive-behavioral group therapy.

PubMed

Gómez-Peña, Mónica; Penelo, Eva; Granero, Roser; Fernández-Aranda, Fernando; Alvarez-Moya, Eva; Santamaría, Juan José; Moragas, Laura; Neus Aymamí, Maria; Gunnard, Katarina; Menchón, José M; Jimenez-Murcia, Susana

2012-07-01

The present study analyzes the association between the motivation to change and the cognitive-behavioral group intervention, in terms of dropouts and relapses, in a sample of male pathological gamblers. The specific objectives were as follows: (a) to estimate the predictive value of baseline University of Rhode Island Change Assessment scale (URICA) scores (i.e., at the start of the study) as regards the risk of relapse and dropout during treatment and (b) to assess the incremental predictive ability of URICA scores, as regards the mean change produced in the clinical status of patients between the start and finish of treatment. The relationship between the URICA and the response to treatment was analyzed by means of a pre-post design applied to a sample of 191 patients who were consecutively receiving cognitive-behavioral group therapy. The statistical analysis included logistic regression models and hierarchical multiple linear regression models. The discriminative ability of the models including the four URICA scores regarding the likelihood of relapse and dropout was acceptable (area under the receiver operating haracteristic curve: .73 and .71, respectively). No significant predictive ability was found as regards the differences between baseline and posttreatment scores (changes in R(2) below 5% in the multiple regression models). The availability of useful measures of motivation to change would enable treatment outcomes to be optimized through the application of specific therapeutic interventions. © 2012 Wiley Periodicals, Inc.

Individuation or Identification? Self-Objectification and the Mother-Adolescent Relationship

PubMed Central

Katz-Wise, Sabra L.; Budge, Stephanie L.; Lindberg, Sara M.; Hyde, Janet S.

2013-01-01

Do adolescents model their mothers’ self-objectification? We measured self-objectification (body surveillance and body shame), body mass index (BMI), body esteem, and quality of the mother-adolescent relationship in 179 female and 162 male adolescents at age 15, as well as self-objectification in their mothers. Initial analyses indicated no improvement in model fit if paths were allowed to differ for females and males; therefore a single model was tested for the combined sample. Findings revealed that mothers’ body surveillance negatively predicted adolescents’ body surveillance. Mothers’ body shame was unrelated to adolescents’ body shame, but positively predicted adolescents’ body surveillance. Results for the relationship between mothers’ and adolescents’ self-objectification suggest that adolescents engaged in more individuation than modeling. A more positive mother-adolescent relationship predicted lower body shame and higher body esteem in adolescents, suggesting that the quality of the relationship with the mother may be a protective factor for adolescents’ body image. Mother-adolescent relationship quality did not moderate the association between mothers’ and adolescents’ self-objectification. These findings contribute to our understanding about the sociocultural role of parents in adolescents’ body image and inform interventions addressing negative body image in this age group. The quality of the mother-adolescent relationship is a clear point of entry for such interventions. Therapists should work with adolescents and their mothers toward a more positive relationship quality, which could then positively impact adolescents’ body image. PMID:24363490

Testing an Expanded Theory of Planned Behavior Model to Explain Marijuana Use among Emerging Adults in a Pro-Marijuana Community

PubMed Central

Ito, Tiffany A.; Henry, Erika A.; Cordova, Kismet A.; Bryan, Angela D.

2015-01-01

Opinions about marijuana use within the US are becoming increasingly favorable, making it important to understand how psychosocial influences impact individuals’ use within this context. Here we use the Theory of Planned Behavior (TPB) to examine the influence of initial attitudes, norms, and efficacy to resist use on initial intentions, and then the effect of initial intentions on actual marijuana use measured one year later using data drawn from a community with relatively high use. We expanded the traditional TPB model by investigating two types of normative influence (descriptive and injunctive) and two types of intentions (use intentions and proximity intentions), reasoning that exposure to high use in the population may produce high descriptive norms and proximity intentions overall, but not necessarily increase actual use. By contrast, we expected greater variability in injunctive norms and use intentions and that only use intentions would predict actual use. Consistent with hypotheses, intentions to use marijuana were predicted by injunctive norms (and attitudes) and in turn predicted marijuana use one year later. By contrast, descriptive norms were relatively high among all participants and did not predict intentions. Moreover, proximity intentions were not predictive of actual use. We also found that increasing intentions to use over a one year period predicted greater use. Given the greater efficacy of theory-based as compared to non-theory-based interventions, these findings provide critical information for the design of successful interventions to decrease marijuana-associated harms. PMID:26168227

Initial Impact of the Fast Track Prevention Trial for Conduct Problems: II. Classroom Effects

PubMed Central

2009-01-01

This study examined the effectiveness of the universal component of the Fast Track prevention model: the PATHS (Promoting Alternative THinking Strategies) curriculum and teacher consultation. This randomized clinical trial involved 198 intervention and 180 comparison classrooms from neighborhoods with greater than average crime in 4 U.S. locations. In the intervention schools, Grade 1 teachers delivered a 57-lesson social competence intervention focused on self-control, emotional awareness, peer relations, and problem solving. Findings indicated significant effects on peer ratings of aggression and hyperactive–disruptive behavior and observer ratings of classroom atmosphere. Quality of implementation predicted variation in assessments of classroom functioning. The results are discussed in terms of both the efficacy of universal, school-based prevention models and the need to examine comprehensive, multiyear programs. PMID:10535231

Cost-Effectiveness Analysis of a Skin Awareness Intervention for Early Detection of Skin Cancer Targeting Men Older Than 50 Years.

PubMed

Gordon, Louisa G; Brynes, Joshua; Baade, Peter D; Neale, Rachel E; Whiteman, David C; Youl, Philippa H; Aitken, Joanne F; Janda, Monika

2017-04-01

To assess the cost-effectiveness of an educational intervention encouraging self-skin examinations for early detection of skin cancers among men older than 50 years. A lifetime Markov model was constructed to combine data from the Skin Awareness Trial and other published sources. The model incorporated a health system perspective and the cost and health outcomes for melanoma, squamous and basal cell carcinomas, and benign skin lesions. Key model outcomes included Australian costs (2015), quality-adjusted life-years (QALYs), life-years, and counts of skin cancers. Univariate and probabilistic sensitivity analyses were undertaken to address parameter uncertainty. The mean cost of the intervention was A$5,298 compared with A$4,684 for usual care, whereas mean QALYs were 7.58 for the intervention group and 7.77 for the usual care group. The intervention was thus inferior to usual care. When only survival gain is considered, the model predicted the intervention would cost A$1,059 per life-year saved. The likelihood that the intervention was cost-effective up to A$50,000 per QALY gained was 43.9%. The model was stable to most data estimates; nevertheless, it relies on the specificity of clinical diagnosis of skin cancers and is subject to limited health utility data for people with skin lesions. Although the intervention improved skin checking behaviors and encouraged men to seek medical advice about suspicious lesions, the overall costs and effects from also detecting more squamous and basal cell carcinomas and benign lesions outweighed the positive health gains from detecting more thin melanomas. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

It's Your Place: Development and Evaluation of an Evidence-Based Bystander Intervention Campaign.

PubMed

Sundstrom, Beth; Ferrara, Merissa; DeMaria, Andrea L; Gabel, Colby; Booth, Kathleen; Cabot, Jeri

2017-06-28

Preventing sexual assault on college campuses is a national priority. Bystander intervention offers a promising approach to change social norms and prevent sexual misconduct. This study presents the implementation and evaluation of a theory-based campaign to promote active bystander intervention. The theory of planned behavior (TPB) served as a conceptual framework throughout campaign development and evaluation. Formative research published elsewhere was used to develop campaign strategies, communication channels, and messages, including "It is your place to prevent sexual assault: You're not ruining a good time." The It's Your Place multi-media campaign fosters a culture of bystander intervention through peer-to-peer facilitation and training, as well as traditional and new media platforms. A cross-sectional post-test only web-based survey was designed to evaluate the campaign and test the TPB's ability to accurately predict intention to intervene. Survey data were collected from 1,505 currently enrolled students. The TPB model predicted intention to intervene. There was a significant effect of campaign exposure on attitude, subjective norms, and perceived behavioral intention. This theory-based communication campaign offers implications for promoting active bystander intervention and reducing sexual assault.

Examining the Predictive Validity of a Dynamic Assessment of Decoding to Forecast Response Tier 2 to Intervention

PubMed Central

Cho, Eunsoo; Compton, Donald L.; Fuchs, Doug; Fuchs, Lynn S.; Bouton, Bobette

2013-01-01

The purpose of this study was to examine the role of a dynamic assessment (DA) of decoding in predicting responsiveness to Tier 2 small group tutoring in a response-to-intervention model. First-grade students (n=134) who did not show adequate progress in Tier 1 based on 6 weeks of progress monitoring received Tier 2 small-group tutoring in reading for 14 weeks. Student responsiveness to Tier 2 was assessed weekly with word identification fluency (WIF). A series of conditional individual growth curve analyses were completed that modeled the correlates of WIF growth (final level of performance and growth). Its purpose was to examine the predictive validity of DA in the presence of 3 sets of variables: static decoding measures, Tier 1 responsiveness indicators, and pre-reading variables (phonemic awareness, rapid letter naming, oral vocabulary, and IQ). DA was a significant predictor of final level and growth, uniquely explaining 3% – 13% of the variance in Tier 2 responsiveness depending on the competing predictors in the model and WIF outcome (final level of performance or growth). Although the additional variances explained uniquely by DA were relatively small, results indicate the potential of DA in identifying Tier 2 nonresponders. PMID:23213050

Examining the predictive validity of a dynamic assessment of decoding to forecast response to tier 2 intervention.

PubMed

Cho, Eunsoo; Compton, Donald L; Fuchs, Douglas; Fuchs, Lynn S; Bouton, Bobette

2014-01-01

The purpose of this study was to examine the role of a dynamic assessment (DA) of decoding in predicting responsiveness to Tier 2 small-group tutoring in a response-to-intervention model. First grade students (n = 134) who did not show adequate progress in Tier 1 based on 6 weeks of progress monitoring received Tier 2 small-group tutoring in reading for 14 weeks. Student responsiveness to Tier 2 was assessed weekly with word identification fluency (WIF). A series of conditional individual growth curve analyses were completed that modeled the correlates of WIF growth (final level of performance and growth). Its purpose was to examine the predictive validity of DA in the presence of three sets of variables: static decoding measures, Tier 1 responsiveness indicators, and prereading variables (phonemic awareness, rapid letter naming, oral vocabulary, and IQ). DA was a significant predictor of final level and growth, uniquely explaining 3% to 13% of the variance in Tier 2 responsiveness depending on the competing predictors in the model and WIF outcome (final level of performance or growth). Although the additional variances explained uniquely by DA were relatively small, results indicate the potential of DA in identifying Tier 2 nonresponders. © Hammill Institute on Disabilities 2012.

Development of a prognostic model based on demographic, environmental and lifestyle information for predicting incidences of symptomatic respiratory or gastrointestinal infection in adult office workers.

PubMed

Hovi, Tapani; Ollgren, Jukka; Haapakoski, Jaason; Savolainen-Kopra, Carita

2016-11-16

Occurrence of respiratory tract infection (RTI) or gastrointestinal tract infection (GTI) is known to vary between individuals and may be a confounding factor in the analysis of the results of intervention trials. We aimed at developing a prognostic model for predicting individual incidences of RTI and GTI on the basis of data collected in a hand-hygiene intervention trial among adult office workers, and comprising a prior-to-onset questionnaire on potential infection-risk factors and weekly electronic follow-up reports on occurrence of symptoms of, and on exposures to RTI or GTI. A mixed-effect negative binomial regression model was used to calculate a predictor-specific incidence rate ratio for each questionnaire variable and for each of the four endpoints, and predicted individual incidences for symptoms of and exposures to RTI and GTI. In the fitting test these were then compared with the observed incidences. Out of 1270 eligible employees of six enterprises, 683 volunteered to participate in the trial. Ninety-two additional participants were recruited during the follow-up. Out of the 775 registered participants, 717 returned the questionnaire with data on potential predictor variables and follow-up reports for determination of outcomes. Age and gender were the strongest predictors of both exposure to, and symptoms of RTI or GTI, although no gender difference was seen in the RTI incidence. In addition, regular use of public transport, and history of seasonal influenza vaccination increased the risk of RTI. The individual incidence values predicted by the model showed moderate correlation with those observed in each of the four categories. According to the Cox-Snell multivariate formula the model explained 11.2% of RTI and 3.3% of GTI incidences. Resampling revealed mean and 90% confidence interval values of 10.9 (CI 6.9-14.5)% for RTI and 2.4 (0.6-4.4)% for GTI. The model created explained a relatively small proportion of the occurrence of RTI or GTI. Unpredictable exposure to disease agents, and individual susceptibility factors are likely to be key determinants of disease emergence. Yet, the model might be useful in prerandomization stratification of study population in RTI intervention trials where the expected difference between trial arms is relatively small. Registered at ClinicalTrials.gov with Identifier NCT00821509 on 12 March 2009.

HIV Treatment as Prevention: Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa

PubMed Central

Eaton, Jeffrey W.; Johnson, Leigh F.; Salomon, Joshua A.; Bärnighausen, Till; Bendavid, Eran; Bershteyn, Anna; Bloom, David E.; Cambiano, Valentina; Fraser, Christophe; Hontelez, Jan A. C.; Humair, Salal; Klein, Daniel J.; Long, Elisa F.; Phillips, Andrew N.; Pretorius, Carel; Stover, John; Wenger, Edward A.; Williams, Brian G.; Hallett, Timothy B.

2012-01-01

Background Many mathematical models have investigated the impact of expanding access to antiretroviral therapy (ART) on new HIV infections. Comparing results and conclusions across models is challenging because models have addressed slightly different questions and have reported different outcome metrics. This study compares the predictions of several mathematical models simulating the same ART intervention programmes to determine the extent to which models agree about the epidemiological impact of expanded ART. Methods and Findings Twelve independent mathematical models evaluated a set of standardised ART intervention scenarios in South Africa and reported a common set of outputs. Intervention scenarios systematically varied the CD4 count threshold for treatment eligibility, access to treatment, and programme retention. For a scenario in which 80% of HIV-infected individuals start treatment on average 1 y after their CD4 count drops below 350 cells/µl and 85% remain on treatment after 3 y, the models projected that HIV incidence would be 35% to 54% lower 8 y after the introduction of ART, compared to a counterfactual scenario in which there is no ART. More variation existed in the estimated long-term (38 y) reductions in incidence. The impact of optimistic interventions including immediate ART initiation varied widely across models, maintaining substantial uncertainty about the theoretical prospect for elimination of HIV from the population using ART alone over the next four decades. The number of person-years of ART per infection averted over 8 y ranged between 5.8 and 18.7. Considering the actual scale-up of ART in South Africa, seven models estimated that current HIV incidence is 17% to 32% lower than it would have been in the absence of ART. Differences between model assumptions about CD4 decline and HIV transmissibility over the course of infection explained only a modest amount of the variation in model results. Conclusions Mathematical models evaluating the impact of ART vary substantially in structure, complexity, and parameter choices, but all suggest that ART, at high levels of access and with high adherence, has the potential to substantially reduce new HIV infections. There was broad agreement regarding the short-term epidemiologic impact of ambitious treatment scale-up, but more variation in longer term projections and in the efficiency with which treatment can reduce new infections. Differences between model predictions could not be explained by differences in model structure or parameterization that were hypothesized to affect intervention impact. Please see later in the article for the Editors' Summary PMID:22802730

Logistic Regression Analyses for Predicting Clinically Important Differences in Motor Capacity, Motor Performance, and Functional Independence after Constraint-Induced Therapy in Children with Cerebral Palsy

ERIC Educational Resources Information Center

Wang, Tien-ni; Wu, Ching-yi; Chen, Chia-ling; Shieh, Jeng-yi; Lu, Lu; Lin, Keh-chung

2013-01-01

Given the growing evidence for the effects of constraint-induced therapy (CIT) in children with cerebral palsy (CP), there is a need for investigating the characteristics of potential participants who may benefit most from this intervention. This study aimed to establish predictive models for the effects of pediatric CIT on motor and functional…

Health Care Expenditures for University and Academic Medical Center Employees Enrolled in a Pilot Workplace Health Partner Intervention.

PubMed

Johnston, Kenton J; Hockenberry, Jason M; Rask, Kimberly J; Cunningham, Lynn; Brigham, Kenneth L; Martin, Greg S

2015-08-01

To evaluate the impact of a pilot workplace health partner intervention delivered by a predictive health institute to university and academic medical center employees on per-member, per-month health care expenditures. We analyzed the health care claims of participants versus nonparticipants, with a 12-month baseline and 24-month intervention period. Total per-member, per-month expenditures were analyzed using two-part regression models that controlled for sex, age, health benefit plan type, medical member months, and active employment months. Our regression results found no statistical differences in total expenditures at baseline and intervention. Further sensitivity analyses controlling for high cost outliers, comorbidities, and propensity to be in the intervention group confirmed these findings. We find no difference in health care expenditures attributable to the health partner intervention. The intervention does not seem to have raised expenditures in the short term.

Effectiveness and cost of reducing particle-related mortality with particle filtration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fisk, W. J.; Chan, W. R.

This study evaluates the mortality-related benefits and costs of improvements in particle filtration in U.S. homes and commercial buildings based on models with empirical inputs. The models account for time spent in various environments as well as activity levels and associated breathing rates. The scenarios evaluated include improvements in filter efficiencies in both forced-air heating and cooling systems of homes and heating, ventilating, and air conditioning systems of workplaces as well as use of portable air cleaners in homes. The predicted reductions in mortality range from approximately 0.25 to 2.4 per 10 000 population. The largest reductions in mortality were frommore » interventions with continuously operating portable air cleaners in homes because, given our scenarios, these portable air cleaners with HEPA filters most reduced particle exposures. For some interventions, predicted annual mortality-related economic benefits exceed $1000 per person. Economic benefits always exceed costs with benefit-to-cost ratios ranging from approximately 3.9 to 133. In conclusion, restricting interventions to homes of the elderly further increases the mortality reductions per unit population and the benefit-to-cost ratios.« less

The Protective Effects of Family Support on the Relationship between Official Intervention and General Delinquency across the Life Course.

PubMed

Dong, Beidi; Krohn, Marvin D

2017-03-01

Previous research on the labeling perspective has identified mediational processes and the long-term effects of official intervention in the life course. However, it is not yet clear what factors may moderate the relationship between labeling and subsequent offending. The current study integrates Cullen's (1994) social support theory to examine how family social support conditions the criminogenic, stigmatizing effects of official intervention on delinquency and whether such protective effects vary by developmental stage. Using longitudinal data from the Rochester Youth Development Study, we estimated negative binomial regression models to investigate the relationships between police arrest, family social support, and criminal offending during both adolescence and young adulthood. Police arrest is a significant predictor of self-reported delinquency in both the adolescent and adult models. Expressive family support exhibits main effects in the adolescent models; instrumental family support exhibits main effects at both developmental stages. Additionally, instrumental family support diminishes some of the predicted adverse effects of official intervention in adulthood. Perception of family support can be critical in reducing general delinquency as well as buffering against the adverse effects of official intervention on subsequent offending. Policies and programs that work with families subsequent to a criminal justice intervention should emphasize the importance of providing a supportive environment for those who are labeled.

Climate-based models for pulsed resources improve predictability of consumer population dynamics: outbreaks of house mice in forest ecosystems.

PubMed

Holland, E Penelope; James, Alex; Ruscoe, Wendy A; Pech, Roger P; Byrom, Andrea E

2015-01-01

Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts) are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT) for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer-resource dynamics to predict invasive house mouse (Mus musculus) outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year's advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer-resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.

Designing and Undertaking a Health Economics Study of Digital Health Interventions.

PubMed

McNamee, Paul; Murray, Elizabeth; Kelly, Michael P; Bojke, Laura; Chilcott, Jim; Fischer, Alastair; West, Robert; Yardley, Lucy

2016-11-01

This paper introduces and discusses key issues in the economic evaluation of digital health interventions. The purpose is to stimulate debate so that existing economic techniques may be refined or new methods developed. The paper does not seek to provide definitive guidance on appropriate methods of economic analysis for digital health interventions. This paper describes existing guides and analytic frameworks that have been suggested for the economic evaluation of healthcare interventions. Using selected examples of digital health interventions, it assesses how well existing guides and frameworks align to digital health interventions. It shows that digital health interventions may be best characterized as complex interventions in complex systems. Key features of complexity relate to intervention complexity, outcome complexity, and causal pathway complexity, with much of this driven by iterative intervention development over time and uncertainty regarding likely reach of the interventions among the relevant population. These characteristics imply that more-complex methods of economic evaluation are likely to be better able to capture fully the impact of the intervention on costs and benefits over the appropriate time horizon. This complexity includes wider measurement of costs and benefits, and a modeling framework that is able to capture dynamic interactions among the intervention, the population of interest, and the environment. The authors recommend that future research should develop and apply more-flexible modeling techniques to allow better prediction of the interdependency between interventions and important environmental influences. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Protection motivation theory and physical activity in the general population: a systematic literature review.

PubMed

Bui, Linh; Mullan, Barbara; McCaffery, Kirsten

2013-01-01

An appropriate theoretical framework may be useful for guiding the development of physical activity interventions. This review investigates the effectiveness of the protection motivation theory (PMT), a model based on the cognitive mediation processes of behavioral change, in the prediction and promotion of physical activity participation. A literature search was conducted using the databases MEDLINE, PsycINFO, PubMed, and Web of Science, and a manual search was conducted on relevant reference lists. Studies were included if they tested or applied the PMT, measured physical activity, and sampled from healthy populations. A total of 20 studies were reviewed, grouped into four design categories: prediction, stage discrimination, experimental manipulation, and intervention. The results indicated that the PMT's coping appraisal construct of self-efficacy generally appears to be the most effective in predicting and promoting physical activity participation. In conclusion, the PMT shows some promise, however, there are still substantial gaps in the evidence.

Paper-based and web-based intervention modeling experiments identified the same predictors of general practitioners' antibiotic-prescribing behavior.

PubMed

Treweek, Shaun; Bonetti, Debbie; Maclennan, Graeme; Barnett, Karen; Eccles, Martin P; Jones, Claire; Pitts, Nigel B; Ricketts, Ian W; Sullivan, Frank; Weal, Mark; Francis, Jill J

2014-03-01

To evaluate the robustness of the intervention modeling experiment (IME) methodology as a way of developing and testing behavioral change interventions before a full-scale trial by replicating an earlier paper-based IME. Web-based questionnaire and clinical scenario study. General practitioners across Scotland were invited to complete the questionnaire and scenarios, which were then used to identify predictors of antibiotic-prescribing behavior. These predictors were compared with the predictors identified in an earlier paper-based IME and used to develop a new intervention. Two hundred seventy general practitioners completed the questionnaires and scenarios. The constructs that predicted simulated behavior and intention were attitude, perceived behavioral control, risk perception/anticipated consequences, and self-efficacy, which match the targets identified in the earlier paper-based IME. The choice of persuasive communication as an intervention in the earlier IME was also confirmed. Additionally, a new intervention, an action plan, was developed. A web-based IME replicated the findings of an earlier paper-based IME, which provides confidence in the IME methodology. The interventions will now be evaluated in the next stage of the IME, a web-based randomized controlled trial. Copyright © 2014 Elsevier Inc. All rights reserved.

A model of the mechanisms of language extinction and revitalization strategies to save endangered languages.

PubMed

Fernando, Chrisantha; Valijärvi, Riitta-Liisa; Goldstein, Richard A

2010-02-01

Why and how have languages died out? We have devised a mathematical model to help us understand how languages go extinct. We use the model to ask whether language extinction can be prevented in the future and why it may have occurred in the past. A growing number of mathematical models of language dynamics have been developed to study the conditions for language coexistence and death, yet their phenomenological approach compromises their ability to influence language revitalization policy. In contrast, here we model the mechanisms underlying language competition and look at how these mechanisms are influenced by specific language revitalization interventions, namely, private interventions to raise the status of the language and thus promote language learning at home, public interventions to increase the use of the minority language, and explicit teaching of the minority language in schools. Our model reveals that it is possible to preserve a minority language but that continued long-term interventions will likely be necessary. We identify the parameters that determine which interventions work best under certain linguistic and societal circumstances. In this way the efficacy of interventions of various types can be identified and predicted. Although there are qualitative arguments for these parameter values (e.g., the responsiveness of children to learning a language as a function of the proportion of conversations heard in that language, the relative importance of conversations heard in the family and elsewhere, and the amplification of spoken to heard conversations of the high-status language because of the media), extensive quantitative data are lacking in this field. We propose a way to measure these parameters, allowing our model, as well as others models in the field, to be validated.

The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.

PubMed

Viboud, Cécile; Sun, Kaiyuan; Gaffey, Robert; Ajelli, Marco; Fumanelli, Laura; Merler, Stefano; Zhang, Qian; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro

2018-03-01

Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens. Published by Elsevier B.V.

Relationship of physical therapy inpatient rehabilitation interventions and patient characteristics to outcomes following spinal cord injury: The SCIRehab project

PubMed Central

Teeter, Laura; Gassaway, Julie; Taylor, Sally; LaBarbera, Jacqueline; McDowell, Shari; Backus, Deborah; Zanca, Jeanne M.; Natale, Audrey; Cabrera, Jordan; Smout, Randall J.; Kreider, Scott E. D.; Whiteneck, Gale

2012-01-01

Background/objective Examine associations of type and quantity of physical therapy (PT) interventions delivered during inpatient spinal cord injury (SCI) rehabilitation and patient characteristics with outcomes at the time of discharge and at 1 year post-injury. Methods Physical therapists delivering routine care documented details of PT interventions provided. Regression modeling was used to predict outcomes at discharge and 1 year post-injury for a 75% subset; models were validated with the remaining 25%. Injury subgroups also were examined: motor complete low tetraplegia, motor complete paraplegia, and American Spinal Injury Association (ASIA) Impairment Scale (AIS) D motor incomplete tetra-/paraplegia. Results PT treatment variables explain more variation in three functionally homogeneous subgroups than in the total sample. Among patients with motor complete low tetraplegia, higher scores for the transfer component of the discharge motor Functional Independence Measure () are strongly associated with more time spent working on manual wheelchair skills. Being male is the most predictive variable for the motor FIM score at discharge for patients with motor complete paraplegia. Admission ASIA lower extremity motor score (LEMS) and change in LEMS were the factors most predictive for having the primary locomotion mode of “walk” or “both (walk and wheelchair)” on the discharge motor FIM for patients with AIS D injuries. Conclusion Injury classification influences type and quantity of PT interventions during inpatient SCI rehabilitation and is a strong predictor of outcomes at discharge and 1 year post-injury. The impact of PT treatment increases when patient groupings become more homogeneous and outcomes become specific to the groupings. Note This is the second of nine articles in the SCIRehab series. PMID:23318034

Patient navigation based on predictive modeling decreases no-show rates in cancer care.

PubMed

Percac-Lima, Sanja; Cronin, Patrick R; Ryan, David P; Chabner, Bruce A; Daly, Emily A; Kimball, Alexandra B

2015-05-15

Patient adherence to appointments is key to improving outcomes in health care. "No-show" appointments contribute to suboptimal resource use. Patient navigation and telephone reminders have been shown to improve cancer care and adherence, particularly in disadvantaged populations, but may not be cost-effective if not targeted at the appropriate patients. In 5 clinics within a large academic cancer center, patients who were considered to be likely (the top 20th percentile) to miss a scheduled appointment without contacting the clinic ahead of time ("no-shows") were identified using a predictive model and then randomized to an intervention versus a usual-care group. The intervention group received telephone calls from a bilingual patient navigator 7 days before and 1 day before the appointment. Over a 5-month period, of the 40,075 appointments scheduled, 4425 patient appointments were deemed to be at high risk of a "no-show" event. After the patient navigation intervention, the no-show rate in the intervention group was 10.2% (167 of 1631), compared with 17.5% in the control group (280 of 1603) (P<.001). Reaching a patient or family member was associated with a significantly lower no-show rate (5.9% and 3.0%, respectively; P<.001 and .006, respectively) compared with leaving a message (14.7%: P = .117) or no contact (no-show rate, 21.6%: P = .857). Telephone navigation targeted at those patients predicted to be at high risk of visit nonadherence was found to effectively and substantially improve patient adherence to cancer clinic appointments. Further studies are needed to determine the long-term impact on patient outcomes, but short-term gains in the optimization of resources can be recognized immediately. © 2015 American Cancer Society.

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

PubMed

Hettige, Nuwan C; Nguyen, Thai Binh; Yuan, Chen; Rajakulendran, Thanara; Baddour, Jermeen; Bhagwat, Nikhil; Bani-Fatemi, Ali; Voineskos, Aristotle N; Mallar Chakravarty, M; De Luca, Vincenzo

2017-07-01

Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide. We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models. All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71. Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts. Copyright © 2017 Elsevier Inc. All rights reserved.

Age-shifting in malaria incidence as a result of induced immunological deficit: a simulation study.

PubMed

Pemberton-Ross, Peter; Smith, Thomas A; Hodel, Eva Maria; Kay, Katherine; Penny, Melissa A

2015-07-25

Effective population-level interventions against Plasmodium falciparum malaria lead to age-shifts, delayed morbidity or rebounds in morbidity and mortality whenever they are deployed in ways that do not permanently interrupt transmission. When long-term intervention programmes target specific age-groups of human hosts, the age-specific morbidity rates ultimately adjust to new steady-states, but it is very difficult to study these rates and the temporal dynamics leading up to them empirically because the changes occur over very long time periods. This study investigates the age and magnitude of age- and time- shifting of incidence induced by either pre-erythrocytic vaccination (PEV) programmes or seasonal malaria chemo-prevention (SMC), using an ensemble of individual-based stochastic simulation models of P. falciparum dynamics. The models made various assumptions about immunity decay, transmission heterogeneity and were parameterized with data on both age-specific infection and disease incidence at different levels of exposure, on the durations of different stages of the parasite life-cycle and on human demography. Effects of transmission intensity, and of levels of access to malaria treatment were considered. While both PEV and SMC programmes are predicted to have overall strongly positive health effects, a shift of morbidity into older children is predicted to be induced by either programme if transmission levels remain static and not reduced by other interventions. Predicted shifting of burden continue into the second decade of the programme. Even if long-term surveillance is maintained it will be difficult to avoid mis-attribution of such long-term changes in age-specific morbidity patterns to other factors. Conversely, short-lived transient changes in incidence measured soon after introduction of a new intervention may give over-positive views of future impacts. Complementary intervention strategies could be designed to specifically protect those age-groups at risk from burden shift.

Social class disparities in health and education: reducing inequality by applying a sociocultural self model of behavior.

PubMed

Stephens, Nicole M; Markus, Hazel Rose; Fryberg, Stephanie A

2012-10-01

The literature on social class disparities in health and education contains 2 underlying, yet often opposed, models of behavior: the individual model and the structural model. These models refer to largely unacknowledged assumptions about the sources of human behavior that are foundational to research and interventions. Our review and theoretical integration proposes that, in contrast to how the 2 models are typically represented, they are not opposed, but instead they are complementary sets of understandings that inform and extend each other. Further, we elaborate the theoretical rationale and predictions for a third model: the sociocultural self model of behavior. This model incorporates and extends key tenets of the individual and structural models. First, the sociocultural self model conceptualizes individual characteristics (e.g., skills) and structural conditions (e.g., access to resources) as interdependent forces that mutually constitute each other and that are best understood together. Second, the sociocultural self model recognizes that both individual characteristics and structural conditions indirectly influence behavior through the selves that emerge in the situation. These selves are malleable psychological states that are a product of the ongoing mutual constitution of individuals and structures and serve to guide people's behavior by systematically shaping how people construe situations. The theoretical foundation of the sociocultural self model lays the groundwork for a more complete understanding of behavior and provides new tools for developing interventions that will reduce social class disparities in health and education. The model predicts that intervention efforts will be more effective at producing sustained behavior change when (a) current selves are congruent, rather than incongruent, with the desired behavior and (b) individual characteristics and structural conditions provide ongoing support for the selves that are necessary to support the desired behavior. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Work and Sleep--A Prospective Study of Psychosocial Work Factors, Physical Work Factors, and Work Scheduling.

PubMed

Åkerstedt, Torbjörn; Garefelt, Johanna; Richter, Anne; Westerlund, Hugo; Magnusson Hanson, Linda L; Sverke, Magnus; Kecklund, Göran

2015-07-01

There is limited knowledge about the prospective relationship between major work characteristics (psychosocial, physical, scheduling) and disturbed sleep. The current study sought to provide such knowledge. Prospective cohort, with measurements on two occasions (T1 and T2) separated by two years. Naturalistic study, Sweden. There were 4,827 participants forming a representative sample of the working population. Questionnaire data on work factors obtained on two occasions were analyzed with structural equation modeling. Competing models were compared in order to investigate temporal relationships. A reciprocal model was found to fit the data best. Sleep disturbances at T2 were predicted by higher work demands at T1 and by lower perceived stress at T1. In addition, sleep disturbances at T1 predicted subsequent higher perception of stress, higher work demands, lower degree of control, and less social support at work at T2. A cross-sectional mediation analysis showed that (higher) perceived stress mediated the relationship between (higher) work demands and sleep disturbances; however, no such association was found longitudinally. Higher work demands predicted disturbed sleep, whereas physical work characteristics, shift work, and overtime did not. In addition, disturbed sleep predicted subsequent higher work demands, perceived stress, less social support, and lower degree of control. The results suggest that remedial interventions against sleep disturbances should focus on psychosocial factors, and that such remedial interventions may improve the psychosocial work situation in the long run. © 2015 Associated Professional Sleep Societies, LLC.

A novel Bayesian approach to predicting reductions in HIV incidence following increased testing interventions among gay, bisexual and other men who have sex with men in Vancouver, Canada.

PubMed

Irvine, Michael A; Konrad, Bernhard P; Michelow, Warren; Balshaw, Robert; Gilbert, Mark; Coombs, Daniel

2018-03-01

Increasing HIV testing rates among high-risk groups should lead to increased numbers of cases being detected. Coupled with effective treatment and behavioural change among individuals with detected infection, increased testing should also reduce onward incidence of HIV in the population. However, it can be difficult to predict the strengths of these effects and thus the overall impact of testing. We construct a mathematical model of an ongoing HIV epidemic in a population of gay, bisexual and other men who have sex with men. The model incorporates different levels of infection risk, testing habits and awareness of HIV status among members of the population. We introduce a novel Bayesian analysis that is able to incorporate potentially unreliable sexual health survey data along with firm clinical diagnosis data. We parameterize the model using survey and diagnostic data drawn from a population of men in Vancouver, Canada. We predict that increasing testing frequency will yield a small-scale but long-term impact on the epidemic in terms of new infections averted, as well as a large short-term impact on numbers of detected cases. These effects are predicted to occur even when a testing intervention is short-lived. We show that a short-lived but intensive testing campaign can potentially produce many of the same benefits as a campaign that is less intensive but of longer duration. © 2018 The Author(s).

Investigating Interventions in Alzheimer's Disease with Computer Simulation Models

PubMed Central

Proctor, Carole J.; Boche, Delphine; Gray, Douglas A.; Nicoll, James A. R.

2013-01-01

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (Aβ) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against Aβ and then demonstrate the effects of this intervention on soluble Aβ, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble Aβ, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against Aβ. Since, soluble Aβ, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against Aβ may not be effective unless it is performed very early in the disease process or combined with other therapies. PMID:24098635

Antecedents and mediators of physical activity in endometrial cancer survivors: Increasing physical activity through Steps to Health

PubMed Central

Cox, Matthew; Carmack, Cindy; Hughes, Daniel; Baum, George; Brown, Jubilee; Jhingran, Anuja; Lu, Karen; Basen-Engquist, Karen

2015-01-01

OBJECTIVE Research shows that physical activity (PA) has a positive effect on cancer survivors including improving quality of life, improving physical fitness, and decreasing risk for cancer recurrence in some cancer types. Theory-based intervention approaches have identified self-efficacy as a potential mediator of PA interventions. This study examines the temporal relationships at four time points (T1–T4) between several social cognitive theory constructs and PA among a group of endometrial cancer survivors receiving a PA intervention. METHOD A sample of 98 sedentary women who were at least six months post treatment for endometrial cancer were given an intervention to increase their PA. The study tested whether modeling, physiological somatic sensations, and social support at previous time points predicted self-efficacy at later time points, which in turn predicted PA at later time points. RESULTS Results indicate that as physiological somatic sensations at T2 decrease, self-efficacy at T3 increases, which leads to an increase in PA at T4. This suggests that self-efficacy is a significant mediator between physiological somatic sensations and PA. Exploratory follow up models suggest model fit can be improved with the addition of contemporaneous effects between self-efficacy and PA at T3 and T4, changing the timing of the mediational relationships. CONCLUSIONS Physiological somatic sensations appear to be an important construct to target in order to increase PA in this population. While self-efficacy appeared to mediate the relationship between physiological somatic sensations and PA, the timing of this relationship is requires further study. PMID:25642840

Synthesising empirical results to improve predictions of post-wildfire runoff and erosion response

USGS Publications Warehouse

Shakesby, Richard A.; Moody, John A.; Martin, Deborah A.; Robichaud, Peter R.

