Predictors of job satisfaction among academic family medicine faculty: Findings from a faculty work-life and leadership survey.

PubMed

Krueger, Paul; White, David; Meaney, Christopher; Kwong, Jeffrey; Antao, Viola; Kim, Florence

2017-03-01

To identify predictors of job satisfaction among academic family medicine faculty members. A comprehensive Web-based survey of all faculty members in an academic department of family medicine. Bivariate and multivariable analyses (logistic regression) were used to identify variables associated with job satisfaction. The Department of Family and Community Medicine at the University of Toronto in Ontario and its 15 affiliated community teaching hospitals and community-based teaching practices. All 1029 faculty members in the Department of Family and Community Medicine were invited to complete the survey. Faculty members' demographic and practice information; teaching, clinical, administration, and research activities; leadership roles; training needs and preferences; mentorship experiences; health status; stress levels; burnout levels; and job satisfaction. Faculty members' perceptions about supports provided, recognition, communication, retention, workload, teamwork, respect, resource distribution, remuneration, and infrastructure support. Faculty members' job satisfaction, which was the main outcome variable, was obtained from the question, "Overall, how satisfied are you with your job?" Of the 1029 faculty members, 687 (66.8%) responded to the survey. Bivariate analyses revealed 26 predictors as being statistically significantly associated with job satisfaction, including faculty members' ratings of their local department and main practice setting, their ratings of leadership and mentorship experiences, health status variables, and demographic variables. The multivariable analyses identified the following 5 predictors of job satisfaction: the Maslach Burnout Inventory subscales of emotional exhaustion and personal accomplishment; being born in Canada; the overall quality of mentorship that was received being rated as very good or excellent; and teamwork being rated as very good or excellent. The findings from this study show that job satisfaction among academic family medicine faculty members is a multi-dimensional construct. Future improvement in overall level of job satisfaction will therefore require multiple strategies. Copyright© the College of Family Physicians of Canada.

Predictability of a favorable outcome in anorexia nervosa.

PubMed

Deter, H C; Schellberg, D; Köpp, W; Friederich, H C; Herzog, W

2005-03-01

In a long-term follow-up of anorexia nervosa (AN) patients, somatic, psychological and social variables at clinical presentation should be investigated using a multilevel approach. This study isolated predictors known from the literature over longer time periods and carried out a separate investigation of predictors in a sample of 81 AN patients of the Heidelberg-Mannheim study over a mean period of 12 years (range 9-19 years). Separate hierarchic regression analyses on the basis of the course of the Morgan-Russell categories were calculated for four individually recorded areas: anamnestic, psychological, somatic and social data sets. Age at the onset of the disease, purging behavior, low serum albumin, high glutamic-oxalo acetic transaminase (GOT) psychopathology (ANSS) and social pathology had the highest predictive value qualities. In survival analysis overall assessment of all six main predictors at clinical presentation could differentiate all patients who recovered from those who remained ill (log-rank test P = 0.019). A small number of variables were important for detecting a good or poor long-term course of AN. At onset of the disease, it seems necessary to evaluate these psychological, somatic and social predictors.

Linking coral river runoff proxies with climate variability, hydrology and land-use in Madagascar catchments.

PubMed

Maina, Joseph; de Moel, Hans; Vermaat, Jan E; Bruggemann, J Henrich; Guillaume, Mireille M M; Grove, Craig A; Madin, Joshua S; Mertz-Kraus, Regina; Zinke, Jens

2012-10-01

Understanding the linkages between coastal watersheds and adjacent coral reefs is expected to lead to better coral reef conservation strategies. Our study aims to examine the main predictors of environmental proxies recorded in near shore corals and therefore how linked near shore reefs are to the catchment physical processes. To achieve these, we developed models to simulate hydrology of two watersheds in Madagascar. We examined relationships between environmental proxies derived from massive Porites spp. coral cores (spectral luminescence and barium/calcium ratios), and corresponding time-series (1950-2006) data of hydrology, climate, land use and human population growth. Results suggest regional differences in the main environmental drivers of reef sedimentation: on annual time-scales, precipitation, river flow and sediment load explained the variability in coral proxies of river discharge for the northeast region, while El Niño-Southern Oscillation (ENSO) and temperature (air and sea surface) were the best predictors in the southwest region. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dengue: recent past and future threats

PubMed Central

Rogers, David J.

2015-01-01

This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases?(2) Is the spatial resolution of a climate dataset an important determinant of model accuracy?(3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit?(4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future?Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite–Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions. PMID:25688021

Paranormal belief, experience, and the Keirsey Temperament Sorter.

PubMed

Fox, J; Williams, C

2000-06-01

121 college students completed the Anomalous Experience Inventory and the Keirsey Temperament Sorter. Multiple regression analyses provided significant models predicting both Paranormal Experience and Belief; the main predictors were the other subscales of the Anomalous Experience Inventory with the Keirsey variables playing only a minor role.

Predictability and prediction of the total number of winter extremely cold days over China

NASA Astrophysics Data System (ADS)

Luo, Xiao; Wang, Bin

2018-03-01

The current dynamical climate models have limited skills in predicting winter temperature in China. The present study uses physics-based empirical models (PEMs) to explore the sources and limits of the seasonal predictability in the total number of extremely cold days (NECD) over China. A combined cluster-rotated EOF analysis reveals two sub-regions of homogeneous variability among hundreds of stations, namely the Northeast China (NE) and Main China (MC). This reduces the large-number of predictands to only two indices, the NCED-NE and NCED-MC, which facilitates detection of the common sources of predictability for all stations. The circulation anomalies associated with the NECD-NE exhibit a zonally symmetric Arctic Oscillation-like pattern, whereas those associated with the NECD-MC feature a North-South dipolar pattern over Asia. The predictability of the NECD originates from SST and snow cover anomalies in the preceding September and October. However, the two regions have different SST predictors: The NE predictor is in the western Eurasian Arctic while the MC predictor is over the tropical-North Pacific. The October snow cover predictors also differ: The NE predictor primarily resides in the central Eurasia while the MC predictor is over the western and eastern Eurasia. The PEM prediction results suggest that about 60% (55%) of the total variance of winter NECD over the NE (Main) China are likely predictable 1 month in advance. The NECD at each station can also be predicted by using the four predictors that were detected for the two indices. The cross-validated temporal correlation skills exceed 0.70 at most stations. The physical mechanisms by which the autumn Arctic sea ice, snow cover, and tropical-North Pacific SST anomalies affect winter NECD over the NE and Main China are discussed.

Leaf growth dynamics in four plant species of the Patagonian Monte, Argentina.

PubMed

Campanella, M Victoria; Bertiller, Mónica B

2013-07-01

Studying plant responses to environmental variables is an elemental key to understand the functioning of arid ecosystems. We selected four dominant species of the two main life forms. The species selected were two evergreen shrubs: Larrea divaricata and Chuquiraga avellanedae and two perennial grasses: Nassella tenuis and Pappostipa speciosa. We registered leaf/shoot growth, leaf production and environmental variables (precipitation, air temperature, and volumetric soil water content at two depths) during summer-autumn and winter-spring periods. Multiple regressions were used to test the predictive power of the environmental variables. During the summer-autumn period, the strongest predictors of leaf/shoot growth and leaf production were the soil water content of the upper layer and air temperature while during the winter-spring period, the strongest predictor was air temperature. In conclusion, we found that the leaf/shoot growth and leaf production were associated with current environmental conditions, specially to soil water content and air temperature.

Binary logistic regression modelling: Measuring the probability of relapse cases among drug addict

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Alias, Siti Nor Shadila

2014-07-01

For many years Malaysia faced the drug addiction issues. The most serious case is relapse phenomenon among treated drug addict (drug addict who have under gone the rehabilitation programme at Narcotic Addiction Rehabilitation Centre, PUSPEN). Thus, the main objective of this study is to find the most significant factor that contributes to relapse to happen. The binary logistic regression analysis was employed to model the relationship between independent variables (predictors) and dependent variable. The dependent variable is the status of the drug addict either relapse, (Yes coded as 1) or not, (No coded as 0). Meanwhile the predictors involved are age, age at first taking drug, family history, education level, family crisis, community support and self motivation. The total of the sample is 200 which the data are provided by AADK (National Antidrug Agency). The finding of the study revealed that age and self motivation are statistically significant towards the relapse cases..

Prediction of employer-employee relationships from sociodemographic variables and social values in Brunei public and private sector workers.

PubMed

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer-employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer-employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer-employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer-employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer-employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer-employee relationship problems. Other significant social value correlates, but not predictors of employer-employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer-employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its possible solutions.

Prediction of employer–employee relationships from sociodemographic variables and social values in Brunei public and private sector workers

PubMed Central

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer–employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer–employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer–employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer–employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer–employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer–employee relationship problems. Other significant social value correlates, but not predictors of employer–employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer–employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its possible solutions. PMID:28769597

Evaluation of Selected Recycling Curricula: Educating the Green Citizen.

ERIC Educational Resources Information Center

Boerschig, Sally; De Young, Raymond

1993-01-01

Solid waste curricula from various programs around the country were reviewed using eight variables identified as predictors of conservation behavior. Scores demonstrated that solid waste curricula focus mainly on knowledge and include, to a lesser extent, attitude change and action strategies. Lists the 14 programs evaluated in the study. (MDH)

Analysis of Eighth Graders' Performance On Standardized Mathematics Tests.

ERIC Educational Resources Information Center

Meyinsse, Joseph; Tashakkori, Abbas

The main objective of this study was to show whether eighth graders' performance on standardized mathematics tests could be predicted from a variety of variables. These predictors included the students' race/ethnicity, gender, attitudes toward mathematics, students' time spent on homework, whether parents helped with homework assignments,…

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Using worldwide edaphic data to model plant species niches: An assessment at a continental extent

PubMed Central

Galvão, Franklin; Villalobos, Fabricio; De Marco Júnior, Paulo

2017-01-01

Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species’ geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist. PMID:29049298

Predictors of patient dependence in mild-to-moderate Alzheimer's disease.

PubMed

Benke, Thomas; Sanin, Günter; Lechner, Anita; Dal-Bianco, Peter; Ransmayr, Gerhard; Uranüs, Margarete; Marksteiner, Josef; Gaudig, Maren; Schmidt, Reinhold

2015-01-01

Patient dependence has rarely been studied in mild-to-moderate Alzheimer's disease (AD). To identify factors which predict patient dependence in mild-to-moderate AD. We studied 398 non-institutionalized AD patients (234 females) of the ongoing Prospective Registry on Dementia (PRODEM) in Austria. The Dependence Scale (DS) was used to assess patient dependence. Patient assessment comprised functional abilities, neuropsychiatric symptoms and cognitive functions. A multiple linear regression analysis was performed to identify predictors of patient dependence. AD patients were mildly-to-moderately impaired (mean scores and SDs were: CDR 0.84 ± 0.43; DAD 74.4 ± 23.3, MMSE = 22.5 ± 3.6). Psychopathology and caregiver burden were in the low range (mean NPI score 13.2, range 0 to 98; mean ZBI score 18, range 0-64). Seventy five percent of patients were classified as having a mild level of patient dependence (DS sum score 0 to 6). Patient dependence correlated significantly and positively with age, functional measures, psychopathology and depression, disease duration, and caregiver burden. Significant negative, but low correlations were found between patient dependence, cognitive variables, and global cognition. Activities of daily living, patient age, and disease severity accounted for 63% of variance in patient dependence, whereas cognitive variables accounted for only 11%. Dependence in this cohort was mainly related to age and functional impairment, and less so to cognitive and neuropsychiatric variables. This differs from studies investigating patients in more advanced disease stages which found abnormal behavior and impairments of cognition as main predictors of patient dependence.

Prediction of placebo responses: a systematic review of the literature

PubMed Central

Horing, Bjoern; Weimer, Katja; Muth, Eric R.; Enck, Paul

2014-01-01

Objective: Predicting who responds to placebo treatment—and under which circumstances—has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR) prediction. Methods: To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities) and in PubMed (for pain only). Articles were selected when (a) they assessed putative predictors prior to placebo treatment and (b) an adequate control group was included when the associations of predictors and PRs were analyzed. Results: Twenty studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism). Other predictors involved behavioral control (desire for control, eating restraint), personality variables (fun seeking, sensation seeking, neuroticism), or biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism). Finally, suggestibility and beliefs in expectation biases, body consciousness, and baseline symptom severity were found to be predictive. Conclusions: While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or expectations of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted. PMID:25324797

Parents’ Primary Professional Sources of Parenting Advice Moderate Predictors of Parental Attitudes toward Corporal Punishment

PubMed Central

Taylor, Catherine A.; McKasson, Sarah; Hoy, Guenevere; DeJong, William

2016-01-01

Despite the risk it poses to children’s mental and physical health, approval and use of corporal punishment (CP) remains high in the United States. Informed by the Theory of Planned Behavior, we examined potential predictors of attitudes supportive of CP while assessing the moderating effects of parents’ (N=500) chosen primary professional source of advice regarding child discipline: pediatricians (47.8%), religious leaders (20.8%), mental health professionals (MHPs) (n=18.4%), or other identified professionals (13.0%). We conducted a random-digit-dial telephone survey among parents ages 18 and over within New Orleans, LA. The main outcome measure was derived from the Attitudes Toward Spanking scale (ATS). The main “predictors” were: perceived injunctive norms (i.e., perceived approval of CP by professionals; and by family and friends), perceived descriptive norms of family and friends regarding CP, and expected outcomes of CP use. We used multivariate OLS models to regress ATS scores on the predictor variables for each subset of parents based on their chosen professional source of advice. Perceived approval of CP by professionals was the strongest predictor of parental attitudes supportive of CP, except for those seeking advice from MHPs. Perceived injunctive and descriptive norms of family and friends were important, but only for those seeking advice from pediatricians or religious leaders. Positive expected outcomes of CP mattered, but only for those seeking advice from religious leaders or MHPs. In conclusion, the strength and relevance of variables predicting attitudes toward CP varied according to the professional from which the parent was most likely to seek advice. PMID:28529440

Most Likely to Succeed: Exploring Predictor Variables for the Counselor Preparation Comprehensive Examination

ERIC Educational Resources Information Center

Hartwig, Elizabeth Kjellstrand; Van Overschelde, James P.

2016-01-01

The authors investigated predictor variables for the Counselor Preparation Comprehensive Examination (CPCE) to examine whether academic variables, demographic variables, and test version were associated with graduate counseling students' CPCE scores. Multiple regression analyses revealed all 3 variables were statistically significant predictors of…

What are the most crucial soil factors for predicting the distribution of alpine plant species?

NASA Astrophysics Data System (ADS)

Buri, A.; Pinto-Figueroa, E.; Yashiro, E.; Guisan, A.

2017-12-01

Nowadays the use of species distribution models (SDM) is common to predict in space and time the distribution of organisms living in the critical zone. The realized environmental niche concept behind the development of SDM imply that many environmental factors must be accounted for simultaneously to predict species distributions. Climatic and topographic factors are often primary included, whereas soil factors are frequently neglected, mainly due to the paucity of soil information available spatially and temporally. Furthermore, among existing studies, most included soil pH only, or few other soil parameters. In this study we aimed at identifying what are the most crucial soil factors for explaining alpine plant distributions and, among those identified, which ones further improve the predictive power of plant SDMs. To test the relative importance of the soil factors, we performed plant SDMs using as predictors 52 measured soil properties of various types such as organic/inorganic compounds, chemical/physical properties, water related variables, mineral composition or grain size distribution. We added them separately to a standard set of topo-climatic predictors (temperature, slope, solar radiation and topographic position). We used ensemble forecasting techniques combining together several predictive algorithms to model the distribution of 116 plant species over 250 sites in the Swiss Alps. We recorded the variable importance for each model and compared the quality of the models including different soil proprieties (one at a time) as predictors to models having only topo-climatic variables as predictors. Results show that 46% of the soil proprieties tested become the second most important variable, after air temperature, to explain spatial distribution of alpine plants species. Moreover, we also assessed that addition of certain soil factors, such as bulk soil water density, could improve over 80% the quality of some plant species models. We confirm that soil pH remains one of the most important soil factor for predicting plant species distributions, closely followed by water, organic and inorganic carbon related properties. Finally, we were able to extract three main categories of important soil properties for plant species distributions: grain size distribution, acidity and water in the soil.

Socio-ecological predictors of participation and dropout in organised sports during childhood.

PubMed

Vella, Stewart A; Cliff, Dylan P; Okely, Anthony D

2014-05-13

The purpose of this study was to explore the socio-ecological determinants of participation and dropout in organised sports in a nationally-representative sample of Australian children. Data were drawn from Waves 3 and 4 of the Longitudinal Study of Australian Children. In total, 4042 children aged 8.25 (SD = 0.44) years at baseline were included, with 24-months between Waves. Socio-ecological predictors were reported by parents and teachers, while cognitive and health measures were assessed by trained professionals. All predictors were assessed at age 8, and used to predict participation and dropout by age 10. Seven variables at age 8 were shown to positively predict participation in organised sports at age 10. These included: sex (boy); fewer people in household; higher household income; main language spoken at home (English); higher parental education; child taken to a sporting event; and, access to a specialist PE teacher during primary school. Four variables predicted dropout from organised sports by age 10: lower household income; main language spoken at home (non-English); lower parental education; and, child not taken to a sporting event. The interplay between child sex, socioeconomic indicators, and parental support is important in predicting children's participation in organised sports. Multilevel and multicomponent interventions to promote participation and prevent dropout should be underpinned by the Socio-Ecological Model and targeted to high risk populations using multiple levels of risk.

Adherence predictors in an Internet-based Intervention program for depression.

PubMed

Castro, Adoración; López-Del-Hoyo, Yolanda; Peake, Christian; Mayoral, Fermín; Botella, Cristina; García-Campayo, Javier; Baños, Rosa María; Nogueira-Arjona, Raquel; Roca, Miquel; Gili, Margalida

2018-05-01

Internet-delivered psychotherapy has been demonstrated to be effective in the treatment of depression. Nevertheless, the study of the adherence in this type of the treatment reported divergent results. The main objective of this study is to analyze predictors of adherence in a primary care Internet-based intervention for depression in Spain. A multi-center, three arm, parallel, randomized controlled trial was conducted with 194 depressive patients, who were allocated in self-guided or supported-guided intervention. Sociodemographic and clinical characteristics were gathered using a case report form. The Mini international neuropsychiatric interview diagnoses major depression. Beck Depression Inventory was used to assess depression severity. The visual analogic scale assesses the respondent's self-rated health and Short Form Health Survey was used to measure the health-related quality of life. Age results a predictor variable for both intervention groups (with and without therapist support). Perceived health is a negative predictor of adherence for the self-guided intervention when change in depression severity was included in the model. Change in depression severity results a predictor of adherence in the support-guided intervention. Our findings demonstrate that in our sample, there are differences in sociodemographic and clinical variables between active and dropout participants and we provide adherence predictors in each intervention condition of this Internet-based program for depression (self-guided and support-guided). It is important to point that further research in this area is essential to improve tailored interventions and to know specific patients groups can benefit from these interventions.

Cancer of the colorectum in Maine, 1995-1998: determinants of stage at diagnosis in a rural state.

PubMed

Parsons, Margaret A; Askland, Kathleen D

2007-01-01

Despite screening for colorectal cancer, mortality in the United States remains substantial. In northern New England, little is known about predictors of stage at diagnosis, an important determinant of survival and mortality. The objective of this study was to identify predictors of late stage at diagnosis for colorectal cancer in a rural state with a predominantly white population and a large Franco-American minority. Incident cases from 1995-1998 were obtained from the Maine Cancer Registry. Individual-level variables (age, sex, race, French ethnicity by surname, and payer) and contextual/town-level variables (socioeconomic status, population density, Franco ancestry proportion, distance to health care, and weather) were modeled with multiple logistic regression for late stage. Increasing distance to primary care provider was associated with late stage for colorectal cancer. Compared to patients aged > or =85 years, those aged 65-84 years were less likely to be diagnosed late, while those aged 35-49 years were more likely--although not significantly--to have late stage at diagnosis. Associations were not found with socioeconomic variables. The finding regarding distance to primary care may be consistent with studies showing that rurality and distance to care predict reduced utilization of health care services and worse health outcomes. The finding regarding age has implications for the education of younger high-risk patients and their physicians. The absence of positive findings with regard to socioeconomic variables may stem from the uniquely mixed sociodemographic profiles in rural and urban regions of Maine. Further research should refine these and other contextual measures to elucidate effects on rural health and should further evaluate the utility of assigning French ethnicity by surname in order to identify health disparities.

Applying Ajzen's Theory of Planned Behavior to a Study of Online Course Adoption in Public Relations Education

ERIC Educational Resources Information Center

Knabe, Ann Peru

2012-01-01

This study used Icek Ajzen's Theory of Planned Behavior to research public relations faculty intentions of teaching online. All of the main predictor variables (Subjective Norms, Attitude toward the Act and Perceived Behavioral Control) were statistically significant at varying degrees in predicting intent to teach public relations online. Of the…

Predicting Student Satisfaction with an Emphasis on Campus Recreational Sports and Cultural Facilities in a Turkish University

ERIC Educational Resources Information Center

Çelik, Ali Kemal; Akyol, Kübra

2015-01-01

The main purpose of this paper was to determine the predictors of student satisfaction focusing on campus recreational sports and cultural facilities. The present study utilized data from a written-questionnaire administered to one thousand adult undergraduate students. The dependent variable used in predicting student satisfaction was…

Prevalence and predictors of healthcare utilization among older people (60+): focusing on ADL dependency and risk of depression.

PubMed

Sandberg, Magnus; Kristensson, Jimmie; Midlöv, Patrik; Fagerström, Cecilia; Jakobsson, Ulf

2012-01-01

The aim of this study was to investigate healthcare utilization patterns over a six-year period among older people (60+), classified as dependent/independent in Activities of Daily Living (ADL) and/or at/not at risk of depression and to identify healthcare utilization predictors. A sample (n=1402) comprising ten age cohorts aged between 60 and 96 years was drawn from the Swedish National study on Aging and Care (SNAC). Baseline data were collected between 2001 and 2003. Number and length of hospital stays were collected for six years after baseline year. Group differences and mean changes over time were investigated. Healthcare utilization predictors were explored using multiple linear regression analysis. The results revealed that 21-24% had at least one hospital stay in the six years after baseline, 29-37% among ADL dependent subjects and 24-33% among those at risk of depression. There was a significant increase of hospital stays in all groups over time. ADL-dependent subjects and those at risk of depression had significant more hospital stays, except for those at/not at risk of depression in years 2, 4 and 5. The healthcare utilization predictors 5-6 years after baseline were mainly age, previous healthcare utilization and various symptoms and, in 1-2 and 3-4 years after baseline, age, various diagnostic groups and various physical variables. Thus healthcare utilization patterns seem to be similar for the different groups, but it is difficult to find universal predictors. This suggests that different variables should be considered, including both ADL and psychosocial variables, when trying to identify future healthcare users. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Low Prevalence of Vitamin D Insufficiency among Nepalese Infants Despite High Prevalence of Vitamin D Insufficiency among Their Mothers

PubMed Central

Haugen, Johanne; Ulak, Manjeswori; Chandyo, Ram K.; Henjum, Sigrun; Thorne-Lyman, Andrew L.; Ueland, Per Magne; Midtun, Øivind; Shrestha, Prakash S.; Strand, Tor A.

2016-01-01

Background: Describing vitamin D status and its predictors in various populations is important in order to target public health measures. Objectives: To describe the status and predictors of vitamin D status in healthy Nepalese mothers and infants. Methods: 500 randomly selected Nepalese mother and infant pairs were included in a cross-sectional study. Plasma 25(OH)D concentrations were measured by LC-MS/MS and multiple linear regression analyses were used to identify predictors of vitamin D status. Results: Among the infants, the prevalence of vitamin D insufficiency (25(OH)D <50 nmol/L) and deficiency (<30 nmol/L) were 3.6% and 0.6%, respectively, in contrast to 59.8% and 14.0% among their mothers. Infant 25(OH)D concentrations were negatively associated with infant age and positively associated with maternal vitamin D status and body mass index (BMI), explaining 22% of the variability in 25(OH)D concentration. Global solar radiation, maternal age and BMI predicted maternal 25(OH)D concentration, explaining 9.7% of its variability. Conclusion: Age and maternal vitamin D status are the main predictors of vitamin D status in infants in Bhaktapur, Nepal, who have adequate vitamin D status despite poor vitamin D status in their mothers. PMID:28009810

Predicting change over time in career planning and career exploration for high school students.

PubMed

Creed, Peter A; Patton, Wendy; Prideaux, Lee-Ann

2007-06-01

This study assessed 166 high school students in Grade 8 and again in Grade 10. Four models were tested: (a) whether the T1 predictor variables (career knowledge, indecision, decision-making self efficacy, self-esteem, demographics) predicted the outcome variable (career planning/exploration) at T1; (b) whether the T1 predictor variables predicted the outcome variable at T2; (c) whether the T1 predictor variables predicted change in the outcome variable from T1-T2; and (d) whether changes in the predictor variables from T1-T2 predicted change in the outcome variable from T1-T2. Strong associations (R(2)=34%) were identified for the T1 analysis (confidence, ability and paid work experience were positively associated with career planning/exploration). T1 variables were less useful predictors of career planning/exploration at T2 (R(2)=9%; having more confidence at T1 was associated with more career planning/exploration at T2) and change in career planning/exploration from T1-T2 (R(2)=11%; less confidence and no work experience were associated with change in career planning/exploration from T1-T2). When testing effect of changes in predictor variables predicting changes in outcome variable (R(2)=22%), three important predictors, indecision, work experience and confidence, were identified. Overall, results indicated important roles for self-efficacy and early work experiences in current and future career planning/exploration of high school students.

Work-home interface stress: an important predictor of emotional exhaustion 15 years into a medical career.

PubMed

Hertzberg, Tuva Kolstad; Rø, Karin Isaksson; Vaglum, Per Jørgen Wiggen; Moum, Torbjørn; Røvik, Jan Ole; Gude, Tore; Ekeberg, Øivind; Tyssen, Reidar

2016-01-01

The importance of work-home interface stress can vary throughout a medical career and between genders. We studied changes in work-home interface stress over 5 yr, and their prediction of emotional exhaustion (main dimension of burn-out), controlled for other variables. A nationwide doctor cohort (NORDOC; n=293) completed questionnaires at 10 and 15 yr after graduation. Changes over the period were examined and predictors of emotional exhaustion analyzed using linear regression. Levels of work-home interface stress declined, whereas emotional exhaustion stayed on the same level. Lack of reduction in work-home interface stress was an independent predictor of emotional exhaustion in year 15 (β=-0.21, p=0.001). Additional independent predictors were reduction in support from colleagues (β=0.11, p=0.04) and emotional exhaustion at baseline (β=0.62, p<0.001). Collegial support was a more important predictor for men than for women. In separate analyses, significant adjusted predictors were lack of reduction in work-home interface stress among women, and reduction of collegial support and lack of reduction in working hours among men. Thus, change in work-home interface stress is a key independent predictor of emotional exhaustion among doctors 15 yr after graduation. Some gender differences in predictors of emotional exhaustion were found.

Socio-ecological predictors of participation and dropout in organised sports during childhood

PubMed Central

2014-01-01

Background The purpose of this study was to explore the socio-ecological determinants of participation and dropout in organised sports in a nationally-representative sample of Australian children. Methods Data were drawn from Waves 3 and 4 of the Longitudinal Study of Australian Children. In total, 4042 children aged 8.25 (SD = 0.44) years at baseline were included, with 24-months between Waves. Socio-ecological predictors were reported by parents and teachers, while cognitive and health measures were assessed by trained professionals. All predictors were assessed at age 8, and used to predict participation and dropout by age 10. Results Seven variables at age 8 were shown to positively predict participation in organised sports at age 10. These included: sex (boy); fewer people in household; higher household income; main language spoken at home (English); higher parental education; child taken to a sporting event; and, access to a specialist PE teacher during primary school. Four variables predicted dropout from organised sports by age 10: lower household income; main language spoken at home (non-English); lower parental education; and, child not taken to a sporting event. Conclusions The interplay between child sex, socioeconomic indicators, and parental support is important in predicting children’s participation in organised sports. Multilevel and multicomponent interventions to promote participation and prevent dropout should be underpinned by the Socio-Ecological Model and targeted to high risk populations using multiple levels of risk. PMID:24885978

In Pursuit of the Elusive Elixir: Predictors of First Grade Reading.

ERIC Educational Resources Information Center

Porter, Robin

Multivariate sets of predictor variables including both cognitive and social variables, different types of preschool experiences, and family environment variables were used to predict the first-grade reading achievement of 144 first-grade boys and girls. Measures for the predictor variables had been taken at school entry and at the end of the…

A Survey of Out-of-Pocket Expenditures for Children with Autism Spectrum Disorder in Israel

ERIC Educational Resources Information Center

Raz, Raanan; Lerner-Geva, Liat; Leon, Odelia; Chodick, Gabriel; Gabis, Lidia V.

2013-01-01

We describe a survey of children with ASD aged 4-10 years. The main dependent variables were out-of-pocket expenditures for health services and hours of therapy. Multivariable logistic regression models were used in order to find independent predictors for service utilization. Parents of 178 of the children (87%) agreed to participate. The average…

Child ADHD Severity and Positive and Negative Parenting as Predictors of Child Social Functioning: Evaluation of Three Theoretical Models

ERIC Educational Resources Information Center

Kaiser, Nina M.; McBurnett, Keith; Pfiffner, Linda J.

2011-01-01

Objective: Prior research has established links between child social functioning and both parenting and child ADHD severity; however, research examining the way that these variables work together is lacking. The current article aims to test three possible models (main effects, mediation, and moderation) by which ADHD severity and positive and…

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Children's reasons for living, self-esteem, and violence.

PubMed

Merwin, Rhonda M; Ellis, Jon B

2004-01-01

Attitudes toward violence and reasons for living in young adolescents with high, moderate, and low self-esteem were examined. The authors devised an Attitudes Toward Violence questionnaire; the Rosenberg's Self-esteem Scale (RSE) and the Brief Reasons for Living in Adolescents (BRFL-A) was used to assess adaptive characteristics. The independent variables were gender and self-esteem. The dependent variables were total Reasons for Living score and Attitudes Toward Violence score. Participants included 138 boys and 95 girls, ages 11 to 15 years (M = 13.3) from a city middle school. The results showed that for the dependent variable attitudes toward violence, main effects were found for both gender and self-esteem. For the dependent variable reasons for living, a main effect was found for self-esteem but not for gender. An inverse relationship was found between violence and reasons for living. Being male and low self-esteem emerged as predictors of more accepting attitudes toward violence. Low self-esteem was significantly related to fewer reasons for living.

Dimensions and predictors of disability—A baseline study of patients entering somatic rehabilitation in secondary care

PubMed Central

2018-01-01

Purpose The purpose of this study was to investigate disability among patients who were accepted for admission to a Norwegian rehabilitation center and to identify predictors of disability. Materials and methods In a cross-sectional study including 967 adult participants, the World Health Organization Disability Assessment Schedule version 2.0 36-item version was used for assessing overall and domain-specific disability as outcome variables. Patients completed the Hospital Anxiety and Depression Scale (HADS), EuroQoL EQ-5D-5L and questions about multi-morbidity, smoking and perceived physical fitness. Additionally, the main health condition, sociodemographic and environmental variables obtained from referrals and public registers were used as predictor variables. Descriptive statistics and linear regression analyses were performed. Results The mean (standard error) overall disability score was 30.0 (0.5), domain scores ranged from 11.9 to 44.7. Neurological diseases, multi-morbidity, low education, impaired physical fitness, pain, and higher HADS depressive score increased the overall disability score. A low HADS depressive score predicted a lower disability score in all domains. Conclusions A moderate overall disability score was found among patients accepted for admission to a rehabilitation center but “life activities” and “participation in society” had the highest domain scores. This should be taken into account when rehabilitation strategies are developed. PMID:29499064

Psychometric and demographic predictors of the perceived risk of terrorist threats and the willingness to pay for terrorism risk management programs.

PubMed

Mumpower, Jeryl L; Shi, Liu; Stoutenborough, James W; Vedlitz, Arnold

2013-10-01

A 2009 national telephone survey of 924 U.S. adults assessed perceptions of terrorism and homeland security issues. Respondents rated severity of effects, level of understanding, number affected, and likelihood of four terrorist threats: poisoned water supply; explosion of a small nuclear device in a major U.S. city; an airplane attack similar to 9/11; and explosion of a bomb in a building, train, subway, or highway. Respondents rated perceived risk and willingness to pay (WTP) for dealing with each threat. Demographic, attitudinal, and party affiliation data were collected. Respondents rated bomb as highest in perceived risk but gave the highest WTP ratings to nuclear device. For both perceived risk and WTP, psychometric variables were far stronger predictors than were demographic ones. OLS regression analyses using both types of variables to predict perceived risk found only two significant demographic predictors for any threat--Democrat (a negative predictor for bomb) and white male (a significant positive predictor for airline attack). In contrast, among psychometric variables, severity, number affected, and likelihood were predictors of all four threats and level of understanding was a predictor for one. For WTP, education was a negative predictor for three threats; no other demographic variables were significant predictors for any threat. Among psychometric variables, perceived risk and number affected were positive predictors of WTP for all four threats; severity and likelihood were predictors for three; level of understanding was a significant predictor for two. © 2013 Society for Risk Analysis.

The Concept of Proportionality as a Predictor of Success at the University of Papua and New Guinea. E.R.U. Report 6.

ERIC Educational Resources Information Center

Jones, John

A main problem encountered by science and mathematics students at secondary and tertiary institutions throughout Papua New Guinea is that of dealing with ratio and proportion. The problem is most clearly defined in science, since this is the area in which the quantitative manipulation of physical variables is most frequently carried out. An…

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.

Tree species distribution in temperate forests is more influenced by soil than by climate.

PubMed

Walthert, Lorenz; Meier, Eliane Seraina

2017-11-01

Knowledge of the ecological requirements determining tree species distributions is a precondition for sustainable forest management. At present, the abiotic requirements and the relative importance of the different abiotic factors are still unclear for many temperate tree species. We therefore investigated the relative importance of climatic and edaphic factors for the abundance of 12 temperate tree species along environmental gradients. Our investigations are based on data from 1,075 forest stands across Switzerland including the cold-induced tree line of all studied species and the drought-induced range boundaries of several species. Four climatic and four edaphic predictors represented the important growth factors temperature, water supply, nutrient availability, and soil aeration. The climatic predictors were derived from the meteorological network of MeteoSwiss, and the edaphic predictors were available from soil profiles. Species cover abundances were recorded in field surveys. The explanatory power of the predictors was assessed by variation partitioning analyses with generalized linear models. For six of the 12 species, edaphic predictors were more important than climatic predictors in shaping species distribution. Over all species, abundances depended mainly on nutrient availability, followed by temperature, water supply, and soil aeration. The often co-occurring species responded similar to these growth factors. Drought turned out to be a determinant of the lower range boundary for some species. We conclude that over all 12 studied tree species, soil properties were more important than climate variables in shaping tree species distribution. The inclusion of appropriate soil variables in species distribution models allowed to better explain species' ecological niches. Moreover, our study revealed that the ecological requirements of tree species assessed in local field studies and in experiments are valid at larger scales across Switzerland.

Prediction models of health-related quality of life in different neck pain conditions: a cross-sectional study.

PubMed

Beltran-Alacreu, Hector; López-de-Uralde-Villanueva, Ibai; Calvo-Lobo, César; La Touche, Roy; Cano-de-la-Cuerda, Roberto; Gil-Martínez, Alfonso; Fernández-Ayuso, David; Fernández-Carnero, Josué

2018-01-01

The main aim of the study was to predict the health-related quality of life (HRQoL) based on physical, functional, and psychological measures in patients with different types of neck pain (NP). This cross-sectional study included 202 patients from a primary health center and the physiotherapy outpatient department of a hospital. Patients were divided into four groups according to their NP characteristics: chronic (CNP), acute whiplash (WHIP), chronic NP associated with temporomandibular dysfunction (NP-TMD), or chronic NP associated with chronic primary headache (NP-PH). The following measures were performed: Short Form-12 Health Survey (SF-12), Neck Disability Index (NDI), visual analog scale (VAS), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BECK), and cervical range of movement (CROM). The regression models based on the SF-12 total HRQoL for CNP and NP-TMD groups showed that only NDI was a significant predictor of the worst HRQoL (48.9% and 48.4% of the variance, respectively). In the WHIP group, the regression model showed that BECK was the only significant predictor variable for the worst HRQoL (31.7% of the variance). Finally, in the NP-PH group, the regression showed that the BECK, STAI, and VAS model predicted the worst HRQoL (75.1% of the variance). Chronic nonspecific NP and chronic NP associated with temporomandibular dysfunction were the main predictors of neck disability. In addition, depression, anxiety, and pain were the main predictors of WHIP or primary headache associated with CNP.

Work-home interface stress: an important predictor of emotional exhaustion 15 years into a medical career

PubMed Central

HERTZBERG, Tuva Kolstad; RØ, Karin Isaksson; VAGLUM, Per Jørgen Wiggen; MOUM, Torbjørn; RØVIK, Jan Ole; GUDE, Tore; EKEBERG, Øivind; TYSSEN, Reidar

2015-01-01

The importance of work-home interface stress can vary throughout a medical career and between genders. We studied changes in work-home interface stress over 5 yr, and their prediction of emotional exhaustion (main dimension of burn-out), controlled for other variables. A nationwide doctor cohort (NORDOC; n=293) completed questionnaires at 10 and 15 yr after graduation. Changes over the period were examined and predictors of emotional exhaustion analyzed using linear regression. Levels of work-home interface stress declined, whereas emotional exhaustion stayed on the same level. Lack of reduction in work-home interface stress was an independent predictor of emotional exhaustion in year 15 (β=−0.21, p=0.001). Additional independent predictors were reduction in support from colleagues (β=0.11, p=0.04) and emotional exhaustion at baseline (β=0.62, p<0.001). Collegial support was a more important predictor for men than for women. In separate analyses, significant adjusted predictors were lack of reduction in work-home interface stress among women, and reduction of collegial support and lack of reduction in working hours among men. Thus, change in work-home interface stress is a key independent predictor of emotional exhaustion among doctors 15 yr after graduation. Some gender differences in predictors of emotional exhaustion were found. PMID:26538002

Situational and Intrapersonal Predictors of School and Life Satisfaction of Elementary School Students

ERIC Educational Resources Information Center

Drost, Amy Linden

2012-01-01

This study examined predictors of school and life satisfaction of fifth-grade students. Two situational predictor variables (school climate and school stress) and two intrapersonal predictor variables (locus of control and academic self-concept) were examined. It was hypothesized that positive school climate, low levels of school stress, internal…

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Evaluation of variable selection methods for random forests and omics data sets.

PubMed

Degenhardt, Frauke; Seifert, Stephan; Szymczak, Silke

2017-10-16

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta.In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings. © The Author 2017. Published by Oxford University Press.

Educational career and predictors of type of education in young adults with spina bifida.

PubMed

Barf, H A; Verhoef, M; Post, M W M; Jennekens-Schinkel, A; Gooskens, R H J M; Mullaart, R A; Prevo, A J H

2004-03-01

Children with spina bifida (SB) often require special education. To date, little information is available about the educational career of these children. This study focuses on educational career and predictors of attending special education of young adults with SB, using a cross-sectional study including 178 young Dutch adults with SB aged from 16-25. The main outcome was attending regular versus special education. For searching predictive power we selected age, gender, type of SB, level of lesion, hydrocephalus (HC), number of surgical interventions, ambulation, continence and cognitive functioning. Chi-square tests and binary logistic regression were used in the data analysis. Participants with HC attended special primary education more often (59%) than participants without HC (17%). For those participants with HC, the necessity of special primary education was associated with below average intelligence (75% versus 35%), wheelchair dependence (82% versus 39%) and surgical interventions (74% versus 44%). Only half of the participants with HC followed regular secondary education, whereas for participants with SB without HC, the outcome in secondary education was similar to that of the general population (92%). Intelligence was the main predictor of attending special secondary education (odds 5.1:1), but HC (odds 4.3:1) and wheelchair dependence (odds 2.6:1) were also a significant. Other variables were not significant predictors of special secondary education.

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

NASA Astrophysics Data System (ADS)

Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.

2014-04-01

Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other organic soils are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new dataset comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip well specific long-term annual mean water level (WL) as well as a transformed form of it (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insights into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying e.g. on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

NASA Astrophysics Data System (ADS)

Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.

2014-09-01

Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other soils with high organic carbon contents are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new data set comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip-well-specific long-term annual mean water level (WL) as well as a transformed form (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insight into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying, for example, on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

Maxillomandibular advancement as the initial treatment of obstructive sleep apnoea: Is the mandibular occlusal plane the key?

PubMed

Rubio-Bueno, P; Landete, P; Ardanza, B; Vázquez, L; Soriano, J B; Wix, R; Capote, A; Zamora, E; Ancochea, J; Naval-Gías, L

2017-11-01

Maxillomandibular advancement (MMA) can be effective for managing obstructive sleep apnoea (OSA); however, limited information is available on the predictor surgical variables. This study investigated whether normalization of the mandibular occlusal plane (MOP) was a determinant factor in curing OSA. Patients with moderate or severe OSA who underwent MMA were evaluated by preoperative and postoperative three-dimensional (3D) scans and polysomnograms. The postoperative value of MOP and the magnitude of skeletal advancement were the predictor variables; change in the apnoea-hypopnoea index (AHI) was the main outcome variable. Thirty-four subjects with a mean age of 41±14years and 58,8% female were analysed. The Epworth Sleepiness Scale (ESS) was 17.4±5.4 and AHI was 38.3±10.7 per hour before surgery. Postoperative AHI was 6.5±4.3 per hour (P<0.001) with 52.94% of the patients considered as cured, and 47.06% suffering from a mild residual OSA with ESS 0.8±1.4 (P<0.001). 3D changes revealed a volume increase of 106.3±38.8%. The mandible was advanced 10.4±3.9mm and maxilla 4.9±3.2mm. MOP postoperative value was concluded to be the best predictor variable. Treatment planning should include MOP normalization and a mandibular advancement between 6 and 10mm. The maxillary advancement would depend on the desired aesthetic changes and final occlusion. Copyright © 2017 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Cognitive predictors of adaptive functioning in children with symptomatic epilepsy.

PubMed

Kerr, Elizabeth N; Fayed, Nora

2017-10-01

The current study sought to understand the contribution of the attention and working memory challenges experienced by children with active epilepsy without an intellectual disability to adaptive functioning (AF) while taking into account intellectual ability, co-occurring brain-based psychosocial diagnoses, and epilepsy-related variables. The relationship of attention and working memory with AF was examined in 76 children with active epilepsy with intellectual ability above the 2nd percentile recruited from a tertiary care center. AF was measured using the Scales of Independent Behavior-Revised (SIB-R) and compared with norm-referenced data. Standardized clinical assessments of attention span, sustained attention, as well as basic and more complex working memory were administered to children. Commonality analysis was used to investigate the importance of the variables with respect to the prediction of AF and to construct parsimonious models to elucidate the factors most important in explaining AF. Seventy-one percent of parents reported that their child experienced mild to severe difficulties with overall AF. Similar proportions of children displayed limitations in domain-specific areas of AF (Motor, Social/Communication, Person Living, and Community Living). The reduced models for Broad and domain-specific AF produced a maximum of seven predictor variables, with little loss in overall explained variance compared to the full models. Intellectual ability was a powerful predictor of Broad and domain-specific AF. Complex working memory was the only other cognitive predictor retained in each of the parsimonious models of AF. Sustained attention and complex working memory explained a large amount of the total variance in Motor AF. Children with a previously diagnosed comorbidity displayed lower Social/Communication, Personal Living, and Broad AF than those without a diagnosis. At least one epilepsy-related variable appeared in each of the reduced models, with age of seizure onset and seizure type (generalized or partial) being the main predictors. Intellectual ability was the most powerful predictor of AF in children with epilepsy whose intellectual functioning was above the 2nd percentile. Co-occurring brain-based cognitive and psychosocial issues experienced by children with living epilepsy, particularly complex working memory and diagnosed comorbidities, contribute to AF and may be amenable to intervention. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Predictors of hydrocephalus as a complication of non-traumatic subarachnoid hemorrhage: a retrospective observational cohort study in 107 patients.

PubMed

Vinas Rios, Juan Manuel; Sanchez-Aguilar, Martin; Kretschmer, Thomas; Heinen, Christian; Medina Govea, Fatima Azucena; Jose Juan, Sanchez-Rodriguez; Schmidt, Thomas

2018-01-01

The predictors of shunt dependency such as amount of subarachnoid blood, acute hydrocephalus (HC), mode of aneurysm repair, clinical grade at admission and cerebro spinal fluid (CSF) drainage in excess of 1500 ml during the 1st week after the subarachnoid hemorrhage (SAH) have been identified as predictors of shunt dependency. Therefore our main objective is to identify predictors of CSF shunt dependency following non-traumatic subarachnoid hemorrhage. We performed a retrospective study including patients from January 1st 2012 to September 30th 2014 between 16 and 89 years old and had a non-traumatic subarachnoid hemorrhage in cranial computed tomography (CCT). We excluded patients with the following characteristics: Patients who died 3 days after admittance, lesions in brainstem, previous surgical treatment in another clinic, traumatic brain injury, pregnancy and disability prior to SAH.We performed a descriptive and comparative analysis as well as a logistic regression with the variables that showed a significant difference ( p  < 0.05). Hence we identified the variables concerning HC after non traumatic SAH and its correlation. One hundred and seven clinical files of patients with non-traumatic SAH were analyzed. Twenty one (48%) later underwent shunt treatment. Shunt patients had significantly clinical and corroborated with doppler ultrasonography vasospasmus ( p  = 0.015), OR = 5.2. The amount of subarachnoidal blood according to modified Fisher grade was ( p  = 0.008) OR = 10.9. Endovascularly treated patients were less often shunted as compared with those undergoing surgical aneurysm repair ( p  = 0.004). Vasospasmus and a large amount of ventricular blood seem to be a predictor concerning hydrocephalus after non-traumatic SAH. Hence according to our results the presence of these two variables could alert the treating physician in the decision whether an early shunt implantation < 7 days after SAH should be necessary.

Generic biomass functions for Norway spruce in Central Europe--a meta-analysis approach toward prediction and uncertainty estimation.

PubMed

Wirth, Christian; Schumacher, Jens; Schulze, Ernst-Detlef

2004-02-01

To facilitate future carbon and nutrient inventories, we used mixed-effect linear models to develop new generic biomass functions for Norway spruce (Picea abies (L.) Karst.) in Central Europe. We present both the functions and their respective variance-covariance matrices and illustrate their application for biomass prediction and uncertainty estimation for Norway spruce trees ranging widely in size, age, competitive status and site. We collected biomass data for 688 trees sampled in 102 stands by 19 authors. The total number of trees in the "base" model data sets containing the predictor variables diameter at breast height (D), height (H), age (A), site index (SI) and site elevation (HSL) varied according to compartment (roots: n = 114, stem: n = 235, dry branches: n = 207, live branches: n = 429 and needles: n = 551). "Core" data sets with about 40% fewer trees could be extracted containing the additional predictor variables crown length and social class. A set of 43 candidate models representing combinations of lnD, lnH, lnA, SI and HSL, including second-order polynomials and interactions, was established. The categorical variable "author" subsuming mainly methodological differences was included as a random effect in a mixed linear model. The Akaike Information Criterion was used for model selection. The best models for stem, root and branch biomass contained only combinations of D, H and A as predictors. More complex models that included site-related variables resulted for needle biomass. Adding crown length as a predictor for needles, branches and roots reduced both the bias and the confidence interval of predictions substantially. Applying the best models to a test data set of 17 stands ranging in age from 16 to 172 years produced realistic allocation patterns at the tree and stand levels. The 95% confidence intervals (% of mean prediction) were highest for crown compartments (approximately +/- 12%) and lowest for stem biomass (approximately +/- 5%), and within each compartment, they were highest for the youngest and oldest stands, respectively.

Need for cognition moderates paranormal beliefs and magical ideation in inconsistent-handers.

PubMed

Prichard, Eric C; Christman, Stephen D

2016-01-01

A growing literature suggests that degree of handedness predicts gullibility and magical ideation. Inconsistent-handers (people who use their non-dominant hand for at least one common manual activity) report more magical ideation and are more gullible. The current study tested whether this effect is moderated by need for cognition. One hundred eighteen university students completed questionnaires assessing handedness, self-reported paranormal beliefs, and self-reported need for cognition. Handedness (Inconsistent vs. Consistent Right) and Need for Cognition (High vs. Low) were treated as categorical predictors. Both paranormal beliefs and magical ideation served as dependent variable's in separate analyses. Neither set of tests yielded main effects for handedness or need for cognition. However, there were a significant handedness by need for cognition interactions. Post-hoc comparisons revealed that low, but not high, need for cognition inconsistent-handers reported relatively elevated levels of paranormal belief and magical ideation. A secondary set of tests treating the predictor variables as continuous instead of categorical obtained the same overall pattern.

Understanding Coupling of Global and Diffuse Solar Radiation with Climatic Variability

NASA Astrophysics Data System (ADS)

Hamdan, Lubna

Global solar radiation data is very important for wide variety of applications and scientific studies. However, this data is not readily available because of the cost of measuring equipment and the tedious maintenance and calibration requirements. Wide variety of models have been introduced by researchers to estimate and/or predict the global solar radiations and its components (direct and diffuse radiation) using other readily obtainable atmospheric parameters. The goal of this research is to understand the coupling of global and diffuse solar radiation with climatic variability, by investigating the relationships between these radiations and atmospheric parameters. For this purpose, we applied multilinear regression analysis on the data of National Solar Radiation Database 1991--2010 Update. The analysis showed that the main atmospheric parameters that affect the amount of global radiation received on earth's surface are cloud cover and relative humidity. Global radiation correlates negatively with both variables. Linear models are excellent approximations for the relationship between atmospheric parameters and global radiation. A linear model with the predictors total cloud cover, relative humidity, and extraterrestrial radiation is able to explain around 98% of the variability in global radiation. For diffuse radiation, the analysis showed that the main atmospheric parameters that affect the amount received on earth's surface are cloud cover and aerosol optical depth. Diffuse radiation correlates positively with both variables. Linear models are very good approximations for the relationship between atmospheric parameters and diffuse radiation. A linear model with the predictors total cloud cover, aerosol optical depth, and extraterrestrial radiation is able to explain around 91% of the variability in diffuse radiation. Prediction analysis showed that the linear models we fitted were able to predict diffuse radiation with efficiency of test adjusted R2 values equal to 0.93, using the data of total cloud cover, aerosol optical depth, relative humidity and extraterrestrial radiation. However, for prediction purposes, using nonlinear terms or nonlinear models might enhance the prediction of diffuse radiation.

Employee Turnover: An Empirical and Methodological Assessment.

ERIC Educational Resources Information Center

Muchinsky, Paul M.; Tuttle, Mark L.

1979-01-01

Reviews research on the prediction of employee turnover. Groups predictor variables into five general categories: attitudinal (job satisfaction), biodata, work-related, personal, and test-score predictors. Consistent relationships between common predictor variables and turnover were found for four categories. Eight methodological problems/issues…

Women and vulnerability to depression: some personality and clinical factors.

PubMed

Carrillo, Jesús M; Rojo, Nieves; Staats, Arthur W

2004-05-01

The purpose of this study is to explore the role of sex differences and personality in vulnerability to depression. Sex differences in personality and some clinical variables are described. We also assess the value of the variables that revealed significant sex differences as predictors of vulnerability to depression. In a group of adult participants (N = 112), 50% males and 50% females (mean age = 41.30; SD = 15.09; range 17-67), we studied sex differences in the three-factor personality model, using the Eysenck Personality Questionnaire, Form A (EPQ-A; Eysenck & Eysenck, 1975), and in the Five-Factor Personality Model, with the NEO Personality Inventory (NEO-PI; Costa & McCrae, 1985). The following clinical scales were used: the Beck Depression Inventory (BDI; Beck, Rush, Shaw, & Emery, 1979), the Schizotypy Questionnaire (STQ; Claridge & Broks, 1984; Spanish version, Carrillo & Rojo, 1999), the THARL Scales (Dua, 1989, 1990; Spanish version, Dua & Carrillo, 1994) and the Adjustment Inventory (Bell, 1937; Spanish version, Cerdá, 1980). Subsequently, simple linear regression analysis, with BDI scores as criterion, were performed to estimate the value of the variables as predictors of vulnerability to depression. The results indicate that a series of personality variables cause women to be more vulnerable to depression than men and that these variables could be explained by a negative emotion main factor. Results are discussed within the framework of the psychological behaviorism theory of depression.

Post-processing method for wind speed ensemble forecast using wind speed and direction

NASA Astrophysics Data System (ADS)

Sofie Eide, Siri; Bjørnar Bremnes, John; Steinsland, Ingelin

2017-04-01

Statistical methods are widely applied to enhance the quality of both deterministic and ensemble NWP forecasts. In many situations, like wind speed forecasting, most of the predictive information is contained in one variable in the NWP models. However, in statistical calibration of deterministic forecasts it is often seen that including more variables can further improve forecast skill. For ensembles this is rarely taken advantage of, mainly due to that it is generally not straightforward how to include multiple variables. In this study, it is demonstrated how multiple variables can be included in Bayesian model averaging (BMA) by using a flexible regression method for estimating the conditional means. The method is applied to wind speed forecasting at 204 Norwegian stations based on wind speed and direction forecasts from the ECMWF ensemble system. At about 85 % of the sites the ensemble forecasts were improved in terms of CRPS by adding wind direction as predictor compared to only using wind speed. On average the improvements were about 5 %, but mainly for moderate to strong wind situations. For weak wind speeds adding wind direction had more or less neutral impact.

Age of acquisition persists as the main factor in picture naming when cumulative word frequency and frequency trajectory are controlled.

PubMed

Pérez, Miguel A

2007-01-01

The aim of this study was to address the effect of objective age of acquisition (AoA) on picture-naming latencies when different measures of frequency (cumulative and adult word frequency) and frequency trajectory are taken into account. A total of 80 Spanish participants named a set of 178 pictures. Several multiple regression analyses assessed the influence of AoA, word frequency, frequency trajectory, object familiarity, name agreement, image agreement, image variability, name length, and orthographic neighbourhood density on naming times. The results revealed that AoA is the main predictor of picture-naming times. Cumulative frequency and adult word frequency (written or spoken) appeared as important factors in picture naming, but frequency trajectory and object familiarity did not. Other significant variables were image agreement, image variability, and neighbourhood density. These results (a) provide additional evidence of the predictive power of AoA in naming times independent of word-frequency and (b) suggest that image variability and neighbourhood density should also be taken into account in models of lexical production.

Modelling Ecuador's rainfall distribution according to geographical characteristics.