2016-01-01

Advances in research into wildfire impacts on runoff and erosion have demonstrated increasing complexity of controlling factors and responses, which, combined with changing fire frequency, present challenges for modellers. We convened a conference attended by experts and practitioners in post-wildfire impacts, meteorology and related research, including modelling, to focus on priority research issues. The aim was to improve our understanding of controls and responses and the predictive capabilities of models. This conference led to the eight selected papers in this special issue. They address aspects of the distinctiveness in the controls and responses among wildfire regions, spatiotemporal rainfall variability, infiltration, runoff connectivity, debris flow formation and modelling applications. Here we summarise key findings from these papers and evaluate their contribution to improving understanding and prediction of post-wildfire runoff and erosion under changes in climate, human intervention and population pressure on wildfire-prone areas.

Stepping Up the Pressure: Arousal Can Be Associated with a Reduction in Male Aggression

PubMed Central

Ward, Andrew; Mann, Traci; Westling, Erika H.; Creswell, J. David; Ebert, Jeffrey P.; Wallaert, Matthew

2009-01-01

The attentional myopia model of behavioral control (Mann & Ward, 2007) was tested in an experiment investigating the relationship between physiological arousal and aggression. Drawing on previous work linking arousal and narrowed attentional focus, the model predicts that arousal will lead to behavior that is relatively disinhibited in situations in which promoting pressures to aggress are highly salient. In situations in which inhibitory pressures are more salient, the model predicts behavior that is relatively restrained. In the experiment, 81 male undergraduates delivered noise-blasts against a provoking confederate while experiencing either high or low levels of physiological arousal and, at the same time, being exposed to cues that served either to promote or inhibit aggression. In addition to supporting the predictions of the model, this experiment provided some of the first evidence for enhanced control of aggression under conditions of heightened physiological arousal. Implications for interventions designed to reduce aggression are discussed. PMID:18561301

Associations of Sexual Victimization, Depression, and Sexual Assertiveness with Unprotected Sex: A Test of the Multifaceted Model of HIV Risk Across Gender

PubMed Central

Morokoff, Patricia J.; Redding, Colleen A.; Harlow, Lisa L.; Cho, Sookhyun; Rossi, Joseph S.; Meier, Kathryn S.; Mayer, Kenneth H.; Koblin, Beryl; Brown-Peterside, Pamela

2014-01-01

This study examined whether the Multifaceted Model of HIV Risk (MMOHR) would predict unprotected sex based on predictors including gender, childhood sexual abuse (CSA), sexual victimization (SV), depression, and sexual assertiveness for condom use. A community-based sample of 473 heterosexually active men and women, aged 18–46 years completed survey measures of model variables. Gender predicted several variables significantly. A separate model for women demonstrated excellent fit, while the model for men demonstrated reasonable fit. Multiple sample model testing supported the use of MMOHR in both men and women, while simultaneously highlighting areas of gender difference. Prevention interventions should focus on sexual assertiveness, especially for CSA and SV survivors, as well as targeting depression, especially among men. PMID:25018617

Individual and Center-Level Factors Affecting Mortality Among Extremely Low Birth Weight Infants

PubMed Central

Alleman, Brandon W.; Li, Lei; Dagle, John M.; Smith, P. Brian; Ambalavanan, Namasivayam; Laughon, Matthew M.; Stoll, Barbara J.; Goldberg, Ronald N.; Carlo, Waldemar A.; Murray, Jeffrey C.; Cotten, C. Michael; Shankaran, Seetha; Walsh, Michele C.; Laptook, Abbot R.; Ellsbury, Dan L.; Hale, Ellen C.; Newman, Nancy S.; Wallace, Dennis D.; Das, Abhik; Higgins, Rosemary D.

2013-01-01

OBJECTIVE: To examine factors affecting center differences in mortality for extremely low birth weight (ELBW) infants. METHODS: We analyzed data for 5418 ELBW infants born at 16 Neonatal Research Network centers during 2006–2009. The primary outcomes of early mortality (≤12 hours after birth) and in-hospital mortality were assessed by using multilevel hierarchical models. Models were developed to investigate associations of center rates of selected interventions with mortality while adjusting for patient-level risk factors. These analyses were performed for all gestational ages (GAs) and separately for GAs <25 weeks and ≥25 weeks. RESULTS: Early and in-hospital mortality rates among centers were 5% to 36% and 11% to 53% for all GAs, 13% to 73% and 28% to 90% for GAs <25 weeks, and 1% to 11% and 7% to 26% for GAs ≥25 weeks, respectively. Center intervention rates significantly predicted both early and in-hospital mortality for infants <25 weeks. For infants ≥25 weeks, intervention rates did not predict mortality. The variance in mortality among centers was significant for all GAs and outcomes. Center use of interventions and patient risk factors explained some but not all of the center variation in mortality rates. CONCLUSIONS: Center intervention rates explain a portion of the center variation in mortality, especially for infants born at <25 weeks’ GA. This finding suggests that deaths may be prevented by standardizing care for very early GA infants. However, differences in patient characteristics and center intervention rates do not account for all of the observed variability in mortality; and for infants with GA ≥25 weeks these differences account for only a small part of the variation in mortality. PMID:23753096

Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews

PubMed Central

Turner, Rebecca M; Davey, Jonathan; Clarke, Mike J; Thompson, Simon G; Higgins, Julian PT

2012-01-01

Background Many meta-analyses contain only a small number of studies, which makes it difficult to estimate the extent of between-study heterogeneity. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, and offers advantages over conventional random-effects meta-analysis. To assist in this, we provide empirical evidence on the likely extent of heterogeneity in particular areas of health care. Methods Our analyses included 14 886 meta-analyses from the Cochrane Database of Systematic Reviews. We classified each meta-analysis according to the type of outcome, type of intervention comparison and medical specialty. By modelling the study data from all meta-analyses simultaneously, using the log odds ratio scale, we investigated the impact of meta-analysis characteristics on the underlying between-study heterogeneity variance. Predictive distributions were obtained for the heterogeneity expected in future meta-analyses. Results Between-study heterogeneity variances for meta-analyses in which the outcome was all-cause mortality were found to be on average 17% (95% CI 10–26) of variances for other outcomes. In meta-analyses comparing two active pharmacological interventions, heterogeneity was on average 75% (95% CI 58–95) of variances for non-pharmacological interventions. Meta-analysis size was found to have only a small effect on heterogeneity. Predictive distributions are presented for nine different settings, defined by type of outcome and type of intervention comparison. For example, for a planned meta-analysis comparing a pharmacological intervention against placebo or control with a subjectively measured outcome, the predictive distribution for heterogeneity is a log-normal (−2.13, 1.582) distribution, which has a median value of 0.12. In an example of meta-analysis of six studies, incorporating external evidence led to a smaller heterogeneity estimate and a narrower confidence interval for the combined intervention effect. Conclusions Meta-analysis characteristics were strongly associated with the degree of between-study heterogeneity, and predictive distributions for heterogeneity differed substantially across settings. The informative priors provided will be very beneficial in future meta-analyses including few studies. PMID:22461129

Predicting dropout using student- and school-level factors: An ecological perspective.

PubMed

Wood, Laura; Kiperman, Sarah; Esch, Rachel C; Leroux, Audrey J; Truscott, Stephen D

2017-03-01

High school dropout has been associated with negative outcomes, including increased rates of unemployment, incarceration, and mortality. Dropout rates vary significantly depending on individual and environmental factors. The purpose of our study was to use an ecological perspective to concurrently explore student- and school-level predictors associated with dropout for the purpose of better understanding how to prevent it. We used the Education Longitudinal Study of 2002 dataset. Participants included 14,106 sophomores across 684 public and private schools. We identified variables of interest based on previous research on dropout and implemented hierarchical generalized linear modeling. In the final model, significant student-level predictors included academic achievement, retention, sex, family socioeconomic status (SES), and extracurricular involvement. Significant school-level predictors included school SES and school size. Race/ethnicity, special education status, born in the United States, English as first language, school urbanicity, and school region did not significantly predict dropout after controlling for the aforementioned predictors. Implications for prevention and intervention efforts within a multitiered intervention model are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Social networks and smoking: exploring the effects of peer influence and smoker popularity through simulations.

PubMed

Schaefer, David R; Adams, Jimi; Haas, Steven A

2013-10-01

Adolescent smoking and friendship networks are related in many ways that can amplify smoking prevalence. Understanding and developing interventions within such a complex system requires new analytic approaches. We draw on recent advances in dynamic network modeling to develop a technique that explores the implications of various intervention strategies targeted toward micro-level processes. Our approach begins by estimating a stochastic actor-based model using data from one school in the National Longitudinal Study of Adolescent Health. The model provides estimates of several factors predicting friendship ties and smoking behavior. We then use estimated model parameters to simulate the coevolution of friendship and smoking behavior under potential intervention scenarios. Namely, we manipulate the strength of peer influence on smoking and the popularity of smokers relative to nonsmokers. We measure how these manipulations affect smoking prevalence, smoking initiation, and smoking cessation. Results indicate that both peer influence and smoking-based popularity affect smoking behavior and that their joint effects are nonlinear. This study demonstrates how a simulation-based approach can be used to explore alternative scenarios that may be achievable through intervention efforts and offers new hypotheses about the association between friendship and smoking.

Social Networks and Smoking: Exploring the Effects of Influence and Smoker Popularity through Simulations

PubMed Central

Schaefer, David R.; adams, jimi; Haas, Steven A.

2015-01-01

Adolescent smoking and friendship networks are related in many ways that can amplify smoking prevalence. Understanding and developing interventions within such a complex system requires new analytic approaches. We draw upon recent advances in dynamic network modeling to develop a technique that explores the implications of various intervention strategies targeted toward micro-level processes. Our approach begins by estimating a stochastic actor-based model using data from one school in the National Longitudinal Study of Adolescent Health. The model provides estimates of several factors predicting friendship ties and smoking behavior. We then use estimated model parameters to simulate the co-evolution of friendship and smoking behavior under potential intervention scenarios. Namely, we manipulate the strength of peer influence on smoking and the popularity of smokers relative to nonsmokers. We measure how these manipulations affect smoking prevalence, smoking initiation, and smoking cessation. Results indicate that both peer influence and smoking-based popularity affect smoking behavior, and that their joint effects are nonlinear. This study demonstrates how a simulation-based approach can be used to explore alternative scenarios that may be achievable through intervention efforts and offers new hypotheses about the association between friendship and smoking. PMID:24084397

Using System Dynamic Model and Neural Network Model to Analyse Water Scarcity in Sudan

NASA Astrophysics Data System (ADS)

Li, Y.; Tang, C.; Xu, L.; Ye, S.

2017-07-01

Many parts of the world are facing the problem of Water Scarcity. Analysing Water Scarcity quantitatively is an important step to solve the problem. Water scarcity in a region is gauged by WSI (water scarcity index), which incorporate water supply and water demand. To get the WSI, Neural Network Model and SDM (System Dynamic Model) that depict how environmental and social factors affect water supply and demand are developed to depict how environmental and social factors affect water supply and demand. The uneven distribution of water resource and water demand across a region leads to an uneven distribution of WSI within this region. To predict WSI for the future, logistic model, Grey Prediction, and statistics are applied in predicting variables. Sudan suffers from severe water scarcity problem with WSI of 1 in 2014, water resource unevenly distributed. According to the result of modified model, after the intervention, Sudan’s water situation will become better.

Modeling Neurocognitive Decline and Recovery During Repeated Cycles of Extended Sleep and Chronic Sleep Deficiency.

PubMed

St Hilaire, Melissa A; Rüger, Melanie; Fratelli, Federico; Hull, Joseph T; Phillips, Andrew J K; Lockley, Steven W

2017-01-01

Intraindividual night-to-night sleep duration is often insufficient and variable. Here we report the effects of such chronic variable sleep deficiency on neurobehavioral performance and the ability of state-of-the-art models to predict these changes. Eight healthy males (mean age ± SD: 23.9 ± 2.4 years) studied at our inpatient intensive physiologic monitoring unit completed an 11-day protocol with a baseline 10-hour sleep opportunity and three cycles of two 3-hour time-in-bed (TIB) and one 10-hour TIB sleep opportunities. Participants received one of three polychromatic white light interventions (200 lux 4100K, 200 or 400 lux 17000K) for 3.5 hours on the morning following the second 3-hour TIB opportunity each cycle. Neurocognitive performance was assessed using the psychomotor vigilance test (PVT) administered every 1-2 hours. PVT data were compared to predictions of five group-average mathematical models that incorporate chronic sleep loss functions. While PVT performance deteriorated cumulatively following each cycle of two 3-hour sleep opportunities, and improved following each 10-hour sleep opportunity, performance declined cumulatively throughout the protocol at a more accelerated rate than predicted by state-of-the-art group-average mathematical models. Subjective sleepiness did not reflect performance. The light interventions had minimal effect. Despite apparent recovery following each extended sleep opportunity, residual performance impairment remained and deteriorated rapidly when rechallenged with subsequent sleep loss. None of the group-average models were capable of predicting both the build-up in impairment and recovery profile of performance observed at the group or individual level, raising concerns regarding their use in real-world settings to predict performance and improve safety. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Regression models for predicting peak and continuous three-dimensional spinal loads during symmetric and asymmetric lifting tasks.

PubMed

Fathallah, F A; Marras, W S; Parnianpour, M

1999-09-01

Most biomechanical assessments of spinal loading during industrial work have focused on estimating peak spinal compressive forces under static and sagittally symmetric conditions. The main objective of this study was to explore the potential of feasibly predicting three-dimensional (3D) spinal loading in industry from various combinations of trunk kinematics, kinetics, and subject-load characteristics. The study used spinal loading, predicted by a validated electromyography-assisted model, from 11 male participants who performed a series of symmetric and asymmetric lifts. Three classes of models were developed: (a) models using workplace, subject, and trunk motion parameters as independent variables (kinematic models); (b) models using workplace, subject, and measured moments variables (kinetic models); and (c) models incorporating workplace, subject, trunk motion, and measured moments variables (combined models). The results showed that peak 3D spinal loading during symmetric and asymmetric lifting were predicted equally well using all three types of regression models. Continuous 3D loading was predicted best using the combined models. When the use of such models is infeasible, the kinematic models can provide adequate predictions. Finally, lateral shear forces (peak and continuous) were consistently underestimated using all three types of models. The study demonstrated the feasibility of predicting 3D loads on the spine under specific symmetric and asymmetric lifting tasks without the need for collecting EMG information. However, further validation and development of the models should be conducted to assess and extend their applicability to lifting conditions other than those presented in this study. Actual or potential applications of this research include exposure assessment in epidemiological studies, ergonomic intervention, and laboratory task assessment.

Predictors of change in life skills in schizophrenia after cognitive remediation.

PubMed

Kurtz, Matthew M; Seltzer, James C; Fujimoto, Marco; Shagan, Dana S; Wexler, Bruce E

2009-02-01

Few studies have investigated predictors of response to cognitive remediation interventions in patients with schizophrenia. Predictor studies to date have selected treatment outcome measures that were either part of the remediation intervention itself or closely linked to the intervention with few studies investigating factors that predict generalization to measures of everyday life-skills as an index of treatment-related improvement. In the current study we investigated the relationship between four measures of neurocognitive function, crystallized verbal ability, auditory sustained attention and working memory, verbal learning and memory, and problem-solving, two measures of symptoms, total positive and negative symptoms, and the process variables of treatment intensity and duration, to change on a performance-based measure of everyday life-skills after a year of computer-assisted cognitive remediation offered as part of intensive outpatient rehabilitation treatment. Thirty-six patients with schizophrenia or schizoaffective disorder were studied. Results of a linear regression model revealed that auditory attention and working memory predicted a significant amount of the variance in change in performance-based measures of everyday life skills after cognitive remediation, even when variance for all other neurocognitive variables in the model was controlled. Stepwise regression revealed that auditory attention and working memory predicted change in everyday life-skills across the trial even when baseline life-skill scores, symptoms and treatment process variables were controlled. These findings emphasize the importance of sustained auditory attention and working memory for benefiting from extended programs of cognitive remediation.

Adolescent Marijuana Use Intentions: Using Theory to Plan an Intervention

ERIC Educational Resources Information Center

Sayeed, Sarah; Fishbein, Martin; Hornik, Robert; Cappella, Joseph; Kirkland Ahern, R.

2005-01-01

This paper uses an integrated model of behavior change to predict intentions to use marijuana occasionally and regularly in a US-based national sample of male and female 12 to 18 year olds (n = 600). The model combines key constructs from the theory of reasoned action and social cognitive theory. The survey was conducted on laptop computers, and…

A National Study Predicting Licensed Social Workers' Levels of Political Participation: The Role of Resources, Psychological Engagement, and Recruitment Networks

ERIC Educational Resources Information Center

Ritter, Jessica A.

2008-01-01

The social work literature is replete with studies evaluating social workers' direct practice interventions, but strikingly few have assessed how well social workers are faring in the political arena. This study tests a major theoretical model, the civic voluntarism model, developed to explain why some citizens become involved in politics, whereas…

Developing a Model for Identifying Students at Risk of Failure in a First Year Accounting Unit

ERIC Educational Resources Information Center

Smith, Malcolm; Therry, Len; Whale, Jacqui

2012-01-01

This paper reports on the process involved in attempting to build a predictive model capable of identifying students at risk of failure in a first year accounting unit in an Australian university. Identifying attributes that contribute to students being at risk can lead to the development of appropriate intervention strategies and support…

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods

PubMed Central

Stone, Bryan L; Sakaguchi, Farrant; Sheng, Xiaoming; Murtaugh, Maureen A

2015-01-01

Background Chronic diseases affect 52% of Americans and consume 86% of health care costs. A small portion of patients consume most health care resources and costs. More intensive patient management strategies, such as case management, are usually more effective at improving health outcomes, but are also more expensive. To use limited resources efficiently, risk stratification is commonly used in managing patients with chronic diseases, such as asthma, chronic obstructive pulmonary disease, diabetes, and heart disease. Patients are stratified based on predicted risk with patients at higher risk given more intensive care. The current risk-stratified patient management approach has 3 limitations resulting in many patients not receiving the most appropriate care, unnecessarily increased costs, and suboptimal health outcomes. First, using predictive models for health outcomes and costs is currently the best method for forecasting individual patient’s risk. Yet, accuracy of predictive models remains poor causing many patients to be misstratified. If an existing model were used to identify candidate patients for case management, enrollment would miss more than half of those who would benefit most, but include others unlikely to benefit, wasting limited resources. Existing models have been developed under the assumption that patient characteristics primarily influence outcomes and costs, leaving physician characteristics out of the models. In reality, both characteristics have an impact. Second, existing models usually give neither an explanation why a particular patient is predicted to be at high risk nor suggestions on interventions tailored to the patient’s specific case. As a result, many high-risk patients miss some suitable interventions. Third, thresholds for risk strata are suboptimal and determined heuristically with no quality guarantee. Objective The purpose of this study is to improve risk-stratified patient management so that more patients will receive the most appropriate care. Methods This study will (1) combine patient, physician profile, and environmental variable features to improve prediction accuracy of individual patient health outcomes and costs; (2) develop the first algorithm to explain prediction results and suggest tailored interventions; (3) develop the first algorithm to compute optimal thresholds for risk strata; and (4) conduct simulations to estimate outcomes of risk-stratified patient management for various configurations. The proposed techniques will be demonstrated on a test case of asthma patients. Results We are currently in the process of extracting clinical and administrative data from an integrated health care system’s enterprise data warehouse. We plan to complete this study in approximately 5 years. Conclusions Methods developed in this study will help transform risk-stratified patient management for better clinical outcomes, higher patient satisfaction and quality of life, reduced health care use, and lower costs. PMID:26503357

The proposed 'concordance-statistic for benefit' provided a useful metric when modeling heterogeneous treatment effects.

PubMed

van Klaveren, David; Steyerberg, Ewout W; Serruys, Patrick W; Kent, David M

2018-02-01

Clinical prediction models that support treatment decisions are usually evaluated for their ability to predict the risk of an outcome rather than treatment benefit-the difference between outcome risk with vs. without therapy. We aimed to define performance metrics for a model's ability to predict treatment benefit. We analyzed data of the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) trial and of three recombinant tissue plasminogen activator trials. We assessed alternative prediction models with a conventional risk concordance-statistic (c-statistic) and a novel c-statistic for benefit. We defined observed treatment benefit by the outcomes in pairs of patients matched on predicted benefit but discordant for treatment assignment. The 'c-for-benefit' represents the probability that from two randomly chosen matched patient pairs with unequal observed benefit, the pair with greater observed benefit also has a higher predicted benefit. Compared to a model without treatment interactions, the SYNTAX score II had improved ability to discriminate treatment benefit (c-for-benefit 0.590 vs. 0.552), despite having similar risk discrimination (c-statistic 0.725 vs. 0.719). However, for the simplified stroke-thrombolytic predictive instrument (TPI) vs. the original stroke-TPI, the c-for-benefit (0.584 vs. 0.578) was similar. The proposed methodology has the potential to measure a model's ability to predict treatment benefit not captured with conventional performance metrics. Copyright © 2017 Elsevier Inc. All rights reserved.

Empirical Validation of the Information–Motivation–Behavioral Skills Model of Diabetes Medication Adherence: A Framework for Intervention

PubMed Central

Mayberry, Lindsay S.; Osborn, Chandra Y.

2014-01-01

OBJECTIVE Suboptimal adherence to diabetes medications is prevalent and associated with unfavorable health outcomes, but it remains unclear what intervention content is necessary to effectively promote medication adherence in diabetes. In other disease contexts, the Information–Motivation–Behavioral skills (IMB) model has effectively explained and promoted medication adherence and thus may have utility in explaining and promoting adherence to diabetes medications. We tested the IMB model’s hypotheses in a sample of adults with type 2 diabetes. RESEARCH DESIGN AND METHODS Participants (N = 314) completed an interviewer-administered survey and A1C test. Structural equation models tested the effects of diabetes medication adherence-related information, motivation, and behavioral skills on medication adherence and the effect of medication adherence on A1C. RESULTS The IMB elements explained 41% of the variance in adherence, and adherence explained 9% of the variance in A1C. As predicted, behavioral skills had a direct effect on adherence (β = 0.59; P < 0.001) and mediated the effects of information (indirect effect 0.08 [0.01–0.15]) and motivation (indirect effect 0.12 [0.05–0.20]) on adherence. Medication adherence significantly predicted glycemic control (β = −0.30; P < 0.001). Neither insulin status nor regimen complexity was associated with adherence, and neither moderated associations between the IMB constructs and adherence. CONCLUSIONS The results support the IMB model’s predictions and identify modifiable and intervenable determinants of diabetes medication adherence. Medication adherence promotion interventions may benefit from content targeting patients’ medication adherence-related information, motivation, and behavioral skills and assessing the degree to which change in these determinants leads to changes in medication adherence behavior. PMID:24598245

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review.

PubMed

Sanchez-Morillo, Daniel; Fernandez-Granero, Miguel A; Leon-Jimenez, Antonio

2016-08-01

Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients. © The Author(s) 2016.

Phenolic and microbial-targeted metabolomics to discovering and evaluating wine intake biomarkers in human urine and plasma.

PubMed

Urpi-Sarda, Mireia; Boto-Ordóñez, María; Queipo-Ortuño, María Isabel; Tulipani, Sara; Corella, Dolores; Estruch, Ramon; Tinahones, Francisco J; Andres-Lacueva, Cristina

2015-09-01

The discovery of biomarkers of intake in nutritional epidemiological studies is essential in establishing an association between dietary intake (considering their bioavailability) and diet-related risk factors for diseases. The aim is to study urine and plasma phenolic and microbial profile by targeted metabolomics approach in a wine intervention clinical trial for discovering and evaluating food intake biomarkers. High-risk male volunteers (n = 36) were included in a randomized, crossover intervention clinical trial. After a washout period, subjects received red wine or gin, or dealcoholized red wine over four weeks. Fasting plasma and 24-h urine were collected at baseline and after each intervention period. A targeted metabolomic analysis of 70 host and microbial phenolic metabolites was performed using ultra performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). Metabolites were subjected to stepwise logistic regression to establish prediction models and received operation curves were performed to evaluate biomarkers. Prediction models based mainly on gallic acid metabolites, obtained sensitivity, specificity and area under the curve (AUC) for the training and validation sets of between 91 and 98% for urine and between 74 and 91% for plasma. Resveratrol, ethylgallate and gallic acid metabolite groups in urine samples also resulted in being good predictors of wine intake (AUC>87%). However, lower values for metabolites were obtained in plasma samples. The highest correlations between fasting plasma and urine were obtained for the prediction model score (r = 0.6, P<0.001), followed by gallic acid metabolites (r = 0.5-0.6, P<0.001). This study provides new insights into the discovery of food biomarkers in different biological samples. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modeling determinants of medication attitudes and poor adherence in early nonaffective psychosis: implications for intervention.

PubMed

Drake, Richard J; Nordentoft, Merete; Haddock, Gillian; Arango, Celso; Fleischhacker, W Wolfgang; Glenthøj, Birte; Leboyer, Marion; Leucht, Stefan; Leweke, Markus; McGuire, Phillip; Meyer-Lindenberg, Andreas; Rujescu, Dan; Sommer, Iris E; Kahn, René S; Lewis, Shon W

2015-05-01

We aimed to design a multimodal intervention to improve adherence following first episode psychosis, consistent with current evidence. Existing literature identified medication attitudes, insight, and characteristics of support as important determinants of adherence to medication: we examined medication attitudes, self-esteem, and insight in an early psychosis cohort better to understand their relationships. Existing longitudinal data from 309 patients with early Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, nonaffective psychosis (83% first episode) were analyzed to test the hypothesis that medication attitudes, while meaningfully different from "insight," correlated with insight and self-esteem, and change in each influenced the others. Rosenberg Self-Esteem Scale, Birchwood Insight Scale, and Positive and Negative Syndrome Scale insight were assessed at presentation, after 6 weeks and 3 and 18 months. Drug Attitudes Inventory (DAI) and treatment satisfaction were rated from 6 weeks onward. Structural equation models of their relationships were compared. Insight measures' and DAI's predictive validity were compared against relapse, readmission, and remission. Analysis found five latent constructs best fitted the data: medication attitudes, self-esteem, accepting need for treatment, self-rated insight, and objective insight. All were related and each affected the others as it changed, except self-esteem and medication attitudes. Low self-reported insight at presentation predicted readmission. Good 6-week insight (unlike drug attitudes) predicted remission. Literature review and data modeling indicated that a multimodal intervention using motivational interviewing, online psychoeducation, and SMS text medication reminders to enhance adherence without damaging self-concept was feasible and appropriate. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Event-rate and delta inflation when evaluating mortality as a primary outcome from randomized controlled trials of nutritional interventions during critical illness: a systematic review.

PubMed

Summers, Matthew J; Chapple, Lee-anne S; McClave, Stephen A; Deane, Adam M

2016-04-01

There is a lack of high-quality evidence that proves that nutritional interventions during critical illness reduce mortality. We evaluated whether power calculations for randomized controlled trials (RCTs) of nutritional interventions that used mortality as the primary outcome were realistic, and whether overestimation was systematic in the studies identified to determine whether this was due to overestimates of event rate or delta. A systematic review of the literature between 2005 and 2015 was performed to identify RCTs of nutritional interventions administered to critically ill adults that had mortality as the primary outcome. Predicted event rate (predicted mortality during the control), predicted mortality during intervention, predicted delta (predicted difference between mortality during the control and intervention), actual event rate (observed mortality during control), observed mortality during intervention, and actual delta (difference between observed mortality during the control and intervention) were recorded. The event-rate gap (predicted event rate minus observed event rate), the delta gap (predicted delta minus observed delta), and the predicted number needed to treat were calculated. Data are shown as median (range). Fourteen articles were extracted, with power calculations provided for 10 studies. The predicted event rate was 29.9% (20.0–52.4%), and the predicted delta was 7.9% (3.0–20.0%). If the study hypothesis was proven correct then, on the basis of the power calculations, the number needed to treat would have been 12.7 (5.0–33.3) patients. The actual event rate was 25.3% (6.1–50.0%), the observed mortality during the intervention was 24.4% (6.3–39.7%), and the actual delta was 0.5% (−10.2–10.3%), such that the event-rate gap was 2.6% (−3.9–23.7%) and delta gap was 7.5% (3.2–25.2%). Overestimates of delta occur frequently in RCTs of nutritional interventions in the critically ill that are powered to determine a mortality benefit. Delta inflation may explain the number of "negative" studies in this field of research.

Household water treatment in developing countries: comparing different intervention types using meta-regression.

PubMed

Hunter, Paul R

2009-12-01

Household water treatment (HWT) is being widely promoted as an appropriate intervention for reducing the burden of waterborne disease in poor communities in developing countries. A recent study has raised concerns about the effectiveness of HWT, in part because of concerns over the lack of blinding and in part because of considerable heterogeneity in the reported effectiveness of randomized controlled trials. This study set out to attempt to investigate the causes of this heterogeneity and so identify factors associated with good health gains. Studies identified in an earlier systematic review and meta-analysis were supplemented with more recently published randomized controlled trials. A total of 28 separate studies of randomized controlled trials of HWT with 39 intervention arms were included in the analysis. Heterogeneity was studied using the "metareg" command in Stata. Initial analyses with single candidate predictors were undertaken and all variables significant at the P < 0.2 level were included in a final regression model. Further analyses were done to estimate the effect of the interventions over time by MonteCarlo modeling using @Risk and the parameter estimates from the final regression model. The overall effect size of all unblinded studies was relative risk = 0.56 (95% confidence intervals 0.51-0.63), but after adjusting for bias due to lack of blinding the effect size was much lower (RR = 0.85, 95% CI = 0.76-0.97). Four main variables were significant predictors of effectiveness of intervention in a multipredictor meta regression model: Log duration of study follow-up (regression coefficient of log effect size = 0.186, standard error (SE) = 0.072), whether or not the study was blinded (coefficient 0.251, SE 0.066) and being conducted in an emergency setting (coefficient -0.351, SE 0.076) were all significant predictors of effect size in the final model. Compared to the ceramic filter all other interventions were much less effective (Biosand 0.247, 0.073; chlorine and safe waste storage 0.295, 0.061; combined coagulant-chlorine 0.2349, 0.067; SODIS 0.302, 0.068). A Monte Carlo model predicted that over 12 months ceramic filters were likely to be still effective at reducing disease, whereas SODIS, chlorination, and coagulation-chlorination had little if any benefit. Indeed these three interventions are predicted to have the same or less effect than what may be expected due purely to reporting bias in unblinded studies With the currently available evidence ceramic filters are the most effective form of HWT in the longterm, disinfection-only interventions including SODIS appear to have poor if any longterm public health benefit.

Using instructional design process to improve design and development of Internet interventions.

PubMed

Hilgart, Michelle M; Ritterband, Lee M; Thorndike, Frances P; Kinzie, Mable B

2012-06-28

Given the wide reach and extensive capabilities of the Internet, it is increasingly being used to deliver comprehensive behavioral and mental health intervention and prevention programs. Their goals are to change user behavior, reduce unwanted complications or symptoms, and improve health status and health-related quality of life. Internet interventions have been found efficacious in addressing a wide range of behavioral and mental health problems, including insomnia, nicotine dependence, obesity, diabetes, depression, and anxiety. Despite the existence of many Internet-based interventions, there is little research to inform their design and development. A model for behavior change in Internet interventions has been published to help guide future Internet intervention development and to help predict and explain behavior changes and symptom improvement outcomes through the use of Internet interventions. An argument is made for grounding the development of Internet interventions within a scientific framework. To that end, the model highlights a multitude of design-related components, areas, and elements, including user characteristics, environment, intervention content, level of intervention support, and targeted outcomes. However, more discussion is needed regarding how the design of the program should be developed to address these issues. While there is little research on the design and development of Internet interventions, there is a rich, related literature in the field of instructional design (ID) that can be used to inform Internet intervention development. ID models are prescriptive models that describe a set of activities involved in the planning, implementation, and evaluation of instructional programs. Using ID process models has been shown to increase the effectiveness of learning programs in a broad range of contexts. ID models specify a systematic method for assessing the needs of learners (intervention users) to determine the gaps between current knowledge and behaviors, and desired outcomes. Through the ID process, designers focus on the needs of learners, taking into account their prior knowledge; set measurable learning objectives or performance requirements; assess learners' achievement of the targeted outcomes; and employ cycles of continuous formative evaluation to ensure that the intervention meets the needs of all stakeholders. The ID process offers a proven methodology for the design of instructional programs and should be considered an integral part of the creation of Internet interventions. By providing a framework for the design and development of Internet interventions and by purposefully focusing on these aspects, as well as the underlying theories supporting these practices, both the theories and the interventions themselves can continue to be refined and improved. By using the behavior change model for Internet interventions along with the best research available to guide design practice and inform development, developers of Internet interventions will increase their ability to achieve desired outcomes.

Using Instructional Design Process to Improve Design and Development of Internet Interventions

PubMed Central

Hilgart, Michelle M; Thorndike, Frances P; Kinzie, Mable B

2012-01-01

Given the wide reach and extensive capabilities of the Internet, it is increasingly being used to deliver comprehensive behavioral and mental health intervention and prevention programs. Their goals are to change user behavior, reduce unwanted complications or symptoms, and improve health status and health-related quality of life. Internet interventions have been found efficacious in addressing a wide range of behavioral and mental health problems, including insomnia, nicotine dependence, obesity, diabetes, depression, and anxiety. Despite the existence of many Internet-based interventions, there is little research to inform their design and development. A model for behavior change in Internet interventions has been published to help guide future Internet intervention development and to help predict and explain behavior changes and symptom improvement outcomes through the use of Internet interventions. An argument is made for grounding the development of Internet interventions within a scientific framework. To that end, the model highlights a multitude of design-related components, areas, and elements, including user characteristics, environment, intervention content, level of intervention support, and targeted outcomes. However, more discussion is needed regarding how the design of the program should be developed to address these issues. While there is little research on the design and development of Internet interventions, there is a rich, related literature in the field of instructional design (ID) that can be used to inform Internet intervention development. ID models are prescriptive models that describe a set of activities involved in the planning, implementation, and evaluation of instructional programs. Using ID process models has been shown to increase the effectiveness of learning programs in a broad range of contexts. ID models specify a systematic method for assessing the needs of learners (intervention users) to determine the gaps between current knowledge and behaviors, and desired outcomes. Through the ID process, designers focus on the needs of learners, taking into account their prior knowledge; set measurable learning objectives or performance requirements; assess learners’ achievement of the targeted outcomes; and employ cycles of continuous formative evaluation to ensure that the intervention meets the needs of all stakeholders. The ID process offers a proven methodology for the design of instructional programs and should be considered an integral part of the creation of Internet interventions. By providing a framework for the design and development of Internet interventions and by purposefully focusing on these aspects, as well as the underlying theories supporting these practices, both the theories and the interventions themselves can continue to be refined and improved. By using the behavior change model for Internet interventions along with the best research available to guide design practice and inform development, developers of Internet interventions will increase their ability to achieve desired outcomes. PMID:22743534

Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models.