NASA Astrophysics Data System (ADS)

Tobar, Vladimiro; Wyseure, Guido

2017-04-01

It is known that rainfall is affected by terrain characteristics and some studies had focussed on its distribution over complex terrain. Ecuador's temporal and spatial rainfall distribution is affected by its location on the ITCZ, the marine currents in the Pacific, the Amazon rainforest, and the Andes mountain range. Although all these factors are important, we think that the latter one may hold a key for modelling spatial and temporal distribution of rainfall. The study considered 30 years of monthly data from 319 rainfall stations having at least 10 years of data available. The relatively low density of stations and their location in accessible sites near to main roads or rivers, leave large and important areas ungauged, making it not appropriate to rely on traditional interpolation techniques to estimate regional rainfall for water balance. The aim of this research was to come up with a useful model for seasonal rainfall distribution in Ecuador based on geographical characteristics to allow its spatial generalization. The target for modelling was the seasonal rainfall, characterized by nine percentiles for each one of the 12 months of the year that results in 108 response variables, later on reduced to four principal components comprising 94% of the total variability. Predictor variables for the model were: geographic coordinates, elevation, main wind effects from the Amazon and Coast, Valley and Hill indexes, and average and maximum elevation above the selected rainfall station to the east and to the west, for each one of 18 directions (50-135°, by 5°) adding up to 79 predictors. A multiple linear regression model by the Elastic-net algorithm with cross-validation was applied for each one of the PC as response to select the most important ones from the 79 predictor variables. The Elastic-net algorithm deals well with collinearity problems, while allowing variable selection in a blended approach between the Ridge and Lasso regression. The model fitting produced explained variances of 59%, 81%, 49% and 17% for PC1, PC2, PC3 and PC4, respectively, backing up the hypothesis of good correlation between geographical characteristics and seasonal rainfall patterns (comprised in the four principal components). With the obtained coefficients from the regression, the 108 rainfall percentiles for each station were back estimated giving very good results when compared with the original ones, with an overall 60% explained variance.

How Variables Uncorrelated with the Dependent Variable Can Actually Make Excellent Predictors: The Important Suppressor Variable Case.

ERIC Educational Resources Information Center

Woolley, Kristin K.

Many researchers are unfamiliar with suppressor variables and how they operate in multiple regression analyses. This paper describes the role suppressor variables play in a multiple regression model and provides practical examples that explain how they can change research results. A variable that when added as another predictor increases the total…

Factors influencing behavioral intention to undergo Papanicolaou testing in early adulthood: Comparison of Japanese and Korean women.

PubMed

Kang, Kyung-Ah; Kim, Shin-Jeong; Kaneko, Noriyo

2017-12-01

In this study, we identified the factors influencing behavioral intention to undergo Papanicolaou testing among Japanese and Korean women in early adulthood. Their behavioral intentions were compared in this cross-sectional descriptive study. In total, 887 women (Japanese = 498, Korean = 389) aged 20-39 years participated in this study. Using a self-report questionnaire, knowledge, attitudes, subjective norm, perceived behavioral control, and behavioral intention were surveyed. There were significant differences between Japanese and Korean women's scores on all main variables. For Japanese women, all the variables moderately correlated with behavioral intention. In comparison, for Korean women, all independent variables, except for knowledge, moderately correlated with behavioral intention. Through a multiple regression analysis, age, undergoing Papanicolaou testing, attitudes, subjective norm, and perceived behavioral control were identified as significant predictors of behavioral intention among Japanese women. Among Korean women, job status, undergoing a Papanicolaou test, attitudes, subjective norm, and perceived behavioral control were demonstrated as significant predictors of behavioral intention. Health professionals should consider these factors to encourage Papanicolaou testing in women in early adulthood. © 2017 John Wiley & Sons Australia, Ltd.

Rainfall Results of the Florida Area Cumulus Experiment, 1970-76.

NASA Astrophysics Data System (ADS)

Woodley, William L.; Jordan, Jill; Barnston, Anthony; Simpson, Joanne; Biondini, Ron; Flueck, John

1982-02-01

The Florida Area Cumulus Experiment of 1970-76 (FACE-1) is a single-area, randomized, exploratory experiment to determine whether seeding cumuli for dynamic effects (dynamic seeding) can be used to augment convective rainfall over a substantial target area (1.3 × 104 km2) in south Florida. Rainfall is estimated using S-band radar observations after adjustment by raingages. The two primary response variables are rain volumes in the total target (TT) and in the floating target (FT), the most intensely treated portion of the target. The experimental unit is the day and the main observational period is the 6 h after initiation of treatment (silver iodide flares on seed days and either no flares or placebos on control days). Analyses without predictors suggest apparent increases in both the location (means and medians) and the dispersion (standard deviation and interquartile range) characteristics of rainfall due to seeding in the FT and TT variables with substantial statistical support for the FT results and lesser statistical support for the TT results. Analyses of covariance using meteorologically meaningful predictor variables suggest a somewhat larger effect of seeding with stronger statistical support. These results are interpreted in terms of the FACE conceptual model.

Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

PubMed Central

Stepanov, Igor I.; Abramson, Charles I.; Hoogs, Marietta; Benedict, Ralph H. B.

2012-01-01

The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(−B2  ∗  (X − 1)) + B4  ∗  (1 − exp(−B2  ∗  (X − 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both. PMID:22745911

Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis.

PubMed

Stepanov, Igor I; Abramson, Charles I; Hoogs, Marietta; Benedict, Ralph H B

2012-01-01

The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1-5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(-B2  ∗  (X - 1)) + B4  ∗  (1 - exp(-B2  ∗  (X - 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both.

Predictors of adjustment and growth in women with recurrent ovarian cancer.

PubMed

Ponto, Julie Ann; Ellington, Lee; Mellon, Suzanne; Beck, Susan L

2010-05-01

To analyze predictors of adjustment and growth in women who had experienced recurrent ovarian cancer using components of the Resiliency Model of Family Stress, Adjustment, and Adaptation as a conceptual framework. Cross-sectional. Participants were recruited from national cancer advocacy groups. 60 married or partnered women with recurrent ovarian cancer. Participants completed an online or paper survey. Independent variables included demographic and illness variables and meaning of illness. Outcome variables were psychological adjustment and post-traumatic growth. A model of five predictor variables (younger age, fewer years in the relationship, poorer performance status, greater symptom distress, and more negative meaning) accounted for 64% of the variance in adjustment but did not predict post-traumatic growth. This study supports the use of a model of adjustment that includes demographic, illness, and appraisal variables for women with recurrent ovarian cancer. Symptom distress and poorer performance status were the most significant predictors of adjustment. Younger age and fewer years in the relationship also predicted poorer adjustment. Nurses have the knowledge and skills to influence the predictors of adjustment to recurrent ovarian cancer, particularly symptom distress and poor performance status. Nurses who recognize the predictors of poorer adjustment can anticipate problems and intervene to improve adjustment for women.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

USGS Publications Warehouse

Zimmermann, N.E.; Edwards, T.C.; Moisen, Gretchen G.; Frescino, T.S.; Blackard, J.A.

2007-01-01

1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. ?? 2007 The Authors.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

PubMed Central

ZIMMERMANN, N E; EDWARDS, T C; MOISEN, G G; FRESCINO, T S; BLACKARD, J A

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. PMID:18642470

Cross-cultural and site-based influences on demographic, well-being, and social network predictors of risk perception in hazard and disaster settings in Ecuador and Mexico: predictors of risk perception in hazard and disaster settings in Ecuador and Mexico.

PubMed

Jones, Eric C; Faas, Albert J; Murphy, Arthur D; Tobin, Graham A; Whiteford, Linda M; McCarty, Christopher

2013-03-01

Although virtually all comparative research about risk perception focuses on which hazards are of concern to people in different culture groups, much can be gained by focusing on predictors of levels of risk perception in various countries and places. In this case, we examine standard and novel predictors of risk perception in seven sites among communities affected by a flood in Mexico (one site) and volcanic eruptions in Mexico (one site) and Ecuador (five sites). We conducted more than 450 interviews with questions about how people feel at the time (after the disaster) regarding what happened in the past, their current concerns, and their expectations for the future. We explore how aspects of the context in which people live have an effect on how strongly people perceive natural hazards in relationship with demographic, well-being, and social network factors. Generally, our research indicates that levels of risk perception for past, present, and future aspects of a specific hazard are similar across these two countries and seven sites. However, these contexts produced different predictors of risk perception-in other words, there was little overlap between sites in the variables that predicted the past, present, or future aspects of risk perception in each site. Generally, current stress was related to perception of past danger of an event in the Mexican sites, but not in Ecuador; network variables were mainly important for perception of past danger (rather than future or present danger), although specific network correlates varied from site to site across the countries.

Prevalence and predictors associated with intestinal infections by protozoa and helminths in southern Brazil.

PubMed

Casavechia, Maria Teresinha Gomes; Lonardoni, Maria Valdrinez Campana; Venazzi, Eneide Aparecida Sabaini; Campanerut-Sá, Paula Aline Zanetti; da Costa Benalia, Hugo Rafael; Mattiello, Matheus Felipe; Menechini, Pedro Victor Lazaretti; Dos Santos, Carlos Aparecido; Teixeira, Jorge Juarez Vieira

2016-06-01

Approximately 2 billion people are infected with soil-transmitted helminths worldwide, mainly in tropical and subtropical areas. This research aimed to investigate the prevalence and predictors associated with parasitic infections in primary health care. A cross-sectional study was performed with a large random sample to identify the prevalence and predictors associated with parasitic infections in primary health care in Marialva, southern Brazil, from April 2011 to September 2013. Stool samples from 775 individuals were analyzed for the presence of protozoan cysts, helminth eggs, and larvae. The overall prevalence of intestinal parasites was 13.94 %, and the prevalence of protozoa and helminths was 15.1 and 2.9 %, respectively. The predictor variables that were associated with intestinal parasites were male gender odds ratio (OR) 1.60, 95 % confidence interval (CI 1.10-2.40) and the absence of a kitchen garden (OR 2.28, 95 % CI, 1.08-4.85). Positive associations were found between Giardia duodenalis and individuals aged ≤18 with high risk (OR 19.0, 95 % CI 2.16-167.52), between Endolimax nana and the absence of a kitchen garden (p < 0.01), and between Trichuris trichiura and the presence of a kitchen garden (p = 0.014). Polyparasitism was present in 27.27 % of infected individuals. Our findings confirmed a relatively low prevalence in primary care, compared to international standards, despite the rare publications in the area. As variables, male gender and the absence of a kitchen garden stood out as important predictors. It is highly relevant that the health conditions of the population comply with consistent standards.

Clinico-pathological and biological prognostic variables in squamous cell carcinoma of the vulva.

PubMed

Gadducci, Angiolo; Tana, Roberta; Barsotti, Cecilia; Guerrieri, Maria Elena; Genazzani, Andrea Riccardo

2012-07-01

Several clinical-pathological parameters have been related to survival of patients with invasive squamous cell carcinoma of the vulva, whereas few studies have investigated the ability of biological variables to predict the clinical outcome of these patients. The present paper reviews the literature data on the prognostic relevance of lymph node-related parameters, primary tumor-related parameters, FIGO stage, blood variables, and tissue biological variables. Regarding these latter, the paper takes into account the analysis of DNA content, cell cycle-regulatory proteins, apoptosis-related proteins, epidermal growth factor receptor [EGFR], and proteins that are involved in tumor invasiveness, metastasis and angiogenesis. At present, the lymph node status and FIGO stage according to the new 2009 classification system are the main predictors for vulvar squamous cell carcinoma, whereas biological variables do not have yet a clinical relevance and their role is still investigational. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Predictors of persistent pain after total knee arthroplasty: a systematic review and meta-analysis.

PubMed

Lewis, G N; Rice, D A; McNair, P J; Kluger, M

2015-04-01

Several studies have identified clinical, psychosocial, patient characteristic, and perioperative variables that are associated with persistent postsurgical pain; however, the relative effect of these variables has yet to be quantified. The aim of the study was to provide a systematic review and meta-analysis of predictor variables associated with persistent pain after total knee arthroplasty (TKA). Included studies were required to measure predictor variables prior to or at the time of surgery, include a pain outcome measure at least 3 months post-TKA, and include a statistical analysis of the effect of the predictor variable(s) on the outcome measure. Counts were undertaken of the number of times each predictor was analysed and the number of times it was found to have a significant relationship with persistent pain. Separate meta-analyses were performed to determine the effect size of each predictor on persistent pain. Outcomes from studies implementing uni- and multivariable statistical models were analysed separately. Thirty-two studies involving almost 30 000 patients were included in the review. Preoperative pain was the predictor that most commonly demonstrated a significant relationship with persistent pain across uni- and multivariable analyses. In the meta-analyses of data from univariate models, the largest effect sizes were found for: other pain sites, catastrophizing, and depression. For data from multivariate models, significant effects were evident for: catastrophizing, preoperative pain, mental health, and comorbidities. Catastrophizing, mental health, preoperative knee pain, and pain at other sites are the strongest independent predictors of persistent pain after TKA. © The Author 2014. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…

Changes in Situational and Dispositional Factors as Predictors of Job Satisfaction

ERIC Educational Resources Information Center

Keller, Anita C.; Semmer, Norbert K.

2013-01-01

Arguably, job satisfaction is one of the most important variables with regard to work. When explaining job satisfaction, research usually focuses on predictor variables in terms of levels but neglects growth rates. Therefore it remains unclear how potential predictors evolve over time and how their development affects job satisfaction. Using…

Predictors of posttraumatic stress symptoms following childbirth

PubMed Central

2014-01-01

Background Posttraumatic stress disorder (PTSD) following childbirth has gained growing attention in the recent years. Although a number of predictors for PTSD following childbirth have been identified (e.g., history of sexual trauma, emergency caesarean section, low social support), only very few studies have tested predictors derived from current theoretical models of the disorder. This study first aimed to replicate the association of PTSD symptoms after childbirth with predictors identified in earlier research. Second, cognitive predictors derived from Ehlers and Clark’s (2000) model of PTSD were examined. Methods N = 224 women who had recently given birth completed an online survey. In addition to computing single correlations between PTSD symptom severities and variables of interest, in a hierarchical multiple regression analyses posttraumatic stress symptoms were predicted by (1) prenatal variables, (2) birth-related variables, (3) postnatal social support, and (4) cognitive variables. Results Wellbeing during pregnancy and age were the only prenatal variables contributing significantly to the explanation of PTSD symptoms in the first step of the regression analysis. In the second step, the birth-related variables peritraumatic emotions and wellbeing during childbed significantly increased the explanation of variance. Despite showing significant bivariate correlations, social support entered in the third step did not predict PTSD symptom severities over and above the variables included in the first two steps. However, with the exception of peritraumatic dissociation all cognitive variables emerged as powerful predictors and increased the amount of variance explained from 43% to a total amount of 68%. Conclusions The findings suggest that the prediction of PTSD following childbirth can be improved by focusing on variables derived from a current theoretical model of the disorder. PMID:25026966

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Measurement-to-measurement blood pressure variability is related to cognitive performance: the Maine Syracuse study.

PubMed

Crichton, Georgina E; Elias, Merrill F; Dore, Gregory A; Torres, Rachael V; Robbins, Michael A

2014-11-01

The objective was to investigate the association between variability in blood pressure (BP) and cognitive function for sitting, standing, and reclining BP values and variability derived from all 15 measures. In previous studies, only sitting BP values have been examined, and only a few cognitive measures have been used. A secondary objective was to examine associations between BP variability and cognitive performance in hypertensive individuals stratified by treatment success. Cross-sectional analyses were performed on 972 participants of the Maine Syracuse Study for whom 15 serial BP clinic measures (5 sitting, 5 recumbent, and 5 standing) were obtained before testing of cognitive performance. Using all 15 measures, higher variability in systolic and diastolic BP was associated with poorer performance on multiple measures of cognitive performance, independent of demographic factors, cardiovascular risk factors, and pulse pressure. When sitting, reclining, and standing systolic BP values were compared, only variability in standing BP was related to measures of cognitive performance. However, for diastolic BP, variability in all 3 positions was related to cognitive performance. Mean BP values were weaker predictors of cognition. Furthermore, higher overall variability in both systolic and diastolic BP was associated with poorer cognitive performance in unsuccessfully treated hypertensive individuals (with BP ≥140/90 mm Hg), but these associations were not evident in those with controlled hypertension. © 2014 American Heart Association, Inc.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

Acculturative and Psychological Predictors of Academic-Related Outcomes Among Cambodian American High School Students

PubMed Central

Dinh, Khanh T.; Weinstein, Traci L.; Kim, Su Yeong; Ho, Ivy K.

2009-01-01

This study examined the acculturative and psychosocial predictors of academic-related outcomes among Cambodian American high school students from an urban school district in the state of Massachusetts. Student participants (N = 163) completed an anonymous survey that assessed demographic characteristics, acculturative experiences, intergenerational conflict, depression, and academic-related outcomes. The main results indicated that acculturative and psychosocial variables were significant predictors of academic-related outcomes. Specifically, students' Cambodian cultural orientation was positively associated with their beliefs about the utility of education and sense of school membership, while students' Anglo/White cultural orientation was positively associated with their grade point average, educational aspirations, and sense of school membership. Results also indicated that Cambodian cultural orientation was negatively associated with intergenerational conflict, which in turn was associated with depression. This study provides important information to developers of school-based and family-based prevention and intervention programs by highlighting the acculturative challenges and how academic success can be fostered for Cambodian American students. PMID:20011458

Cognitive and attitudinal predictors related to graphing achievement among pre-service elementary teachers

NASA Astrophysics Data System (ADS)

Szyjka, Sebastian P.

The purpose of this study was to determine the extent to which six cognitive and attitudinal variables predicted pre-service elementary teachers' performance on line graphing. Predictors included Illinois teacher education basic skills sub-component scores in reading comprehension and mathematics, logical thinking performance scores, as well as measures of attitudes toward science, mathematics and graphing. This study also determined the strength of the relationship between each prospective predictor variable and the line graphing performance variable, as well as the extent to which measures of attitude towards science, mathematics and graphing mediated relationships between scores on mathematics, reading, logical thinking and line graphing. Ninety-four pre-service elementary education teachers enrolled in two different elementary science methods courses during the spring 2009 semester at Southern Illinois University Carbondale participated in this study. Each subject completed five different instruments designed to assess science, mathematics and graphing attitudes as well as logical thinking and graphing ability. Sixty subjects provided copies of primary basic skills score reports that listed subset scores for both reading comprehension and mathematics. The remaining scores were supplied by a faculty member who had access to a database from which the scores were drawn. Seven subjects, whose scores could not be found, were eliminated from final data analysis. Confirmatory factor analysis (CFA) was conducted in order to establish validity and reliability of the Questionnaire of Attitude Toward Line Graphs in Science (QALGS) instrument. CFA tested the statistical hypothesis that the five main factor structures within the Questionnaire of Attitude Toward Statistical Graphs (QASG) would be maintained in the revised QALGS. Stepwise Regression Analysis with backward elimination was conducted in order to generate a parsimonious and precise predictive model. This procedure allowed the researcher to explore the relationships among the affective and cognitive variables that were included in the regression analysis. The results for CFA indicated that the revised QALGS measure was sound in its psychometric properties when tested against the QASG. Reliability statistics indicated that the overall reliability for the 32 items in the QALGS was .90. The learning preferences construct had the lowest reliability (.67), while enjoyment (.89), confidence (.86) and usefulness (.77) constructs had moderate to high reliabilities. The first four measurement models fit the data well as indicated by the appropriate descriptive and statistical indices. However, the fifth measurement model did not fit the data well statistically, and only fit well with two descriptive indices. The results addressing the research question indicated that mathematical and logical thinking ability were significant predictors of line graph performance among the remaining group of variables. These predictors accounted for 41% of the total variability on the line graph performance variable. Partial correlation coefficients indicated that mathematics ability accounted for 20.5% of the variance on the line graphing performance variable when removing the effect of logical thinking. The logical thinking variable accounted for 4.7% of the variance on the line graphing performance variable when removing the effect of mathematics ability.

Life Span Studies of ADHD-Conceptual Challenges and Predictors of Persistence and Outcome.

PubMed

Caye, Arthur; Swanson, James; Thapar, Anita; Sibley, Margaret; Arseneault, Louise; Hechtman, Lily; Arnold, L Eugene; Niclasen, Janni; Moffitt, Terrie; Rohde, Luis Augusto

2016-12-01

There is a renewed interest in better conceptualizing trajectories of attention-deficit/hyperactivity disorder (ADHD) from childhood to adulthood, driven by an increased recognition of long-term impairment and potential persistence beyond childhood and adolescence. This review addresses the following major issues relevant to the course of ADHD in light of current evidence from longitudinal studies: (1) conceptual and methodological issues related to measurement of persistence of ADHD, (2) estimates of persistence rate from childhood to adulthood and its predictors, (3) long-term negative outcomes of childhood ADHD and their early predictors, and (4) the recently proposed new adult-onset ADHD. Estimates of persistence vary widely in the literature, and diagnostic criteria, sample characteristics, and information source are the most important factors explaining variability among studies. Evidence indicates that ADHD severity, comorbid conduct disorder and major depressive disorder, and treatment for ADHD are the main predictors of ADHD persistence from childhood to adulthood. Comorbid conduct disorder and ADHD severity in childhood are the most important predictors of adverse outcomes in adulthood among children with ADHD. Three recent population studies suggested the existence of a significant proportion of individuals who report onset of ADHD symptoms and impairments after childhood. Finally, we highlight areas for improvement to increase our understanding of ADHD across the life span.

Life Span Studies of ADHD—Conceptual Challenges and Predictors of Persistence and Outcome

PubMed Central

Caye, Arthur; Swanson, James; Thapar, Anita; Sibley, Margaret; Arseneault, Louise; Hechtman, Lily; Arnold, L. Eugene; Niclasen, Janni; Moffitt, Terrie

2018-01-01

There is a renewed interest in better conceptualizing trajectories of attention-deficit/hyperactivity disorder (ADHD) from childhood to adulthood, driven by an increased recognition of long-term impairment and potential persistence beyond childhood and adolescence. This review addresses the following major issues relevant to the course of ADHD in light of current evidence from longitudinal studies: (1) conceptual and methodological issues related to measurement of persistence of ADHD, (2) estimates of persistence rate from childhood to adulthood and its predictors, (3) long-term negative outcomes of childhood ADHD and their early predictors, and (4) the recently proposed new adult-onset ADHD. Estimates of persistence vary widely in the literature, and diagnostic criteria, sample characteristics, and information source are the most important factors explaining variability among studies. Evidence indicates that ADHD severity, comorbid conduct disorder and major depressive disorder, and treatment for ADHD are the main predictors of ADHD persistence from childhood to adulthood. Comorbid conduct disorder and ADHD severity in childhood are the most important predictors of adverse outcomes in adulthood among children with ADHD. Three recent population studies suggested the existence of a significant proportion of individuals who report onset of ADHD symptoms and impairments after childhood. Finally, we highlight areas for improvement to increase our understanding of ADHD across the life span. PMID:27783340

Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants.

PubMed

González, Mari Feli; Facal, David; Juncos-Rabadán, Onésimo; Yanguas, Javier

2017-10-01

Cognitive performance is not easily predicted, since different variables play an important role in the manifestation of age-related declines. The objective of this study is to analyze the predictors of cognitive performance in a Spanish sample over 50 years from a multidimensional perspective, including socioeconomic, affective, and physical variables. Some of them are well-known predictors of cognition and others are emergent variables in the study of cognition. The total sample, drawn from the "Longitudinal Study Aging in Spain (ELES)" project, consisted of 832 individuals without signs of cognitive impairment. Cognitive function was measured with tests evaluating episodic and working memory, visuomotor speed, fluency, and naming. Thirteen independent variables were selected as predictors belonging to socioeconomic, emotional, and physical execution areas. Multiple linear regressions, following the enter method, were calculated for each age group in order to study the influence of these variables in cognitive performance. Education is the variable which best predicts cognitive performance in the 50-59, 60-69, and 70-79 years old groups. In the 80+ group, the best predictor is objective economic status and education does not enter in the model. Age-related decline can be modified by the influence of educational and socioeconomic variables. In this context, it is relevant to take into account how easy is to modify certain variables, compared to others which depend on each person's life course.

Sleep characteristics as predictor variables of stress systems markers in insomnia disorder.

PubMed

Floam, Samantha; Simpson, Norah; Nemeth, Emese; Scott-Sutherland, Jennifer; Gautam, Shiva; Haack, Monika

2015-06-01

This study investigates the extent to which sleep characteristics serve as predictor variables for inflammatory, hypothalamic-pituitary-adrenal and autonomic systems markers. Twenty-nine participants with a diagnosis of insomnia disorder based on the Diagnostic Statistical Manual of Mental Disorders, Fifth Edition (age 25.3 ± 1.6 years, insomnia duration 6.6 ± 0.8 years) and 19 healthy control sleepers (age 25.4 ± 1.4 years) underwent a 2-week at-home evaluation keeping a sleep diary and wearing an actigraph, followed by a visit to the Research Center to measure blood pressure, and collect blood and urine samples. The actigraphy- and diary-based variables of sleep duration, sleep-onset latency, wake after sleep onset and sleep fragmentation/number of night-time awakenings were averaged and entered as dependent variables in regression analyses. Composite scores were calculated for the autonomic (blood pressure, norepinephrine), inflammatory (monocyte counts, interleukin-6, C-reactive protein) and hypothalamic-pituitary-adrenal systems (cortisol), and used as predictor variables in regression models. Compared with controls, individuals with insomnia had a shorter sleep duration (P < 0.05), and a higher hypothalamic-pituitary-adrenal and inflammatory composite score (P < 0.05). The higher inflammatory score was mainly due to higher circulating monocytes (P < 0.05), rather than differences in interleukin-6 or C-reactive protein. In persistent insomnia disorder, cortisol is upregulated and associated with actigraphy- and diary-based wake after sleep onset, suggesting that wake after sleep onset may serve as a marker to identify individuals at increased risks for disorders associated with a hyperactive hypothalamic-pituitary-adrenal system. The absence of autonomic and pro-inflammatory changes (interleukin-6, C-reactive protein), despite a substantial decrease in actigraphic sleep duration, may relate to a higher resilience to the adverse biological consequences of insomnia in this young age group. © 2014 European Sleep Research Society.

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

PubMed

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

Oral Language, Sex and Socio-Economic Status as Predictors of Reading Achievement.

ERIC Educational Resources Information Center

Ebert, Dorothy Jo Williamson

This study was designed to discover the degree of relationship between a number of predictor variables and reading achievement for 65 black second grade students in two Austin, Texas, schools. The seven predictor variables used were: oral language performance as measured by the Gloria and David Beginning English, Series 20, Test 6 (GDBE); an…

Evaluation of the effects of climate and man intervention on ground waters and their dependent ecosystems using time series analysis

NASA Astrophysics Data System (ADS)

Gemitzi, Alexandra; Stefanopoulos, Kyriakos

2011-06-01

SummaryGroundwaters and their dependent ecosystems are affected both by the meteorological conditions as well as from human interventions, mainly in the form of groundwater abstractions for irrigation needs. This work aims at investigating the quantitative effects of meteorological conditions and man intervention on groundwater resources and their dependent ecosystems. Various seasonal Auto-Regressive Integrated Moving Average (ARIMA) models with external predictor variables were used in order to model the influence of meteorological conditions and man intervention on the groundwater level time series. Initially, a seasonal ARIMA model that simulates the abstraction time series using as external predictor variable temperature ( T) was prepared. Thereafter, seasonal ARIMA models were developed in order to simulate groundwater level time series in 8 monitoring locations, using the appropriate predictor variables determined for each individual case. The spatial component was introduced through the use of Geographical Information Systems (GIS). Application of the proposed methodology took place in the Neon Sidirochorion alluvial aquifer (Northern Greece), for which a 7-year long time series (i.e., 2003-2010) of piezometric and groundwater abstraction data exists. According to the developed ARIMA models, three distinct groups of groundwater level time series exist; the first one proves to be dependent only on the meteorological parameters, the second group demonstrates a mixed dependence both on meteorological conditions and on human intervention, whereas the third group shows a clear influence from man intervention. Moreover, there is evidence that groundwater abstraction has affected an important protected ecosystem.

Health related quality of life in parents of six to eight year old children with Down syndrome.

PubMed

Marchal, Jan Pieter; Maurice-Stam, Heleen; Hatzmann, Janneke; van Trotsenburg, A S Paul; Grootenhuis, Martha A

2013-11-01

Raising a child with Down syndrome (DS) has been found to be associated with lowered health related quality of life (HRQoL) in the domains cognitive functioning, social functioning, daily activities and vitality. We aimed to explore which socio-demographics, child functioning and psychosocial variables were related to these HRQoL domains in parents of children with DS. Parents of 98 children with DS completed the TNO-AZL adult quality of life questionnaire (TAAQOL) and a questionnaire assessing socio-demographic, child functioning and psychosocial predictors. Using multiple linear regression analyses for each category of predictors, we selected relevant predictors for the final models. The final multiple linear regression models revealed that cognitive functioning was best predicted by the sleep of the child (β=.29, p<.01) and by the parent having given up a hobby (β=-.29, p<.01), social functioning by the quality of the partner relation (β=.34, p<.001), daily activities by the parent having to care for an ill friend or family member (β=-.31, p<.01), and vitality by the parent having enough personal time (β=.32, p<.01). Overall, psychosocial variables rather than socio-demographics or child functioning showed most consistent and powerful relations to the HRQoL domains of cognitive functioning, social functioning, daily activities and vitality. These psychosocial variables mainly related to social support and time pressure. Systematic screening of parents to detect problems timely, and interventions targeting the supportive network and the demands in time are recommended. Copyright © 2013 Elsevier Ltd. All rights reserved.

What variables are important in predicting bovine viral diarrhea virus? A random forest approach.

PubMed

Machado, Gustavo; Mendoza, Mariana Recamonde; Corbellini, Luis Gustavo

2015-07-24

Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.

Effect of Escitalopram on Hot Flash Interference: A Randomized, Controlled Trial

PubMed Central

Carpenter, Janet S.; Guthrie, Katherine A.; Larson, Joseph C.; Freeman, Ellen W.; Joffe, Hadine; Reed, Susan D.; Ensrud, Kristine E.; LaCroix, Andrea Z.

2012-01-01

Objectives To estimate the effect of escitalopram 10–20 mg/day versus placebo for reducing hot flash interference in daily life and understand correlates and predictors of reductions in hot flash interference, a key measure of quality of life. Design Multi-site, randomized, double-blind, placebo-controlled clinical trial. Patients 205 midlife women (46% African-American) who met criteria participated. Setting MsFLASH clinical sites in Boston, Indianapolis, Oakland, and Philadelphia. Intervention After baseline, women were randomized to 1 pill of escitalopram 10 mg/day (n=104) or placebo (n=101) with follow-up at 4- and 8-weeks. At week 4, those not achieving 50% fewer hot flashes were increased to 2 pills daily (20 mg/day or 2 placebo pills). Main outcome measures The Hot Flash Related Daily Interference Scale; Correlates were variables from hot flash diaries; Predictors were baseline demographics, clinical variables, depression, anxiety, sleep quality, and hot flashes. Results Compared to placebo, escitalopram significantly reduced hot flash interference by 6.0 points at week 4 and 3.4 points at week 8 more than placebo (p=0.012). Reductions in hot flash interference correlated with changes in hot flash diary variables. However, baseline variables did not significantly predict reductions in hot flash interference. Conclusions Escitalopram 10–20mg/day for 8 weeks improves women’s quality of life and this benefit did not vary by demographic, clinical, mood, sleep, or hot flash variables. PMID:22480818

Unmet dental needs and barriers to dental care among children with autism spectrum disorders.

PubMed

Lai, Bien; Milano, Michael; Roberts, Michael W; Hooper, Stephen R

2012-07-01

Mail-in pilot-tested questionnaires were sent to a stratified random sample of 1,500 families from the North Carolina Autism Registry. Multivariate logistic regression analysis was used to determine the significance of unmet dental needs and other predictors. Of 568 surveys returned (Response Rate = 38%), 555 were complete and usable. Sixty-five (12%) children had unmet dental needs. Of 516 children (93%) who had been to a dentist, 11% still reported unmet needs. The main barriers were child's behavior, cost, and lack of insurance. The significant predictor variables of unmet needs were child's behavior (p = 0.01), child's dental health (p < 0.001), and caregiver's last dental visit greater than 6 months (p = 0.002). Type of ASD did not have an effect on having unmet dental needs.

Perceived Difficulty Quitting Predicts Enrollment in a Smoking-Cessation Program for Patients With Head and Neck Cancer

PubMed Central

Duffy, Sonia A.; Scheumann, Angela L.; Fowler, Karen E.; Darling-Fisher, Cynthia; Terrell, Jeffrey E.

2013-01-01

Purpose/Objectives To determine the predictors of participation in a smoking-cessation program among patients with head and neck cancer. Design This cross-sectional study is a substudy of a larger, randomized trial of patients with head and neck cancer that determined the predictors of smokers’ participation in a cessation intervention. Setting Otolaryngology clinics at three Veterans Affairs medical centers (Ann Arbor, MI, Gainesville, FL, and Dallas, TX), and the University of Michigan Hospital in Ann Arbor. Sample 286 patients who had smoked within six months of the screening survey were eligible for a smoking-cessation intervention. Methods Descriptive statistics and bivariate and multivariate logistic regression were used to determine the independent predictors of smokers’ participation in an intervention study. Main Research Variables Perceived difficulty quitting (as a construct of self-efficacy), health behaviors (i.e., smoking and problem drinking), clinical characteristics (i.e., depression and cancer site and stage), and demographic variables. Findings Forty-eight percent of those eligible participated. High perceived difficulty quitting was the only statistically significant predictor of participation, whereas problem drinking, lower depressive symptoms, and laryngeal cancer site approached significance. Conclusions Special outreach may be needed to reach patients with head and neck cancer who are overly confident in quitting, problem drinkers, and patients with laryngeal cancer. Implications for Nursing Oncology nurses are in an opportune position to assess patients’ perceived difficulty quitting smoking and motivate them to enroll in cessation programs, ultimately improving quality of life, reducing risk of recurrence, and increasing survival for this population. PMID:20439219

VR Employment Outcomes of Individuals with Autism Spectrum Disorders: A Decade in the Making.

PubMed

Alverson, Charlotte Y; Yamamoto, Scott H

2018-01-01

This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school, secondary disability status, and total number of VR services. Competitive employment was the criterion variable. Only one predictor variable, Total Number of VR Services, was significant across all 10 years. IEP status in high school was not significant in any year. The remaining predictors were significant in one or more years. Further research and implications for researchers and practitioners are included.

Modeling Predictors of Duties Not Including Flying Status.

PubMed

Tvaryanas, Anthony P; Griffith, Converse

2018-01-01

The purpose of this study was to reuse available datasets to conduct an analysis of potential predictors of U.S. Air Force aircrew nonavailability in terms of being in "duties not to include flying" (DNIF) status. This study was a retrospective cohort analysis of U.S. Air Force aircrew on active duty during the period from 2003-2012. Predictor variables included age, Air Force Specialty Code (AFSC), clinic location, diagnosis, gender, pay grade, and service component. The response variable was DNIF duration. Nonparametric methods were used for the exploratory analysis and parametric methods were used for model building and statistical inference. Out of a set of 783 potential predictor variables, 339 variables were identified from the nonparametric exploratory analysis for inclusion in the parametric analysis. Of these, 54 variables had significant associations with DNIF duration in the final model fitted to the validation data set. The predicted results of this model for DNIF duration had a correlation of 0.45 with the actual number of DNIF days. Predictor variables included age, 6 AFSCs, 7 clinic locations, and 40 primary diagnosis categories. Specific demographic (i.e., age), occupational (i.e., AFSC), and health (i.e., clinic location and primary diagnosis category) DNIF drivers were identified. Subsequent research should focus on the application of primary, secondary, and tertiary prevention measures to ameliorate the potential impact of these DNIF drivers where possible.Tvaryanas AP, Griffith C Jr. Modeling predictors of duties not including flying status. Aerosp Med Hum Perform. 2018; 89(1):52-57.

Justification, perception of severity and harm, and criminalization of wife abuse in the Palestinian society.

PubMed

Haj-Yahia, Muhammad M; Wilson, Rula M; Naqvi, Syed Agha M

2012-07-01

The purpose of this study was to examine the perceptions of Palestinian adults toward different dimensions of wife abuse. A cross-sectional survey, using a combination of self-administered questionnaires and interviews, was conducted among a systematic random sample of 624 adult Palestinian men and women from the West Bank and Gaza Strip (18 years or older). Study results indicated a strong tendency to justify wife beating in different situations, such as when the wife is perceived as having an affair with another man or as physically attacking her husband. Participants considered the following acts of husband's violence against wife as most severe: using a weapon (86%), having sex with the wife against her will (67%), and hitting her with his fist (57%). The majority of participants thought that wife beating should be considered a crime (82.3%). Traditional marital role expectations was the main significant predictor for all of the study criterion variables. Gender, place of residence, age, and marital status were significant predictors of some of the criterion variables.

Geographically weighted lasso (GWL) study for modeling the diarrheic to achieve open defecation free (ODF) target

NASA Astrophysics Data System (ADS)

Arumsari, Nurvita; Sutidjo, S. U.; Brodjol; Soedjono, Eddy S.

2014-03-01

Diarrhea has been one main cause of morbidity and mortality to children around the world, especially in the developing countries According to available data that was mentioned. It showed that sanitary and healthy lifestyle implementation by the inhabitants was not good yet. Inadequacy of environmental influence and the availability of health services were suspected factors which influenced diarrhea cases happened followed by heightened percentage of the diarrheic. This research is aimed at modelling the diarrheic by using Geographically Weighted Lasso method. With the existence of spatial heterogeneity was tested by Breusch Pagan, it was showed that diarrheic modeling with weighted regression, especially GWR and GWL, can explain the variation in each location. But, the absence of multi-collinearity cases on predictor variables, which were affecting the diarrheic, resulted in GWR and GWL modelling to be not different or identical. It is shown from the resulting MSE value. While from R2 value which usually higher on GWL model showed a significant variable predictor based on more parametric shrinkage value.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388

Assimilating Non-linear Effects of Customized Large-Scale Climate Predictors on Downscaled Precipitation over the Tropical Andes

NASA Astrophysics Data System (ADS)

Molina, J. M.; Zaitchik, B. F.

2016-12-01

Recent findings considering high CO2 emission scenarios (RCP8.5) suggest that the tropical Andes may experience a massive warming and a significant precipitation increase (decrease) during the wet (dry) seasons by the end of the 21st century. Variations on rainfall-streamflow relationships and seasonal crop yields significantly affect human development in this region and make local communities highly vulnerable to climate change and variability. We developed an expert-informed empirical statistical downscaling (ESD) algorithm to explore and construct robust global climate predictors to perform skillful RCP8.5 projections of in-situ March-May (MAM) precipitation required for impact modeling and adaptation studies. We applied our framework to a topographically-complex region of the Colombian Andes where a number of previous studies have reported El Niño-Southern Oscillation (ENSO) as the main driver of climate variability. Supervised machine learning algorithms were trained with customized and bias-corrected predictors from NCEP reanalysis, and a cross-validation approach was implemented to assess both predictive skill and model selection. We found weak and not significant teleconnections between precipitation and lagged seasonal surface temperatures over El Niño3.4 domain, which suggests that ENSO fails to explain MAM rainfall variability in the study region. In contrast, series of Sea Level Pressure (SLP) over American Samoa -likely associated with the South Pacific Convergence Zone (SPCZ)- explains more than 65% of the precipitation variance. The best prediction skill was obtained with Selected Generalized Additive Models (SGAM) given their ability to capture linear/nonlinear relationships present in the data. While SPCZ-related series exhibited a positive linear effect in the rainfall response, SLP predictors in the north Atlantic and central equatorial Pacific showed nonlinear effects. A multimodel (MIROC, CanESM2 and CCSM) ensemble of ESD projections revealed an increased variability and a positive and significant trend in the MAM precipitation mean in the next decades, with accentuated changes and projection uncertainty after 2050. ESD traces (2050-2100) from MIROC presented the highest changes in the precipitation mean ( 60%) when compared with the observations.

[Modeling employee stress in psychiatric rehabilitation--effects of personal and organizational factors].

PubMed

Queri, S; Konrad, M; Keller, K

2012-08-01

Increasing stress-associated health problems in Germany often are attributed to problems on the job, in particular to rising work demands. The study includes several stress predictors from other results and from literature in one predictive model for the field of work of "psychiatric rehabilitation".A cross-sectional design was used to measure personal and organizational variables with quantitative standard questionnaires as self-ratings from n=243 pedagogically active employees from various professions. Overall stress and job stress were measured with different instruments.The sample showed above-average overall stress scores along with below-average job stress scores. The multivariate predictive model for explaining the heightened stress shows pathogenetic and salutogenetic main effects for organizational variables such as "gratification crisis" and personal variables such as "occupational self-efficacy expectations" as well as an interaction of both types of variables. There are relevant gender-specific results concerning empathy and differences between professions concerning the extent of occupational self-efficacy.The results are a matter of particular interest for the practice of workplace health promotion as well as for social work schools, the main group in our sample being social workers. © Georg Thieme Verlag KG Stuttgart · New York.

Modelling the effects of environmental conditions on the acoustic occurrence and behaviour of Antarctic blue whales

PubMed Central

Shabangu, Fannie W.; Yemane, Dawit; Stafford, Kathleen M.; Ensor, Paul; Findlay, Ken P.

2017-01-01

Harvested to perilously low numbers by commercial whaling during the past century, the large scale response of Antarctic blue whales Balaenoptera musculus intermedia to environmental variability is poorly understood. This study uses acoustic data collected from 586 sonobuoys deployed in the austral summers of 1997 through 2009, south of 38°S, coupled with visual observations of blue whales during the IWC SOWER line-transect surveys. The characteristic Z-call and D-call of Antarctic blue whales were detected using an automated detection template and visual verification method. Using a random forest model, we showed the environmental preferences pattern, spatial occurrence and acoustic behaviour of Antarctic blue whales. Distance to the southern boundary of the Antarctic Circumpolar Current (SBACC), latitude and distance from the nearest Antarctic shores were the main geographic predictors of blue whale call occurrence. Satellite-derived sea surface height, sea surface temperature, and productivity (chlorophyll-a) were the most important environmental predictors of blue whale call occurrence. Call rates of D-calls were strongly predicted by the location of the SBACC, latitude and visually detected number of whales in an area while call rates of Z-call were predicted by the SBACC, latitude and longitude. Satellite-derived sea surface height, wind stress, wind direction, water depth, sea surface temperatures, chlorophyll-a and wind speed were important environmental predictors of blue whale call rates in the Southern Ocean. Blue whale call occurrence and call rates varied significantly in response to inter-annual and long term variability of those environmental predictors. Our results identify the response of Antarctic blue whales to inter-annual variability in environmental conditions and highlighted potential suitable habitats for this population. Such emerging knowledge about the acoustic behaviour, environmental and habitat preferences of Antarctic blue whales is important in improving the management and conservation of this highly depleted species. PMID:28222124

Modelling the effects of environmental conditions on the acoustic occurrence and behaviour of Antarctic blue whales.

PubMed

Shabangu, Fannie W; Yemane, Dawit; Stafford, Kathleen M; Ensor, Paul; Findlay, Ken P

2017-01-01

Harvested to perilously low numbers by commercial whaling during the past century, the large scale response of Antarctic blue whales Balaenoptera musculus intermedia to environmental variability is poorly understood. This study uses acoustic data collected from 586 sonobuoys deployed in the austral summers of 1997 through 2009, south of 38°S, coupled with visual observations of blue whales during the IWC SOWER line-transect surveys. The characteristic Z-call and D-call of Antarctic blue whales were detected using an automated detection template and visual verification method. Using a random forest model, we showed the environmental preferences pattern, spatial occurrence and acoustic behaviour of Antarctic blue whales. Distance to the southern boundary of the Antarctic Circumpolar Current (SBACC), latitude and distance from the nearest Antarctic shores were the main geographic predictors of blue whale call occurrence. Satellite-derived sea surface height, sea surface temperature, and productivity (chlorophyll-a) were the most important environmental predictors of blue whale call occurrence. Call rates of D-calls were strongly predicted by the location of the SBACC, latitude and visually detected number of whales in an area while call rates of Z-call were predicted by the SBACC, latitude and longitude. Satellite-derived sea surface height, wind stress, wind direction, water depth, sea surface temperatures, chlorophyll-a and wind speed were important environmental predictors of blue whale call rates in the Southern Ocean. Blue whale call occurrence and call rates varied significantly in response to inter-annual and long term variability of those environmental predictors. Our results identify the response of Antarctic blue whales to inter-annual variability in environmental conditions and highlighted potential suitable habitats for this population. Such emerging knowledge about the acoustic behaviour, environmental and habitat preferences of Antarctic blue whales is important in improving the management and conservation of this highly depleted species.

The effects of the Omagh bomb on adolescent mental health: a school-based study.

PubMed

Duffy, Michael; McDermott, Maura; Percy, Andrew; Ehlers, Anke; Clark, David M; Fitzgerald, Michael; Moriarty, John

2015-02-06

The main objective of this study was to assess psychiatric morbidity among adolescents following the Omagh car bombing in Northern Ireland in 1998. Data was collected within schools from adolescents aged between 14 and 18 years via a self-completion booklet comprised of established predictors of PTSD; type of exposure, initial emotional response, long-term adverse physical problems, predictors derived from Ehlers and Clark's (2000) cognitive model, a PTSD symptoms measure (PDS) and the General Health Questionnaire (GHQ). Those with more direct physical exposure were significantly more likely to meet caseness on the GHQ and the PDS. The combined pre and peri trauma risk factors highlighted in previous meta-analyses accounted for 20% of the variance in PDS scores but the amount of variance accounted for increased to 56% when the variables highlighted in Ehlers and Clark's cognitive model for PTSD were added. High rates of chronic PTSD were observed in adolescents exposed to the bombing. Whilst increased exposure was associated with increased psychiatric morbidity, the best predictors of PTSD were specific aspects of the trauma ('seeing someone you think is dying'), what you are thinking during the event ('think you are going to die') and the cognitive mechanisms employed after the trauma. As these variables are in principle amenable to treatment the results have implications for teams planning treatment interventions after future traumas.

Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

NASA Astrophysics Data System (ADS)

Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

2017-04-01

The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

Peer Educators and Close Friends as Predictors of Male College Students' Willingness to Prevent Rape

ERIC Educational Resources Information Center

Stein, Jerrold L.

2007-01-01

Astin's (1977, 1991, 1993) input-environment-outcome (I-E-O) model provided a conceptual framework for this study which measured 156 male college students' willingness to prevent rape (outcome variable). Predictor variables included personal attitudes (input variable), perceptions of close friends' attitudes toward rape and rape prevention…

The Impact of Individual Differences on E-Learning System Behavioral Intention

NASA Astrophysics Data System (ADS)

Liao, Peiwen; Yu, Chien; Yi, Chincheh

This study investigated the impact of contingent variables on the relationship between four predictors and employees' behavioral intention with e-learning. Seven hundred and twenty-two employees in online training and education were asked to answer questionnaires about their learning styles, perceptions of the quality of the proposed predictors and behavioral intention with e-learning systems. The results of analysis showed that three contingent variables, gender, job title and industry, significantly influenced the perceptions of predictors and employees' behavioral intention with the e-learning system. This study also found a statistically significant moderating effect of two contingent variables, gender, job title and industry, on the relationship between predictors and e-learning system behavioral intention. The results suggest that a serious consideration of contingent variables is crucial for improving e-learning system behavioral intention. The implications of these results for the management of e-learning systems are discussed.

On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental.

PubMed

Shieh, Gwowen

2010-05-28

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term. This article attempts to clarify the misconception of multicollinearity in MMR studies. The counterintuitive yet beneficial effects of multicollinearity on the ability to detect moderator relationships are explored. Comprehensive treatments and numerical investigations are presented for the simplest interaction model and more complex three-predictor setting. The results provide critical insight that both helps avoid misleading interpretations and yields better understanding for the impact of intercorrelation among predictor variables in MMR analyses.

The impact of sociodemographic factors vs. gender roles on female hospital workers' health: do we need to shift emphasis?

PubMed

Musshauser, Doris; Bader, Angelika; Wildt, Beatrice; Hochleitner, Margarethe

2006-09-01

The aim of the present study was to evaluate the physical and mental health status of female workers from five different occupational groups and to identify possible sociodemographic and gender-coded family-related factors as well as work characteristics influencing women's health. The identified predictors of health status were subjected to a gender-sensitive analysis and their relations to one another are discussed. A total of 1083 female hospital workers including medical doctors, technical and administrative personnel, nurses and a group mainly consisting of scientific personnel and psychologists completed a questionnaire measuring work- and family-related variables, sociodemographic data and the Short-form 36 Health Questionnaire (SF-36). Data were analysed by multivariate regression analyses. Female medical doctors reported highest scores for all physical health dimensions except General Health. Our study population showed general low mental health status among administrative personnel and the heterogeneous group, others, scored highest on all mental health component scores. A series of eight regression analyses were performed. Three variables contributed highly significantly to all SF-36 subscale scores: age, satisfaction with work schedule, and the unpaid work variable. Age had the strongest influence on all physical dimensions except General Health (beta=-0.17) and had no detectable influence on mental health scores. The unpaid work variable (beta=-0.23; p<0.001) exerted a stronger influence on General Health than did age. Nevertheless, these variables were limited predictors of physical and mental health status. In all occupational groups the amount of time spent daily on child care and household tasks, as a traditional gender-coded factor, and satisfaction with work schedule were the only contributors to mental health among working women in this study. Traditional sociodemographic data had no effect on mental health status. In addition to age, these factors were shown to be the only predictors of physical health status of female workers. Gender coded-factors matter. These findings underline the importance of including gender-coded family- and work-related variables in medical research over and above basic sociodemographic data in order to describe study populations more clearly.

Work-place predictors of duration of breastfeeding among female physicians.

PubMed

Sattari, Maryam; Serwint, Janet R; Neal, Dan; Chen, Si; Levine, David M

2013-12-01

To identify work-related predictors of breastfeeding duration among female physicians. Data on 238 children were obtained from 50 female physicians, whose main affiliation was with Johns Hopkins University (Baltimore, MD), and 80 female physicians, whose main affiliation was with the University of Florida (Gainesville, FL). We used a mixed linear model to determine which variables were significant predictors of breastfeeding duration when controlling for maternal demographics and taking into account the clustering of observations on study location and mothers. Although female physicians intended to breastfeed 56% of the infants for at least 12 months and 97% of infants were breastfed at birth, only 34% of infants continued to receive breast milk at 12 months. Duration of lactation among female physicians correlated with the following work-related factors: (1) not having to make up missed call/work that occurred as result of pregnancy or maternity leave; (2) longer length of maternity leave; (3) sufficiency of time at work for milk expression; and (4) perceived level of support for breastfeeding efforts at work from colleagues, program director, or division/section chiefs. Our findings support the importance of work-related factors in breastfeeding maintenance among female physicians and suggest that a tailored intervention, providing time and institutional encouragement, might result in significant improvement in their breastfeeding duration. Copyright © 2013 Mosby, Inc. All rights reserved.

Predicting Change over Time in Career Planning and Career Exploration for High School Students

ERIC Educational Resources Information Center

Creed, Peter A.; Patton, Wendy; Prideaux, Lee-Ann

2007-01-01

This study assessed 166 high school students in Grade 8 and again in Grade 10. Four models were tested: (a) whether the T1 predictor variables (career knowledge, indecision, decision-making selfefficacy, self-esteem, demographics) predicted the outcome variable (career planning/exploration) at T1; (b) whether the T1 predictor variables predicted…

Organizational commitment as a predictor variable in nursing turnover research: literature review.

PubMed

Wagner, Cheryl M

2007-11-01

This paper is a report of a literature review to (1) demonstrate the predictability of organizational commitment as a variable, (2) compare organizational commitment and job satisfaction as predictor variables and (3) determine the usefulness of organizational commitment in nursing turnover research. Organizational commitment is not routinely selected as a predictor variable in nursing studies, although the evidence suggests that it is a reliable predictor. Findings from turnover studies can help determine the previous performance of organizational commitment, and be compared to those of studies using the more conventional variable of job satisfaction. Published research studies in English were accessed for the period 1960-2006 using the CINAHL, EBSCOHealthsource Nursing, ERIC, PROQUEST, Journals@OVID, PubMed, PsychINFO, Health and Psychosocial Instruments (HAPI) and COCHRANE library databases and Business Source Premier. The search terms included nursing turnover, organizational commitment or job satisfaction. Only studies reporting mean comparisons, R(2) or beta values related to organizational commitment and turnover or turnover antecedents were included in the review. There were 25 studies in the final data set, with a subset of 23 studies generated to compare the variables of organizational commitment and job satisfaction. Results indicated robust indirect predictability of organizational commitment overall, with greater predictability by organizational commitment vs job satisfaction. Organizational commitment is a useful predictor of turnover in nursing research, and effective as a variable with the most direct impact on antecedents of turnover such as intent to stay. The organizational commitment variable should be routinely employed in nursing turnover research studies.

Assessment of the uncertainty and predictive power of large-scale predictors for nonlinear precipitation downscaling in the European Arctic (Invited)

NASA Astrophysics Data System (ADS)

Sauter, T.

2013-12-01

Despite the extensive research on downscaling methods there is still little consensus about the choice of useful atmospheric predictor variables. Besides the general decision of a proper statistical downscaling model, the selection of an informative predictor set is crucial for the accuracy and stability of the resulting downscaled time series. These requirements must be fullfilled by both the atmospheric variables and the predictor domains in terms of geographical location and spatial extend, to which in general not much attention is paid. However, only a limited number of studies is interested in the predictive capability of the predictor domain size or shape, and the question to what extent variability of neighboring grid points influence local-scale events. In this study we emphasized the spatial relationships between observed daily precipitation and selected number of atmospheric variables for the European Arctic. Several nonlinear regression models are used to link the large-scale predictors obtained from reanalysed Weather Research and Forecast model runs to the local-scale observed precipitation. Inferences on the sources of uncertainty are then drawn from variance based sensitivity measures, which also permit to capture interaction effects between individual predictors. The information is further used to develop more parsimonious downscaling models with only small decreases in accuracy. Individual predictors (without interactions) account for almost 2/3 of the total output variance, while the remaining fraction is solely due to interactions. Neglecting predictor interactions in the screening process will lead to some loss of information. Hence, linear screening methods are insufficient as they neither account for interactions nor for non-additivity as given by many nonlinear prediction algorithms.

Predictive factors of difficulty in lower third molar extraction: A prospective cohort study.

PubMed

Alvira-González, J; Figueiredo, R; Valmaseda-Castellón, E; Quesada-Gómez, C; Gay-Escoda, C

2017-01-01

Several publications have measured the difficulty of third molar removal, trying to establish the main risk factors, however several important preoperative and intraoperative variables are overlooked. A prospective cohort study comprising a total of 130 consecutive lower third molar extractions was performed. The outcome variables used to measure the difficulty of the extraction were operation time and a 100mm visual analogue scale filled by the surgeon at the end of the surgical procedure. The predictors were divided into 4 different groups (demographic, anatomic, radiographic and operative variables). A descriptive, bivariate and multivariate analysis of the data was performed. Patients' weight, the presence of bulbous roots, the need to perform crown and root sectioning of the lower third molar and Pell and Gregory 123 classification significantly influenced both outcome variables (p< 0.05). Certain anatomical, radiological and operative variables appear to be important factors in the assessment of surgical difficulty in the extraction of lower third molars.

Patient or treatment centre? Where are efforts invested to improve cancer patients' psychosocial outcomes?

PubMed Central

Carey, ML; Clinton-McHarg, T; Sanson-Fisher, RW; Campbell, S; Douglas, HE

2011-01-01

The psychosocial outcomes of cancer patients may be influenced by individual-level, social and treatment centre predictors. This paper aimed to examine the extent to which individual, social and treatment centre variables have been examined as predictors or targets of intervention for psychosocial outcomes of cancer patients. Medline was searched to find studies in which the psychological outcomes of cancer patient were primary variables. Papers published in English between 1999 and 2009 that reported primary data relevant to psychosocial outcomes for cancer patients were included, with 20% randomly selected for further coding. Descriptive studies were coded for inclusion of individual, social or treatment centre variables. Intervention studies were coded to determine if the unit of intervention was the individual patient, social unit or treatment centre. After random sampling, 412 publications meeting the inclusion criteria were identified, 169 were descriptive and 243 interventions. Of the descriptive papers 95.0% included individual predictors, and 5.0% social predictors. None of the descriptive papers examined treatment centre variables as predictors of psychosocial outcomes. Similarly, none of the interventions evaluated the effectiveness of treatment centre interventions for improving psychosocial outcomes. Potential reasons for the overwhelming dominance of individual predictors and individual-focused interventions in psychosocial literature are discussed. PMID:20646035

Variability in baseline travel behaviour as a predictor of changes in commuting by active travel, car and public transport: a natural experimental study

PubMed Central

Heinen, Eva; Ogilvie, David

2016-01-01

Purpose To strengthen our understanding of the impact of baseline variability in mode choice on the likelihood of travel behaviour change. Methods Quasi-experimental analyses in a cohort study of 450 commuters exposed to a new guided busway with a path for walking and cycling in Cambridge, UK. Exposure to the intervention was defined using the shortest network distance from each participant’s home to the busway. Variability in commuter travel behaviour at baseline was defined using the Herfindahl–Hirschman Index, the number of different modes of transport used over a week, and the proportion of trips made by the main (combination of) mode(s). The outcomes were changes in the share of commute trips (i) involving any active travel, (ii) involving any public transport, and (iii) made entirely by car. Variability and change data were derived from a self-reported seven-day record collected before (2009) and after (2012) the intervention. Separate multinomial regression models were estimated to assess the influence of baseline variability on behaviour change, both independently and as an interaction effect with exposure to the intervention. Results All three measures of variability predicted changes in mode share in most models. The effect size for the intervention was slightly strengthened after including variability. Commuters with higher baseline variability were more likely to increase their active mode share (e.g. for HHI: relative risk ratio [RRR] for interaction 3.34, 95% CI 1.41, 7.89) and decrease their car mode share in response to the intervention (e.g. for HHI: RRR 7.50, 95% CI 2.52, 22.34). Conclusions People reporting a higher level of variability in mode choice were more likely to change their travel behaviour following an intervention. Future research should consider such variability as a potential predictor and effect modifier of travel and physical activity behaviour change, and its significance for the design and targeting of interventions. PMID:27200265

Predictors of Daily Mobility of Adults in Peri-Urban South India.

PubMed

Sanchez, Margaux; Ambros, Albert; Salmon, Maëlle; Bhogadi, Santhi; Wilson, Robin T; Kinra, Sanjay; Marshall, Julian D; Tonne, Cathryn

2017-07-14

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women's activity spaces were smaller and more circular than men's. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health.

Predictors of Daily Mobility of Adults in Peri-Urban South India

PubMed Central

Kinra, Sanjay; Marshall, Julian D.; Tonne, Cathryn

2017-01-01

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women’s activity spaces were smaller and more circular than men’s. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health. PMID:28708095

Structural MRI Predictors of Late-Life Cognition Differ Across African Americans, Hispanics, and Whites.

PubMed

Zahodne, Laura B; Manly, Jennifer J; Narkhede, Atul; Griffith, Erica Y; DeCarli, Charles; Schupf, Nicole S; Mayeux, Richard; Brickman, Adam M

2015-01-01

Structural magnetic resonance imaging (MRI) provides key biomarkers to predict onset and track progression of Alzheimer's disease (AD). However, most published reports of relationships between MRI variables and cognition in older adults include racially, ethnically, and socioeconomically homogenous samples. Racial/ethnic differences in MRI variables and cognitive performance, as well as health, socioeconomic status and psychological factors, raise the possibility that brain-behavior relationships may be stronger or weaker in different groups. The current study tested whether MRI predictors of cognition differ in African Americans and Hispanics, compared with non-Hispanic Whites. Participants were 638 non-demented older adults (29% non-Hispanic White, 36% African American, 35% Hispanic) in the Washington Heights-Inwood Columbia Aging Project. Composite scores of memory, language, speed/executive functioning, and visuospatial function were derived from a neuropsychological battery. Hippocampal volume, regional cortical thickness, infarcts, and white matter hyperintensity (WMH) volumes were quantified with FreeSurfer and in-house developed procedures. Multiple-group regression analysis, in which each cognitive composite score was regressed onto MRI variables, demographics, and cardiovascular health, tested which paths differed across groups. Larger WMH volume was associated with worse language and speed/executive functioning among African Americans, but not among non-Hispanic Whites. Larger hippocampal volume was more strongly associated with better memory among non-Hispanic Whites compared with Hispanics. Cortical thickness and infarcts were similarly associated with cognition across groups. The main finding of this study was that certain MRI predictors of cognition differed across racial/ethnic groups. These results highlight the critical need for more diverse samples in the study of cognitive aging, as the type and relation of neurobiological substrates of cognitive functioning may be different for different groups.

Factors predictive of obstructive sleep apnea in patients undergoing pre-operative evaluation for bariatric surgery and referred to a sleep laboratory for polysomnography

PubMed Central

Duarte, Ricardo Luiz de Menezes; Magalhães-da-Silveira, Flavio José

2015-01-01

Objective: To identify the main predictive factors for obtaining a diagnosis of obstructive sleep apnea (OSA) in patients awaiting bariatric surgery. Methods: Retrospective study of consecutive patients undergoing pre-operative evaluation for bariatric surgery and referred for in-laboratory polysomnography. Eight variables were evaluated: sex, age, neck circumference (NC), BMI, Epworth Sleepiness Scale (ESS) score, snoring, observed apnea, and hypertension. We employed ROC curve analysis to determine the best cut-off value for each variable and multiple linear regression to identify independent predictors of OSA severity. Results: We evaluated 1,089 patients, of whom 781 (71.7%) were female. The overall prevalence of OSA-defined as an apnea/hypopnea index (AHI) ≥ 5.0 events/h-was 74.8%. The best cut-off values for NC, BMI, age, and ESS score were 42 cm, 42 kg/m2, 37 years, and 10 points, respectively. All eight variables were found to be independent predictors of a diagnosis of OSA in general, and all but one were found to be independent predictors of a diagnosis of moderate/severe OSA (AHI ≥ 15.0 events/h), the exception being hypertension. We devised a 6-item model, designated the NO-OSAS model (NC, Obesity, Observed apnea, Snoring, Age, and Sex), with a cut-off value of ≥ 3 for identifying high-risk patients. For a diagnosis of moderate/severe OSA, the model showed 70.8% accuracy, 82.8% sensitivity, and 57.9% specificity. Conclusions: In our sample of patients awaiting bariatric surgery, there was a high prevalence of OSA. At a cut-off value of ≥ 3, the proposed 6-item model showed good accuracy for a diagnosis of moderate/severe OSA. PMID:26578136

Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia

PubMed Central

Chalghaf, Bilel; Chlif, Sadok; Mayala, Benjamin; Ghawar, Wissem; Bettaieb, Jihène; Harrabi, Myriam; Benie, Goze Bertin; Michael, Edwin; Salah, Afif Ben

2016-01-01

Cutaneous leishmaniasis is a very complex disease involving multiple factors that limit its emergence and spatial distribution. Prediction of cutaneous leishmaniasis epidemics in Tunisia remains difficult because most of the epidemiological tools used so far are descriptive in nature and mainly focus on a time dimension. The purpose of this work is to predict the potential geographic distribution of Phlebotomus papatasi and zoonotic cutaneous leishmaniasis caused by Leishmania major in Tunisia using Grinnellian ecological niche modeling. We attempted to assess the importance of environmental factors influencing the potential distribution of P. papatasi and cutaneous leishmaniasis caused by L. major. Vectors were trapped in central Tunisia during the transmission season using CDC light traps (John W. Hock Co., Gainesville, FL). A global positioning system was used to record the geographical coordinates of vector occurrence points and households tested positive for cutaneous leishmaniasis caused by L. major. Nine environmental layers were used as predictor variables to model the P. papatasi geographical distribution and five variables were used to model the L. major potential distribution. Ecological niche modeling was used to relate known species' occurrence points to values of environmental factors for these same points to predict the presence of the species in unsampled regions based on the value of the predictor variables. Rainfall and temperature contributed the most as predictors for sand flies and human case distributions. Ecological niche modeling anticipated the current distribution of P. papatasi with the highest suitability for species occurrence in the central and southeastern part of Tunisian. Furthermore, our study demonstrated that governorates of Gafsa, Sidi Bouzid, and Kairouan are at highest epidemic risk. PMID:26856914

Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia.

PubMed

Chalghaf, Bilel; Chlif, Sadok; Mayala, Benjamin; Ghawar, Wissem; Bettaieb, Jihène; Harrabi, Myriam; Benie, Goze Bertin; Michael, Edwin; Salah, Afif Ben

2016-04-01

Cutaneous leishmaniasis is a very complex disease involving multiple factors that limit its emergence and spatial distribution. Prediction of cutaneous leishmaniasis epidemics in Tunisia remains difficult because most of the epidemiological tools used so far are descriptive in nature and mainly focus on a time dimension. The purpose of this work is to predict the potential geographic distribution of Phlebotomus papatasi and zoonotic cutaneous leishmaniasis caused by Leishmania major in Tunisia using Grinnellian ecological niche modeling. We attempted to assess the importance of environmental factors influencing the potential distribution of P. papatasi and cutaneous leishmaniasis caused by L. major. Vectors were trapped in central Tunisia during the transmission season using CDC light traps (John W. Hock Co., Gainesville, FL). A global positioning system was used to record the geographical coordinates of vector occurrence points and households tested positive for cutaneous leishmaniasis caused by L. major. Nine environmental layers were used as predictor variables to model the P. papatasi geographical distribution and five variables were used to model the L. major potential distribution. Ecological niche modeling was used to relate known species' occurrence points to values of environmental factors for these same points to predict the presence of the species in unsampled regions based on the value of the predictor variables. Rainfall and temperature contributed the most as predictors for sand flies and human case distributions. Ecological niche modeling anticipated the current distribution of P. papatasi with the highest suitability for species occurrence in the central and southeastern part of Tunisian. Furthermore, our study demonstrated that governorates of Gafsa, Sidi Bouzid, and Kairouan are at highest epidemic risk. © The American Society of Tropical Medicine and Hygiene.

Clustering of haemostatic variables and the effect of high cashew and walnut diets on these variables in metabolic syndrome patients.

PubMed

Pieters, Marlien; Oosthuizen, Welma; Jerling, Johann C; Loots, Du Toit; Mukuddem-Petersen, Janine; Hanekom, Susanna M

2005-09-01

We investigated the effect of a high walnut and cashew diet on haemostatic variables in people with the metabolic syndrome. Factor analysis was used to determine how the haemostatic variables cluster with other components of the metabolic syndrome and multiple regression to determine possible predictors. This randomized, control, parallel, controlled-feeding trial included 68 subjects who complied with the Third National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol criteria. After a 3-week run-in following the control diet, subjects were divided into three groups receiving either walnuts or cashews (20 energy%) or a control diet for 8 weeks. The nut intervention had no significant effect on von Willebrand factor antigen, fibrinogen, factor VII coagulant activity, plasminogen activator inhibitor 1 activity, tissue plasminogen activator activity or thrombin activatable fibrinolysis inhibitor. Statistically, fibrinogen clustered with the body-mass-correlates and acute phase response factors, and factor VII coagulant activity clustered with high-density lipoprotein cholesterol (HDL-C). Tissue plasminogen activator activity, plasminogen activator inhibitor 1 activity and von Willebrand factor antigen clustered into a separate endothelial function factor. HDL-C and markers of obesity were the strongest predictors of the haemostatic variables. We conclude that high walnut and cashew diets did not influence haemostatic factors in this group of metabolic syndrome subjects. The HDL-C increase and weight loss may be the main focus of dietary intervention for the metabolic syndrome. Furthermore, diet composition may have only limited effects if weight loss is not achieved.

Multicollinearity and Regression Analysis

NASA Astrophysics Data System (ADS)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

Drug Concentration Thresholds Predictive of Therapy Failure and Death in Children With Tuberculosis: Bread Crumb Trails in Random Forests

PubMed Central

Swaminathan, Soumya; Pasipanodya, Jotam G.; Ramachandran, Geetha; Hemanth Kumar, A. K.; Srivastava, Shashikant; Deshpande, Devyani; Nuermberger, Eric; Gumbo, Tawanda

2016-01-01

Background. The role of drug concentrations in clinical outcomes in children with tuberculosis is unclear. Target concentrations for dose optimization are unknown. Methods. Plasma drug concentrations measured in Indian children with tuberculosis were modeled using compartmental pharmacokinetic analyses. The children were followed until end of therapy to ascertain therapy failure or death. An ensemble of artificial intelligence algorithms, including random forests, was used to identify predictors of clinical outcome from among 30 clinical, laboratory, and pharmacokinetic variables. Results. Among the 143 children with known outcomes, there was high between-child variability of isoniazid, rifampin, and pyrazinamide concentrations: 110 (77%) completed therapy, 24 (17%) failed therapy, and 9 (6%) died. The main predictors of therapy failure or death were a pyrazinamide peak concentration <38.10 mg/L and rifampin peak concentration <3.01 mg/L. The relative risk of these poor outcomes below these peak concentration thresholds was 3.64 (95% confidence interval [CI], 2.28–5.83). Isoniazid had concentration-dependent antagonism with rifampin and pyrazinamide, with an adjusted odds ratio for therapy failure of 3.00 (95% CI, 2.08–4.33) in antagonism concentration range. In regard to death alone as an outcome, the same drug concentrations, plus z scores (indicators of malnutrition), and age <3 years, were highly ranked predictors. In children <3 years old, isoniazid 0- to 24-hour area under the concentration-time curve <11.95 mg/L × hour and/or rifampin peak <3.10 mg/L were the best predictors of therapy failure, with relative risk of 3.43 (95% CI, .99–11.82). Conclusions. We have identified new antibiotic target concentrations, which are potential biomarkers associated with treatment failure and death in children with tuberculosis. PMID:27742636

Long-term outcome of major depressive disorder in psychiatric patients is variable.

PubMed

Holma, K Mikael; Holma, Irina A K; Melartin, Tarja K; Rytsälä, Heikki J; Isometsä, Erkki T

2008-02-01

The prevailing view of outcome of major depressive disorder (MDD), based on mostly inpatient cohorts sampled from tertiary centers, emphasizes chronicity and frequent recurrences. We investigated the long-term outcome of a regionally representative psychiatric MDD cohort comprising mainly outpatients. The Vantaa Depression Study included 163 patients with DSM-IV MDD (71.5% of those eligible) diagnosed using structured and semistructured interviews and followed up at 6 months, 18 months, and 5 years with a life chart between February 1, 1997, and April 30, 2004. The effects of comorbid disorders and other predictors on outcome were comprehensively investigated. Over the 5-year follow-up, 98.8% of patients achieved a symptom state below major depressive episode (MDE) criteria, and 88.4% reached full remission, with the median time to full remission being 11.0 months. Nearly one third (29.3%) had no recurrences, whereas 30.0% experienced 1, 12.9% experienced 2, and 27.9% experienced 3 or more recurrences. Preceding dysthymic disorder (p = .028), cluster C personality disorder (p = .041), and longer MDE duration prior to entry (p = .011) were the most significant predictors of longer time in achieving full remission. Severity of MDD and comorbidity, especially social phobia, predicted probability of, shorter time to, and number of recurrences. Previous literature on mostly inpatient MDD may have, by generalizing from patients with the most severe psychopathology, overemphasized chronicity of MDD. The long-term outcome of MDD in psychiatric care is variable, with about one tenth of patients having poor, one third having intermediate, and one half having favorable outcomes. In addition to known predictors, cluster C personality disorders and social phobia warrant further attention as predictors of MDD outcome among outpatients.

Predictors of cognitive impairment assessed by Mini Mental State Examination in community-dwelling older adults: relevance of the step test.

PubMed

Muscari, Antonio; Spiller, Ilaria; Bianchi, Giampaolo; Fabbri, Elisa; Forti, Paola; Magalotti, Donatella; Pandolfi, Paolo; Zoli, Marco

2018-07-15

Several predictors of cognitive impairment assessed by Mini Mental State Examination (MMSE) have previously been identified. However, which predictors are the most relevant and what is their effect on MMSE categories remains unclear. Cross-sectional and longitudinal study using data from 1116 older adults (72.6 ± 5.6 years, 579 female), 350 of whom were followed for 7 years. At baseline, the following variables were collected: personal data, marital status, occupation, anthropometric measures, risk factors, previous cardiovascular events, self-rated health and physical activity during the last week. Furthermore, routine laboratory tests, abdominal echography and a step test (with measurement of the time needed to ascend and descend two steps 20 times) were performed. The associations of these variables with cross-sectional cognitive deficit (MMSE < 24) and longitudinal cognitive decline (decrease of MMSE score over 7 years of follow-up) were investigated using logistic regression models. Cross-sectional cognitive deficit was independently associated with school education ≤ 5 years, prolonged step test duration, having been blue collar or housewife (P ≤ 0.0001 for all) and, with lower significance, with advanced age, previous stroke and poor recent physical activity (P < 0.05). Longitudinal cognitive decline was mainly associated with step test duration (P = 0.0001) and diastolic blood pressure (P = 0.0002). The MMSE categories mostly associated with step test duration were orientation, attention, calculation and language, while memory appeared to be poorly or not affected. In our cohort of older adults, step test duration was the most relevant predictor of cognitive impairment. Copyright © 2018 Elsevier Inc. All rights reserved.

The use of generalised additive models (GAM) in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.

Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.

PubMed

Melcher, Michael; Scharl, Theresa; Luchner, Markus; Striedner, Gerald; Leisch, Friedrich

2017-02-01

The quality of biopharmaceuticals and patients' safety are of highest priority and there are tremendous efforts to replace empirical production process designs by knowledge-based approaches. Main challenge in this context is that real-time access to process variables related to product quality and quantity is severely limited. To date comprehensive on- and offline monitoring platforms are used to generate process data sets that allow for development of mechanistic and/or data driven models for real-time prediction of these important quantities. Ultimate goal is to implement model based feed-back control loops that facilitate online control of product quality. In this contribution, we explore structured additive regression (STAR) models in combination with boosting as a variable selection tool for modeling the cell dry mass, product concentration, and optical density on the basis of online available process variables and two-dimensional fluorescence spectroscopic data. STAR models are powerful extensions of linear models allowing for inclusion of smooth effects or interactions between predictors. Boosting constructs the final model in a stepwise manner and provides a variable importance measure via predictor selection frequencies. Our results show that the cell dry mass can be modeled with a relative error of about ±3%, the optical density with ±6%, the soluble protein with ±16%, and the insoluble product with an accuracy of ±12%. Biotechnol. Bioeng. 2017;114: 321-334. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Predictors of Health Service Utilization Among Older Men in Jamaica.

PubMed

Willie-Tyndale, Douladel; McKoy Davis, Julian; Holder-Nevins, Desmalee; Mitchell-Fearon, Kathryn; James, Kenneth; Waldron, Norman K; Eldemire-Shearer, Denise

2018-01-03

To determine the relative influence of sociodemographic, socioeconomic, psychosocial, and health variables on health service utilization in the last 12 months. Data were analyzed for 1,412 men ≥60 years old from a 2012 nationally representative community-based survey in Jamaica. Associations between six health service utilization variables and several explanatory variables were explored. Logistic regression models were used to identify independent predictors of each utilization measure and determine the strengths of associations. More than 75% reported having health visits and blood pressure checks. Blood sugar (69.6%) and cholesterol (63.1%) checks were less common, and having a prostate check (35.1%) was the least utilized service. Adjusted models confirmed that the presence of chronic diseases and health insurance most strongly predicted utilization. A daughter or son as the main source of financial support (vs self) doubled or tripled, respectively, the odds of routine doctors' visits. Compared with primary or lower education, tertiary education doubled [2.37 (1.12, 4.95)] the odds of a blood pressure check. Regular attendance at club/society/religious organizations' meetings increased the odds of having a prostate check by 45%. Although need and financial resources most strongly influenced health service utilization, psychosocial variables may be particularly influential for underutilized services. © The Author(s) 2018. 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.

Hospital employees' perceptions of fairness and job satisfaction at a time of transformational change.

PubMed

Brandis, Susan; Fisher, Ron; McPhail, Ruth; Rice, John; Eljiz, Kathy; Fitzgerald, Anneke; Gapp, Rod; Marshall, Andrea

2016-06-01

Objective This study examines the relationships between job satisfaction and organisational justice during a time of transformational change. Methods Data collection occurred immediately before a major regional hospital's move to a greenfield site. Existing measures of job satisfaction and organisational justice were used. Data were analysed (n=316) using descriptive, correlation and regression methods together with interactions between predictor variables. Results Correlation coefficients for satisfaction and organisational justice variables were high and significant at the P<0.001 level. Results of a robust regression model (adjusted R(2)=0.568) showed all three components of organisational justice contributed significantly to employee job satisfaction. Interactions between the predictor variables showed that job satisfaction increased as the interactions between the predictor variables increased. Conclusions The finding that even at a time of transformational change staff perceptions of fair treatment will in the main result in high job satisfaction extends the literature in this area. In addition, it was found that increasing rewards for staff who perceive low levels of organisational justice does not increase satisfaction as much as for staff who perceive high levels of fairness. If people feel negative about their role, but feel they are well paid, they probably still have negative feelings overall. What is known about the topic? Despite much research highlighting the importance of job satisfaction and organisational justice in healthcare, no research has examined the influence of transformational change, such as a healthcare organisational relocation, on these factors. What does this paper add? The research adds to academic literature relating to job satisfaction and organisational justice. It highlights the importance of organisational justice in influencing the job satisfaction of staff. What are the implications for practitioners? Financial rewards do not necessarily motivate staff but low rewards do demotivate. Shortages of health professionals are often linked to a lack of job satisfaction, and recruitment and retention strategies are often based on salary.

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

Atmospheric circulation types and extreme areal precipitation in southern central Europe

NASA Astrophysics Data System (ADS)

Jacobeit, Jucundus; Homann, Markus; Philipp, Andreas; Beck, Christoph

2017-04-01

Gridded daily rainfall data for southern central Europe are aggregated to regions of similar precipitation variability by means of S-mode principal component analyses separately for the meteorological seasons. Atmospheric circulation types (CTs) are derived by a particular clustering technique including large-scale fields of SLP, vertical wind and relative humidity at the 700 hPa level as well as the regional rainfall time series. Multiple regression models with monthly CT frequencies as predictors are derived for monthly frequencies and amounts of regional precipitation extremes (beyond the 95 % percentile). Using predictor output from different global climate models (ECHAM6, ECHAM5, EC-EARTH) for different scenarios (RCP4.5, RCP8.5, A1B) and two projection periods (2021-2050, 2071-2100) leads to assessments of future changes in regional precipitation extremes. Most distinctive changes are indicated for the summer season with mainly increasing extremes for the earlier period and widespread decreasing extremes towards the end of the 21st century, mostly for the strong scenario. Considerable uncertainties arise from the predictor use of different global climate models, especially during the winter and spring seasons.

Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era.

PubMed

Zador, Zsolt; Huang, Wendy; Sperrin, Matthew; Lawton, Michael T

2018-06-01

Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. We reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

[Parenthood and cancer: dyadic analysis of psychological distress and -health-related quality of life of cancer parents with minor children].

PubMed

Kühne, Franziska; Krattenmacher, Thomas; Bergelt, Corinna; Bierbaum, Anna-Lena; Christine Ernst, Johanna; Flechtner, Hans-Henning; Keller, Monika; Klitzing, Kai V; Romer, Georg; Möller, Birgit

2013-12-01

The purpose of this study was the analysis of psychological distress and health-related quality of life (HRQoL) of parents with minor children during curative resp. palliative treatment.Cross-sectional design with a sample of N=89 parent dyads. Dyadic analysis of demographic, illness and family variables via mixed linear models.Patients and healthy partners indicated psychological distress on different subscales. Intradyadic correlations were small-moderate. Most important predictors of psychological distress and HRQoL were treatment stadium, gender, family functioning, and employment status.Dependent on demographic variables, psychooncological support was evident mainly for parents in palliative care and for families with dysfunctional functioning. © Georg Thieme Verlag KG Stuttgart · New York.

Pharmacogenetics of schizophrenia.

PubMed

Reynolds, Gavin P; Templeman, Lucy A; Godlewska, Beata R

2006-08-01

There is substantial unexplained interindividual variability in the drug treatment of schizophrenia. A substantial proportion of patients respond inadequately to antipsychotic drugs, and many experience limiting side effects. As genetic factors are likely to contribute to this variability, the pharmacogenetics of schizophrenia has attracted substantial effort. The approaches have mainly been limited to association studies of polymorphisms in candidate genes, which have been indicated by the pharmacology of antipsychotic drugs. Although some advances have been made, particularly in understanding the pharmacogenetics of some limiting side effects, genetic prediction of symptom response remains elusive. Nevertheless, with improvements in defining the response phenotype in carefully assessed and homogeneous subject groups, the near future is likely to see the identification of genetic predictors of outcome that may inform the choice of pharmacotherapy.

Psychosocial predictors of the onset of anxiety disorders in women: Results from a prospective 3-year longitudinal study

PubMed Central

Calkins, Amanda W.; Otto, Michael W.; Cohen, Lee S.; Soares, Claudio N.; Vitonis, Alison F.; Hearon, Bridget A.; Harlow, Bernard L.

2009-01-01

In a prospective, longitudinal, population-based study of 643 women participating in the Harvard Study of Moods and Cycles we examined whether psychosocial variables predicted a new or recurrent onset of an anxiety disorder. Presence of anxiety disorders was assessed every six months over three years via structured clinical interviews. Among individuals who had a new episode of anxiety, we confirmed previous findings that history of anxiety, increased anxiety sensitivity (the fear of anxiety related sensations), and increased neuroticism were significant predictors. We also found trend level support for assertiveness as a predictor of anxiety onset. However, of these variables, only history of anxiety and anxiety sensitivity provided unique prediction. We did not find evidence for negative life events as a predictor of onset of anxiety either alone or in interaction with other variables in a diathesis-stress model. These findings from a prospective longitudinal study are discussed in relation to the potential role of such predictors in primary or relapse prevention efforts. PMID:19699609

Incidence of workers compensation indemnity claims across socio-demographic and job characteristics.

PubMed

Du, Juan; Leigh, J Paul

2011-10-01

We hypothesized that low socioeconomic status, employer-provided health insurance, low wages, and overtime were predictors of reporting workers compensation indemnity claims. We also tested for gender and race disparities. Responses from 17,190 (person-years) Americans participating in the Panel Study of Income Dynamics, 1997-2005, were analyzed with logistic regressions. The dependent variable indicated whether the subject collected benefits from a claim. Odds ratios for men and African-Americans were relatively large and strongly significant predictors of claims; significance for Hispanics was moderate and confounded by education. Odds ratios for variables measuring education were the largest for all statistically significant covariates. Neither low wages nor employer-provided health insurance was a consistent predictor. Due to confounding from the "not salaried" variable, overtime was not a consistently significant predictor. Few studies use nationally representative longitudinal data to consider which demographic and job characteristics predict reporting workers compensation indemnity cases. This study did and tested some common hypotheses about predictors. Copyright © 2011 Wiley-Liss, Inc.

Short-term variability and predictors of urinary pentachlorophenol levels in Ohio preschool children

EPA Science Inventory

Pentachlorophenol (PCP) is a persistent and ubiquitous environmental contaminant. No published data exist on the temporal variability or important predictors of urinary PCP concentrations in young children. In this further analysis of study data, we have examined the associations...

The no-show patient in the model family practice unit.

PubMed

Dervin, J V; Stone, D L; Beck, C H

1978-12-01

Appointment breaking by patients causes problems for the physician's office. Patients who neither keep nor cancel their appointments are often referred to as "no shows." Twenty variables were identified as potential predictors of no-show behavior. These predictors were applied to 291 Family Practice Center patients during a one-month study in April 1977. A discriminant function and multiple regression procedure were utilized ascertain the predictability of the selected variables. Predictive accuracy of the variables was 67.4 percent compared to the presently utilized constant predictor technique, which is 73 percent accurate. Modification of appointment schedules based upon utilization of the variables studies as predictors of show/no-show behavior does not appear to be an effective strategy in the Family Practice Center of the Community Hospital of Sonoma County, Santa Rosa, due to the high proportion of patients who do, in fact, show. In clinics with lower show rates, the technique may prove to be an effective strategy.

[The quality of the work-home and home-work relationship in the context of personal resources].

PubMed

Szymańska, Paulina

2014-01-01

The aim of the study was to examine whether gain or loss of personal resources is associated with conflict or facilitation between work and family. The study involved 90 employees (mean age: 34.5 years). The Polish version of COR-Evaluation (Conservation of Resources-Evaluation) questionnaire, developed by Hobfoll and adapted by Dudek et al, was used to assess personal resources. The questionnaire enables to estimate gain and loss of 40 resources and calculate the overall level of gained or lost resources. SWING Questionnaire (Survey Work-Home Interaction, Nijmegen), developed by Geurts et al. and adapted by Mośicka-Teske and Merecz), was used to examine the quality of work-home and home-work relationship. The gain of personal resources positively correlates with both home-work facilitation (HWF) and work-home facilitation (WHF). Improvement of the family relations proved to be the most significant predictor of HWF and WHF. The loss of personal resources coincides with high level of conflict between the investigated areas of life. The main predictor of home-work conflict (HWC) was the variable relating to restrictions of access to medical services. In case of work-home conflict (WHC) the reduction of material security in case of dramatic life events was the major predictor. The results confirmed that the gain of resources is crucial for HWF/WHF, while their loss is an important factor, when the HWC/WHC is considered. The resources, which proved to be the main predictors of work-home and home-work relatiohship were alsoindicated. The obtained information may be beneficial to human resources managers, especially in designing the activities aimed at increasing the satisfaction and effectiveness of employees.

Physiological and behavioral indices of emotion dysregulation as predictors of outcome from cognitive behavioral therapy and acceptance and commitment therapy for anxiety.

PubMed

Davies, Carolyn D; Niles, Andrea N; Pittig, Andre; Arch, Joanna J; Craske, Michelle G

2015-03-01

Identifying for whom and under what conditions a treatment is most effective is an essential step toward personalized medicine. The current study examined pre-treatment physiological and behavioral variables as predictors and moderators of outcome in a randomized clinical trial comparing cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT) for anxiety disorders. Sixty individuals with a DSM-IV defined principal anxiety disorder completed 12 sessions of either CBT or ACT. Baseline physiological and behavioral variables were measured prior to entering treatment. Self-reported anxiety symptoms were assessed at pre-treatment, post-treatment, and 6- and 12-month follow-up from baseline. Higher pre-treatment heart rate variability was associated with worse outcome across ACT and CBT. ACT outperformed CBT for individuals with high behavioral avoidance. Subjective anxiety levels during laboratory tasks did not predict or moderate treatment outcome. Due to small sample sizes of each disorder, disorder-specific predictors were not tested. Future research should examine these predictors in larger samples and across other outcome variables. Lower heart rate variability was identified as a prognostic indicator of overall outcome, whereas high behavioral avoidance was identified as a prescriptive indicator of superior outcome from ACT versus CBT. Investigation of pre-treatment physiological and behavioral variables as predictors and moderators of outcome may help guide future treatment-matching efforts. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predictors of Inappropriate Use of Diagnostic Tests and Management of Bronchiolitis

PubMed Central

Sarmiento, Lorena; Rojas-Soto, Gladys E.

2017-01-01

Background The aim of the present study was to determine predictors of inappropriate use of diagnostic tests and management of bronchiolitis in a population of hospitalized infants. Methods In an analytical cross-sectional study, we determined independent predictors of the inappropriate use of diagnostic tests and management of bronchiolitis in a population of hospitalized infants. We defined a composite outcome score as the main outcome variable. Results Of the 303 included patients, 216 (71.3%) experienced an inappropriate use of diagnostic tests and treatment of bronchiolitis. After controlling for potential confounders, it was found that atopic dermatitis (OR 5.30; CI 95% 1.14–24.79; p = 0.034), length of hospital stay (OR 1.48; CI 95% 1.08–2.03; p = 0.015), and the number of siblings (OR 1.92; CI 95% 1.13–3.26; p = 0.015) were independent predictors of an inappropriate use of diagnostic tests and treatment of the disease. Conclusions Inappropriate use of diagnostic tests and treatment of bronchiolitis was a highly prevalent outcome in our population of study. Participants with atopic dermatitis, a longer hospital stay, and a greater number of siblings were at increased risk for inappropriate use of diagnostic tests and management of the disease. PMID:28758127

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.

PubMed

Heidema, A Geert; Boer, Jolanda M A; Nagelkerke, Nico; Mariman, Edwin C M; van der A, Daphne L; Feskens, Edith J M

2006-04-21

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

Regression calibration for models with two predictor variables measured with error and their interaction, using instrumental variables and longitudinal data.

PubMed

Strand, Matthew; Sillau, Stefan; Grunwald, Gary K; Rabinovitch, Nathan

2014-02-10

Regression calibration provides a way to obtain unbiased estimators of fixed effects in regression models when one or more predictors are measured with error. Recent development of measurement error methods has focused on models that include interaction terms between measured-with-error predictors, and separately, methods for estimation in models that account for correlated data. In this work, we derive explicit and novel forms of regression calibration estimators and associated asymptotic variances for longitudinal models that include interaction terms, when data from instrumental and unbiased surrogate variables are available but not the actual predictors of interest. The longitudinal data are fit using linear mixed models that contain random intercepts and account for serial correlation and unequally spaced observations. The motivating application involves a longitudinal study of exposure to two pollutants (predictors) - outdoor fine particulate matter and cigarette smoke - and their association in interactive form with levels of a biomarker of inflammation, leukotriene E4 (LTE 4 , outcome) in asthmatic children. Because the exposure concentrations could not be directly observed, we used measurements from a fixed outdoor monitor and urinary cotinine concentrations as instrumental variables, and we used concentrations of fine ambient particulate matter and cigarette smoke measured with error by personal monitors as unbiased surrogate variables. We applied the derived regression calibration methods to estimate coefficients of the unobserved predictors and their interaction, allowing for direct comparison of toxicity of the different pollutants. We used simulations to verify accuracy of inferential methods based on asymptotic theory. Copyright © 2013 John Wiley & Sons, Ltd.

Miscarriage: A Special Type of Family Crisis.

ERIC Educational Resources Information Center

Day, Randal D.; Hooks, Daniel

1987-01-01

Surveyed 102 women about their experience with miscarriage. Found that family resource variables were a much stronger predictor of level of crisis and recovery than were personal or community resource variables. Adaptation and cohesion were significant predictors of speed or recovery and level of crisis, respectively. (Author/NB)

The relationship among young adult college students' depression, anxiety, stress, demographics, life satisfaction, and coping styles.

PubMed

Mahmoud, Jihan Saber Raja; Staten, Ruth; Hall, Lynne A; Lennie, Terry A

2012-03-01

Recent research indicates that young adult college students experience increased levels of depression, anxiety, and stress. It is less clear what strategies college health care providers might use to assist students in decreasing these mental health concerns. In this paper, we examine the relative importance of coping style, life satisfaction, and selected demographics in predicting undergraduates' depression, anxiety, and stress. A total of 508 full-time undergraduate students aged 18-24 years completed the study measures and a short demographics information questionnaire. Coping strategies and life satisfaction were assessed using the Brief COPE Inventory and an adapted version of the Brief Students' Multidimensional Life Satisfaction Scale. Depression, anxiety, and stress were measured using the Depression Anxiety and Stress Scale-21 (DASS-21). Multiple regression analyses were used to examine the relative influence of each of the independent variables on depression, anxiety, and stress. Maladaptive coping was the main predictor of depression, anxiety, and stress. Adaptive coping was not a significant predictor of any of the three outcome variables. Reducing maladaptive coping behaviors may have the most positive impact on reducing depression, anxiety, and stress in this population.

Predicting life satisfaction of the Angolan elderly: a structural model.

PubMed

Gutiérrez, M; Tomás, J M; Galiana, L; Sancho, P; Cebrià, M A

2013-01-01

Satisfaction with life is of particular interest in the study of old age well-being because it has arisen as an important component of old age. A considerable amount of research has been done to explain life satisfaction in the elderly, and there is growing empirical evidence on best predictors of life satisfaction. This research evaluates the predictive power of some aging process variables, on Angolan elderly people's life satisfaction, while including perceived health into the model. Data for this research come from a cross-sectional survey of elderly people living in the capital of Angola, Luanda. A total of 1003 Angolan elderly were surveyed on socio-demographic information, perceived health, active engagement, generativity, and life satisfaction. A Multiple Indicators Multiple Causes model was built to test variables' predictive power on life satisfaction. The estimated theoretical model fitted the data well. The main predictors were those related to active engagement with others. Perceived health also had a significant and positive effect on life satisfaction. Several processes together may predict life satisfaction in the elderly population of Angola, and the variance accounted for it is large enough to be considered relevant. The key factor associated to life satisfaction seems to be active engagement with others.

Demographic, psychometric, and case progression information as predictors of return-to-work in teachers undergoing occupational rehabilitation.

PubMed

Young, A E; Russell, J

1995-12-01

Occupational stress is a significant problem and is of particular concern for educational organizations. It was the aim of the current project to identify variables that could predict return-to-work outcomes in a group of teachers who had taken leave for a work-related stress condition. Demographic, psychometric, and case progression data were collected for 119 teachers who had taken Workers' Compensation Leave and were participating in a rehabilitation program. The participants' return to work outcomes were followed-up at least 12 months after they initially left their workplace. Hierarchical discriminant function analysis indicated that 84.62% of the cases could be correctly classified as either "returning to work" or "not returning to work due to illness." The main predictor variables were: if the individual had attempted to return to work within 505 days of injury, the individual's health behaviors, the sex of the individual, and the type of school in which he or she was employed (primary or secondary). It is suggested that the derived model could be further developed and used to predict return to work from stress-related illnesses.

A comparison of acoustic and observed sediment classifications as predictor variables for modelling biotope distributions in Galway Bay, Ireland

NASA Astrophysics Data System (ADS)

O'Carroll, Jack P. J.; Kennedy, Robert; Ren, Lei; Nash, Stephen; Hartnett, Michael; Brown, Colin

2017-10-01

The INFOMAR (Integrated Mapping For the Sustainable Development of Ireland's Marine Resource) initiative has acoustically mapped and classified a significant proportion of Ireland's Exclusive Economic Zone (EEZ), and is likely to be an important tool in Ireland's efforts to meet the criteria of the MSFD. In this study, open source and relic data were used in combination with new grab survey data to model EUNIS level 4 biotope distributions in Galway Bay, Ireland. The correct prediction rates of two artificial neural networks (ANNs) were compared to assess the effectiveness of acoustic sediment classifications versus sediments that were visually classified by an expert in the field as predictor variables. To test for autocorrelation between predictor variables the RELATE routine with Spearman rank correlation method was used. Optimal models were derived by iteratively removing predictor variables and comparing the correct prediction rates of each model. The models with the highest correct prediction rates were chosen as optimal. The optimal models each used a combination of salinity (binary; 0 = polyhaline and 1 = euhaline), proximity to reef (binary; 0 = within 50 m and 1 = outside 50 m), depth (continuous; metres) and a sediment descriptor (acoustic or observed) as predictor variables. As the status of benthic habitats is required to be assessed under the MSFD the Ecological Status (ES) of the subtidal sediments of Galway Bay was also assessed using the Infaunal Quality Index. The ANN that used observed sediment classes as predictor variables could correctly predict the distribution of biotopes 67% of the time, compared to 63% for the ANN using acoustic sediment classes. Acoustic sediment ANN predictions were affected by local sediment heterogeneity, and the lack of a mixed sediment class. The all-round poor performance of ANNs is likely to be a result of the temporally variable and sparsely distributed data within the study area.

Relationship between behavioural problems and use of mental health services in patients with severe mental illness and the mediating role of the perceived burden of care.

PubMed

Bellido-Zanin, Gloria; Vázquez-Morejón, Antonio J; Pérez-San-Gregorio, Maria Ángeles; Martín-Rodríguez, Agustín

2017-10-01

Mental health models proposed for predicting more use of mental health resources by patients with severe mental illness are including a wider variety of predictor variables, but there are still many more remaining to be explored for a complete model. The purpose of this study was to enquire into the relationship between two variables, behaviour problems and burden of care, and the use of mental health resources in patients with severe mental illness. Our hypothesis was that perceived burden of care mediates between behaviour problems of patients with serious mental illness and the use of mental health resources. The Behaviour Problem Inventory, which was filled out by the main caregiver, was used to evaluate 179 patients cared for in a community mental health unit. They also answered a questionnaire on perceived family burden. A structural equation analysis was done to test our hypothesis. The results showed that both the behaviour problems and perceived burden of care are good predictors of the use of mental health resources, where perceived burden of care mediates between behaviour problems and use of resources. These variables seem to be relevant for inclusion in complete models for predicting use of mental health resources. Copyright © 2017 Elsevier B.V. All rights reserved.

Variables that Predict Serve Efficacy in Elite Men’s Volleyball with Different Quality of Opposition Sets

PubMed Central

Valhondo, Álvaro; Fernández-Echeverría, Carmen; González-Silva, Jara; Claver, Fernando; Moreno, M. Perla

2018-01-01

Abstract The objective of this study was to determine the variables that predicted serve efficacy in elite men’s volleyball, in sets with different quality of opposition. 3292 serve actions were analysed, of which 2254 were carried out in high quality of opposition sets and 1038 actions were in low quality of opposition sets, corresponding to a total of 24 matches played during the Men’s European Volleyball Championships held in 2011. The independent variables considered in this study were the serve zone, serve type, serving player, serve direction, reception zone, receiving player and reception type; the dependent variable was serve efficacy and the situational variable was quality of opposition sets. The variables that acted as predictors in both high and low quality of opposition sets were the serving player, reception zone and reception type. The serve type variable only acted as a predictor in high quality of opposition sets, while the serve zone variable only acted as a predictor in low quality of opposition sets. These results may provide important guidance in men’s volleyball training processes. PMID:29599869

Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation

NASA Technical Reports Server (NTRS)

Vybiral, T.; Glaeser, D. H.; Goldberger, A. L.; Rigney, D. R.; Hess, K. R.; Mietus, J.; Skinner, J. E.; Francis, M.; Pratt, C. M.

1993-01-01

OBJECTIVES. The purpose of this report was to study heart rate variability in Holter recordings of patients who experienced ventricular fibrillation during the recording. BACKGROUND. Decreased heart rate variability is recognized as a long-term predictor of overall and arrhythmic death after myocardial infarction. It was therefore postulated that heart rate variability would be lowest when measured immediately before ventricular fibrillation. METHODS. Conventional indexes of heart rate variability were calculated from Holter recordings of 24 patients with structural heart disease who had ventricular fibrillation during monitoring. The control group consisted of 19 patients with coronary artery disease, of comparable age and left ventricular ejection fraction, who had nonsustained ventricular tachycardia but no ventricular fibrillation. RESULTS. Heart rate variability did not differ between the two groups, and no consistent trends in heart rate variability were observed before ventricular fibrillation occurred. CONCLUSIONS. Although conventional heart rate variability is an independent long-term predictor of adverse outcome after myocardial infarction, its clinical utility as a short-term predictor of life-threatening arrhythmias remains to be elucidated.

Independent Predictors of Prognosis Based on Oral Cavity Squamous Cell Carcinoma Surgical Margins.

PubMed

Buchakjian, Marisa R; Ginader, Timothy; Tasche, Kendall K; Pagedar, Nitin A; Smith, Brian J; Sperry, Steven M

2018-05-01

Objective To conduct a multivariate analysis of a large cohort of oral cavity squamous cell carcinoma (OCSCC) cases for independent predictors of local recurrence (LR) and overall survival (OS), with emphasis on the relationship between (1) prognosis and (2) main specimen permanent margins and intraoperative tumor bed frozen margins. Study Design Retrospective cohort study. Setting Tertiary academic head and neck cancer program. Subjects and Methods This study included 426 patients treated with OCSCC resection between 2005 and 2014 at University of Iowa Hospitals and Clinics. Patients underwent excision of OCSCC with intraoperative tumor bed frozen margin sampling and main specimen permanent margin assessment. Multivariate analysis of the data set to predict LR and OS was performed. Results Independent predictors of LR included nodal involvement, histologic grade, and main specimen permanent margin status. Specifically, the presence of a positive margin (odds ratio, 6.21; 95% CI, 3.3-11.9) or <1-mm/carcinoma in situ margin (odds ratio, 2.41; 95% CI, 1.19-4.87) on the main specimen was an independent predictor of LR, whereas intraoperative tumor bed margins were not predictive of LR on multivariate analysis. Similarly, independent predictors of OS on multivariate analysis included nodal involvement, extracapsular extension, and a positive main specimen margin. Tumor bed margins did not independently predict OS. Conclusion The main specimen margin is a strong independent predictor of LR and OS on multivariate analysis. Intraoperative tumor bed frozen margins do not independently predict prognosis. We conclude that emphasis should be placed on evaluating the main specimen margins when estimating prognosis after OCSCC resection.

[Anxiety, self-esteem and self-perceived satisfaction as predictors of health: differences between men and women].

PubMed

Sánchez López, María Pilar; Aparicio García, Marta Evelia; Dresch, Virginia

2006-08-01

The main objective of this research is to analyze whether there are differences in physical health between men and women when considering their working situation. Three psychological variables are analyzed (anxiety, self-esteem and satisfaction) as well as several indicators of physical health for different working situations. The results seem to indicate that although women have worse health than men (when the group is analyzed in general), these differences vary when we take into account the working condition of the participants, and the differences even disappear. The psychological variables used in this survey only explain the variance of the subjective indicators of physical health, most of all, the Physiological Anxiety, which is responsible for the highest rate of the explained variance. The psychological variables predict women's physical health more than men's, what seems to indicate that women's physical health is closely related to psychological health.

Relationships between Speech Intelligibility and Word Articulation Scores in Children with Hearing Loss

PubMed Central

Ertmer, David J.

2012-01-01

Purpose This investigation sought to determine whether scores from a commonly used word-based articulation test are closely associated with speech intelligibility in children with hearing loss. If the scores are closely related, articulation testing results might be used to estimate intelligibility. If not, the importance of direct assessment of intelligibility would be reinforced. Methods Forty-four children with hearing losses produced words from the Goldman-Fristoe Test of Articulation-2 and sets of 10 short sentences. Correlation analyses were conducted between scores for seven word-based predictor variables and percent-intelligible scores derived from listener judgments of stimulus sentences. Results Six of seven predictor variables were significantly correlated with percent-intelligible scores. However, regression analysis revealed that no single predictor variable or multi- variable model accounted for more than 25% of the variability in intelligibility scores. Implications The findings confirm the importance of assessing connected speech intelligibility directly. PMID:20220022

Estimates of self, parental, and partner multiple intelligence and their relationship with personality, values, and demographic variables: a study in Britain and France.

PubMed

Swami, Viren; Furnham, Adrian; Zilkha, Susan

2009-11-01

In the present study, 151 British and 151 French participants estimated their own, their parents' and their partner's overall intelligence and 13 'multiple intelligences.' In accordance with previous studies, men rated themselves as higher on almost all measures of intelligence, but there were few cross-national differences. There were also important sex differences in ratings of parental and partner intelligence. Participants generally believed they were more intelligent than their parents but not their partners. Regressions indicated that participants believed verbal, logical-mathematical, and spatial intelligence to be the main predictors of intelligence. Regressions also showed that participants' Big Five personality scores (in particular, Extraversion and Openness), but not values or beliefs about intelligence and intelligences tests, were good predictors of intelligence. Results were discussed in terms of the influence of gender-role stereotypes.

Cross-national comparisons of college students' attitudes toward diet/fitness apps on smartphones.

PubMed

Cho, Jaehee; Lee, H Erin; Quinlan, Margaret

2017-10-01

Based on the technology acceptance model (TAM), we explored the nationally-bounded roles of four predictors (subjective norms, entertainment, recordability, and networkability) in determining the TAM variables of perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI) to use diet/fitness apps on smartphones. College students in the US and South Korea were invited to participate in a survey. We obtained 508 questionnaires (304 from the US and 204 from Korea). Data were analyzed mainly through path analysis. The four factors positively predicted the PU and PEOU of diet/fitness apps. While the effects of the predictors on the three TAM components were generally stronger among the US students than Korean students, the effect of subjective norms on the BI of diet/fitness apps was weaker among Korean students. Findings from the cross-national comparisons were helpful for thoroughly understanding the contextualized mechanisms involved in the adoption of diet/fitness apps.

The differential impact of job isostrain and home-work interference on indicators of physical and mental health in women and men.

PubMed

Casini, Annalisa; Clays, Els; Godin, Isabelle; De Backer, Guy; Kornitzer, Marcel; Kittel, France

2010-12-01

To evaluate (1) whether the physical and mental health of male workers differs from that of female workers, and, if so, whether (2) this is affected by the interplay between work and nonwork burden. We pooled two large Belgian databases (BELSTRESS III, SOMSTRESS) comprising data on 4810 (2847 women). Gender-specific logistic regressions were performed using a four-level variable as predictor. This combined two predictors: isolated job strain (isostrain) and home-work interference (HWI). Male workers are at greater risk of chronic fatigue when they experience high isostrain but not high HWI. Although accumulated high isostrain and high HWI affect women mainly via chronic fatigue, the same pattern has a greater impact on men's perceived health. There was no difference for the other patterns. To improve workers' well-being, organizations should develop work and nonwork balance policies specific for men and women.

Predictors of First-Year Sultan Qaboos University Students' Grade Point Average

ERIC Educational Resources Information Center

Alkhausi, Hussain Ali; Al-Yahmadi, Hamad; Al-Kalbani, Muna; Clayton, David; Al-Barwani, Thuwayba; Al-Sulaimani, Humaira; Neisler, Otherine; Khan, Mohammad Athar

2015-01-01

This study investigated predictors of first-year university grade point average (GPA) using academic and nonacademic variables. Data were collected from 1511 Omani students selected conveniently from the population of students entering Sultan Qaboos University (SQU) in Fall 2010. Variables considered in the analysis were general education diploma…

Life Expectancy of Persons with Down Syndrome.

ERIC Educational Resources Information Center

Eyman, Richard K.; And Others

1991-01-01

Longevity of 12,543 Down's syndrome clients of the California Department of Developmental Services was examined. Findings indicated that predictors of survival were not different from mortality-related variables in the general population. Lack of mobility or poor feeding skills were better predictors of early death than variables associated with…

Centering Effects in HLM Level-1 Predictor Variables.

ERIC Educational Resources Information Center

Schumacker, Randall E.; Bembry, Karen

Research has suggested that important research questions can be addressed with meaningful interpretations using hierarchical linear modeling (HLM). The proper interpretation of results, however, is invariably linked to the choice of centering for the Level-1 predictor variables that produce the outcome measure for the Level-2 regression analysis.…

A Study of Predictors of College Completion among SEEK Immigrant Students

ERIC Educational Resources Information Center

Nazon, Marie C.

2010-01-01

This study examined the strength of the relationship between eight situational and demographic variables and college completion among immigrant students in SEEK, an educational opportunity program. The eight variables studied as possible predictors of college completion included household composition, length of residency, English as a primary…

Predicting the In-Hospital Responsiveness to Treatment of Alcoholics. Social Factors as Predictors of Outcome. Brain Damage as a Factor in Treatment Outcome of Chronic Alcoholic Patients.

ERIC Educational Resources Information Center

Mascia, George V.; And Others

The authors attempt to locate predictor variables associated with the outcome of alcoholic treatment programs. Muscia's study focuses on the predictive potential of: (1) response to a GSR conditioning procedure; (2) several personality variables; and (3) age and IQ measures. Nine variables, reflecting diverse perspectives, were selected as a basis…

Clinical verification of a theory for predicting side branch stenosis after main vessel stenting in coronary bifurcation lesions.

PubMed

Vassilev, Dobrin; Gil, Robert

2008-12-01

To verify in a clinical scenario a theory for predicting side branch (SB) stenosis after main vessel stent implantation in coronary bifurcation lesions. Many unresolved issues remain regarding SB compromise when the parent vessel is stented. Bifurcation lesions (all Medina types) were subjected to angiographic analysis to determine the angle, defined as alpha, between the axes of the parent vessel and the SB. Using the prediction that the percent diameter stenosis (%DS) is equal to the cosine of angle alpha and relating it to a formula to determine the minimal lumen diameter (MLD) led to the following equation: MLD = ds x (1 -cos alpha); ds refers to the diameter of the SB. The predicted and observed SB stenosis values following angiography were compared. Fifty-two patients with 57 lesions were included in the analysis. Patient demographics and characteristics were similar to those in previous studies. There was a high coefficient of determination between the predicted and observed values of %DS (r(2)= 0.82, P < 0.001) and MLD (r(2)= 0.86, P < 0.001). We determined a cutoff value of 70% for predicted %DS for SB closure. When using multivariate regression analysis, the only predictor of SB ostial stenosis after stenting was alpha angle, whereas the predictors of MLD included the angle alpha and the RVD of the SB. Our analysis shows that the most powerful independent predictor of SB compromise is a new variable angle alpha.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Parent involvement in school: English speaking versus Spanish speaking families.