PubMed

Baquero, Oswaldo Santos; Santana, Lidia Maria Reis; Chiaravalloti-Neto, Francisco

2018-01-01

Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city-São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times higher than the benchmark and 1.47 higher that the next best performing model. The predictive models captured the seasonal patterns but differed in their capacity to anticipate large epidemics and all outperformed the benchmark. In addition to be able to predict epidemics of unprecedented magnitude, the best model had computational advantages, since its training and tuning was straightforward and required seconds or at most few minutes. These are desired characteristics to provide timely results for decision makers. However, it should be noted that predictions are made just one month ahead and this is a limitation that future studies could try to reduce.

Planning versus action: Different decision-making processes predict plans to change one's diet versus actual dietary behavior.

PubMed

Kiviniemi, Marc T; Brown-Kramer, Carolyn R

2015-05-01

Most health decision-making models posit that deciding to engage in a health behavior involves forming a behavioral intention which then leads to actual behavior. However, behavioral intentions and actual behavior may not be functionally equivalent. Two studies examined whether decision-making factors predicting dietary behaviors were the same as or distinct from those predicting intentions. Actual dietary behavior was proximally predicted by affective associations with the behavior. By contrast, behavioral intentions were predicted by cognitive beliefs about behaviors, with no contribution of affective associations. This dissociation has implications for understanding individual regulation of health behaviors and for behavior change interventions. © The Author(s) 2015.

Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention.

PubMed

Zhang, Yanhang; Barocas, Victor H; Berceli, Scott A; Clancy, Colleen E; Eckmann, David M; Garbey, Marc; Kassab, Ghassan S; Lochner, Donna R; McCulloch, Andrew D; Tran-Son-Tay, Roger; Trayanova, Natalia A

2016-09-01

Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications.

Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention

PubMed Central

Zhang, Yanhang; Barocas, Victor H.; Berceli, Scott A.; Clancy, Colleen E.; Eckmann, David M.; Garbey, Marc; Kassab, Ghassan S.; Lochner, Donna R.; McCulloch, Andrew D.; Tran-Son-Tay, Roger; Trayanova, Natalia A.

2016-01-01

Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications. PMID:27138523

Ultrasound waiting lists: rational queue or extended capacity?

PubMed

Brasted, Christopher

2008-06-01

The features and issues regarding clinical waiting lists in general and general ultrasound waiting lists in particular are reviewed, and operational aspects of providing a general ultrasound service are also discussed. A case study is presented describing a service improvement intervention in a UK NHS hospital's ultrasound department, from which arises requirements for a predictive planning model for an ultrasound waiting list. In the course of this, it becomes apparent that a booking system is a more appropriate way of describing the waiting list than a conventional queue. Distinctive features are identified from the literature and the case study as the basis for a predictive model, and a discrete event simulation model is presented which incorporates the distinctive features.

Interest level in 2-year-olds with autism spectrum disorder predicts rate of verbal, nonverbal, and adaptive skill acquisition.

PubMed

Klintwall, Lars; Macari, Suzanne; Eikeseth, Svein; Chawarska, Katarzyna

2015-11-01

Recent studies have suggested that skill acquisition rates for children with autism spectrum disorders receiving early interventions can be predicted by child motivation. We examined whether level of interest during an Autism Diagnostic Observation Schedule assessment at 2 years predicts subsequent rates of verbal, nonverbal, and adaptive skill acquisition to the age of 3 years. A total of 70 toddlers with autism spectrum disorder, mean age of 21.9 months, were scored using Interest Level Scoring for Autism, quantifying toddlers' interest in toys, social routines, and activities that could serve as reinforcers in an intervention. Adaptive level and mental age were measured concurrently (Time 1) and again after a mean of 16.3 months of treatment (Time 2). Interest Level Scoring for Autism score, Autism Diagnostic Observation Schedule score, adaptive age equivalent, verbal and nonverbal mental age, and intensity of intervention were entered into regression models to predict rates of skill acquisition. Interest level at Time 1 predicted subsequent acquisition rate of adaptive skills (R(2) = 0.36) and verbal mental age (R(2) = 0.30), above and beyond the effects of Time 1 verbal and nonverbal mental ages and Autism Diagnostic Observation Schedule scores. Interest level at Time 1 also contributed (R(2) = 0.30), with treatment intensity, to variance in development of nonverbal mental age. © The Author(s) 2014.

Vaccine Effects on Heterogeneity in Susceptibility and Implications for Population Health Management

PubMed Central

Wargo, Andrew R.; Jones, Darbi R.; Viss, Jessie R.; Rutan, Barbara J.; Egan, Nicholas A.; Sá-Guimarães, Pedro; Kim, Min Sun; Kurath, Gael; Gomes, M. Gabriela M.

2017-01-01

ABSTRACT Heterogeneity in host susceptibility is a key determinant of infectious disease dynamics but is rarely accounted for in assessment of disease control measures. Understanding how susceptibility is distributed in populations, and how control measures change this distribution, is integral to predicting the course of epidemics with and without interventions. Using multiple experimental and modeling approaches, we show that rainbow trout have relatively homogeneous susceptibility to infection with infectious hematopoietic necrosis virus and that vaccination increases heterogeneity in susceptibility in a nearly all-or-nothing fashion. In a simple transmission model with an R0 of 2, the highly heterogeneous vaccine protection would cause a 35 percentage-point reduction in outbreak size over an intervention inducing homogenous protection at the same mean level. More broadly, these findings provide validation of methodology that can help to reduce biases in predictions of vaccine impact in natural settings and provide insight into how vaccination shapes population susceptibility. PMID:29162706

Predicting physical activity and fruit and vegetable intake in adolescents: a test of the information, motivation, behavioral skills model.

PubMed

Kelly, Stephanie; Melnyk, Bernadette Mazurek; Belyea, Michael

2012-04-01

Most adolescents do not meet national recommendations regarding physical activity and/or the intake of fruits and vegetables. The purpose of this study was to explore whether variables in the information, motivation, behavioral skills (IMB) model of health promotion predicted physical activity and fruit and vegetable intake in 404 adolescents from 2 high schools in the Southwest United States using structural equation modeling (SEM). The SEM models included theoretical constructs, contextual variables, and moderators. The theoretical relationships in the IMB model were confirmed and were moderated by gender and race. Interventions that incorporate cognitive-behavioral skills building may be a key factor for promoting physical activity as well as fruit and vegetable intake in adolescents. Copyright © 2012 Wiley Periodicals, Inc.

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.

PubMed

Majnarić-Trtica, Ljiljana; Vitale, Branko

2011-10-01

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

Group‐Based Trajectory Models: Assessing Adherence to Antihypertensive Medication in Older Adults in a Community Pharmacy Setting

PubMed Central

Stewart, Derek; Smith, Susan M.; Gallagher, Paul; Cousins, Gráinne

2017-01-01

Antihypertensive medication nonadherence is highly prevalent, leading to uncontrolled blood pressure. Methods that facilitate the targeting and tailoring of adherence interventions in clinical settings are required. Group‐Based Trajectory Modeling (GBTM) is a newer method to evaluate adherence using pharmacy dispensing (refill) data that has advantages over traditional refill adherence metrics (e.g. Proportion of Days Covered) by identifying groups of patients who may benefit from adherence interventions, and identifying patterns of adherence behavior over time that may facilitate tailoring of an adherence intervention. We evaluated adherence to antihypertensive medication in 905 patients over a 12‐month period in a community pharmacy setting using GBTM, identifying three subgroups of adherence patterns: 52.8%, 40.7%, and 6.5% had very high, high, and low adherence, respectively. However, GBTM failed to demonstrate predictive validity with blood pressure at 12 months. Further research on the validity of adherence measures that facilitate interventions in clinical settings is required. PMID:28875569

Child-Level Predictors of Responsiveness to Evidence-Based Mathematics Intervention.

PubMed

Powell, Sarah R; Cirino, Paul T; Malone, Amelia S

2017-07-01

We identified child-level predictors of responsiveness to 2 types of mathematics (calculation and word-problem) intervention among 2nd-grade children with mathematics difficulty. Participants were 250 children in 107 classrooms in 23 schools pretested on mathematics and general cognitive measures and posttested on mathematics measures. We assigned classrooms randomly assigned to calculation intervention, word-problem intervention, or business-as-usual control. Intervention lasted 17 weeks. Path analyses indicated that scores on working memory and language comprehension assessments moderated responsiveness to calculation intervention. No moderators were identified for responsiveness to word-problem intervention. Across both intervention groups and the control group, attentive behavior predicted both outcomes. Initial calculation skill predicted the calculation outcome, and initial language comprehension predicted word-problem outcomes. These results indicate that screening for calculation intervention should include a focus on working memory, language comprehension, attentive behavior, and calculations. Screening for word-problem intervention should focus on attentive behavior and word problems.

Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution.

PubMed

Chakrabarti, Shaon; Michor, Franziska

2017-07-15

The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression, a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive strategies for combination therapies. We applied our approach to an ongoing phase Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the MTD are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction, and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials. Cancer Res; 77(14); 3908-21. ©2017 AACR . ©2017 American Association for Cancer Research.

Supraspinal Control Predicts Locomotor Function and Forecasts Responsiveness to Training after Spinal Cord Injury

PubMed Central

Field-Fote, Edelle C.; Yang, Jaynie F.; Basso, D. Michele; Gorassini, Monica A.

2017-01-01

Abstract Restoration of walking ability is an area of great interest in the rehabilitation of persons with spinal cord injury. Because many cortical, subcortical, and spinal neural centers contribute to locomotor function, it is important that intervention strategies be designed to target neural elements at all levels of the neuraxis that are important for walking ability. While to date most strategies have focused on activation of spinal circuits, more recent studies are investigating the value of engaging supraspinal circuits. Despite the apparent potential of pharmacological, biological, and genetic approaches, as yet none has proved more effective than physical therapeutic rehabilitation strategies. By making optimal use of the potential of the nervous system to respond to training, strategies can be developed that meet the unique needs of each person. To complement the development of optimal training interventions, it is valuable to have the ability to predict future walking function based on early clinical presentation, and to forecast responsiveness to training. A number of clinical prediction rules and association models based on common clinical measures have been developed with the intent, respectively, to predict future walking function based on early clinical presentation, and to delineate characteristics associated with responsiveness to training. Further, a number of variables that are correlated with walking function have been identified. Not surprisingly, most of these prediction rules, association models, and correlated variables incorporate measures of volitional lower extremity strength, illustrating the important influence of supraspinal centers in the production of walking behavior in humans. PMID:27673569

Evaluation of a Mathematical Model of Rat Body Weight Regulation in Application to Caloric Restriction and Drug Treatment Studies.

PubMed

Selimkhanov, Jangir; Thompson, W Clayton; Patterson, Terrell A; Hadcock, John R; Scott, Dennis O; Maurer, Tristan S; Musante, Cynthia J

2016-01-01

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology.

Evaluation of a Mathematical Model of Rat Body Weight Regulation in Application to Caloric Restriction and Drug Treatment Studies

PubMed Central

Selimkhanov, Jangir; Patterson, Terrell A.; Scott, Dennis O.; Maurer, Tristan S.; Musante, Cynthia J.

2016-01-01

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology. PMID:27227543

Developmental Pathways of Youth Gang Membership: A Structural Test of the Social Development Model

PubMed Central

Hill, Karl G.; Gilman, Amanda B.; Howell, James C.; Catalano, Richard F.; Hawkins, J. David

2017-01-01

As a result of nearly 40 years of research using a risk and protective factor approach, much is known about the predictors of gang onset. Little theoretical work, however, has been done to situate this approach to studying gang membership within a more comprehensive developmental model. Using structural equation modeling techniques, the current study is the first to test the capacity of the social development model (SDM) to predict the developmental pathways that increase and decrease the likelihood of gang membership. Results suggest that the SDM provides a good accounting of the social developmental processes at age 13 that are predictive of later gang membership. These findings support the promotion of a theoretical understanding of gang membership that specifies both pro- and antisocial developmental pathways. Additionally, as the SDM is intended as a model that can guide preventive intervention, results also hold practical utility for designing strategies that can be implemented in early adolescence to address the likelihood of later gang involvement. Three key preventive intervention points to address gang membership are discussed, including promoting efforts to enhance social skills, increasing the availability of prosocial opportunities and rewarding engagement in these opportunities, and reducing antisocial socialization experiences throughout the middle- and high school years. PMID:29403146

Computational Fluid Dynamics modeling of contrast transport in basilar aneurysms following flow-altering surgeries.

PubMed

Vali, Alireza; Abla, Adib A; Lawton, Michael T; Saloner, David; Rayz, Vitaliy L

2017-01-04

In vivo measurement of blood velocity fields and flow descriptors remains challenging due to image artifacts and limited resolution of current imaging methods; however, in vivo imaging data can be used to inform and validate patient-specific computational fluid dynamics (CFD) models. Image-based CFD can be particularly useful for planning surgical interventions in complicated cases such as fusiform aneurysms of the basilar artery, where it is crucial to alter pathological hemodynamics while preserving flow to the distal vasculature. In this study, patient-specific CFD modeling was conducted for two basilar aneurysm patients considered for surgical treatment. In addition to velocity fields, transport of contrast agent was simulated for the preoperative and postoperative conditions using two approaches. The transport of a virtual contrast passively following the flow streamlines was simulated to predict post-surgical flow regions prone to thrombus deposition. In addition, the transport of a mixture of blood with an iodine-based contrast agent was modeled to compare and verify the CFD results with X-ray angiograms. The CFD-predicted patterns of contrast flow were qualitatively compared to in vivo X-ray angiograms acquired before and after the intervention. The results suggest that the mixture modeling approach, accounting for the flow rates and properties of the contrast injection, is in better agreement with the X-ray angiography data. The virtual contrast modeling assessed the residence time based on flow patterns unaffected by the injection procedure, which makes the virtual contrast modeling approach better suited for prediction of thrombus deposition, which is not limited to the peri-procedural state. Copyright © 2016 Elsevier Ltd. All rights reserved.

Participation in physical play and leisure: developing a theory- and evidence-based intervention for children with motor impairments.

PubMed

Kolehmainen, Niina; Francis, Jillian J; Ramsay, Craig R; Owen, Christine; McKee, Lorna; Ketelaar, Marjolijn; Rosenbaum, Peter

2011-11-07

Children with motor impairments (e.g. difficulties with motor control, muscle tone or balance) experience significant difficulties in participating in physical play and leisure. Current interventions are often poorly defined, lack explicit hypotheses about why or how they might work, and have insufficient evidence about effectiveness. This project will identify (i) the 'key ingredients' of an effective intervention to increase participation in physical play and leisure in children with motor impairments; and (ii) how these ingredients can be combined in a feasible and acceptable intervention. The project draws on the WHO International Classification of Functioning, Disability and Health and the UK Medical Research Council guidance for developing 'complex interventions'. There will be five steps: 1) identifying biomedical, personal and environmental factors proposed to predict children's participation in physical play and leisure; 2) developing an explicit model of the key predictors; 3) selecting intervention strategies to target the predictors, and specifying the pathways to change; 4) operationalising the strategies in a feasible and acceptable intervention; and 5) modelling the intervention processes and outcomes within single cases. The primary output from this project will be a detailed protocol for an intervention. The intervention, if subsequently found to be effective, will support children with motor difficulties to attain life-long well-being and participation in society. The project will also be an exemplar of methodology for a systematic development of non-drug interventions for children.

Participation in physical play and leisure: developing a theory- and evidence-based intervention for children with motor impairments

PubMed Central

2011-01-01

Background Children with motor impairments (e.g. difficulties with motor control, muscle tone or balance) experience significant difficulties in participating in physical play and leisure. Current interventions are often poorly defined, lack explicit hypotheses about why or how they might work, and have insufficient evidence about effectiveness. This project will identify (i) the 'key ingredients' of an effective intervention to increase participation in physical play and leisure in children with motor impairments; and (ii) how these ingredients can be combined in a feasible and acceptable intervention. Methods/Design The project draws on the WHO International Classification of Functioning, Disability and Health and the UK Medical Research Council guidance for developing 'complex interventions'. There will be five steps: 1) identifying biomedical, personal and environmental factors proposed to predict children's participation in physical play and leisure; 2) developing an explicit model of the key predictors; 3) selecting intervention strategies to target the predictors, and specifying the pathways to change; 4) operationalising the strategies in a feasible and acceptable intervention; and 5) modelling the intervention processes and outcomes within single cases. Discussion The primary output from this project will be a detailed protocol for an intervention. The intervention, if subsequently found to be effective, will support children with motor difficulties to attain life-long well-being and participation in society. The project will also be an exemplar of methodology for a systematic development of non-drug interventions for children. PMID:22061203

[Empowerment, stress vulnerability and burnout among Portuguese nursing staff].

PubMed

Orgambídez-Ramos, Alejandro; Borrego-Alés, Yolanda; Ruiz-Frutos, Carlos

2018-01-01

The work environment in Portuguese hospitals, characterized by economic cutbacks, can lead to higher levels of burnout experienced by nursing staff. Furthermore, vulnerability to stress can negatively affect the perception of burnout in the workplace. However, structural empowerment is an organizational process that can prevent and decrease burnout among nurses. Consequently, the aim of the study was to examine to what extent structural empowerment and vulnerability to stress can play a predictive role in core burnout in a sample of Portuguese nurses. A convenience sample of 297 nursing staff members from Portuguese hospitals was used in this study. Core burnout was negatively and significantly related to all the dimensions of structural empowerment, and it was positively and significantly related to vulnerability to stress. Regression models showed that core burnout was significantly predicted by access to funds, access to opportunities and vulnerability to stress. Organizational administrations must make every effort in designing interventions focused on structural empowerment, as well as interventions focused on individual interventions that enhance skills for coping with stress.

Memory Self-Efficacy Predicts Responsiveness to Inductive Reasoning Training in Older Adults

PubMed Central

Jackson, Joshua J.; Hill, Patrick L.; Gao, Xuefei; Roberts, Brent W.; Stine-Morrow, Elizabeth A. L.

2012-01-01

Objectives. In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Methods. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Results. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Discussion. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood. PMID:21743037

Why do individuals not lose more weight from an exercise intervention at a defined dose? An energy balance analysis

PubMed Central

Thomas, D. M.; Bouchard, C.; Church, T.; Slentz, C.; Kraus, W. E.; Redman, L. M.; Martin, C. K.; Silva, A. M.; Vossen, M.; Westerterp, K.; Heymsfield, S. B.

2013-01-01

Summary Weight loss resulting from an exercise intervention tends to be lower than predicted. Modest weight loss can arise from an increase in energy intake, physiological reductions in resting energy expenditure, an increase in lean tissue or a decrease in non-exercise activity. Lower than expected, weight loss could also arise from weak and invalidated assumptions within predictive models. To investigate these causes, we systematically reviewed studies that monitored compliance to exercise prescriptions and measured exercise-induced change in body composition. Changed body energy stores were calculated to determine the deficit between total daily energy intake and energy expenditures. This information combined with available measurements was used to critically evaluate explanations for low exercise-induced weight loss. We conclude that the small magnitude of weight loss observed from the majority of evaluated exercise interventions is primarily due to low doses of prescribed exercise energy expenditures compounded by a concomitant increase in caloric intake. PMID:22681398

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

NASA Technical Reports Server (NTRS)

Diallo, Ousmane H.

2012-01-01

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Physical activity and parents of very young children: The role of beliefs and social-cognitive factors.

PubMed

Cowie, Eloise; White, Katherine; Hamilton, Kyra

2018-05-14

Despite the unequivocal benefits of regular physical activity, many parents engage in lower levels of physical activity (PA) following the birth of a child. Drawing on the theory of planned behaviour (TPB) and health action process approach (HAPA), an integrative model was developed to examine variables predicting PA in parents of very young children. In addition, key beliefs related to PA intentions and behaviour among parents of very young children were investigated. A prospective-correlational design with two waves of data collection, spaced one week apart, was adopted. Parents (N = 297) completed an online- or paper-based questionnaire assessing TPB global constructs and belief-based items as well as family social support and planning from the HAPA. One week later, parents self-reported their PA behaviour. Data were analysed using latent variable structural equation modelling. Findings revealed the model was a good fit to the data, accounting for 62% and 27% of the variance in PA intentions and behaviour, respectively. Attitude, subjective norm, and perceived behavioural control predicted intentions. Family social support failed to predict both planning and intentions. Physical activity was predicted by planning only, with an indirect effect occurring from intentions to behaviour through planning. A number of key beliefs on intentions and behaviour were also identified. This formative research provides further understanding of the factors that influence the PA behaviour of parents of very young children. Results provide targets for future interventions to increase PA for parents in a transition phase where PA levels decline. Statement of Contribution What is already known on this subject? Despite physical activity benefits, many parents are inactive following the birth of a child Social-cognitive models have demonstrated efficacy in predicting physical activity Weaknesses are inherent in the use of single theories to explain behaviour What does this study add? Use of integrative models allows for meaningful prediction of parental physical activity A range of key beliefs were found to be related to parental physical activity Results can inform future physical activity interventions for parents of very young children. © 2018 The British Psychological Society.

ADVANCIS Score Predicts Acute Kidney Injury After Percutaneous Coronary Intervention for Acute Coronary Syndrome.

PubMed

Fan, Pei-Chun; Chen, Tien-Hsing; Lee, Cheng-Chia; Tsai, Tsung-Yu; Chen, Yung-Chang; Chang, Chih-Hsiang

2018-01-01

Acute kidney injury (AKI), a common and crucial complication of acute coronary syndrome (ACS) after receiving percutaneous coronary intervention (PCI), is associated with increased mortality and adverse outcomes. This study aimed to develop and validate a risk prediction model for incident AKI after PCI for ACS. We included 82,186 patients admitted for ACS and receiving PCI between 1997 and 2011 from the Taiwan National Health Insurance Research Database and randomly divided them into a training cohort (n = 57,630) and validation cohort (n = 24,656) for risk model development and validation, respectively. Risk factor analysis revealed that age, diabetes mellitus, ventilator use, prior AKI, number of intervened vessels, chronic kidney disease (CKD), intra-aortic balloon pump (IABP) use, cardiogenic shock, female sex, prior stroke, peripheral arterial disease, hypertension, and heart failure were significant risk factors for incident AKI after PCI for ACS. The reduced model, ADVANCIS, comprised 8 clinical parameters (age, diabetes mellitus, ventilator use, prior AKI, number of intervened vessels, CKD, IABP use, cardiogenic shock), with a score scale ranging from 0 to 22, and performed comparably with the full model (area under the receiver operating characteristic curve, 87.4% vs 87.9%). An ADVANCIS score of ≥6 was associated with higher in-hospital mortality risk. In conclusion, the ADVANCIS score is a novel, simple, robust tool for predicting the risk of incident AKI after PCI for ACS, and it can aid in risk stratification to monitor patient care.

Application of Transtheoretical (Stages of Change) Model in Studying Attitudes and Behaviors of Adults with Hearing Loss: A Descriptive Review.

PubMed

Manchaiah, Vinaya; Hernandez, Barbara Michiels; Beck, Douglas L

2018-06-01

Health Behavior Change (HBC) refers to facilitating changes to habits and/or behaviors related to health. There are a number of models/theories of HBC, which provide a structured framework to better understand the HBCs of individuals. The Transtheoretical Model (TTM, aka "the Stages of Change" model) is an integrative model used to conceptualize the process of intentional behavior change and is applied to a variety of behaviors, populations, and settings. In the last few years, use of TTM by the profession of audiology has been increasing. This descriptive literature review was aimed at identifying and presenting a summary of research studies, which use TTM to study the attitudes and behaviors of adults with hearing loss. A literature review was conducted. This review included 13 empirical studies. A literature review was conducted using the EBSCOhost and included the databases Cumulative Index to Nursing and Allied Health, MEDLINE, and PsycINFO. The review suggests TTM is useful in studying the attitudes and behaviors of adults with hearing loss. There are positive associations between stages of change and help-seeking, intervention uptake, and hearing rehabilitation outcome (i.e., benefit and satisfaction). However, associations with intervention decisions and intervention use were not evident. It appears help-seeking, intervention uptake, and successful outcomes are usually displayed in people in the later stages of change as those with greater hearing loss are often in the later stages of change. Understanding the readiness toward help-seeking and uptake of intervention in people with hearing loss based on TTM may help clinicians develop more focused management strategies. However, additional longitudinal and interventional studies are needed to further test the predictive validity of the stages of change model. American Academy of Audiology.

Applying the health promotion model to development of a worksite intervention.

PubMed

Lusk, S L; Kerr, M J; Ronis, D L; Eakin, B L

1999-01-01

Consistent use of hearing protection devices (HPDs) decreases noise-induced hearing loss, however, many workers do not use them consistently. Past research has supported the need to use a conceptual framework to understand behaviors and guide intervention programs; however, few reports have specified a process to translate a conceptual model into an intervention. The strongest predictors from the Health Promotion Model were used to design a training program to increase HPD use among construction workers. Carpenters (n = 118), operating engineers (n = 109), and plumber/pipefitters (n = 129) in the Midwest were recruited to participate in the study. Written questionnaires including scales measuring the components of the Health Promotion Model were completed in classroom settings at worker trade group meetings. All items from scales predicting HPD use were reviewed to determine the basis for the content of a program to promote the use of HPDs. Three selection criteria were developed: (1) correlation with use of hearing protection (at least .20), (2) amenability to change, and (3) room for improvement (mean score not at ceiling). Linear regression and Pearson's correlation were used to assess the components of the model as predictors of HPD use. Five predictors had statistically significant regression coefficients: perceived noise exposure, self-efficacy, value of use, barriers to use, and modeling of use of hearing protection. Using items meeting the selection criteria, a 20-minute videotape with written handouts was developed as the core of an intervention. A clearly defined practice session was also incorporated in the training intervention. Determining salient factors for worker populations and specific protective equipment prior to designing an intervention is essential. These predictors provided the basis for a training program that addressed the specific needs of construction workers. Results of tests of the effectiveness of the program will be available in the near future.

What Matters When Children Play: Influence of Social Cognitive Theory and Perceived Environment on Levels of Physical Activity Among Elementary-Aged Youth

PubMed Central

Harmon, Brook E.; Nigg, Claudio R.; Long, Camonia; Amato, Katie; Anwar, Mahabub-Ul; Kutchman, Eve; Anthamatten, Peter; Browning, Raymond C.; Brink, Lois; Hill, James O.

2014-01-01

Objectives Social Cognitive Theory (SCT) has often been used as a guide to predict and modify physical activity (PA) behavior. We assessed the ability of commonly investigated SCT variables and perceived school environment variables to predict PA among elementary students. We also examined differences in influences between Hispanic and non-Hispanic students. Design This analysis used baseline data collected from eight schools who participated in a four-year study of a combined school-day curriculum and environmental intervention. Methods Data were collected from 393 students. A 3-step linear regression was used to measure associations between PA level, SCT variables (self-efficacy, social support, enjoyment), and perceived environment variables (schoolyard structures, condition, equipment/supervision). Logistic regression assessed associations between variables and whether students met PA recommendations. Results School and sex explained 6% of the moderate-to-vigorous PA models' variation. SCT variables explained an additional 15% of the models' variation, with much of the model's predictive ability coming from self-efficacy and social support. Sex was more strongly associated with PA level among Hispanic students, while self-efficacy was more strongly associated among non-Hispanic students. Perceived environment variables contributed little to the models. Conclusions Our findings add to the literature on the influences of PA among elementary-aged students. The differences seen in the influence of sex and self-efficacy among non-Hispanic and Hispanic students suggests these are areas where PA interventions could be tailored to improve efficacy. Additional research is needed to understand if different measures of perceived environment or perceptions at different ages may better predict PA. PMID:24772004

From Risk Assessment to Risk Management: Matching Interventions to Adolescent Offenders’ Strengths and Vulnerabilities

PubMed Central

Singh, Jay P.; Desmarais, Sarah L.; Sellers, Brian G.; Hylton, Tatiana; Tirotti, Melissa; Van Dorn, Richard A.

2013-01-01

Though considerable research has examined the validity of risk assessment tools in predicting adverse outcomes in justice-involved adolescents, the extent to which risk assessments are translated into risk management strategies and, importantly, the association between this link and adverse outcomes has gone largely unexamined. To address these shortcomings, the Risk-Need-Responsivity (RNR) model was used to examine associations between identified strengths and vulnerabilities, interventions, and institutional outcomes for justice-involved youth. Data were collected from risk assessments completed using the Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV) for 120 adolescent offenders (96 boys and 24 girls). Interventions and outcomes were extracted from institutional records. Mixed evidence of adherence to RNR principles was found. Accordant to the risk principle, adolescent offenders judged to have more strengths had more strength-based interventions in their service plans, though adolescent offenders with more vulnerabilities did not have more interventions targeting their vulnerabilities. With respect to the need and responsivity principles, vulnerabilities and strengths identified as particularly relevant to the individual youth's risk of adverse outcomes were addressed in the service plans about half and a quarter of the time, respectively. Greater adherence to the risk and need principles was found to predict significantly the likelihood of externalizing outcomes. Findings suggest some gaps between risk assessment and risk management and highlight the potential usefulness of strength-based approaches to intervention. PMID:25346561

Understanding adolescent response to a technology-based depression prevention program.

PubMed

Gladstone, Tracy; Marko-Holguin, Monika; Henry, Jordan; Fogel, Joshua; Diehl, Anne; Van Voorhees, Benjamin W

2014-01-01

Guided by the Behavioral Vaccine Theory of prevention, this study uses a no-control group design to examine intervention variables that predict favorable changes in depressive symptoms at 6- to 8-week follow-up in at-risk adolescents who participated in a primary care, Internet-based prevention program. Participants included 83 adolescents from primary care settings ages 14 to 21 (M = 17.5, SD = 2.04), 56.2% female, with 41% non-White. Participants completed self-report measures, met with a physician, and then completed a 14-module Internet intervention targeting the prevention of depression. Linear regression models indicated that several intervention factors (duration on website in days, the strength of the relationship with the physician, perceptions of ease of use, and the perceived relevance of the material presented) were significantly associated with greater reductions in depressive symptoms from baseline to follow-up. Automatic negative thoughts significantly mediated the relation between change in depressive symptoms scores and both duration of use and physician relationship. Several intervention variables predicted favorable changes in depressive symptom scores among adolescents who participated in an Internet-based prevention program, and the strength of two of these variables was mediated by automatic negative thoughts. These findings support the importance of cognitive factors in preventing adolescent depression and suggest that modifiable aspects of technology-based intervention experience and relationships should be considered in optimizing intervention design.

The Abdominal Aortic Aneurysm Statistically Corrected Operative Risk Evaluation (AAA SCORE) for predicting mortality after open and endovascular interventions.

PubMed

Ambler, Graeme K; Gohel, Manjit S; Mitchell, David C; Loftus, Ian M; Boyle, Jonathan R

2015-01-01

Accurate adjustment of surgical outcome data for risk is vital in an era of surgeon-level reporting. Current risk prediction models for abdominal aortic aneurysm (AAA) repair are suboptimal. We aimed to develop a reliable risk model for in-hospital mortality after intervention for AAA, using rigorous contemporary statistical techniques to handle missing data. Using data collected during a 15-month period in the United Kingdom National Vascular Database, we applied multiple imputation methodology together with stepwise model selection to generate preoperative and perioperative models of in-hospital mortality after AAA repair, using two thirds of the available data. Model performance was then assessed on the remaining third of the data by receiver operating characteristic curve analysis and compared with existing risk prediction models. Model calibration was assessed by Hosmer-Lemeshow analysis. A total of 8088 AAA repair operations were recorded in the National Vascular Database during the study period, of which 5870 (72.6%) were elective procedures. Both preoperative and perioperative models showed excellent discrimination, with areas under the receiver operating characteristic curve of .89 and .92, respectively. This was significantly better than any of the existing models (area under the receiver operating characteristic curve for best comparator model, .84 and .88; P < .001 and P = .001, respectively). Discrimination remained excellent when only elective procedures were considered. There was no evidence of miscalibration by Hosmer-Lemeshow analysis. We have developed accurate models to assess risk of in-hospital mortality after AAA repair. These models were carefully developed with rigorous statistical methodology and significantly outperform existing methods for both elective cases and overall AAA mortality. These models will be invaluable for both preoperative patient counseling and accurate risk adjustment of published outcome data. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Cost-effectiveness of a community-based intervention for reducing the transmission of Schistosoma haematobium and HIV in Africa

PubMed Central

Ndeffo Mbah, Martial L.; Kjetland, Eyrun F.; Atkins, Katherine E.; Poolman, Eric M.; Orenstein, Evan W.; Meyers, Lauren Ancel; Townsend, Jeffrey P.; Galvani, Alison P.

2013-01-01

Epidemiological studies from sub-Saharan Africa show that genital infection with Schistosoma haematobium may increase the risk for HIV infection in young women. Therefore, preventing schistosomiasis has the potential to reduce HIV transmission in sub-Saharan Africa. We developed a transmission model of female genital schistosomiasis and HIV infections that we fit to epidemiological data of HIV and female genital schistosomiasis prevalence and coinfection in rural Zimbabwe. We used the model to evaluate the cost-effectiveness of a multifaceted community-based intervention for preventing schistosomiasis and, consequently, HIV infections in rural Zimbabwe, from the perspective of a health payer. The community-based intervention combined provision of clean water, sanitation, and health education (WSH) with administration of praziquantel to school-aged children. Considering variation in efficacy between 10% and 70% of WSH for reducing S. haematobium transmission, our model predicted that community-based intervention is likely to be cost-effective in Zimbabwe at an aggregated WSH cost corresponding to US $725–$1,000 per individual over a 20-y intervention period. These costs compare favorably with empirical measures of WSH provision in developing countries, indicating that integrated community-based intervention for reducing the transmission of S. haematobium is an economically attractive strategy for reducing schistosomiasis and HIV transmission in sub-Saharan Africa that would have a powerful impact on averting infections and saving lives. PMID:23589884

The Protective Effects of Family Support on the Relationship between Official Intervention and General Delinquency across the Life Course

PubMed Central

Dong, Beidi; Krohn, Marvin D.

2016-01-01

Purpose Previous research on the labeling perspective has identified mediational processes and the long-term effects of official intervention in the life course. However, it is not yet clear what factors may moderate the relationship between labeling and subsequent offending. The current study integrates Cullen’s (1994) social support theory to examine how family social support conditions the criminogenic, stigmatizing effects of official intervention on delinquency and whether such protective effects vary by developmental stage. Methods Using longitudinal data from the Rochester Youth Development Study, we estimated negative binomial regression models to investigate the relationships between police arrest, family social support, and criminal offending during both adolescence and young adulthood. Results Police arrest is a significant predictor of self-reported delinquency in both the adolescent and adult models. Expressive family support exhibits main effects in the adolescent models; instrumental family support exhibits main effects at both developmental stages. Additionally, instrumental family support diminishes some of the predicted adverse effects of official intervention in adulthood. Conclusions Perception of family support can be critical in reducing general delinquency as well as buffering against the adverse effects of official intervention on subsequent offending. Policies and programs that work with families subsequent to a criminal justice intervention should emphasize the importance of providing a supportive environment for those who are labeled. PMID:28729962

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Guven, Erhan; Moniz, Linda; Babin, Steven M.; Chretien, Jean-Paul

2016-01-01

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article. PMID:27127415

A Confidant Support and Problem Solving Model of Divorced Fathers’ Parenting

PubMed Central

DeGarmo, David S.; Forgatch, Marion S.