PubMed

Lee, Sang Min; Thorn, Antoinette; Bloomdahl, Susana Contreras; Ha, Jung Hee; Nam, Suk Kyung; Lee, Jayoung

2012-07-01

The purpose of the present study was to explore the relationships between three predictor variables (attitude toward school, parent-child communication, and school commitment action) and the criterion variable (parent involvement) in a representative sample and to examine if these relationships were consistent across three groups (English speaking Caucasian family, English speaking Latino family, and Spanish speaking Latino families). Using a national database (N = 9.841), multi-group SEM analyses were conducted to investigate the relationship between three predictor variables and the criterion variable in three family groups. While all three predictor variables significantly predicted parent involvement in English speaking Caucasian and Latino families, only two variables (parent-child communication and school commitment actions), significantly predicted parent involvement in Spanish speaking Latino families. The results of this study suggest that when administrators, teachers and counselors in school strive to share specific school-related information with Latino families, Spanish speaking families are more likely to become involved with schools.

Quantitatively measured tremor in hand-arm vibration-exposed workers.

PubMed

Edlund, Maria; Burström, Lage; Hagberg, Mats; Lundström, Ronnie; Nilsson, Tohr; Sandén, Helena; Wastensson, Gunilla

2015-04-01

The aim of the present study was to investigate the possible increase in hand tremor in relation to hand-arm vibration (HAV) exposure in a cohort of exposed and unexposed workers. Participants were 178 male workers with or without exposure to HAV. The study is cross-sectional regarding the outcome of tremor and has a longitudinal design with respect to exposure. The dose of HAV exposure was collected via questionnaires and measurements at several follow-ups. The CATSYS Tremor Pen(®) was used for measuring postural tremor. Multiple linear regression methods were used to analyze associations between different tremor variables and HAV exposure, along with predictor variables with biological relevance. There were no statistically significant associations between the different tremor variables and cumulative HAV or current exposure. Age was a statistically significant predictor of variation in tremor outcomes for three of the four tremor variables, whereas nicotine use was a statistically significant predictor of either left or right hand or both hands for all four tremor variables. In the present study, there was no evidence of an exposure-response association between HAV exposure and measured postural tremor. Increase in age and nicotine use appeared to be the strongest predictors of tremor.

Global diversity patterns in sandy beach macrofauna: a biogeographic analysis.

PubMed

Barboza, Francisco Rafael; Defeo, Omar

2015-09-28

Unlike the advances generated on land, the knowledge of global diversity patterns in marine ecosystems is limited to a small number of studies. For sandy beaches, which dominate the world's ocean shores, previous meta-analyses highlighted the role of beach morphodynamics in explaining species richness patterns. Oceanographic variables and historical processes have not been considered, even though they could be main predictors of community structure. Our work, based on 256 sandy beaches around the world, analysed species richness considering for the first time temperature, salinity and primary productivity. Biogeographic units (realms, provinces and ecoregions) were used to incorporate historical factors in modelling processes. Ecoregions, which implicitly include isolation and coastal complexity among other historical geographic factors, best represented trends in species richness worldwide. Temperature was a main predictor of species richness, which increased from temperate to tropical sandy beaches. Species richness increased with tide range and towards wide beaches with gentle slopes and fine grains, which is consistent with the hypothesis that habitat availability has an important role in structuring sandy beach communities. The role of temperature and habitat availability suggests that ocean warming and sea level rise could affect the distribution of obligate species living in these narrow ecosystems.

Global diversity patterns in sandy beach macrofauna: a biogeographic analysis

PubMed Central

Rafael Barboza, Francisco; Defeo, Omar

2015-01-01

Unlike the advances generated on land, the knowledge of global diversity patterns in marine ecosystems is limited to a small number of studies. For sandy beaches, which dominate the world’s ocean shores, previous meta-analyses highlighted the role of beach morphodynamics in explaining species richness patterns. Oceanographic variables and historical processes have not been considered, even though they could be main predictors of community structure. Our work, based on 256 sandy beaches around the world, analysed species richness considering for the first time temperature, salinity and primary productivity. Biogeographic units (realms, provinces and ecoregions) were used to incorporate historical factors in modelling processes. Ecoregions, which implicitly include isolation and coastal complexity among other historical geographic factors, best represented trends in species richness worldwide. Temperature was a main predictor of species richness, which increased from temperate to tropical sandy beaches. Species richness increased with tide range and towards wide beaches with gentle slopes and fine grains, which is consistent with the hypothesis that habitat availability has an important role in structuring sandy beach communities. The role of temperature and habitat availability suggests that ocean warming and sea level rise could affect the distribution of obligate species living in these narrow ecosystems. PMID:26411697

Global Risk Score and Clinical SYNTAX Score as Predictors of Clinical Outcomes of Patients Undergoing Unprotected Left Main Percutaneous Catheter Intervention

PubMed Central

Cuenza, Lucky; Collado, Marianne P.; Ho Khe Sui, James

2017-01-01

Background Risk stratification is an important component of left main percutaneous catheter intervention (PCI) which has emerged as a feasible alternative to cardiac surgery. We sought to compare the clinical SYNTAX score and the global risk score in predicting outcomes of patients undergoing unprotected left main PCI in our institution. Methods Clinical, angiographic and procedural characteristics of 92 patients who underwent unprotected left main PCI (mean age 62 ± 12.1 years) were analyzed. Patients were risk stratified into tertiles of high, intermediate and low risk using the global risk score (GRS) and the clinical SYNTAX score (CSS) and were prospectively followed up at 1 year for the occurrence of major adverse cardiovascular events (MACEs), defined as a composite of all cause mortality, cardiac mortality, non-fatal myocardial infarction, stroke, coronary artery bypass, and target vessel revascularization. Results There were 26 (28.2%) who experienced MACEs, of which 10 (10.8%) patients died. Multivariable hazards analysis showed that the GRS (hazard ratio (HR) = 5.5, P = 0.001) and CSS (HR = 4.3, P = 0.001) were both independent predictors of MACEs. Kaplan-Meier analysis showed higher incidence of MACEs with the intermediate and higher risk categories compared to those classified as low risk. Receiver-operator characteristic analysis showed that the GRS has better discriminatory ability than the CSS in the prediction of 1 year MACEs (0.891 vs. 0.743, P = 0.007). Conclusion The GRS and CSS are predictive of outcomes after left main PCI. The GRS appears to have superior predictive and prognostic utility compared to the CSS. This study emphasizes the importance of combining both anatomic and clinical variables for optimum prognostication and management decisions in left main PCI. PMID:29317974

Detection of major climatic and environmental predictors of liver fluke exposure risk in Ireland using spatial cluster analysis.

PubMed

Selemetas, Nikolaos; de Waal, Theo

2015-04-30

Fasciolosis caused by Fasciola hepatica (liver fluke) can cause significant economic and production losses in dairy cow farms. The aim of the current study was to identify important weather and environmental predictors of the exposure risk to liver fluke by detecting clusters of fasciolosis in Ireland. During autumn 2012, bulk-tank milk samples from 4365 dairy farms were collected throughout Ireland. Using an in-house antibody-detection ELISA, the analysis of BTM samples showed that 83% (n=3602) of dairy farms had been exposed to liver fluke. The Getis-Ord Gi* statistic identified 74 high-risk and 130 low-risk significant (P<0.01) clusters of fasciolosis. The low-risk clusters were mostly located in the southern regions of Ireland, whereas the high-risk clusters were mainly situated in the western part. Several climatic variables (monthly and seasonal mean rainfall and temperatures, total wet days and rain days) and environmental datasets (soil types, enhanced vegetation index and normalised difference vegetation index) were used to investigate dissimilarities in the exposure to liver fluke between clusters. Rainfall, total wet days and rain days, and soil type were the significant classes of climatic and environmental variables explaining the differences between significant clusters. A discriminant function analysis was used to predict the exposure risk to liver fluke using 80% of data for modelling and the remaining subset of 20% for post hoc model validation. The most significant predictors of the model risk function were total rainfall in August and September and total wet days. The risk model presented 100% sensitivity and 91% specificity and an accuracy of 95% correctly classified cases. A risk map of exposure to liver fluke was constructed with higher probability of exposure in western and north-western regions. The results of this study identified differences between clusters of fasciolosis in Ireland regarding climatic and environmental variables and detected significant predictors of the exposure risk to liver fluke. Copyright © 2015 Elsevier B.V. All rights reserved.

Partner relationship satisfaction and maternal emotional distress in early pregnancy

PubMed Central

2011-01-01

Background Recognition of maternal emotional distress during pregnancy and the identification of risk factors for this distress are of considerable clinical- and public health importance. The mental health of the mother is important both for herself, and for the physical and psychological health of her children and the welfare of the family. The first aim of the present study was to identify risk factors for maternal emotional distress during pregnancy with special focus on partner relationship satisfaction. The second aim was to assess interaction effects between relationship satisfaction and the main predictors. Methods Pregnant women enrolled in the Norwegian Mother and Child Cohort Study (n = 51,558) completed a questionnaire with questions about maternal emotional distress, relationship satisfaction, and other risk factors. Associations between 37 predictor variables and emotional distress were estimated by multiple linear regression analysis. Results Relationship dissatisfaction was the strongest predictor of maternal emotional distress (β = 0.25). Other predictors were dissatisfaction at work (β = 0.11), somatic disease (β = 0.11), work related stress (β = 0.10) and maternal alcohol problems in the preceding year (β = 0.09). Relationship satisfaction appeared to buffer the effects of frequent moving, somatic disease, maternal smoking, family income, irregular working hours, dissatisfaction at work, work stress, and mother's sick leave (P < 0.05). Conclusions Dissatisfaction with the partner relationship is a significant predictor of maternal emotional distress in pregnancy. A good partner relationship can have a protective effect against some stressors. PMID:21401914

Personal and organizational predictors of workplace sexual harassment of women by men.

PubMed

Dekker, I; Barling, J

1998-01-01

The authors investigated the predictors of workplace sexual harassment in 278 male university faculty and staff (M age = 45 years). Workplace variables (perceptions of organizational sanctions against harassment and perceptions of a sexualized workplace) and personal variables (adversarial sexual beliefs, sexual harassment beliefs, perspective taking, and self-esteem) were studied as predictors of sexualized and gender harassment. Social desirability was controlled. Both organizational variables and beliefs about sexual harassment predicted gender harassment and sexualized harassment. Perspective taking, adversarial sexual beliefs, and sexual harassment beliefs moderated the effects of perceived organizational sanctions against harassment on sexualized harassment. Findings are discussed as they relate to organizational efforts to reduce or prevent sexual harassment.

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

a Latent Variable Path Analysis Model of Secondary Physics Enrollments in New York State.

NASA Astrophysics Data System (ADS)

Sobolewski, Stanley John

The Percentage of Enrollment in Physics (PEP) at the secondary level nationally has been approximately 20% for the past few decades. For a more scientifically literate citizenry as well as specialists to continue scientific research and development, it is desirable that more students enroll in physics. Some of the predictor variables for physics enrollment and physics achievement that have been identified previously includes a community's socioeconomic status, the availability of physics, the sex of the student, the curriculum, as well as teacher and student data. This study isolated and identified predictor variables for PEP of secondary schools in New York. Data gathered by the State Education Department for the 1990-1991 school year was used. The source of this data included surveys completed by teachers and administrators on student characteristics and school facilities. A data analysis similar to that done by Bryant (1974) was conducted to determine if the relationships between a set of predictor variables related to physics enrollment had changed in the past 20 years. Variables which were isolated included: community, facilities, teacher experience, number of type of science courses, school size and school science facilities. When these variables were isolated, latent variable path diagrams were proposed and verified by the Linear Structural Relations computer modeling program (LISREL). These diagrams differed from those developed by Bryant in that there were more manifest variables used which included achievement scores in the form of Regents exam results. Two criterion variables were used, percentage of students enrolled in physics (PEP) and percent of students enrolled passing the Regents physics exam (PPP). The first model treated school and community level variables as exogenous while the second model treated only the community level variables as exogenous. The goodness of fit indices for the models was 0.77 for the first model and 0.83 for the second model. No dramatic differences were found between the relationship of predictor variables to physics enrollment in 1972 and 1991. New models indicated that smaller school size, enrollment in previous science and math courses and other school variables were more related to high enrollment rather than achievement. Exogenous variables such as community size were related to achievement. It was shown that achievement and enrollment were related to a different set of predictor variables.

Psychosocial predictors of human papillomavirus vaccination intentions for young women 18 to 26: religiosity, morality, promiscuity, and cancer worry.

PubMed

Krakow, Melinda M; Jensen, Jakob D; Carcioppolo, Nick; Weaver, Jeremy; Liu, Miao; Guntzviller, Lisa M

2015-01-01

To determine whether five psychosocial variables, namely, religiosity, morality, perceived promiscuity, cancer worry frequency, and cancer worry severity, predict young women's intentions to receive the human papillomavirus (HPV) vaccination. Female undergraduate students (n=408) completed an online survey. Questions pertaining to hypothesized predictors were analyzed through bivariate correlations and hierarchical regression equations. Regressions examined whether the five psychosocial variables of interest predicted intentions to vaccinate above and beyond controls. Proposed interactions among predictor variables were also tested. Study findings supported cancer worry as a direct predictor of HPV vaccination intention, and religiosity and sexual experience as moderators of the relationship between concerns of promiscuity reputation and intentions to vaccinate. One dimension of cancer worry (severity) emerged as a particularly robust predictor for this population. This study provides support for several important, yet understudied, factors contributing to HPV vaccination intentions among college-aged women: cancer worry severity and religiosity. Future research should continue to assess the predictive contributions of these variables and evaluate how messages and campaigns to increase HPV vaccination uptake can utilize religious involvement and worry about cancer to promote more effectively HPV vaccination as a cancer prevention strategy. Copyright © 2015 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Ecological and personal predictors of science achievement in an urban center

NASA Astrophysics Data System (ADS)

Guidubaldi, John Michael

This study sought to examine selected personal and environmental factors that predict urban students' achievement test scores on the science subject area of the Ohio standardized test. Variables examined were in the general categories of teacher/classroom, student, and parent/home. It assumed that these clusters might add independent variance to a best predictor model, and that discovering relative strength of different predictors might lead to better selection of intervention strategies to improve student performance. This study was conducted in an urban school district and was comprised of teachers and students enrolled in ninth grade science in three of this district's high schools. Consenting teachers (9), students (196), and parents (196) received written surveys with questions designed to examine the predictive power of each variable cluster. Regression analyses were used to determine which factors best correlate with student scores and classroom science grades. Selected factors were then compiled into a best predictive model, predicting success on standardized science tests. Students t tests of gender and racial subgroups confirmed that there were racial differences in OPT scores, and both gender and racial differences in science grades. Additional examinations were therefore conducted for all 12 variables to determine whether gender and race had an impact on the strength of individual variable predictions and on the final best predictor model. Of the 15 original OPT and cluster variable hypotheses, eight showed significant positive relationships that occurred in the expected direction. However, when more broadly based end-of-the-year science class grade was used as a criterion, 13 of the 15 hypotheses showed significant relationships in the expected direction. With both criteria, significant gender and racial differences were observed in the strength of individual predictors and in the composition of best predictor models.

Factors influencing teamwork and collaboration within a tertiary medical center

PubMed Central

Chien, Shu Feng; Wan, Thomas TH; Chen, Yu-Chih

2012-01-01

AIM: To understand how work climate and related factors influence teamwork and collaboration in a large medical center. METHODS: A survey of 3462 employees was conducted to generate responses to Sexton’s Safety Attitudes Questionnaire (SAQ) to assess perceptions of work environment via a series of five-point, Likert-scaled questions. Path analysis was performed, using teamwork (TW) and collaboration (CO) as endogenous variables. The exogenous variables are effective communication (EC), safety culture (SC), job satisfaction (JS), work pressure (PR), and work climate (WC). The measurement instruments for the variables or summated subscales are presented. Reliability of each sub-scale are calculated. Alpha Cronbach coefficients are relatively strong: TW (0.81), CO (0.76), EC (0.70), SC (0.83), JS (0.91), WP (0.85), and WC (0.78). Confirmatory factor analysis was performed for each of these constructs. RESULTS: Path analysis enables to identify statistically significant predictors of two endogenous variables, teamwork and intra-organizational collaboration. Significant amounts of variance in perceived teamwork (R2 = 0.59) and in collaboration (R2 = 0.75) are accounted for by the predictor variables. In the initial model, safety culture is the most important predictor of perceived teamwork, with a β weight of 0.51, and work climate is the most significant predictor of collaboration, with a β weight of 0.84. After eliminating statistically insignificant causal paths and allowing correlated predictors1, the revised model shows that work climate is the only predictor positively influencing both teamwork (β = 0.26) and collaboration (β = 0.88). A relatively weak positive (β = 0.14) but statistically significant relationship exists between teamwork and collaboration when the effects of other predictors are simultaneously controlled. CONCLUSION: Hospital executives who are interested in improving collaboration should assess the work climate to ensure that employees are operating in a setting conducive to intra-organizational collaboration. PMID:25237612

Popularity as a Predictor of Early Alcohol Use and Moderator of Other Risk Processes

PubMed Central

Guyll, Max; Madon, Stephanie; Spoth, Richard; Lannin, Daniel G

2014-01-01

Objective: This study tested the relationship between popularity and early adolescent alcohol use and examined whether popularity moderated the influence of several risk processes. Method: Longitudinal data provided by 1,196 youth (590 girls) were analyzed to assess main and interactive effects of popularity, friends’ alcohol use attitudes, own alcohol use attitude, risk taking, and aggressive–disruptive behavior on changes in alcohol use during seventh grade. Results: When we controlled for demographic variables and baseline alcohol use, popularity and the other predictors of interest exhibited linear main effects on alcohol use, with popularity and the attitude variables also demonstrating curvilinear relationships. Further analysis indicated that popularity moderated the effect of aggressive–disruptive behavior, the latter being associated with greater alcohol use among more popular adolescents. Additional moderation results revealed that friends’ favorable attitudes toward alcohol use also potentiated aggressive–disruptive behavior’s relationship with alcohol use and that male youth were more likely than female youth to use alcohol, but only among low risk takers. Conclusions: Popular youth may attempt to maintain status through early alcohol use, and their social competencies may facilitate risk processes associated with aggressive–disruptive behavior. Findings suggest the utility of providing universal prevention at developmentally crucial times to address substance use overall, and particularly to decrease early use among popular youth, which may serve to slow the growth of substance use in the larger cohort. Although aggressive–disruptive youth who are popular seem to be at particular risk, they may resist traditional interventions, indicating the potential value of less obvious intervention strategies. PMID:25343648

Popularity as a predictor of early alcohol use and moderator of other risk processes.

PubMed

Guyll, Max; Madon, Stephanie; Spoth, Richard; Lannin, Daniel G

2014-11-01

This study tested the relationship between popularity and early adolescent alcohol use and examined whether popularity moderated the influence of several risk processes. Longitudinal data provided by 1,196 youth (590 girls) were analyzed to assess main and interactive effects of popularity, friends' alcohol use attitudes, own alcohol use attitude, risk taking, and aggressive-disruptive behavior on changes in alcohol use during seventh grade. When we controlled for demographic variables and baseline alcohol use, popularity and the other predictors of interest exhibited linear main effects on alcohol use, with popularity and the attitude variables also demonstrating curvilinear relationships. Further analysis indicated that popularity moderated the effect of aggressive-disruptive behavior, the latter being associated with greater alcohol use among more popular adolescents. Additional moderation results revealed that friends' favorable attitudes toward alcohol use also potentiated aggressive-disruptive behavior's relationship with alcohol use and that male youth were more likely than female youth to use alcohol, but only among low risk takers. Popular youth may attempt to maintain status through early alcohol use, and their social competencies may facilitate risk processes associated with aggressive-disruptive behavior. Findings suggest the utility of providing universal prevention at developmentally crucial times to address substance use overall, and particularly to decrease early use among popular youth, which may serve to slow the growth of substance use in the larger cohort. Although aggressive-disruptive youth who are popular seem to be at particular risk, they may resist traditional interventions, indicating the potential value of less obvious intervention strategies.

Job Satisfaction in Mexican Faculty: An Analysis of its Predictor Variables. ASHE Annual Meeting Paper.

ERIC Educational Resources Information Center

Galaz-Fontes, Jesus Francisco; Gil-Anton, Manuel

This study examined overall job satisfaction among college faculty in Mexico. The study used data from a 1992-93 Carnegie International Faculty Survey. Secondary multiple regression analysis identified predictor variables for several faculty subgroups. Results were interpreted by differentiating between work-related and intrinsic factors, as well…

Teacher and Child Predictors of Achieving IEP Goals of Children with Autism

ERIC Educational Resources Information Center

Ruble, Lisa; McGrew, John H.

2013-01-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in "The…

On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental

ERIC Educational Resources Information Center

Shieh, Gwowen

2010-01-01

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term.…

Motivation for change as a predictor of treatment response for dysthymia.

PubMed

Frías Ibáñez, Álvaro; González Vallespí, Laura; Palma Sevillano, Carol; Farriols Hernando, Núria

2016-05-01

Dysthymia constitutes a chronic, mild affective disorder characterized by heterogeneous treatment effects. Several predictors of clinical response and attendance have been postulated, although research on the role of the psychological variables involved in this mental disorder is still scarce. Fifty-four adult patients, who met criteria for dysthymia completed an ongoing naturalistic treatment based on the brief interpersonal psychotherapy (IPT-B), which was delivered bimonthly over 16 months. As potential predictor variables, the therapeutic alliance, coping strategies, perceived self-efficacy, and motivation for change were measured at baseline. Outcome variables were response to treatment (Clinical Global Impression and Beck’s Depression Inventory) and treatment attendance. Stepwise multiple linear regression analyses revealed that higher motivation for change predicted better response to treatment. Moreover, higher motivation for change also predicted treatment attendance. Therapeutic alliance was not a predictor variable of neither clinical response nor treatment attendance. These preliminary findings support the adjunctive use of motivational interviewing (MI) techniques in the treatment of dysthymia. Further research with larger sample size and follow-up assessment is warranted.

Predictors of short-term outcome to exercise and manual therapy for people with hip osteoarthritis.

PubMed

French, Helen P; Galvin, Rose; Cusack, Tara; McCarthy, Geraldine M

2014-01-01

Physical therapy for hip osteoarthritis (OA) has shown short-term effects but limited long-term benefit. There has been limited research, with inconsistent results, in identifying prognostic factors associated with a positive response to physical therapy. The purpose of this study was to identify potential predictors of response to physical therapy (exercise therapy [ET] with or without adjunctive manual therapy [MT]) for hip OA based on baseline patient-specific and clinical characteristics. A prognostic study was conducted. Secondary analysis of data from a multicenter randomized controlled trial (RCT) (N=131) that evaluated the effectiveness of ET and ET+MT for hip OA was undertaken. Treatment response was defined using OMERACT/OARSI responder criteria. Ten baseline measures were used as predictor variables. Regression analyses were undertaken to identify predictors of outcome. Discriminative ability (sensitivity, specificity, and likelihood ratios) of significant variables was calculated. The RCT results showed no significant difference in most outcomes between ET and ET+MT at 9 and 18 weeks posttreatment. Forty-six patients were classified as responders at 9 weeks, and 36 patients were classified as responders at 18 weeks. Four baseline variables were predictive of a positive outcome at 9 weeks: male sex, pain with activity (<6/10), Western Ontario and McMaster Universities Osteoarthritis Index physical function subscale score (<34/68), and psychological health (Hospital Anxiety and Depression Scale score <9/42). No predictor variables were identified at the 18-week follow-up. Prognostic accuracy was fair for all 4 variables (sensitivity=0.5-0.58, specificity=0.57-0.72, likelihood ratios=1.25-1.77), indicating fair discriminative ability at predicting treatment response. The short-term follow-up limits the interpretation of results, and the low number of identified responders may have resulted in possible overfitting of the predictor model. The authors were unable to identify baseline variables in patients with hip OA that indicate those most likely to respond to treatment due to low discriminative ability. Further validation studies are needed to definitively define the best predictors of response to physical therapy in people with hip OA.

Preadmission Predictors of On-time Graduation in a Doctor of Pharmacy Program.

PubMed

Allen, Rondall E; Diaz, Carroll; Gant, Kisha; Taylor, Ashley; Onor, Ifeanyi

2016-04-25

Objective. To determine which preadmission variables or combination of variables are able to predict on-time graduation in a doctor of pharmacy program. Methods. Transcripts and student files were reviewed for 460 students who entered the college between 2007 and 2009. Results. The preadmission variables with significant correlations to on-time graduation included having a prior degree, student type, the number of unsatisfactory grades (nonscience and math-science courses, and the combination), prepharmacy cumulative grade point average (GPA), and math-science GPA. Of these variables, the significant predictors of on-time graduation were prior degree, the presence of no unsatisfactory grades in nonscience courses, and prepharmacy cumulative GPA. Conclusion. Having a prior degree, lack of unsatisfactory grades in nonscience courses, and prepharmacy GPA were identified as significant predictors of on-time graduation.

Preadmission Predictors of On-time Graduation in a Doctor of Pharmacy Program

PubMed Central

Diaz, Carroll; Gant, Kisha; Taylor, Ashley; Onor, Ifeanyi

2016-01-01

Objective. To determine which preadmission variables or combination of variables are able to predict on-time graduation in a doctor of pharmacy program. Methods. Transcripts and student files were reviewed for 460 students who entered the college between 2007 and 2009. Results. The preadmission variables with significant correlations to on-time graduation included having a prior degree, student type, the number of unsatisfactory grades (nonscience and math-science courses, and the combination), prepharmacy cumulative grade point average (GPA), and math-science GPA. Of these variables, the significant predictors of on-time graduation were prior degree, the presence of no unsatisfactory grades in nonscience courses, and prepharmacy cumulative GPA. Conclusion. Having a prior degree, lack of unsatisfactory grades in nonscience courses, and prepharmacy GPA were identified as significant predictors of on-time graduation. PMID:27170814

Neuropsychological deficits in preschool as predictors of ADHD symptoms and academic achievement in late adolescence

PubMed Central

Sjöwall, Douglas; Bohlin, Gunilla; Rydell, Ann-Margret; Thorell, Lisa B

2017-01-01

High levels of ADHD symptoms are related to severe negative outcomes, which underscore the importance of identifying early markers of these behavior problems. The main aim of the present study was therefore to investigate whether neuropsychological deficits in preschool are related to later ADHD symptoms and academic achievement, over and above the influence of early ADHD symptom levels. The present study is unique because it includes a broader range of predictors compared to previous studies and the participants are followed over time for as long as 13 years (i.e., ages 5–18 years). Preschool data included measures of executive functioning and reaction time variability as well as emotional reactivity and emotion regulation of both positive and negative emotions. When controlling for early ADHD symptom levels, working memory, reaction time variability, and regulation of happiness/exuberance were significantly related to inattention whereas regulation of happiness/exuberance and anger reactivity were significantly related to hyperactivity/impulsivity. Furthermore, working memory and reaction time variability in preschool were significantly related to academic achievement in late adolescence beyond the influence of early ADHD symptoms. These findings could suggest that it is possible to screen for early neuropsychological deficits and thereby identify children who are at risk of negative outcomes. Furthermore, our results suggest that interventions need to look beyond executive functioning deficits in ADHD and also target the role of emotional functioning and reaction time variability. The importance of including both the positive and negative aspects of emotional functioning and distinguishing between emotion regulation and emotional reactivity was also demonstrated. PMID:26212755

The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: A case study in Wrocław.

PubMed

Kamińska, Joanna A

2018-07-01

Random forests, an advanced data mining method, are used here to model the regression relationships between concentrations of the pollutants NO 2 , NO x and PM 2.5 , and nine variables describing meteorological conditions, temporal conditions and traffic flow. The study was based on hourly values of wind speed, wind direction, temperature, air pressure and relative humidity, temporal variables, and finally traffic flow, in the two years 2015 and 2016. An air quality measurement station was selected on a main road, located a short distance (40 m) from a large intersection equipped with a traffic flow measurement system. Nine different time subsets were defined, based among other things on the climatic conditions in Wrocław. An analysis was made of the fit of models created for those subsets, and of the importance of the predictors. Both the fit and the importance of particular predictors were found to be dependent on season. The best fit was obtained for models created for the six-month warm season (April-September) and for the summer season (June-August). The most important explanatory variable in the models of concentrations of nitrogen oxides was traffic flow, while in the case of PM 2.5 the most important were meteorological conditions, in particular temperature, wind speed and wind direction. Temporal variables (except for month in the case of PM 2.5 ) were found to have no significant effect on the concentrations of the studied pollutants. Copyright © 2018 Elsevier Ltd. All rights reserved.

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

PubMed

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

Evaluation of teledermatology adoption by health-care professionals using a modified Technology Acceptance Model.

PubMed

Orruño, Estibalitz; Gagnon, Marie Pierre; Asua, José; Ben Abdeljelil, Anis

2011-01-01

We examined the main factors affecting the intention of physicians to use teledermatology using a modified Technology Acceptance Model (TAM). The investigation was carried out during a teledermatology pilot study conducted in Spain. A total of 276 questionnaires were sent to physicians by email and 171 responded (62%). Cronbach's alpha was acceptably high for all constructs. Theoretical variables were well correlated with each other and with the dependent variable (Intention to Use). Logistic regression indicated that the original TAM model was good at predicting physicians' intention to use teledermatology and that the variables Perceived Usefulness and Perceived Ease of Use were both significant (odds ratios of 8.4 and 7.4, respectively). When other theoretical variables were added, the model was still significant and it also became more powerful. However, the only significant predictor in the modified model was Facilitators with an odds ratio of 9.9. Thus the TAM was good at predicting physicians' intention to use teledermatology. However, the most important variable was the perception of Facilitators to using the technology (e.g. infrastructure, training and support).

Predictive factors of difficulty in lower third molar extraction: A prospective cohort study

PubMed Central

Alvira-González, Joaquín; Valmaseda-Castellón, Eduard; Quesada-Gómez, Carmen; Gay-Escoda, Cosme

2017-01-01

Background Several publications have measured the difficulty of third molar removal, trying to establish the main risk factors, however several important preoperative and intraoperative variables are overlooked. Material and Methods A prospective cohort study comprising a total of 130 consecutive lower third molar extractions was performed. The outcome variables used to measure the difficulty of the extraction were operation time and a 100mm visual analogue scale filled by the surgeon at the end of the surgical procedure. The predictors were divided into 4 different groups (demographic, anatomic, radiographic and operative variables). A descriptive, bivariate and multivariate analysis of the data was performed. Results Patients’ weight, the presence of bulbous roots, the need to perform crown and root sectioning of the lower third molar and Pell and Gregory 123 classification significantly influenced both outcome variables (p< 0.05). Conclusions Certain anatomical, radiological and operative variables appear to be important factors in the assessment of surgical difficulty in the extraction of lower third molars. Key words:Third molar, surgical extraction, surgical difficulty. PMID:27918736

A variant of sparse partial least squares for variable selection and data exploration.

PubMed

Olson Hunt, Megan J; Weissfeld, Lisa; Boudreau, Robert M; Aizenstein, Howard; Newman, Anne B; Simonsick, Eleanor M; Van Domelen, Dane R; Thomas, Fridtjof; Yaffe, Kristine; Rosano, Caterina

2014-01-01

When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference. In response, an approach termed "all-possible" SPLS is proposed, which fits a SPLS model for all tuning parameter values across a set grid. Noted is the percentage of time a given predictor is chosen, as well as the average non-zero parameter estimate. Using a "large" number of multicollinear predictors, simulation confirmed variables not associated with the outcome were least likely to be chosen as sparsity increased across the grid of tuning parameters, while the opposite was true for those strongly associated. Lastly, variables with a weak association were chosen more often than those with no association, but less often than those with a strong relationship to the outcome. Similarly, predictors most strongly related to the outcome had the largest average parameter estimate magnitude, followed by those with a weak relationship, followed by those with no relationship. Across two independent studies regarding the relationship between volumetric MRI measures and a cognitive test score, this method confirmed a priori hypotheses about which brain regions would be selected most often and have the largest average parameter estimates. In conclusion, the percentage of time a predictor is chosen is a useful measure for ordering the strength of the relationship between the independent and dependent variables, serving as a form of inference. The average parameter estimates give further insight regarding the direction and strength of association. As a result, all-possible SPLS gives more information than the dichotomous output of traditional SPLS, making it useful when undertaking data exploration and hypothesis generation for a large number of potential predictors.

Latitude of residence and position in time zone are predictors of cancer incidence, cancer mortality, and life expectancy at birth.

PubMed

Borisenkov, Mikhail F

2011-03-01

According to the hypothesis of circadian disruption, external factors that disturb the function of the circadian system can raise the risk of malignant neoplasm and reduce life span. Recent work has shown that the functionality of the circadian system is dependent not only on latitude of residence but also on the region's position in the time zone. The purpose of the present research was to examine the influence of latitude and time zone on cancer incidence, cancer mortality, and life expectancy at birth. A stepwise multiple regression analysis was carried out on residents of 59 regions of the European part of the Russian Federation (EPRF) using age-standardized parameters (per 100,000) of cancer incidence (CI), cancer mortality (CM), and life expectancy at birth (LE, yrs) as dependent variables. The geographical coordinates (latitude and position in the time zone) of the regions were used as independent variables, controlling for the level of economic development in the regions. The same analysis was carried out for LE in 31 regions in China. Latitude was the strongest predictor of LE in the EPRF population; it explained 48% and 45% of the variability in LE of women and men, respectively. Position within the time zone accounted for an additional 4% and 3% variability of LE in women and men, respectively. The highest values for LE were observed in the southeast of the EPRF. In China, latitude was not a predictor of LE, whereas position in the time zone explained 15% and 18% of the LE variability in women and men, respectively. The highest values of LE were observed in the eastern regions of China. Both latitude and position within the time zone were predictors for CI and CM of the EPRF population. Latitude was the best predictor of stomach CI and CM; this predictor explained 46% and 50% of the variability, respectively. Position within the time zone was the best predictor of female breast CM; it explained 15% of the variability. In most cases, CI and CM increased with increasing latitude of residence, from the eastern to the western border of the time zone, and with increasing level of economic development within the region. The dependence of CI, CM, and LE on the geographical coordinates of residence is in agreement with the hypothesis of circadian disruption.

Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah

Treesearch

N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species...

Modeling the human development index and the percentage of poor people using quantile smoothing splines

NASA Astrophysics Data System (ADS)

Mulyani, Sri; Andriyana, Yudhie; Sudartianto

2017-03-01

Mean regression is a statistical method to explain the relationship between the response variable and the predictor variable based on the central tendency of the data (mean) of the response variable. The parameter estimation in mean regression (with Ordinary Least Square or OLS) generates a problem if we apply it to the data with a symmetric, fat-tailed, or containing outlier. Hence, an alternative method is necessary to be used to that kind of data, for example quantile regression method. The quantile regression is a robust technique to the outlier. This model can explain the relationship between the response variable and the predictor variable, not only on the central tendency of the data (median) but also on various quantile, in order to obtain complete information about that relationship. In this study, a quantile regression is developed with a nonparametric approach such as smoothing spline. Nonparametric approach is used if the prespecification model is difficult to determine, the relation between two variables follow the unknown function. We will apply that proposed method to poverty data. Here, we want to estimate the Percentage of Poor People as the response variable involving the Human Development Index (HDI) as the predictor variable.

Partnership-Level Analysis of African American Women's Risky Sexual Behavior in Main and Non-Main Partnerships.

PubMed

Broaddus, Michelle; Owczarzak, Jill; Pacella, Maria; Pinkerton, Steven; Wright, Cassandra

2016-12-01

The majority of research on risky sexual behavior in African American women has examined global associations between individual-level predictors and behavior. However, this method obscures the potentially significant impact of the specific relationship or relationship partner on risky sexual behavior. To address this gap, we conducted partnership-level analysis of risky sexual behavior among 718 African American women recruited from HIV counseling, testing, and referral sites in four states. Using mixed model regressions, we tested relationships between condomless vaginal intercourse with men and variables drawn from the Theory of Planned Behavior, Theory of Gender and Power, and previous research specifically on sexual risks among African American women. Significant associations with risky sexual behavior indicate the need for continued emphasis on condom attitudes, condom negotiation behaviors, and overcoming partner resistance to condoms within both main and non-main partnerships when implementing interventions designed to address HIV and sexually transmitted infection risks among African American women.

Can You Hack It? Validating Predictors for IT Boot Camps

NASA Astrophysics Data System (ADS)

Gear, Courtney C.

Given the large number of information technology jobs open and lack of qualified individuals to fill them, coding boot camps have sprung up in response to this skill gap by offering a specialized training program in an accelerated format. This fast growth has created a need to measure these training programs and understand their effectiveness. In the present study, a series of analyses examined whether specific or combinations of predictors were valid for training performance in this coding academy. Self-rated, daily efficacy scores were used as outcome variables of training success and correlation results showed a positive relationship with efficacy scores and the logic test score as a predictor. Exploratory analyses indicated a Dunning-Kruger effect where students with lower education levels experience higher overall mood during the training program. Limitations of the study included small sample size, severe range restriction in predictor scores, lack of variance in predictor scores, and low variability in training program success. These limitations made identifying jumps between training stages difficult to identify. By identifying which predictors matter most for each stage of skill acquisition, further research should consider more objective variables such as instructor scores which can serve as a guideline to better asses what stage learners join at and how to design curriculum and assignments accordingly (Honken, 2013).

Difficulties with Regression Analysis of Age-Adjusted Rates.

DTIC Science & Technology

1982-09-01

variables used in those analyses, such as death rates in various states, have been age adjusted, whereas the predictor variables have not been age adjusted...The use of crude state death rates as the outcome variable with crude covariates and age as predictors can avoid the problem, at least under some...should be regressed on age-adjusted exposure Z+B+ Although age-specific death rates , Yas+’ may be available, it is often difficult to obtain age

Student-Related Variables as Predictors of Academic Achievement among Some Undergraduate Psychology Students in Barbados

ERIC Educational Resources Information Center

Fayombo, Grace Adebisi

2011-01-01

This study examined some student-related variables (interest in higher education, psychological resilience and study habit) as predictors of academic achievement among 131 (M (mean) = 28.17, SD (standard deviation) = 1.61) first year psychology students in the Introduction to Developmental Psychology class in UWI (The University of the West…

Whistle-Blowing and the Code of Silence in Police Agencies: Policy and Structural Predictors

ERIC Educational Resources Information Center

Rothwell, Gary R.; Baldwin, J. Norman

2007-01-01

This article reports the findings from a study that investigates predictors of police willingness to blow the whistle and police frequency of blowing the whistle on seven forms of misconduct. It specifically investigates the capacity of nine policy and structural variables to predict whistle-blowing. The results indicate that two variables, a…

Item Structural Properties as Predictors of Item Difficulty and Item Association.

ERIC Educational Resources Information Center

Solano-Flores, Guillermo

1993-01-01

Studied the ability of logical test design (LTD) to predict student performance in reading Roman numerals for 211 sixth graders in Mexico City tested on Roman numeral items varying on LTD-related and non-LTD-related variables. The LTD-related variable item iterativity was found to be the best predictor of item difficulty. (SLD)

A Study of the Relationship between Parenting Stress and Spirituality among Mothers of Elementary Children in Selected Korean Churches

ERIC Educational Resources Information Center

Choi, Seong Ji

2012-01-01

Problem: The problem of this study was to determine the relationship between parenting stress and six specified predictor variables of spirituality among mothers of elementary children attending selected Korean Baptist churches located in the Dallas/Ft. Worth area. The specified predictor variables of spirituality were awareness, instability,…

Individualism-Collectivism, Social-Network Orientation, and Acculturation as Predictors of Attitudes toward Seeking Professional Psychological Help among Chinese Americans.

ERIC Educational Resources Information Center

Tata, Shiraz Piroshaw; Leong, Frederick T. L.

1994-01-01

Used several culturally based variables (individualism-collectivism, social support attitudes, acculturation) and gender to predict patterns of help-seeking attitudes among Chinese American college students (n=219). Each of the independent variables was found to be a significant predictor of attitudes toward seeking professional psychological…

Predicting Preservice Music Teachers' Performance Success in Instrumental Courses Using Self-Regulated Study Strategies and Predictor Variables

ERIC Educational Resources Information Center

Ersozlu, Zehra N.; Nietfeld, John L.; Huseynova, Lale

2017-01-01

The purpose of this study was to examine the extent to which self-regulated study strategies and predictor variables predict performance success in instrumental performance college courses. Preservice music teachers (N = 123) from a music education department in two state universities in Turkey completed the Music Self-Regulated Studying…

Predicting Middle School Students' Use of Web 2.0 Technologies out of School Using Home and School Technological Variables

ERIC Educational Resources Information Center

Hughes, Joan E.; Read, Michelle F.; Jones, Sara; Mahometa, Michael

2015-01-01

This study used multiple regression to identify predictors of middle school students' Web 2.0 activities out of school, a construct composed of 15 technology activities. Three middle schools participated, where sixth- and seventh-grade students completed a questionnaire. Independent predictor variables included three demographic and five computer…

Psychosocial Variables as Predictors of School Adjustment of Gifted Students with Learning Disabilities in Nigeria

ERIC Educational Resources Information Center

Fakolade, O. A.; Oyedokun, S. O.

2015-01-01

The paper considered several psychosocial variables as predictors of school adjustment of 40 gifted students with learning disabilities in Junior Secondary School in Ikenne Local Government Council Area of Ogun State, Nigeria. Purposeful random sampling was employed to select four schools from 13 junior secondary schools in the area, six…

Resolution of Unwanted Pregnancy during Adolescence through Abortion versus Childbirth: Individual and Family Predictors and Psychological Consequences

ERIC Educational Resources Information Center

Coleman, Priscilla K.

2006-01-01

Using data from the National Longitudinal Study of Adolescent Health, various demographic, psychological, educational, and family variables were explored as predictors of pregnancy resolution. Only 2 of the 17 variables examined were significantly associated with pregnancy resolution (risk-taking and the desire to leave home). After controlling…

Towards an automatic statistical model for seasonal precipitation prediction and its application to Central and South Asian headwater catchments

NASA Astrophysics Data System (ADS)

Gerlitz, Lars; Gafurov, Abror; Apel, Heiko; Unger-Sayesteh, Katy; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

Statistical climate forecast applications typically utilize a small set of large scale SST or climate indices, such as ENSO, PDO or AMO as predictor variables. If the predictive skill of these large scale modes is insufficient, specific predictor variables such as customized SST patterns are frequently included. Hence statistically based climate forecast models are either based on a fixed number of climate indices (and thus might not consider important predictor variables) or are highly site specific and barely transferable to other regions. With the aim of developing an operational seasonal forecast model, which is easily transferable to any region in the world, we present a generic data mining approach which automatically selects potential predictors from gridded SST observations and reanalysis derived large scale atmospheric circulation patterns and generates robust statistical relationships with posterior precipitation anomalies for user selected target regions. Potential predictor variables are derived by means of a cellwise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability based cluster analysis. Finally for every month and lead time, an individual random forest based forecast model is automatically calibrated and evaluated by means of the preliminary generated predictor variables. The model is exemplarily applied and evaluated for selected headwater catchments in Central and South Asia. Particularly the for winter and spring precipitation (which is associated with westerly disturbances in the entire target domain) the model shows solid results with correlation coefficients up to 0.7, although the variability of precipitation rates is highly underestimated. Likewise for the monsoonal precipitation amounts in the South Asian target areas a certain skill of the model could be detected. The skill of the model for the dry summer season in Central Asia and the transition seasons over South Asia is found to be low. A sensitivity analysis by means on well known climate indices reveals the major large scale controlling mechanisms for the seasonal precipitation climate of each target area. For the Central Asian target areas, both, the El Nino Southern Oscillation and the North Atlantic Oscillation are identified as important controlling factors for precipitation totals during moist spring season. Drought conditions are found to be triggered by a warm ENSO phase in combination with a positive phase of the NAO. For the monsoonal summer precipitation amounts over Southern Asia, the model suggests a distinct negative response to El Nino events.

Bullying among adolescents in North Cyprus and Turkey: testing a multifactor model.

PubMed

Bayraktar, Fatih

2012-04-01

Peer bullying has been studied since the 1970s. Therefore, a vast literature has accumulated about the various predictors of bullying. However, to date there has been no study which has combined individual-, peer-, parental-, teacher-, and school-related predictors of bullying within a model. In this sense, the main aim of this study was to test a multifactor model of bullying among adolescents in North Cyprus and Turkey. A total of 1,052 adolescents (554 girls, 498 boys) aged between 13 and 18 (M = 14.7, SD = 1.17) were recruited from North Cyprus and Turkey. Before testing the multifactor models, the measurement models were tested according to structural equation modeling propositions. Both models indicated that the psychological climate of the school, teacher attitudes within classroom, peer relationships, parental acceptance-rejection, and individual social competence factors had significant direct effects on bullying behaviors. Goodness-of-fit indexes indicated that the proposed multifactor model fitted both data well. The strongest predictors of bullying were the psychological climate of the school following individual social competence factors and teacher attitudes within classroom in both samples. All of the latent variables explained 44% and 51% of the variance in bullying in North Cyprus and Turkey, respectively.

Triggers of Eating in Everyday Life

PubMed Central

Tomiyama, A. Janet; Mann, Traci; Comer, Lisa

2009-01-01

Understanding the triggers of eating in everyday life is crucial for the creation of interventions to promote healthy eating and to prevent overeating. Here, the proximal predictors of eating are explored in a natural setting. Research from laboratory settings suggests that restrained eaters overeat after experiencing anxiety, distraction, and the presence of positive or negative moods, but not hunger; whereas the only factor that triggers eating in unrestrained eaters is hunger. In this study, 137 female participants reported hourly for two days on these potential predictors and their eating using electronic diaries, allowing us to establish the relationships between these factors while participants went about their normal daily activities. The main outcome variables were the number of servings eaten and whether or not food was eaten. Contrary to findings from laboratory settings, in everyday life restrained eaters (1) did not overeat in response to anxiety; (2) ate less in the presence of positive or negative moods; and (3) ate more in response to hunger. The relationships between these factors and eating among unrestrained eaters were closer to those found in laboratory settings. In conclusion, predictors of eating must be studied in everyday life to develop successful interventions. PMID:18773931

Teacher and child predictors of achieving IEP goals of children with autism.

PubMed

Ruble, Lisa; McGrew, John H

2013-12-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in The collaborative model for promoting competence and success for students with ASD. Springer, New York, 2012a) were analyzed. Predictors evaluated against child IEP goal attainment included child, teacher, intervention practice, and implementation practice variables based on an implementation science framework (Dunst and Trivette in J Soc Sci 8:143-148, 2012). Findings revealed one child (engagement), one teacher (exhaustion), two intervention quality (IEP quality for targeted and not targeted elements), and no implementation quality variables accounted for variance in child outcomes when analyzed separately. When the four significant variables were compared against each other in a single regression analysis, IEP quality accounted for one quarter of the variance in child outcomes.

Teacher and Child Predictors of Achieving IEP Goals of Children with Autism

PubMed Central

Ruble, Lisa; McGrew, John H.

2013-01-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in The collaborative model for promoting competence and success for students with ASD. Springer, New York, 2012a) were analyzed. Predictors evaluated against child IEP goal attainment included child, teacher, intervention practice, and implementation practice variables based on an implementation science framework (Dunst and Trivette in J Soc Sci 8:143–148, 2012). Findings revealed one child (engagement), one teacher (exhaustion), two intervention quality (IEP quality for targeted and not targeted elements), and no implementation quality variables accounted for variance in child outcomes when analyzed separately. When the four significant variables were compared against each other in a single regression analysis, IEP quality accounted for one quarter of the variance in child outcomes. PMID:23838728

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model

PubMed Central

2013-01-01

Background Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. Methods The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. Results The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance < mean) property. Our study also identify several significant predictors of the outcome variable namely mother’s education, father’s education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. Conclusions Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh. PMID:23297699

Spatial variability of Chinook salmon spawning distribution and habitat preferences

USGS Publications Warehouse

Cram, Jeremy M.; Torgersen, Christian E.; Klett, Ryan S.; Pess, George R.; May, Darran; Pearsons, Todd N.; Dittman, Andrew H.

2017-01-01

We investigated physical habitat conditions associated with the spawning sites of Chinook Salmon Oncorhynchus tshawytscha and the interannual consistency of spawning distribution across multiple spatial scales using a combination of spatially continuous and discrete sampling methods. We conducted a census of aquatic habitat in 76 km of the upper main-stem Yakima River in Washington and evaluated spawning site distribution using redd survey data from 2004 to 2008. Interannual reoccupation of spawning areas was high, ranging from an average Pearson’s correlation of 0.62 to 0.98 in channel subunits and 10-km reaches, respectively. Annual variance in the interannual correlation of spawning distribution was highest in channel units and subunits, but it was low at reach scales. In 13 of 15 models developed for individual years (2004–2008) and reach lengths (800 m, 3 km, 6 km), stream power and depth were the primary predictors of redd abundance. Multiple channels and overhead cover were patchy but were important secondary and tertiary predictors of reach-scale spawning site selection. Within channel units and subunits, pool tails and thermal variability, which may be associated with hyporheic exchange, were important predictors of spawning. We identified spawning habitat preferences within reaches and channel units that are relevant for salmonid habitat restoration planning. We also identified a threshold (i.e., 2-km reaches) beyond which interannual spawning distribution was markedly consistent, which may be informative for prioritizing habitat restoration or conservation. Management actions may be improved through enhanced understanding of spawning habitat preferences and the consistency with which Chinook Salmon reoccupy spawning areas at different spatial scales.

Droughts in Amazonia: Spatiotemporal Variability, Teleconnections, and Seasonal Predictions

NASA Astrophysics Data System (ADS)

Lima, Carlos H. R.; AghaKouchak, Amir

2017-12-01

Most Amazonia drought studies have focused on rainfall deficits and their impact on river discharges, while the analysis of other important driver variables, such as temperature and soil moisture, has attracted less attention. Here we try to better understand the spatiotemporal dynamics of Amazonia droughts and associated climate teleconnections as characterized by the Palmer Drought Severity Index (PDSI), which integrates information from rainfall deficit, temperature anomalies, and soil moisture capacity. The results reveal that Amazonia droughts are most related to one dominant pattern across the entire region, followed by two seesaw kind of patterns: north-south and east-west. The main two modes are correlated with sea surface temperature (SST) anomalies in the tropical Pacific and Atlantic oceans. The teleconnections associated with global SST are then used to build a seasonal forecast model for PDSI over Amazonia based on predictors obtained from a sparse canonical correlation analysis approach. A unique feature of the presented drought prediction method is using only a few number of predictors to avoid excessive noise in the predictor space. Cross-validated results show correlations between observed and predicted spatial average PDSI up to 0.60 and 0.45 for lead times of 5 and 9 months, respectively. To the best of our knowledge, this is the first study in the region that, based on cross-validation results, leads to appreciable forecast skills for lead times beyond 4 months. This is a step forward in better understanding the dynamics of Amazonia droughts and improving risk assessment and management, through improved drought forecasting.

Prevalence of positive ppd in a cohort of rheumatoid arthritis patients.

PubMed

Tamborenea, Maria Natalia; Tate, Guillermo; Mysler, Eduardo; Debonis, Jose; Schijedman, Adrian

2010-03-01

The main objective of this study is to determine the prevalence of positive and anergic tuberculin skin test (ppd) in a rheumatoid arthritis cohort of patients (RA) and assess the association among ppd results and clinical and treatment variables. Patients with RA diagnosis were included. The ppd was done by Mantoux method. Positive result was considered when indurations were equal or greater than 5 mm. Anergic reaction was defined when the indurations was 0 mm. We included 105 patients (N = 105). The prevalence of positive ppd was 12.4% (n = 13), while the 87.6% (n = 92) presented a negative result. The 69.5% (n = 73) of the population were anergic to ppd. Patients with negative result received higher steroids dosages than patients with positive ppd (p < 0.04). In the multivariable model, the steroids dosage was a significant and independent predictor of negative ppd (p = 0.021, OR 0.72, 95% CI 0.55-0.95). Anergic and non-anergic patients were separated in groups, and a new analysis was done. The higher dosage of methotrexate was associated to tuberculine anergy (p = 0.025). In the multivariable model, the methotrexate dosage was a significant and independent predictor of tuberculine anergy (p = 0.005, OR 1.14, 95% CIs 1.04-1.24). In conclusion, in our cohort, the prevalence of positive ppd was lower than others studies. Among analyzed variables, the high steroid dose was a significant and independent predictor of negative ppd. The methotrexate treatment and dose were associated with ppd anergy.

Prevalence and occupational predictors of psychological distress in the offshore petroleum industry: a prospective study.

PubMed

Nielsen, Morten Birkeland; Tvedt, Sturle Danielsen; Matthiesen, Stig Berge

2013-11-01

This study investigates the prevalence of psychological distress and stressors in the work environment as prospective predictors of distress, among employees in the offshore petroleum industry. Correlation and logistic regression analyses were employed to examine longitudinal relationships between stressors and distress in a randomly drawn sample of 741 employees from the Norwegian petroleum offshore industry. Time lag between baseline and follow-up was 6 months. Work environment stressors included safety factors, leadership, and job characteristics. The prevalence of psychological distress was 9 % at baseline and 8 % at follow-up. All investigated work environment factors correlated with subsequent distress. In bivariate logistic regression analyses, caseness of distress was predicted by baseline distress, near miss accidents, risk perception, poor safety climate, tyrannical leadership, laissez-faire leadership, job demands, and workplace bullying. After adjustment for baseline distress, control variables, and other predictors, laissez-faire leadership (OR = 1.69; 95 % CI: 1.12-2.54) and exposure to bullying (OR = 1.49; 95 % CI: 1.07-2.10) emerged as the most robust predictors of subsequent distress. The findings show that the prevalence of psychological distress is lower among offshore employees than in the general population. Although offshore workers operate in a physically challenging context, their mental health is mainly influenced by stressors in the psychosocial work environment. This highlights the importance of developing and implementing psychosocial safety interventions within the offshore industry.

Relation between troponin T concentration and mortality in patients presenting with an acute stroke: observational study

PubMed Central

James, P; Ellis, C J; Whitlock, R M L; McNeil, A R; Henley, J; Anderson, N E

2000-01-01

Objective To assess whether a raised serum troponin T concentration would be an independent predictor of death in patients with an acute ischaemic stroke. Design Observational study. Setting Auckland Hospital, Auckland, New Zealand. Subjects All 181 patients with an acute ischaemic stroke admitted over nine months in 1997-8, from a total of 8057 patients admitted to the acute medical service. Main outcome measures Blood samples for measuring troponin T concentration were collected 12-72 hours after admission; other variables previously associated with severity of stroke were also recorded and assessed as independent predictors of inpatient mortality. Results Troponin T concentration was raised (>0.1 μg/l) in 17% (30) of patients admitted with an acute ischaemic stroke. Thirty one patients died in hospital (12/30 (40%) patients with a raised troponin T concentration v 19/151 (13%) patients with a normal concentration (relative risk 3.2 (95% confidence 1.7 to 5.8; P=0.0025)). Of 17 possible predictors of death, assessed in a multivariate stepwise model, only a raised troponin T concentration (P=0.0002), age (P=0.0008), and an altered level of consciousness at presentation (P=0.0074) independently predicted an adverse outcome. Conclusions Serum troponin T concentration at hospital admission is a powerful predictor of mortality in patients admitted with an acute ischaemic stroke. PMID:10834890

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

PubMed Central

Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-01-01

Objective: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. Methods: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan–Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Results: Kaplan–Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Conclusion: Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. Advances in knowledge: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour. PMID:27319577

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.