2011-01-01

This study tested a hypothesized social interaction learning (SIL) model of confidant support and paternal parenting. The latent growth curve analysis employed 230 recently divorced fathers, of which 177 enrolled support confidants, to test confidant support as a predictor of problem solving outcomes and problem solving outcomes as predictors of change in fathers’ parenting. Fathers’ parenting was hypothesized to predict growth in child behavior. Observational measures of support behaviors and problem solving outcomes were obtained from structured discussions of personal and parenting issues faced by the fathers. Findings replicated and extended prior cross-sectional studies with divorced mothers and their confidants. Confidant support predicted better problem solving outcomes, problem solving predicted more effective parenting, and parenting in turn predicted growth in children’s reduced total problem behavior T scores over 18 months. Supporting a homophily perspective, fathers’ antisociality was associated with confidant antisociality but only fathers’ antisociality influenced the support process model. Intervention implications are discussed regarding SIL parent training and social support. PMID:21541814

Influences of acute alcohol consumption, sexual precedence, and relationship motivation on women’s relationship and sex appraisals and unprotected sex intentions

PubMed Central

Jacques-Tiura, Angela J.; Norris, Jeanette; Kiekel, Preston A.; Davis, Kelly Cue; Zawacki, Tina; Morrison, Diane M.; George, William H.; Abdallah, Devon Alisa

2014-01-01

Guided by the cognitive mediation model of sexual decision making (Norris, Masters, & Zawacki, 2004. Cognitive mediation of women’s sexual decision making: The influence of alcohol, contextual factors, and background variables. Annual Review of Sex Research, 15, 258–296), we examined female social drinkers’ (N = 162) in-the-moment risky sexual decision making by testing how individual differences (relationship motivation) and situational factors (alcohol consumption and sexual precedence conditions) influenced cognitive appraisals and sexual outcomes in a hypothetical sexual scenario. In a path model, acute intoxication, sexual precedence, and relationship motivation interactively predicted primary relationship appraisals and independently predicted primary sex appraisals. Primary appraisals predicted secondary appraisals related to relationship and unprotected sex, which predicted unprotected sex intentions. Sexual precedence directly increased unprotected sex intentions. Findings support the cognitive mediation model and suggest that sexual risk reduction interventions should address alcohol, relationship, sexual, and cognitive factors. PMID:25755302

A confidant support and problem solving model of divorced fathers' parenting.

PubMed

Degarmo, David S; Forgatch, Marion S

2012-03-01

This study tested a hypothesized social interaction learning (SIL) model of confidant support and paternal parenting. The latent growth curve analysis employed 230 recently divorced fathers, of which 177 enrolled support confidants, to test confidant support as a predictor of problem solving outcomes and problem solving outcomes as predictors of change in fathers' parenting. Fathers' parenting was hypothesized to predict growth in child behavior. Observational measures of support behaviors and problem solving outcomes were obtained from structured discussions of personal and parenting issues faced by the fathers. Findings replicated and extended prior cross-sectional studies with divorced mothers and their confidants. Confidant support predicted better problem solving outcomes, problem solving predicted more effective parenting, and parenting in turn predicted growth in children's reduced total problem behavior T scores over 18 months. Supporting a homophily perspective, fathers' antisociality was associated with confidant antisociality but only fathers' antisociality influenced the support process model. Intervention implications are discussed regarding SIL parent training and social support.

Biological Factors Contributing to the Response to Cognitive Training in Mild Cognitive Impairment.

PubMed

Peter, Jessica; Schumacher, Lena V; Landerer, Verena; Abdulkadir, Ahmed; Kaller, Christoph P; Lahr, Jacob; Klöppel, Stefan

2018-01-01

In mild cognitive impairment (MCI), small benefits from cognitive training were observed for memory functions but there appears to be great variability in the response to treatment. Our study aimed to improve the characterization and selection of those participants who will benefit from cognitive intervention. We evaluated the predictive value of disease-specific biological factors for the outcome after cognitive training in MCI (n = 25) and also considered motivation of the participants. We compared the results of the cognitive intervention group with two independent control groups of MCI patients (local memory clinic, n = 20; ADNI cohort, n = 302). The primary outcome measure was episodic memory as measured by verbal delayed recall of a 10-word list. Episodic memory remained stable after treatment and slightly increased 6 months after the intervention. In contrast, in MCI patients who did not receive an intervention, episodic memory significantly decreased during the same time interval. A larger left entorhinal cortex predicted more improvement in episodic memory after treatment and so did higher levels of motivation. Adding disease-specific biological factors significantly improved the prediction of training-related change compared to a model based simply on age and baseline performance. Bootstrapping with resampling (n = 1000) verified the stability of our finding. Cognitive training might be particularly helpful in individuals with a bigger left entorhinal cortex as individuals who did not benefit from intervention showed 17% less volume in this area. When extended to alternative treatment options, stratification based on disease-specific biological factors is a useful step towards individualized medicine.

Brief Report: Relationships Among Spousal Communication, Self-Efficacy, and Motivation Among Expectant Latino Fathers Who Smoke

PubMed Central

Khaddouma, Alexander; Gordon, Kristina Coop; Fish, Laura J.; Bilheimer, Alecia; Gonzalez, Alecia; Pollak, Kathryn I.

2015-01-01

Objective Cigarette smoking is a prevalent problem among Latinos, yet little is known about what factors motivate them to quit smoking or make them feel more confident that they can. Given cultural emphases on familial bonds among Latinos (e.g., familismo), it is possible that communication processes among Latino spouses play an important role. The present study tested a mechanistic model in which perceived spousal constructive communication patterns predicted changes in level of motivation for smoking cessation through changes in self-efficacy among Latino expectant fathers. Methods Latino males (n = 173) and their pregnant partners participated in a couple-based intervention targeting males’ smoking. Couples completed self-report measures of constructive communication, self-efficacy (male partners only), and motivation to quit (male partners only) at four time points throughout the intervention. Results Higher levels of perceived constructive communication among Latino male partners predicted subsequent increases in male’s partners’ self-efficacy and, to a lesser degree, motivation to quit smoking; however, self-efficacy did not mediate associations between constructive communication and motivation to quit smoking. Furthermore, positive relationships with communication were only significant at measurements taken after completion of the intervention. Female partners’ level of perceived constructive communication did not predict male partners’ outcomes. Conclusion These results provide preliminary evidence to support the utility of couple-based interventions for Latino men who smoke. Findings also suggest that perceptions of communication processes among Latino partners (particularly male partners) may be an important target for interventions aimed increasing desire and perceived ability to quit smoking among Latino men. PMID:25844907

Investigating the effect of Alcohol Brief Interventions within accident and emergency departments using a data informatics methodology.

PubMed

Baldacchino, Alex; O'Rourke, Louise; Humphris, Gerry

2018-07-01

Alcohol Brief Interventions (ABI) have been implemented throughout Scotland since 2008 and aim to reduce hazardous drinking through a Scottish Government funded initiative delivered in a range of settings, including Accident and Emergency (A and E) departments. To study the extent to which Alcohol Brief Interventions (ABI) are associated with later health service use. An opportunistic informatics approach was applied. A unique patient identifier was used to link patient data with core datasets spanning two years previous and two years post ABI. Variables included inpatient attendance, outpatient attendance, psychiatric admissions, and A and E attendance and prescribing. Patients (N = 1704) who presented at A and E departments who reported an average alcohol consumption of more than 8 units daily received the ABI. Fast Alcohol Screening Test (FAST) was used to assess patients for hazardous alcohol consumption. Multilevel linear modelling was employed to predict post-intervention utilisation using pre-ABI variables and controlling for person characteristics and venue. Significant decrease in A and E usage was found at one and two years following the ABI intervention. Previous health service use was predictive of later service use. A single question (Item 4) on the FAST was predictive of A and E attendance at one and two years. This investigation and methodology used provide support for the delivery of the ABI. However, it cannot be ascertained whether this is due to the ABI or simply is a result of making contact with a specialist in the addiction field. Copyright © 2018 Elsevier B.V. All rights reserved.

Testing an integrated behavioural and biomedical model of disability in N-of-1 studies with chronic pain.

PubMed

Quinn, Francis; Johnston, Marie; Johnston, Derek W

2013-01-01

Previous research has supported an integrated biomedical and behavioural model explaining activity limitations. However, further tests of this model are required at the within-person level, because while it proposes that the constructs are related within individuals, it has primarily been tested between individuals in large group studies. We aimed to test the integrated model at the within-person level. Six correlational N-of-1 studies in participants with arthritis, chronic pain and walking limitations were carried out. Daily measures of theoretical constructs were collected using a hand-held computer (PDA), the activity was assessed by self-report and accelerometer and the data were analysed using time-series analysis. The biomedical model was not supported as pain impairment did not predict activity, so the integrated model was supported partially. Impairment predicted intention to move around, while perceived behavioural control (PBC) and intention predicted activity. PBC did not predict activity limitation in the expected direction. The integrated model of disability was partially supported within individuals, especially the behavioural elements. However, results suggest that different elements of the model may drive activity (limitations) for different individuals. The integrated model provides a useful framework for understanding disability and suggests interventions, and the utility of N-of-1 methodology for testing theory is illustrated.

Single non-invasive model to diagnose non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH).

PubMed

Otgonsuren, Munkhzul; Estep, Michael J; Hossain, Nayeem; Younossi, Elena; Frost, Spencer; Henry, Linda; Hunt, Sharon; Fang, Yun; Goodman, Zachary; Younossi, Zobair M

2014-12-01

Non-alcoholic steatohepatitis (NASH) is the progressive form of non-alcoholic fatty liver disease (NAFLD). A liver biopsy is considered the "gold standard" for diagnosing/staging NASH. Identification of NAFLD/NASH using non-invasive tools is important for intervention. The study aims were to: develop/validate the predictive performance of a non-invasive model (index of NASH [ION]); assess the performance of a recognized non-invasive model (fatty liver index [FLI]) compared with ION for NAFLD diagnosis; determine which non-invasive model (FLI, ION, or NAFLD fibrosis score [NFS]) performed best in predicting age-adjusted mortality. From the National Health and Nutrition Examination Survey III database, anthropometric, clinical, ultrasound, laboratory, and mortality data were obtained (n = 4458; n = 861 [19.3%] NAFLD by ultrasound) and used to develop the ION model, and then to compare the ION and FLI models for NAFLD diagnosis. For validation and diagnosis of NASH, liver biopsy data were used (n = 152). Age-adjusted Cox proportional hazard modeling estimated the association among the three non-invasive tests (FLI, ION, and NFS) and mortality. FLI's threshold score > 60 and ION's threshold score > 22 had similar specificity (FLI = 80% vs ION = 82%) for NAFLD diagnosis; FLI < 30 (80% sensitivity) and ION < 11 (81% sensitivity) excluded NAFLD. An ION score > 50 predicted histological NASH (92% specificity); the FLI model did not predict NASH or mortality. The ION model was best in predicting cardiovascular/diabetes-related mortality; NFS predicted overall or diabetes-related mortality. The ION model was superior in predicting NASH and mortality compared with the FLI model. Studies are needed to validate ION. © 2014 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

CMOST: an open-source framework for the microsimulation of colorectal cancer screening strategies.

PubMed

Prakash, Meher K; Lang, Brian; Heinrich, Henriette; Valli, Piero V; Bauerfeind, Peter; Sonnenberg, Amnon; Beerenwinkel, Niko; Misselwitz, Benjamin

2017-06-05

Colorectal cancer (CRC) is a leading cause of cancer-related mortality. CRC incidence and mortality can be reduced by several screening strategies, including colonoscopy, but randomized CRC prevention trials face significant obstacles such as the need for large study populations with long follow-up. Therefore, CRC screening strategies will likely be designed and optimized based on computer simulations. Several computational microsimulation tools have been reported for estimating efficiency and cost-effectiveness of CRC prevention. However, none of these tools is publicly available. There is a need for an open source framework to answer practical questions including testing of new screening interventions and adapting findings to local conditions. We developed and implemented a new microsimulation model, Colon Modeling Open Source Tool (CMOST), for modeling the natural history of CRC, simulating the effects of CRC screening interventions, and calculating the resulting costs. CMOST facilitates automated parameter calibration against epidemiological adenoma prevalence and CRC incidence data. Predictions of CMOST were highly similar compared to a large endoscopic CRC prevention study as well as predictions of existing microsimulation models. We applied CMOST to calculate the optimal timing of a screening colonoscopy. CRC incidence and mortality are reduced most efficiently by a colonoscopy between the ages of 56 and 59; while discounted life years gained (LYG) is maximal at 49-50 years. With a dwell time of 13 years, the most cost-effective screening is at 59 years, at $17,211 discounted USD per LYG. While cost-efficiency varied according to dwell time it did not influence the optimal time point of screening interventions within the tested range. Predictions of CMOST are highly similar compared to a randomized CRC prevention trial as well as those of other microsimulation tools. This open source tool will enable health-economics analyses in for various countries, health-care scenarios and CRC prevention strategies. CMOST is freely available under the GNU General Public License at https://gitlab.com/misselwb/CMOST.

Towards Actionable Learning Analytics Using Dispositions

ERIC Educational Resources Information Center

Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan

2017-01-01

Studies in the field of learning analytics (LA) have shown students' demographics and learning management system (LMS) data to be effective identifiers of "at risk" performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students…

Transactional Associations Between Youths’ Responses to Peer Stress and Depression: The Moderating Roles of Sex and Stress Exposure

PubMed Central

Agoston, Anna Monica; Rudolph, Karen D.

2011-01-01

This study examined transactional associations between responses to peer stress and depression in youth. Specifically, it tested the hypotheses that (a) depression would predict fewer effortful responses and more involuntary, dysregulated responses to peer stress over time; and (b) fewer adaptive and more maladaptive responses would predict subsequent depression. Youth (M age = 12.41; SD = 1.19; 86 girls, 81 boys) and their maternal caregivers completed semi-structured interviews and questionnaires at three annual waves. Multi-group comparison path analyses were conducted to examine sex and stress-level differences in the proposed reciprocal-influence model. In girls and in youth exposed to high levels of peer stress, maladaptive stress responses predicted more depressive symptoms and adaptive stress responses predicted fewer depressive symptoms at each wave. These findings suggest the utility of preventive interventions for depression designed to enhance the quality of girls’ stress responses. In boys, depression predicted less adaptive and more maladaptive stress responses, but only at the second wave. These findings suggest that interventions designed to reduce boys’ depressive symptoms may help them develop more adaptive stress responses. PMID:20852929

Disability prevention and communication among workers, physicians, employers, and insurers--current models and opportunities for improvement.

PubMed

Pransky, Glenn; Shaw, William; Franche, Renee-Louise; Clarke, Andrew

2004-06-03

To review prevailing models of disability management and prevention with respect to communication, and to suggest alternative approaches. Review of selected articles. Effective disability management and return to work strategies have been the focus of an increasing number of intervention programmes and associated research studies, spanning a variety of worker populations and provider and business perspectives. Although primary and secondary disability prevention approaches have addressed theoretical basis, methods and costs, few identify communication as a key factor influencing disability outcomes. Four prevailing models of disability management and prevention (medical model, physical rehabilitation model, job-match model, and managed care model) are identified. The medical model emphasizes the physician's role to define functional limitations and job restrictions. In the physical rehabilitation model, rehabilitation professionals communicate the importance of exercise and muscle reconditioning for resuming normal work activities. The job-match model relies on the ability of employers to accurately communicate physical job requirements. The managed care model focuses on dissemination of acceptable standards for medical treatment and duration of work absence, and interventions by case managers when these standards are exceeded. Despite contrary evidence for many health impairments, these models share a common assumption that medical disability outcomes are highly predictable and unaffected by either individual or contextual factors. As a result, communication is often authoritative and unidirectional, with workers and employers in a passive role. Improvements in communication may be responsible for successes across a variety of new interventions. Communication-based interventions may further improve disability outcomes, reduce adversarial relationships, and prove cost-effective; however, controlled trials are needed.

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses

PubMed Central

Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A.

2015-01-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test–post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences. PMID:29881032

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses.

PubMed

Cho, Sun-Joo; Preacher, Kristopher J; Bottge, Brian A

2015-11-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test-post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences.

A Proposed Method to Predict Preterm Birth Using Clinical Data, Standard Maternal Serum Screening, and Cholesterol

PubMed Central

ALLEMAN, Brandon W.; SMITH, Amanda R.; BYERS, Heather M.; BEDELL, Bruce; RYCKMAN, Kelli K.; MURRAY, Jeffrey C.; BOROWSKI, Kristi S.

2013-01-01

Objective To create a predictive model for preterm birth (PTB) from available clinical data and serum analytes. Study Design Serum analytes, routine pregnancy screening plus cholesterol and corresponding health information were linked to birth certificate data for a cohort of 2699 Iowa women with serum sampled in the first and second trimester. Stepwise logistic regression was used to select the best predictive model for PTB. Results Serum screening markers remained significant predictors of PTB even after controlling for maternal characteristics. The best predictive model included maternal characteristics, first trimester total cholesterol (TC), TC change between trimesters and second trimester alpha-fetoprotein and inhibin A. The model showed better discriminatory ability than PTB history alone and performed similarly in subgroups of women without past PTB. Conclusions Using clinical and serum screening data a potentially useful predictor of PTB was constructed. Validation and replication in other populations, and incorporation of other measures that identify PTB risk, like cervical length, can be a step towards identifying additional women who may benefit from new or currently available interventions. PMID:23500456

Simulating indoor concentrations of NO(2) and PM(2.5) in multifamily housing for use in health-based intervention modeling.

PubMed

Fabian, P; Adamkiewicz, G; Levy, J I

2012-02-01

Residents of low-income multifamily housing can have elevated exposures to multiple environmental pollutants known to influence asthma. Simulation models can characterize the health implications of changing indoor concentrations, but quantifying the influence of interventions on concentrations is challenging given complex airflow and source characteristics. In this study, we simulated concentrations in a prototype multifamily building using CONTAM, a multizone airflow and contaminant transport program. Contaminants modeled included PM(2.5) and NO(2) , and parameters included stove use, presence and operability of exhaust fans, smoking, unit level, and building leakiness. We developed regression models to explain variability in CONTAM outputs for individual sources, in a manner that could be utilized in simulation modeling of health outcomes. To evaluate our models, we generated a database of 1000 simulated households with characteristics consistent with Boston public housing developments and residents and compared the predicted levels of NO(2) and PM(2.5) and their correlates with the literature. Our analyses demonstrated that CONTAM outputs could be readily explained by available parameters (R(2) between 0.89 and 0.98 across models), but that one-compartment box models would mischaracterize concentrations and source contributions. Our study quantifies the key drivers for indoor concentrations in multifamily housing and helps to identify opportunities for interventions. Many low-income urban asthmatics live in multifamily housing that may be amenable to ventilation-related interventions such as weatherization or air sealing, wall and ceiling hole repairs, and exhaust fan installation or repair, but such interventions must be designed carefully given their cost and their offsetting effects on energy savings as well as indoor and outdoor pollutants. We developed models to take into account the complex behavior of airflow patterns in multifamily buildings, which can be used to identify and evaluate environmental and non-environmental interventions targeting indoor air pollutants which can trigger asthma exacerbations. © 2011 John Wiley & Sons A/S.

Virtual reality in radiology: virtual intervention

NASA Astrophysics Data System (ADS)

Harreld, Michael R.; Valentino, Daniel J.; Duckwiler, Gary R.; Lufkin, Robert B.; Karplus, Walter J.

1995-04-01

Intracranial aneurysms are the primary cause of non-traumatic subarachnoid hemorrhage. Morbidity and mortality remain high even with current endovascular intervention techniques. It is presently impossible to identify which aneurysms will grow and rupture, however hemodynamics are thought to play an important role in aneurysm development. With this in mind, we have simulated blood flow in laboratory animals using three dimensional computational fluid dynamics software. The data output from these simulations is three dimensional, complex and transient. Visualization of 3D flow structures with standard 2D display is cumbersome, and may be better performed using a virtual reality system. We are developing a VR-based system for visualization of the computed blood flow and stress fields. This paper presents the progress to date and future plans for our clinical VR-based intervention simulator. The ultimate goal is to develop a software system that will be able to accurately model an aneurysm detected on clinical angiography, visualize this model in virtual reality, predict its future behavior, and give insight into the type of treatment necessary. An associated database will give historical and outcome information on prior aneurysms (including dynamic, structural, and categorical data) that will be matched to any current case, and assist in treatment planning (e.g., natural history vs. treatment risk, surgical vs. endovascular treatment risks, cure prediction, complication rates).

The prevention of diabetes and cardiovascular disease in people with schizophrenia.

PubMed

Holt, R I G

2015-08-01

Primary prevention of diabetes and cardiovascular disease is an important priority for people with schizophrenia. This review aims to identify lifestyle and pharmacological interventions that reduce diabetes and cardiovascular disease in people with schizophrenia. PubMed and other electronic databases were searched to identify relevant articles. Lifestyle interventions that focus on diet and physical activity reduce the incidence of diabetes. Similar programmes in people with schizophrenia have led to significant weight loss and may reasonably be expected to reduce diabetes in the long-term. Metformin may be considered when lifestyle change is not feasible or effective. Lifestyle interventions, particularly smoking cessation, are likely to be effective in reducing cardiovascular disease in people with schizophrenia. Although cardiovascular prevention trials with statins have not been performed in people with schizophrenia, similar reductions in cholesterol has been seen as in the general population and statins should be considered for those at high risk. Traditional cardiovascular risk prediction models perform well in identifying those at high cardiovascular risk, but bespoke prediction models using data from people with schizophrenia perform better. Reducing diabetes and cardiovascular disease requires a coordinated and concerted effort from mental and physical health teams working across primary and secondary care. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Quantifying health improvements from water quantity enhancement: an engineering perspective applied to rainwater harvesting in West Africa.

PubMed

Fry, Lauren M; Cowden, Joshua R; Watkins, David W; Clasen, Thomas; Mihelcic, James R

2010-12-15

Knowledge of potential benefits resulting from technological interventions informs decision making and planning of water, sanitation, and hygiene programs. The public health field has built a body of literature showing health benefits from improvements in water quality. However, the connection between improvements in water quantity and health is not well documented. Understanding the connection between technological interventions and water use provides insight into this problem. We present a model predicting reductions in diarrhea disease burden when the water demands from hygiene and sanitation improvements are met by domestic rainwater harvesting (DRWH). The model is applied in a case study of 37 West African cities. For all cities, with a total population of over 10 million, we estimate that DRWH with 400 L storage capacity could result in a 9% reduction in disability-affected life years (DALYs). If DRWH is combined with point of use (POU) treatment, this potential impact is nearly doubled, to a 16% reduction in DALYs. Seasonal variability of diarrheal incidence may have a small to moderate effect on the effectiveness of DRWH, depending on the storage volume used. Similar predictions could be made for other interventions that improve water quantity in other locations where disease burden from diarrhea is known.

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

State tobacco control expenditures and tax paid cigarette sales

PubMed Central

Tauras, John A.; Xu, Xin; Huang, Jidong; King, Brian; Lavinghouze, S. Rene; Sneegas, Karla S.; Chaloupka, Frank J.

2018-01-01

This research is the first nationally representative study to examine the relationship between actual state-level tobacco control spending in each of the 5 CDC’s Best Practices for Comprehensive Tobacco Control Program categories and cigarette sales. We employed several alternative two-way fixed-effects regression techniques to estimate the determinants of cigarette sales in the United States for the years 2008–2012. State spending on tobacco control was found to have a negative and significant impact on cigarette sales in all models that were estimated. Spending in the areas of cessation interventions, health communication interventions, and state and community interventions were found to have a negative impact on cigarette sales in all models that were estimated, whereas spending in the areas of surveillance and evaluation, and administration and management were found to have negative effects on cigarette sales in only some models. Our models predict that states that spend up to seven times their current levels could still see significant reductions in cigarette sales. The findings from this research could help inform further investments in state tobacco control programs. PMID:29652890

A model of aging as accumulated damage matches observed mortality patterns and predicts the life-extending effects of prospective interventions

PubMed Central

Phoenix, Chris

2007-01-01

The relative insensitivity of lifespan to environmental factors constitutes compelling evidence that the physiological decline associated with aging derives primarily from the accumulation of intrinsic molecular and cellular side-effects of metabolism. Here we model that accumulation starting from a biologically based interpretation of the way in which those side-effects interact. We first validate this model by showing that it very accurately reproduces the distribution of ages at death seen in typical populations that are well protected from age-independent causes of death. We then exploit the mechanistic basis of this model to explore the impact on lifespans of interventions that combat aging, with an emphasis on interventions that repair (rather than merely retard) the direct molecular or cellular consequences of metabolism and thus prevent them from accumulating to pathogenic levels. Our results strengthen the case that an indefinite extension of healthy and total life expectancy can be achieved by a plausible rate of progress in the development of such therapies, once a threshold level of efficacy of those therapies has been reached. PMID:19424837

Anxiety, social skills, friendship quality, and peer victimization: an integrated model.

PubMed

Crawford, A Melissa; Manassis, Katharina

2011-10-01

This cross-sectional study investigated whether anxiety and social functioning interact in their prediction of peer victimization. A structural equation model linking anxiety, social skills, and friendship quality to victimization was tested separately for children with anxiety disorders and normal comparison children to explore whether the processes involved in victimization differ for these groups. Participants were 8-14 year old children: 55 (34 boys, 21 girls) diagnosed with an anxiety disorder and 85 (37 boys, 48 girls) normal comparison children. The final models for both groups yielded two independent pathways to victimization: (a) anxiety independently predicted being victimized; and (b) poor social skills predicted lower friendship quality, which in turn, placed a child at risk for victimization. These findings have important implications for the treatment of childhood anxiety disorders and for school-based anti-bullying interventions, but replication with larger samples is indicated. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predicting employees' well-being using work-family conflict and job strain models.

PubMed

Karimi, Leila; Karimi, Hamidreza; Nouri, Aboulghassem

2011-04-01

The present study examined the effects of two models of work–family conflict (WFC) and job-strain on the job-related and context-free well-being of employees. The participants of the study consisted of Iranian employees from a variety of organizations. The effects of three dimensions of the job-strain model and six forms of WFC on affective well-being were assessed. The results of hierarchical multiple regression analysis revealed that the number of working hours, strain-based work interfering with family life (WIF) along with job characteristic variables (i.e. supervisory support, job demands and job control) all make a significant contribution to the prediction of job-related well-being. On the other hand, strain-based WIF and family interfering with work (FIW) significantly predicted context-free well-being. Implications are drawn and recommendations made regarding future research and interventions in the workplace.

Core self-evaluations and Snyder's hope theory in persons with spinal cord injuries.

PubMed

Smedema, Susan Miller; Chan, Jacob Yuichung; Phillips, Brian N

2014-11-01

The objective of the study was to evaluate a motivational model of core self-evaluations (CSE), hope (agency and pathways thinking), participation, and life satisfaction in persons with spinal cord injuries. A cross-sectional, correlational design with path analysis was used to evaluate the model. 187 adults with spinal cord injuries participated in this study. The results indicated an excellent fit between the data and the proposed model. Specifically, CSE was found to directly predict agency and pathways thinking, participation, and life satisfaction. CSE was also found to indirectly predict participation and life satisfaction through agency thinking. Although CSE contributes directly to participation and life satisfaction, it also has a unique role in increasing individuals' motivation to pursue goals, which also predicts participation and life satisfaction. Counseling interventions should be multifaceted and address the components of CSE to increase hope, participation, and life satisfaction. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Optimality Principles for Model-Based Prediction of Human Gait

PubMed Central

Ackermann, Marko; van den Bogert, Antonie J.

2010-01-01

Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient’s gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait. PMID:20074736

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes.

PubMed

Achana, Felix A; Cooper, Nicola J; Bujkiewicz, Sylwia; Hubbard, Stephanie J; Kendrick, Denise; Jones, David R; Sutton, Alex J

2014-07-21

Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis. Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately.

Development and validation of a cost-utility model for Type 1 diabetes mellitus.

PubMed

Wolowacz, S; Pearson, I; Shannon, P; Chubb, B; Gundgaard, J; Davies, M; Briggs, A

2015-08-01

To develop a health economic model to evaluate the cost-effectiveness of new interventions for Type 1 diabetes mellitus by their effects on long-term complications (measured through mean HbA1c ) while capturing the impact of treatment on hypoglycaemic events. Through a systematic review, we identified complications associated with Type 1 diabetes mellitus and data describing the long-term incidence of these complications. An individual patient simulation model was developed and included the following complications: cardiovascular disease, peripheral neuropathy, microalbuminuria, end-stage renal disease, proliferative retinopathy, ketoacidosis, cataract, hypoglycemia and adverse birth outcomes. Risk equations were developed from published cumulative incidence data and hazard ratios for the effect of HbA1c , age and duration of diabetes. We validated the model by comparing model predictions with observed outcomes from studies used to build the model (internal validation) and from other published data (external validation). We performed illustrative analyses for typical patient cohorts and a hypothetical intervention. Model predictions were within 2% of expected values in the internal validation and within 8% of observed values in the external validation (percentages represent absolute differences in the cumulative incidence). The model utilized high-quality, recent data specific to people with Type 1 diabetes mellitus. In the model validation, results deviated less than 8% from expected values. © 2014 Research Triangle Institute d/b/a RTI Health Solutions. Diabetic Medicine © 2014 Diabetes UK.

Individualized pharmacokinetic risk assessment for development of diabetes in high risk population.

PubMed

Gupta, N; Al-Huniti, N H; Veng-Pedersen, P

2007-10-01

The objective of this study is to propose a non-parametric pharmacokinetic prediction model that addresses the individualized risk of developing type-2 diabetes in subjects with family history of type-2 diabetes. All selected 191 healthy subjects had both parents as type-2 diabetic. Glucose was administered intravenously (0.5 g/kg body weight) and 13 blood samples taken at specified times were analyzed for plasma insulin and glucose concentrations. All subjects were followed for an average of 13-14 years for diabetic or normal (non-diabetic) outcome. The new logistic regression model predicts the development of diabetes based on body mass index and only one blood sample at 90 min analyzed for insulin concentration. Our model correctly identified 4.5 times more subjects (54% versus 11.6%) predicted to develop diabetes and more than twice the subjects (99% versus 46.4%) predicted not to develop diabetes compared to current non-pharmacokinetic probability estimates for development of type-2 diabetes. Our model can be useful for individualized prediction of development of type-2 diabetes in subjects with family history of type-2 diabetes. This improved prediction may be an important mediating factor for better perception of risk and may result in an improved intervention.

Lymphatic filariasis transmission risk map of India, based on a geo-environmental risk model.

PubMed

Sabesan, Shanmugavelu; Raju, Konuganti Hari Kishan; Subramanian, Swaminathan; Srivastava, Pradeep Kumar; Jambulingam, Purushothaman

2013-09-01

The strategy adopted by a global program to interrupt transmission of lymphatic filariasis (LF) is mass drug administration (MDA) using chemotherapy. India also followed this strategy by introducing MDA in the historically known endemic areas. All other areas, which remained unsurveyed, were presumed to be nonendemic and left without any intervention. Therefore, identification of LF transmission risk areas in the entire country has become essential so that they can be targeted for intervention. A geo-environmental risk model (GERM) developed earlier was used to create a filariasis transmission risk map for India. In this model, a Standardized Filariasis Transmission Risk Index (SFTRI, based on geo-environmental risk variables) was used as a predictor of transmission risk. The relationship between SFTRI and endemicity (historically known) of an area was quantified by logistic regression analysis. The quantified relationship was validated by assessing the filarial antigenemia status of children living in the unsurveyed areas through a ground truth study. A significant positive relationship was observed between SFTRI and the endemicity of an area. Overall, the model prediction of filarial endemic status of districts was found to be correct in 92.8% of the total observations. Thus, among the 190 districts hitherto unsurveyed, as many as 113 districts were predicted to be at risk, and the remaining at no risk. The GERM developed on geographic information system (GIS) platform is useful for LF spatial delimitation on a macrogeographic/regional scale. Furthermore, the risk map developed will be useful for the national LF elimination program by identifying areas at risk for intervention and for undertaking surveillance in no-risk areas.

Engaging Mexican Origin Families in a School-Based Preventive Intervention

PubMed Central

Mauricio, Anne M.; Gonzales, Nancy A.; Millsap, Roger E.; Meza, Connie M.; Dumka, Larry E.; Germán, Miguelina; Genalo, M. Toni

2009-01-01

This study describes a culturally sensitive approach to engage Mexican origin families in a school-based, family-focused preventive intervention trial. The approach was evaluated via assessing study enrollment and intervention program participation, as well as examining predictors of engagement at each stage. Incorporating traditional cultural values into all aspects of engagement resulted in participation rates higher than reported rates of minority-focused trials not emphasizing cultural sensitivity. Family preferred language (English or Spanish) or acculturation status predicted engagement at all levels, with less acculturated families participating at higher rates. Spanish-language families with less acculturated adolescents participated at higher rates than Spanish-language families with more acculturated adolescents. Other findings included two-way interactions between family language and the target child’s familism values, family single- vs. dual-parent status, and number of hours the primary parent worked in predicting intervention participation. Editors’ Strategic Implications: The authors present a promising approach—which requires replication—to engaging and retaining Mexican American families in a school-based prevention program. The research also highlights the importance of considering acculturation status when implementing and studying culturally tailored aspects of prevention models. PMID:18004659

Driving Green: Toward the Prediction and Influence of Efficient Driving Behavior

NASA Astrophysics Data System (ADS)

Newsome, William D.

Sub-optimal efficiency in activities involving the consumption of fossil fuels, such as driving, contribute to a miscellany of negative environmental, political, economic and social externalities. Demonstrations of the effectiveness of feedback interventions can be found in countless organizational settings, as can demonstrations of individual differences in sensitivity to feedback interventions. Mechanisms providing feedback to drivers about fuel economy are becoming standard equipment in most new vehicles, but vary considerably in their constitution. A keystone of Radical Behaviorism is the acknowledgement that verbal behavior appears to play a role in mediating apparent susceptibility to influence by contingencies of varying delay. In the current study, samples of verbal behavior (rules) were collected in the context of a feedback intervention to improve driving efficiency. In an analysis of differences in individual responsiveness to the feedback intervention, the rate of novel rules per week generated by drivers is revealed to account for a substantial proportion of the variability in relative efficiency gains across participants. The predictive utility of conceptual tools, such as the basic distinction among contingency-shaped and rule governed behavior, the elaboration of direct-acting and indirect-acting contingencies, and the psychological flexibility model, is bolstered by these findings.

Workflow and intervention times of MR-guided focused ultrasound - Predicting the impact of new techniques.

PubMed

Loeve, Arjo J; Al-Issawi, Jumana; Fernandez-Gutiérrez, Fabiola; Langø, Thomas; Strehlow, Jan; Haase, Sabrina; Matzko, Matthias; Napoli, Alessandro; Melzer, Andreas; Dankelman, Jenny

2016-04-01

Magnetic resonance guided focused ultrasound surgery (MRgFUS) has become an attractive, non-invasive treatment for benign and malignant tumours, and offers specific benefits for poorly accessible locations in the liver. However, the presence of the ribcage and the occurrence of liver motion due to respiration limit the applicability MRgFUS. Several techniques are being developed to address these issues or to decrease treatment times in other ways. However, the potential benefit of such improvements has not been quantified. In this research, the detailed workflow of current MRgFUS procedures was determined qualitatively and quantitatively by using observation studies on uterine MRgFUS interventions, and the bottlenecks in MRgFUS were identified. A validated simulation model based on discrete events simulation was developed to quantitatively predict the effect of new technological developments on the intervention duration of MRgFUS on the liver. During the observation studies, the duration and occurrence frequencies of all actions and decisions in the MRgFUS workflow were registered, as were the occurrence frequencies of motion detections and intervention halts. The observation results show that current MRgFUS uterine interventions take on average 213min. Organ motion was detected on average 2.9 times per intervention, of which on average 1.0 actually caused a need for rework. Nevertheless, these motion occurrences and the actions required to continue after their detection consumed on average 11% and up to 29% of the total intervention duration. The simulation results suggest that, depending on the motion occurrence frequency, the addition of new technology to automate currently manual MRgFUS tasks and motion compensation could potentially reduce the intervention durations by 98.4% (from 256h 5min to 4h 4min) in the case of 90% motion occurrence, and with 24% (from 5h 19min to 4h 2min) in the case of no motion. In conclusion, new tools were developed to predict how intervention durations will be affected by future workflow changes and by the introduction of new technology. Copyright © 2016 Elsevier Inc. All rights reserved.

GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.

PubMed

Panayidou, Klea; Gsteiger, Sandro; Egger, Matthias; Kilcher, Gablu; Carreras, Máximo; Efthimiou, Orestis; Debray, Thomas P A; Trelle, Sven; Hummel, Noemi

2016-09-01

The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real-world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software. We identified 12 articles in which four approaches were used: multi-state models, discrete event simulation models, physiology-based models and survival and generalized linear models. Studies predicted outcomes over longer time periods in different patient populations, including patients with lower levels of adherence or persistence to treatment or examined doses not tested in trials. Eight studies included individual patient data. Seven examined cardiovascular and metabolic diseases and three neurological conditions. Most studies included sensitivity analyses, but external validation was performed in only three studies. We conclude that mathematical modelling to predict real-world effectiveness of drug interventions is not widely used at present and not well validated. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

A Process Evaluation of an Efficacious Family-Based Intervention to Promote Healthy Eating: The Entre Familia: Reflejos de Salud Study.