PubMed

Molina, David; Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-07-04

The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T 1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Heterogeneity measures computed on the post-contrast pre-operative T 1 weighted MR images of patients with GBM are predictors of survival. Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour.

Multiple regression for physiological data analysis: the problem of multicollinearity.

PubMed

Slinker, B K; Glantz, S A

1985-07-01

Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

A Community Study of the Psychological Effects of the Omagh Car Bomb on Adults

PubMed Central

Duffy, Michael; Bolton, David; Gillespie, Kate; Ehlers, Anke; Clark, David M.

2013-01-01

Background The main aims of the study were to assess psychological morbidity among adults nine months after a car bomb explosion in the town of Omagh, Northern Ireland and to identify predictors of chronic posttraumatic stress disorder symptoms. Method A questionnaire was sent to all adults in households in The Omagh District Council area. The questionnaire comprised established predictors of PTSD (such as pre-trauma personal characteristics, type of exposure, initial emotional response and long-term adverse physical or financial problems), predictors derived from the Ehlers and Clark (2000) cognitive model, a measure of PTSD symptoms and the General Health Questionnaire. Results Among respondents (n = 3131) the highest rates of PTSD symptoms and probable casesness (58.5%) were observed among people who were present in the street when the bomb exploded but elevated rates were also observed in people who subsequently attended the scene (21.8% probable caseness) and among people for whom someone close died (11.9%). People with a near miss (left the scene before the explosion) did not show elevated rates. Exposure to the bombing increased PTSD symptoms to a greater extent than general psychiatric symptoms. Previously established predictors accounted for 42% of the variance in PTSD symptoms among people directly exposed to the bombing. Predictors derived from the cognitive model accounted for 63%. Conclusions High rates of chronic PTSD were observed in individuals exposed to the bombing. Psychological variables that are in principle amenable to treatment were the best predictors of PTSD symptoms. Teams planning treatment interventions for victims of future bombings and other traumas may wish to take these results into account. PMID:24098795

Predictors of Urgent Findings on Abdominopelvic CT in Patients with Crohn's Disease Presenting to the Emergency Department.

PubMed

Jung, Yoon Suk; Park, Dong Il; Hong, Sung Noh; Kim, Eun Ran; Kim, Young Ho; Cheon, Jae Hee; Eun, Chang Soo; Han, Dong Soo; Lee, Chang Kyun; Kim, Jae Hak; Huh, Kyu Chan; Yoon, Soon Man; Song, Hyun Joo; Shin, Jeong Eun; Jeon, Seong Ran

2015-04-01

Patients with Crohn's disease (CD) are frequently exposed to diagnostic radiation, mainly as a result of abdominopelvic computed tomography (APCT) examinations. However, there are limited data on the impact of APCT on clinical management in this population. To investigate clinical predictors of urgent findings on APCT in patients with CD who presented to the emergency department (ED). A retrospective study was performed among patients with CD presenting to 11 EDs with a gastrointestinal complaint. The primary outcome, OPAN (obstruction, perforation, abscess, or non-CD-related urgent findings), included new or worsening CD-related urgent findings or non-CD-related urgent findings that required urgent or emergency treatment. Variables with P < 0.1 in univariate analyses were included in a multivariable logistic regression model. Of the 266 APCTs performed, 103 (38.7 %) had OPAN and 113 (42.5 %) required changes in treatment plan. Stricturing or penetrating disease (odds ratio [OR] 2.72, 95 % confidence interval [CI] 1.21-6.13), heart rate >100 beats/min (OR 2.33, 95 % CI 1.10-4.93), leukocyte count >10,000/mm(3) (OR 4.38, 95 % CI 2.10-9.13), and CRP >2.5 mg/dL (OR 3.11, 95 % CI 1.23-7.86) were identified as the independent predictors of OPAN, whereas biologic agent use (OR 0.37; 95 % CI 0.15-0.90) was identified as the negative predictor in patients with CD. Only 39 % of the APCTs performed in the ED among patients with CD showed urgent findings. Stricturing or penetrating disease, tachycardia, leukocytosis, and high CRP level were predictors of urgent CT findings, while biologic agent use was a negative predictor. To reduce unnecessary radiation exposure, the selection process for CD patients referred for APCT must be improved.

Using social cognitive theory to explain discretionary, "leisure-time" physical exercise among high school students.

PubMed

Winters, Eric R; Petosa, Rick L; Charlton, Thomas E

2003-06-01

To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.

Prognostic value of echocardiographic indices of left atrial morphology and function in dogs with myxomatous mitral valve disease

PubMed Central

Romito, Giovanni; Guglielmini, Carlo; Diana, Alessia; Pelle, Nazzareno G.; Contiero, Barbara; Cipone, Mario

2018-01-01

Background The prognostic relevance of left atrial (LA) morphological and functional variables, including those derived from speckle tracking echocardiography (STE), has been little investigated in veterinary medicine. Objectives To assess the prognostic value of several echocardiographic variables, with a focus on LA morphological and functional variables in dogs with myxomatous mitral valve disease (MMVD). Animals One‐hundred and fifteen dogs of different breeds with MMVD. Methods Prospective cohort study. Conventional morphologic and echo‐Doppler variables, LA areas and volumes, and STE‐based LA strain analysis were performed in all dogs. A survival analysis was performed to test for the best echocardiographic predictors of cardiac‐related death. Results Most of the tested variables, including all LA STE‐derived variables were univariate predictors of cardiac death in Cox proportional hazard analysis. Because of strong correlation between many variables, only left atrium to aorta ratio (LA/Ao > 1.7), mitral valve E wave velocity (MV E vel > 1.3 m/s), LA maximal volume (LAVmax > 3.53 mL/kg), peak atrial longitudinal strain (PALS < 30%), and contraction strain index (CSI per 1% increase) were entered in the univariate analysis, and all were predictors of cardiac death. However, only the MV E vel (hazard ratio [HR], 4.45; confidence interval [CI], 1.76‐11.24; P < .001) and LAVmax (HR, 2.32; CI, 1.10‐4.89; P = .024) remained statistically significant in the multivariable analysis. Conclusions and Clinical Importance The assessment of LA dimension and function provides useful prognostic information in dogs with MMVD. Considering all the LA variables, LAVmax appears the strongest predictor of cardiac death, being superior to LA/Ao and STE‐derived variables. PMID:29572938

Multivariate outcome prediction in traumatic brain injury with focus on laboratory values.

PubMed

Nelson, David W; Rudehill, Anders; MacCallum, Robert M; Holst, Anders; Wanecek, Michael; Weitzberg, Eddie; Bellander, Bo-Michael

2012-11-20

Traumatic brain injury (TBI) is a major cause of morbidity and mortality. Identifying factors relevant to outcome can provide a better understanding of TBI pathophysiology, in addition to aiding prognostication. Many common laboratory variables have been related to outcome but may not be independent predictors in a multivariate setting. In this study, 757 patients were identified in the Karolinska TBI database who had retrievable early laboratory variables. These were analyzed towards a dichotomized Glasgow Outcome Scale (GOS) with logistic regression and relevance vector machines, a non-linear machine learning method, univariately and controlled for the known important predictors in TBI outcome: age, Glasgow Coma Score (GCS), pupil response, and computed tomography (CT) score. Accuracy was assessed with Nagelkerke's pseudo R². Of the 18 investigated laboratory variables, 15 were found significant (p<0.05) towards outcome in univariate analyses. In contrast, when adjusting for other predictors, few remained significant. Creatinine was found an independent predictor of TBI outcome. Glucose, albumin, and osmolarity levels were also identified as predictors, depending on analysis method. A worse outcome related to increasing osmolarity may warrant further study. Importantly, hemoglobin was not found significant when adjusted for post-resuscitation GCS as opposed to an admission GCS, and timing of GCS can thus have a major impact on conclusions. In total, laboratory variables added an additional 1.3-4.4% to pseudo R².

Health, lifestyle, belief and knowledge differences between two ethnic groups with specific reference to tobacco, diet and physical activity.

PubMed

Anthony, Denis; Baggott, Rob; Tanner, Judith; Jones, Kathryn; Evans, Hala; Perkins, Gill; Palmer, Hilary

2012-11-01

  To compare physical activity levels, body mass index, habitual diet, tobacco use and prevalence of non-communicable disease between the two ethnic groups and to identify predictors for differences between groups.   Tobacco use, poor diet and physical inactivity are major lifestyle risk factors for chronic cardiovascular diseases, certain cancers, diabetes and chronic lung diseases. There are higher risk and incidence of these diseases in some ethnic groups, for example Asians have higher incidence of diabetes.   Cross sectional survey.   Cross sectional survey of Asians of Indian descent and white British adults conducted between October-December 2009. Main outcome variables were lifestyle behaviours and BMI. Self-reported disease diagnosis was also collected. In a regression analysis, predictors of outcome variables were demographic variables and beliefs/attitudes/knowledge towards lifestyle behaviours.   Body mass index, tobacco use and non-communicable disease (except diabetes) were lower in Indians. Indians reported lower physical activity levels and greater salt use than Whites. Tobacco use was higher in Whites, but knowledge, attitudes and beliefs were similar between Whites and Indians.   Health risk behaviour and morbidity are different between the two ethnic groups. Gender, age, educational level, beliefs, attitudes and knowledge do not explain these differences. Health promotion that aims to improve knowledge will probably not work and innovative methods are needed to improve health in high risk groups. © 2012 Blackwell Publishing Ltd.

Differential Event Rates and Independent Predictors of Long-Term Major Cardiovascular Events and Death in 5795 Patients With Unprotected Left Main Coronary Artery Disease Treated With Stents, Bypass Surgery, or Medication: Insights From a Large International Multicenter Registry.

PubMed

Kang, Se Hun; Ahn, Jung-Min; Lee, Cheol Hyun; Lee, Pil Hyung; Kang, Soo-Jin; Lee, Seung-Whan; Kim, Young-Hak; Lee, Cheol Whan; Park, Seong-Wook; Park, Duk-Woo; Park, Seung-Jung

2017-07-01

Identifying predictive factors for major cardiovascular events and death in patients with unprotected left main coronary artery disease is of great clinical value for risk stratification and possible guidance for tailored preventive strategies. The Interventional Research Incorporation Society-Left MAIN Revascularization registry included 5795 patients with unprotected left main coronary artery disease (percutaneous coronary intervention, n=2850; coronary-artery bypass grafting, n=2337; medication alone, n=608). We analyzed the incidence and independent predictors of major adverse cardiac and cerebrovascular events (MACCE; a composite of death, MI, stroke, or repeat revascularization) and all-cause mortality in each treatment stratum. During follow-up (median, 4.3 years), the rates of MACCE and death were substantially higher in the medical group than in the percutaneous coronary intervention and coronary-artery bypass grafting groups ( P <0.001). In the percutaneous coronary intervention group, the 3 strongest predictors for MACCE were chronic renal failure, old age (≥65 years), and previous heart failure; those for all-cause mortality were chronic renal failure, old age, and low ejection fraction. In the coronary-artery bypass grafting group, old age, chronic renal failure, and low ejection fraction were the 3 strongest predictors of MACCE and death. In the medication group, old age, low ejection fraction, and diabetes mellitus were the 3 strongest predictors of MACCE and death. Among patients with unprotected left main coronary artery disease, the key clinical predictors for MACCE and death were generally similar regardless of index treatment. This study provides effect estimates for clinically relevant predictors of long-term clinical outcomes in real-world left main coronary artery patients, providing possible guidance for tailored preventive strategies. URL: https://clinicaltrials.gov. Unique identifier: NCT01341327. © 2017 American Heart Association, Inc.

Kindergarten predictors of second versus eighth grade reading comprehension impairments.

PubMed

Adlof, Suzanne M; Catts, Hugh W; Lee, Jaehoon

2010-01-01

Multiple studies have shown that kindergarten measures of phonological awareness and alphabet knowledge are good predictors of reading achievement in the primary grades. However, less attention has been given to the early predictors of later reading achievement. This study used a modified best-subsets variable-selection technique to examine kindergarten predictors of early versus later reading comprehension impairments. Participants included 433 children involved in a longitudinal study of language and reading development. The kindergarten test battery assessed various language skills in addition to phonological awareness, alphabet knowledge, naming speed, and nonverbal cognitive ability. Reading comprehension was assessed in second and eighth grades. Results indicated that different combinations of variables were required to optimally predict second versus eighth grade reading impairments. Although some variables effectively predicted reading impairments in both grades, their relative contributions shifted over time. These results are discussed in light of the changing nature of reading comprehension over time. Further research will help to improve the early identification of later reading disabilities.

Crop weather models of corn and soybeans for Agrophysical Units (APU's) in Iowa using monthly meteorological predictors

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.

First-Grade Cognitive Abilities as Long-Term Predictors of Reading Comprehension and Disability Status

PubMed Central

Fuchs, Douglas; Compton, Donald L.; Fuchs, Lynn S.; Bryant, V. Joan; Hamlett, Carol L.; Lambert, Warren

2012-01-01

In a sample of 195 first graders selected for poor reading performance, the authors explored four cognitive predictors of later reading comprehension and reading disability (RD) status. In fall of first grade, the authors measured the children’s phonological processing, rapid automatized naming (RAN), oral language comprehension, and nonverbal reasoning. Throughout first grade, they also modeled the students’ reading progress by means of weekly Word Identification Fluency (WIF) tests to derive December and May intercepts. The authors assessed their reading comprehension in the spring of Grades 1–5. With the four cognitive variables and the WIF December intercept as predictors, 50.3% of the variance in fifth-grade reading comprehension was explained: 52.1% of this 50.3% was unique to the cognitive variables, 13.1% to the WIF December intercept, and 34.8% was shared. All five predictors were statistically significant. The same four cognitive variables with the May (rather than December) WIF intercept produced a model that explained 62.1% of the variance. Of this amount, the cognitive variables and May WIF intercept accounted for 34.5% and 27.7%, respectively; they shared 37.8%. All predictors in this model were statistically significant except RAN. Logistic regression analyses indicated that the accuracy with which the cognitive variables predicted end-of-fifth-grade RD status was 73.9%. The May WIF intercept contributed reliably to this prediction; the December WIF intercept did not. Results are discussed in terms of a role for cognitive abilities in identifying, classifying, and instructing students with severe reading problems. PMID:22539057

First-grade cognitive abilities as long-term predictors of reading comprehension and disability status.

PubMed

Fuchs, Douglas; Compton, Donald L; Fuchs, Lynn S; Bryant, V Joan; Hamlett, Carol L; Lambert, Warren

2012-01-01

In a sample of 195 first graders selected for poor reading performance, the authors explored four cognitive predictors of later reading comprehension and reading disability (RD) status. In fall of first grade, the authors measured the children's phonological processing, rapid automatized naming (RAN), oral language comprehension, and nonverbal reasoning. Throughout first grade, they also modeled the students' reading progress by means of weekly Word Identification Fluency (WIF) tests to derive December and May intercepts. The authors assessed their reading comprehension in the spring of Grades 1-5. With the four cognitive variables and the WIF December intercept as predictors, 50.3% of the variance in fifth-grade reading comprehension was explained: 52.1% of this 50.3% was unique to the cognitive variables, 13.1% to the WIF December intercept, and 34.8% was shared. All five predictors were statistically significant. The same four cognitive variables with the May (rather than December) WIF intercept produced a model that explained 62.1% of the variance. Of this amount, the cognitive variables and May WIF intercept accounted for 34.5% and 27.7%, respectively; they shared 37.8%. All predictors in this model were statistically significant except RAN. Logistic regression analyses indicated that the accuracy with which the cognitive variables predicted end-of-fifth-grade RD status was 73.9%. The May WIF intercept contributed reliably to this prediction; the December WIF intercept did not. Results are discussed in terms of a role for cognitive abilities in identifying, classifying, and instructing students with severe reading problems.

Constrained Stochastic Extended Redundancy Analysis.

PubMed

DeSarbo, Wayne S; Hwang, Heungsun; Stadler Blank, Ashley; Kappe, Eelco

2015-06-01

We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA).

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Discrimination, acculturation and other predictors of depression among pregnant Hispanic women.

PubMed

Walker, Janiece L; Ruiz, R Jeanne; Chinn, Juanita J; Marti, Nathan; Ricks, Tiffany N

2012-01-01

The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. A prospective observational design was used. Central and Gulf coast areas of Texas in obstetrical offices. A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22-24 weeks gestation was used to collect data. The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy.

Predictors of Sustainability of Social Programs

ERIC Educational Resources Information Center

Savaya, Riki; Spiro, Shimon E.

2012-01-01

This article presents the findings of a large scale study that tested a comprehensive model of predictors of three manifestations of sustainability: continuation, institutionalization, and duration. Based on the literature the predictors were arrayed in four groups: variables pertaining to the project, the auspice organization, the community, and…

Estimating the Classification Efficiency of a Test Battery.

ERIC Educational Resources Information Center

De Corte, Wilfried

2000-01-01

Shows how a theorem proven by H. Brogden (1951, 1959) can be used to estimate the allocation average (a predictor based classification of a test battery) assuming that the predictor intercorrelations and validities are known and that the predictor variables have a joint multivariate normal distribution. (SLD)

Effects of Internship Predictors on Successful Field Experience.

ERIC Educational Resources Information Center

Beard, Fred; Morton, Linda

1999-01-01

Finds that a majority of advertising and public-relations interns found their internships successful. Indicates that successful internships depend on predictors given the least attention by school programs: quality of supervision was the most important single predictor variable, followed in importance by organizational practices/policies, positive…

Relations among Socioeconomic Status, Age, and Predictors of Phonological Awareness

ERIC Educational Resources Information Center

McDowell, Kimberly D.; Lonigan, Christopher J.; Goldstein, Howard

2007-01-01

Purpose: This study simultaneously examined predictors of phonological awareness within the framework of 2 theories: the phonological distinctness hypothesis and the lexical restructuring model. Additionally, age as a moderator of the relations between predictor variables and phonological awareness was examined. Method: This cross-sectional…

[Psychiatry of the life span?--relevance of age in psychiatric research].

PubMed

Sikorski, Claudia; Motzek, Tom

2010-11-01

The aim of this study was to determine to what extent studies published in two German journals took the age of their sample into consideration. All publications of the two journals were viewed. Only empirical research papers were included. It was then assessed whether they included information on age of the sample and, if that was the case, the studies were further categorized as only giving descriptive sample information, reporting age-specific results of dependent variables or using age as a predictor in regression analyses. Furthermore, the age range covered was assessed. 88 % of all studies included information on age. Of those, about half only provided descriptive information on the age of the study sample, while more than one third used the age variable as a predictor in multivariate models. Few studies reported age-specific outcomes. Main focus of research was on adult populations aged 18 to 65. Only few studies concentrated on children and adolescents. In light of demographic change and age specificity of psychological disorders, it will be necessary to further differentiate and report age-specific results of psychiatric research. A change in what is considered normative aging and developmental tasks for certain age groups calls for further research in those age groups. © Georg Thieme Verlag KG Stuttgart · New York.

Carrying capacity for species richness as context for conservation: a case study of North American birds

Treesearch

Andrew J. Hansen; Linda Bowers Phillips; Curtis H. Flather; Jim Robinson-Cox

2011-01-01

We evaluated the leading hypotheses on biophysical factors affecting species richness for Breeding Bird Survey routes from areas with little influence of human activities.We then derived a best model based on information theory, and used this model to extrapolate SK across North America based on the biophysical predictor variables. The predictor variables included the...

Cognitive and Affective Variables and Their Relationships to Performance in a Lotus 1-2-3 Class.

ERIC Educational Resources Information Center

Guster, Dennis; Batt, Richard

1989-01-01

Describes study of two-year college students that was conducted to determine whether variables that were predictors of success in a programing class were also predictors of success in a package-oriented computer class using Lotus 1-2-3. Diagraming skill, critical thinking ability, spatial discrimination, and test anxiety level were examined. (11…

A Study of the Relationship between Social Support and Clergy Family Stress among Korean-American Baptist Pastors and Their Wives

ERIC Educational Resources Information Center

Shin, Min Young

2012-01-01

Problem: The first problem of this study was to determine the relationship between the clergy family stress scores as measured by the Clergy Family Inventory (CFLI) and the specified predictor variables of social support among Korean-American Baptist pastors. The specified predictor variables included tangible support, appraisal support,…

Strategic Interviewing to Detect Deception: Cues to Deception across Repeated Interviews

PubMed Central

Masip, Jaume; Blandón-Gitlin, Iris; Martínez, Carmen; Herrero, Carmen; Ibabe, Izaskun

2016-01-01

Previous deception research on repeated interviews found that liars are not less consistent than truth tellers, presumably because liars use a “repeat strategy” to be consistent across interviews. The goal of this study was to design an interview procedure to overcome this strategy. Innocent participants (truth tellers) and guilty participants (liars) had to convince an interviewer that they had performed several innocent activities rather than committing a mock crime. The interview focused on the innocent activities (alibi), contained specific central and peripheral questions, and was repeated after 1 week without forewarning. Cognitive load was increased by asking participants to reply quickly. The liars’ answers in replying to both central and peripheral questions were significantly less accurate, less consistent, and more evasive than the truth tellers’ answers. Logistic regression analyses yielded classification rates ranging from around 70% (with consistency as the predictor variable), 85% (with evasive answers as the predictor variable), to over 90% (with an improved measure of consistency that incorporated evasive answers as the predictor variable, as well as with response accuracy as the predictor variable). These classification rates were higher than the interviewers’ accuracy rate (54%). PMID:27847493

Antecedents of narcotic use and addiction. A study of 898 Vietnam veterans.

PubMed

Helzer, J E; Robins, L N; Davis, D H

1976-02-01

Previous studies of predictors of narcotic abuse have been retrospective and based on samples of long-term addicts obtained from legal or medical channels. There are several methodological problems in this approach. The present study is an attempt to test certain alleged predictors of narcotic use in a cohort of 898 Vietnam veterans. The design overcomes several of the methodological weaknesses of previous studies. Eight variables which have been reported as predictors of drug use or addiction in the drug literature were inquired about during a personal interview which included the premilitary life of each subject. The antecedent variables were socioeconomic background, inner city residence, psychiatric illness, broken home, race, employment history, education and antisocial history. Using information obtained from interviews and military records, we then tested the predictive value of each of these antecedents by comparing narcotic used and addiction in Vietman and use after Vietnam in men differing with respect to each antecedent. Results indicate that some of the variables were very poor, and others very good predictors of the various levels of narcotic involvement. The predictive value and overall importance of each of the variables we tested are discussed.

Childhood Depression: Relation to Adaptive, Clinical and Predictor Variables

PubMed Central

Garaigordobil, Maite; Bernarás, Elena; Jaureguizar, Joana; Machimbarrena, Juan M.

2017-01-01

The study had two goals: (1) to explore the relations between self-assessed childhood depression and other adaptive and clinical variables (2) to identify predictor variables of childhood depression. Participants were 420 students aged 7–10 years old (53.3% boys, 46.7% girls). Results revealed: (1) positive correlations between depression and clinical maladjustment, school maladjustment, emotional symptoms, internalizing and externalizing problems, problem behaviors, emotional reactivity, and childhood stress; and (2) negative correlations between depression and personal adaptation, global self-concept, social skills, and resilience (sense of competence and affiliation). Linear regression analysis including the global dimensions revealed 4 predictors of childhood depression that explained 50.6% of the variance: high clinical maladjustment, low global self-concept, high level of stress, and poor social skills. However, upon introducing the sub-dimensions, 9 predictor variables emerged that explained 56.4% of the variance: many internalizing problems, low family self-concept, high anxiety, low responsibility, low personal self-assessment, high social stress, few aggressive behaviors toward peers, many health/psychosomatic problems, and external locus of control. The discussion addresses the importance of implementing prevention programs for childhood depression at early ages. PMID:28572787

Do Cognitive Models Help in Predicting the Severity of Posttraumatic Stress Disorder, Phobia, and Depression After Motor Vehicle Accidents? A Prospective Longitudinal Study

PubMed Central

Ehring, Thomas; Ehlers, Anke; Glucksman, Edward

2008-01-01

The study investigated the power of theoretically derived cognitive variables to predict posttraumatic stress disorder (PTSD), travel phobia, and depression following injury in a motor vehicle accident (MVA). MVA survivors (N = 147) were assessed at the emergency department on the day of their accident and 2 weeks, 1 month, 3 months, and 6 months later. Diagnoses were established with the Structured Clinical Interview for DSM–IV. Predictors included initial symptom severities; variables established as predictors of PTSD in E. J. Ozer, S. R. Best, T. L. Lipsey, and D. S. Weiss's (2003) meta-analysis; and variables derived from cognitive models of PTSD, phobia, and depression. Results of nonparametric multiple regression analyses showed that the cognitive variables predicted subsequent PTSD and depression severities over and above what could be predicted from initial symptom levels. They also showed greater predictive power than the established predictors, although the latter showed similar effect sizes as in the meta-analysis. In addition, the predictors derived from cognitive models of PTSD and depression were disorder-specific. The results support the role of cognitive factors in the maintenance of emotional disorders following trauma. PMID:18377119

Empirical downscaling of atmospheric key variables above a tropical glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, M.; Kaser, G.; Mölg, T.; Juen, I.; Wagnon, P.

2009-04-01

Glaciers in the outer tropical Cordillera Blanca (Peru, South America) are of major socio-economic importance, since glacier runoff represents the primary water source during the dry season, when little or no rainfall occurs. Due to their location at high elevations, the glaciers moreover provide important information about climate change in the tropical troposphere, where measurements are sparse. This study targets the local reconstruction of air temperature, specific humidity and wind speed above the surface of an outer tropical glacier from NCEP/NCAR reanalysis data as large scale predictors. Since a farther scope is to provide input data for process based glacier mass balance modelling, the reconstruction pursues a high temporal resolution. Hence an empirical downscaling scheme is developed, based on a few years' time series of hourly observations from automatic weather stations, located at the glacier Artesonraju and nearby moraines (Northern Cordillera Blanca). Principal component and multiple regression analyses are applied to define the appropriate spatial downscaling domain, suitable predictor variables, and the statistical transfer functions. The model performance is verified using an independent data set. The best predictors are lower tropospheric air temperature and specific humidity, at reanalysis model grid points that represent the Bolivian Altiplano, located in the South of the Cordillera Blanca. The developed downscaling model explaines a considerable portion (more than 60%) of the diurnal variance of air temperature and specific humidity at the moraine stations, and air temperature above the glacier surface. Specific humidity above the glacier surface, however, can be reconstructed well in the seasonal, but not in the required diurnal time resolution. Wind speed can only be poorly determined by the large scale predictors (r² lower than 0.3) at both sites. We assume a complex local interaction between valley and glacier wind system to be the main cause for the differences between model and observations.

Towards malaria risk prediction in Afghanistan using remote sensing.

PubMed

Adimi, Farida; Soebiyanto, Radina P; Safi, Najibullah; Kiang, Richard

2010-05-13

Malaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistan's diverse landscape and terrain contributes to the heterogeneous malaria prevalence across the country. Understanding the role of environmental variables on malaria transmission can further the effort for malaria control programme. Provincial malaria epidemiological data (2004-2007) collected by the health posts in 23 provinces were used in conjunction with space-borne observations from NASA satellites. Specifically, the environmental variables, including precipitation, temperature and vegetation index measured by the Tropical Rainfall Measuring Mission and the Moderate Resolution Imaging Spectoradiometer, were used. Regression techniques were employed to model malaria cases as a function of environmental predictors. The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation. Vegetation index, in general, is the strongest predictor, reflecting the fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time series are modelled well, with provincial average R2 of 0.845. Although the R2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases. The provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a cost-effective surveillance system that includes forecasting, early warning and detection. The predictive and early warning capabilities shown in this paper support this strategy.

Drug Concentration Thresholds Predictive of Therapy Failure and Death in Children With Tuberculosis: Bread Crumb Trails in Random Forests.

PubMed

Swaminathan, Soumya; Pasipanodya, Jotam G; Ramachandran, Geetha; Hemanth Kumar, A K; Srivastava, Shashikant; Deshpande, Devyani; Nuermberger, Eric; Gumbo, Tawanda

2016-11-01

 The role of drug concentrations in clinical outcomes in children with tuberculosis is unclear. Target concentrations for dose optimization are unknown.  Plasma drug concentrations measured in Indian children with tuberculosis were modeled using compartmental pharmacokinetic analyses. The children were followed until end of therapy to ascertain therapy failure or death. An ensemble of artificial intelligence algorithms, including random forests, was used to identify predictors of clinical outcome from among 30 clinical, laboratory, and pharmacokinetic variables.  Among the 143 children with known outcomes, there was high between-child variability of isoniazid, rifampin, and pyrazinamide concentrations: 110 (77%) completed therapy, 24 (17%) failed therapy, and 9 (6%) died. The main predictors of therapy failure or death were a pyrazinamide peak concentration <38.10 mg/L and rifampin peak concentration <3.01 mg/L. The relative risk of these poor outcomes below these peak concentration thresholds was 3.64 (95% confidence interval [CI], 2.28-5.83). Isoniazid had concentration-dependent antagonism with rifampin and pyrazinamide, with an adjusted odds ratio for therapy failure of 3.00 (95% CI, 2.08-4.33) in antagonism concentration range. In regard to death alone as an outcome, the same drug concentrations, plus z scores (indicators of malnutrition), and age <3 years, were highly ranked predictors. In children <3 years old, isoniazid 0- to 24-hour area under the concentration-time curve <11.95 mg/L × hour and/or rifampin peak <3.10 mg/L were the best predictors of therapy failure, with relative risk of 3.43 (95% CI, .99-11.82).  We have identified new antibiotic target concentrations, which are potential biomarkers associated with treatment failure and death in children with tuberculosis. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America.

A method for deterministic statistical downscaling of daily precipitation at a monsoonal site in Eastern China

NASA Astrophysics Data System (ADS)

Liu, Yonghe; Feng, Jinming; Liu, Xiu; Zhao, Yadi

2017-12-01

Statistical downscaling (SD) is a method that acquires the local information required for hydrological impact assessment from large-scale atmospheric variables. Very few statistical and deterministic downscaling models for daily precipitation have been conducted for local sites influenced by the East Asian monsoon. In this study, SD models were constructed by selecting the best predictors and using generalized linear models (GLMs) for Feixian, a site in the Yishu River Basin and Shandong Province. By calculating and mapping Spearman rank correlation coefficients between the gridded standardized values of five large-scale variables and daily observed precipitation, different cyclonic circulation patterns were found for monsoonal precipitation in summer (June-September) and winter (November-December and January-March); the values of the gridded boxes with the highest absolute correlations for observed precipitation were selected as predictors. Data for predictors and predictands covered the period 1979-2015, and different calibration and validation periods were divided when fitting and validating the models. Meanwhile, the bootstrap method was also used to fit the GLM. All the above thorough validations indicated that the models were robust and not sensitive to different samples or different periods. Pearson's correlations between downscaled and observed precipitation (logarithmically transformed) on a daily scale reached 0.54-0.57 in summer and 0.56-0.61 in winter, and the Nash-Sutcliffe efficiency between downscaled and observed precipitation reached 0.1 in summer and 0.41 in winter. The downscaled precipitation partially reflected exact variations in winter and main trends in summer for total interannual precipitation. For the number of wet days, both winter and summer models were able to reflect interannual variations. Other comparisons were also made in this study. These results demonstrated that when downscaling, it is appropriate to combine a correlation-based predictor selection across a spatial domain with GLM modeling.

A Socioecological Predication Model of Posttraumatic Stress Disorder in Low-Income, High-Risk Prenatal Native Hawaiian/Pacific Islander Women.

PubMed

Dodgson, Joan E; Oneha, Mary Frances; Choi, Myunghan

2014-01-01

Only recently has perinatal posttraumatic stress disorder (PTSD) been researched in any depth; however, the causes and consequences of this serious illness remain unclear. Most commonly, childbirth trauma and interpersonal violence have been reported as contributing factors. However, not all Native Hawaiian/Pacific Islander (NHPI) women who experience these events experience PTSD. The factors affecting PTSD are many and complex, intertwining individual, family, and community contexts. Using a socioecological framework, 3 levels of contextual variables were incorporated in this study (individual, family, and social/community). The purpose of this study was to determine the socioecological predictors associated with prenatal PTSD among NHPI. A case-control design was used to collect retrospective data about socioecological variables from medical record data. The sample was low-income, high-risk NHPI women receiving perinatal health care at a rural community health center in Hawaii who screened positive (n = 55) or negative (n = 91) for PTSD. Hierarchical logistic regression was conducted to determine socioecological predictors of positive PTSD screening. Although the majority of women (66.4%) experienced some form of interpersonal violence, a constellation of significant predictor variables from all 3 levels of the model were identified: depression (individual level), lack of family support and family stress (family level), and violence (social/community level). Each of the predictor variables has been identified by other researchers as significantly affecting perinatal PTSD. However, it is because these variables occur together that a more complex picture emerges, suggesting the importance of considering multiple variables in context when identifying and caring for these women. Although additional research is needed, it is possible that the significant predictor variables could be useful in identifying women who are at higher risk for PTSD in other similar populations. © 2014 by the American College of Nurse‐Midwives.

Predictors of Post-Operative Pain Relief in Patients with Chronic Pancreatitis Undergoing the Frey or Whipple Procedure.

PubMed

Sinha, Amitasha; Patel, Yuval A; Cruise, Michael; Matsukuma, Karen; Zaheer, Atif; Afghani, Elham; Yadav, Dhiraj; Makary, Martin A; Hirose, Kenzo; Andersen, Dana K; Singh, Vikesh K

2016-04-01

Post-operative pain relief in chronic pancreatitis (CP) is variable. Our objective was to determine clinical imaging or histopathologic predictor(s) of post-operative pain relief in CP patients undergoing the Whipple or Frey procedure. All patients who underwent a Whipple (n = 30) or Frey procedure (n = 30) for painful CP between January 2003 and September 2013 were evaluated. A toxic etiology was defined as a history of alcohol use and/or smoking. The pre-operative abdominal CT was evaluated for calcification(s) and main pancreatic duct (MPD) dilation (≥5 mm). The post-operative histopathology was evaluated for severe fibrosis. Clinical imaging and histopathologic features were evaluated as predictors of post-operative pain relief using univariable and multivariable regression analysis. A total of 60 patients (age 51.6 years, 53% males) were included in our study, of whom 42 (70%) reported post-operative pain relief over a mean follow-up of 1.1 years. There were 37 (62%) patients with toxic etiology, 36 (60%) each with calcification(s) and MPD dilation. A toxic etiology, calcifications, and severe fibrosis were associated with post-operative pain relief on univariable analysis (all p < 0.01). However, only a toxic etiology was an independent predictor of post-operative pain relief (OR 5.7, 95% CI 1.3, 24.5, p = 0.02). Only a toxic etiology, and not imaging or histopathologic findings, independently predicts post-operative pain relief in CP patients undergoing the Whipple or Frey procedure.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Political determinants of progress in the MDGs in Sub-Saharan Africa.

PubMed

Atti, Emma; Gulis, Gabriel

2017-11-01

Sub-Saharan Africa (SSA) lagged furthest behind in achieving targets for the millennium development goals (MDG). We investigate the hypothesis that its slow progress is influenced by political factors. Longitudinal data on three health MDG indicators: under-five mortality, maternal mortality and HIV prevalence rates were collated from 1990 to 2012 in 48 countries. Countries were grouped into geo-political and eco-political groups. Groupings were based on conflict trends in geographical regions and the International Monetary Fund's classification of SSA countries based on gross national income and development assistance respectively. Cumulative progress in each group was derived and main effects tested using ANOVA. Correlation analysis was conducted between political variables - POLITY 2, fragile state index (FSI), voter turnout rates, civil liberty scores (CLS) and the health variables. Our results suggest a significant main effect of eco-political and geo-political groups on some of the health variables. Political conflict as measured by FSI and political participation as measured by CLS were stronger predictors of slow progress in reducing under-five mortality rates and maternal mortality ratios. Our findings highlight the need for further research on political determinants of mortality in SSA. Cohesive effort should focus on strengthening countries' political, economic and social capacities in order to achieve sustainable goals beyond 2015.

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

PubMed Central

Esperón-Rodríguez, Manuel; Baumgartner, John B.; Beaumont, Linda J.

2017-01-01

Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. PMID:28652933

How Binary Skills Obscure the Transition from Non-Mastery to Mastery

ERIC Educational Resources Information Center

Karelitz, Tzur M.

2008-01-01

What is the nature of latent predictors that facilitate diagnostic classification? Rupp and Templin (this issue) suggest that these predictors should be multidimensional, categorical variables that can be combined in various ways. Diagnostic Classification Models (DCM) typically use multiple categorical predictors to classify respondents into…

Examining Preservice Science Teacher Understanding of Nature of Science: Discriminating Variables on the Aspects of Nature of Science

NASA Astrophysics Data System (ADS)

Jones, William I.

This study examined the understanding of nature of science among participants in their final year of a 4-year undergraduate teacher education program at a Midwest liberal arts university. The Logic Model Process was used as an integrative framework to focus the collection, organization, analysis, and interpretation of the data for the purpose of (1) describing participant understanding of NOS and (2) to identify participant characteristics and teacher education program features related to those understandings. The Views of Nature of Science Questionnaire form C (VNOS-C) was used to survey participant understanding of 7 target aspects of Nature of Science (NOS). A rubric was developed from a review of the literature to categorize and score participant understanding of the target aspects of NOS. Participants' high school and college transcripts, planning guides for their respective teacher education program majors, and science content and science teaching methods course syllabi were examined to identify and categorize participant characteristics and teacher education program features. The R software (R Project for Statistical Computing, 2010) was used to conduct an exploratory analysis to determine correlations of the antecedent and transaction predictor variables with participants' scores on the 7 target aspects of NOS. Fourteen participant characteristics and teacher education program features were moderately and significantly ( p < .01) correlated with participant scores on the target aspects of NOS. The 6 antecedent predictor variables were entered into multiple regression analyses to determine the best-fit model of antecedent predictor variables for each target NOS aspect. The transaction predictor variables were entered into separate multiple regression analyses to determine the best-fit model of transaction predictor variables for each target NOS aspect. Variables from the best-fit antecedent and best-fit transaction models for each target aspect of NOS were then combined. A regression analysis for each of the combined models was conducted to determine the relative effect of these variables on the target aspects of NOS. Findings from the multiple regression analyses revealed that each of the fourteen predictor variables was present in the best-fit model for at least 1 of the 7 target aspects of NOS. However, not all of the predictor variables were statistically significant (p < .007) in the models and their effect (beta) varied. Participants in the teacher education program who had higher ACT Math scores, completed more high school science credits, and were enrolled either in the Middle Childhood with a science concentration program major or in the Adolescent/Young Adult Science Education program major were more likely to have an informed understanding on each of the 7 target aspects of NOS. Analyses of the planning guides and the course syllabi in each teacher education program major revealed differences between the program majors that may account for the results.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Response variability in rapid automatized naming predicts reading comprehension

PubMed Central

Li, James J.; Cutting, Laurie E.; Ryan, Matthew; Zilioli, Monica; Denckla, Martha B.; Mahone, E. Mark

2009-01-01

A total of 37 children ages 8 to 14 years, screened for word-reading difficulties (23 with attention-deficit/hyperactivity disorder, ADHD; 14 controls) completed oral reading and rapid automatized naming (RAN) tests. RAN trials were segmented into pause and articulation time and intraindividual variability. There were no group differences on reading or RAN variables. Color- and letter-naming pause times and number-naming articulation time were significant predictors of reading fluency. In contrast, number and letter pause variability were predictors of comprehension. Results support analysis of subcomponents of RAN and add to literature emphasizing intraindividual variability as a marker for response preparation, which has relevance to reading comprehension. PMID:19221923

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

Predictors of self-reported negative mood following a depressive mood induction procedure across previously depressed, currently anxious, and control individuals.

PubMed

Scherrer, Martin C; Dobson, Keith S; Quigley, Leanne

2014-09-01

This study identified and examined a set of potential predictors of self-reported negative mood following a depressive mood induction procedure (MIP) in a sample of previously depressed, clinically anxious, and control participants. The examined predictor variables were selected on the basis of previous research and theories of depression, and included symptoms of depression and anxiety, negative and positive affect, negative and positive automatic thoughts, dysfunctional beliefs, rumination, self-concept, and occurrence and perceived unpleasantness of recent negative events. The sample consisted of 33 previously depressed, 22 currently anxious, and 26 non-clinical control participants, recruited from community sources. Participant group status was confirmed through structured diagnostic interviews. Participants completed the Velten negative self-statement MIP as well as self-report questionnaires of affective, cognitive, and psychosocial variables selected as potential predictors of mood change. Symptoms of anxiety were associated with increased self-reported negative mood shift following the MIP in previously depressed participants, but not clinically anxious or control participants. Increased occurrence of recent negative events was a marginally significant predictor of negative mood shift for the previously depressed participants only. None of the other examined variables was significant predictors of MIP response for any of the participant groups. These results identify factors that may increase susceptibility to negative mood states in previously depressed individuals, with implications for theory and prevention of relapse to depression. The findings also identify a number of affective, cognitive, and psychosocial variables that do not appear to influence mood change following a depressive MIP in previously depressed, currently anxious, and control individuals. Limitations of the study and directions for future research are discussed. Current anxiety symptomatology was a significant predictor and occurrence of recent negative events was a marginally significant predictor of greater negative mood shift following the depressive mood induction for previously depressed individuals. None of the examined variables predicted change in mood following the depressive mood induction for currently anxious or control individuals. These results suggest that anxiety symptoms and experience with negative events may increase risk for experiencing depressive mood states among individuals with a vulnerability to depression. The generalizability of the present results to individuals with comorbid depression and anxiety is limited. Future research employing appropriate statistical approaches for confirmatory research is needed to test and confirm the present results. © 2014 The British Psychological Society.

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

Optimising predictor domains for spatially coherent precipitation downscaling

NASA Astrophysics Data System (ADS)

Radanovics, S.; Vidal, J.-P.; Sauquet, E.; Ben Daoud, A.; Bontron, G.

2013-10-01

Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an ensemble of near-optimum predictor domains for a statistical downscaling method. This algorithm is applied to find five-member ensembles of near-optimum geopotential predictor domains for an analogue downscaling method for 608 individual target zones covering France. Results first show that very similar downscaling performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east-west band around 47° N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east-west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation downscaling over France, and therefore de facto ensuring the spatial coherence required for hydrological applications.

Motor recovery in post-stroke patients with aphasia: the role of specific linguistic abilities.

PubMed

Ginex, Valeria; Veronelli, Laura; Vanacore, Nicola; Lacorte, Eleonora; Monti, Alessia; Corbo, Massimo

2017-09-01

Aphasia is a serious consequence of stroke but aphasics patients have been routinely excluded from participation in some areas of stroke research. To assess the role of specific linguistic and non-verbal cognitive abilities on the short-term motor recovery of patients with aphasia due to first-ever stroke to the left hemisphere after an intensive rehabilitation treatment. 48 post-acute aphasic patients, who underwent physiotherapy and speech language therapy, were enrolled for this retrospective cohort-study. Four types of possible predictive factors were taken into account: clinical variables, functional status, language and non-verbal cognitive abilities. The motor FIM at discharge was used as the main dependent variable. Patients were classified as follows: 6 amnestic, 9 Broca's, 7 Wernicke's, and 26 global aphasics. Motor FIM at admission (p = 0.003) and at discharge (p = 0.042), all linguistic subtests of Aachener AphasieTest (p = 0.001), and non-verbal reasoning abilities (Raven's CPM, p = 0.006) resulted significantly different across different types of aphasia. Post-hoc analyses showed differences only between global aphasia and the other groups. A Multiple Linear Regression shows that admission motor FIM (p = 0.001) and Token test (p = 0.040), adjusted for clinical, language, and non-verbal reasoning variables, resulted as independent predictors of motor FIM scores at discharge, while Raven's CPM resulted close to statistical significance. Motor function at admission resulted as the variable that most affects the motor recovery of post-stroke patients with aphasia after rehabilitation. A linguistic test requiring also non-linguistic abilities, including attention and working memory (i.e. Token test) is an independent predictor as well.

Does Gender Inequity Increase the Risk of Intimate Partner Violence among Women? Evidence from a National Bangladeshi Sample

PubMed Central

Rahman, Mosiur; Nakamura, Keiko; Seino, Kaoruko; Kizuki, Masashi

2013-01-01

Background Evidence from developing countries regarding the association between gender inequity and intimate partner violence (IPV) victimization in women has been suggestive but inconclusive. Using nationally representative population-based data from Bangladesh, we examined the association between multidimensional aspects of gender inequity and the risk of IPV. Methods We used data from the 2007 Bangladesh Demographic Health Survey. The analyses were based on the responses of 4,467 married women. The main explanatory variable was gender inequity, which reflects the multidimensional aspects of women's autonomy and the relationship inequality between women and their partner. The experience of physical and/or sexual IPV was the main outcome variable of interest. Results Over 53% of married Bangladeshi women experienced physical and/or sexual violence from their husbands. In the adjusted models, women who had a higher level of autonomy (adjusted odds ratio [AOR] 0.48; 99% confidence interval [CI] 0.37–0.61), a particularly high level of economic-decision-making autonomy (AOR 0.12; 99% CI 0.08–0.17), and a higher level of non-supportive attitudes towards wife beating or raping (AOR 0.61; 99% CI 0.47–0.83) were less likely to report having experienced IPV. Education level, age at marriage, and occupational discrepancy between spouses were also found to be significant predictors of IPV. Conclusions In conclusion, dimensions of gender inequities were significant predictors of IPV among married women in Bangladesh. An investigation of the causal link between multidimensional aspects of gender inequity and IPV will be critical to developing interventions to reduce the risk of IPV and should be considered a public health research priority. PMID:24376536

The interaction rainfall vs. weight as determinant of total mercury concentration in fish from a tropical estuary.

PubMed

Barletta, M; Lucena, L R R; Costa, M F; Barbosa-Cintra, S C T; Cysneiros, F J A

2012-08-01

Mercury loads in tropical estuaries are largely controlled by the rainfall regime that may cause biodilution due to increased amounts of organic matter (both live and non-living) in the system. Top predators, as Trichiurus lepturus, reflect the changing mercury bioavailability situations in their muscle tissues. In this work two variables [fish weight (g) and monthly total rainfall (mm)] are presented as being important predictors of total mercury concentration (T-Hg) in fish muscle. These important explanatory variables were identified by a Weibull Regression model, which best fit the dataset. A predictive model using readily available variables as rainfall is important, and can be applied for human and ecological health assessments and decisions. The main contribution will be to further protect vulnerable groups as pregnant women and children. Nature conservation directives could also improve by considering monitoring sample designs that include this hypothesis, helping to establish complete and detailed mercury contamination scenarios. Copyright © 2012 Elsevier Ltd. All rights reserved.

Finite element techniques for the Navier-Stokes equations in the primitive variable formulation and the vorticity stream-function formulation

NASA Technical Reports Server (NTRS)

Glaisner, F.; Tezduyar, T. E.

1987-01-01

Finite element procedures for the Navier-Stokes equations in the primitive variable formulation and the vorticity stream-function formulation have been implemented. For both formulations, streamline-upwind/Petrov-Galerkin techniques are used for the discretization of the transport equations. The main problem associated with the vorticity stream-function formulation is the lack of boundary conditions for vorticity at solid surfaces. Here an implicit treatment of the vorticity at no-slip boundaries is incorporated in a predictor-multicorrector time integration scheme. For the primitive variable formulation, mixed finite-element approximations are used. A nine-node element and a four-node + bubble element have been implemented. The latter is shown to exhibit a checkerboard pressure mode and a numerical treatment for this spurious pressure mode is proposed. The two methods are compared from the points of view of simulating internal and external flows and the possibilities of extensions to three dimensions.

Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method

NASA Astrophysics Data System (ADS)

Mansuy, N. R.; Paré, D.; Thiffault, E.

2015-12-01

Large-scale mapping of soil properties is increasingly important for environmental resource management. Whileforested areas play critical environmental roles at local and global scales, forest soil maps are typically at lowresolution.The objective of this study was to generate continuous national maps of selected soil variables (C, N andsoil texture) for the Canadian managed forest landbase at 250 m resolution. We produced these maps using thekNN method with a training dataset of 538 ground-plots fromthe National Forest Inventory (NFI) across Canada,and 18 environmental predictor variables. The best predictor variables were selected (7 topographic and 5 climaticvariables) using the Least Absolute Shrinkage and Selection Operator method. On average, for all soil variables,topographic predictors explained 37% of the total variance versus 64% for the climatic predictors. Therelative root mean square error (RMSE%) calculated with the leave-one-out cross-validation method gave valuesranging between 22% and 99%, depending on the soil variables tested. RMSE values b 40% can be considered agood imputation in light of the low density of points used in this study. The study demonstrates strong capabilitiesfor mapping forest soil properties at 250m resolution, compared with the current Soil Landscape of CanadaSystem, which is largely oriented towards the agricultural landbase. The methodology used here can potentiallycontribute to the national and international need for spatially explicit soil information in resource managementscience.

Reliability, reference values and predictor variables of the ulnar sensory nerve in disease free adults.

PubMed

Ruediger, T M; Allison, S C; Moore, J M; Wainner, R S

2014-09-01

The purposes of this descriptive and exploratory study were to examine electrophysiological measures of ulnar sensory nerve function in disease free adults to determine reliability, determine reference values computed with appropriate statistical methods, and examine predictive ability of anthropometric variables. Antidromic sensory nerve conduction studies of the ulnar nerve using surface electrodes were performed on 100 volunteers. Reference values were computed from optimally transformed data. Reliability was computed from 30 subjects. Multiple linear regression models were constructed from four predictor variables. Reliability was greater than 0.85 for all paired measures. Responses were elicited in all subjects; reference values for sensory nerve action potential (SNAP) amplitude from above elbow stimulation are 3.3 μV and decrement across-elbow less than 46%. No single predictor variable accounted for more than 15% of the variance in the response. Electrophysiologic measures of the ulnar sensory nerve are reliable. Absent SNAP responses are inconsistent with disease free individuals. Reference values recommended in this report are based on appropriate transformations of non-normally distributed data. No strong statistical model of prediction could be derived from the limited set of predictor variables. Reliability analyses combined with relatively low level of measurement error suggest that ulnar sensory reference values may be used with confidence. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Self Efficacy and Some Demographic Variables as Predictors of Occupational Stress among Primary School Teachers in Delta State of Nigeria

ERIC Educational Resources Information Center

Akpochafo, G. O.

2014-01-01

This study investigated self efficacy and some demographic variables as predictors of occupational stress among primary school teachers in Delta State. Three hypotheses were formulated to guide the study. The study adopted a descriptive survey design that utilized an expost-facto research type. A sample of one hundred and twenty primary school…

Identification of Variables Associated with Group Separation in Descriptive Discriminant Analysis: Comparison of Methods for Interpreting Structure Coefficients

ERIC Educational Resources Information Center

Finch, Holmes

2010-01-01

Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…

Influence of Selected Personal Characteristics and County Situational Factors on Time Allocated to Dairy Subjects by Extension Agents in Selected Tennessee Counties.

ERIC Educational Resources Information Center

Northcutt, Sherwin Dean; And Others

The study deals with various predictors of time spent on dairy subjects by Extension agents and predictors of contacts made by agents with dairy clientele. Purposes were to determine the relationships, if any, between various independent variables and groups of independent variables (agents' background and training, county dairy situation, agents'…

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

PubMed

Jaime-González, Carlos; Acebes, Pablo; Mateos, Ana; Mezquida, Eduardo T

2017-01-01

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.

BPH progression: concept and key learning from MTOPS, ALTESS, COMBAT, and ALF-ONE.

PubMed

Roehrborn, Claus G

2008-03-01

Benign prostatic hyperplasia (BPH) represents a significant burden in ageing men due to frequently associated lower urinary tract symptoms (LUTS), which may impair quality of life. BPH is also a progressive disease, mainly characterized by a deterioration of LUTS over time, and in some patients by the occurrence of serious outcomes such as acute urinary retention (AUR) and need for BPH-related surgery. The goals of therapy for BPH are not only to improve bothersome LUTS but also to identify those patients at risk of unfavourable outcomes, to optimize their management. In selected patients, combination of an alpha(1)-blocker and a 5alpha-reductase inhibitor is the most effective form of BPH medical therapy to reduce the risk of clinical progression and relieve LUTS. Monotherapy also significantly reduces the risk of BPH clinical progression, mainly through a reduction of LUTS deterioration for alpha(1)-blockers while 5alpha-reductase inhibitors also reduce the risk of AUR and need for BPH-related surgery. Enlarged prostate and high serum prostate-specific antigen levels have been consistently found to be good clinical predictors of AUR and BPH-related surgery in longitudinal population-based studies and placebo arms of controlled studies. High post-void residual urine (PVR) is also associated with an increased risk of LUTS deterioration and should thus be reconsidered in practice as a predictor of BPH progression. Conversely, baseline LUTS severity and low peak flow rate, initially identified as predictors of unfavourable outcomes in community setting, behave paradoxically in controlled trials, probably as a consequence of strict inclusion criteria and subsequent regression to the mean and glass ceiling effects. Lastly, there is increasing evidence that dynamic variables, such as LUTS and PVR worsening, and lack of symptomatic improvement with alpha(1)-blockers are important predictors of future LUTS/BPH-related events, allowing better identification and management of patients at risk of BPH progression.

Psychosocial variables and time to injury onset: a hurdle regression analysis model.

PubMed

Sibold, Jeremy; Zizzi, Samuel

2012-01-01

Psychological variables have been shown to be related to athletic injury and time missed from participation in sport. We are unaware of any empirical examination of the influence of psychological variables on time to onset of injury. To examine the influence of orthopaedic and psychosocial variables on time to injury in college athletes. One hundred seventy-seven (men 5 116, women 5 61; age 5 19.45 6 1.39 years) National Collegiate Athletic Association Division II athletes. Hurdle regression analysis (HRA) was used to determine the influence of predictor variables on days to first injury. Worry (z = 2.98, P = .003), concentration disruption (z = -3.95, P < .001), and negative life-event stress (z = 5.02, P < .001) were robust predictors of days to injury. Orthopaedic risk score was not a predictor (z = 1.28, P = .20). These findings support previous research on the stress-injury relationship, and our group is the first to use HRA in athletic injury data. These data support the addition of psychological screening as part of preseason health examinations for collegiate athletes.

Heart rate variability: Pre-deployment predictor of post-deployment PTSD symptoms

PubMed Central

Pyne, Jeffrey M.; Constans, Joseph I.; Wiederhold, Mark D.; Gibson, Douglas P.; Kimbrell, Timothy; Kramer, Teresa L.; Pitcock, Jeffery A.; Han, Xiaotong; Williams, D. Keith; Chartrand, Don; Gevirtz, Richard N.; Spira, James; Wiederhold, Brenda K.; McCraty, Rollin; McCune, Thomas R.