PubMed

Schmied, Emily; Parada, Humberto; Horton, Lucy; Ibarra, Leticia; Ayala, Guadalupe

2015-10-01

Entre Familia: Reflejos de Salud was a successful family-based randomized controlled trial designed to improve dietary behaviors and intake among U.S. Latino families, specifically fruit and vegetable intake. The novel intervention design merged a community health worker (promotora) model with an entertainment-education component. This process evaluation examined intervention implementation and assessed relationships between implementation factors and dietary change. Participants included 180 mothers randomized to an intervention condition. Process evaluation measures were obtained from participant interviews and promotora notes and included fidelity, dose delivered (i.e., minutes of promotora in-person contact with families, number of promotora home visits), and dose received (i.e., participant use of and satisfaction with intervention materials). Outcome variables included changes in vegetable intake and the use of behavioral strategies to increase dietary fiber and decrease dietary fat intake. Participant satisfaction was high, and fidelity was achieved; 87.5% of families received the planned number of promotora home visits. In the multivariable model, satisfaction with intervention materials predicted more frequent use of strategies to increase dietary fiber (p ≤ .01). Trends suggested that keeping families in the prescribed intervention timeline and obtaining support from other social network members through sharing of program materials may improve changes. Study findings elucidate the relationship between specific intervention processes and dietary changes. © 2015 Society for Public Health Education.

Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data.

PubMed

Ma, Handong; Weng, Chunhua

2016-04-01

To link public data resources for predicting post-marketing drug safety label changes by analyzing the Convergent Focus Shift patterns among drug testing trials. We identified 256 top-selling prescription drugs between 2003 and 2013 and divided them into 83 BBW drugs (drugs with at least one black box warning label) and 173 ROBUST drugs (drugs without any black box warning label) based on their FDA black box warning (BBW) records. We retrieved 7499 clinical trials that each had at least one of these drugs for intervention from the ClinicalTrials.gov. We stratified all the trials by pre-marketing or post-marketing status, study phase, and study start date. For each trial, we retrieved drug and disease concepts from clinical trial summaries to model its study population using medParser and SNOMED-CT. Convergent Focus Shift (CFS) pattern was calculated and used to assess the temporal changes in study populations from pre-marketing to post-marketing trials for each drug. Then we selected 68 candidate drugs, 18 with BBW warning and 50 without, that each had at least nine pre-marketing trials and nine post-marketing trials for predictive modeling. A random forest predictive model was developed to predict BBW acquisition incidents based on CFS patterns among these drugs. Pre- and post-marketing trials of BBW and ROBUST drugs were compared to look for their differences in CFS patterns. Among the 18 BBW drugs, we consistently observed that the post-marketing trials focused more on recruiting patients with medical conditions previously unconsidered in the pre-marketing trials. In contrast, among the 50 ROBUST drugs, the post-marketing trials involved a variety of medications for testing their associations with target intervention(s). We found it feasible to predict BBW acquisitions using different CFS patterns between the two groups of drugs. Our random forest predictor achieved an AUC of 0.77. We also demonstrated the feasibility of the predictor for identifying long-term BBW acquisition events without compromising prediction accuracy. This study contributes a method for post-marketing pharmacovigilance using Convergent Focus Shift (CFS) patterns in clinical trial study populations mined from linked public data resources. These signals are otherwise unavailable from individual data resources. We demonstrated the added value of linked public data and the feasibility of integrating ClinicalTrials.gov summaries and drug safety labels for post-marketing surveillance. Future research is needed to ensure better accessibility and linkage of heterogeneous drug safety data for efficient pharmacovigilance. Copyright © 2016 Elsevier Inc. All rights reserved.

Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the ‘Black Box’ of Machine Learning

PubMed Central

Gillingham, Philip

2016-01-01

Recent developments in digital technology have facilitated the recording and retrieval of administrative data from multiple sources about children and their families. Combined with new ways to mine such data using algorithms which can ‘learn’, it has been claimed that it is possible to develop tools that can predict which individual children within a population are most likely to be maltreated. The proposed benefit is that interventions can then be targeted to the most vulnerable children and their families to prevent maltreatment from occurring. As expertise in predictive modelling increases, the approach may also be applied in other areas of social work to predict and prevent adverse outcomes for vulnerable service users. In this article, a glimpse inside the ‘black box’ of predictive tools is provided to demonstrate how their development for use in social work may not be straightforward, given the nature of the data recorded about service users and service activity. The development of predictive risk modelling (PRM) in New Zealand is focused on as an example as it may be the first such tool to be applied as part of ongoing reforms to child protection services. PMID:27559213

Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the 'Black Box' of Machine Learning.

PubMed

Gillingham, Philip

2016-06-01

Recent developments in digital technology have facilitated the recording and retrieval of administrative data from multiple sources about children and their families. Combined with new ways to mine such data using algorithms which can 'learn', it has been claimed that it is possible to develop tools that can predict which individual children within a population are most likely to be maltreated. The proposed benefit is that interventions can then be targeted to the most vulnerable children and their families to prevent maltreatment from occurring. As expertise in predictive modelling increases, the approach may also be applied in other areas of social work to predict and prevent adverse outcomes for vulnerable service users. In this article, a glimpse inside the 'black box' of predictive tools is provided to demonstrate how their development for use in social work may not be straightforward, given the nature of the data recorded about service users and service activity. The development of predictive risk modelling (PRM) in New Zealand is focused on as an example as it may be the first such tool to be applied as part of ongoing reforms to child protection services.

Gender differences in condom use prediction with Theory of Reasoned Action and Planned Behaviour: the role of self-efficacy and control.

PubMed

Muñoz-Silva, A; Sánchez-García, M; Nunes, C; Martins, A

2007-10-01

There is much evidence that demonstrates that programs and interventions based on the theoretical models of the Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour (TPB) have been effective in the prevention of the sexual transmission of HIV. The objective of this work is to compare the effectiveness of both models in the prediction of condom use, distinguishing two components inside the variable Perceived Behavioural Control of the TPB model: self-efficacy and control. The perspective of gender differences is also added. The study was carried out in a sample of 601 Portuguese and Spanish university students. The results show that the females have a higher average in all the TPB variables than males, except in the frequency of condom use: females request the use of condoms less frequently than males. On the other hand, for both females and males the TPB model predicts better condom-use intention than the TRA. However there are no differences between the two models in relation to the prediction of condom-use behaviour. For prediction of intention, the most outstanding variable among females is attitude, while among males they are subjective norm and self-efficacy. Finally, we analyze the implications of these data from a theoretical and practical point of view.

High-Density Lipoprotein Cholesterol, Blood Urea Nitrogen, and Serum Creatinine Can Predict Severe Acute Pancreatitis.

PubMed

Hong, Wandong; Lin, Suhan; Zippi, Maddalena; Geng, Wujun; Stock, Simon; Zimmer, Vincent; Xu, Chunfang; Zhou, Mengtao

2017-01-01

Early prediction of disease severity of acute pancreatitis (AP) would be helpful for triaging patients to the appropriate level of care and intervention. The aim of the study was to develop a model able to predict Severe Acute Pancreatitis (SAP). A total of 647 patients with AP were enrolled. The demographic data, hematocrit, High-Density Lipoprotein Cholesterol (HDL-C) determinant at time of admission, Blood Urea Nitrogen (BUN), and serum creatinine (Scr) determinant at time of admission and 24 hrs after hospitalization were collected and analyzed statistically. Multivariate logistic regression indicated that HDL-C at admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. A logistic regression function (LR model) was developed to predict SAP as follows: -2.25-0.06 HDL-C (mg/dl) at admission + 0.06 BUN (mg/dl) at 24 hours + 0.66 Scr (mg/dl) at 24 hours. The optimism-corrected c-index for LR model was 0.832 after bootstrap validation. The area under the receiver operating characteristic curve for LR model for the prediction of SAP was 0.84. The LR model consists of HDL-C at admission and BUN and Scr at 24 hours, representing an additional tool to stratify patients at risk of SAP.

Improving the Targeting of Treatment: Evidence from College Remediation

ERIC Educational Resources Information Center

Scott-Clayton, Judith; Crosta, Peter M.; Belfield, Clive R.

2014-01-01

Remediation is one of the largest single interventions intended to improve outcomes for underprepared college students, yet little is known about the remedial screening process. Using administrative data and a rich predictive model, we find that severe mis-assignments are common using current test-score-cutoff-based policies, with…

Mortality and Rates of Secondary Intervention After EVAR in an Unselected Population: Influence of Simple Clinical Categories and Implications for Surveillance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hammond, Christopher J., E-mail: christopherhammond@nhs.net; Shah, Asim H.; Snoddon, Andrew

IntroductionPost-EVAR surveillance has a major impact upon patients, carers and healthcare resources. We hypothesised that elective indication, on-IFU anatomy, use of a modern device or normal first CTA, or a combination of these categories, might predict a rate of secondary intervention low enough to alter current surveillance protocols.MethodsPatients undergoing EVAR in our institution between 01.05.2007 and 28.02.2013 were assessed. Data on indication (elective, emergency), anatomy relative to IFU, device, first month CTA result, secondary intervention and mortality were obtained. Kaplan–Meier charts of mortality and freedom from secondary intervention were produced. Statistical analysis was by log-rank test and Cox proportional hazardmore » modelling.Results234 patients underwent EVAR (188 elective, 208 on-IFU). Most implants were Endurant (106) or Talent (98). 151 patients had a normal first CTA. By median follow-up of 38.6 months, 39 patients underwent secondary intervention. A normal first CTA and elective indication were significantly associated with reduced risk of secondary intervention (p < 0.001 and p = 0.042 respectively), but device type and placement on- or off-IFU were not. Elective placement with a normal first CTA was 93 % predictive of freedom from secondary intervention by 32 months post-EVAR. Of nine patients undergoing secondary intervention in this group, eight presented symptomatically.DiscussionIn optimal procedural circumstances with normal post-procedural imaging, only 7 % of patients undergoing EVAR require secondary intervention, a minority of which is driven by surveillance. These data support a change to surveillance more tailored to the individual patient, and highlight the need for further qualitative and quantitative research.« less

Predicting stillbirth in a low resource setting.

PubMed

Kayode, Gbenga A; Grobbee, Diederick E; Amoakoh-Coleman, Mary; Adeleke, Ibrahim Taiwo; Ansah, Evelyn; de Groot, Joris A H; Klipstein-Grobusch, Kerstin

2016-09-20

Stillbirth is a major contributor to perinatal mortality and it is particularly common in low- and middle-income countries, where annually about three million stillbirths occur in the third trimester. This study aims to develop a prediction model for early detection of pregnancies at high risk of stillbirth. This retrospective cohort study examined 6,573 pregnant women who delivered at Federal Medical Centre Bida, a tertiary level of healthcare in Nigeria from January 2010 to December 2013. Descriptive statistics were performed and missing data imputed. Multivariable logistic regression was applied to examine the associations between selected candidate predictors and stillbirth. Discrimination and calibration were used to assess the model's performance. The prediction model was validated internally and over-optimism was corrected. We developed a prediction model for stillbirth that comprised maternal comorbidity, place of residence, maternal occupation, parity, bleeding in pregnancy, and fetal presentation. As a secondary analysis, we extended the model by including fetal growth rate as a predictor, to examine how beneficial ultrasound parameters would be for the predictive performance of the model. After internal validation, both calibration and discriminative performance of both the basic and extended model were excellent (i.e. C-statistic basic model = 0.80 (95 % CI 0.78-0.83) and extended model = 0.82 (95 % CI 0.80-0.83)). We developed a simple but informative prediction model for early detection of pregnancies with a high risk of stillbirth for early intervention in a low resource setting. Future research should focus on external validation of the performance of this promising model.

An online spatio-temporal prediction model for dengue fever epidemic in Kaohsiung,Taiwan

NASA Astrophysics Data System (ADS)

Cheng, Ming-Hung; Yu, Hwa-Lung; Angulo, Jose; Christakos, George

2013-04-01

Dengue Fever (DF) is one of the most serious vector-borne infectious diseases in tropical and subtropical areas. DF epidemics occur in Taiwan annually especially during summer and fall seasons. Kaohsiung city has been one of the major DF hotspots in decades. The emergence and re-emergence of the DF epidemic is complex and can be influenced by various factors including space-time dynamics of human and vector populations and virus serotypes as well as the associated uncertainties. This study integrates a stochastic space-time "Susceptible-Infected-Recovered" model under Bayesian maximum entropy framework (BME-SIR) to perform real-time prediction of disease diffusion across space-time. The proposed model is applied for spatiotemporal prediction of the DF epidemic at Kaohsiung city during 2002 when the historical series of high DF cases was recorded. The online prediction by BME-SIR model updates the parameters of SIR model and infected cases across districts over time. Results show that the proposed model is rigorous to initial guess of unknown model parameters, i.e. transmission and recovery rates, which can depend upon the virus serotypes and various human interventions. This study shows that spatial diffusion can be well characterized by BME-SIR model, especially at the district surrounding the disease outbreak locations. The prediction performance at DF hotspots, i.e. Cianjhen and Sanmin, can be degraded due to the implementation of various disease control strategies during the epidemics. The proposed online disease prediction BME-SIR model can provide the governmental agency with a valuable reference to timely identify, control, and efficiently prevent DF spread across space-time.

Metabolic Syndrome Components and Their Response to Lifestyle and Metformin Interventions are Associated with Differences in Diabetes Risk in Persons with Impaired Glucose Tolerance

PubMed Central

Florez, Hermes; Temprosa, Marinella G; Orchard, Trevor J; Mather, Kieren J; Marcovina, Santica M; Barrett-Connor, Elizabeth; Horton, Edward; Saudek, Christopher; Pi-Sunyer, Xavier F; Ratner, Robert E; Goldberg, Ronald B

2013-01-01

Aims To determine the association of metabolic syndrome (MetS) and its components with diabetes risk in participants with impaired glucose tolerance (IGT), and whether intervention-related changes in MetS lead to differences in diabetes incidence. Methods We used the NCEP/ATP III revised MetS definition at baseline and intervention-related changes of its components to predict incident diabetes using Cox models in 3234 Diabetes Prevention Program (DPP) participants with IGT over an average follow-up of 3.2 years. Results In an intention-to-treat analysis, the demographic-adjusted hazard ratios (95%CI) for diabetes in those with MetS (versus no MetS) at baseline were 1.7(1.3-2.3), 1.7(1.2-2.3), and 2.0(1.3-3.0) for placebo, metformin, and lifestyle groups, respectively. Higher levels of fasting plasma glucose and triglycerides at baseline were independently associated with increased risk of diabetes. Greater waist circumference (WC) was associated with higher risk in placebo and lifestyle groups, but not in the metformin group. In a multivariate model, favorable changes in WC (placebo and lifestyle) and HDLc (placebo and metformin) contributed to reduced diabetes risk. Conclusions MetS and some of its components are associated with increased diabetes incidence in persons with IGT in a manner that differed according to DPP intervention. After hyperglycemia, the most predictive factors for diabetes were baseline hypertriglyceridemia and both baseline and lifestyle-associated changes in waist circumference. Targeting these cardio-metabolic risk factors may help to assess the benefits of interventions that reduce diabetes incidence. PMID:24118860

Cognitive Rehabilitation in Bilateral Vestibular Patients: A Computational Perspective.

PubMed

Ellis, Andrew W; Schöne, Corina G; Vibert, Dominique; Caversaccio, Marco D; Mast, Fred W

2018-01-01

There is evidence that vestibular sensory processing affects, and is affected by, higher cognitive processes. This is highly relevant from a clinical perspective, where there is evidence for cognitive impairments in patients with peripheral vestibular deficits. The vestibular system performs complex probabilistic computations, and we claim that understanding these is important for investigating interactions between vestibular processing and cognition. Furthermore, this will aid our understanding of patients' self-motion perception and will provide useful information for clinical interventions. We propose that cognitive training is a promising way to alleviate the debilitating symptoms of patients with complete bilateral vestibular loss (BVP), who often fail to show improvement when relying solely on conventional treatment methods. We present a probabilistic model capable of processing vestibular sensory data during both passive and active self-motion. Crucially, in our model, knowledge from multiple sources, including higher-level cognition, can be used to predict head motion. This is the entry point for cognitive interventions. Despite the loss of sensory input, the processing circuitry in BVP patients is still intact, and they can still perceive self-motion when the movement is self-generated. We provide computer simulations illustrating self-motion perception of BVP patients. Cognitive training may lead to more accurate and confident predictions, which result in decreased weighting of sensory input, and thus improved self-motion perception. Using our model, we show the possible impact of cognitive interventions to help vestibular rehabilitation in patients with BVP.

Potential for reduction of burden and local elimination of malaria by reducing Plasmodium falciparum malaria transmission: a mathematical modelling study

PubMed Central

Griffin, Jamie T; Bhatt, Samir; Sinka, Marianne E; Gething, Peter W; Lynch, Michael; Patouillard, Edith; Shutes, Erin; Newman, Robert D; Alonso, Pedro; Cibulskis, Richard E; Ghani, Azra C

2016-01-01

Summary Background Rapid declines in malaria prevalence, cases, and deaths have been achieved globally during the past 15 years because of improved access to first-line treatment and vector control. We aimed to assess the intervention coverage needed to achieve further gains over the next 15 years. Methods We used a mathematical model of the transmission of Plasmodium falciparum malaria to explore the potential effect on case incidence and malaria mortality rates from 2015 to 2030 of five different intervention scenarios: remaining at the intervention coverage levels of 2011–13 (Sustain), for which coverage comprises vector control and access to treatment; two scenarios of increased coverage to 80% (Accelerate 1) and 90% (Accelerate 2), with a switch from quinine to injectable artesunate for management of severe disease and seasonal malaria chemoprevention where recommended for both Accelerate scenarios, and rectal artesunate for pre-referral treatment at the community level added to Accelerate 2; a near-term innovation scenario (Innovate), which included longer-lasting insecticidal nets and expansion of seasonal malaria chemoprevention; and a reduction in coverage to 2006–08 levels (Reverse). We did the model simulations at the first administrative level (ie, state or province) for the 80 countries with sustained stable malaria transmission in 2010, accounting for variations in baseline endemicity, seasonality in transmission, vector species, and existing intervention coverage. To calculate the cases and deaths averted, we compared the total number of each under the five scenarios between 2015 and 2030 with the predicted number in 2015, accounting for population growth. Findings With an increase to 80% coverage, we predicted a reduction in case incidence of 21% (95% credible intervals [CrI] 19–29) and a reduction in mortality rates of 40% (27–61) by 2030 compared with 2015 levels. Acceleration to 90% coverage and expansion of treatment at the community level was predicted to reduce case incidence by 59% (Crl 56–64) and mortality rates by 74% (67–82); with additional near-term innovation, incidence was predicted to decline by 74% (70–77) and mortality rates by 81% (76–87). These scenarios were predicted to lead to local elimination in 13 countries under the Accelerate 1 scenario, 20 under Accelerate 2, and 22 under Innovate by 2030, reducing the proportion of the population living in at-risk areas by 36% if elimination is defined at the first administrative unit. However, failing to maintain coverage levels of 2011–13 is predicted to raise case incidence by 76% (Crl 71–80) and mortality rates by 46% (39–51) by 2020. Interpretation Our findings show that decreases in malaria transmission and burden can be accelerated over the next 15 years if the coverage of key interventions is increased. Funding UK Medical Research Council, UK Department for International Development, the Bill & Melinda Gates Foundation, the Swiss Development Agency, and the US Agency for International Development. PMID:26809816

Surrogate inaccuracy in predicting older adults' desire for life-sustaining interventions in the event of decisional incapacity: is it due in part to erroneous quality-of-life assessments?

PubMed

Bravo, Gina; Sene, Modou; Arcand, Marcel

2017-07-01

Family members are often called upon to make decisions for an incapacitated relative. Yet they have difficulty predicting a loved one's desire to receive treatments in hypothetical situations. We tested the hypothesis that this difficulty could in part be explained by discrepant quality-of-life assessments. The data come from 235 community-dwelling adults aged 70 years and over who rated their quality of life and desire for specified interventions in four health states (current state, mild to moderate stroke, incurable brain cancer, and severe dementia). All ratings were made on Likert-type scales. Using identical rating scales, a surrogate chosen by the older adult was asked to predict the latter's responses. Linear mixed models were fitted to determine whether differences in quality-of-life ratings between the older adult and surrogate were associated with surrogates' inaccuracy in predicting desire for treatment. The difference in quality-of-life ratings was a significant predictor of prediction inaccuracy for the three hypothetical health states (p < 0.01) and nearly significant for the current health state (p = 0.077). All regression coefficients were negative, implying that the more the surrogate overestimated quality of life compared to the older adult, the more he or she overestimated the older adult's desire to be treated. Discrepant quality-of-life ratings are associated with surrogates' difficulty in predicting desire for life-sustaining interventions in hypothetical situations. This finding underscores the importance of discussing anticipated quality of life in states of cognitive decline, to better prepare family members for making difficult decisions for their loved ones. ISRCTN89993391.

Assessing patient risk of central line-associated bacteremia via machine learning.

PubMed

Beeler, Cole; Dbeibo, Lana; Kelley, Kristen; Thatcher, Levi; Webb, Douglas; Bah, Amadou; Monahan, Patrick; Fowler, Nicole R; Nicol, Spencer; Judy-Malcolm, Alisa; Azar, Jose

2018-04-13

Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLABSIs and, in real time, prevent them from occurring. A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables. Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best-performing model was 0.82 in production. This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost-effective but otherwise time-limited interventions. Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection. Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Public health research: lost in translation or speaking the wrong language?

PubMed

Kansagra, Susan M; Farley, Thomas A

2011-12-01

Public health leaders, like physicians, need to make decisions that impact health based on strong evidence. To generate useful evidence for public health leaders, research must focus on interventions that have potential to impact population-level health. Often policy and environmental changes are the interventions with the greatest potential impact on population health, but studying these is difficult because of limitations in the methods typically used and emphasized in health research. To create useful evidence for policy and environmental interventions, other research methods are needed, including observational studies, the use of surveillance data for evaluation, and predictive mathematical modeling. More emphasis is needed on these types of study designs by researchers, funding agencies, and scientific journals.

The VACS index accurately predicts mortality and treatment response among multi-drug resistant HIV infected patients participating in the options in management with antiretrovirals (OPTIMA) study.

PubMed

Brown, Sheldon T; Tate, Janet P; Kyriakides, Tassos C; Kirkwood, Katherine A; Holodniy, Mark; Goulet, Joseph L; Angus, Brian J; Cameron, D William; Justice, Amy C

2014-01-01

The VACS Index is highly predictive of all-cause mortality among HIV infected individuals within the first few years of combination antiretroviral therapy (cART). However, its accuracy among highly treatment experienced individuals and its responsiveness to treatment interventions have yet to be evaluated. We compared the accuracy and responsiveness of the VACS Index with a Restricted Index of age and traditional HIV biomarkers among patients enrolled in the OPTIMA study. Using data from 324/339 (96%) patients in OPTIMA, we evaluated associations between indices and mortality using Kaplan-Meier estimates, proportional hazards models, Harrel's C-statistic and net reclassification improvement (NRI). We also determined the association between study interventions and risk scores over time, and change in score and mortality. Both the Restricted Index (c = 0.70) and VACS Index (c = 0.74) predicted mortality from baseline, but discrimination was improved with the VACS Index (NRI = 23%). Change in score from baseline to 48 weeks was more strongly associated with survival for the VACS Index than the Restricted Index with respective hazard ratios of 0.26 (95% CI 0.14-0.49) and 0.39(95% CI 0.22-0.70) among the 25% most improved scores, and 2.08 (95% CI 1.27-3.38) and 1.51 (95%CI 0.90-2.53) for the 25% least improved scores. The VACS Index predicts all-cause mortality more accurately among multi-drug resistant, treatment experienced individuals and is more responsive to changes in risk associated with treatment intervention than an index restricted to age and HIV biomarkers. The VACS Index holds promise as an intermediate outcome for intervention research.

Kinematic measures of Arm-trunk movements during unilateral and bilateral reaching predict clinically important change in perceived arm use in daily activities after intensive stroke rehabilitation.

PubMed

Chen, Hao-ling; Lin, Keh-chung; Liing, Rong-jiuan; Wu, Ching-yi; Chen, Chia-ling

2015-09-21

Kinematic analysis has been used to objectively evaluate movement patterns, quality, and strategies during reaching tasks. However, no study has investigated whether kinematic variables during unilateral and bilateral reaching tasks predict a patient's perceived arm use during activities of daily living (ADL) after an intensive intervention. Therefore, this study investigated whether kinematic measures during unilateral and bilateral reaching tasks before an intervention can predict clinically meaningful improvement in perceived arm use during ADL after intensive poststroke rehabilitation. The study was a secondary analysis of 120 subjects with chronic stroke who received 90-120 min of intensive intervention every weekday for 3-4 weeks. Reaching kinematics during unilateral and bilateral tasks and the Motor Activity Log (MAL) were evaluated before and after the intervention. Kinematic variables explained 22 and 11 % of the variance in actual amount of use (AOU) and quality of movement (QOM), respectively, of MAL improvement during unilateral reaching tasks. Kinematic variables also explained 21 and 31 % of the variance in MAL-AOU and MAL-QOM, respectively, during bilateral reaching tasks. Selected kinematic variables, including endpoint variables, trunk involvement, and joint recruitment and interjoint coordination, were significant predictors for improvement in perceived arm use during ADL (P < 0.05). Arm-trunk kinematics may be used to predict clinically meaningful improvement in perceived arm use during ADL after intensive rehabilitation. Involvement of interjoint coordination and trunk control variables as predictors in bilateral reaching models indicates that a high level of motor control (i.e., multijoint coordination) and trunk stability may be important in obtaining treatment gains in arm use, especially for bilateral daily activities, in intensive rehabilitation after stroke.

From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support

PubMed Central

Constantinou, Anthony Costa; Fenton, Norman; Marsh, William; Radlinski, Lukasz

2016-01-01

Objectives 1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; 2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; 3) To ensure the BN model can be used for interventional analysis; 4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. Method The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. Results When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. Conclusions This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way. PMID:26830286

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support.

PubMed

Constantinou, Anthony Costa; Fenton, Norman; Marsh, William; Radlinski, Lukasz

2016-02-01

(1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way. Copyright © 2016 Elsevier B.V. All rights reserved.

Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models.

PubMed

Tildesley, Michael J; Ryan, Sadie J

2012-01-01

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.

Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models

PubMed Central

Tildesley, Michael J.; Ryan, Sadie J.

2012-01-01

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock. PMID:23133352

A dynamic model for predicting growth in zinc-deficient stunted infants given supplemental zinc.

PubMed

Wastney, Meryl E; McDonald, Christine M; King, Janet C

2018-05-01

Zinc deficiency limits infant growth and increases susceptibility to infections, which further compromises growth. Zinc supplementation improves the growth of zinc-deficient stunted infants, but the amount, frequency, and duration of zinc supplementation required to restore growth in an individual child is unknown. A dynamic model of zinc metabolism that predicts changes in weight and length of zinc-deficient, stunted infants with dietary zinc would be useful to define effective zinc supplementation regimens. The aims of this study were to develop a dynamic model for zinc metabolism in stunted, zinc-deficient infants and to use that model to predict the growth response when those infants are given zinc supplements. A model of zinc metabolism was developed using data on zinc kinetics, tissue zinc, and growth requirements for healthy 9-mo-old infants. The kinetic model was converted to a dynamic model by replacing the rate constants for zinc absorption and excretion with functions for these processes that change with zinc intake. Predictions of the dynamic model, parameterized for zinc-deficient, stunted infants, were compared with the results of 5 published zinc intervention trials. The model was then used to predict the results for zinc supplementation regimes that varied in the amount, frequency, and duration of zinc dosing. Model predictions agreed with published changes in plasma zinc after zinc supplementation. Predictions of weight and length agreed with 2 studies, but overpredicted values from a third study in which other nutrient deficiencies may have been growth limiting; the model predicted that zinc absorption was impaired in that study. The model suggests that frequent, smaller doses (5-10 mg Zn/d) are more effective for increasing growth in stunted, zinc-deficient 9-mo-old infants than are larger, less-frequent doses. The dose amount affects the duration of dosing necessary to restore and maintain plasma zinc concentration and growth.

Assessment of soil erosion risk in Komering watershed, South Sumatera, using SWAT model

NASA Astrophysics Data System (ADS)

Salsabilla, A.; Kusratmoko, E.

2017-07-01

Changes in land use watershed led to environmental degradation. Estimated loss of soil erosion is often difficult due to some factors such as topography, land use, climate and human activities. This study aims to predict soil erosion hazard and sediment yield using the Soil and Water Assessment Tools (SWAT) hydrological model. The SWAT was chosen because it can simulate the model with limited data. The study area is Komering watershed (806,001 Ha) in South Sumatera Province. There are two factors land management intervention: 1) land with agriculture, and 2) land with cultivation. These factors selected in accordance with the regulations of spatial plan area. Application of the SWAT demonstrated that the model can predict surface runoff, soil erosion loss and sediment yield. The erosion risk for each watershed can be classified and predicted its changes based on the scenarios which arranged. In this paper, we also discussed the relationship between the distribution of erosion risk and watershed's characteristics in a spatial perspective.

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

Goal clarity and financial planning activities as determinants of retirement savings contributions.

PubMed

Stawski, Robert S; Hershey, Douglas A; Jacobs-Lawson, Joy M

2007-01-01

Retirement counselors, financial service professionals, and retirement intervention specialists routinely emphasize the importance of developing clear goals for the future; however, few empirical studies have focused on the benefits of retirement goal setting. In the present study, the extent to which goal clarity and financial planning activities predict retirement savings practices was examined among 100 working adults. Path analysis techniques were used to test two competing models, both of which were designed to predict savings contributions. Findings provide support for the model in which retirement goal clarity is a significant predictor of planning practices, and planning, in turn, predicts savings tendencies. Two demographic variables-income and age-were also revealed to be important elements of the model, with income accounting for roughly half of the explained variance in savings contributions. The results of this study have implications for the development of age-based models of planning, as well as implications for retirement counselors and financial planners who advise workers on long-term saving strategies.

A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

USGS Publications Warehouse

Vitousek, Sean; Barnard, Patrick; Limber, Patrick W.; Erikson, Li; Cole, Blake

2017-01-01

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea-level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea-level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea-level rise scenarios of 0.93 to 2.0 m.

A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

NASA Astrophysics Data System (ADS)

Vitousek, Sean; Barnard, Patrick L.; Limber, Patrick; Erikson, Li; Cole, Blake

2017-04-01

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea level rise scenarios of 0.93 to 2.0 m.

A Model to Predict the Use of Surgical Resection for Advanced-Stage Non-Small Cell Lung Cancer Patients.

PubMed

David, Elizabeth A; Andersen, Stina W; Beckett, Laurel A; Melnikow, Joy; Kelly, Karen; Cooke, David T; Brown, Lisa M; Canter, Robert J

2017-11-01

For advanced-stage non-small cell lung cancer, chemotherapy and chemoradiotherapy are the primary treatments. Although surgical intervention in these patients is associated with improved survival, the effect of selection bias is poorly defined. Our objective was to characterize selection bias and identify potential surgical candidates by constructing a Surgical Selection Score (SSS). Patients with clinical stage IIIA, IIIB, or IV non-small cell lung cancer were identified in the National Cancer Data Base from 1998 to 2012. Logistic regression was used to develop the SSS based on clinical characteristics. Estimated area under the receiver operating characteristic curve was used to assess discrimination performance of the SSS. Kaplan-Meier analysis was used to compare patients with similar SSSs. We identified 300,572 patients with stage IIIA, IIIB, or IV non-small cell lung cancer without missing data; 6% (18,701) underwent surgical intervention. The surgical cohort was 57% stage IIIA (n = 10,650), 19% stage IIIB (n = 3,483), and 24% stage IV (n = 4,568). The areas under the receiver operating characteristic curve from the best-fit logistic regression model in the training and validation sets were not significantly different, at 0.83 (95% confidence interval, 0.82 to 0.83) and 0.83 (95% confidence interval, 0.82 to 0.83). The range of SSS is 43 to 1,141. As expected, SSS was a good predictor of survival. Within each quartile of SSS, patients in the surgical group had significantly longer survival than nonsurgical patients (p < 0.001). A prediction model for selection of patients for surgical intervention was created. Once validated and prospectively tested, this model may be used to identify patients who may benefit from surgical intervention. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Simplified Models of Vector Control Impact upon Malaria Transmission by Zoophagic Mosquitoes

PubMed Central

Kiware, Samson S.; Chitnis, Nakul; Moore, Sarah J.; Devine, Gregor J.; Majambere, Silas; Merrill, Stephen; Killeen, Gerry F.

2012-01-01

Background High coverage of personal protection measures that kill mosquitoes dramatically reduce malaria transmission where vector populations depend upon human blood. However, most primary malaria vectors outside of sub-Saharan Africa can be classified as “very zoophagic,” meaning they feed occasionally (<10% of blood meals) upon humans, so personal protection interventions have negligible impact upon their survival. Methods and Findings We extended a published malaria transmission model to examine the relationship between transmission, control, and the baseline proportion of bloodmeals obtained from humans (human blood index). The lower limit of the human blood index enables derivation of simplified models for zoophagic vectors that (1) Rely on only three field-measurable parameters. (2) Predict immediate and delayed (with and without assuming reduced human infectivity, respectively) impacts of personal protection measures upon transmission. (3) Illustrate how appreciable indirect communal-level protection for non-users can be accrued through direct personal protection of users. (4) Suggest the coverage and efficacy thresholds required to attain epidemiological impact. The findings suggest that immediate, indirect, community-wide protection of users and non-users alike may linearly relate to the efficacy of a user’s direct personal protection, regardless of whether that is achieved by killing or repelling mosquitoes. High protective coverage and efficacy (≥80%) are important to achieve epidemiologically meaningful impact. Non-users are indirectly protected because the two most common species of human malaria are strict anthroponoses. Therefore, the small proportion of mosquitoes that are killed or diverted while attacking humans can represent a large proportion of those actually transmitting malaria. Conclusions Simplified models of malaria transmission by very zoophagic vectors may be used by control practitioners to predict intervention impact interventions using three field-measurable parameters; the proportion of human exposure to mosquitoes occurring when an intervention can be practically used, its protective efficacy when used, and the proportion of people using it. PMID:22701527

Reducing Internalizing Symptoms among High-Risk, Hispanic Adolescents: Mediators of a Preventive Family Intervention

PubMed Central

Perrino, Tatiana; Brincks, Ahnalee; Howe, George; Brown, C. Hendricks; Prado, Guillermo; Pantin, Hilda

2016-01-01

Familias Unidas is a family-focused preventive intervention that has been found to reduce drug use and sexual risk behaviors among Hispanic adolescents. In some trials, Familias Unidas has also been found to be efficacious in reducing adolescent internalizing symptoms (i.e., depressive and anxiety symptoms), even though the intervention did not specifically target internalizing symptoms. This study examines potential mediators or mechanisms by which Familias Unidas influences internalizing symptoms, specifically the role of intervention-targeted improvements in parent-adolescent communication and reductions in youth externalizing behaviors. A total of 213 Hispanic eighth grade students with a history of externalizing behavior problems and their primary caregivers were recruited from the public school system. Participants, with a mean age of 13.8 years, were randomized into the Familias Unidas intervention or community practice control condition, and assessed at baseline, 6-months, 18-months, and 30-months post-baseline. A cascading mediation model was tested in which the Familias Unidas intervention was hypothesized to decrease adolescent internalizing symptoms through two mediators: improvements in parent-adolescent communication leading to decreases in externalizing behaviors. Findings show that the intervention had significant direct effects on youth internalizing symptoms at 30-months post-baseline. In addition, the cascading mediation model was supported in which the Familias Unidas intervention predicted significant improvements in parent-adolescent communication at 6-months, subsequently decreasing externalizing behaviors at 18-months, and ultimately reducing youth internalizing symptoms at 30-months post-baseline. Implications for prevention interventions are discussed. PMID:27154768

Use of mathematical modelling to assess the impact of vaccines on antibiotic resistance.

PubMed

Atkins, Katherine E; Lafferty, Erin I; Deeny, Sarah R; Davies, Nicholas G; Robotham, Julie V; Jit, Mark

2018-06-01

Antibiotic resistance is a major global threat to the provision of safe and effective health care. To control antibiotic resistance, vaccines have been proposed as an essential intervention, complementing improvements in diagnostic testing, antibiotic stewardship, and drug pipelines. The decision to introduce or amend vaccination programmes is routinely based on mathematical modelling. However, few mathematical models address the impact of vaccination on antibiotic resistance. We reviewed the literature using PubMed to identify all studies that used an original mathematical model to quantify the impact of a vaccine on antibiotic resistance transmission within a human population. We reviewed the models from the resulting studies in the context of a new framework to elucidate the pathways through which vaccination might impact antibiotic resistance. We identified eight mathematical modelling studies; the state of the literature highlighted important gaps in our understanding. Notably, studies are limited in the range of pathways represented, their geographical scope, and the vaccine-pathogen combinations assessed. Furthermore, to translate model predictions into public health decision making, more work is needed to understand how model structure and parameterisation affects model predictions and how to embed these predictions within economic frameworks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predictors of Program Use and Child and Parent Outcomes of A Brief Online Parenting Intervention.

PubMed

Baker, Sabine; Sanders, Matthew R

2017-10-01

Web-based parenting interventions have the potential to increase the currently low reach of parenting programs, but few evidence-based online programs are available, and little is known about who benefits from this delivery format. This study investigated if improvements in child behavior and parenting, following participation in a brief online parenting program (Triple P Online Brief), can be predicted by family and program-related factors. Participants were 100 parents of 2-9-year-old children displaying disruptive behavior problems. Regression analyses showed that higher baseline levels of child behavior problems, older parental age and more intense conflict over parenting pre-intervention predicted greater improvement in child behavior at 9-month follow-up. Improvement in parenting was predicted by higher pre-intervention levels of ineffective parenting. Family demographics, parental adjustment and program related factors did not predict treatment outcomes. Younger child age and lower disagreement over parenting pre-intervention predicted completion of the recommended minimum dose of the program.

PRIME – PRocess modelling in ImpleMEntation research: selecting a theoretical basis for interventions to change clinical practice

PubMed Central

Walker, Anne E; Grimshaw, Jeremy; Johnston, Marie; Pitts, Nigel; Steen, Nick; Eccles, Martin

2003-01-01

Background Biomedical research constantly produces new findings but these are not routinely translated into health care practice. One way to address this problem is to develop effective interventions to translate research findings into practice. Currently a range of empirical interventions are available and systematic reviews of these have demonstrated that there is no single best intervention. This evidence base is difficult to use in routine settings because it cannot identify which intervention is most likely to be effective (or cost effective) in a particular situation. We need to establish a scientific rationale for interventions. As clinical practice is a form of human behaviour, theories of human behaviour that have proved useful in other similar settings may provide a basis for developing a scientific rationale for the choice of interventions to translate research findings into clinical practice. The objectives of the study are: to amplify and populate scientifically validated theories of behaviour with evidence from the experience of health professionals; to use this as a basis for developing predictive questionnaires using replicable methods; to identify which elements of the questionnaire (i.e., which theoretical constructs) predict clinical practice and distinguish between evidence compliant and non-compliant practice; and on the basis of these results, to identify variables (based on theoretical constructs) that might be prime targets for behaviour change interventions. Methods We will develop postal questionnaires measuring two motivational, three action and one stage theory to explore five behaviours with 800 general medical and 600 general dental practitioners. We will collect data on performance for each of the behaviours. The relationships between predictor variables (theoretical constructs) and outcome measures (data on performance) in each survey will be assessed using multiple regression analysis and structural equation modelling. In the final phase of the project, the findings from all surveys will be analysed simultaneously adopting a random effects approach to investigate whether the relationships between predictor variables and outcome measures are modified by behaviour, professional group or geographical location. PMID:14683530

Predictors of second language acquisition in Latino children with specific language impairment.

PubMed

Gutiérrez-Clellen, Vera; Simon-Cereijido, Gabriela; Sweet, Monica

2012-02-01

This study evaluated the extent to which the language of intervention, the child's development in Spanish, and the effects of English vocabulary, use, proficiency, and exposure predict differences in the rates of acquisition of English in Latino children with specific language impairment (SLI). In this randomized controlled trial, 188 Latino preschoolers with SLI participated in a small-group academic enrichment program for 12 weeks and were followed up 3 and 5 months later. Children were randomly assigned to either a bilingual or an English-only program. Predictors of English growth included measures of Spanish language skills and English vocabulary, use, proficiency, and exposure. Performance on English outcomes (i.e., picture description and narrative sample) was assessed over time. A series of longitudinal models were tested via multilevel modeling with baseline and posttreatment measures nested within child. Children demonstrated growth on the English outcomes over time. The language of intervention, Spanish skills, English vocabulary, and English use significantly predicted differences in rates of growth across children for specific measures of English development. This study underscores the role of the child's first language skills, the child's level of English vocabulary development, and level of English use for predicting differences in English acquisition in Latino preschoolers with SLI. These factors should be carefully considered in making clinical decisions.

Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction.

PubMed

Ding, Zhongxiang; Zhang, Han; Lv, Xiao-Fei; Xie, Fei; Liu, Lizhi; Qiu, Shijun; Li, Li; Shen, Dinggang

2018-01-01

Radiation therapy, a major method of treatment for brain cancer, may cause severe brain injuries after many years. We used a rare and unique cohort of nasopharyngeal carcinoma patients with normal-appearing brains to study possible early irradiation injury in its presymptomatic phase before severe, irreversible necrosis happens. The aim is to detect any structural or functional imaging biomarker that is sensitive to early irradiation injury, and to understand the recovery and progression of irradiation injury that can shed light on outcome prediction for early clinical intervention. We found an acute increase in local brain activity that is followed by extensive reductions in such activity in the temporal lobe and significant loss of functional connectivity in a distributed, large-scale, high-level cognitive function-related brain network. Intriguingly, these radiosensitive functional alterations were found to be fully or partially recoverable. In contrast, progressive late disruptions to the integrity of the related far-end white matter structure began to be significant after one year. Importantly, early increased local brain functional activity was predictive of severe later temporal lobe necrosis. Based on these findings, we proposed a dynamic, multifactorial model for radiation injury and another preventive model for timely clinical intervention. Hum Brain Mapp 39:407-427, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Risk assessment models to predict caries recurrence after oral rehabilitation under general anaesthesia: a pilot study.

PubMed

Lin, Yai-Tin; Kalhan, Ashish Chetan; Lin, Yng-Tzer Joseph; Kalhan, Tosha Ashish; Chou, Chein-Chin; Gao, Xiao Li; Hsu, Chin-Ying Stephen

2018-05-08

Oral rehabilitation under general anaesthesia (GA), commonly employed to treat high caries-risk children, has been associated with high economic and individual/family burden, besides high post-GA caries recurrence rates. As there is no caries prediction model available for paediatric GA patients, this study was performed to build caries risk assessment/prediction models using pre-GA data and to explore mid-term prognostic factors for early identification of high-risk children prone to caries relapse post-GA oral rehabilitation. Ninety-two children were identified and recruited with parental consent before oral rehabilitation under GA. Biopsychosocial data collection at baseline and the 6-month follow-up were conducted using questionnaire (Q), microbiological assessment (M) and clinical examination (C). The prediction models constructed using data collected from Q, Q + M and Q + M + C demonstrated an accuracy of 72%, 78% and 82%, respectively. Furthermore, of the 83 (90.2%) patients recalled 6 months after GA intervention, recurrent caries was identified in 54.2%, together with reduced bacterial counts, lower plaque index and increased percentage of children toothbrushing for themselves (all P < 0.05). Additionally, meal-time and toothbrushing duration were shown, through bivariate analyses, to be significant prognostic determinants for caries recurrence (both P < 0.05). Risk assessment/prediction models built using pre-GA data may be promising in identifying high-risk children prone to post-GA caries recurrence, although future internal and external validation of predictive models is warranted. © 2018 FDI World Dental Federation.

Predictors of sustained reduction in energy and fat intake in the Diabetes Prevention Program Outcomes Study intensive lifestyle intervention.

PubMed

Davis, Nichola J; Ma, Yong; Delahanty, Linda M; Hoffman, Heather J; Mayer-Davis, Elizabeth; Franks, Paul W; Brown-Friday, Janet; Isonaga, Mae; Kriska, Andrea M; Venditti, Elizabeth M; Wylie-Rosett, Judith

2013-11-01

Few lifestyle intervention studies examine long-term sustainability of dietary changes. To describe sustainability of dietary changes over 9 years in the Diabetes Prevention Program and its outcomes study, the Diabetes Prevention Program Outcomes Study, among participants receiving the intensive lifestyle intervention. One thousand seventy-nine participants were enrolled in the intensive lifestyle intervention arm of the Diabetes Prevention Program; 910 continued participation in the Diabetes Prevention Program Outcomes Study. Fat and energy intake derived from food frequency questionnaires at baseline and post-randomization Years 1 and 9 were examined. Parsimonious models determined whether baseline characteristics and intensive lifestyle intervention session participation predicted sustainability. Self-reported energy intake was reduced from a median of 1,876 kcal/day (interquartile range [IQR]=1,452 to 2,549 kcal/day) at baseline to 1,520 kcal/day (IQR=1,192 to 1,986 kcal/day) at Year 1, and 1,560 kcal/day (IQR=1,223 to 2,026 kcal/day) at Year 9. Dietary fat was reduced from a median of 70.4 g (IQR=49.3 to 102.5 g) to 45 g (IQR=32.2 to 63.8 g) at Year 1 and increased to 61.0 g (IQR=44.6 to 82.7 g) at Year 9. Percent energy from fat was reduced from a median of 34.4% (IQR=29.6% to 38.5%) to 27.1% (IQR=23.1% to 31.5%) at Year 1 but increased to 35.3% (IQR=29.7% to 40.2%) at Year 9. Lower baseline energy intake and Year 1 dietary reduction predicted lower energy and fat gram intake at Year 9. Higher leisure physical activity predicted lower fat gram intake but not energy intake. Intensive lifestyle intervention can result in reductions in total energy intake for up to 9 years. Initial success in achieving reductions in fat and energy intake and success in attaining activity goals appear to predict long-term success at maintaining changes. Copyright © 2013 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Can Rheumatologists Predict Eventual Need for Orthopaedic Intervention in Patients with Rheumatoid Arthritis? Results of a Systematic Review and Analysis of Two UK Inception Cohorts.

PubMed

Nikiphorou, Elena; Carpenter, Lewis; Norton, Sam; Morris, Stephen; MacGregor, Alex; Dixey, Josh; Williams, Peter; Kiely, Patrick; Walsh, David Andrew; Young, Adam

2017-03-01

The structural damage caused by rheumatoid arthritis (RA) can often be mitigated by orthopaedic surgery in late disease. This study evaluates the value of predictive factors for orthopaedic intervention. A systematic review of literature was undertaken to identify papers describing predictive factors for orthopaedic surgery in RA. Manuscripts were selected if they met inclusion criteria of cohort study design, diagnosis of RA, follow-up duration/disease duration ≥3 years, any orthopaedic surgical interventions recorded, and then summarised for predictive factors. A separate predictive analysis was performed on two consecutive UK Early RA cohorts, linked to national datasets. The literature search identified 15 reports examining predictive factors for orthopaedic intervention, 4 inception, 5 prospective and 6 retrospective. Despite considerable variation, acute phase, x-ray scores, women and genotyping were the most commonly reported prognostic markers. The current predictive analysis included 1602 procedures performed in 711 patients (25-year cumulative incidence 26%). Earlier recruitment year, erosions and lower haemoglobin predicted both intermediate and major surgery (P<0.05). Studies report variations in type of and predictive power of clinical and laboratory parameters for different surgical interventions suggesting specific contributions from different pathological and/or patient-level factors. Our current analysis suggests that attention to non-inflammatory factors in addition to suppression of inflammation is needed to minimise the burden of orthopaedic surgery.

Enteric disease episodes and the risk of acquiring a future sexually transmitted infection: a prediction model in Montreal residents.

PubMed

Caron, Melissa; Allard, Robert; Bédard, Lucie; Latreille, Jérôme; Buckeridge, David L

2016-11-01

The sexual transmission of enteric diseases poses an important public health challenge. We aimed to build a prediction model capable of identifying individuals with a reported enteric disease who could be at risk of acquiring future sexually transmitted infections (STIs). Passive surveillance data on Montreal residents with at least 1 enteric disease report was used to construct the prediction model. Cases were defined as all subjects with at least 1 STI report following their initial enteric disease episode. A final logistic regression prediction model was chosen using forward stepwise selection. The prediction model with the greatest validity included age, sex, residential location, number of STI episodes experienced prior to the first enteric disease episode, type of enteric disease acquired, and an interaction term between age and male sex. This model had an area under the curve of 0.77 and had acceptable calibration. A coordinated public health response to the sexual transmission of enteric diseases requires that a distinction be made between cases of enteric diseases transmitted through sexual activity from those transmitted through contaminated food or water. A prediction model can aid public health officials in identifying individuals who may have a higher risk of sexually acquiring a reportable disease. Once identified, these individuals could receive specialized intervention to prevent future infection. The information produced from a prediction model capable of identifying higher risk individuals can be used to guide efforts in investigating and controlling reported cases of enteric diseases and STIs. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Applying an extended theory of planned behaviour to predict breakfast consumption in adolescents.

PubMed

Kennedy, S; Davies, E L; Ryan, L; Clegg, M E

2017-05-01

Breakfast skipping increases during adolescence and is associated with lower levels of physical activity and weight gain. Theory-based interventions promoting breakfast consumption in adolescents report mixed findings, potentially because of limited research identifying which determinants to target. This study aimed to: (i) utilise the Theory of Planned Behaviour (TPB) to identify the relative contribution of attitudes (affective, cognitive and behavioural) to predict intention to eat breakfast and breakfast consumption in adolescents and (ii) determine whether demographic factors moderate the relationship between TPB variables, intention and behaviour. Questionnaires were completed by 434 students (mean 14±0.9 years) measuring breakfast consumption (0-2, 3-6 or 7 days), physical activity levels and TPB measures. Data were analysed by breakfast frequency and demographics using hierarchical and multinomial regression analyses. Breakfast was consumed everyday by 57% of students, with boys more likely to eat a regular breakfast, report higher activity levels and report more positive attitudes towards breakfast than girls (P<0.001). The TPB predicted 58% of the variation in intentions. Overall, the model was predictive of breakfast behaviours (P<0.001), but the relative contribution of TPB constructs varied depending on breakfast frequency. Interactions between gender and intentions were significant when comparing 0-2- and 3-6-day breakfast eaters only highlighting a stronger intention-behaviour relationship for girls. Findings confirm that the TPB is a successful model for predicting breakfast intentions and behaviours in adolescents. The potential for a direct effect of attitudes on behaviours should be considered in the implementation and design of breakfast interventions.

Putting theory to the test: Examining family context, caregiver motivation, and conflict in the Family Check-Up model

PubMed Central

Fosco, Gregory M.; Van Ryzin, Mark; Stormshak, Elizabeth A.; Dishion, Thomas J.

2014-01-01

This study examined contextual factors (caregiver depression, family resources, ethnicity, and initial levels of youth problem behavior) related to the effectiveness of the Family Check-Up (FCU) and evaluated family processes as a mediator of FCU intervention response and adolescent antisocial behavior. We followed a sample of 180 ethnically diverse youths of families who engaged in the FCU intervention. Family data were collected as part of the FCU assessment, and youth data were collected over 4 years, from sixth through ninth grade. Findings indicated that caregiver depression and minority status predicted greater caregiver motivation to change. In turn, caregiver motivation was the only direct predictor of FCU intervention response during a 1-year period. Growth in family conflict from sixth through eighth grade mediated the link between FCU response and ninth-grade antisocial behavior. This study explicitly tested core aspects of the FCU intervention model and demonstrated that caregiver motivation is a central factor that underlies family response to the FCU. The study also provided support for continued examination of family process mechanisms that account for enduring effects of the FCU and other family-centered interventions. PMID:24438894

Putting theory to the test: examining family context, caregiver motivation, and conflict in the Family Check-Up model.

PubMed

Fosco, Gregory M; Van Ryzin, Mark; Stormshak, Elizabeth A; Dishion, Thomas J

2014-05-01

This study examined contextual factors (caregiver depression, family resources, ethnicity, and initial levels of youth problem behavior) related to the effectiveness of the Family Check-Up (FCU) and evaluated family processes as a mediator of FCU intervention response and adolescent antisocial behavior. We followed a sample of 180 ethnically diverse youths of families who engaged in the FCU intervention. Family data were collected as part of the FCU assessment, and youth data were collected over 4 years, from sixth through ninth grade. Findings indicated that caregiver depression and minority status predicted greater caregiver motivation to change. In turn, caregiver motivation was the only direct predictor of FCU intervention response during a 1-year period. Growth in family conflict from sixth through eighth grade mediated the link between FCU response and ninth-grade antisocial behavior. This study explicitly tested core aspects of the FCU intervention model and demonstrated that caregiver motivation is a central factor that underlies family response to the FCU. The study also provided support for continued examination of family process mechanisms that account for enduring effects of the FCU and other family-centered interventions.

Respiratory motion estimation in x-ray angiography for improved guidance during coronary interventions

NASA Astrophysics Data System (ADS)

Baka, N.; Lelieveldt, B. P. F.; Schultz, C.; Niessen, W.; van Walsum, T.

2015-05-01

During percutaneous coronary interventions (PCI) catheters and arteries are visualized by x-ray angiography (XA) sequences, using brief contrast injections to show the coronary arteries. If we could continue visualizing the coronary arteries after the contrast agent passed (thus in non-contrast XA frames), we could potentially lower contrast use, which is advantageous due to the toxicity of the contrast agent. This paper explores the possibility of such visualization in mono-plane XA acquisitions with a special focus on respiratory based coronary artery motion estimation. We use the patient specific coronary artery centerlines from pre-interventional 3D CTA images to project on the XA sequence for artery visualization. To achieve this, a framework for registering the 3D centerlines with the mono-plane 2D + time XA sequences is presented. During the registration the patient specific cardiac and respiratory motion is learned. We investigate several respiratory motion estimation strategies with respect to accuracy, plausibility and ease of use for motion prediction in XA frames with and without contrast. The investigated strategies include diaphragm motion based prediction, and respiratory motion extraction from the guiding catheter tip motion. We furthermore compare translational and rigid respiratory based heart motion. We validated the accuracy of the 2D/3D registration and the respiratory and cardiac motion estimations on XA sequences of 12 interventions. The diaphragm based motion model and the catheter tip derived motion achieved 1.58 mm and 1.83 mm median 2D accuracy, respectively. On a subset of four interventions we evaluated the artery visualization accuracy for non-contrast cases. Both diaphragm, and catheter tip based prediction performed similarly, with about half of the cases providing satisfactory accuracy (median error < 2 mm).

An online spatiotemporal prediction model for dengue fever epidemic in Kaohsiung (Taiwan).

PubMed

Yu, Hwa-Lung; Angulo, José M; Cheng, Ming-Hung; Wu, Jiaping; Christakos, George

2014-05-01

The emergence and re-emergence of disease epidemics is a complex question that may be influenced by diverse factors, including the space-time dynamics of human populations, environmental conditions, and associated uncertainties. This study proposes a stochastic framework to integrate space-time dynamics in the form of a Susceptible-Infected-Recovered (SIR) model, together with uncertain disease observations, into a Bayesian maximum entropy (BME) framework. The resulting model (BME-SIR) can be used to predict space-time disease spread. Specifically, it was applied to obtain a space-time prediction of the dengue fever (DF) epidemic that took place in Kaohsiung City (Taiwan) during 2002. In implementing the model, the SIR parameters were continually updated and information on new cases of infection was incorporated. The results obtained show that the proposed model is rigorous to user-specified initial values of unknown model parameters, that is, transmission and recovery rates. In general, this model provides a good characterization of the spatial diffusion of the DF epidemic, especially in the city districts proximal to the location of the outbreak. Prediction performance may be affected by various factors, such as virus serotypes and human intervention, which can change the space-time dynamics of disease diffusion. The proposed BME-SIR disease prediction model can provide government agencies with a valuable reference for the timely identification, control, and prevention of DF spread in space and time. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Revascularization of smokers with claudication is not predicted to limit quality of life despite a higher risk of late failure.

PubMed

Mixson, Joshua D; Brothers, Thomas E

2017-01-01

Tobacco smoking after lower extremity revascularization for claudication has repeatedly been shown to increase the risk of adverse events, such that many vascular specialists consider that refusal to abstain from smoking constitutes a major contraindication to open surgical bypass or endovascular intervention. A Markov decision analysis (DA) model was used to compare the options of direct revascularization vs medical therapy only in smokers with claudication. The primary outcome was calculated quality of life (cQoL), determined for each patient at follow-up based on the outcomes of the treatment received. Markov DA software was used to predict the QoL for each treatment option preoperatively based on smoking status. Among patients referred during a recent 64-month period with vasculogenic claudication, 94 were actively smoking compared with 217 who were not. The DA model predicted that if the patients who smoked were to discontinue smoking, the best therapy would be bypass surgery for 77% and endovascular intervention for 17%. However, despite at least doubling the risks with intervention in the patients who continue to smoke, the DA model still predicted that 78% and 9% would fare better with open surgical or endovascular intervention, respectively. Among actively smoking patients, open surgical (3%) or endovascular (4%) therapies were initially performed in few patients, whereas 93% were offered only medical therapy. Among initial nonsmokers, revascularization was performed by open (27%) or endovascular (42%) means. At 3 years, the median (interquartile range [IQR]) cQoL was lower in initial smokers than in nonsmokers (0.73 [IQR, 0.73-0.77] vs 0.82 [IQR, 0.75-0.86]; P < .0001), primarily because of a lack of revascularization for smokers. Among initial smokers who did undergo revascularization initially, because of progression of symptoms, or after smoking cessation, cQoL was similar to initial nonsmokers (0.77 [IQR, 0.73-0.84] vs 0.73 [IQR, 0.73-0.73]; P = .37). Although 26% of initial smokers had stopped by the time of their last follow-up, 10% of initially nonsmoking patients were smoking at follow-up. However, among all patients undergoing intervention, the cQoL of patients smoking at the time of last their follow-up was similar to nonsmokers (0.82 [IQR, 0.82-0.86] vs 0.83 [IQR, 0.73-0.86]; P = .99). Patients with claudication who smoke may be denied the symptom improvement associated with revascularization, yet recidivism for smoking also occurs among patients who have stopped smoking in order to receive revascularization. The strategy not to directly revascularize patients with claudication who continue to smoke does not appear to maximize patient midterm QoL. Published by Elsevier Inc.

Simulation based teaching in interventional radiology training: is it effective?

PubMed

Patel, R; Dennick, R

2017-03-01

To establish the educational effectiveness of simulation teaching in interventional radiology training. Electronic databases (MEDLINE, ERIC, Embase, OvidSP, and Cochrane Library) were searched (January 2000 to May 2015). Studies specifically with educational outcomes conducted on radiologists were eligible. All forms of simulation in interventional training were included. Data were extracted based on the population, intervention, comparison, and outcome (PICO) model. Kirkpatrick's hierarchy was used to establish educational intervention effectiveness. The quality of studies was assessed using the Cochrane risk of bias tool. Search resulted in 377 articles, of which 15 met the inclusion criteria. Thirteen of the 15 studies achieved level 2 of Kirkpatrick's hierarchy with only one reaching level 4. Statistically significant improvements in performance metrics as objective measures, demonstrating trainee competence were seen in 12/15 studies. Subjective improvements in confidence were noted in 13/15. Only one study demonstrated skills transferability and improvements in patient outcomes. Results demonstrate the relevance of simulated training to current education models in improving trainee competence; however, this is limited to the simulated environment as there is a lack of literature investigating its predictive validity and the effect on patient outcomes. The requirement for further research in this field is highlighted. Simulation is thus currently only deemed useful as an adjunct to current training models with the potential to play an influential role in the future of the interventional radiology training curriculum. Copyright © 2016. Published by Elsevier Ltd.

Diagnoses, Intervention Strategies, and Rates of Functional Improvement in Integrated Behavioral Health Care Patients

PubMed Central

Bridges, Ana J.; Gregus, Samantha J.; Rodriguez, Juventino Hernandez; Andrews, Arthur R.; Villalobos, Bianca T.; Pastrana, Freddie A.; Cavell, Timothy A.

2016-01-01

Objective Compared with more traditional mental health care, integrated behavioral health care (IBHC) offers greater access to services and earlier identification and intervention of behavioral and mental health difficulties. The current study examined demographic, diagnostic, and intervention factors that predict positive changes for IBHC patients. Method Participants were 1,150 consecutive patients (mean age = 30.10 years, 66.6% female, 60.1% Hispanic, 47.9% uninsured) seen for IBHC services at 2 primary care clinics over a 34-month period. Patients presented with depressive (23.2%), anxiety (18.6%), adjustment (11.3%), and childhood externalizing (7.6%) disorders, with 25.7% of patients receiving no diagnosis. Results The most commonly delivered interventions included behavioral activation (26.1%), behavioral medicine-specific consultation (14.6%), relaxation training (10.3%), and parent-management training (8.5%). There was high concordance between diagnoses and evidence-based intervention selection. We used latent growth curve modeling to explore predictors of baseline global assessment of functioning (GAF) and improvements in GAF across sessions, utilizing data from a subset of 117 patients who attended at least 3 behavioral health visits. Hispanic ethnicity and being insured predicted higher baseline GAF, while patients with an anxiety disorder had lower baseline GAF than patients with other diagnoses. Controlling for primary diagnosis, patients receiving behavioral activation or exposure therapy improved at faster rates than patients receiving other interventions. Demographic variables did not relate to rates of improvement. Conclusion Results suggest even brief IBHC interventions can be focused, targeting specific patient concerns with evidence-based treatment components. PMID:25774786

Predicting Teacher Participation in a Classroom-Based, Integrated Preventive Intervention for Preschoolers.

PubMed

Baker, Courtney N; Kupersmidt, Janis B; Voegler-Lee, Mary Ellen; Arnold, David H; Willoughby, Michael T

2010-01-01

Preschools provide a promising setting in which to conduct preventive interventions for childhood problems, but classroom programs can only be effective if teachers are willing and able to implement them. This study is one of the first to investigate predictors of the frequency of teacher participation in a classroom-based, randomized controlled trial of an integrated prevention program for preschoolers. The intervention was designed to promote school readiness with an integrated social and academic program, to be implemented by teachers with the support of classroom consultants. The current study is part of a larger project conducted with Head Start and community child care centers that serve primarily economically disadvantaged families; 49 teachers from 30 centers participated in this study. Overall, teachers conducted approximately 70% of the program activities. Participation decreased significantly over time from the first to the final week of the intervention, and also decreased within each week of the intervention, from the first to the final weekly activity. Teachers working at community child care centers implemented more intervention activities than did Head Start teachers. Teacher concerns about the intervention, assessed prior to training, predicted less participation. In addition, teachers' participation was positively related to their perception that their centers and directors were supportive, collegial, efficient, and fair, as well as their job satisfaction and commitment. Teacher experience, education, ethnicity, and self-efficacy were not significantly related to participation. In multi-level models that considered center as a level of analysis, substantial variance was accounted for by centers, pointing to the importance of considering center-level predictors in future research.

Indirect Effects of the Fast Track Intervention on Conduct Disorder Symptoms and Callous-Unemotional Traits: Distinct Pathways Involving Discipline and Warmth

PubMed Central

Pasalich, Dave S.; Witkiewitz, Katie; McMahon, Robert J.; Pinderhughes, Ellen E.

2016-01-01

Little is known about intervening processes that explain how prevention programs improve particular youth antisocial outcomes. We examined whether parental harsh discipline and warmth in childhood differentially account for Fast Track intervention effects on conduct disorder (CD) symptoms and callous-unemotional (CU) traits in early adolescence. Participants included 891 high-risk kindergarteners (69% male; 51% African American) from urban and rural United States communities who were randomized into either the Fast Track intervention (n = 445) or non-intervention control (n = 446) groups. The 10-year intervention included parent management training and other services (e.g., social skills training, universal classroom curriculum) targeting various risk factors for the development of conduct problems. Harsh discipline (Grades 1 through 3) and warmth (Grades 1 and 2) were measured using parent responses to vignettes and direct observations of parent-child interaction, respectively. Parents reported on children’s CD symptoms in Grade 6 and CU traits in Grade 7. Results demonstrated indirect effects of the Fast Track intervention on reducing risk for youth antisocial outcomes. That is, Fast Track was associated with lower scores on harsh discipline, which in turn predicted decreased levels of CD symptoms. In addition, Fast Track was associated with higher scores on warmth, which in turn predicted reduced levels of CU traits. Our findings inform developmental and intervention models of youth antisocial behavior by providing evidence for the differential role of harsh discipline and warmth in accounting for indirect effects of Fast Track on CD symptoms versus CU traits, respectively. PMID:26242993

Predicting Teacher Participation in a Classroom-Based, Integrated Preventive Intervention for Preschoolers

PubMed Central

Baker, Courtney N.; Kupersmidt, Janis B.; Voegler-Lee, Mary Ellen; Arnold, David H.; Willoughby, Michael T.

2009-01-01

Preschools provide a promising setting in which to conduct preventive interventions for childhood problems, but classroom programs can only be effective if teachers are willing and able to implement them. This study is one of the first to investigate predictors of the frequency of teacher participation in a classroom-based, randomized controlled trial of an integrated prevention program for preschoolers. The intervention was designed to promote school readiness with an integrated social and academic program, to be implemented by teachers with the support of classroom consultants. The current study is part of a larger project conducted with Head Start and community child care centers that serve primarily economically disadvantaged families; 49 teachers from 30 centers participated in this study. Overall, teachers conducted approximately 70% of the program activities. Participation decreased significantly over time from the first to the final week of the intervention, and also decreased within each week of the intervention, from the first to the final weekly activity. Teachers working at community child care centers implemented more intervention activities than did Head Start teachers. Teacher concerns about the intervention, assessed prior to training, predicted less participation. In addition, teachers' participation was positively related to their perception that their centers and directors were supportive, collegial, efficient, and fair, as well as their job satisfaction and commitment. Teacher experience, education, ethnicity, and self-efficacy were not significantly related to participation. In multi-level models that considered center as a level of analysis, substantial variance was accounted for by centers, pointing to the importance of considering center-level predictors in future research. PMID:21103189

Indirect Effects of the Fast Track Intervention on Conduct Disorder Symptoms and Callous-Unemotional Traits: Distinct Pathways Involving Discipline and Warmth.

PubMed

Pasalich, Dave S; Witkiewitz, Katie; McMahon, Robert J; Pinderhughes, Ellen E

2016-04-01

Little is known about intervening processes that explain how prevention programs improve particular youth antisocial outcomes. We examined whether parental harsh discipline and warmth in childhood differentially account for Fast Track intervention effects on conduct disorder (CD) symptoms and callous-unemotional (CU) traits in early adolescence. Participants included 891 high-risk kindergarteners (69% male; 51% African American) from urban and rural United States communities who were randomized into either the Fast Track intervention (n = 445) or non-intervention control (n = 446) groups. The 10-year intervention included parent management training and other services (e.g., social skills training, universal classroom curriculum) targeting various risk factors for the development of conduct problems. Harsh discipline (Grades 1 to 3) and warmth (Grades 1 and 2) were measured using parent responses to vignettes and direct observations of parent-child interaction, respectively. Parents reported on children's CD symptoms in Grade 6 and CU traits in Grade 7. Results demonstrated indirect effects of the Fast Track intervention on reducing risk for youth antisocial outcomes. That is, Fast Track was associated with lower scores on harsh discipline, which in turn predicted decreased levels of CD symptoms. In addition, Fast Track was associated with higher scores on warmth, which in turn predicted reduced levels of CU traits. Our findings inform developmental and intervention models of youth antisocial behavior by providing evidence for the differential role of harsh discipline and warmth in accounting for indirect effects of Fast Track on CD symptoms versus CU traits, respectively.

Developing and Testing a Model to Predict Outcomes of Organizational Change

PubMed Central

Gustafson, David H; Sainfort, François; Eichler, Mary; Adams, Laura; Bisognano, Maureen; Steudel, Harold

2003-01-01

Objective To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. PMID:12785571

A Model for Generating Multi-hazard Scenarios

NASA Astrophysics Data System (ADS)

Lo Jacomo, A.; Han, D.; Champneys, A.

2017-12-01

Communities in mountain areas are often subject to risk from multiple hazards, such as earthquakes, landslides, and floods. Each hazard has its own different rate of onset, duration, and return period. Multiple hazards tend to complicate the combined risk due to their interactions. Prioritising interventions for minimising risk in this context is challenging. We developed a probabilistic multi-hazard model to help inform decision making in multi-hazard areas. The model is applied to a case study region in the Sichuan province in China, using information from satellite imagery and in-situ data. The model is not intended as a predictive model, but rather as a tool which takes stakeholder input and can be used to explore plausible hazard scenarios over time. By using a Monte Carlo framework and varrying uncertain parameters for each of the hazards, the model can be used to explore the effect of different mitigation interventions aimed at reducing the disaster risk within an uncertain hazard context.

Prediction of Knee Joint Contact Forces From External Measures Using Principal Component Prediction and Reconstruction.

PubMed

Saliba, Christopher M; Clouthier, Allison L; Brandon, Scott C E; Rainbow, Michael J; Deluzio, Kevin J

2018-05-29

Abnormal loading of the knee joint contributes to the pathogenesis of knee osteoarthritis. Gait retraining is a non-invasive intervention that aims to reduce knee loads by providing audible, visual, or haptic feedback of gait parameters. The computational expense of joint contact force prediction has limited real-time feedback to surrogate measures of the contact force, such as the knee adduction moment. We developed a method to predict knee joint contact forces using motion analysis and a statistical regression model that can be implemented in near real-time. Gait waveform variables were deconstructed using principal component analysis and a linear regression was used to predict the principal component scores of the contact force waveforms. Knee joint contact force waveforms were reconstructed using the predicted scores. We tested our method using a heterogenous population of asymptomatic controls and subjects with knee osteoarthritis. The reconstructed contact force waveforms had mean (SD) RMS differences of 0.17 (0.05) bodyweight compared to the contact forces predicted by a musculoskeletal model. Our method successfully predicted subject-specific shape features of contact force waveforms and is a potentially powerful tool in biofeedback and clinical gait analysis.

Dispositional and Situational Avoidance and Approach as Predictors of Physical Symptom Bother Following Breast Cancer Diagnosis

PubMed Central

Bauer, Margaret R.; Harris, Lauren N.; Wiley, Joshua F.; Crespi, Catherine M.; Krull, Jennifer L.; Weihs, Karen L.; Stanton, Annette L.

2016-01-01

Background Few studies examine whether dispositional approach and avoidance coping and stressor-specific coping strategies differentially predict physical adjustment to cancer-related stress. Purpose This study examines dispositional and situational avoidance and approach coping as unique predictors of the bother women experience from physical symptoms after breast cancer treatment, as well as whether situational coping mediates the prediction of bother from physical symptoms by dispositional coping. Method Breast cancer patients (N=460) diagnosed within the past 3 months completed self-report measures of dispositional coping at study entry and of situational coping and bother from physical symptoms every 6 weeks through 6 months. Results In multilevel structural equation modeling analyses, both dispositional and situational avoidance predict greater symptom bother. Dispositional, but not situational, approach predicts less symptom bother. Supporting mediation models, dispositional avoidance predicts more symptom bother indirectly through greater situational avoidance. Dispositional approach predicts less symptom bother through less situational avoidance. Conclusion Psychosocial interventions to reduce cancer-related avoidance coping are warranted for cancer survivors who are high in dispositional avoidance and/or low in dispositional approach. PMID:26769023

Dispositional and Situational Avoidance and Approach as Predictors of Physical Symptom Bother Following Breast Cancer Diagnosis.