2017-01-01

Heart rate variability is a physiological measure associated with autonomic nervous system activity. This study hypothesized that lower pre-deployment HRV would be associated with higher post-deployment post-traumatic stress disorder (PTSD) symptoms. Three-hundred-forty-three Army National Guard soldiers enrolled in the Warriors Achieving Resilience (WAR) study were analyzed. The primary outcome was PTSD symptom severity using the PTSD Checklist – Military version (PCL) measured at baseline, 3- and 12-month post-deployment. Heart rate variability predictor variables included: high frequency power (HF) and standard deviation of the normal cardiac inter-beat interval (SDNN). Generalized linear mixed models revealed that the pre-deployment PCL*ln(HF) interaction term was significant (p < 0.0001). Pre-deployment SDNN was not a significant predictor of post-deployment PCL. Covariates included age, pre-deployment PCL, race/ethnicity, marital status, tobacco use, childhood abuse, pre-deployment traumatic brain injury, and previous combat zone deployment. Pre-deployment heart rate variability predicts post-deployment PTSD symptoms in the context of higher pre-deployment PCL scores. PMID:27773678

Assessing the accuracy and stability of variable selection ...

EPA Pesticide Factsheets

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used, or stepwise procedures are employed which iteratively add/remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating dataset consists of the good/poor condition of n=1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p=212) of landscape features from the StreamCat dataset. Two types of RF models are compared: a full variable set model with all 212 predictors, and a reduced variable set model selected using a backwards elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors, and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substanti

Role of maternal health and infant inflammation in nutritional and neurodevelopmental outcomes of two-year-old Bangladeshi children.

PubMed

Donowitz, Jeffrey R; Cook, Heather; Alam, Masud; Tofail, Fahmida; Kabir, Mamun; Colgate, E Ross; Carmolli, Marya P; Kirkpatrick, Beth D; Nelson, Charles A; Ma, Jennie Z; Haque, Rashidul; Petri, William A

2018-05-01

Previous studies have shown maternal, inflammatory, and socioeconomic variables to be associated with growth and neurodevelopment in children from low-income countries. However, these outcomes are multifactorial and work describing which predictors most strongly influence them is lacking. We conducted a longitudinal study of Bangladeshi children from birth to two years to assess oral vaccine efficacy. Variables pertaining to maternal and perinatal health, socioeconomic status, early childhood enteric and systemic inflammation, and anthropometry were collected. Bayley-III neurodevelopmental assessment was conducted at two years. As a secondary analysis, we employed hierarchical cluster and random forests techniques to identify and rank which variables predicted growth and neurodevelopment. Cluster analysis demonstrated three distinct groups of predictors. Mother's weight and length-for-age Z score (LAZ) at enrollment were the strongest predictors of LAZ at two years. Cognitive score on Bayley-III was strongly predicted by weight-for-age (WAZ) at enrollment, income, and LAZ at enrollment. Top predictors of language included Rotavirus vaccination, plasma IL 5, sCD14, TNFα, mother's weight, and male gender. Motor function was best predicted by fecal calprotectin, WAZ at enrollment, fecal neopterin, and plasma CRP index. The strongest predictors for social-emotional score included plasma sCD14, income, WAZ at enrollment, and LAZ at enrollment. Based on the random forests' predictions, the estimated percentage of variation explained was 35.4% for LAZ at two years, 34.3% for ΔLAZ, 42.7% for cognitive score, 28.1% for language, 40.8% for motor, and 37.9% for social-emotional score. Birth anthropometry and maternal weight were strong predictors of growth while enteric and systemic inflammation had stronger associations with neurodevelopment. Birth anthropometry was a powerful predictor for all outcomes. These data suggest that further study of stunting in low-income settings should include variables relating to maternal and prenatal health, while investigations focusing on neurodevelopmental outcomes should additionally target causes of systemic and enteric inflammation.

Distribution and predictors of wing shape and size variability in three sister species of solitary bees

PubMed Central

Prunier, Jérôme G.; Dewulf, Alexandre; Kuhlmann, Michael; Michez, Denis

2017-01-01

Morphological traits can be highly variable over time in a particular geographical area. Different selective pressures shape those traits, which is crucial in evolutionary biology. Among these traits, insect wing morphometry has already been widely used to describe phenotypic variability at the inter-specific level. On the contrary, fewer studies have focused on intra-specific wing morphometric variability. Yet, such investigations are relevant to study potential convergences of variation that could highlight micro-evolutionary processes. The recent sampling and sequencing of three solitary bees of the genus Melitta across their entire species range provides an excellent opportunity to jointly analyse genetic and morphometric variability. In the present study, we first aim to analyse the spatial distribution of the wing shape and centroid size (used as a proxy for body size) variability. Secondly, we aim to test different potential predictors of this variability at both the intra- and inter-population levels, which includes genetic variability, but also geographic locations and distances, elevation, annual mean temperature and precipitation. The comparison of spatial distribution of intra-population morphometric diversity does not reveal any convergent pattern between species, thus undermining the assumption of a potential local and selective adaptation at the population level. Regarding intra-specific wing shape differentiation, our results reveal that some tested predictors, such as geographic and genetic distances, are associated with a significant correlation for some species. However, none of these predictors are systematically identified for the three species as an important factor that could explain the intra-specific morphometric variability. As a conclusion, for the three solitary bee species and at the scale of this study, our results clearly tend to discard the assumption of the existence of a common pattern of intra-specific signal/structure within the intra-specific wing shape and body size variability. PMID:28273178

Predictors of College Retention and Performance between Regular and Special Admissions

ERIC Educational Resources Information Center

Kim, Johyun

2015-01-01

This predictive correlational research study examined the effect of cognitive, demographic, and socioeconomic variables as predictors of regular and special admission students' first-year GPA and retention among a sample of 7,045 students. Findings indicated high school GPA and ACT scores were the two most effective predictors of regular and…

Examination of Predictors and Moderators for Self-Help Treatments of Binge-Eating Disorder

ERIC Educational Resources Information Center

Masheb, Robin M.; Grilo, Carlos M.

2008-01-01

Predictors and moderators of outcomes were examined in 75 overweight patients with binge-eating disorder (BED) who participated in a randomized clinical trial of guided self-help treatments. Age variables, psychiatric and personality disorder comorbidity, and clinical characteristics were tested as predictors and moderators of treatment outcomes.…

Adolescent Mothers and Depression: Predictors of Resilience and Risk through the Toddler Years

ERIC Educational Resources Information Center

Eshbaugh, Elaine M.

2006-01-01

This study investigated predictors of depression in 278 African-American, 206 European-American, and 122 Hispanic teen mothers approximately 36 months after the birth while controlling for depression 14 months after the birth. Predictor variables were age, ethnicity, mastery, knowledge of development, and parental distress. Younger teens were not…

Measuring Teacher Quality: Continuing the Search for Policy-Relevant Predictors of Student Achievement

ERIC Educational Resources Information Center

Knoeppel, Robert C.; Logan, Joyce P.; Keiser, Clare M.

2005-01-01

The purpose of this study was to investigate the potential viability of the variable certification by the National Board for Professional Teaching Standards (NBPTS) as a policy-relevant predictor of student achievement. Because research has identified the teacher as the most important school-related predictor of student achievement, more research…

Physical Activity and Perceived Self-Efficacy in Older Adults.

ERIC Educational Resources Information Center

Langan, Mary E.; Marotta, Sylvia A.

2000-01-01

The purpose of this study was to examine predictors of self-efficacy in older adults, with physical activity, age, and sex as the predictor variables. Regression analyses revealed physical activity to be the only statistically significant predictor of self-efficacy. These findings may be of interest to counselors who work with older people.…

Serial position effects are sensitive predictors of conversion from MCI to Alzheimer's disease dementia.

PubMed

Egli, Simone C; Beck, Irene R; Berres, Manfred; Foldi, Nancy S; Monsch, Andreas U; Sollberger, Marc

2014-10-01

It is unclear whether the predictive strength of established cognitive variables for progression to Alzheimer's disease (AD) dementia from mild cognitive impairment (MCI) varies depending on time to conversion. We investigated which cognitive variables were best predictors, and which of these variables remained predictive for patients with longer times to conversion. Seventy-five participants with MCI were assessed on measures of learning, memory, language, and executive function. Relative predictive strengths of these measures were analyzed using Cox regression models. Measures of word-list position-namely, serial position scores-together with Short Delay Free Recall of word-list learning best predicted conversion to AD dementia. However, only serial position scores predicted those participants with longer time to conversion. Results emphasize that the predictive strength of cognitive variables varies depending on time to conversion to dementia. Moreover, finer measures of learning captured by serial position scores were the most sensitive predictors of AD dementia. Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Prediction of problematic wine fermentations using artificial neural networks.

PubMed

Román, R César; Hernández, O Gonzalo; Urtubia, U Alejandra

2011-11-01

Artificial neural networks (ANNs) have been used for the recognition of non-linear patterns, a characteristic of bioprocesses like wine production. In this work, ANNs were tested to predict problems of wine fermentation. A database of about 20,000 data from industrial fermentations of Cabernet Sauvignon and 33 variables was used. Two different ways of inputting data into the model were studied, by points and by fermentation. Additionally, different sub-cases were studied by varying the predictor variables (total sugar, alcohol, glycerol, density, organic acids and nitrogen compounds) and the time of fermentation (72, 96 and 256 h). The input of data by fermentations gave better results than the input of data by points. In fact, it was possible to predict 100% of normal and problematic fermentations using three predictor variables: sugars, density and alcohol at 72 h (3 days). Overall, ANNs were capable of obtaining 80% of prediction using only one predictor variable at 72 h; however, it is recommended to add more fermentations to confirm this promising result.

Predictors of physical performance and functional ability in people 50+ with and without fibromyalgia.

PubMed

Jones, C Jessie; Rutledge, Dana N; Aquino, Jordan

2010-07-01

The purposes of this study were to determine whether people with and without fibromyalgia (FM) age 50 yr and above showed differences in physical performance and perceived functional ability and to determine whether age, gender, depression, and physical activity level altered the impact of FM status on these factors. Dependent variables included perceived function and 6 performance measures (multidimensional balance, aerobic endurance, overall functional mobility, lower body strength, and gait velocity-normal or fast). Independent (predictor) variables were FM status, age, gender, depression, and physical activity level. Results indicated significant differences between adults with and without FM on all physical-performance measures and perceived function. Linear-regression models showed that the contribution of significant predictors was in expected directions. All regression models were significant, accounting for 16-65% of variance in the dependent variables.

The role of lexical variables in the visual recognition of Chinese characters: A megastudy analysis.

PubMed

Sze, Wei Ping; Yap, Melvin J; Rickard Liow, Susan J

2015-01-01

Logographic Chinese orthography partially represents both phonology and semantics. By capturing the online processing of a large pool of Chinese characters, we were able to examine the relative salience of specific lexical variables when this nonalphabetic script is read. Using a sample of native mainland Chinese speakers (N = 35), lexical decision latencies for 1560 single characters were collated into a database, before the effects of a comprehensive range of variables were explored. Hierarchical regression analyses determined the unique item-level variance explained by orthographic (frequency, stroke count), semantic (age of learning, imageability, number of meanings), and phonological (consistency, phonological frequency) factors. Orthographic and semantic variables, respectively, accounted for more collective variance than the phonological variables. Significant main effects were further observed for the individual orthographic and semantic predictors. These results are consistent with the idea that skilled readers tend to rely on orthographic and semantic information when processing visually presented characters. This megastudy approach marks an important extension to existing work on Chinese character recognition, which hitherto has relied on factorial designs. Collectively, the findings reported here represent a useful set of empirical constraints for future computational models of character recognition.

Nitrogen deposition outweighs climatic variability in driving annual growth rate of canopy beech trees: Evidence from long-term growth reconstruction across a geographic gradient.

PubMed

Gentilesca, Tiziana; Rita, Angelo; Brunetti, Michele; Giammarchi, Francesco; Leonardi, Stefano; Magnani, Federico; van Noije, Twan; Tonon, Giustino; Borghetti, Marco

2018-07-01

In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160 years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (G V ) and height (G H ) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (i.e. mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO 2 concentration, including tree cambial age among growth predictors. Generalized additive models (GAM), linear mixed-effects models (LMM), and Bayesian regression models (BRM) were independently employed to assess explanatory variables. The main results from our study were as follows: (i) tree age was the main explanatory variable for long-term growth variability; (ii) GAM, LMM, and BRM results consistently indicated climatic variables and CO 2 effects on G V and G H were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; (iii) instead, significant positive nitrogen deposition (N dep ) effects were repeatedly observed in G V and G H ; the positive effects of N dep on canopy height growth rates, which tended to level off at N dep values greater than approximately 1.0 g m -2  y -1 , were interpreted as positive impacts on forest stand above-ground net productivity at the selected study sites. © 2018 John Wiley & Sons Ltd.

Social connectedness and self-esteem: predictors of resilience in mental health among maltreated homeless youth.

PubMed

Dang, Michelle T

2014-03-01

The purpose of this cross-sectional study was to explore social connectedness and self-esteem as predictors of resilience among homeless youth with histories of maltreatment. Connectedness variables included family connectedness, school connectedness, and affiliation with prosocial peers. The sample included 150 homeless youth aged 14 to 21 (mean age = 18 years) with the majority being an ethnic minority. Participants completed surveys using audio-CASI. Results revealed that youth with higher levels of social connectedness and self-esteem reported lower levels of psychological distress. When all predictor variables were controlled in the analysis, self-esteem remained significant for predicting better mental health.

Predictors of workplace violence among female sex workers in Tijuana, Mexico.

PubMed

Katsulis, Yasmina; Durfee, Alesha; Lopez, Vera; Robillard, Alyssa

2015-05-01

For sex workers, differences in rates of exposure to workplace violence are likely influenced by a variety of risk factors, including where one works and under what circumstances. Economic stressors, such as housing insecurity, may also increase the likelihood of exposure. Bivariate analyses demonstrate statistically significant associations between workplace violence and selected predictor variables, including age, drug use, exchanging sex for goods, soliciting clients outdoors, and experiencing housing insecurity. Multivariate regression analysis shows that after controlling for each of these variables in one model, only soliciting clients outdoors and housing insecurity emerge as statistically significant predictors for workplace violence. © The Author(s) 2014.

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

PubMed

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

Gender and health services use for a mental health problem

PubMed Central

Albizu-Garcia, Carmen E.; Alegría, Margarita; Freeman, Daniel; Vera, Mildred

2013-01-01

This study addresses whether the predictors of seeking help for a mental health problem differ by gender. An adaptation of Andersen’s Socio-Behavioral Model is used to identify factors associated with seeking care for a mental health problem. Data are derived from two waves of a community survey undertaken in 1992–1993 and in 1993–1994 among a probability sample of adults (18–69 years), residing in poor areas of Puerto Rico. Paired data was used from those individuals who responded to both waves of the survey for a total of 3221 community respondents. Responses from wave 1 were used to predict mental health service use in wave 2. The dependent variable is any use of outpatient mental health services in the year preceding the second interview. Logistic regression was used to model the effects of the independent variables on use. Males and females were found to use mental health services in nearly equal proportions. Gender did not have a main effect on use when other covariates were controlled. Significant interactions with gender were found for several predictors of use. The largest intervention effects were encountered in our need for care indicators. Having a definite need for mental health care and poor self-rated mental health had a larger effect on predicting use of services for men than they do for women. It is concluded that strategies designed to improve access to mental health services for minority disadvantaged populations ought to take into account gender differences in the predictors of use. Studies addressing factors influencing health services utilization for a mental health problem should consider stratifying their sample by gender. Future research should establish whether or not these findings are sustained with other population groups. PMID:11522134

Surgery confounds biology: the predictive value of stage-, grade- and prostate-specific antigen for recurrence after radical prostatectomy as a function of surgeon experience.

PubMed

Vickers, Andrew J; Savage, Caroline J; Bianco, Fernando J; Klein, Eric A; Kattan, Michael W; Secin, Fernando P; Guilloneau, Bertrand D; Scardino, Peter T

2011-04-01

Statistical models predicting cancer recurrence after surgery are based on biologic variables. We have shown previously that prostate cancer recurrence is related to both tumor biology and to surgical technique. Here, we evaluate the association between several biological predictors and biochemical recurrence across varying surgical experience. The study included two separate cohorts: 6,091 patients treated by open radical prostatectomy and an independent replication set of 2,298 patients treated laparoscopically. We calculated the odds ratios for biological predictors of biochemical recurrence-stage, Gleason grade and prostate-specific antigen (PSA)-and also the predictive accuracy (area under the curve, AUC) of a multivariable model, for subgroups of patients defined by the experience of their surgeon. In the open cohort, the odds ratio for Gleason score 8+ and advanced pathologic stage, though not PSA or Gleason score 7, increased dramatically when patients treated by surgeons with lower levels of experience were excluded (Gleason 8+: odds ratios 5.6 overall vs. 13.0 for patients treated by surgeons with 1,000+ prior cases; locally advanced disease: odds ratios of 6.6 vs. 12.2, respectively). The AUC of the multivariable model was 0.750 for patients treated by surgeons with 50 or fewer cases compared to 0.849 for patients treated by surgeons with 500 or more. Although predictiveness was lower overall for the independent replication set cohort, the main findings were replicated. Surgery confounds biology. Although our findings have no direct clinical implications, studies investigating biological variables as predictors of outcome after curative resection of cancer should consider the impact of surgeon-specific factors. Copyright © 2010 UICC.

Mandibular bone structure, bone mineral density, and clinical variables as fracture predictors: a 15-year follow-up of female patients in a dental clinic.

PubMed

Jonasson, Grethe; Billhult, Annika

2013-09-01

To compare three mandibular trabeculation evaluation methods, clinical variables, and osteoporosis as fracture predictors in women. One hundred and thirty-six female dental patients (35-94 years) answered a questionnaire in 1996 and 2011. Using intra-oral radiographs from 1996, five methods were compared as fracture predictors: (1) mandibular bone structure evaluated with a visual radiographic index, (2) bone texture, (3) size and number of intertrabecular spaces calculated with Jaw-X software, (4) fracture probability calculated with a fracture risk assessment tool (FRAX), and (5) osteoporosis diagnosis based on dual-energy-X-ray absorptiometry. Differences were assessed with the Mann-Whitney test and relative risk calculated. Previous fracture, gluco-corticoid medication, and bone texture were significant indicators of future and total (previous plus future) fracture. Osteoporosis diagnosis, sparse trabeculation, Jaw-X, and FRAX were significant predictors of total but not future fracture. Clinical and oral bone variables may identify individuals at greatest risk of fracture. Copyright © 2013 Elsevier Inc. All rights reserved.

Nonsuicidal self-injury in community adolescents: A systematic review of prospective predictors, mediators and moderators.

PubMed

Valencia-Agudo, Fatima; Burcher, Georgina Corbet; Ezpeleta, Lourdes; Kramer, Tami

2018-06-01

Nonsuicidal self-injury (NSSI) usually starts during adolescence and is associated with an array of psychological and psychiatric symptoms and future suicide attempts. The aim of this study is to determine prospective predictors, mediators and moderators of NSSI in adolescent community samples in order to target prevention and treatment strategies. Two team members searched online databases independently. Thirty-nine studies were included in the review. Several variables were seen to prospectively predict NSSI: female gender, family-related variables, peer victimisation, depression, previous NSSI and self-concept. Few studies analysed mediators and moderators. Low self-concept was highlighted as a relevant moderator in the relationship between intra/interpersonal variables and NSSI. Implications of these findings are discussed. The considerable heterogeneity between studies posed a limitation to determine robust predictors of NSSI. Further prospective studies using standardised measures of predictors and outcomes are needed to ascertain the most at risk individuals and develop prevention strategies. Copyright © 2018 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Predicting academic success among deaf college students.

PubMed

Convertino, Carol M; Marschark, Marc; Sapere, Patricia; Sarchet, Thomastine; Zupan, Megan

2009-01-01

For both practical and theoretical reasons, educators and educational researchers seek to determine predictors of academic success for students at different levels and from different populations. Studies involving hearing students at the postsecondary level have documented significant predictors of success relating to various demographic factors, school experience, and prior academic attainment. Studies involving deaf and hard-of-hearing students have focused primarily on younger students and variables such as degree of hearing loss, use of cochlear implants, educational placement, and communication factors-although these typically are considered only one or two at a time. The present investigation utilizes data from 10 previous experiments, all using the same paradigm, in an attempt to discern significant predictors of readiness for college (utilizing college entrance examination scores) and classroom learning at the college level (utilizing scores from tests in simulated classrooms). Academic preparation was a clear and consistent predictor in both domains, but the audiological and communication variables examined were not. Communication variables that were significant reflected benefits of language flexibility over skills in either spoken language or American Sign Language.

Ethnic Variables and Negative Life Events as Predictors of Depressive Symptoms and Suicidal Behaviors in Latino College Students: On the Centrality of "Receptivo a los Demás"

ERIC Educational Resources Information Center

Chang, Edward C.; Yu, Elizabeth A.; Yu, Tina; Kahle, Emma R.; Hernandez, Viviana; Kim, Jean M.; Jeglic, Elizabeth L.; Hirsch, Jameson K.

2016-01-01

In the present study, we examined ethnic variables (viz., multigroup ethnic identity and other group orientation) along with negative life events as predictors of depressive symptoms and suicidal behaviors in a sample of 156 (38 male and 118 female) Latino college students. Results of conducting hierarchical regression analyses indicated that the…

Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors

ERIC Educational Resources Information Center

Waller, Niels; Jones, Jeff

2011-01-01

We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…

The influence of physical and cognitive factors on reactive agility performance in men basketball players.

PubMed

Scanlan, Aaron; Humphries, Brendan; Tucker, Patrick S; Dalbo, Vincent

2014-01-01

This study explored the influence of physical and cognitive measures on reactive agility performance in basketball players. Twelve men basketball players performed multiple sprint, Change of Direction Speed Test, and Reactive Agility Test trials. Pearson's correlation analyses were used to determine relationships between the predictor variables (stature, mass, body composition, 5-m, 10-m and 20-m sprint times, peak speed, closed-skill agility time, response time and decision-making time) and reactive agility time (response variable). Simple and stepwise regression analyses determined the individual influence of each predictor variable and the best predictor model for reactive agility time. Morphological (r = -0.45 to 0.19), sprint (r = -0.40 to 0.41) and change-of-direction speed measures (r = 0.43) had small to moderate correlations with reactive agility time. Response time (r = 0.76, P = 0.004) and decision-making time (r = 0.58, P = 0.049) had large to very large relationships with reactive agility time. Response time was identified as the sole predictor variable for reactive agility time in the stepwise model (R(2) = 0.58, P = 0.004). In conclusion, cognitive measures had the greatest influence on reactive agility performance in men basketball players. These findings suggest reaction and decision-making drills should be incorporated in basketball training programmes.

Developing models to predict 8th grade students' achievement levels on timss science based on opportunity-to-learn variables

NASA Astrophysics Data System (ADS)

Whitford, Melinda M.

Science educational reforms have placed major emphasis on improving science classroom instruction and it is therefore vital to study opportunity-to-learn (OTL) variables related to student science learning experiences and teacher teaching practices. This study will identify relationships between OTL and student science achievement and will identify OTL predictors of students' attainment at various distinct achievement levels (low/intermediate/high/advanced). Specifically, the study (a) address limitations of previous studies by examining a large number of independent and control variables that may impact students' science achievement and (b) it will test hypotheses of structural relations to how the identified predictors and mediating factors impact on student achievement levels. The study will follow a multi-stage and integrated bottom-up and top-down approach to identify predictors of students' achievement levels on standardized tests using TIMSS 2011 dataset. Data mining or pattern recognition, a bottom-up approach will identify the most prevalent association patterns between different student achievement levels and variables related to student science learning experiences, teacher teaching practices and home and school environments. The second stage is a top-down approach, testing structural equation models of relations between the significant predictors and students' achievement levels according.

Predictors of Calcium Retention in Adolescent Boys

PubMed Central

Hill, Kathleen M.; Braun, Michelle; Kern, Mark; Martin, Berdine R.; Navalta, James W.; Sedlock, Darlene A.; McCabe, Linda; McCabe, George P.; Peacock, Munro; Weaver, Connie M.

2008-01-01

Context: The relationship between calcium (Ca) intake and Ca retention in adolescent boys was recently reported. Objective: This study evaluated the influence of Ca intake, serum hormone levels, biomarkers of bone metabolism, habitual physical activity, habitual Ca intake, and physical fitness on Ca retention in the same sample. Design: This study was a randomized, cross-over design that consisted of two 3-wk metabolic balance periods. Setting: The study took place on a university campus as a summer camp. Patients or Other Participants: A total of 31 American white boys (13–15 yr) participated in the study. Interventions: Each subject consumed a controlled diet with one of five high-low Ca intake pairs that ranged from 670-2003 mg/d, which was manipulated utilizing a fortified beverage. Main Outcome Measures: Ca retention was determined by Ca intake minus urinary and fecal Ca excretion during each balance period. Results: Ca intake explained 21.7% of the variability in Ca retention, and serum IGF-I concentration explained an additional 11.5%. Other serum hormone levels did not significantly add to the model. Biomarkers of bone metabolism, habitual physical activity, habitual Ca intake, and physical fitness were not significant predictors of Ca retention in adolescent boys. Conclusions: IGF-I, a regulator of growth during puberty, is an important predictor of Ca retention in adolescent boys. However, dietary Ca intake is an even greater predictor of Ca retention during this period of growth. PMID:18840643

Analysis of mechanical system of extreme rainfall events using backward tracking on information from the atmosphere circulation pattern for the 2000-2015 precipitation record in South Korea

NASA Astrophysics Data System (ADS)

So, B. J.; Kwon, H. H.

2016-12-01

A natural disaster for flood and drought have occurred in different parts of the world, and the disasters caused by significant extreme hydrological event in past years. Several studies examining stochastic analysis based nonstationary analysis reported for forecasting and outlook for extreme hydrological events, but there is the procedure to select predictor variables. In this study, we analyzed mechanical system of extreme rainfall events using backward tracking to determine the predictors of nonstationary considering the atmosphere circulation pattern. First, observed rainfall data of KMA (Korea Meteorological Administration) and ECMWF ERA-Interm data were constructed during the 2000-2015 period. Then, the 7day backward tracking were performed to establish the path of air mass using the LAGRANTO Tool considering the observed rainfall stations located in S. Korea as a starting point, The tracking information for rainfall event were clustered and then, we extracts the main influence factor based on the categorized tracking path considering to information of rainfall magnitude (e.g,, mega-sized, medium-sized). Finally, the nonstationary predictors are determined through a combination of factors affecting the nonstationary rainfall simulation techniques. The predictors based on a mechanical structure is expected to be able to respond to external factors such as climate change. In addition, this method can be used to determine the prediction factor in different geographical areas by different position.

Identifying health insurance predictors and the main reported reasons for being uninsured among US immigrants by legal authorization status.

PubMed

Vargas Bustamante, Arturo; Chen, Jie; Fang, Hai; Rizzo, John A; Ortega, Alexander N

2014-01-01

This study identifies differences in health insurance predictors and investigates the main reported reasons for lacking health insurance coverage between short-stayed (≤ 10 years) and long-stayed (>10 years) US immigrant adults to parse the possible consequences of the Affordable Care Act among immigrants by length of stay and documentation status. Foreign-born adults (18-64 years of age) from the 2009 California Health Interview Survey are the study population. Health insurance coverage predictors and the main reasons for being uninsured are compared across cohorts and by documentation status. A logistic-regression two-part multivariate model is used to adjust for confounding factors. The analyses determine that legal status is a strong health insurance predictor, particularly among long-stayed undocumented immigrants. Immigration status is the main reported reason for lacking health insurance. Although long-stayed documented immigrants are likely to benefit from the Affordable Care Act implementation, undocumented immigrants and short-stayed documented immigrants may encounter difficulties getting health insurance coverage. Copyright © 2013 John Wiley & Sons, Ltd.

Identifying health insurance predictors and the main reported reasons for being uninsured among US immigrants by legal authorization status

PubMed Central

Bustamante, Arturo Vargas; Chen, Jie; Fang, Hai; Rizzo, John A.; Ortega, Alexander N.

2014-01-01

SUMMARY This study identifies differences in health insurance predictors and investigates the main reported reasons for lacking health insurance coverage between short-stayed (≤ 10 years) and long-stayed (> 10 years) US immigrant adults to parse the possible consequences of the Affordable Care Act among immigrants by length of stay and documentation status. Foreign-born adults (18–64 years of age) from the 2009 California Health Interview Survey are the study population. Health insurance coverage predictors and the main reasons for being uninsured are compared across cohorts and by documentation status. A logistic-regression two-part multivariate model is used to adjust for confounding factors. The analyses determine that legal status is a strong health insurance predictor, particularly among long-stayed undocumented immigrants. Immigration status is the main reported reason for lacking health insurance. Although long-stayed documented immigrants are likely to benefit from the Affordable Care Act implementation, undocumented immigrants and short-stayed documented immigrants may encounter difficulties getting health insurance coverage. PMID:24038524

Childhood maltreatment history as a risk factor for sexual harassment among U.S. Army soldiers.

PubMed

Rosen, L N; Martin, L

1998-01-01

Four different types of childhood maltreatment were examined as predictors of unwanted sexual experiences and acknowledged sexual harassment among male and female active duty soldiers in the United States Army. Predictor variables included childhood sexual abuse, physical-emotional abuse, physical neglect, and emotional neglect. Three types of unwanted sexual experiences in the workplace were examined as outcome variables: gender harassment, unwanted sexual attention, and coercion. Both sexual and physical-emotional abuse during childhood were found to be predictors of unwanted sexual experiences and of acknowledged sexual harassment in the workplace. Among female soldiers, the most severe type of unwanted experience-coercion-was predicted only by childhood physical-emotional abuse. Among male soldiers childhood sexual abuse was the strongest predictor of coercion. A greater variety of types of childhood maltreatment predicted sexual harassment outcomes for male soldiers. Childhood maltreatment and adult sexual harassment were predictors of psychological well-being for soldiers of both genders.

Predictors of outcome for cognitive behaviour therapy in binge eating disorder.

PubMed

Lammers, Mirjam W; Vroling, Maartje S; Ouwens, Machteld A; Engels, Rutger C M E; van Strien, Tatjana

2015-05-01

The aim of this naturalistic study was to identify pretreatment predictors of response to cognitive behaviour therapy in treatment-seeking patients with binge eating disorder (BED; N = 304). Furthermore, we examined end-of-treatment factors that predict treatment outcome 6 months later (N = 190). We assessed eating disorder psychopathology, general psychopathology, personality characteristics and demographic variables using self-report questionnaires. Treatment outcome was measured using the bulimia subscale of the Eating Disorder Inventory 1. Predictors were determined using hierarchical linear regression analyses. Several variables significantly predicted outcome, four of which were found to be both baseline predictors of treatment outcome and end-of-treatment predictors of follow-up: Higher levels of drive for thinness, higher levels of interoceptive awareness, lower levels of binge eating pathology and, in women, lower levels of body dissatisfaction predicted better outcome in the short and longer term. Based on these results, several suggestions are made to improve treatment outcome for BED patients. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Discrimination, Acculturation and Other Predictors of Depression among Pregnant Hispanic Women

PubMed Central

Walker, Janiece L.; Ruiz, R. Jeanne; Chinn, Juanita J.; Marti, Nathan; Ricks, Tiffany N.

2012-01-01

Objective The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. Design A prospective observational design was used. Setting Central and Gulf coast areas of Texas in obstetrical offices. Participants A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22–24 weeks gestation was used to collect data. Measures The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Results Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. Conclusions It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy. PMID:23140083

Metacommunity ecology meets biogeography: effects of geographical region, spatial dynamics and environmental filtering on community structure in aquatic organisms.

PubMed

Heino, Jani; Soininen, Janne; Alahuhta, Janne; Lappalainen, Jyrki; Virtanen, Risto

2017-01-01

Metacommunity patterns and underlying processes in aquatic organisms have typically been studied within a drainage basin. We examined variation in the composition of six freshwater organismal groups across various drainage basins in Finland. We first modelled spatial structures within each drainage basin using Moran eigenvector maps. Second, we partitioned variation in community structure among three groups of predictors using constrained ordination: (1) local environmental variables, (2) spatial variables, and (3) dummy variable drainage basin identity. Third, we examined turnover and nestedness components of multiple-site beta diversity, and tested the best fit patterns of our datasets using the "elements of metacommunity structure" analysis. Our results showed that basin identity and local environmental variables were significant predictors of community structure, whereas within-basin spatial effects were typically negligible. In half of the organismal groups (diatoms, bryophytes, zooplankton), basin identity was a slightly better predictor of community structure than local environmental variables, whereas the opposite was true for the remaining three organismal groups (insects, macrophytes, fish). Both pure basin and local environmental fractions were, however, significant after accounting for the effects of the other predictor variable sets. All organismal groups exhibited high levels of beta diversity, which was mostly attributable to the turnover component. Our results showed consistent Clementsian-type metacommunity structures, suggesting that subgroups of species responded similarly to environmental factors or drainage basin limits. We conclude that aquatic communities across large scales are mostly determined by environmental and basin effects, which leads to high beta diversity and prevalence of Clementsian community types.

A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

NASA Astrophysics Data System (ADS)

Madonna, Erica; Ginsbourger, David; Martius, Olivia

2018-05-01

In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April-September) in the period 2002-2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

A New Analytic Framework for Moderation Analysis --- Moving Beyond Analytic Interactions

PubMed Central

Tang, Wan; Yu, Qin; Crits-Christoph, Paul; Tu, Xin M.

2009-01-01

Conceptually, a moderator is a variable that modifies the effect of a predictor on a response. Analytically, a common approach as used in most moderation analyses is to add analytic interactions involving the predictor and moderator in the form of cross-variable products and test the significance of such terms. The narrow scope of such a procedure is inconsistent with the broader conceptual definition of moderation, leading to confusion in interpretation of study findings. In this paper, we develop a new approach to the analytic procedure that is consistent with the concept of moderation. The proposed framework defines moderation as a process that modifies an existing relationship between the predictor and the outcome, rather than simply a test of a predictor by moderator interaction. The approach is illustrated with data from a real study. PMID:20161453

The nature and use of prediction skills in a biological computer simulation

NASA Astrophysics Data System (ADS)

Lavoie, Derrick R.; Good, Ron

The primary goal of this study was to examine the science process skill of prediction using qualitative research methodology. The think-aloud interview, modeled after Ericsson and Simon (1984), let to the identification of 63 program exploration and prediction behaviors.The performance of seven formal and seven concrete operational high-school biology students were videotaped during a three-phase learning sequence on water pollution. Subjects explored the effects of five independent variables on two dependent variables over time using a computer-simulation program. Predictions were made concerning the effect of the independent variables upon dependent variables through time. Subjects were identified according to initial knowledge of the subject matter and success at solving three selected prediction problems.Successful predictors generally had high initial knowledge of the subject matter and were formal operational. Unsuccessful predictors generally had low initial knowledge and were concrete operational. High initial knowledge seemed to be more important to predictive success than stage of Piagetian cognitive development.Successful prediction behaviors involved systematic manipulation of the independent variables, note taking, identification and use of appropriate independent-dependent variable relationships, high interest and motivation, and in general, higher-level thinking skills. Behaviors characteristic of unsuccessful predictors were nonsystematic manipulation of independent variables, lack of motivation and persistence, misconceptions, and the identification and use of inappropriate independent-dependent variable relationships.

Verification of relationships between anthropometric variables among ureteral stents recipients and ureteric lengths: a challenge for Vitruvian-da Vinci theory.

PubMed

Acelam, Philip A

2015-01-01

To determine and verify how anthropometric variables correlate to ureteric lengths and how well statistical models approximate the actual ureteric lengths. In this work, 129 charts of endourological patients (71 females and 58 males) were studied retrospectively. Data were gathered from various research centers from North and South America. Continuous data were studied using descriptive statistics. Anthropometric variables (age, body surface area, body weight, obesity, and stature) were utilized as predictors of ureteric lengths. Linear regressions and correlations were used for studying relationships between the predictors and the outcome variables (ureteric lengths); P-value was set at 0.05. To assess how well statistical models were capable of predicting the actual ureteric lengths, percentages (or ratios of matched to mismatched results) were employed. The results of the study show that anthropometric variables do not correlate well to ureteric lengths. Statistical models can partially estimate ureteric lengths. Out of the five anthropometric variables studied, three of them: body frame, stature, and weight, each with a P<0.0001, were significant. Two of the variables: age (R (2)=0.01; P=0.20) and obesity (R (2)=0.03; P=0.06), were found to be poor estimators of ureteric lengths. None of the predictors reached the expected (match:above:below) ratio of 1:0:0 to qualify as reliable predictors of ureteric lengths. There is not sufficient evidence to conclude that anthropometric variables can reliably predict ureteric lengths. These variables appear to lack adequate specificity as they failed to reach the expected (match:above:below) ratio of 1:0:0. Consequently, selections of ureteral stents continue to remain a challenge. However, height (R (2)=0.68) with the (match:above:below) ratio of 3:3:4 appears suited for use as estimator, but on the basis of decision rule. Additional research is recommended for stent improvements and ureteric length determinations.

Verification of relationships between anthropometric variables among ureteral stents recipients and ureteric lengths: a challenge for Vitruvian-da Vinci theory

PubMed Central

Acelam, Philip A

2015-01-01

Objective To determine and verify how anthropometric variables correlate to ureteric lengths and how well statistical models approximate the actual ureteric lengths. Materials and methods In this work, 129 charts of endourological patients (71 females and 58 males) were studied retrospectively. Data were gathered from various research centers from North and South America. Continuous data were studied using descriptive statistics. Anthropometric variables (age, body surface area, body weight, obesity, and stature) were utilized as predictors of ureteric lengths. Linear regressions and correlations were used for studying relationships between the predictors and the outcome variables (ureteric lengths); P-value was set at 0.05. To assess how well statistical models were capable of predicting the actual ureteric lengths, percentages (or ratios of matched to mismatched results) were employed. Results The results of the study show that anthropometric variables do not correlate well to ureteric lengths. Statistical models can partially estimate ureteric lengths. Out of the five anthropometric variables studied, three of them: body frame, stature, and weight, each with a P<0.0001, were significant. Two of the variables: age (R2=0.01; P=0.20) and obesity (R2=0.03; P=0.06), were found to be poor estimators of ureteric lengths. None of the predictors reached the expected (match:above:below) ratio of 1:0:0 to qualify as reliable predictors of ureteric lengths. Conclusion There is not sufficient evidence to conclude that anthropometric variables can reliably predict ureteric lengths. These variables appear to lack adequate specificity as they failed to reach the expected (match:above:below) ratio of 1:0:0. Consequently, selections of ureteral stents continue to remain a challenge. However, height (R2=0.68) with the (match:above:below) ratio of 3:3:4 appears suited for use as estimator, but on the basis of decision rule. Additional research is recommended for stent improvements and ureteric length determinations. PMID:26317082

Prognostic value of echocardiographic indices of left atrial morphology and function in dogs with myxomatous mitral valve disease.

PubMed

Baron Toaldo, Marco; Romito, Giovanni; Guglielmini, Carlo; Diana, Alessia; Pelle, Nazzareno G; Contiero, Barbara; Cipone, Mario

2018-05-01

The prognostic relevance of left atrial (LA) morphological and functional variables, including those derived from speckle tracking echocardiography (STE), has been little investigated in veterinary medicine. To assess the prognostic value of several echocardiographic variables, with a focus on LA morphological and functional variables in dogs with myxomatous mitral valve disease (MMVD). One-hundred and fifteen dogs of different breeds with MMVD. Prospective cohort study. Conventional morphologic and echo-Doppler variables, LA areas and volumes, and STE-based LA strain analysis were performed in all dogs. A survival analysis was performed to test for the best echocardiographic predictors of cardiac-related death. Most of the tested variables, including all LA STE-derived variables were univariate predictors of cardiac death in Cox proportional hazard analysis. Because of strong correlation between many variables, only left atrium to aorta ratio (LA/Ao > 1.7), mitral valve E wave velocity (MV E vel > 1.3 m/s), LA maximal volume (LAVmax > 3.53 mL/kg), peak atrial longitudinal strain (PALS < 30%), and contraction strain index (CSI per 1% increase) were entered in the univariate analysis, and all were predictors of cardiac death. However, only the MV E vel (hazard ratio [HR], 4.45; confidence interval [CI], 1.76-11.24; P < .001) and LAVmax (HR, 2.32; CI, 1.10-4.89; P = .024) remained statistically significant in the multivariable analysis. The assessment of LA dimension and function provides useful prognostic information in dogs with MMVD. Considering all the LA variables, LAVmax appears the strongest predictor of cardiac death, being superior to LA/Ao and STE-derived variables. Copyright © 2018 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Prevalence and differential profile of patients with drug addiction problems who commit intimate partner violence.

PubMed

Arteaga, Alfonso; Fernández-Montalvo, Javier; López-Goñi, José J

2015-12-01

The objectives of this study were, first, to explore the prevalence of aggressors with lifetime intimate partner violence (IPV) among patients in the Proyecto Hombre of Navarra (Spain) addiction treatment programme; and second, to know the specific and differential characteristics of patients presenting IPV as aggressors. A sample of 162 patients (119 men and 43 women) was assessed. Data on socio-demographic and substance consumption characteristics, IPV variables, psychopathological symptoms, and personality variables were obtained. The profiles of patients in addiction treatment with and without a history of violence towards their partners were compared. The results showed that 33.6% of people in treatment for addiction had committed violence against their partners. This prevalence was significantly higher (χ(2)  = 15.6, p < .001) in women (63.3%) than in men (24.2%). In the 98.4% of the cases the IPV was bidirectional. Patients with a history of IPV perpetration showed greater severity in substance consumption variables, psychopathological symptoms, and personality traits. Gender, the family scale on the European version of the Addiction Severity Index (EuropASI), and the aggressive-sadistic scale on the Millon Clinical Multiaxial Inventory (MCMI-III) were the main variables related to the presence of IPV as aggressors. There was a differential profile in patients with IPV perpetration, showing more psychopathological and personality symptoms. Moreover, in this study being a woman was one of the main predictors of committing IPV. © American Academy of Addiction Psychiatry.

Hazardous Alcohol Drinking as Predictor of Smoking Relapse (3-, 6-, and 12-Months Follow-Up) by Gender.

PubMed

Rodríguez-Cano, Rubén; López-Durán, Ana; Martínez-Vispo, Carmela; Martínez, Úrsula; Fernández Del Río, Elena; Becoña, Elisardo

2016-12-01

Diverse studies have found a relation between alcohol consumption and smoking relapse. Few studies have analyzed the relation of smoking relapse with pretreatment alcohol consumption and gender differences. The main purpose of this study is to analyze the influence of alcohol consumption in smoking relapse over 12 months (3-, 6-, and 12-months follow-up) and to determine possible gender differences. The sample included 374 smokers who quit smoking by participating in a psychological smoking cessation treatment. We assessed hazardous pretreatment alcohol drinking (AUDIT), cigarette consumption (FTND; number of cigarettes) and sociodemographic variables. Higher scores on hazardous pretreatment alcohol drinking predict smoking relapse at 3-, 6-, and 12-months after smoking cessation. In males, higher scores on hazardous pretreatment alcohol drinking predict relapse at 6 and at 12 months. In females, higher scores on hazardous pretreatment alcohol drinking predict tobacco relapse at 3 months. Hazardous pretreatment alcohol drinking predicts relapse at all intervals after smoking cessation (3-, 6-, and 12-months follow-up). However, the influence of hazardous pretreatment alcohol drinking on smoking relapse differs as a function of gender, as it is a short-term predictor in women (3 months) and a long-term predictor in men (6 and 12 months). Copyright © 2016 Elsevier Inc. All rights reserved.

Post-traumatic stress disorder in a sample of Syrian refugees in Lebanon.

PubMed

Kazour, Francois; Zahreddine, Nada R; Maragel, Michel G; Almustafa, Mustafa A; Soufia, Michel; Haddad, Ramzi; Richa, Sami

2017-01-01

Lebanon is the main hosting country for the Syrian crisis, with more than one million Syrian refugees. The objective of this study was to determine the prevalence of post-traumatic stress disorder (PTSD), and identify its possible predictors, in a sample of Syrian refugees living in camps in Lebanon. We conducted a household survey on Syrian refugees between 18 and 65years old in 6 camps of the Central Bekaa region, using the Mini International Neuropsychiatric Interview (M.I.N.I.) as a diagnostic tool. Among the 452 respondents, we found a lifetime prevalence of PTSD of 35.4%, and a point prevalence of 27.2%. The lifetime prevalence of SUD was 1.99% and the point prevalence 0.66%. Multivariate logistic regression could not identify any predictor of current PTSD among a list of demographic variables, but identified the Syrian hometown as a significant predictor of lifetime PTSD (p=.013), with refugees from Aleppo having significantly more PTSD than those coming from Homs (adjusted OR 2.14, 95% CI [1.28, 3.56], p=.004). PTSD was a real mental health issue in our sample of adult Syrian refugees in Central Bekaa camps, unlike SUD. Copyright © 2016 Elsevier Inc. All rights reserved.

PCSK9 and lipoprotein (a) levels are two predictors of coronary artery calcification in asymptomatic patients with familial hypercholesterolemia.

PubMed

Alonso, Rodrigo; Mata, Pedro; Muñiz, Ovidio; Fuentes-Jimenez, Francisco; Díaz, Jose Luis; Zambón, Daniel; Tomás, Marta; Martin, Cesar; Moyon, Thomas; Croyal, Mikaël; Thedrez, Aurélie; Lambert, Gilles

2016-11-01

We aimed to assess whether elevated PCSK9 and lipoprotein (a) [Lp(a)] levels associate with coronary artery calcification (CAC), a good marker of atherosclerosis burden, in asymptomatic familial hypercholesterolemia. We selected 161 molecularly defined FH patients treated with stable doses of statins for more than a year. CAC was measured using the Agatston method and quantified as categorical variable. Fasting plasma samples were collected and analyzed for lipids and lipoproteins. PCSK9 was measured by ELISA, Lp(a) and apolipoprotein (a) concentrations by inmunoturbidimetry and LC-MS/MS, respectively. Circulating PCSK9 levels were significantly reduced in patients without CAC (n = 63), compared to those with CAC (n = 99). Patients with the highest CAC scores (above 100) had the highest levels of circulating PCSK9 and Lp(a). In multivariable regression analyses, the main predictors for a positive CAC score was age and sex followed by circulating PCSK9 and Lp(a) levels. In statin treated asymptomatic FH patients, elevated PCSK9 and Lp(a) levels are independently associated with the presence and severity of CAC, a good predictor of coronary artery disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Revisiting Factors Associated With Screen Time Media Use: A Structural Study Among School-Aged Adolescents.

PubMed

Ngantcha, Marcus; Janssen, Eric; Godeau, Emmanuelle; Ehlinger, Virginie; Le-Nezet, Olivier; Beck, François; Spilka, Stanislas

2018-06-01

Screen-based media overuse has been related to harmful consequences especially among children and adolescents. Given their complex interrelationships, predictors of screen time (ST) should be analyzed simultaneously rather than individually to avoid incomplete conclusions. Structural equation models were conducted to examine associations between media ST (television, video games, and computers) along with harmful consequences in adolescents' well-being, such as underweight and overweight, depression, and school failure. Predictors included individual (gender, age, and physical activity), family (structure and socioeconomic background), and substance use variables. We used the Health Behaviour in School-aged Children survey organized in 2014, including eighth- and ninth-grade students living in France (N = 3720). Students reported spending 3 hours per day in front of each media. Spending more than 2 hours behind each of those 3 media was associated with lower life satisfaction, less physical activity, active school bullying, and grade repetition. Socioeconomic status was the most important predictor of ST, whereas regular substance uses showed modest associations. The main implication of our findings is to sensitize parents and stakeholders about the limitation of ST, including their own use that adolescents are likely to mimic. Alternative measures such as off-line time should be encouraged.

Condom use among young women: Modeling the Theory of Gender and Power

PubMed Central

DePadilla, Lara; Windle, Michael; Wingood, Gina; Cooper, Hannah; DiClemente, Ralph

2012-01-01

Objective This study sought to articulate pathways between constructs from the Theory of Gender and Power (TGP) and their associations with sexual behavior. Design The data were collected pre-intervention during a randomized controlled HIV prevention trial. Participants were 701 sexually active, unmarried African-American females, aged 14–20, who were not pregnant, and were recruited from three health clinics in a southeastern U.S. city. Structural equation modeling was used for the analyses. Main Outcome Measure Self-reported condom use. Results Theoretical associations yielded a well-fitting structural model across initial and cross-validation samples. A significant amount of variance was explained for the variables of condom use (R2=.31, .18), partner communication (R2=.30, .26), substance use during sex (R2=.32, .51), and negative personal affect (R2=.36, .48). Partner communication (.35, .38) was the strongest predictor of condom use, negative personal affect (−.41, −.37) was the strongest predictor of partner communication, and physical risk (.54, .54) was the strongest predictor of negative personal affect. Conclusion This model provides evidence to support both direct and indirect associations between social and behavioral risk factors and condom use. Associations between TGP constructs and condom use can facilitate future development and analyses of interventions based on this theory. PMID:21553975

Predicting the admission into medical school of African American college students who have participated in summer academic enrichment programs.

PubMed

Hesser, A; Cregler, L L; Lewis, L

1998-02-01

To identify cognitive and noncognitive variables as predictors of the admission into medical school of African American college students who have participated in summer academic enrichment programs (SAEPs). The study sample comprised 309 African American college students who participated in SAEPs at the Medical College of Georgia School of Medicine from 1980 to 1989 and whose educational and occupational statuses were determined by follow-up tracking. A three-step logistic regression was used to analyze the data (with alpha = .05); the criterion variable was admission to medical school. The 17 predictor variables studied were one of two types, cognitive and noncognitive. The cognitive variables were (1) Scholastic Aptitude Test mathematics (SAT-M) score, (2) SAT verbal score, (3) college grade-point average (GPA), (4) college science GPA, (5) SAEP GPA, and (6) SAEP basic science GPA (BSGPA). The noncognitive variables were (1) gender, (2) highest college level at the time of the last SAEP application, (3) type of college attended (historically African American or predominately white), (4) number of SAEPs attended, (5) career aspiration (physician or another health science option) (6) parents who were professionals, (7) parents who were health care role models, (8) evidence of leadership, (9) evidence of community service, (10) evidence of special motivation, and (11) strength of letter of recommendation in the SAEP application. For each student the rating scores for the last four noncognitive variables were determined by averaging the ratings of two judges who reviewed relevant information in each student's file. In step 1, which explained 20% of the admission decision variance, SAT-M score, SAEP BSGPA, and college GPA were the three significant cognitive predictors identified. In step 2, which explained 31% of the variance, the three cognitive predictors identified in step 1 were joined by three noncognitive predictors: career aspiration, type of college, and number of SAEPs attended. In step 3, which explained 29% of the variance, two cognitive variables (SAT-M score and SAEP BSGPA) and two noncognitive variables (career aspiration and strength of recommendation letter) were identified. The results support the concept of using both cognitive and noncognitive variables when selecting African American students for pre-medical school SAEPs.

Predictor sort sampling and one-sided confidence bounds on quantiles

Treesearch

Steve Verrill; Victoria L. Herian; David W. Green

2002-01-01

Predictor sort experiments attempt to make use of the correlation between a predictor that can be measured prior to the start of an experiment and the response variable that we are investigating. Properly designed and analyzed, they can reduce necessary sample sizes, increase statistical power, and reduce the lengths of confidence intervals. However, if the non- random...

Investigation of Remedial Education Course Scores as a Predictor of Introduction-Level Course Performances

ERIC Educational Resources Information Center

Ulmer, Ward; Means, Darris R.; Cawthon, Tony W.; Kristensen, Sheryl A.

2016-01-01

This study explores whether performance in remedial English and remedial math is a predictor of success in a college-level introduction English or college-level math class; and whether demographic variables increase the likelihood of remedial English and remedial math as a predictor of success in a college-level introduction English or…

Who Is Retained in School, and When? Survival Analysis of Predictors of Grade Retention in Luxembourgish Secondary School

ERIC Educational Resources Information Center

Klapproth, Florian; Schaltz, Paule

2015-01-01

Based on a large longitudinal sample (N?=?9031) of Luxembourgish secondary school students, this study examined whether variables reflecting the sociodemographic background of the students (gender, nationality and socioeconomic status) as well as the school track proved to be predictors of grade retention. These possible predictors of grade…

Dispositional hope and life satisfaction among older adults attending lifelong learning programs.

PubMed

Oliver, A; Tomás, J M; Montoro-Rodriguez, J

2017-09-01

The aim of this study is to explore the indirect effects of dispositional hope in the life satisfaction of older adults attending a lifelong learning program at the University of Valencia, Spain. We examine the mediating impact of dispositional hope regarding its ability to impact life satisfaction while considering affective and confidant social support, perceived health and leisure activities, consciousness and spirituality as predictors. Analysis were based on survey data (response rate 77.4%) provided by 737 adults 55 years old or more (Mean age=65.41, SD=6.60; 69% woman). A structural model with latent variables was specified and estimated in Mplus. The results show the ability of just a few variables to sum up a reasonable model to apply to successful aging population. All these variables are correlated and significantly predict hope with the exception of health. The model additionally includes significant positive indirect effects from spirituality, affective support and consciousness on satisfaction. The model has a good fit in terms of both the measurement and structural model. Regarding predictive power, these comprehensive four main areas of successful aging account for 42% of hope and finally for one third of the life satisfaction variance. Results support the mediating role of dispositional hope on the life satisfaction among older adults attending lifelong learning programs. These findings also support the MacArthur model of successful aging adapted to older adults with high levels of functional, social and cognitive ability. Dispositional hope, perceived health, and social support were the strongest predictors of satisfaction with life. Copyright © 2017 Elsevier B.V. All rights reserved.

Climate and soil attributes determine plant species turnover in global drylands

PubMed Central

Maestre, Fernando T.; Gotelli, Nicholas J.; Quero, José L.; Delgado-Baquerizo, Manuel; Bowker, Matthew A.; Eldridge, David J.; Ochoa, Victoria; Gozalo, Beatriz; Valencia, Enrique; Berdugo, Miguel; Escolar, Cristina; García-Gómez, Miguel; Escudero, Adrián; Prina, Aníbal; Alfonso, Graciela; Arredondo, Tulio; Bran, Donaldo; Cabrera, Omar; Cea, Alex; Chaieb, Mohamed; Contreras, Jorge; Derak, Mchich; Espinosa, Carlos I.; Florentino, Adriana; Gaitán, Juan; Muro, Victoria García; Ghiloufi, Wahida; Gómez-González, Susana; Gutiérrez, Julio R.; Hernández, Rosa M.; Huber-Sannwald, Elisabeth; Jankju, Mohammad; Mau, Rebecca L.; Hughes, Frederic Mendes; Miriti, Maria; Monerris, Jorge; Muchane, Muchai; Naseri, Kamal; Pucheta, Eduardo; Ramírez-Collantes, David A.; Raveh, Eran; Romão, Roberto L.; Torres-Díaz, Cristian; Val, James; Veiga, José Pablo; Wang, Deli; Yuan, Xia; Zaady, Eli

2015-01-01

Aim Geographic, climatic, and soil factors are major drivers of plant beta diversity, but their importance for dryland plant communities is poorly known. This study aims to: i) characterize patterns of beta diversity in global drylands, ii) detect common environmental drivers of beta diversity, and iii) test for thresholds in environmental conditions driving potential shifts in plant species composition. Location 224 sites in diverse dryland plant communities from 22 geographical regions in six continents. Methods Beta diversity was quantified with four complementary measures: the percentage of singletons (species occurring at only one site), Whittake’s beta diversity (β(W)), a directional beta diversity metric based on the correlation in species occurrences among spatially contiguous sites (β(R2)), and a multivariate abundance-based metric (β(MV)). We used linear modelling to quantify the relationships between these metrics of beta diversity and geographic, climatic, and soil variables. Results Soil fertility and variability in temperature and rainfall, and to a lesser extent latitude, were the most important environmental predictors of beta diversity. Metrics related to species identity (percentage of singletons and β(W)) were most sensitive to soil fertility, whereas those metrics related to environmental gradients and abundance ((β(R2)) and β(MV)) were more associated with climate variability. Interactions among soil variables, climatic factors, and plant cover were not important determinants of beta diversity. Sites receiving less than 178 mm of annual rainfall differed sharply in species composition from more mesic sites (> 200 mm). Main conclusions Soil fertility and variability in temperature and rainfall are the most important environmental predictors of variation in plant beta diversity in global drylands. Our results suggest that those sites annually receiving ~ 178 mm of rainfall will be especially sensitive to future climate changes. These findings may help to define appropriate conservation strategies for mitigating effects of climate change on dryland vegetation. PMID:25914437

Accommodation and Health Costs of Deinstitutionalized People with Mental Illness Living in Residential Services in Brazil.

PubMed

Razzouk, Denise

2018-04-30

Health costs are the main hindrances for expanding community mental health services. Exploring patient profiles and cost predictors may be useful for optimising financial resources. However, the deinstitutionalisation process may burden health budgets in terms of supporting multiple community services based on varying levels of need. This study assessed accommodation and health service costs, quality of life and clinical and psychosocial profiles among individuals receiving mental healthcare through residential services. Specific accommodation cost predictors were also verified. Health costs were assessed from the perspective of a public health provider using a microcosting bottom-up approach at 20 residential services in São Paulo, Brazil. Instruments used to assess health costs and patient profiles included the Brazilian version of the Client Socio-demographic and Service Receipt Inventory (CSSRI), the Mini International Neuropsychiatric Interview (MINI), the Clinical Global Impression-Severity Scale (CGI-S), the Independent Living Skills Survey (ILLS), the Social Behaviour Scale (SBS) and the Quality of Life Scale (QLS). One hundred and forty-seven residents, predominantly experiencing psychotic disorders, were interviewed. The geographical region and length of time spent living in residential services or in a psychiatric hospital predicted 66% of the variance in accommodation costs. The CGI-S and ILLS scores and years of education explained 52.7% of the variance in quality of life. Accommodation costs were not driven by patient profile variables, while region and time spent in a hospital or in residential services were the main cost predictors. Semi-staffed homes may be an alternative for resource optimisation among individuals with mild impairment, particularly if strategies for psychosocial rehabilitation and improving quality of life are implemented.

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

NASA Astrophysics Data System (ADS)

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-01

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation

PubMed Central

MANEL, STÉPHANIE; GUGERLI, FELIX; THUILLER, WILFRIED; ALVAREZ, NADIR; LEGENDRE, PIERRE; HOLDEREGGER, ROLF; GIELLY, LUDOVIC; TABERLET, PIERRE

2014-01-01

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran’s eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps. PMID:22680783

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data.

PubMed

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-05

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

Methodological development for selection of significant predictors explaining fatal road accidents.

PubMed

Dadashova, Bahar; Arenas-Ramírez, Blanca; Mira-McWilliams, José; Aparicio-Izquierdo, Francisco

2016-05-01

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000-2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. Published by Elsevier Ltd.

Downscaling reanalysis data to high-resolution variables above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Mölg, Thomas; Marzeion, Ben; Kaser, Georg

2010-05-01

Recently initiated observation networks in the Cordillera Blanca provide temporally high-resolution, yet short-term atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly NCEP/NCAR reanalysis data to the local target variables, measured at the tropical glacier Artesonraju (Northern Cordillera Blanca). The approach is particular in the context of ESD for two reasons. First, the observational time series for model calibration are short (only about two years). Second, unlike most ESD studies in climate research, we focus on variables at a high temporal resolution (i.e., six-hourly values). Our target variables are two important drivers in the surface energy balance of tropical glaciers; air temperature and specific humidity. The selection of predictor fields from the reanalysis data is based on regression analyses and climatologic considerations. The ESD modelling procedure includes combined empirical orthogonal function and multiple regression analyses. Principal component screening is based on cross-validation using the Akaike Information Criterion as model selection criterion. Double cross-validation is applied for model evaluation. Potential autocorrelation in the time series is considered by defining the block length in the resampling procedure. Apart from the selection of predictor fields, the modelling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice by using both single- and mixed-field predictors of the variables air temperature (1000 hPa), specific humidity (1000 hPa), and zonal wind speed (500 hPa). The chosen downscaling domain ranges from 80 to 50 degrees west and from 0 to 20 degrees south. Statistical transfer functions are derived individually for different months and times of day (month/hour-models). The forecast skill of the month/hour-models largely depends on month and time of day, ranging from 0 to 0.8, but the mixed-field predictors generally perform better than the single-field predictors. At all time scales, the ESD model shows added value against two simple reference models; (i) the direct use of reanalysis grid point values, and (ii) mean diurnal and seasonal cycles over the calibration period. The ESD model forecast 1960 to 2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation, but is sensitive to the chosen predictor type. So far, we have not assessed the performance of NCEP/NCAR reanalysis data against other reanalysis products. The developed ESD model is computationally cheap and applicable wherever measurements are available for model calibration.

Meteorological Influences on the Seasonality of Lyme Disease in the United States

PubMed Central

Moore, Sean M.; Eisen, Rebecca J.; Monaghan, Andrew; Mead, Paul

2014-01-01

Lyme disease (Borrelia burgdorferi infection) is the most common vector-transmitted disease in the United States. The majority of human Lyme disease (LD) cases occur in the summer months, but the timing of the peak occurrence varies geographically and from year to year. We calculated the beginning, peak, end, and duration of the main LD season in 12 highly endemic states from 1992 to 2007 and then examined the association between the timing of these seasonal variables and several meteorological variables. An earlier beginning to the LD season was positively associated with higher cumulative growing degree days through Week 20, lower cumulative precipitation, a lower saturation deficit, and proximity to the Atlantic coast. The timing of the peak and duration of the LD season were also associated with cumulative growing degree days, saturation deficit, and cumulative precipitation, but no meteorological predictors adequately explained the timing of the end of the LD season. PMID:24470565

Parent-child attachment, academic performance and the process of high-school dropout: a narrative review.

PubMed

Ramsdal, Gro; Bergvik, Svein; Wynn, Rolf

2015-01-01

Poor academic performance is a strong predictor of school dropout. Researchers have tried to disentangle variables influencing academic performance. However, studies on preschool and early care variables are seldom examined when explaining the school dropout process. We reviewed the literature on the relationship between caregiver-child attachment and academic performance, including attachment studies from preschool years, seeking out potential contributions to academic performance and the dropout process. The review was organized according to a model of four main mediating hypotheses: the attachment-teaching hypothesis, the social network hypothesis, the attachment-cooperation hypothesis, and the attachment self-regulation hypothesis. The results of the review are summed up in a model. There is some support for all four hypotheses. The review indicates that attachment and early care contribute substantially to dropout and graduation processes. Mediation effects should be given far more attention in future research.

Personality, organizational stress, and attitudes toward work as prospective predictors of professional burnout in hospital nurses

PubMed Central

Hudek-Knežević, Jasna; Kalebić Maglica, Barbara; Krapić, Nada

2011-01-01

Aim To examine to what extent personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness), organizational stress, and attitudes toward work and interactions between personality and either organizational stress or attitudes toward work prospectively predict 3 components of burnout. Methods The study was carried out on 118 hospital nurses. Data were analyzed by a set of hierarchical regression analyses, in which personality traits, measures of organizational stress, and attitudes toward work, as well as interactions between personality and either organizational stress or attitudes toward work were included as predictors, while 3 indices of burnout were measured 4 years later as criteria variables. Results Personality traits proved to be significant but weak prospective predictors of burnout and as a group predicted only reduced professional efficacy (R2 = 0.10), with agreeableness being a single negative predictor. Organizational stress was positive, affective-normative commitment negative predictor, while continuance commitment was not related to any dimension of burnout. We found interactions between neuroticism as well as conscientiousness and organizational stress, measured as role conflict and work overload, on reduced professional efficacy (βNRCWO = -0.30; ßcRCWO = -0.26). We also found interactions between neuroticism and affective normative commitment (β = 0.24) and between openness and continuance commitment on reduced professional efficacy (β = -0.23), as well as interactions between conscientiousness and continuance commitment on exhaustion. Conclusion Although contextual variables were strong prospective predictors and personality traits weak predictors of burnout, the results suggested the importance of the interaction between personality and contextual variables in predicting burnout. PMID:21853549

Comparison of correlated correlations.

PubMed

Cohen, A

1989-12-01

We consider a problem where kappa highly correlated variables are available, each being a candidate for predicting a dependent variable. Only one of the kappa variables can be chosen as a predictor and the question is whether there are significant differences in the quality of the predictors. We review several tests derived previously and propose a method based on the bootstrap. The motivating medical problem was to predict 24 hour proteinuria by protein-creatinine ratio measured at either 08:00, 12:00 or 16:00. The tests which we discuss are illustrated by this example and compared using a small Monte Carlo study.

The behaviour of random forest permutation-based variable importance measures under predictor correlation.

PubMed

Nicodemus, Kristin K; Malley, James D; Strobl, Carolin; Ziegler, Andreas

2010-02-27

Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results. In the case when both predictor correlation was present and predictors were associated with the outcome (HA), the unconditional RF VIM attributed a higher share of importance to correlated predictors, while under the null hypothesis that no predictors are associated with the outcome (H0) the unconditional RF VIM was unbiased. Conditional VIMs showed a decrease in VIM values for correlated predictors versus the unconditional VIMs under HA and was unbiased under H0. Scaled VIMs were clearly biased under HA and H0. Unconditional unscaled VIMs are a computationally tractable choice for large datasets and are unbiased under the null hypothesis. Whether the observed increased VIMs for correlated predictors may be considered a "bias" - because they do not directly reflect the coefficients in the generating model - or if it is a beneficial attribute of these VIMs is dependent on the application. For example, in genetic association studies, where correlation between markers may help to localize the functionally relevant variant, the increased importance of correlated predictors may be an advantage. On the other hand, we show examples where this increased importance may result in spurious signals.

Predictor Variables of Developing Anterior Pituitary Deficiencies in a Group of Paediatric Patients with Central Diabetes Insipidus and Langerhans Cell Histiocytosis.

PubMed

Vaiani, Elisa; Malossetti, Carmen; Vega, Lina Margarita; Zubizarreta, Pedro; Braier, Jorge; Belgorosky, Alicia

2017-01-01

Langerhans cell histiocytosis (LCH) is a rare histiocytic disorder of unknown etiopathogenesis. Central diabetes insipidus (CDI) is the most frequent endocrine manifestation and is a known risk factor for the development of further anterior pituitary hormone deficiencies (APD). However, not all CDI patients develop APD, as observed during prolonged periods of follow-up. To find predictors of developing APD in LCH children with CDI followed in our institution. We retrospectively analysed 44 patients over a median period (quartiles) of 12.3 years (8.79-14.24). Patients were subdivided into group 1 and group 2, according to absence or presence of APD, respectively. The main variables studied were: (1) chronological age (CA) at LCH diagnosis, (2) the primary site of LCH at diagnosis: low risk (LR) and multisystemic risk organs, and (3) the presence of reactivation. Multivariate Cox regression analysis showed that APD was positively associated with CA at LCH diagnosis [relative risk (RR) 1.14, p < 0.01], the LR clinical form (RR 8.6, p < 0.03), and negatively associated with the presence of reactivations (RR 0.3, p < 0.01). Patients with older CA at LCH diagnosis, LR clinical forms, and fewer reactivation episodes might represent a subgroup of paediatric LCH CDI patients with a higher risk of developing APD. © 2016 S. Karger AG, Basel.

Variability in symptom expression among sexually abused girls: developing multivariate models.

PubMed

Spaccarelli, S; Fuchs, C

1997-03-01

Examined which of several apparent risk variables were predictors of internalizing and externalizing problems in 48 girls who were referred for therapy after disclosing sexual abuse. Specifically, the effects of abuse characteristics, support from nonoffending parents, victims' coping strategies, and victims' cognitive appraisals on symptomatology were assessed. As hypothesized, results indicated that internalizing and externalizing problems were associated with different sets of predictor variables. Victims' self-reports of depression and anxiety were related to lower perceived support from nonoffending parents, more use of cognitive avoidance coping, and more negative appraisals of the abuse. These results were partially replicated when using parent-report measures of depression, but were not replicated for parent reports of victim anxiety. Incest was the only variable that was significantly related to parent-reported anxiety. Parent-reported aggressive behaviors were predicted by level of abuse-related stress; and aggression, social problems, and sexual problems were all related to the tendency to cope by controlling others. Social problems were also related to coping by self-distraction. Regression analyses were done for each dependent variable to examine which predictors accounted for unique variance when controlling for other significant zero-order correlates. Implications of these results for understanding variability in symptom expression among sexual abuse victims are discussed.

School and Neighborhood Predictors of Physical Fitness in Elementary School Students.

PubMed

Kahan, David; McKenzie, Thomas L

2017-06-01

We assessed the associations of 5 school and 7 neighborhood variables with fifth-grade students achieving Healthy Fitness Zone (HFZ) or Needs Improvement-Health Risk (NI-HR) on aerobic capacity (AC) and body composition (BC) physical fitness components of the state-mandated FITNESSGRAM ® physical fitness test. Data for outcome (physical fitness) and predictor (school and neighborhood) variables were extracted from various databases (eg, Data Quest, Walk Score ® ) for 160 schools located in San Diego, California. Predictor variables that were at least moderately correlated (|r| ≥ .30) with ≥1 outcome variables in univariate analyses were retained for ordinary least squares regression analyses. The mean percentages of students achieving HFZ AC (65.7%) and BC (63.5%) were similar (t = 1.13, p = .26), while those for NI-HR zones were significantly different (AC = 6.0% vs BC = 18.6%; t = 12.60, p < .001). Correlations were greater in magnitude for school than neighborhood demographics and stronger for BC than AC. The school variables free/reduced-price lunch (negative) and math achievement (positive) predicted fitness scores. Among neighborhood variables, percent Hispanic predicted failure of meeting the HFZ BC criterion. Creating school and neighborhood environments conducive to promoting physical activity and improving fitness is warranted. © 2017, American School Health Association.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.

PubMed

Reid, Colleen E; Jerrett, Michael; Petersen, Maya L; Pfister, Gabriele G; Morefield, Philip E; Tager, Ira B; Raffuse, Sean M; Balmes, John R

2015-03-17

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis.

PubMed

Zador, Zsolt; Sperrin, Matthew; King, Andrew T

2016-01-01

Traumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance. We analyzed the corticosteroid randomization after significant head injury (CRASH) trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT) findings and Glasgow Outcome Scale (GCS) were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients). Predictions of clinical outcome (death or severe disability at 6 months) were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC) under the receiver operating curve (ROC) and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUCSP) and sensitivity (pAUCSE) intervals of 90-100%. Probabilistic associations were depicted using Bayesian networks. Complete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138-0.8336) for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUCSP = 0.6523, 95% CI: 0.6402-0.6641 and pAUCSE = 0.6332, 95% CI: 0.62-0.6477) compared to the complete models (pAUCSP = 0.6664, 95% CI: 0.6543-0.679, pAUCSE = 0.6436, 95% CI: 0.6289-0.6585, de Long p value 0.1165 and 0.3448 respectively). Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did. We demonstrate that importance based variable selection allows simplified predictive models to be created while maintaining prediction accuracy. Variable selection targeting specificity confirmed key components of clinical assessment in TBI whereas sensitivity based ranking suggested extracranial injury as one of the important predictors. These results help refine our approach to head injury assessment, decision-making and outcome prediction targeted at model sensitivity and specificity. Bayesian networks proved to be a comprehensive tool for depicting probabilistic associations for key predictors giving insight into why the simplified model has maintained accuracy.

Tumble Graphs: Avoiding Misleading End Point Extrapolation When Graphing Interactions From a Moderated Multiple Regression Analysis

ERIC Educational Resources Information Center

Bodner, Todd E.

2016-01-01

This article revisits how the end points of plotted line segments should be selected when graphing interactions involving a continuous target predictor variable. Under the standard approach, end points are chosen at ±1 or 2 standard deviations from the target predictor mean. However, when the target predictor and moderator are correlated or the…

The cognitive foundations of reading and arithmetic skills in 7- to 10-year-olds.

PubMed

Durand, Marianne; Hulme, Charles; Larkin, Rebecca; Snowling, Margaret

2005-06-01

A range of possible predictors of arithmetic and reading were assessed in a large sample (N=162) of children between ages 7 years 5 months and 10 years 4 months. A confirmatory factor analysis of the predictors revealed a good fit to a model consisting of four latent variables (verbal ability, nonverbal ability, search speed, and phonological memory) and two manifest variables (digit comparison and phoneme deletion). A path analysis showed that digit comparison and verbal ability were unique predictors of variations in arithmetic skills, whereas phoneme deletion and verbal ability were unique predictors of variations in reading skills. These results confirm earlier findings that phoneme deletion ability appears to be a critical foundation for learning to read (decode). In addition, variations in the speed of accessing numerical quantity information appear to be a critical foundation for the development of arithmetic skills.

Downscaling GCM Output with Genetic Programming Model

NASA Astrophysics Data System (ADS)

Shi, X.; Dibike, Y. B.; Coulibaly, P.

2004-05-01

Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP downscaling results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the downscaling results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP downscaling; GCM.

Boosting for detection of gene-environment interactions.

PubMed

Pashova, H; LeBlanc, M; Kooperberg, C

2013-01-30

In genetic association studies, it is typically thought that genetic variants and environmental variables jointly will explain more of the inheritance of a phenotype than either of these two components separately. Traditional methods to identify gene-environment interactions typically consider only one measured environmental variable at a time. However, in practice, multiple environmental factors may each be imprecise surrogates for the underlying physiological process that actually interacts with the genetic factors. In this paper, we develop a variant of L(2) boosting that is specifically designed to identify combinations of environmental variables that jointly modify the effect of a gene on a phenotype. Because the effect modifiers might have a small signal compared with the main effects, working in a space that is orthogonal to the main predictors allows us to focus on the interaction space. In a simulation study that investigates some plausible underlying model assumptions, our method outperforms the least absolute shrinkage and selection and Akaike Information Criterion and Bayesian Information Criterion model selection procedures as having the lowest test error. In an example for the Women's Health Initiative-Population Architecture using Genomics and Epidemiology study, the dedicated boosting method was able to pick out two single-nucleotide polymorphisms for which effect modification appears present. The performance was evaluated on an independent test set, and the results are promising. Copyright © 2012 John Wiley & Sons, Ltd.

Climate variability, rice production and groundwater depletion in India

NASA Astrophysics Data System (ADS)

Bhargava, Alok

2018-03-01

This paper modeled the proximate determinants of rice outputs and groundwater depths in 27 Indian states during 1980-2010. Dynamic random effects models were estimated by maximum likelihood at state and well levels. The main findings from models for rice outputs were that temperatures and rainfall levels were significant predictors, and the relationships were quadratic with respect to rainfall. Moreover, nonlinearities with respect to population changes indicated greater rice production with population increases. Second, groundwater depths were positively associated with temperatures and negatively with rainfall levels and there were nonlinear effects of population changes. Third, dynamic models for in situ groundwater depths in 11 795 wells in mainly unconfined aquifers, accounting for latitudes, longitudes and altitudes, showed steady depletion. Overall, the results indicated that population pressures on food production and environment need to be tackled via long-term healthcare, agricultural, and groundwater recharge policies in India.

Prediction of first episode of panic attack among white-collar workers.

PubMed

Watanabe, Akira; Nakao, Kazuhisa; Tokuyama, Madoka; Takeda, Masatoshi

2005-04-01

The purpose of the present study was to elucidate a longitudinal matrix of the etiology for first-episode panic attack among white-collar workers. A path model was designed for this purpose. A 5-year, open-cohort study was carried out in a Japanese company. To evaluate the risk factors associated with the onset of a first episode of panic attack, the odds ratios of a new episode of panic attack were calculated by logistic regression. The path model contained five predictor variables: gender difference, overprotection, neuroticism, lifetime history of major depression, and recent stressful life events. The logistic regression analysis indicated that a person with a lifetime history of major depression and recent stressful life events had a fivefold and a threefold higher risk of panic attacks at follow up, respectively. The path model for the prediction of a first episode of panic attack fitted the data well. However, this model presented low accountability for the variance in the ultimate dependent variables, the first episode of panic attack. Three predictors (neuroticism, lifetime history of major depression, and recent stressful life events) had a direct effect on the risk for a first episode of panic attack, whereas gender difference and overprotection had no direct effect. The present model could not fully predict first episodes of panic attack in white-collar workers. To make a path model for the prediction of the first episode of panic attack, other strong predictor variables, which were not surveyed in the present study, are needed. It is suggested that genetic variables are among the other strong predictor variables. A new path model containing genetic variables (e.g. family history etc.) will be needed to predict the first episode of panic attack.

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Predictive score for mortality in patients with COPD exacerbations attending hospital emergency departments

PubMed Central

2014-01-01

Background Limited information is available about predictors of short-term outcomes in patients with exacerbation of chronic obstructive pulmonary disease (eCOPD) attending an emergency department (ED). Such information could help stratify these patients and guide medical decision-making. The aim of this study was to develop a clinical prediction rule for short-term mortality during hospital admission or within a week after the index ED visit. Methods This was a prospective cohort study of patients with eCOPD attending the EDs of 16 participating hospitals. Recruitment started in June 2008 and ended in September 2010. Information on possible predictor variables was recorded during the time the patient was evaluated in the ED, at the time a decision was made to admit the patient to the hospital or discharge home, and during follow-up. Main short-term outcomes were death during hospital admission or within 1 week of discharge to home from the ED, as well as at death within 1 month of the index ED visit. Multivariate logistic regression models were developed in a derivation sample and validated in a validation sample. The score was compared with other published prediction rules for patients with stable COPD. Results In total, 2,487 patients were included in the study. Predictors of death during hospital admission, or within 1 week of discharge to home from the ED were patient age, baseline dyspnea, previous need for long-term home oxygen therapy or non-invasive mechanical ventilation, altered mental status, and use of inspiratory accessory muscles or paradoxical breathing upon ED arrival (area under the curve (AUC) = 0.85). Addition of arterial blood gas parameters (oxygen and carbon dioxide partial pressures (PO2 and PCO2)) and pH) did not improve the model. The same variables were predictors of death at 1 month (AUC = 0.85). Compared with other commonly used tools for predicting the severity of COPD in stable patients, our rule was significantly better. Conclusions Five clinical predictors easily available in the ED, and also in the primary care setting, can be used to create a simple and easily obtained score that allows clinicians to stratify patients with eCOPD upon ED arrival and guide the medical decision-making process. PMID:24758312

The Impact of Reporting a Prior Penicillin Allergy on the Treatment of Methicillin-Sensitive Staphylococcus aureus Bacteremia

PubMed Central

Shenoy, Erica S.; Huang, Mingshu; Kuhlen, James L.; Ware, Winston A.; Parker, Robert A.; Walensky, Rochelle P.

2016-01-01

Background Methicillin-sensitive Staphylococcus aureus (MSSA) bacteremia is a morbid infection with mortality benefit from receipt of parenteral β-lactam therapy. A substantial portion of MSSA bacteremia patients report penicillin allergy, but infrequently have true allergy. Objective To determine the frequency and predictors of optimal and adequate therapy in patients with MSSA bacteremia. Design Retrospective cohort. Participants Adult inpatients with MSSA bacteremia, January 2009 through October 2013. Main Measures The primary measure was a trial of optimal therapy (OT), defined as ≥3 inpatient days or discharge on any first-line agents (nafcillin, oxacillin, cefazolin, or penicillin G, if susceptible). The secondary measure was completion of adequate therapy (AT), defined as ≥10 inpatient days or discharge on an agent appropriate for MSSA bacteremia. Data were electronically gathered with key variables manually validated through chart review. Log-binomial regression models were used to determine the frequency and predictors of outcomes. Key Results Of 456 patients, 346 (76%) received a trial of OT. Patients reporting penicillin allergy (13%) were less likely to receive OT trial than those without penicillin allergy (47% vs. 80%, p <0.001). Adjusting for other factors, penicillin allergy was the largest negative predictor of OT trial (RR 0.64 [0.49, 0.83]). Infectious Disease (ID) consultation was the largest positive predictor of OT trial across all patients (RR 1.34 [1.14, 1.57]). Allergy/Immunology consultation was the single most important predictor of OT trial among patients reporting penicillin allergy (RR 2.33 [1.44, 3.77]). Of 440 patients, 391 (89%) completed AT, with ID consultation the largest positive predictor of the outcome (RR 1.28 [1.15, 1.43]). Conclusions Nearly 25% of patients with MSSA bacteremia did not receive OT trial and about 10% did not receive AT completion. Reported penicillin allergy reduced, and ID consult increased, the likelihood of OT. Allergy evaluation, coupled with ID consultation, may improve outcomes in MSSA bacteremic patients. PMID:27438379

Rank-based estimation in the {ell}1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data.

PubMed

Johnson, Brent A

2009-10-01

We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selection in such partial linear model has direct application to high-dimensional survival analyses that attempt to adjust for clinical predictors. In the microarray setting, previous methods can adjust for other clinical predictors by assuming that clinical and gene expression data enter the model linearly in the same fashion. Here, we select important variables after adjusting for prognostic clinical variables but the clinical effects are assumed nonlinear. Our estimator is based on stratification and can be extended naturally to account for multiple nonlinear effects. We illustrate the utility of our method through simulation studies and application to the Wisconsin prognostic breast cancer data set.

A study of the factors affecting advancement and graduation for engineering students

NASA Astrophysics Data System (ADS)

Fletcher, John Thomas

The purpose of this study was, first, to determine whether a set of predictor variables could be identified from pre-enrollment and post-enrollment data that would differentiate students who advance to a major in engineering from non-advancers and, further, to determine if the predictor variables would differentiate students who graduate from the College of Engineering from non-graduates and graduates of other colleges at Auburn University. A second purpose was to determine if the predictor variables would correctly identify male and female students with the same degree of accuracy. The third purpose was to determine if there were significant relationships between the predictor variables studied and grades earned in a set of 15 courses that have enrollments over 100 students and are part of the pre-engineering curriculum. The population for this study was the 868 students who entered the pre-engineering program at Auburn University as freshmen during the Summer and Fall Quarters of 1991. The variables selected to differentiate the different groups were ACT scores, high school grade indices, and first quarter college grade point average. Two sets of classification matrices were developed using analysis and holdout samples that were divided based on sex. With respect to the question about advancement to the professional engineering program, structure coefficients derived from discriminant analysis procedures performed on all the cases combined indicated that first quarter college grade point average, high school math index, ACT math score, and high school science grade index were important predictor variables in classifying students who advanced to the professional engineering program and those who did not. Further, important structure coefficients with respect to graduation with a degree from the College of Engineering were first quarter college grade point average, high school math index, ACT math score, and high school science grade index. The results of this study indicated that significant differences existed in the model's ability to predict advancement and graduation for male and female students. This difference was not unexpected based on the male-dominated population. However, the models identified predicted at a high rate for both male and female students. Finally, many significant relationships were found to exist between the predictor variables and the 15 pre-engineering courses that were selected. The strength of the relationships ranged from a high of .82, p < .001 (Chemistry 103 grade with total high school grade index) to a low of .07, p > .05 (Chemistry 102 with ACT science score).

Predictors of Parent-Teacher Agreement in Youth with Autism Spectrum Disorder and Their Typically Developing Siblings.

PubMed

Stratis, Elizabeth A; Lecavalier, Luc

2017-08-01

This study evaluated the magnitude of informant agreement and predictors of agreement on behavior and emotional problems and autism symptoms in 403 children with autism and their typically developing siblings. Parent-teacher agreement was investigated on the Child Behavior Checklist (CBCL) and Social Responsiveness Scale (SRS). Agreement between parents and teachers fell in the low to moderate range. Multiple demographic and clinical variables were considered as predictors, and only some measures of parent broad autism traits were associated with informant agreement. Parent report on the SRS was a positive predictor of agreement, while teacher report was a negative predictor. Parent report on the CBCL emerged as a positive predictor of agreement, while teacher report emerged as a negative predictor.

Combining climatic and soil properties better predicts covers of Brazilian biomes.

PubMed

Arruda, Daniel M; Fernandes-Filho, Elpídio I; Solar, Ricardo R C; Schaefer, Carlos E G R

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km 2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Combining climatic and soil properties better predicts covers of Brazilian biomes

NASA Astrophysics Data System (ADS)

Arruda, Daniel M.; Fernandes-Filho, Elpídio I.; Solar, Ricardo R. C.; Schaefer, Carlos E. G. R.

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Insight, rumination, and self-reflection as predictors of well-being.

PubMed

Harrington, Rick; Loffredo, Donald A

2011-01-01

Dispositional private self-focused attention variables such as insight, internal self-awareness (ISA), and self-reflectiveness (SR) have been found to relate to well-being. The present study sought to determine which dispositional private self-focused attention variables have the most predictive power for subjective well-being as measured by the Satisfaction With Life Scale (E. Diener, R. A. Emmons, R. J. Larsen, & S. Griffin, 1985) and for a eudaemonic form of well-being as measured by the Psychological Well-Being Scale (C. D. Ryff, 1989). A total of 121 college student participants completed an online version of the Self-Consciousness Scale-Revised, the Rumination-Reflection Questionnaire, the Self-Reflection and Insight Scale, the Satisfaction With Life Scale, and the Psychological WellBeing Scale. Results of a multivariate regression analysis using the Self-Consciousness Scale-Revised's (M. F. Scheier & C. S. Carver, 1985) subfactors of SR and ISA, the Rumination-Reflection Questionnaire's (P. D. Trapnell & J. D. Campbell, 1999) subscales of Rumination and Reflection, and the Self-Reflection and Insight Scale's (A. M. Grant, J. Franklin, & P. Langford, 2002) Self-Reflection and Insight subscales revealed that the Insight subscale was the only statistically significant predictor (a positive predictor) for all 6 dimensions of psychological well-being. Insight was also the only significant positive predictor for satisfaction with life. The Rumination subscale was a significant negative predictor for 3 dimensions of psychological well-being, and the Reflection subscale was a significant positive predictor for 1 dimension. Implications of dispositional self-awareness variables and their relation to dimensions of well-being are discussed.

Chronic stress and sexual function in women

PubMed Central

Hamilton, Lisa Dawn; Meston, Cindy M.

2014-01-01

Introduction Chronic stress is known to have negative effects on reproduction, but little is known about how it affects the sexual response cycle. The present study examined the relationship between chronic stress and sexual arousal and the mechanisms that mediate this relationship. Aim To test the relationship between chronic stress and sexual arousal and identify mechanisms that may explain this relationship. We predicted that women experiencing high levels of chronic stress would show lower levels of genital arousal & DHEAS and higher levels of cortisol and cognitive distraction compared to women with average levels of stress. Methods Women who were categorized as high in chronic stress (high stress group, n = 15) or average in chronic stress (average stress group; n = 15) provided saliva samples and watched an erotic film while having their genital and psychological arousal measured. Main Outcome Measures Main outcome measures were vaginal pulse amplitude, psychological arousal, salivary cortisol, salivary DHEAS, and heart rate and compared them between women with high and average levels of chronic stress. Results Women in the high stress group had lower levels of genital, but not psychological arousal, had higher levels of cortisol, and reported more distraction during the erotic film than women in the average stress group. The main predictor of decreased genital sexual arousal was participants’ distraction scores. Conclusions High levels of chronic stress were related to lower levels of genital sexual arousal. Both psychological (distraction) and hormonal (increased cortisol) factors were related to the lower levels of sexual arousal seen in women high in chronic stress, but distraction was the only significant predictor when controlling for other variables. PMID:23841462

Multivariate analyses of tinnitus complaint and change in tinnitus complaint: a masker study.

PubMed

Jakes, S; Stephens, S D

1987-11-01

Multivariate statistical techniques were used to re-analyse the data from the recent DHSS multi-centre masker study. These analyses were undertaken to three ends. First, to clarify and attempt to replicate the previously found factor structure of complaints about tinnitus. Secondly, to attempt to identify common factors in the change or improvement measures pre- and post-masker treatment. Thirdly, to identify predictors of any such outcome factors. Two complaint factors were identified; 'Distress' and 'intrusiveness'. A series of analyses were conducted on change measures using different numbers of subjects and variables. When only semantic differential scales were used, the change factors were very similar to the complaint factors noted above. When variables measuring other aspects of improvement were included, several other factors were identified. These included; 'tinnitus helped', 'masking effects', 'residual inhibition' and 'matched loudness'. Twenty-five conceptually distinct predictors of outcome were identified. These predictor variables were quite different for different outcome factors. For example, high-frequency hearing loss was a predictor of tinnitus being helped by the masker, and a low frequency match and a low masking threshold predicted therapeutic success on residual inhibition. Decrease in matched loudness was predicted by louder tinnitus initially.

Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

PubMed Central

Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

2016-01-01

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

Student performance on levels 1 and 2-CE of COMLEX-USA: do elective upper-level undergraduate science courses matter?

PubMed

Wong, Stanley K; Ramirez, Juan R; Helf, Scott C

2009-11-01

The effect of a variety of preadmission variables, including the number of elective preadmission upper-level science courses, on academic achievement is not well established. To investigate the relationship between number of preadmission variables and overall student academic achievement in osteopathic medical school. Academic records of osteopathic medical students in the 2008 and 2009 graduating classes of Western University of Health Sciences College of Osteopathic Medicine of the Pacific in Pomona, California, were analyzed. Multivariate linear regression analyses were performed to identify predictors of academic achievement based on Medical College Admission Test (MCAT) subscores, undergraduate grade point average (GPA), GPA in medical school basic science (preclinical GPA) and clinical clerkship (clinical GPA), and scores on the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 and Level 2-Cognitive Evaluation (CE). Records of 358 osteopathic medical students were evaluated. Analysis of beta coefficients suggested that undergraduate science GPA was the most important predictor of overall student academic achievement (P<.01). Biological sciences MCAT subscore was a more modest but still statistically significant predictor of preclinical GPA and COMLEX-USA Level 1 score (P<.01). Physical sciences MCAT subscore was also a statistically significant predictor of preclinical GPA, and verbal reasoning MCAT subscore was a statistically significant predictor of COMLEX-USA Level 2-CE score (both P<.01). Women had statistically significantly higher preclinical GPA and COMLEX-USA Level 2-CE scores than men (P<.05). Differences in some outcome variables were also associated with racial-ethnic background and age. Number of preadmission elective upper-level science courses taken by students before matriculation was not significantly correlated with any academic achievement variable. Although undergraduate science GPA and MCAT biological sciences subscore were significant predictors of overall academic achievement for osteopathic medical students, the number of elective upper-level science courses taken preadmission had no predictive value.

Predictors of relapse in patients with major depressive disorder in a 52-week, fixed dose, double blind, randomized trial of selegiline transdermal system (STS).

PubMed

Jang, Saeheon; Jung, Sungwon; Pae, Chiun; Kimberly, Blanchard Portland; Craig Nelson, J; Patkar, Ashwin A

2013-12-01

We investigated patient and disease characteristics predictive of relapse of MDD during a 52-week placebo controlled trial of selegiline transdermal system (STS) to identify patient characteristics relevant for STS treatment. After 10 weeks of open-label stabilization with STS, 322 remitted patients with MDD were randomized to 52-weeks of double-blind treatment with STS (6 mg/24h) or placebo (PLB). Relapse was defined as Hamilton Depression Rating Scale (HAMD-17) score of ≥ 14 and a CGI-S score of ≥ 3 with at least 2-point increase from the beginning of the double blind phase on 2 consecutive visits. Cox's proportional hazards regression was used to examine the effect of potential predictors (age, sex, age at onset of first MDD, early response pattern, number of previous antidepressant trials, severity of index episode, number of previous episodes, melancholic features, atypical features and anxious feature) on outcome. Exploratory analyses examined additional clinical variables (medical history, other psychiatric history, and individual items of HAM-D 28) on relapse. For all predictor variables analyzed, treatment Hazard Ratio (HR=0.48~0.54) was significantly in favor of STS (i.e., lower relapse risk than PLB). Age of onset was significantly predictive of relapse. Type, duration, and severity of depressive episodes, previous antidepressant trials, or demographic variables did not predict relapse. In additional exploratory analysis, eating disorder history and suicidal ideation were significant predictors of relapse after controlling for the effect of treatment in individual predictor analysis. While age of onset, eating disorder history and suicidal ideation were significant predictors, the majority of clinical and demographic variables were not predictive of relapse. Given the post-hoc nature of analysis, the findings need confirmation from a prospective study. It appears that selegiline transdermal system was broadly effective in preventing relapse across different subtypes and symptoms clusters of MDD. © 2013 Published by Elsevier B.V.

Insecticide treated bednet strategy in rural settings: can we exploit women's decision making power?

PubMed

Tilak, Rina; Tilak, V W; Bhalwar, R

2007-01-01

Use of insecticide treated bednets in prevention of malaria is a widely propagated global strategy, however, its use has been reported to be influenced and limited by many variables especially gender bias. A cross sectional field epidemiological study was conducted in a rural setting with two outcome variables, 'Bednet use'(primary outcome variable) and 'Women's Decision Making Power' which were studied in reference to various predictor variables. Analysis reveals a significant effect on the primary outcome variable 'Bednet use' of the predictor variables- age, occupation, bednet purchase decision, women's decision making power, husband's education and knowledge about malaria and its prevention. The study recommends IEC on treated bednets to be disseminated through TV targeting the elderly women who have better decision making power and mobilizing younger women who were found to prefer bednets for prevention of mosquito bites for optimizing the use of treated bednets in similar settings.

Variable Selection through Correlation Sifting

NASA Astrophysics Data System (ADS)

Huang, Jim C.; Jojic, Nebojsa

Many applications of computational biology require a variable selection procedure to sift through a large number of input variables and select some smaller number that influence a target variable of interest. For example, in virology, only some small number of viral protein fragments influence the nature of the immune response during viral infection. Due to the large number of variables to be considered, a brute-force search for the subset of variables is in general intractable. To approximate this, methods based on ℓ1-regularized linear regression have been proposed and have been found to be particularly successful. It is well understood however that such methods fail to choose the correct subset of variables if these are highly correlated with other "decoy" variables. We present a method for sifting through sets of highly correlated variables which leads to higher accuracy in selecting the correct variables. The main innovation is a filtering step that reduces correlations among variables to be selected, making the ℓ1-regularization effective for datasets on which many methods for variable selection fail. The filtering step changes both the values of the predictor variables and output values by projections onto components obtained through a computationally-inexpensive principal components analysis. In this paper we demonstrate the usefulness of our method on synthetic datasets and on novel applications in virology. These include HIV viral load analysis based on patients' HIV sequences and immune types, as well as the analysis of seasonal variation in influenza death rates based on the regions of the influenza genome that undergo diversifying selection in the previous season.

Use of generalized regression tree models to characterize vegetation favoring Anopheles albimanus breeding.

PubMed

Hernandez, J E; Epstein, L D; Rodriguez, M H; Rodriguez, A D; Rejmankova, E; Roberts, D R

1997-03-01

We propose the use of generalized tree models (GTMs) to analyze data from entomological field studies. Generalized tree models can be used to characterize environments with different mosquito breeding capacity. A GTM simultaneously analyzes a set of predictor variables (e.g., vegetation coverage) in relation to a response variable (e.g., counts of Anopheles albimanus larvae), and how it varies with respect to a set of criterion variables (e.g., presence of predators). The algorithm produces a treelike graphical display with its root at the top and 2 branches stemming down from each node. At each node, conditions on the value of predictors partition the observations into subgroups (environments) in which the relation between response and criterion variables is most homogeneous.

Dreams Fulfilled and Shattered: Determinants of Segmented Assimilation in the Second Generation*

PubMed Central

Haller, William; Portes, Alejandro; Lynch, Scott M.

2013-01-01

We summarize prior theories on the adaptation process of the contemporary immigrant second generation as a prelude to presenting additive and interactive models showing the impact of family variables, school contexts and academic outcomes on the process. For this purpose, we regress indicators of educational and occupational achievement in early adulthood on predictors measured three and six years earlier. The Children of Immigrants Longitudinal Study (CILS), used for the analysis, allows us to establish a clear temporal order among exogenous predictors and the two dependent variables. We also construct a Downward Assimilation Index (DAI), based on six indicators and regress it on the same set of predictors. Results confirm a pattern of segmented assimilation in the second generation, with a significant proportion of the sample experiencing downward assimilation. Predictors of the latter are the obverse of those of educational and occupational achievement. Significant interaction effects emerge between these predictors and early school contexts, defined by different class and racial compositions. Implications of these results for theory and policy are examined. PMID:24223437

Predictors and moderators of response to cognitive behavioral therapy and medication for the treatment of binge eating disorder.

PubMed

Grilo, Carlos M; Masheb, Robin M; Crosby, Ross D

2012-10-01

To examine predictors and moderators of response to cognitive behavioral therapy (CBT) and medication treatments for binge-eating disorder (BED). 108 BED patients in a randomized double-blind placebo-controlled trial testing CBT and fluoxetine treatments were assessed prior, throughout, and posttreatment. Demographic factors, psychiatric and personality disorder comorbidity, eating disorder psychopathology, psychological features, and 2 subtyping methods (negative affect, overvaluation of shape/weight) were tested as predictors and moderators for the primary outcome of remission from binge eating and 4 secondary dimensional outcomes (binge-eating frequency, eating disorder psychopathology, depression, and body mass index). Mixed-effects models analyzed all available data for each outcome variable. In each model, effects for baseline value and treatment were included with tests of both prediction and moderator effects. Several demographic and clinical variables significantly predicted and/or moderated outcomes. One demographic variable signaled a statistical advantage for medication only (younger participants had greater binge-eating reductions), whereas several demographic and clinical variables (lower self-esteem, negative affect, and overvaluation of shape/weight) signaled better improvements if receiving CBT. Overvaluation was the most salient predictor/moderator of outcomes. Overvaluation significantly predicted binge-eating remission (29% of participants with vs. 57% of participants without overvaluation remitted). Overvaluation was especially associated with lower remission rates if receiving medication only (10% vs. 42% for participants without overvaluation). Overvaluation moderated dimensional outcomes: Participants with overvaluation had significantly greater reductions in eating disorder psychopathology and depression levels if receiving CBT. Overvaluation predictor/moderator findings persisted after controlling for negative affect. Our findings have clinical utility for prescription of CBT and medication and implications for refinement of the BED diagnosis. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013

PubMed Central

Robles, Brenda; Kuo, Tony

2017-01-01

Background Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs. Methods Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals). Results 3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were. Conclusions These findings have implications for public health practice and community planning in local health jurisdictions. PMID:28087545

[Cost analysis of radiotherapy provided in inpatient setting -  testing potential predictors for a new prospective payment system].

PubMed

Sedo, J; Bláha, M; Pavlík, T; Klika, P; Dušek, L; Büchler, T; Abrahámová, J; Srámek, V; Slampa, P; Komínek, L; Pospíšil, P; Sláma, O; Vyzula, R

2014-01-01

As a part of the development of a new prospective payment model for radiotherapy we analyzed data on costs of care provided by three comprehensive cancer centers in the Czech Republic. Our aim was to find a combination of variables (predictors) which could be used to sort hospitalization cases into groups according to their costs, with each group having the same reimbursement rate. We tested four variables as possible predictors -  number of fractions, stage of disease, radiotherapy technique and diagnostic group. We analyzed 7,440 hospitalization cases treated in three comprehensive cancer centers from 2007 to 2011. We acquired data from the I COP database developed by Institute of Biostatistics and Analyses of Masaryk University in cooperation with oncology centers that contains records from the National Oncological Registry along with data supplied by healthcare providers to insurance companies for the purpose of retrospective reimbursement. When comparing the four variables mentioned above we found that number of fractions and radiotherapy technique were much stronger predictors than the other two variables. Stage of disease did not prove to be a relevant indicator of cost distinction. There were significant differences in costs among diagnostic groups but these were mostly driven by the technique of radiotherapy and the number of fractions. Within the diagnostic groups, the distribution of costs was too heterogeneous for the purpose of the new payment model. The combination of number of fractions and radiotherapy technique appears to be the most appropriate cost predictors to be involved in the prospective payment model proposal. Further analysis is planned to test the predictive value of intention of radiotherapy in order to determine differences in costs between palliative and curative treatment.

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013.

PubMed

Robles, Brenda; Kuo, Tony

2017-01-13

Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs. Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals). 3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were. These findings have implications for public health practice and community planning in local health jurisdictions. 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/.

Predictors and Moderators of Response to Cognitive Behavioral Therapy and Medication for the Treatment of Binge Eating Disorder

PubMed Central

Grilo, Carlos. M.; Masheb, Robin M.; Crosby, Ross D.

2012-01-01

Objective To examine predictors and moderators of response to cognitive-behavioral therapy (CBT) and medication treatments for binge-eating disorder (BED). Method 108 BED patients in a randomized double-blind placebo-controlled trial testing CBT and fluoxetine treatments were assessed prior, throughout-, and post-treatment. Demographic factors, psychiatric and personality-disorder co-morbidity, eating-disorder psychopathology, psychological features, and two sub-typing methods (negative-affect, overvaluation of shape/weight) were tested as predictors and moderators for the primary outcome of remission from binge-eating and four secondary dimensional outcomes (binge-eating frequency, eating-disorder psychopathology, depression, and body mass index). Mixed-effects-models analyzed all available data for each outcome variable. In each model, effects for baseline value and treatment were included with tests of both prediction and moderator effects. Results Several demographic and clinical variables significantly predicted and/or moderated outcomes. One demographic variable signaled a statistical advantage for medication-only (younger participants had greater binge-eating reductions) whereas several demographic and clinical variables (lower self-esteem, negative-affect, and overvaluation of shape/weight) signaled better improvements if receiving CBT. Overvaluation was the most salient predictor/moderator of outcomes. Overvaluation significantly predicted binge-eating remission (29% of participants with versus 57% of participants without overvaluation remitted). Overvaluation was especially associated with lower remission rates if receiving medication-only (10% versus 42% for participants without overvaluation). Overvaluation moderated dimensional outcomes: participants with overvaluation had significantly greater reductions in eating-disorder psychopathology and depression levels if receiving CBT. Overvaluation predictor/moderator findings persisted after controlling for negative-affect. Conclusions Our findings have clinical utility for prescription of CBT and medication and implications for refinement of the BED diagnosis. PMID:22289130

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Predictors of Entering a Hearing Aid Evaluation Period: A Prospective Study in Older Hearing-Help Seekers

PubMed Central

Deeg, Dorly J.H.; Versfeld, Niek J.; Heymans, Martijn W.; Naylor, Graham; Kramer, Sophia E.

2017-01-01

This study aimed to determine the predictors of entering a hearing aid evaluation period (HAEP) using a prospective design drawing on the health belief model and the transtheoretical model. In total, 377 older persons who presented with hearing problems to an Ear, Nose, and Throat specialist (n = 110) or a hearing aid dispenser (n = 267) filled in a baseline questionnaire. After 4 months, it was determined via a telephone interview whether or not participants had decided to enter a HAEP. Multivariable logistic regression analyses were applied to determine which baseline variables predicted HAEP status. A priori, candidate predictors were divided into ‘likely’ and ‘novel’ predictors based on the literature. The following variables turned out to be significant predictors: more expected hearing aid benefits, greater social pressure, and greater self-reported hearing disability. In addition, greater hearing loss severity and stigma were predictors in women but not in men. Of note, the predictive effect of self-reported hearing disability was modified by readiness such that with higher readiness, the positive predictive effect became stronger. None of the ‘novel’ predictors added significant predictive value. The results support the notion that predictors of hearing aid uptake are also predictive of entering a HAEP. This study shows that some of these predictors appear to be gender specific or are dependent on a person’s readiness for change. After assuring the external validity of the predictors, an important next step would be to develop prediction rules for use in clinical practice, so that older persons’ hearing help-seeking journey can be facilitated. PMID:29237333

Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy)

NASA Astrophysics Data System (ADS)

Angileri, Silvia Eleonora; Conoscenti, Christian; Hochschild, Volker; Märker, Michael; Rotigliano, Edoardo; Agnesi, Valerio

2016-06-01

Soil erosion by water constitutes a serious problem affecting various countries. In the last few years, a number of studies have adopted statistical approaches for erosion susceptibility zonation. In this study, the Stochastic Gradient Treeboost (SGT) was tested as a multivariate statistical tool for exploring, analyzing and predicting the spatial occurrence of rill-interrill erosion and gully erosion. This technique implements the stochastic gradient boosting algorithm with a tree-based method. The study area is a 9.5 km2 river catchment located in central-northern Sicily (Italy), where water erosion processes are prevalent, and affect the agricultural productivity of local communities. In order to model soil erosion by water, the spatial distribution of landforms due to rill-interrill and gully erosion was mapped and 12 environmental variables were selected as predictors. Four calibration and four validation subsets were obtained by randomly extracting sets of negative cases, both for rill-interrill erosion and gully erosion models. The results of validation, based on receiving operating characteristic (ROC) curves, showed excellent to outstanding accuracies of the models, and thus a high prediction skill. Moreover, SGT allowed us to explore the relationships between erosion landforms and predictors. A different suite of predictor variables was found to be important for the two models. Elevation, aspect, landform classification and land-use are the main controlling factors for rill-interrill erosion, whilst the stream power index, plan curvature and the topographic wetness index were the most important independent variables for gullies. Finally, an ROC plot analysis made it possible to define a threshold value to classify cells according to the presence/absence of the two erosion processes. Hence, by heuristically combining the resulting rill-interrill erosion and gully erosion susceptibility maps, an integrated water erosion susceptibility map was created. The adopted method offers the advantages of an objective and repeatable procedure, whose result is useful for local administrators to identify the areas that are most susceptible to water erosion and best allocate resources for soil conservation strategies.

Predictors of early breastfeeding initiation among mothers of children under 24 months of age in rural part of West Ethiopia.

PubMed

Hailemariam, Tsedeke Wolde; Adeba, Emiru; Sufa, Alem

2015-10-21

The World Health Organization recommends initiation of breastfeeding within the first hour after childbirth. In developing countries alone, early initiation of breastfeeding could save as many as 1.45 million lives each year by reducing deaths mainly due to diarrheal disorders and lower respiratory tract infections in children. The current study aimed to determine the rate and the predictors of breastfeeding initiation in East Wollega Zones of West Ethiopia. A community-based, cross-sectional study was conducted from April to May 2014 among 594 mothers who had children less than 24 months. Multi stage cluster sampling method was used to select the study population. Eligible mothers were invited to interview using pretested questionnaires to gather data regarding sociodemographics, health-related variables, breastfeeding initiation, and current breastfeeding practices. A multivariable logistic regression analysis was used to identify independent predictors of early initiation of breastfeeding after controlling for confounding variables. A sample of 593 mothers was included in the study. Breastfeeding was initiated by 83.1 % of mothers within the first hour of childbirth. Being a housewife (AOR (95 % CI) = 2.48 (1.54- 3.99)) and infant received colostrum (AOR (95 % CI) =2.22 (1.08-4.55)) were significant positive predictors for early breastfeeding initiation as revealed by logistic regression. The multivariable logistic regression analysis showed that the mothers who had no radio and/or TV in the household (AOR (95 % CI = 0.55 (0.35-0.88)), were not exposure to health information (AOR (95 % CI) = 0.44 (0.25-0.75)), and infants were provided with prelacteal feeds (AOR (95 % CI)=0.30 (0.14-0.65)) were less likely to initiate breastfeeding. The rate of timely initiation of breastfeeding was high. Breastfeeding promotion program is essential to encourage the practice of timely initiation of breastfeeding, and reduce the practice of providing prelacteal feeds within three days of life. Thus appropriate health information is vital to boost early initiation of breastfeeding.

Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system

NASA Astrophysics Data System (ADS)

Robinson, Neethu; Guan, Cuntai; Vinod, A. P.

2015-12-01

Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational time. Significance. The proposed system provides a real time movement control system using EEG-BCI with control over movement speed and position. These results are higher and statistically significant compared to existing techniques in EEG based systems and thus promise the applicability of the proposed method for efficient estimation of movement parameters and for continuous motor control.

Encke-Beta Predictor for Orion Burn Targeting and Guidance

NASA Technical Reports Server (NTRS)

Robinson, Shane; Scarritt, Sara; Goodman, John L.

2016-01-01

The state vector prediction algorithm selected for Orion on-board targeting and guidance is known as the Encke-Beta method. Encke-Beta uses a universal anomaly (beta) as the independent variable, valid for circular, elliptical, parabolic, and hyperbolic orbits. The variable, related to the change in eccentric anomaly, results in integration steps that cover smaller arcs of the trajectory at or near perigee, when velocity is higher. Some burns in the EM-1 and EM-2 mission plans are much longer than burns executed with the Apollo and Space Shuttle vehicles. Burn length, as well as hyperbolic trajectories, has driven the use of the Encke-Beta numerical predictor by the predictor/corrector guidance algorithm in place of legacy analytic thrust and gravity integrals.

Self-care resources and activity as predictors of quality of life in persons after myocardial infarction.

PubMed

Baas, Linda S

2004-01-01

An ex post facto correlational study was conducted to examine predictors of quality of life in persons 3 to 6 months after a myocardial infarction. Self-care resources, self-care knowledge (needs), activity level, and selected demographic variables were examined as predictor variables. A convenience sample of 86 subjects with a mean age of 61 years, was recruited for participation in this study. The study that explained 35% of the variance in quality of life included self-care resources available, activity level, and self-care needs. Modeling and Role Modeling Paradigm provided a useful explanation of how self-care resources and self-care knowledge can be applied to persons recovering from myocardial infarction.

Work stress, role conflict, social support, and psychological burnout among teachers.