PubMed

Bauer, Margaret R; Harris, Lauren N; Wiley, Joshua F; Crespi, Catherine M; Krull, Jennifer L; Weihs, Karen L; Stanton, Annette L

2016-06-01

Few studies examine whether dispositional approach and avoidance coping and stressor-specific coping strategies differentially predict physical adjustment to cancer-related stress. This study examines dispositional and situational avoidance and approach coping as unique predictors of the bother women experience from physical symptoms after breast cancer treatment, as well as whether situational coping mediates the prediction of bother from physical symptoms by dispositional coping. Breast cancer patients (N = 460) diagnosed within the past 3 months completed self-report measures of dispositional coping at study entry and of situational coping and bother from physical symptoms every 6 weeks through 6 months. In multilevel structural equation modeling analyses, both dispositional and situational avoidance predict greater symptom bother. Dispositional, but not situational, approach predicts less symptom bother. Supporting mediation models, dispositional avoidance predicts more symptom bother indirectly through greater situational avoidance. Dispositional approach predicts less symptom bother through less situational avoidance. Psychosocial interventions to reduce cancer-related avoidance coping are warranted for cancer survivors who are high in dispositional avoidance and/or low in dispositional approach.

Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy.

PubMed

Asadi, Hamed; Dowling, Richard; Yan, Bernard; Mitchell, Peter

2014-01-01

Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke. We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data. We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408). We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less

Attitudes and Beliefs of Adolescent Experimental Smokers: A Smoking Prevention Perspective.

ERIC Educational Resources Information Center

Wang, Min Qi; And Others

1996-01-01

Examines the relationships of smoking-related beliefs, attitudes, and smoking status, with a focus on experimental smoking. Survey results of 9,774 respondents suggest that attitude and belief variables can adequately predict smoking stages of adolescents as defined by the stage model of smoking acquisition. Argues that intervention with…

School Climate and Bullying Victimization: A Latent Class Growth Model Analysis

ERIC Educational Resources Information Center

Gage, Nicholas A.; Prykanowski, Debra A.; Larson, Alvin

2014-01-01

Researchers investigating school-level approaches for bullying prevention are beginning to discuss and target school climate as a construct that (a) may predict prevalence and (b) be an avenue for school-wide intervention efforts (i.e., increasing positive school climate). Although promising, research has not fully examined and established the…

Decision support systems for plant disease and insect management in commercial nurseries in the Midwest: A perspective review

USDA-ARS?s Scientific Manuscript database

Decision-support systems (DDSs) are techniques that help decision makers utilize models to solve problems under complex and uncertain conditions. Predicting conditions that warrant intervention is a key tenet of the concept of integrated pest management (IPM) with the use of expert systems and pest ...

Predicting First-Grade Reading Performance from Kindergarten Response to Tier 1 Instruction

ERIC Educational Resources Information Center

Al Otaiba, Stephanie; Folsom, Jessica S.; Schatschneider, Christopher; Wanzek, Jeanne; Greulich, Luana; Meadows, Jane; Li, Zhi; Connor, Carol M.

2011-01-01

Many schools are implementing multitier response-to-intervention (RTI) models to reduce reading difficulties. This study was part of our larger ongoing longitudinal RTI investigation within the Florida Learning Disabilities Center grant and was conducted in 7 ethnically and socioeconomically diverse schools. We observed reading instruction in 20…

How to optimize tuberculosis case finding: explorations for Indonesia with a health system model

PubMed Central

2009-01-01

Background A mathematical model was designed to explore the impact of three strategies for better tuberculosis case finding. Strategies included: (1) reducing the number of tuberculosis patients who do not seek care; (2) reducing diagnostic delay; and (3) engaging non-DOTS providers in the referral of tuberculosis suspects to DOTS services in the Indonesian health system context. The impact of these strategies on tuberculosis mortality and treatment outcome was estimated using a mathematical model of the Indonesian health system. Methods The model consists of multiple compartments representing logical movement of a respiratory symptomatic (tuberculosis suspect) through the health system, including patient- and health system delays. Main outputs of the model are tuberculosis death rate and treatment outcome (i.e. full or partial cure). We quantified the model parameters for the Jogjakarta province context, using a two round Delphi survey with five Indonesian tuberculosis experts. Results The model validation shows that four critical model outputs (average duration of symptom onset to treatment, detection rate, cure rate, and death rate) were reasonably close to existing available data, erring towards more optimistic outcomes than are actually reported. The model predicted that an intervention to reduce the proportion of tuberculosis patients who never seek care would have the biggest impact on tuberculosis death prevention, while an intervention resulting in more referrals of tuberculosis suspects to DOTS facilities would yield higher cure rates. This finding is similar for situations where the alternative sector is a more important health resource, such as in most other parts of Indonesia. Conclusion We used mathematical modeling to explore the impact of Indonesian health system interventions on tuberculosis treatment outcome and deaths. Because detailed data were not available regarding the current Indonesian population, we relied on expert opinion to quantify the parameters. The fact that the model output showed similar results to epidemiological data suggests that the experts had an accurate understanding of this subject, thereby reassuring the quality of our predictions. The model highlighted the potential effectiveness of active case finding of tuberculosis patients with limited access to DOTS facilities in the developing country setting. PMID:19505296

Sample entropy predicts lifesaving interventions in trauma patients with normal vital signs.

PubMed

Naraghi, L; Mejaddam, A Y; Birkhan, O A; Chang, Y; Cropano, C M; Mesar, T; Larentzakis, A; Peev, M; Sideris, A C; Van der Wilden, G M; Imam, A M; Hwabejire, J O; Velmahos, G C; Fagenholz, P J; Yeh, D; de Moya, M A; King, D R

2015-08-01

Heart rate complexity, commonly described as a "new vital sign," has shown promise in predicting injury severity, but its use in clinical practice is not yet widely adopted. We previously demonstrated the ability of this noninvasive technology to predict lifesaving interventions (LSIs) in trauma patients. This study was conducted to prospectively evaluate the utility of real-time, automated, noninvasive, instantaneous sample entropy (SampEn) analysis to predict the need for an LSI in a trauma alert population presenting with normal vital signs. Prospective enrollment of patients who met criteria for trauma team activation and presented with normal vital signs was conducted at a level I trauma center. High-fidelity electrocardiogram recording was used to calculate SampEn and SD of the normal-to-normal R-R interval (SDNN) continuously in real time for 2 hours with a portable, handheld device. Patients who received an LSI were compared to patients without any intervention (non-LSI). Multivariable analysis was performed to control for differences between the groups. Treating clinicians were blinded to results. Of 129 patients enrolled, 38 (29%) received 136 LSIs within 24 hours of hospital arrival. Initial systolic blood pressure was similar in both groups. Lifesaving intervention patients had a lower Glasgow Coma Scale. The mean SampEn on presentation was 0.7 (0.4-1.2) in the LSI group compared to 1.5 (1.1-2.0) in the non-LSI group (P < .0001). The area under the curve with initial SampEn alone was 0.73 (95% confidence interval [CI], 0.64-0.81) and increased to 0.93 (95% CI, 0.89-0.98) after adding sedation to the model. Sample entropy of less than 0.8 yields sensitivity, specificity, negative predictive value, and positive predictive value of 58%, 86%, 82%, and 65%, respectively, with an overall accuracy of 76% for predicting an LSI. SD of the normal-to-normal R-R interval had no predictive value. In trauma patients with normal presenting vital signs, decreased SampEn is an independent predictor of the need for LSI. Real-time SampEn analysis may be a useful adjunct to standard vital signs monitoring. Adoption of real-time, instantaneous SampEn monitoring for trauma patients, especially in resource-constrained environments, should be considered. Copyright © 2015 Elsevier Inc. All rights reserved.

Predictive Models of Cognitive Outcomes of Developmental Insults

NASA Astrophysics Data System (ADS)

Chan, Yupo; Bouaynaya, Nidhal; Chowdhury, Parimal; Leszczynska, Danuta; Patterson, Tucker A.; Tarasenko, Olga

2010-04-01

Representatives of Arkansas medical, research and educational institutions have gathered over the past four years to discuss the relationship between functional developmental perturbations and their neurological consequences. We wish to track the effect on the nervous system by developmental perturbations over time and across species. Except for perturbations, the sequence of events that occur during neural development was found to be remarkably conserved across mammalian species. The tracking includes consequences on anatomical regions and behavioral changes. The ultimate goal is to develop a predictive model of long-term genotypic and phenotypic outcomes that includes developmental insults. Such a model can subsequently be fostered into an educated intervention for therapeutic purposes. Several datasets were identified to test plausible hypotheses, ranging from evoked potential datasets to sleep-disorder datasets. An initial model may be mathematical and conceptual. However, we expect to see rapid progress as large-scale gene expression studies in the mammalian brain permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions. These genes ultimately control behavior. By using a validated model we endeavor to make useful predictions.

Assessing the population health impact of market interventions to improve access to antiretroviral treatment

PubMed Central

Bärnighausen, Till; Kyle, Margaret; Salomon, Joshua A; Waning, Brenda

2012-01-01

Despite extraordinary global progress in increasing coverage of antiretroviral treatment (ART), the majority of people needing ART currently are not receiving treatment. Both the number of people needing ART and the average ART price per patient-year are expected to increase in coming years, which will dramatically raise funding needs for ART. Several international organizations are using interventions in ART markets to decrease ART price or to improve ART quality, delivery and innovation, with the ultimate goal of improving population health. These organizations need to select those market interventions that are most likely to substantially affect population health outcomes (ex ante assessment) and to evaluate whether implemented interventions have improved health outcomes (ex post assessment). We develop a framework to structure ex ante and ex post assessment of the population health impact of market interventions, which is transmitted through effects in markets and health systems. Ex ante assessment should include evaluation of the safety and efficacy of the ART products whose markets will be affected by the intervention; theoretical consideration of the mechanisms through which the intervention will affect population health; and predictive modelling to estimate the potential population health impact of the intervention. For ex post assessment, analysts need to consider which outcomes to estimate empirically and which to model based on empirical findings and understanding of the economic and biological mechanisms along the causal pathway from market intervention to population health. We discuss methods for ex post assessment and analyse assessment issues (unintended intervention effects, interaction effects between different interventions, and assessment impartiality and cost). We offer seven recommendations for ex ante and ex post assessment of population health impact of market interventions. PMID:21914713

Auditory Processing in Noise: A Preschool Biomarker for Literacy.

PubMed

White-Schwoch, Travis; Woodruff Carr, Kali; Thompson, Elaine C; Anderson, Samira; Nicol, Trent; Bradlow, Ann R; Zecker, Steven G; Kraus, Nina

2015-07-01

Learning to read is a fundamental developmental milestone, and achieving reading competency has lifelong consequences. Although literacy development proceeds smoothly for many children, a subset struggle with this learning process, creating a need to identify reliable biomarkers of a child's future literacy that could facilitate early diagnosis and access to crucial early interventions. Neural markers of reading skills have been identified in school-aged children and adults; many pertain to the precision of information processing in noise, but it is unknown whether these markers are present in pre-reading children. Here, in a series of experiments in 112 children (ages 3-14 y), we show brain-behavior relationships between the integrity of the neural coding of speech in noise and phonology. We harness these findings into a predictive model of preliteracy, revealing that a 30-min neurophysiological assessment predicts performance on multiple pre-reading tests and, one year later, predicts preschoolers' performance across multiple domains of emergent literacy. This same neural coding model predicts literacy and diagnosis of a learning disability in school-aged children. These findings offer new insight into the biological constraints on preliteracy during early childhood, suggesting that neural processing of consonants in noise is fundamental for language and reading development. Pragmatically, these findings open doors to early identification of children at risk for language learning problems; this early identification may in turn facilitate access to early interventions that could prevent a life spent struggling to read.

The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30-day readmission.

PubMed

Baillie, Charles A; VanZandbergen, Christine; Tait, Gordon; Hanish, Asaf; Leas, Brian; French, Benjamin; Hanson, C William; Behta, Maryam; Umscheid, Craig A

2013-12-01

Identification of patients at high risk for readmission is a crucial step toward improving care and reducing readmissions. The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions. To develop and implement an automated prediction model integrated into our health system's EHR that identifies on admission patients at high risk for readmission within 30 days of discharge. Retrospective and prospective cohort. Healthcare system consisting of 3 hospitals. All adult patients admitted from August 2009 to September 2012. An automated readmission risk flag integrated into the EHR. Thirty-day all-cause and 7-day unplanned healthcare system readmissions. Using retrospective data, a single risk factor, ≥ 2 inpatient admissions in the past 12 months, was found to have the best balance of sensitivity (40%), positive predictive value (31%), and proportion of patients flagged (18%), with a C statistic of 0.62. Sensitivity (39%), positive predictive value (30%), proportion of patients flagged (18%), and C statistic (0.61) during the 12-month period after implementation of the risk flag were similar. There was no evidence for an effect of the intervention on 30-day all-cause and 7-day unplanned readmission rates in the 12-month period after implementation. An automated prediction model was effectively integrated into an existing EHR and identified patients on admission who were at risk for readmission within 30 days of discharge. © 2013 Society of Hospital Medicine.

Disease elimination and re-emergence in differential-equation models.

PubMed

Greenhalgh, Scott; Galvani, Alison P; Medlock, Jan

2015-12-21

Traditional differential equation models of disease transmission are often used to predict disease trajectories and evaluate the effectiveness of alternative intervention strategies. However, such models cannot account explicitly for probabilistic events, such as those that dominate dynamics when disease prevalence is low during the elimination and re-emergence phases of an outbreak. To account for the dynamics at low prevalence, i.e. the elimination and risk of disease re-emergence, without the added analytical and computational complexity of a stochastic model, we develop a novel application of control theory. We apply our approach to analyze historical data of measles elimination and re-emergence in Iceland from 1923 to 1938, predicting the temporal trajectory of local measles elimination and re-emerge as a result of disease migration from Copenhagen, Denmark. Copyright © 2015 Elsevier Ltd. All rights reserved.

Achieving Remission in Gulf War Illness: A Simulation-Based Approach to Treatment Design.

PubMed

Craddock, Travis J A; Del Rosario, Ryan R; Rice, Mark; Zysman, Joel P; Fletcher, Mary Ann; Klimas, Nancy G; Broderick, Gordon

2015-01-01

Gulf War Illness (GWI) is a chronic multi-symptom disorder affecting up to one-third of the 700,000 returning veterans of the 1991 Persian Gulf War and for which there is no known cure. GWI symptoms span several of the body's principal regulatory systems and include debilitating fatigue, severe musculoskeletal pain, cognitive and neurological problems. Using computational models, our group reported previously that GWI might be perpetuated at least in part by natural homeostatic regulation of the neuroendocrine-immune network. In this work, we attempt to harness these regulatory dynamics to identify treatment courses that might produce lasting remission. Towards this we apply a combinatorial optimization scheme to the Monte Carlo simulation of a discrete ternary logic model that represents combined hypothalamic-pituitary-adrenal (HPA), gonadal (HPG), and immune system regulation in males. In this work we found that no single intervention target allowed a robust return to normal homeostatic control. All combined interventions leading to a predicted remission involved an initial inhibition of Th1 inflammatory cytokines (Th1Cyt) followed by a subsequent inhibition of glucocorticoid receptor function (GR). These first two intervention events alone ended in stable and lasting return to the normal regulatory control in 40% of the simulated cases. Applying a second cycle of this combined treatment improved this predicted remission rate to 2 out of 3 simulated subjects (63%). These results suggest that in a complex illness such as GWI, a multi-tiered intervention strategy that formally accounts for regulatory dynamics may be required to reset neuroendocrine-immune homeostasis and support extended remission.

Evidence-based selection of theories for designing behaviour change interventions: using methods based on theoretical construct domains to understand clinicians' blood transfusion behaviour.

PubMed

Francis, Jill J; Stockton, Charlotte; Eccles, Martin P; Johnston, Marie; Cuthbertson, Brian H; Grimshaw, Jeremy M; Hyde, Chris; Tinmouth, Alan; Stanworth, Simon J

2009-11-01

Many theories of behaviour are potentially relevant to predictive and intervention studies but most studies investigate a narrow range of theories. Michie et al. (2005) agreed 12 'theoretical domains' from 33 theories that explain behaviour change. They developed a 'Theoretical Domains Interview' (TDI) for identifying relevant domains for specific clinical behaviours, but the framework has not been used for selecting theories for predictive studies. It was used here to investigate clinicians' transfusion behaviour in intensive care units (ICU). Evidence suggests that red blood cells transfusion could be reduced for some patients without reducing quality of care. (1) To identify the domains relevant to transfusion practice in ICUs and neonatal intensive care units (NICUs), using the TDI. (2) To use the identified domains to select appropriate theories for a study predicting transfusion behaviour. An adapted TDI about managing a patient with borderline haemoglobin by watching and waiting instead of transfusing red blood cells was used to conduct semi-structured, one-to-one interviews with 18 intensive care consultants and neonatologists across the UK. Relevant theoretical domains were: knowledge, beliefs about capabilities, beliefs about consequences, social influences, behavioural regulation. Further analysis at the construct level resulted in selection of seven theoretical approaches relevant to this context: Knowledge-Attitude-Behaviour Model, Theory of Planned Behaviour, Social Cognitive Theory, Operant Learning Theory, Control Theory, Normative Model of Work Team Effectiveness and Action Planning Approaches. This study illustrated, the use of the TDI to identify relevant domains in a complex area of inpatient care. This approach is potentially valuable for selecting theories relevant to predictive studies and resulted in greater breadth of potential explanations than would be achieved if a single theoretical model had been adopted.

Predicting having condoms available among adolescents: the role of personal norm and enjoyment.

PubMed

Jellema, Ilke J; Abraham, Charles; Schaalma, Herman P; Gebhardt, Winifred A; van Empelen, Pepijn

2013-05-01

Having condoms available has been shown to be an important predictor of condom use. We examined whether or not personal norm and goal enjoyment contribute to predicting having condoms available in the context of cognition specified by the theory of planned behaviour (TPB). Prospective survey study, with a baseline and follow-up measurement (at 3 months). Data were gathered using an online survey. In total 282 adolescents (mean age = 15.6, 74% female adolescents) completed both questionnaires. At baseline, demographics, sexual experience, condom use, TPB variables, descriptive norm, personal norm, and enjoyment towards having condoms available were measured. At T2 (3 months later) having condoms available was measured. Direct and moderating effects of personal norm and goal enjoyment were examined by means of hierarchical linear regression analyses. Regression analyses yielded a direct effect of self-efficacy and personal norm on condom availability. In addition, moderation of the intention-behaviour relation by goal enjoyment added to the variance explained. The final model explained approximately 35% of the variance in condom availability. Personal norm and goal enjoyment add to the predictive utility of a TPB model of having condoms available and may be useful intervention targets. What is already known about this subject? Having condoms available is an important prerequisite for actual condom use. The theory of planned behaviour has successfully been applied to explain condom availability behaviour. The theory of planned behaviour has been criticized for not adequately taking into account affective motivation. What does this study add? Personal norm and goal enjoyment add to the predictive utility of the model. Personal norm explains condom availability directly, enjoyment increases intention enactment. Personal norm and goal enjoyment therefore are useful intervention targets. © 2012 The British Psychological Society.

Behavior predictors of language development over 2 years in children with autism spectrum disorders.

PubMed

Bopp, Karen D; Mirenda, Pat; Zumbo, Bruno D

2009-10-01

This exploratory study examined predictive relationships between 5 types of behaviors and the trajectories of vocabulary and language development in young children with autism over 2 years. Participants were 69 children with autism assessed using standardized measures prior to the initiation of early intervention (T1) and 6 months (T2), 12 months (T3), and 24 months (T4) later. Growth curve modeling examined the extent to which behaviors at T1 and changes in behaviors between T1 and T2 predicted changes in development from T1 to T4. Regardless of T1 nonverbal IQ and autism severity, high scores for inattentive behaviors at T1 predicted lower rates of change in vocabulary production and language comprehension over 2 years. High scores for social unresponsiveness at T1 predicted lower rates of change in vocabulary comprehension and production and in language comprehension over 2 years. Scores for insistence on sameness behaviors, repetitive stereotypic motor behaviors, and acting-out behaviors at T1 did not predict the rate of change of any child measure over 2 years beyond differences accounted for by T1 autism severity and nonverbal IQ status. The results are discussed with regard to their implications for early intervention and understanding the complex factors that affect developmental outcomes.

High adherence is necessary to realize health gains from water quality interventions.

PubMed

Brown, Joe; Clasen, Thomas

2012-01-01

Safe drinking water is critical for health. Household water treatment (HWT) has been recommended for improving access to potable water where existing sources are unsafe. Reports of low adherence to HWT may limit the usefulness of this approach, however. We constructed a quantitative microbial risk model to predict gains in health attributable to water quality interventions based on a range of assumptions about pre-treatment water quality; treatment effectiveness in reducing bacteria, viruses, and protozoan parasites; adherence to treatment interventions; volume of water consumed per person per day; and other variables. According to mean estimates, greater than 500 DALYs may be averted per 100,000 person-years with increased access to safe water, assuming moderately poor pre-treatment water quality that is a source of risk and high treatment adherence (>90% of water consumed is treated). A decline in adherence from 100% to 90% reduces predicted health gains by up to 96%, with sharpest declines when pre-treatment water quality is of higher risk. Results suggest that high adherence is essential in order to realize potential health gains from HWT.

Potential Impact of Graphic Health Warnings on Cigarette Packages in Reducing Cigarette Demand and Smoking-Related Deaths in Vietnam.

PubMed

Minh, Hoang Van; Chung, Le Hong; Giang, Kim Bao; Duc, Duong Minh; Hinh, Nguyen Duc; Mai, Vu Quynh; Cuong, Nguyen Manh; Manh, Pham Duc; Duc, Ha Anh; Yang, Jui-Chen

2016-01-01

Two years after implementation of the graphic health warning intervention in Vietnam, it is very important to evaluate the intervention's potential impact. The objective of this paper was to predict effects of graphic health warnings on cigarette packages, particularly in reducing cigarette demand and smoking-associated deaths in Vietnam. In this study, a discrete choice experiment (DCE) method was used to evaluate the potential impact of graphic tobacco health warnings on smoking demand. To predict the impact of GHWs on reducing premature deaths associated with smoking, we constructed different static models. We adapted the method developed by University of Toronto, Canada and found that GHWs had statistically significant impact on reducing cigarette demand (up to 10.1% through images of lung damage), resulting in an overall decrease of smoking prevalence in Vietnam. We also found that between 428,417- 646,098 premature deaths would be prevented as a result of the GHW intervention. The potential impact of the GHW labels on reducing premature smoking-associated deaths in Vietnam were shown to be stronger among lower socio-economic groups.

High Adherence Is Necessary to Realize Health Gains from Water Quality Interventions

PubMed Central

Brown, Joe; Clasen, Thomas

2012-01-01

Background Safe drinking water is critical for health. Household water treatment (HWT) has been recommended for improving access to potable water where existing sources are unsafe. Reports of low adherence to HWT may limit the usefulness of this approach, however. Methods and Findings We constructed a quantitative microbial risk model to predict gains in health attributable to water quality interventions based on a range of assumptions about pre-treatment water quality; treatment effectiveness in reducing bacteria, viruses, and protozoan parasites; adherence to treatment interventions; volume of water consumed per person per day; and other variables. According to mean estimates, greater than 500 DALYs may be averted per 100,000 person-years with increased access to safe water, assuming moderately poor pre-treatment water quality that is a source of risk and high treatment adherence (>90% of water consumed is treated). A decline in adherence from 100% to 90% reduces predicted health gains by up to 96%, with sharpest declines when pre-treatment water quality is of higher risk. Conclusions Results suggest that high adherence is essential in order to realize potential health gains from HWT. PMID:22586491

Determinants of Antibiotic Consumption - Development of a Model using Partial Least Squares Regression based on Data from India.

PubMed

Tamhankar, Ashok J; Karnik, Shreyasee S; Stålsby Lundborg, Cecilia

2018-04-23

Antibiotic resistance, a consequence of antibiotic use, is a threat to health, with severe consequences for resource constrained settings. If determinants for human antibiotic use in India, a lower middle income country, with one of the highest antibiotic consumption in the world could be understood, interventions could be developed, having implications for similar settings. Year wise data for India, for potential determinants and antibiotic consumption, was sourced from publicly available databases for the years 2000-2010. Data was analyzed using Partial Least Squares regression and correlation between determinants and antibiotic consumption was evaluated, formulating 'Predictors' and 'Prediction models'. The 'prediction model' with the statistically most significant predictors (root mean square errors of prediction for train set-377.0 and test set-297.0) formulated from a combination of Health infrastructure + Surface transport infrastructure (HISTI), predicted antibiotic consumption within 95% confidence interval and estimated an antibiotic consumption of 11.6 standard units/person (14.37 billion standard units totally; standard units = number of doses sold in the country; a dose being a pill, capsule, or ampoule) for India for 2014. The HISTI model may become useful in predicting antibiotic consumption for countries/regions having circumstances and data similar to India, but without resources to measure actual data of antibiotic consumption.

Toward computational crime prediction. Comment on "Statistical physics of crime: A review" by M.R. D'Orsogna and M. Perc

NASA Astrophysics Data System (ADS)

Ferrara, Emilio

2015-03-01

Containing the spreading of crime in modern society in an ongoing battle: our understanding of the dynamics underlying criminal events and the motifs behind individuals therein involved is crucial to design cost-effective prevention policies and intervention strategies. During recent years we witnessed various research fields joining forces, sharing models and methods, toward modeling and quantitatively characterizing crime and criminal behavior.

Determinants of Prosocial Behavior in Included Versus Excluded Contexts

PubMed Central

Cuadrado, Esther; Tabernero, Carmen; Steinel, Wolfgang

2016-01-01

Prosocial behavior (PSB) is increasingly becoming necessary as more and more individuals experience exclusion. In this context it is important to understand the motivational determinants of PSB. Here we report two experiments which analyzed the influence of dispositional (prosocialness; rejection sensitivity) and motivational variables (prosocial self-efficacy; prosocial collective efficacy; trust; anger; social affiliation motivation) on PSB under neutral contexts (Study 1), and once under inclusion or exclusion conditions (Study 2). Both studies provided evidence for the predicted mediation of PSB. Results in both neutral and inclusion and exclusion conditions supported our predictive model of PSB. In the model dispositional variables predicted motivational variables, which in turn predicted PSB. We showed that the investigated variables predicted PSB; this suggests that to promote PSB one could (1) foster prosocialness, prosocial self and collective efficacy, trust in others and affiliation motivation and (2) try to reduce negative feelings and the tendency to dread rejection in an attempt to reduce the negative impact that these variables have on PSB. Moreover, the few differences that emerged in the model between the inclusion and exclusion contexts suggested that in interventions with excluded individuals special care emphasis should be placed on addressing rejection sensitivity and lack of trust. PMID:26779103

Determinants of Prosocial Behavior in Included Versus Excluded Contexts.

PubMed

Cuadrado, Esther; Tabernero, Carmen; Steinel, Wolfgang

2015-01-01

Prosocial behavior (PSB) is increasingly becoming necessary as more and more individuals experience exclusion. In this context it is important to understand the motivational determinants of PSB. Here we report two experiments which analyzed the influence of dispositional (prosocialness; rejection sensitivity) and motivational variables (prosocial self-efficacy; prosocial collective efficacy; trust; anger; social affiliation motivation) on PSB under neutral contexts (Study 1), and once under inclusion or exclusion conditions (Study 2). Both studies provided evidence for the predicted mediation of PSB. Results in both neutral and inclusion and exclusion conditions supported our predictive model of PSB. In the model dispositional variables predicted motivational variables, which in turn predicted PSB. We showed that the investigated variables predicted PSB; this suggests that to promote PSB one could (1) foster prosocialness, prosocial self and collective efficacy, trust in others and affiliation motivation and (2) try to reduce negative feelings and the tendency to dread rejection in an attempt to reduce the negative impact that these variables have on PSB. Moreover, the few differences that emerged in the model between the inclusion and exclusion contexts suggested that in interventions with excluded individuals special care emphasis should be placed on addressing rejection sensitivity and lack of trust.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitchell, Hugh D.; Eisfeld, Amie J.; Sims, Amy

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metricsmore » that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.« less

Prefrontal mediation of the reading network predicts intervention response in dyslexia.

PubMed

Aboud, Katherine S; Barquero, Laura A; Cutting, Laurie E

2018-04-01

A primary challenge facing the development of interventions for dyslexia is identifying effective predictors of intervention response. While behavioral literature has identified core cognitive characteristics of response, the distinction of reading versus executive cognitive contributions to response profiles remains unclear, due in part to the difficulty of segregating these constructs using behavioral outputs. In the current study we used functional neuroimaging to piece apart the mechanisms of how/whether executive and reading network relationships are predictive of intervention response. We found that readers who are responsive to intervention have more typical pre-intervention functional interactions between executive and reading systems compared to nonresponsive readers. These findings suggest that intervention response in dyslexia is influenced not only by domain-specific reading regions, but also by contributions from intervening domain-general networks. Our results make a significant gain in identifying predictive bio-markers of outcomes in dyslexia, and have important implications for the development of personalized clinical interventions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of initiation and cessation of breastfeeding from late pregnancy to 16 weeks: the Feeding Your Baby (FYB) cohort study

PubMed Central

Donnan, Peter T; Dalzell, Janet; Symon, Andrew; Rauchhaus, Petra; Monteith-Hodge, Ewa; Kellett, Gillian; Wyatt, Jeremy C; Whitford, Heather M

2013-01-01

Objective To derive prediction models for both initiation and cessation of breastfeeding using demographic, psychological and obstetric variables. Design A prospective cohort study. Setting Women delivering at Ninewells Hospital, Dundee, UK. Data sources Demographic data and psychological measures were obtained during pregnancy by questionnaire. Birth details, feeding method at birth and at hospital discharge were obtained from the Ninewells hospital database, Dundee, UK. Breastfeeding women were followed up by text messages every 2 weeks until 16 weeks or until breastfeeding was discontinued to ascertain feeding method and feeding intentions. Participants Pregnant women over 30 weeks gestation aged 16 years and above, living in Dundee, booked to deliver at Ninewells Hospital, Dundee, and able to speak English. Main outcome measure Initiation and cessation of breastfeeding. Results From the total cohort of women at delivery (n=344) 68% (95% CI 63% to 73%) of women had started breastfeeding at discharge. Significant predictors of initiating breastfeeding were older age, parity, greater intention to breastfeed from a Theory of Planned Behaviour (TPB)-based questionnaire, higher Iowa Infant Feeding Assessment Scale (IIFAS) score as well as living with a husband or partner. For the final model, the AUROC was 0.967. For those who initiated breastfeeding (n=233), the strongest predictors of stopping were low intention to breastfeed from TPB, low IIFAS score and non-managerial/professional occupations. Conclusions The findings from this study will be used to inform the protocol for an intervention study to encourage and support prolonged breastfeeding as intentions appear to be a key intervention focus for initiation. The predictive models could be used to identify women at high risk of not initiating and also women at high risk of stopping for interventions to improve the longevity of breastfeeding. PMID:23906958

Understanding and seasonal forecasting of hydrological drought in the Anthropocene

NASA Astrophysics Data System (ADS)

Yuan, Xing; Zhang, Miao; Wang, Linying; Zhou, Tian

2017-11-01

Hydrological drought is not only caused by natural hydroclimate variability but can also be directly altered by human interventions including reservoir operation, irrigation, groundwater exploitation, etc. Understanding and forecasting of hydrological drought in the Anthropocene are grand challenges due to complicated interactions among climate, hydrology and humans. In this paper, five decades (1961-2010) of naturalized and observed streamflow datasets are used to investigate hydrological drought characteristics in a heavily managed river basin, the Yellow River basin in north China. Human interventions decrease the correlation between hydrological and meteorological droughts, and make the hydrological drought respond to longer timescales of meteorological drought. Due to large water consumptions in the middle and lower reaches, there are 118-262 % increases in the hydrological drought frequency, up to 8-fold increases in the drought severity, 21-99 % increases in the drought duration and the drought onset is earlier. The non-stationarity due to anthropogenic climate change and human water use basically decreases the correlation between meteorological and hydrological droughts and reduces the effect of human interventions on hydrological drought frequency while increasing the effect on drought duration and severity. A set of 29-year (1982-2010) hindcasts from an established seasonal hydrological forecasting system are used to assess the forecast skill of hydrological drought. In the naturalized condition, the climate-model-based approach outperforms the climatology method in predicting the 2001 severe hydrological drought event. Based on the 29-year hindcasts, the former method has a Brier skill score of 11-26 % against the latter for the probabilistic hydrological drought forecasting. In the Anthropocene, the skill for both approaches increases due to the dominant influence of human interventions that have been implicitly incorporated by the hydrological post-processing, while the difference between the two predictions decreases. This suggests that human interventions can outweigh the climate variability for the hydrological drought forecasting in the Anthropocene, and the predictability for human interventions needs more attention.

Predictive models of control strategies involved in containing indoor airborne infections.

PubMed

Chen, S-C; Chang, C-F; Liao, C-M

2006-12-01

Recently developed control measure modeling approaches for containing airborne infections, including engineering controls with respiratory protection and public health interventions, are readily amenable to an integrated-scale analysis. Here we show that such models can be derived from an integrated-scale analysis generated from three different types of functional relationship: Wells-Riley mathematical model, competing-risks model, and Von Foerster equation, both of the key epidemiological determinants involved and of the functional connections between them. We examine mathematically the impact of engineering control measures such as enhanced air exchange and air filtration rates with personal masking combined with public health interventions such as vaccination, isolation, and contact tracing in containing the spread of indoor airborne infections including influenza, chickenpox, measles, and severe acute respiratory syndrome (SARS). If enhanced engineering controls could reduce the basic reproductive number (R0) below 1.60 for chickenpox and 3 for measles, our simulations show that in such a prepared response with public health interventions would have a high probability of containing the indoor airborne infections. Combinations of engineering control measures and public health interventions could moderately contain influenza strains with an R0 as high as 4. Our analysis indicates that effective isolation of symptomatic patients with low-efficacy contact tracing is sufficient to control a SARS outbreak. We suggest that a valuable added dimension to public health inventions could be provided by systematically quantifying transmissibility and proportion of asymptomatic infection of indoor airborne infection. Practical Implications We have developed a flexible mathematical model that can help determine the best intervention strategies for containing indoor airborne infections. The approach presented here is scalable and can be extended to include additional control efficacies. If a newly emergent airborne infection should appear, the model could be quickly calibrated to data and intervention options at the early stage of the outbreak. Data could be provided from the field to estimate value of R0, the serial interval between cases, the distributions of the latent, incubation, and infectious periods, case fatality rates, and secondary spread within important mixing groups. The combination of enhanced engineering control measures and assigned effective public health interventions would have a high probability for containing airborne infection.

Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS): rationale and design for meta-analyses of individual patient data of randomized controlled trials that evaluate the effect of physical activity and psychosocial interventions on health-related quality of life in cancer survivors

PubMed Central

2013-01-01

Background Effective interventions to improve quality of life of cancer survivors are essential. Numerous randomized controlled trials have evaluated the effects of physical activity or psychosocial interventions on health-related quality of life of cancer survivors, with generally small sample sizes and modest effects. Better targeted interventions may result in larger effects. To realize such targeted interventions, we must determine which interventions that are presently available work for which patients, and what the underlying mechanisms are (that is, the moderators and mediators of physical activity and psychosocial interventions). Individual patient data meta-analysis has been described as the ‘gold standard’ of systematic review methodology. Instead of extracting aggregate data from study reports or from authors, the original research data are sought directly from the investigators. Individual patient data meta-analyses allow for adequate statistical analysis of intervention effects and moderators of such effects. Here, we report the rationale and design of the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) Consortium. The primary aim of POLARIS is 1) to conduct meta-analyses based on individual patient data to evaluate the effect of physical activity and psychosocial interventions on the health-related quality of life of cancer survivors; 2) to identify important demographic, clinical, personal, or intervention-related moderators of the effect; and 3) to build and validate clinical prediction models identifying the most relevant predictors of intervention success. Methods/Design We will invite investigators of randomized controlled trials that evaluate the effects of physical activity and/or psychosocial interventions on health-related quality of life compared with a wait-list, usual care or attention control group among adult cancer survivors to join the POLARIS consortium and share their data for use in pooled analyses that will address the proposed aims. We are in the process of identifying eligible randomized controlled trials through literature searches in four databases. To date, we have identified 132 eligible and unique trials. Discussion The POLARIS consortium will conduct the first individual patient data meta-analyses in order to generate evidence essential to targeting physical activity and psychosocial programs to the individual survivor’s characteristics, capabilities, and preferences. Registration PROSPERO: International prospective register of systematic reviews, CRD42013003805 PMID:24034173

The role of tobacco outlet density in a smoking cessation intervention for urban youth.

PubMed

Mennis, Jeremy; Mason, Michael; Way, Thomas; Zaharakis, Nikola

2016-03-01

This study investigates the role of tobacco outlet density in a randomized controlled trial of a text messaging-based smoking cessation intervention conducted among a sample of 187 primarily African American youth in a midsize U.S. city. A moderated mediation model was used to test whether the indirect effect of residential tobacco outlet density on future smoking was mediated by the intention to smoke, and whether this indirect effect differed between adolescents who received the intervention and those who did not. Results indicated that tobacco outlet density is associated with intention to smoke, which predicts future smoking, and that the indirect effect of tobacco outlet density on future smoking is moderated by the intervention. Tobacco outlet density and the intervention can be viewed as competing forces on future smoking behavior, where higher tobacco outlet density acts to mitigate the sensitivity of an adolescent to the intervention's intended effect. Smoking cessation interventions applied to youth should consider tobacco outlet density as a contextual condition that can influence treatment outcomes. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus.

PubMed

Sweeting, Arianne N; Wong, Jencia; Appelblom, Heidi; Ross, Glynis P; Kouru, Heikki; Williams, Paul F; Sairanen, Mikko; Hyett, Jon A

2018-06-13

Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model. A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM. © 2018 S. Karger AG, Basel.

Spatial Prediction of Coxiella burnetii Outbreak Exposure via Notified Case Counts in a Dose-Response Model.

PubMed

Brooke, Russell J; Kretzschmar, Mirjam E E; Hackert, Volker; Hoebe, Christian J P A; Teunis, Peter F M; Waller, Lance A

2017-01-01

We develop a novel approach to study an outbreak of Q fever in 2009 in the Netherlands by combining a human dose-response model with geostatistics prediction to relate probability of infection and associated probability of illness to an effective dose of Coxiella burnetii. The spatial distribution of the 220 notified cases in the at-risk population are translated into a smooth spatial field of dose. Based on these symptomatic cases, the dose-response model predicts a median of 611 asymptomatic infections (95% range: 410, 1,084) for the 220 reported symptomatic cases in the at-risk population; 2.78 (95% range: 1.86, 4.93) asymptomatic infections for each reported case. The low attack rates observed during the outbreak range from (Equation is included in full-text article.)to (Equation is included in full-text article.). The estimated peak levels of exposure extend to the north-east from the point source with an increasing proportion of asymptomatic infections further from the source. Our work combines established methodology from model-based geostatistics and dose-response modeling allowing for a novel approach to study outbreaks. Unobserved infections and the spatially varying effective dose can be predicted using the flexible framework without assuming any underlying spatial structure of the outbreak process. Such predictions are important for targeting interventions during an outbreak, estimating future disease burden, and determining acceptable risk levels.

Health belief model and reasoned action theory in predicting water saving behaviors in yazd, iran.

PubMed

Morowatisharifabad, Mohammad Ali; Momayyezi, Mahdieh; Ghaneian, Mohammad Taghi

2012-01-01

People's behaviors and intentions about healthy behaviors depend on their beliefs, values, and knowledge about the issue. Various models of health education are used in deter¬mining predictors of different healthy behaviors but their efficacy in cultural behaviors, such as water saving behaviors, are not studied. The study was conducted to explain water saving beha¬viors in Yazd, Iran on the basis of Health Belief Model and Reasoned Action Theory. The cross-sectional study used random cluster sampling to recruit 200 heads of households to collect the data. The survey questionnaire was tested for its content validity and reliability. Analysis of data included descriptive statistics, simple correlation, hierarchical multiple regression. Simple correlations between water saving behaviors and Reasoned Action Theory and Health Belief Model constructs were statistically significant. Health Belief Model and Reasoned Action Theory constructs explained 20.80% and 8.40% of the variances in water saving beha-viors, respectively. Perceived barriers were the strongest Predictor. Additionally, there was a sta¬tistically positive correlation between water saving behaviors and intention. In designing interventions aimed at water waste prevention, barriers of water saving behaviors should be addressed first, followed by people's attitude towards water saving. Health Belief Model constructs, with the exception of perceived severity and benefits, is more powerful than is Reasoned Action Theory in predicting water saving behavior and may be used as a framework for educational interventions aimed at improving water saving behaviors.

Health Belief Model and Reasoned Action Theory in Predicting Water Saving Behaviors in Yazd, Iran

PubMed Central

Morowatisharifabad, Mohammad Ali; Momayyezi, Mahdieh; Ghaneian, Mohammad Taghi

2012-01-01

Background: People's behaviors and intentions about healthy behaviors depend on their beliefs, values, and knowledge about the issue. Various models of health education are used in deter¬mining predictors of different healthy behaviors but their efficacy in cultural behaviors, such as water saving behaviors, are not studied. The study was conducted to explain water saving beha¬viors in Yazd, Iran on the basis of Health Belief Model and Reasoned Action Theory. Methods: The cross-sectional study used random cluster sampling to recruit 200 heads of households to collect the data. The survey questionnaire was tested for its content validity and reliability. Analysis of data included descriptive statistics, simple correlation, hierarchical multiple regression. Results: Simple correlations between water saving behaviors and Reasoned Action Theory and Health Belief Model constructs were statistically significant. Health Belief Model and Reasoned Action Theory constructs explained 20.80% and 8.40% of the variances in water saving beha-viors, respectively. Perceived barriers were the strongest Predictor. Additionally, there was a sta¬tistically positive correlation between water saving behaviors and intention. Conclusion: In designing interventions aimed at water waste prevention, barriers of water saving behaviors should be addressed first, followed by people's attitude towards water saving. Health Belief Model constructs, with the exception of perceived severity and benefits, is more powerful than is Reasoned Action Theory in predicting water saving behavior and may be used as a framework for educational interventions aimed at improving water saving behaviors. PMID:24688927

Clinical time series prediction: towards a hierarchical dynamical system framework

PubMed Central

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. PMID:25534671

Predictive factors of alcohol and tobacco use in adolescents.

PubMed

Alvarez-Aguirre, Alicia; Alonso-Castillo, María Magdalena; Zanetti, Ana Carolina Guidorizzi

2014-01-01

to analyze the effect of self-esteem, assertiveness, self-efficacy and resiliency on alcohol and tobacco consumption in adolescents. a descriptive and correlational study was undertaken with 575 adolescents in 2010. The Self-Esteem Scale, the Situational Confidence Scale, the Assertiveness Questionnaire and the Resiliency Scale were used. the adjustment of the logistic regression model, considering age, sex, self-esteem, assertiveness, self-efficacy and resiliency, demonstrates significance in the consumption of alcohol and tobacco. Age, resiliency and assertiveness predict alcohol consumption in the lifetime and assertiveness predicts alcohol consumption in the last year. Similarly, age and sex predict tobacco consumption in the lifetime and age in the last year. this study can offer important information to plan nursing interventions involving adolescent alcohol and tobacco users.

Predicting Student Success using Analytics in Course Learning Management Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olama, Mohammed M; Thakur, Gautam; McNair, Wade

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems,more » called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.« less

Predicting student success using analytics in course learning management systems

NASA Astrophysics Data System (ADS)

Olama, Mohammed M.; Thakur, Gautam; McNair, Allen W.; Sukumar, Sreenivas R.

2014-05-01

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students' scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.

Application of Machine Learning to Predict Dietary Lapses During Weight Loss.

PubMed

Goldstein, Stephanie P; Zhang, Fengqing; Thomas, John G; Butryn, Meghan L; Herbert, James D; Forman, Evan M

2018-05-01

Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of research showing that lapses are predictable using a variety of physiological, environmental, and psychological indicators. With recent technological advancements, it may be possible to assess these triggers and predict dietary lapses in real time. The current study sought to use machine learning techniques to predict lapses and evaluate the utility of combining both group- and individual-level data to enhance lapse prediction. The current study trained and tested a machine learning algorithm capable of predicting dietary lapses from a behavioral weight loss program among adults with overweight/obesity (n = 12). Participants were asked to follow a weight control diet for 6 weeks and complete ecological momentary assessment (EMA; repeated brief surveys delivered via smartphone) regarding dietary lapses and relevant triggers. WEKA decision trees were used to predict lapses with an accuracy of 0.72 for the group of participants. However, generalization of the group algorithm to each individual was poor, and as such, group- and individual-level data were combined to improve prediction. The findings suggest that 4 weeks of individual data collection is recommended to attain optimal model performance. The predictive algorithm could be utilized to provide in-the-moment interventions to prevent dietary lapses and therefore enhance weight losses. Furthermore, methods in the current study could be translated to other types of health behavior lapses.

Grade of hypospadias is the only factor predicting for re-intervention after primary hypospadias repair: a multivariate analysis from a cohort of 474 patients.

PubMed

Spinoit, Anne-Françoise; Poelaert, Filip; Van Praet, Charles; Groen, Luitzen-Albert; Van Laecke, Erik; Hoebeke, Piet

2015-04-01

There is an ongoing quest on how to minimize complications in hypospadias surgery. There is however a lack of high-quality data on the following parameters that might influence the outcome of primary hypospadias repair: age at initial surgery, the type of suture material, the initial technique, and the type of hypospadias. The objective of this study was to identify independent predictors for re-intervention in primary hypospadias repair. We retrospectively analyzed our database of 474 children undergoing primary hypospadias surgery. Univariate and multivariate logistic regression was performed to identify variables associated with re-intervention. A p-value <0.05 was considered statistically significant and therefore considered as a prognostic factor for re-intervention. Distal penile hypospadias was reported in 77.2% (n = 366), midpenile in 11.4% (n = 54) and proximal in 11.4% (n = 54) of children. Initial repair was based on an incised plate technique in 39.9% (n = 189), meatal advancement in 36.0% (n = 171), an onlay flap in 17.3% (n = 82) and other or combined techniques in 5.3% (n = 25). In 114 patients (24.1%) re-intervention was required (n = 114) of which 54 re-interventions (47.4%) were performed within the first year post-surgery, 17 (14.9%) in the second year and 43 (37.7%) later than 2 years after initial surgery. The reason for the first re-intervention was fistula in 52 patients (46.4%), meatal stenosis in 32 (28.6%), cosmesis in 35 (31.3%) and other in 14 (12.5%). The median time for re-intervention was 14 months after surgery [range 0-114]. Significant predictors for re-intervention on univariate logistic regression (polyglactin suture material versus poliglecaprone, proximal hypospadias, lower age at operation and other than meatal advancement repair) were put in a multivariate logistic regression model. Of all significant variables, only proximal hypospadias remained an independent predictor for re-intervention (OR 3.27; p = 0.012). The grade of hypospadias remains according to our retrospective analysis the only objective independent predicting factor for re-intervention in hypospadias surgery. This finding is rather obvious for everyone operating hypospadias. Curiously midpenile hypospadias cases were doing slightly better than distal hypospadias in terms of re-intervention rates. Our study however has also some shortcomings. First of all, data was gathered retrospectively and follow-up time was ill-balanced for several variables. We tried to correct this by applying sensitivity analysis, but possible associations between some variables and re-intervention might still be obscured by this. Standard questionnaires to analyze surgical outcome were not available. Therefore, we focused our analysis on re-intervention rate as this is a hard and clinically relevant end point. This retrospective analysis of a large hypospadias database with long-term follow-up indicates that the long-lasting debate about factors influencing the reoperation rate in hypospadias surgery might be futile: in experienced hands, the only variable that independently predicts for re-intervention is the severity of hypospadias, the only factor we cannot modify. This retrospective multivariate analysis of a large hypospadias database with long-term follow-up suggests that the only significant independent predictive factor for re-intervention is proximal hypospadias. In our series, technique did not influence the re-intervention rate. Copyright © 2015 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

The effects of school closures on influenza outbreaks and pandemics: systematic review of simulation studies.

PubMed

Jackson, Charlotte; Mangtani, Punam; Hawker, Jeremy; Olowokure, Babatunde; Vynnycky, Emilia

2014-01-01

School closure is a potential intervention during an influenza pandemic and has been investigated in many modelling studies. To systematically review the effects of school closure on influenza outbreaks as predicted by simulation studies. We searched Medline and Embase for relevant modelling studies published by the end of October 2012, and handsearched key journals. We summarised the predicted effects of school closure on the peak and cumulative attack rates and the duration of the epidemic. We investigated how these predictions depended on the basic reproduction number, the timing and duration of closure and the assumed effects of school closures on contact patterns. School closures were usually predicted to be most effective if they caused large reductions in contact, if transmissibility was low (e.g. a basic reproduction number <2), and if attack rates were higher in children than in adults. The cumulative attack rate was expected to change less than the peak, but quantitative predictions varied (e.g. reductions in the peak were frequently 20-60% but some studies predicted >90% reductions or even increases under certain assumptions). This partly reflected differences in model assumptions, such as those regarding population contact patterns. Simulation studies suggest that school closure can be a useful control measure during an influenza pandemic, particularly for reducing peak demand on health services. However, it is difficult to accurately quantify the likely benefits. Further studies of the effects of reactive school closures on contact patterns are needed to improve the accuracy of model predictions.

Reduced Reward-driven Eating Accounts for the Impact of a Mindfulness-Based Diet and Exercise Intervention on Weight Loss: Data from the SHINE Randomized Controlled Trial

PubMed Central

Mason, Ashley E.; Epel, Elissa S.; Aschbacher, Kirstin; Lustig, Robert H.; Acree, Michael; Kristeller, Jean; Cohn, Michael; Dallman, Mary; Moran, Patricia J.; Bacchetti, Peter; Laraia, Barbara; Hecht, Frederick M.; Daubenmier, Jennifer

2016-01-01

Many individuals with obesity report overeating despite intentions to maintain or lose weight. Two barriers to long-term weight loss are reward-driven eating, which is characterized by a lack of control over eating, a preoccupation with food, and a lack of satiety; and psychological stress. Mindfulness training may address these barriers by promoting awareness of hunger and satiety cues, self-regulatory control, and stress reduction. We examined these two barriers as potential mediators of weight loss in the Supporting Health by Integrating Nutrition and Exercise (SHINE) randomized controlled trial, which compared the effects of a 5.5-month diet and exercise intervention with or without mindfulness training on weight loss among adults with obesity. Intention-to-treat multiple mediation models tested whether post-intervention reward-driven eating and psychological stress mediated the impact of intervention arm on weight loss at 12-and 18-months post-baseline among 194 adults with obesity (BMI: 30–45). Mindfulness (relative to control) participants had significant reductions in reward-driven eating at 6 months (post-intervention), which, in turn, predicted weight loss at 12 months. Post-intervention reward-driven eating mediated 47.1% of the total intervention arm effect on weight loss at 12 months [β=-0.06, SE(β)=0.03, p=.030, 95% CI (−0.12, −0.01)]. This mediated effect was reduced when predicting weight loss at 18 months (p=.396), accounting for 23.0% of the total intervention effect, despite similar weight loss at 12 months. Psychological stress did not mediate the effect of intervention arm on weight loss at 12 or 18 months. In conclusion, reducing reward-driven eating, which can be achieved using a diet and exercise intervention that includes mindfulness training, may promote weight loss (clinicaltrials.gov registration: NCT00960414). PMID:26867697

Reduced reward-driven eating accounts for the impact of a mindfulness-based diet and exercise intervention on weight loss: Data from the SHINE randomized controlled trial.

PubMed

Mason, Ashley E; Epel, Elissa S; Aschbacher, Kirstin; Lustig, Robert H; Acree, Michael; Kristeller, Jean; Cohn, Michael; Dallman, Mary; Moran, Patricia J; Bacchetti, Peter; Laraia, Barbara; Hecht, Frederick M; Daubenmier, Jennifer

2016-05-01

Many individuals with obesity report over eating despite intentions to maintain or lose weight. Two barriers to long-term weight loss are reward-driven eating, which is characterized by a lack of control over eating, a preoccupation with food, and a lack of satiety; and psychological stress. Mindfulness training may address these barriers by promoting awareness of hunger and satiety cues, self-regulatory control, and stress reduction. We examined these two barriers as potential mediators of weight loss in the Supporting Health by Integrating Nutrition and Exercise (SHINE) randomized controlled trial, which compared the effects of a 5.5-month diet and exercise intervention with or without mindfulness training on weight loss among adults with obesity. Intention-to-treat multiple mediation models tested whether post-intervention reward-driven eating and psychological stress mediated the impact of intervention arm on weight loss at 12- and 18-months post-baseline among 194 adults with obesity (BMI: 30-45). Mindfulness (relative to control) participants had significant reductions in reward-driven eating at 6 months (post-intervention), which, in turn, predicted weight loss at 12 months. Post-intervention reward-driven eating mediated 47.1% of the total intervention arm effect on weight loss at 12 months [β = -0.06, SE(β) = 0.03, p = .030, 95% CI (-0.12, -0.01)]. This mediated effect was reduced when predicting weight loss at 18 months (p = .396), accounting for 23.0% of the total intervention effect, despite similar weight loss at 12 months. Psychological stress did not mediate the effect of intervention arm on weight loss at 12 or 18 months. In conclusion, reducing reward-driven eating, which can be achieved using a diet and exercise intervention that includes mindfulness training, may promote weight loss (clinicaltrials.gov registration: NCT00960414). Published by Elsevier Ltd.

Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors.

PubMed

Vuong, Kylie; Armstrong, Bruce K; Weiderpass, Elisabete; Lund, Eiliv; Adami, Hans-Olov; Veierod, Marit B; Barrett, Jennifer H; Davies, John R; Bishop, D Timothy; Whiteman, David C; Olsen, Catherine M; Hopper, John L; Mann, Graham J; Cust, Anne E; McGeechan, Kevin

2016-08-01

Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies. To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors. We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women's Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma). We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women's Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness. The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women's Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction model to classify individuals as high risk compared with classifying all individuals as high risk. The melanoma risk prediction model performs well and may be useful in prevention interventions reliant on a risk assessment using self-assessed risk factors.

Effect of virtual reality on time perception in patients receiving chemotherapy

PubMed Central

Kisby, Cassandra K.; Flint, Elizabeth P.

2013-01-01

Purpose Virtual reality (VR) during chemotherapy has resulted in an elapsed time compression effect, validating the attention diversion capabilities of VR. Using the framework of the pacemaker–accumulator cognitive model of time perception, this study explored the influence of age, gender, state anxiety, fatigue, and cancer diagnosis in predicting the difference between actual time elapsed during receipt of intravenous chemotherapy while immersed in a VR environment versus patient’s retrospective estimates of time elapsed during this treatment. Materials and methods This secondary analysis from three studies yielded a pooled sample of N=137 participants with breast, lung, or colon cancer. Each study employed a crossover design requiring two matched intravenous chemotherapy treatments, with participants randomly assigned to receive VR during one treatment. Regressions modeled the effect of demographic variables, diagnosis, and Piper Fatigue Scale and State Anxiety Inventory scores on the difference between actual and estimated time elapsed during chemotherapy with VR. Results In a forward regression model, three predictors (diagnosis, gender, and anxiety) explained a significant portion of the variability for altered time perception (F=5.06, p=0.0008). Diagnosis was the strongest predictor; individuals with breast and colon cancer perceived time passed more quickly. Conclusions VR is a noninvasive intervention that can make chemotherapy treatments more tolerable. Women with breast cancer are more likely and lung cancer patients less likely to experience altered time perception during VR (a possible indicator of effectiveness for this distraction intervention). Understanding factors that predict responses to interventions can help clinicians tailor coping strategies to meet each patient’s needs. PMID:20336327

Forecasting burden of long-term disability from neonatal conditions: results from the Projahnmo I trial, Sylhet, Bangladesh.

PubMed

Shillcutt, Samuel D; Lefevre, Amnesty E; Lee, Anne C C; Baqui, Abdullah H; Black, Robert E; Darmstadt, Gary L

2013-07-01

The burden of disease resulting from neonatal conditions is substantial in developing countries. From 2003 to 2005, the Projahnmo I programme delivered community-based interventions for maternal and newborn health in Sylhet, Bangladesh. This analysis quantifies burden of disability and incorporates non-fatal outcomes into cost-effectiveness analysis of interventions delivered in the Projahnmo I programme. A decision tree model was created to predict disability resulting from preterm birth, neonatal meningitis and intrapartum-related hypoxia ('birth asphyxia'). Outcomes were defined as the years lost to disability (YLD) component of disability-adjusted life years (DALYs). Calculations were based on data from the Projahnmo I trial, supplemented with values from published literature and expert opinion where data were absent. 195 YLD per 1000 neonates [95% confidence interval (CI): 157-241] were predicted in the main calculation, sensitive to different DALY assumptions, disability weights and alternative model structures. The Projahnmo I home care intervention may have averted 2.0 (1.3-2.8) YLD per 1000 neonates. Compared with calculations based on reductions in mortality alone, the cost-effectiveness ratio decreased by only 0.6% from $105.23 to $104.62 ($65.15-$266.60) when YLD were included, with 0.6% more DALYs averted [total 338/1000 (95% CI: 131-542)]. A significant burden of disability results from neonatal conditions in Sylhet, Bangladesh. Adding YLD has very little impact on recommendations based on cost-effectiveness, even at the margin of programme adoption. This model provides guidance for collecting data on disabilities in new settings.

Simplifying contrast-induced acute kidney injury prediction after primary percutaneous coronary intervention: the age, creatinine and ejection fraction score.

PubMed

Araujo, Gustavo N; Pivatto Junior, Fernando; Fuhr, Bruno; Cassol, Elvis P; Machado, Guilherme P; Valle, Felipe H; Bergoli, Luiz C; Wainstein, Rodrigo V; Polanczyk, Carisi A; Wainstein, Marco V

2017-05-24

Contrast-induced acute kidney injury (CI-AKI) is a common event after percutaneous coronary intervention (PCI). Presently, the main strategy to avoid CI-AKI lies in saline hydration, since to date none pharmacologic prophylaxis proved beneficial. Our aim was to determine if a low complexity mortality risk model is able to predict CI-AKI in patients undergoing PCI after ST elevation myocardial infarction (STEMI). We have included patients with STEMI submitted to primary PCI in a tertiary hospital. The definition of CI-AKI was a raise of 0.3 mg/dL or 50% in post procedure (24-72 h) serum creatinine compared to baseline. Age, glomerular filtration and ejection fraction were used to calculate ACEF-MDRD score. We have included 347 patients with mean age of 60 years. In univariate analysis, age, diabetes, previous ASA use, Killip 3 or 4 at admission, ACEF-MDRD and Mehran scores were predictors of CI-AKI. After multivariate adjustment, only ACEF-MDRD score and diabetes remained CI-AKI predictors. Areas under the ROC curve of ACEF-MDRD and Mehran scores were 0.733 (0.68-0.78) and 0.649 (0.59-0.70), respectively. When we compared both scores with DeLong test ACEF-MDRDs AUC was greater than Mehran's (P = 0.03). An ACEF-MDRD score of 2.33 or lower has a negative predictive value of 92.6% for development of CI-AKI. ACEF-MDRD score is a user-friendly tool that has an excellent CI-AKI predictive accuracy in patients undergoing primary percutaneous coronary intervention. Moreover, a low ACEF-MDRD score has a very good negative predictive value for CI-AKI, which makes this complication unlikely in patients with an ACEF-MDRD score of <2.33.

A proposed analytic framework for determining the impact of an antimicrobial resistance intervention.

PubMed

Grohn, Yrjo T; Carson, Carolee; Lanzas, Cristina; Pullum, Laura; Stanhope, Michael; Volkova, Victoriya

2017-06-01

Antimicrobial use (AMU) is increasingly threatened by antimicrobial resistance (AMR). The FDA is implementing risk mitigation measures promoting prudent AMU in food animals. Their evaluation is crucial: the AMU/AMR relationship is complex; a suitable framework to analyze interventions is unavailable. Systems science analysis, depicting variables and their associations, would help integrate mathematics/epidemiology to evaluate the relationship. This would identify informative data and models to evaluate interventions. This National Institute for Mathematical and Biological Synthesis AMR Working Group's report proposes a system framework to address the methodological gap linking livestock AMU and AMR in foodborne bacteria. It could evaluate how AMU (and interventions) impact AMR. We will evaluate pharmacokinetic/dynamic modeling techniques for projecting AMR selection pressure on enteric bacteria. We study two methods to model phenotypic AMR changes in bacteria in the food supply and evolutionary genotypic analyses determining molecular changes in phenotypic AMR. Systems science analysis integrates the methods, showing how resistance in the food supply is explained by AMU and concurrent factors influencing the whole system. This process is updated with data and techniques to improve prediction and inform improvements for AMU/AMR surveillance. Our proposed framework reflects both the AMR system's complexity, and desire for simple, reliable conclusions.

By Ounce or By Calorie: The Differential Effects of Alternative Sugar-Sweetened Beverage Tax Strategies

PubMed Central

Zhen, Chen; Brissette, Ian F.; Ruff, Ryan R.

2014-01-01

The obesity epidemic and excessive consumption of sugar-sweetened beverages have led to proposals of economics-based interventions to promote healthy eating in the United States. Targeted food and beverage taxes and subsidies are prominent examples of such potential intervention strategies. This paper examines the differential effects of taxing sugar-sweetened beverages by calories and by ounces on beverage demand. To properly measure the extent of substitution and complementarity between beverage products, we developed a fully modified distance metric model of differentiated product demand that endogenizes the cross-price effects. We illustrated the proposed methodology in a linear approximate almost ideal demand system, although other flexible demand systems can also be used. In the empirical application using supermarket scanner data, the product-level demand model consists of 178 beverage products with combined market share of over 90%. The novel demand model outperformed the conventional distance metric model in non-nested model comparison tests and in terms of the economic significance of model predictions. In the fully modified model, a calorie-based beverage tax was estimated to cost $1.40 less in compensating variation than an ounce-based tax per 3,500 beverage calories reduced. This difference in welfare cost estimates between two tax strategies is more than three times as much as the difference estimated by the conventional distance metric model. If applied to products purchased from all sources, a 0.04-cent per kcal tax on sugar-sweetened beverages is predicted to reduce annual per capita beverage intake by 5,800 kcal. PMID:25414517

The impact of a social network intervention on retention in Belgian therapeutic communities: a quasi-experimental study.

PubMed

Soyez, Veerle; De Leon, George; Broekaert, Eric; Rosseel, Yves

2006-07-01

Although numerous studies recognize the importance of social network support in engaging substance abusers into treatment, there is only limited knowledge of the impact of network involvement and support during treatment. The primary objective of this research was to enhance retention in Therapeutic Community treatment utilizing a social network intervention. The specific goals of this study were (1) to determine whether different pre-treatment factors predicted treatment retention in a Therapeutic Community; and (2) to determine whether participation of significant others in a social network intervention predicted treatment retention. Consecutive admissions to four long-term residential Therapeutic Communities were assessed at intake (n = 207); the study comprised a mainly male (84.9%) sample of polydrug (41.1%) and opiate (20.8%) abusers, of whom 64.4% had ever injected drugs. Assessment involved the European version of the Addiction Severity Index (EuropASI), the Circumstances, Motivation, Readiness scales (CMR), the Dutch version of the family environment scale (GKS/FES) and an in-depth interview on social network structure and perceived social support. Network members of different cohorts were assigned to a social network intervention, which consisted of three elements (a video, participation at an induction day and participation in a discussion session). Hierarchical regression analyses showed that client-perceived social support (F1,198 = 10.9, P = 0.001) and treatment motivation and readiness (F1,198 = 8.8; P = 0.003) explained a significant proportion of the variance in treatment retention (model fit: F7,197 = 4.4; P = 0.000). By including the variable 'significant others' participation in network intervention' (network involvement) in the model, the fit clearly improved (F1,197 = 6.2; P = 0.013). At the same time, the impact of perceived social support decreased (F1,197 = 2.9; P = 0.091). Participation in the social network intervention was associated with improved treatment retention controlling for other client characteristics. This suggests that the intervention may be of benefit in the treatment of addicted individuals.

Seeing the "Big" Picture: Big Data Methods for Exploring Relationships Between Usage, Language, and Outcome in Internet Intervention Data.

PubMed

Carpenter, Jordan; Crutchley, Patrick; Zilca, Ran D; Schwartz, H Andrew; Smith, Laura K; Cobb, Angela M; Parks, Acacia C

2016-08-31

Assessing the efficacy of Internet interventions that are already in the market introduces both challenges and opportunities. While vast, often unprecedented amounts of data may be available (hundreds of thousands, and sometimes millions of participants with high dimensions of assessed variables), the data are observational in nature, are partly unstructured (eg, free text, images, sensor data), do not include a natural control group to be used for comparison, and typically exhibit high attrition rates. New approaches are therefore needed to use these existing data and derive new insights that can augment traditional smaller-group randomized controlled trials. Our objective was to demonstrate how emerging big data approaches can help explore questions about the effectiveness and process of an Internet well-being intervention. We drew data from the user base of a well-being website and app called Happify. To explore effectiveness, multilevel models focusing on within-person variation explored whether greater usage predicted higher well-being in a sample of 152,747 users. In addition, to explore the underlying processes that accompany improvement, we analyzed language for 10,818 users who had a sufficient volume of free-text response and timespan of platform usage. A topic model constructed from this free text provided language-based correlates of individual user improvement in outcome measures, providing insights into the beneficial underlying processes experienced by users. On a measure of positive emotion, the average user improved 1.38 points per week (SE 0.01, t122,455=113.60, P<.001, 95% CI 1.36-1.41), about an 11% increase over 8 weeks. Within a given individual user, more usage predicted more positive emotion and less usage predicted less positive emotion (estimate 0.09, SE 0.01, t6047=9.15, P=.001, 95% CI .07-.12). This estimate predicted that a given user would report positive emotion 1.26 points (or 1.26%) higher after a 2-week period when they used Happify daily than during a week when they didn't use it at all. Among highly engaged users, 200 automatically clustered topics showed a significant (corrected P<.001) effect on change in well-being over time, illustrating which topics may be more beneficial than others when engaging with the interventions. In particular, topics that are related to addressing negative thoughts and feelings were correlated with improvement over time. Using observational analyses on naturalistic big data, we can explore the relationship between usage and well-being among people using an Internet well-being intervention and provide new insights into the underlying mechanisms that accompany it. By leveraging big data to power these new types of analyses, we can explore the workings of an intervention from new angles, and harness the insights that surface to feed back into the intervention and improve it further in the future.

Seeing the “Big” Picture: Big Data Methods for Exploring Relationships Between Usage, Language, and Outcome in Internet Intervention Data

PubMed Central

Carpenter, Jordan; Crutchley, Patrick; Zilca, Ran D; Schwartz, H Andrew; Smith, Laura K; Cobb, Angela M

2016-01-01

Background Assessing the efficacy of Internet interventions that are already in the market introduces both challenges and opportunities. While vast, often unprecedented amounts of data may be available (hundreds of thousands, and sometimes millions of participants with high dimensions of assessed variables), the data are observational in nature, are partly unstructured (eg, free text, images, sensor data), do not include a natural control group to be used for comparison, and typically exhibit high attrition rates. New approaches are therefore needed to use these existing data and derive new insights that can augment traditional smaller-group randomized controlled trials. Objective Our objective was to demonstrate how emerging big data approaches can help explore questions about the effectiveness and process of an Internet well-being intervention. Methods We drew data from the user base of a well-being website and app called Happify. To explore effectiveness, multilevel models focusing on within-person variation explored whether greater usage predicted higher well-being in a sample of 152,747 users. In addition, to explore the underlying processes that accompany improvement, we analyzed language for 10,818 users who had a sufficient volume of free-text response and timespan of platform usage. A topic model constructed from this free text provided language-based correlates of individual user improvement in outcome measures, providing insights into the beneficial underlying processes experienced by users. Results On a measure of positive emotion, the average user improved 1.38 points per week (SE 0.01, t122,455=113.60, P<.001, 95% CI 1.36–1.41), about a 27% increase over 8 weeks. Within a given individual user, more usage predicted more positive emotion and less usage predicted less positive emotion (estimate 0.09, SE 0.01, t6047=9.15, P=.001, 95% CI .07–.12). This estimate predicted that a given user would report positive emotion 1.26 points higher after a 2-week period when they used Happify daily than during a week when they didn’t use it at all. Among highly engaged users, 200 automatically clustered topics showed a significant (corrected P<.001) effect on change in well-being over time, illustrating which topics may be more beneficial than others when engaging with the interventions. In particular, topics that are related to addressing negative thoughts and feelings were correlated with improvement over time. Conclusions Using observational analyses on naturalistic big data, we can explore the relationship between usage and well-being among people using an Internet well-being intervention and provide new insights into the underlying mechanisms that accompany it. By leveraging big data to power these new types of analyses, we can explore the workings of an intervention from new angles, and harness the insights that surface to feed back into the intervention and improve it further in the future. PMID:27580524

Probabilistic Model for Listeria monocytogenes Growth during Distribution, Retail Storage, and Domestic Storage of Pasteurized Milk ▿

PubMed Central

Koutsoumanis, Konstantinos; Pavlis, Athanasios; Nychas, George-John E.; Xanthiakos, Konstantinos

2010-01-01

A survey on the time-temperature conditions of pasteurized milk in Greece during transportation to retail, retail storage, and domestic storage and handling was performed. The data derived from the survey were described with appropriate probability distributions and introduced into a growth model of Listeria monocytogenes in pasteurized milk which was appropriately modified for taking into account strain variability. Based on the above components, a probabilistic model was applied to evaluate the growth of L. monocytogenes during the chill chain of pasteurized milk using a Monte Carlo simulation. The model predicted that, in 44.8% of the milk cartons released in the market, the pathogen will grow until the time of consumption. For these products the estimated mean total growth of L. monocytogenes during transportation, retail storage, and domestic storage was 0.93 log CFU, with 95th and 99th percentiles of 2.68 and 4.01 log CFU, respectively. Although based on EU regulation 2073/2005 pasteurized milk produced in Greece belongs to the category of products that do not allow the growth of L. monocytogenes due to a shelf life (defined by law) of 5 days, the above results show that this shelf life limit cannot prevent L. monocytogenes from growing under the current chill chain conditions. The predicted percentage of milk cartons—initially contaminated with 1 cell/1-liter carton—in which the pathogen exceeds the safety criterion of 100 cells/ml at the time of consumption was 0.14%. The probabilistic model was used for an importance analysis of the chill chain factors, using rank order correlation, while selected intervention and shelf life increase scenarios were evaluated. The results showed that simple interventions, such as excluding the door shelf from the domestic storage of pasteurized milk, can effectively reduce the growth of the pathogen. The door shelf was found to be the warmest position in domestic refrigerators, and it was most frequently used by the consumers for domestic storage of pasteurized milk. Furthermore, the model predicted that a combination of this intervention with a decrease of the mean temperature of domestic refrigerators by 2°C may allow an extension of pasteurized milk shelf life from 5 to 7 days without affecting the current consumer exposure to L. monocytogenes. PMID:20139308

Developing an objective evaluation method to estimate diabetes risk in community-based settings.

PubMed

Kenya, Sonjia; He, Qing; Fullilove, Robert; Kotler, Donald P

2011-05-01

Exercise interventions often aim to affect abdominal obesity and glucose tolerance, two significant risk factors for type 2 diabetes. Because of limited financial and clinical resources in community and university-based environments, intervention effects are often measured with interviews or questionnaires and correlated with weight loss or body fat indicated by body bioimpedence analysis (BIA). However, self-reported assessments are subject to high levels of bias and low levels of reliability. Because obesity and body fat are correlated with diabetes at different levels in various ethnic groups, data reflecting changes in weight or fat do not necessarily indicate changes in diabetes risk. To determine how exercise interventions affect diabetes risk in community and university-based settings, improved evaluation methods are warranted. We compared a noninvasive, objective measurement technique--regional BIA--with whole-body BIA for its ability to assess abdominal obesity and predict glucose tolerance in 39 women. To determine regional BIA's utility in predicting glucose, we tested the association between the regional BIA method and blood glucose levels. Regional BIA estimates of abdominal fat area were significantly correlated (r = 0.554, P < 0.003) with fasting glucose. When waist circumference and family history of diabetes were added to abdominal fat in multiple regression models, the association with glucose increased further (r = 0.701, P < 0.001). Regional BIA estimates of abdominal fat may predict fasting glucose better than whole-body BIA as well as provide an objective assessment of changes in diabetes risk achieved through physical activity interventions in community settings.