PubMed

Burke, R J; Greenglass, E

1993-10-01

This study examined a research model developed to understand psychological burnout among school-based educators. Data were collected from 833 school-based educators using questionnaires completed anonymously. Four groups of predictor variables identified in previous research were considered: individual demographic and situational variables, work stressors, role conflict, and social support. Some support for the model was found. Work stressors were strong predictors of psychological burnout. Individual demographic characteristics, role conflict, and social support had little effect on psychological burnout.

Sex-typed personality traits and gender identity as predictors of young adults' career interests.

PubMed

Dinella, Lisa M; Fulcher, Megan; Weisgram, Erica S

2014-04-01

Gender segregation of careers is still prominent in the U.S. workforce. The current study was designed to investigate the role of sex-typed personality traits and gender identity in predicting emerging adults' interests in sex-typed careers. Participants included 586 university students (185 males, 401 females). Participants reported their sex-typed personality traits (masculine and feminine traits), gender identities (gender typicality, contentment, felt pressure to conform, and intergroup bias), and interests in sex-typed careers. Results indicated both sex-typed personality traits and gender identity were important predictors of young adults' career interests, but in varying degrees and differentially for men and women. Men's sex-typed personality traits and gender typicality were predictive of their masculine career interests even more so when the interaction of their masculine traits and gender typicality were considered. When gender typicality and sex-typed personality traits were considered simultaneously, gender typicality was negatively related to men's feminine career interests and gender typicality was the only significant predictor of men's feminine career interests. For women, sex-typed personality traits and gender typicality were predictive of their sex-typed career interests. The level of pressure they felt to conform to their gender also positively predicted interest in feminine careers. The interaction of sex-typed personality traits and gender typicality did not predict women's career interests more than when these variables were considered as main effects. Results of the multidimensional assessment of gender identity confirmed that various dimensions of gender identity played different roles in predicting career interests and gender typicality was the strongest predictor of career interests.

Predictors for additional anterior cruciate ligament reconstruction: data from the Swedish national ACL register.

PubMed

Fältström, Anne; Hägglund, Martin; Magnusson, Henrik; Forssblad, Magnus; Kvist, Joanna

2016-03-01

To identify predictors for additional anterior cruciate ligament (ACL) reconstruction. Patients from the Swedish national ACL register who underwent ACL reconstruction between January 2005 and February 2013 (follow-up duration 6-104 months) were included. Cox regression analyses included the following independent variables regarding primary injury: age, sex, time between injury and primary ACL reconstruction, activity at primary injury, concomitant injuries, injury side, graft type, and pre-surgery KOOS and EQ-5D scores. Among ACL reconstruction procedures, 93% involved hamstring tendon (HT) autografts. Graft type did not predict additional ACL reconstruction. Final regression models only included patients with HT autograft (n = 20,824). Of these, 702 had revision and 591 contralateral ACL reconstructions. The 5-year post-operative rates of revision and contralateral ACL reconstruction were 4.3 and 3.8%, respectively. Significant predictors for additional ACL reconstruction were age (fourfold increased rate for <16-year-old patients vs. >35-year-old patients), time between injury and primary surgery (two to threefold increased rate for ACL reconstruction within 0-90 days vs. >365 days), and playing football at primary injury. This study identified younger age, having ACL reconstruction early after the primary injury, and incurring the primary injury while playing football as the main predictors for revision and contralateral ACL reconstruction. This suggests that the rate of additional ACL reconstruction is increased in a selected group of young patients aiming to return to strenuous sports after primary surgery and should be taken into consideration when discussing primary ACL reconstruction, return to sports, and during post-surgery rehabilitation. II.

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

PubMed Central

Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

2018-01-01

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes. PMID:29453930

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting.

PubMed

Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

2018-02-17

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes.

Individual and contextual factors influencing dental health care utilization by preschool children: a multilevel analysis

PubMed

Piovesan, Chaiana; Ardenghi, Thiago Machado; Mendes, Fausto Medeiros; Agostini, Bernardo Antonio; Michel-Crosato, Edgard

2017-03-30

The effect of contextual factors on dental care utilization was evaluated after adjustment for individual characteristics of Brazilian preschool children. This cross-sectional study assessed 639 preschool children aged 1 to 5 years from Santa Maria, a town in Rio Grande do Sul State, located in southern Brazil. Participants were randomly selected from children attending the National Children's Vaccination Day and 15 health centers were selected for this research. Visual examinations followed the ICDAS criteria. Parents answered a questionnaire about demographic and socioeconomic characteristics. Contextual influences on children's dental care utilization were obtained from two community-related variables: presence of dentists and presence of workers' associations in the neighborhood. Unadjusted and adjusted multilevel logistic regression models were used to describe the association between outcome and predictor variables. A prevalence of 21.6% was found for regular use of dental services. The unadjusted assessment of the associations of dental health care utilization with individual and contextual factors included children's ages, family income, parents' schooling, mothers' participation in their children's school activities, dental caries, and presence of workers' associations in the neighborhood as the main outcome covariates. Individual variables remained associated with the outcome after adding contextual variables in the model. In conclusion, individual and contextual variables were associated with dental health care utilization by preschool children.

Self-reported posttraumatic growth predicts greater subsequent posttraumatic stress amidst war and terrorism.

PubMed

Zalta, Alyson K; Gerhart, James; Hall, Brian J; Rajan, Kumar B; Vechiu, Catalina; Canetti, Daphna; Hobfoll, Stevan E

2017-03-01

This study tested three alternative explanations for research indicating a positive, but heterogeneous relationship between self-reported posttraumatic growth (PTG) and posttraumatic stress symptoms (PSS): (a) the third-variable hypothesis that the relationship between PTG and PSS is a spurious one driven by positive relationships with resource loss, (b) the growth over time hypothesis that the relationship between PTG and PSS is initially a positive one, but becomes negative over time, and (c) the moderator hypothesis that resource loss moderates the relationship between PTG and PSS such that PTG is associated with lower levels of PSS as loss increases. A nationally representative sample (N = 1622) of Israelis was assessed at three time points during a period of ongoing violence. PTG, resource loss, and the interaction between PTG and loss were examined as lagged predictors of PSS to test the proposed hypotheses. Results were inconsistent with all three hypotheses, showing that PTG positively predicted subsequent PSS when accounting for main and interactive effects of loss. Our results suggest that self-reported PTG is a meaningful but counterintuitive predictor of poorer mental health following trauma.

Predicting impending death: inconsistency in speed is a selective and early marker.

PubMed

Macdonald, Stuart W S; Hultsch, David F; Dixon, Roger A

2008-09-01

Among older adults, deficits in both level and variability of speeded performance are linked to neurological impairment. This study examined whether and when speed (rate), speed (inconsistency), and traditional accuracy-based markers of cognitive performance foreshadow terminal decline and impending death. Victoria Longitudinal Study data spanning 12 years (5 waves) of measurement were assembled for 707 adults aged 59 to 95 years. Whereas 442 survivors completed all waves and relevant measures, 265 decedents participated on at least 1 occasion and subsequently died. Four main results were observed. First, Cox regressions evaluating the 3 cognitive predictors of mortality replicated previous results for cognitive accuracy predictors. Second, level (rate) of speeded performance predicted survival independent of demographic indicators, cardiovascular health, and cognitive performance level. Third, inconsistency in speed predicted survival independent of all influences combined. Fourth, follow-up random-effects models revealed increases in inconsistency in speed per year closer to death, with advancing age further moderating the accelerated growth. Hierarchical prediction patterns support the view that inconsistency in speed is an early behavioral marker of neurological dysfunction associated with impending death. (c) 2008 APA, all rights reserved

Predicting Impending Death: Inconsistency in Speed is a Selective and Early Marker

PubMed Central

MacDonald, Stuart W.S.; Hultsch, David F.; Dixon, Roger A.

2008-01-01

Among older adults, deficits in both level and variability of speeded performance are linked to neurological impairment. This study examined whether and when speed (rate), speed (inconsistency), and traditional accuracy-based markers of cognitive performance foreshadow terminal decline and impending death. Victoria Longitudinal Study data spanning 12 years (5 waves) of measurement were assembled for 707 adults aged 59 to 95 years. Whereas 442 survivors completed all waves and relevant measures, 265 decedents participated on at least one occasion and subsequently died. Four main results were observed. First, Cox regressions evaluating the three cognitive predictors of mortality replicated previous results for cognitive accuracy predictors. Second, level (rate) of speeded performance predicted survival independent of demographic indicators, cardiovascular health, and cognitive performance level. Third, inconsistency in speed predicted survival independent of all influences combined. Fourth, follow-up random-effects models revealed increases in inconsistency in speed per year closer to death, with advancing age further moderating the accelerated growth. Hierarchical prediction patterns support the view that inconsistency in speed is an early behavioral marker of neurological dysfunction associated with impending death. PMID:18808249

In-school service predictors of employment for individuals with intellectual disability.

PubMed

Park, Jiyoon; Bouck, Emily

2018-06-01

Although there are many secondary data analyses of the National Longitudinal Transition Study-2 (NLTS-2) to investigate post-school outcome for students with disabilities, there has been a lack of research with in-school service predictors and post-school outcome for students with specific disability categories. This study was a secondary data analysis of NLTS-2 to investigate the relationship between current employment status and in-school services for individuals with intellectual disability. Statistical methods such as descriptive statistics and logistic regression were used to analyze NLTS-2 data set. The main findings included that in-school services were correlated with current employment status, and that primary disability (i.e., mild intellectual disability and moderate/severe intellectual disability) was associated with current employment status. In-school services are critical in predicting current employment for individuals with intellectual disability. Also, data suggest additional research is needed to investigate various in-school services and variables that could predict employment differences between individuals with mild and moderate/severe intellectual disability. Copyright © 2018 Elsevier Ltd. All rights reserved.

Determinants of passive smoking in children in Edinburgh, Scotland.

PubMed Central

Jarvis, M J; Strachan, D P; Feyerabend, C

1992-01-01

OBJECTIVES. Using saliva cotinine as a quantitative marker, we examined the contribution of factors other than parental smoking to children's passive exposure to tobacco smoke. METHODS. Saliva specimens from a random sample of 734 7-year-old schoolchildren in Edinburgh, Scotland, were analyzed for cotinine. Their parents completed a questionnaire covering smoking habits and conditions in the home. RESULTS. A number of independent predictors of cotinine were identified in addition to the main one of smoking by household members. These predictors included home ownership, social class, day of the week, season of the year, number of parents present, crowding in the home, the number of children in the household, and sex. Cotinine was higher in children from less advantaged backgrounds, during winter, on Mondays, in girls, and when fewer other children were present. The effects were similar between children from nonsmoking and smoking homes. CONCLUSIONS. Questionnaire measures of parental smoking are insufficient to fully characterize young children's exposure to passive smoking. Because socioeconomic variables contribute to measured exposure, passive-smoking studies that treat class as a confounder and control for it may be overcontrolling. PMID:1503162

Predictor variables for a half marathon race time in recreational male runners

PubMed Central

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the ‘Half Marathon Basel’ completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = −0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r2 = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m2) −3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable. PMID:24198577

Predictor variables for a half marathon race time in recreational male runners.

PubMed

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the 'Half Marathon Basel' completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = -0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r(2) = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m(2)) -3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.

Species distribution model transferability and model grain size - finer may not always be better.

PubMed

Manzoor, Syed Amir; Griffiths, Geoffrey; Lukac, Martin

2018-05-08

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Predictors and Moderators of Treatment Response in Childhood Anxiety Disorders: Results from the CAMS Trial

PubMed Central

Compton, Scott N.; Peris, Tara S.; Almirall, Daniel; Birmaher, Boris; Sherrill, Joel; Kendall, Phillip C.; March, John S.; Gosch, Elizabeth A.; Ginsburg, Golda S.; Rynn, Moira A.; Piacentini, John C.; McCracken, James T.; Keeton, Courtney P.; Suveg, Cynthia M.; Aschenbrand, Sasha G.; Sakolsky, Dara; Iyengar, Satish; Walkup, John T.; Albano, Anne Marie

2014-01-01

Objective To examine predictors and moderators of treatment outcomes among 488 youth ages 7-17 years (50% female; 74% ≤ 12 years) with DSM-IV diagnoses of separation anxiety disorder, social phobia, or generalized anxiety disorder who were randomly assigned to receive either cognitive behavior therapy (CBT), sertraline (SRT), their combination (COMB), or medication management with pill placebo (PBO) in the Child/Adolescent Anxiety Multimodal Study (CAMS). Method Six classes of predictor and moderator variables (22 variables) were identified from the literature and examined using continuous (Pediatric Anxiety Ratings Scale; PARS) and categorical (Clinical Global Impression Scale-Improvement; CGI-I) outcome measures. Results Three baseline variables predicted better outcomes (independent of treatment condition) on the PARS, including low anxiety severity (as measured by parents and independent evaluators) and caregiver strain. No baseline variables were found to predict week 12 responder status (CGI-I). Participant's principal diagnosis moderated treatment outcomes, but only on the PARS. No baseline variables were found to moderate treatment outcomes on week 12 responder status (CGI-I). Discussion Overall, anxious children responded favorably to CAMS treatments. However, having more severe and impairing anxiety, greater caregiver strain, and a principal diagnosis of social phobia were associated with less favorable outcomes. Clinical implications of these findings are discussed. PMID:24417601

The pulse-mass index as a predictor of cardiovascular events in women with systemic lupus erythematosus.

PubMed

García-Villegas, Elsy Aidé; Márquez-González, Horacio; Flores-Suárez, Luis Felipe; Villa-Romero, Antonio Rafael

2017-01-20

Patients with systemic lupus erythematosus (SLE) have 3times the risk of death compared to the rest of the population, with cardiovascular events (CVD) being one of the main causes. Índices such as waist-height (W-Ht I), waist-hip (W-Hp I) and pulse-mass (PMI) predict CVD, though the behaviour is unknown in patients with SLE. The aim of this study was to determine the prognostic value of PMI in the development of CVD in premenopausal women with SLE. Cohort study. Included were premenopausal women with SLE without prior CVD; excluded were those patients with antiphospholipid syndrome (APS), pregnancy, thyroid disease, recent liposuction, and chronic kidney disease. Exposure variables were: PMI, W-Ht I, W-Hp I and metabolic syndrome at onset of the cohort. Considered confounding variables were time of evolution, disease activity, cumulative damage and treatment. Through semi-annual appointments, accident and emergency admittance and hospitalisation records the CVD were screened. Analysis was performed with Cox for proportional hazards and survival with Kaplan Meier. We included 238 women with a median age of 31 (18-52) years, with a follow-up of 8years. We identified 22 (9.6%) cases of CVD. In the Cox proportional hazards analysis, the prognostic variables were: PMI with HR=8.1 (95% CI: 1.1-65), metabolic syndrome with 2.4 (95% CI: 1-5.8), cumulative damage with HR=1.5 (95% CI: 1.1-2.2) and body fat percentage HR=2.8 (95% CI: 1.1-6.9) CONCLUSIONS: The PMI is a better predictor factor of CVD in women with SLE. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

[Predictors of the duration of non-work-related sick leave due to anxiety disorders].

PubMed

Catalina-Romero, Carlos; Martínez-Muñoz, Paloma; Quevedo-Aguado, Luis; Ruiz-Moraga, Montserrat; Fernández-Labandera, Carlos; Calvo-Bonacho, Eva

2013-01-01

To analyze the duration of non-work-related sick leave due to anxiety disorders and to identify demographic, occupational and clinical variables that may contribute to its prediction. We performed a prospective cohort study of 1,161 workers with an episode of non-work-related sick leave due to an anxiety disorder, belonging to the insured population of a mutual insurance company. We assessed the duration of non-work-related sick leave episodes and the main potentially related demographic, occupational and clinical variables. All non-work-related sick leave processes were followed-up until discharge. Cox regression analyses were conducted to establish the predictors of non-work-related sick leave duration. The median duration of non-work-related sick leave due to anxiety disorders was 83 days. In a multivariate analysis, the following factors were identified as being significantly associated with increases in the duration of sick leave (p <0.05): age of over 35 years, lower educational level (primary school studies, secondary school studies or high-school diploma vs. university degree), and the existence of comorbidity and unemployment occurring during the sick leave. In contrast, being separated or divorced was associated with an earlier return to work (p <0.05). Anxiety disorders are associated with long periods of non-work-related sick leave compared with other disorders and standard time duration. Demographic, occupational and clinical variables collected at the initial assessment of the sick leave episode would help to identify groups with an increased risk of prolonged sick leave, requiring strategies to facilitate return to work. Copyright © 2011 SESPAS. Published by Elsevier Espana. All rights reserved.

Religiousness as a Predictor of Alcohol Use in High School Students.

ERIC Educational Resources Information Center

Park, Hae-Seong; Bauer, Scott; Oescher, Jeffrey

2001-01-01

Examines the relationship between religiousness and alcohol use of adolescents based on a sample of high school seniors. Results provide support for examining religiousness variables as predictors of alcohol use patterns of adolescents. (Contains 16 references and 4 tables.) (GCP)

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time‐to‐Event Analysis

PubMed Central

Gong, Xiajing; Hu, Meng

2018-01-01

Abstract Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time‐to‐event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high‐dimensional data featured by a large number of predictor variables. Our results showed that ML‐based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high‐dimensional data. The prediction performances of ML‐based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML‐based methods provide a powerful tool for time‐to‐event analysis, with a built‐in capacity for high‐dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. PMID:29536640

Baseline Predictors for Success Following Strategy-Based Cognitive Remediation Group Training in Schizophrenia.

PubMed

Farreny, Aida; Aguado, Jaume; Corbera, Silvia; Ochoa, Susana; Huerta-Ramos, Elena; Usall, Judith

2016-08-01

Our aim was to examine predictive variables associated with the improvement in cognitive, clinical, and functional outcomes after outpatient participation in REPYFLEC strategy-based Cognitive Remediation (CR) group training. In addition, we investigated which factors might be associated with some long-lasting effects at 6 months' follow-up. Predictors of improvement after CR were studied in a sample of 29 outpatients with schizophrenia. Partial correlations were computed between targeted variables and outcomes of response to explore significant associations. Subsequently, we built linear regression models for each outcome variable and predictors of improvement. The improvement in negative symptoms at posttreatment was linked to faster performance in the Trail Making Test B. Disorganization and cognitive symptoms were related to changes in executive function at follow-up. Lower levels of positive symptoms were related to durable improvements in life skills. Levels of symptoms and cognition were associated with improvements following CR, but the pattern of resulting associations was nonspecific.

Anthropometry as a predictor of high speed performance.

PubMed

Caruso, J F; Ramey, E; Hastings, L P; Monda, J K; Coday, M A; McLagan, J; Drummond, J

2009-07-01

To assess anthropometry as a predictor of high-speed performance, subjects performed four seated knee- and hip-extension workouts with their left leg on an inertial exercise trainer (Impulse Technologies, Newnan GA). Workouts, done exclusively in either the tonic or phasic contractile mode, entailed two one-minute sets separated by a 90-second rest period and yielded three performance variables: peak force, average force and work. Subjects provided the following anthropometric data: height, weight, body mass index, as well as total, upper and lower left leg lengths. Via multiple regression, anthropometry attempted to predict the variance per performance variable. Anthropometry explained a modest (R2=0.27-0.43) yet significant degree of variance from inertial exercise trainer workouts. Anthropometry was a better predictor of peak force variance from phasic workouts, while it accounted for a significant degree of average force and work variance solely from tonic workouts. Future research should identify variables that account for the unexplained variance from high-speed exercise performance.

Predictors of Nursing Students' Performance in a One-Semester Organic and Biochemistry Course

NASA Astrophysics Data System (ADS)

van Lanen, Robert J.; Lockie, Nancy M.; McGannon, Thomas

2000-06-01

In an effort to empower nursing students to successfully persist in chemistry, predictors of success for undergraduate nursing students enrolled in a one-semester organic and biochemistry course were identified. The sample consisted of 308 undergraduate nursing students enrolled in Chemistry 108 (Principles of Organic and Biochemistry) during a period of seven semesters. In this study, Supplemental Instruction (SI) is a nonremedial academic support program offered for Chemistry 108 students. Placement tests in Mathematics, Reading, and English are required of all entering students. The English Placement Test assesses proficiency in analytical reading and writing; the Nelson Denny Reading Test (Form E) assesses the student's understanding of written vocabulary and the mastery of reading comprehension, and the Mathematics Placement Test measures the student's mastery of arithmetic and algebraic calculations. Both demographic and academic variables were examined. For the entire sample, five predictor variables were identified: Mathematics Placement Test score, Chemistry 107 grade (a prerequisite), total number of SI sessions attended, Nelson Denny Reading Test (Form E) score, and age. Predictors for various subpopulations of the sample were also identified. Predictors for students of traditional age were Mathematics Placement Test score, total number of SI sessions attended, and Chemistry 107 grade. The best predictors for continuing education students were Chemistry 107 grade and Nelson Denny Test score.

Predictors of posttreatment drinking outcomes in patients with alcohol dependence.

PubMed

Flórez, Gerardo; Saiz, Pilar A; García-Portilla, Paz; De Cos, Francisco J; Dapía, Sonia; Alvarez, Sandra; Nogueiras, Luis; Bobes, Julio

2015-01-01

This cohort study examined how predictors of alcohol dependence treatment outcomes work together over time by comparing pretreatment and posttreatment predictors. A sample of 274 alcohol-dependent patients was recruited and assessed at baseline, 6 months after treatment initiation (end of the active intervention phase), and 18 months after treatment initiation (end of the 12-month research follow-up phase). At each assessment point, the participants completed a battery of standardized tests [European Addiction Severity Index (EuropASI), Obsessive Compulsive Drinking Scale (OCDS), Alcohol Timeline Followback (TLFB), Fagerström, and International Personality Disorder Examination (IPDE)] that measured symptom severity and consequences; biological markers of alcohol consumption were also tested at each assessment point. A sequential strategy with univariate and multivariate analyses was used to identify how pretreatment and posttreatment predictors influence outcomes up to 1 year after treatment. Pretreatment variables had less predictive power than posttreatment ones. OCDS scores and biological markers of alcohol consumption were the most significant variables for the prediction of posttreatment outcomes. Prior pharmacotherapy treatment and relapse prevention interventions were also associated with posttreatment outcomes. The findings highlight the positive impact of pharmacotherapy during the first 6 months after treatment initiation and of relapse prevention during the first year after treatment and how posttreatment predictors are more important than pretreatment predictors.

[The clinical predictors of heteroaggressive behaviour of the women serving sentence in penitentiary].

PubMed

Shaklein, K N; Bardenshtein, L M; Demcheva, N K

To identify clinical predictors of heteroaggressive behavior. Three hundreds and three women serving sentence in a penal colony were examined using clinical, neurologic and statistical methods. The main group consisted of 225 women with heteroaggressive behavior, the control group included 78 women without aggressive behavior. Differences between the main and control groups in the structure of mental disorders and key syndromes were revealed. The authors conclude that the states with elements of dysphoria, dysthymia, decompensation of personality disorders, which are defined in the various forms of mental pathology, are the most significant predictors of heteroaggressive behavior in women in the penal colony.

Influence of economic and demographic factors on quality of life in renal transplant recipients.

PubMed

Chisholm, Marie A; Spivey, Christina A; Nus, Audrey Van

2007-01-01

The purpose of this study was to determine the influence of annual income, Medicare status, and demographic variables on the health-related quality of life (HQoL) of renal transplant recipients. A cross-sectional survey was mailed to 146 Georgia renal transplant recipients who had functional grafts. Data were collected using the SF-12 Health Survey (version 2), a demographics survey, and 2003 tax documents. One-way ANOVAs and Pearson's R correlations were used to examine relationships between annual income, Medicare status, demographic variables and SF-12 scores. Significant variables were included in stepwise multiple regression analyses. Data from 130 participants (89% response rate) were collected. Recipients with no Medicare coverage had significantly higher scores on the Physical Functioning and Role Physical SF-12 scales (p = 0.005) compared to recipients with Medicare. Annual income was positively correlated with General Health (p < 0.05). Age and race were significant predictors of Vitality (p = 0.004) and Physical Component Summary (p < 0.001) scores. Age, race, and Medicare status were significant predictors of Physical Functioning and Role Physical scores (p < 0.001). Age, annual income, race, and years post-transplant were significant predictors of General Health score (p < 0.001). Age was the sole predictor of Bodily Pain score (p = 0.002), and marital status was the sole predictor of Social Functioning score (p = 0.005). Interventions designed to offset financial barriers may be needed to bolster renal transplant recipients' HQoL.

Work life and mental wellbeing of single and non-single working mothers in Scandinavia.

PubMed

Bull, Torill; Mittelmark, Maurice B

2009-08-01

This study examined levels and predictors of mental wellbeing in Scandinavian working single and non-single mothers, with a special focus on financial stress, job characteristics and work-family conflict. The European Social Survey Round 2 (2005) provided questionnaire data from 73 single and 432 non-single working mothers in Denmark, Sweden and Norway. Respondents answered questions about the outcome variables life satisfaction, happiness, and positive affect, and predictor variables financial stress, job characteristics, work-family conflict, and social support. Hierarchical multiple regression was used to assess the relationships between predictor variables and mental wellbeing outcomes. Single working mothers scored significantly lower on life satisfaction and happiness, but not on positive affect, than did non-single mothers. Financial stress was higher in the single mother group. There were no significant differences in levels of enriching or stressful job characteristics, or in levels of social support. While financial stress and work-family conflict were important predictors in both groups, the relationship between financial stress and wellbeing was far stronger in the single mother group. Confidant support was a significant predictor only in the single mother group, and social participation only in the non-single mothers group. This study suggests that the Scandinavian welfare democracies have not yet been successful in relieving the financial pressure experienced by single working mothers. Development of efficient financial support systems should be prioritized. Ways to reduce work-family conflict in both single and non-single mothers in Scandinavia should also be given increased attention.

Predictors of health-related and global quality of life among young adults with difficult-to-treat epilepsy and mild intellectual disability.

PubMed

Endermann, Michael

2013-02-01

This study evaluated predictors of health-related quality of life (HRQOL) and global quality of life (QOL) among young adults with difficult-to-treat epilepsy and mild intellectual disability. One hundred and forty-two persons with epilepsy and cognitive problems were routinely screened on HRQOL, global QOL, and psychological distress four weeks after admission to a time-limited residential rehabilitation unit. The PESOS scales (PE = PErformance, SO = SOciodemographic aspects, S = Subjective evaluation/estimation) on epilepsy-specific problems were administered as measures of HRQOL; a questionnaire on life satisfaction and an item on overall QOL were used as measures of global QOL. Psychological distress was captured with the Symptom Checklist 90-R. Further data were gained from medical files. Quality-of- life predictors were identified using univariate methods and stepwise regression analyses. Psychological distress was the only predictor of all HRQOL and global QOL parameters. Seizure frequency was a predictor of most HRQOL variables. Other epilepsy variables affected only some HRQOL variables but were not associated with global QOL. Health-related quality of life did not seem to be strongly impaired. Only low correlations were found between HRQOL and global QOL. The notion of psychological distress as the most influential predictor of all QOL measures is in line with most findings on QOL in epilepsy. Former observations of weak associations between HRQOL and global QOL among patients with epilepsy and mild intellectual disability are supported. Thus, interventions to reduce psychological distress, besides epilepsy treatment, seem to be of great importance to improve QOL. Copyright © 2012 Elsevier Inc. All rights reserved.

Predictors of free flap loss in the head and neck region: A four-year retrospective study with 451 microvascular transplants at a single centre.

PubMed

Mücke, Thomas; Ritschl, Lucas M; Roth, Maximilian; Güll, Florian D; Rau, Andrea; Grill, Sonja; Kesting, Marco R; Wolff, Klaus-Dietrich; Loeffelbein, Denys J

2016-09-01

Microvascular free flaps have become an essential part of reconstructive surgery following head and neck tumour ablation. The authors' aim was to investigate the influence of cardiovascular risk factors, preoperative irradiation, previous operations and metabolically active medication on free flap loss in order to predict patients at risk and to improve their therapy. All patients who underwent reconstructive surgery with microvascular free flaps in the head and neck region between 2009 and 2013 were retrospectively analysed. Uni- and multivariate logistic regressions were performed to determine the association between possible predictor variables for free flap loss. We included 451 patients in our analysis. The overall free flap failure rate was 4.0%. Multivariate regression analysis revealed significantly increased risks of free flap failure depending on prior attempts at microvascular transplants (p < 0.001, OR = 14.21) and length of hospitalisation (p = 0.007, OR = 1.05). With consistently low rates of flap failure, microvascular reconstruction of defects in the head and neck region has proven to be highly reliable, even in patients with comorbidities. The expertise of the operating team seems to remain the main factor affecting flap success. The only discerned independent predictor was previously failed attempts at microvascular reconstruction. Copyright © 2016 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

[Productivity costs of rheumatoid arthritis in Germany. Cost composition and prediction of main cost components].

PubMed

Merkesdal, S; Huelsemann, J L; Mittendorf, T; Zeh, S; Zeidler, H; Ruof, J

2006-10-01

Identification of predictors for the productivity cost components: (1) sick leave, and (2) work disability in gainfully employed and (3) impaired household productivity in unemployed patients with rheumatoid arthritis (RA) from the societal perspective. Investigation of productivity costs was linked to a multicenter, randomized, controlled trial evaluating the effectiveness of clinical quality management in 338 patients with RA. The productivity losses were assessed according to the German Guidelines on Health Economic Evaluation. By means of multivariate logistic regression analyses, predictors of sick leave, work disability (employed patients, n=96), and for days confined to bed in unemployed patient (n=242) were determined. Mean annual costs of 970 EUR arose per person taking into consideration all patients (453 EUR sick leave, 63 EUR work disability, 454 EUR impaired productivity of unemployed patients). Disease activity, disease severity, and impaired physical function were global predictors for all of the cost components investigated. Sick leave costs were predicted by prior sick leave periods and the vocational status blue collar worker, work disability costs by sociodemographic variables (marital status, schooling), and the productivity costs of unemployed patients by impaired mental health and impaired physical functions. Interventions such as reduction in disease progression and control of disease activity, early vocational rehabilitation measures and vocational retraining in patients at risk of quitting working life, and self-management programs to learn coping strategies might decrease future RA-related productivity costs.

Continuation Power Flow with Variable-Step Variable-Order Nonlinear Predictor

NASA Astrophysics Data System (ADS)

Kojima, Takayuki; Mori, Hiroyuki

This paper proposes a new continuation power flow calculation method for drawing a P-V curve in power systems. The continuation power flow calculation successively evaluates power flow solutions through changing a specified value of the power flow calculation. In recent years, power system operators are quite concerned with voltage instability due to the appearance of deregulated and competitive power markets. The continuation power flow calculation plays an important role to understand the load characteristics in a sense of static voltage instability. In this paper, a new continuation power flow with a variable-step variable-order (VSVO) nonlinear predictor is proposed. The proposed method evaluates optimal predicted points confirming with the feature of P-V curves. The proposed method is successfully applied to IEEE 118-bus and IEEE 300-bus systems.

Pursuit of STEM: Factors shaping degree completion for African American females in STEM

NASA Astrophysics Data System (ADS)

Wilkins, Ashlee N.

The primary purpose of the study was to examine secondary data from the Cooperative Institutional Research Program (CIRP) Freshman and College Senior Surveys to investigate factors shaping degree aspirations for African American female undergraduates partaking in science, technology, engineering, and mathematics (STEM) majors. Hierarchical multiple regression was used to analyze the data and identify relationships between independent variables in relation to the dependent variable. The findings of the study reveal four key variables that were predictive of degree completion for African American females in STEM. Father's education, SAT composite, highest degree planned, and self-perception were positive predictors for females; while independent variable overall sense of community among students remained a negative predictor. Lastly implications for education and recommendations for future research were discussed.

Analysis of Setting Efficacy in Young Male and Female Volleyball Players.

PubMed

González-Silva, Jara; Domínguez, Alberto Moreno; Fernández-Echeverría, Carmen; Rabaz, Fernando Claver; Arroyo, M Perla Moreno

2016-12-01

The main objective of this study was to analyse the variables that predicted setting efficacy in complex I (KI) in volleyball, in formative categories and depending on gender. The study sample was comprised of 5842 game actions carried out by the 16 male category and the 18 female category teams that participated in the Under-16 Spanish Championship. The dependent variable was setting efficacy. The independent variables were grouped into: serve variables (a serve zone, the type of serve, striking technique, an in-game role of the server and serve direction), reception variables (a reception zone, a receiver player and reception efficacy) and setting variables (a setter's position, a setting zone, the type of a set, setting technique, a set's area and tempo of a set). Multinomial logistic regression showed that the best predictive variables of setting efficacy, both in female and male categories, were reception efficacy, setting technique and tempo of a set. In the male category, the jump serve was the greatest predictor of setting efficacy, while in the female category, it was the set's area. Therefore, in the male category, it was not only the preceding action that affected setting efficacy, but also the serve. On the contrary, in the female category, only variables of the action itself and of the previous action, reception, affected setting efficacy. The results obtained in the present study should be taken into account in the training process of both male and female volleyball players in formative stages.

Treatment processes and demographic variables as predictors of dropout from trauma-focused cognitive behavioral therapy (TF-CBT) for youth.

PubMed

Yasinski, Carly; Hayes, Adele M; Alpert, Elizabeth; McCauley, Thomas; Ready, C Beth; Webb, Charles; Deblinger, Esther

2018-05-22

Premature dropout is a significant concern in trauma-focused psychotherapy for youth. Previous studies have primarily examined pre-treatment demographic and symptom-related predictors of dropout, but few consistent findings have been reported. The current study examined demographic, symptom, and in-session process variables as predictors of dropout from Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) for youth. Participants were a diverse sample of Medicaid-eligible youth (ages 7-17; n = 108) and their nonoffending caregivers (n = 86), who received TF-CBT through an effectiveness study in a community setting. In-session process variables were coded from audio-recorded sessions, and these and pre-treatment demographic variables and symptom levels were examined as predictors of dropout prior to receiving an adequate dose of TF-CBT (<7 sessions). Twenty-nine children were classified as dropouts and 79 as completers. Binary logistic regression analyses revealed that higher levels of child and caregiver avoidance expressed during early sessions, as well as greater relationship difficulties between the child and therapist, predicted dropout. Those children who were in foster care during treatment were less likely to drop out than children living with parents or relatives. No other demographic or symptom-related factors predicted dropout. These findings highlight the importance of addressing avoidance and therapeutic relationship difficulties in early sessions of TF-CBT to help reduce dropout, and they have implications for improving efforts to disseminate evidence-based trauma-focused treatments. Copyright © 2018 Elsevier Ltd. All rights reserved.

Drivers for spatial variability in agricultural soil organic carbon stocks in Germany

NASA Astrophysics Data System (ADS)

Vos, Cora; Don, Axel; Hobley, Eleanor; Prietz, Roland; Heidkamp, Arne; Freibauer, Annette

2017-04-01

Soil organic carbon is one of the largest components of the global carbon cycle. It has recently gained importance in global efforts to mitigate climate change through carbon sequestration. In order to find locations suitable for carbon sequestration, and estimate the sequestration potential, however, it is necessary to understand the factors influencing the high spatial variability of soil organic carbon stocks. Due to numerous interacting factors that influence its dynamics, soil organic carbon stocks are difficult to predict. In the course of the German Agricultural Soil Inventory over 2500 agricultural sites were sampled and their soil organic carbon stocks determined. Data relating to more than 200 potential drivers of SOC stocks were compiled from laboratory measurements, farmer questionnaires and climate stations. The aims of this study were to 1) give an overview of soil organic carbon stocks in Germany's agricultural soils, 2) to quantify and explain the influence of explanatory variables on soil organic carbon stocks. Two different machine learning algorithms were used to identify the most important variables and multiple regression models were used to explore the influence of those variables. Models for predicting carbon stocks in different depth increments between 0-100 cm were developed, explaining up to 62% (validation, 98% calibration) of total variance. Land-use, land-use history, clay content and electrical conductivity were main predictors in the topsoil, while bedrock material, relief and electrical conductivity governed the variability of subsoil carbon stocks. We found 32% of all soils to be deeply anthropogenically transformed. The influence of climate related variables was surprisingly small (≤5% of explained variance), while site variables explained a large share of soil carbon variability (46-100% of explained variance), in particular in the subsoil. Thus, the understanding of SOC dynamics at regional scale requires a thorough description of the variability in soil physical parameters. Agronomic management impact on SOC stocks is important near the soil surface, but is mainly attributable to land-use and not to other management factors on this large regional scale. The importance of historical land-use practices as well as anthropogenic soil transformations to SOC stocks highlights the need for prudent soil management and conservation policies.

Impact of marriage on HIV/AIDS risk behaviors among impoverished, at-risk couples: a multilevel latent variable approach.

PubMed

Stein, Judith A; Nyamathi, Adeline; Ullman, Jodie B; Bentler, Peter M

2007-01-01

Studies among normative samples generally demonstrate a positive impact of marriage on health behaviors and other related attitudes. In this study, we examine the impact of marriage on HIV/AIDS risk behaviors and attitudes among impoverished, highly stressed, homeless couples, many with severe substance abuse problems. A multilevel analysis of 368 high-risk sexually intimate married and unmarried heterosexual couples assessed individual and couple-level effects on social support, substance use problems, HIV/AIDS knowledge, perceived HIV/AIDS risk, needle-sharing, condom use, multiple sex partners, and HIV/AIDS testing. More variance was explained in the protective and risk variables by couple-level latent variable predictors than by individual latent variable predictors, although some gender effects were found (e.g., more alcohol problems among men). The couple-level variable of marriage predicted lower perceived risk, less deviant social support, and fewer sex partners but predicted more needle-sharing.

Statistical prediction of seasonal discharge in Central Asia for water resources management: development of a generic (pre-)operational modeling tool

NASA Astrophysics Data System (ADS)

Apel, Heiko; Baimaganbetov, Azamat; Kalashnikova, Olga; Gavrilenko, Nadejda; Abdykerimova, Zharkinay; Agalhanova, Marina; Gerlitz, Lars; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Gafurov, Abror

2017-04-01

The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien-Shan and Pamirs. During the summer months the snow and glacier melt dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for a sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydromet services, this study aims at the development of a generic tool for deriving statistical forecast models of seasonal river discharge. The generic model is kept as simple as possible in order to be driven by available hydrological and meteorological data, and be applicable for all catchments with their often limited data availability in the region. As snowmelt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature as recorded by climatological stations in the catchments. These data sets are accompanied by snow cover predictors derived from the operational ModSnow tool, which provides cloud free snow cover data for the selected catchments based on MODIS satellite images. In addition to the meteorological data antecedent streamflow is used as a predictor variable. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to 3 or 4 predictors. A user selectable number of best models according to pre-defined performance criteria is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross validation. Based on the cross validation the predictive uncertainty was quantified for every prediction model. According to the official procedures of the hydromet services forecasts of the mean seasonal discharge of the period April to September are derived every month starting from January until June. The application of the model for several catchments in Central Asia - ranging from small to the largest rivers - for the period 2000-2015 provided skillful forecasts for most catchments already in January. The skill of the prediction increased every month, with R2 values often in the range 0.8 - 0.9 in April just before the prediction period. The forecasts further improve in the following months, most likely due to the integration of spring precipitation, which is not included in the predictors before May, or spring discharge, which contains indicative information for the overall seasonal discharge. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of eventual operational implementation.

Main predictors for repetition of suicidal behaviour among women referred to a single public sector tertiary care hospital in Iran.

PubMed

Mostafazadeh, Babak; Farzaneh, Esmaeil

2017-09-01

To assess the main predictors for repetition of suicidal behaviour among women. This cross-sectional study was conducted at Loghman Hakim Hospital, Tehran, Iran, in 2014, and comprised women patients. The patients were divided into two groups, i.e. women repeating suicide and women without repeating suicide. Data was collected through a checklist and then analysed with SPSS 20. Of the 300 women, 121(40.3%) repeated suicide and 179(59.7%) did not. The overall mean age was 26.9±9.1 years (range: 14-80 years). High prevalence of psychological drug usage, alcohol use, history of self-mutilation (self-harm), psychotic disturbances, sexual relationships, as well as smoking and opium addition was revealed as major factors in repeated suicidal behaviour in women when compared with other women. The result of multivariate logistic regression model showed two factors of self-mutilation (odds ratio =2.692, p=0.002) and underlying psychotic disorders (odds ratio = 2.780, p<0.001) as main predictors of suicide in women. In this regard, demographic characteristics could not predict repeating suicidal attempts (p>0.05). The presence of underlying psychotic disorders and self-mutilation were main predictors for repetition of suicidal behaviour.

Crisis Management Research Summaries

ERIC Educational Resources Information Center

Brock, Stephen E., Ed.; Zhe, Elizabeth; Torem, Chris; Comeaux, Natashia; Dempsey, Allison

2010-01-01

This article presents a summary of recent crisis management publications. The first research report summarized, "Predictors of PTSD," was a study of predictor variables for responses to the World Trade Center attack. The second paper, "Effective Mental Health Response to Catastrophic Events," looked at effective responses following Hurricane…

Predictors of Immigrant Children's School Achievement: A Comparative Study

ERIC Educational Resources Information Center

Moon, Sung Seek; Kang, Suk-Young; An, Soonok

2009-01-01

This paper examines the predictors and indicators of immigrant children's school achievement, using the two of the most predominant groups of American immigrants (103 Koreans and 100 Mexicans). Regression analyses were conducted to determine which independent variables (acculturation, parenting school involvement, parenting style, parent…

Sex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos.

PubMed

Shah, Neomi; Hanna, David B; Teng, Yanping; Sotres-Alvarez, Daniela; Hall, Martica; Loredo, Jose S; Zee, Phyllis; Kim, Mimi; Yaggi, H Klar; Redline, Susan; Kaplan, Robert C

2016-06-01

We developed and validated the first-ever sleep apnea (SA) risk calculator in a large population-based cohort of Hispanic/Latino subjects. Cross-sectional data on adults from the Hispanic Community Health Study/Study of Latinos (2008-2011) were analyzed. Subjective and objective sleep measurements were obtained. Clinically significant SA was defined as an apnea-hypopnea index ≥ 15 events per hour. Using logistic regression, four prediction models were created: three sex-specific models (female-only, male-only, and a sex × covariate interaction model to allow differential predictor effects), and one overall model with sex included as a main effect only. Models underwent 10-fold cross-validation and were assessed by using the C statistic. SA and its predictive variables; a total of 17 variables were considered. A total of 12,158 participants had complete sleep data available; 7,363 (61%) were women. The population-weighted prevalence of SA (apnea-hypopnea index ≥ 15 events per hour) was 6.1% in female subjects and 13.5% in male subjects. Male-only (C statistic, 0.808) and female-only (C statistic, 0.836) prediction models had the same predictor variables (ie, age, BMI, self-reported snoring). The sex-interaction model (C statistic, 0.836) contained sex, age, age × sex, BMI, BMI × sex, and self-reported snoring. The final overall model (C statistic, 0.832) contained age, BMI, snoring, and sex. We developed two websites for our SA risk calculator: one in English (https://www.montefiore.org/sleepapneariskcalc.html) and another in Spanish (http://www.montefiore.org/sleepapneariskcalc-es.html). We created an internally validated, highly discriminating, well-calibrated, and parsimonious prediction model for SA. Contrary to the study hypothesis, the variables did not have different predictive magnitudes in male and female subjects. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

The Simulation of Daily Temperature Time Series from GCM Output. Part II: Sensitivity Analysis of an Empirical Transfer Function Methodology.

NASA Astrophysics Data System (ADS)

Winkler, Julie A.; Palutikof, Jean P.; Andresen, Jeffrey A.; Goodess, Clare M.

1997-10-01

Empirical transfer functions have been proposed as a means for `downscaling' simulations from general circulation models (GCMs) to the local scale. However, subjective decisions made during the development of these functions may influence the ensuing climate scenarios. This research evaluated the sensitivity of a selected empirical transfer function methodology to 1) the definition of the seasons for which separate specification equations are derived, 2) adjustments for known departures of the GCM simulations of the predictor variables from observations, 3) the length of the calibration period, 4) the choice of function form, and 5) the choice of predictor variables. A modified version of the Climatological Projection by Model Statistics method was employed to generate control (1 × CO2) and perturbed (2 × CO2) scenarios of daily maximum and minimum temperature for two locations with diverse climates (Alcantarilla, Spain, and Eau Claire, Michigan). The GCM simulations used in the scenario development were from the Canadian Climate Centre second-generation model (CCC GCMII).Variations in the downscaling methodology were found to have a statistically significant impact on the 2 × CO2 climate scenarios, even though the 1 × CO2 scenarios for the different transfer function approaches were often similar. The daily temperature scenarios for Alcantarilla and Eau Claire were most sensitive to the decision to adjust for deficiencies in the GCM simulations, the choice of predictor variables, and the seasonal definitions used to derive the functions (i.e., fixed seasons, floating seasons, or no seasons). The scenarios were less sensitive to the choice of function form (i.e., linear versus nonlinear) and to an increase in the length of the calibration period.The results of Part I, which identified significant departures of the CCC GCMII simulations of two candidate predictor variables from observations, together with those presented here in Part II, 1) illustrate the importance of detailed comparisons of observed and GCM 1 × CO2 series of candidate predictor variables as an initial step in impact analysis, 2) demonstrate that decisions made when developing the transfer functions can have a substantial influence on the 2 × CO2 scenarios and their interpretation, 3) highlight the uncertainty in the appropriate criteria for evaluating transfer function approaches, and 4) suggest that automation of empirical transfer function methodologies is inappropriate because of differences in the performance of transfer functions between sites and because of spatial differences in the GCM's ability to adequately simulate the predictor variables used in the functions.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Flood regionalization: A hybrid geographic and predictor-variable region-of-influence regression method

USGS Publications Warehouse

Eng, K.; Milly, P.C.D.; Tasker, Gary D.

2007-01-01

To facilitate estimation of streamflow characteristics at an ungauged site, hydrologists often define a region of influence containing gauged sites hydrologically similar to the estimation site. This region can be defined either in geographic space or in the space of the variables that are used to predict streamflow (predictor variables). These approaches are complementary, and a combination of the two may be superior to either. Here we propose a hybrid region-of-influence (HRoI) regression method that combines the two approaches. The new method was applied with streamflow records from 1,091 gauges in the southeastern United States to estimate the 50-year peak flow (Q50). The HRoI approach yielded lower root-mean-square estimation errors and produced fewer extreme errors than either the predictor-variable or geographic region-of-influence approaches. It is concluded, for Q50 in the study region, that similarity with respect to the basin characteristics considered (area, slope, and annual precipitation) is important, but incomplete, and that the consideration of geographic proximity of stations provides a useful surrogate for characteristics that are not included in the analysis. ?? 2007 ASCE.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss.

PubMed

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. © 2016 S. Karger GmbH, Freiburg.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss

PubMed Central

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

Objective To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. Methods 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. Results 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Conclusion Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. PMID:26745715

Neonatal intensive care unit: predictive models for length of stay.

PubMed

Bender, G J; Koestler, D; Ombao, H; McCourt, M; Alskinis, B; Rubin, L P; Padbury, J F

2013-02-01

Hospital length of stay (LOS) is important to administrators and families of neonates admitted to the neonatal intensive care unit (NICU). A prediction model for NICU LOS was developed using predictors birth weight, gestational age and two severity of illness tools, the score for neonatal acute physiology, perinatal extension (SNAPPE) and the morbidity assessment index for newborns (MAIN). Consecutive admissions (n=293) to a New England regional level III NICU were retrospectively collected. Multiple predictive models were compared for complexity and goodness-of-fit, coefficient of determination (R (2)) and predictive error. The optimal model was validated prospectively with consecutive admissions (n=615). Observed and expected LOS was compared. The MAIN models had best Akaike's information criterion, highest R (2) (0.786) and lowest predictive error. The best SNAPPE model underestimated LOS, with substantial variability, yet was fairly well calibrated by birthweight category. LOS was longer in the prospective cohort than the retrospective cohort, without differences in birth weight, gestational age, MAIN or SNAPPE. LOS prediction is improved by accounting for severity of illness in the first week of life, beyond factors known at birth. Prospective validation of both MAIN and SNAPPE models is warranted.

Transactional sex and economic exchange with partners among young South African men in the rural Eastern Cape: prevalence, predictors, and associations with gender-based violence

PubMed Central

Dunkle, Kristin L; Jewkes, Rachel; Nduna, Mzikazi; Jama, Nwabisa; Levin, Jonathan; Sikweyiya, Yandisa; Koss, Mary P

2009-01-01

We explored the prevalence and predictors of transactional sex with casual partners and main girlfriends among 1,288 men aged 15-26 from 70 villages in the rural Eastern Cape province of South Africa. Data were collected through face-to-face interviews with young men enrolling in the Stepping Stones HIV prevention trial. A total of 17.7% of participants reported giving material resources or money to casual sex partners and 6.6% received resources from a casual partner. Transactionally motivated relationships with main girlfriends were more balanced between giving (14.9%) and getting (14.3%). We constructed multivariable models to identify the predictors for giving and for getting material resources in casual and in main relationships. Each model resulted in remarkably similar predictors. All four types of exchange were associated with higher socio-economic status, more adverse childhood experiences, more lifetime sexual partners, and alcohol use. Men who were more resistant to peer pressure to have sex were less likely to report transactional sex with casual partners, and men who reported more equitable gender attitudes were less likely to report main partnerships underpinned by exchange. The most consistent predictor of all four types of transaction was the perpetration of intimate partner violence and rape against women other than a main partner. The strong and consistent association between perpetration of gender-based violence and both giving and getting material goods from female partners suggests that transactional sex in both main and casual relationships can be viewed within a broader continuum of men's exercise of gendered power and control. HIV prevention interventions need to explicitly address transactional sex in the context of ideas about masculinity which place a high emphasis on heterosexual success with, and control of, women. PMID:17560702

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison.

PubMed

Vervloet, Marlies; Van den Noortgate, Wim; Ceulemans, Eva

2018-02-12

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights' estimates unstable (i.e., the "bouncing beta" problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.

Predicting the onset of smoking in boys and girls.

PubMed

Charlton, A; Blair, V

1989-01-01

The problem of the high prevalence of smoking among girls and young women is of great concern. In an attempt to identify the factors which influence girls and boys respectively to attempt smoking, the study examines social background, advertising and brand awareness, knowledge, teaching and personal beliefs in conjunction as predictors of smoking. In this study which involved the administration of identical pre- and post-test questionnaires to a sample of boys and girls aged 12 and 13 years, nine variables expressed by never-smokers at pre-test stage were assessed as predictors of immediate future smoking. The two tests were administered 4 months apart to 1125 boys and 1213 girls in northern England. The nine variables included were parental smoking, best friends' smoking, perceived positive values of smoking, perceived negative values of smoking, correct health knowledge, cigarette-brand awareness, having a favourite cigarette advertisement, having a cigarette-brand sponsored sport in four top favourites on television. One group received teaching about smoking between the pre- and post-tests and this was also included as a variable. For boys, no variable investigated had any consistently statistically significant correlation with the uptake of smoking. The most important predictor of smoking for boys, having a best friend who smoked, was significant on application of the chi 2 test (P 0.037), although it was non-significant when included singly in a logistic regression model (0.094); the discrepancy was probably due to the small number of best friends known to smoke. For girls, four variables were found to be significant predictors of smoking when included singly in a logistic regression.(ABSTRACT TRUNCATED AT 250 WORDS)

SCD-HeFT: Use of RR Interval Statistics for Long-term Risk Stratification for Arrhythmic Sudden Cardiac Death

PubMed Central

Au-yeung, Wan-tai M.; Reinhall, Per; Poole, Jeanne E.; Anderson, Jill; Johnson, George; Fletcher, Ross D.; Moore, Hans J.; Mark, Daniel B.; Lee, Kerry L.; Bardy, Gust H.

2015-01-01

Background In the SCD-HeFT a significant fraction of the congestive heart failure (CHF) patients ultimately did not die suddenly from arrhythmic causes. CHF patients will benefit from better tools to identify if ICD therapy is needed. Objective To identify predictor variables from baseline SCD-HeFT patients’ RR intervals that correlate to arrhythmic sudden cardiac death (SCD) and mortality and to design an ICD therapy screening test. Methods Ten predictor variables were extracted from pre-randomization Holter data from 475 patients enrolled in the SCD-HeFT ICD arm using novel and traditional heart rate variability methods. All variables were correlated to SCD using Mann Whitney-Wilcoxon test and receiver operating characteristic analysis. ICD therapy screening tests were designed by minimizing the cost of false classifications. Survival analysis, including log-rank test and Cox models, was also performed. Results α1 and α2 from detrended fluctuation analysis, the ratio of low to high frequency power, the number of PVCs per hour and heart rate turbulence slope are all statistically significant for predicting the occurrences of SCD (p<0.001) and survival (log-rank p<0.01). The most powerful multivariate predictor tool using the Cox Proportional Hazards was α2 with a hazard ratio of 0.0465 (95% CI: 0.00528 – 0.409, p<0.01). Conclusion Predictor variables from RR intervals correlate to the occurrences of SCD and distinguish survival among SCD-HeFT ICD patients. We believe SCD prediction models should incorporate Holter based RR interval analysis to refine ICD patient selection especially in removing patients who are unlikely to benefit from ICD therapy. PMID:26096609

Short-term dynamics of indoor and outdoor endotoxin exposure: Case of Santiago, Chile, 2012.

PubMed

Barraza, Francisco; Jorquera, Héctor; Heyer, Johanna; Palma, Wilfredo; Edwards, Ana María; Muñoz, Marcelo; Valdivia, Gonzalo; Montoya, Lupita D

2016-01-01

Indoor and outdoor endotoxin in PM2.5 was measured for the very first time in Santiago, Chile, in spring 2012. Average endotoxin concentrations were 0.099 and 0.094 [EU/m(3)] for indoor (N=44) and outdoor (N=41) samples, respectively; the indoor-outdoor correlation (log-transformed concentrations) was low: R=-0.06, 95% CI: (-0.35 to 0.24), likely owing to outdoor spatial variability. A linear regression model explained 68% of variability in outdoor endotoxins, using as predictors elemental carbon (a proxy of traffic emissions), chlorine (a tracer of marine air masses reaching the city) and relative humidity (a modulator of surface emissions of dust, vegetation and garbage debris). In this study, for the first time a potential source contribution function (PSCF) was applied to outdoor endotoxin measurements. Wind trajectory analysis identified upwind agricultural sources as contributors to the short-term, outdoor endotoxin variability. Our results confirm an association between combustion particles from traffic and outdoor endotoxin concentrations. For indoor endotoxins, a predictive model was developed but it only explained 44% of endotoxin variability; the significant predictors were tracers of indoor PM2.5 dust (Si, Ca), number of external windows and number of hours with internal doors open. Results suggest that short-term indoor endotoxin variability may be driven by household dust/garbage production and handling. This would explain the modest predictive performance of published models that use answers to household surveys as predictors. One feasible alternative is to increase the sampling period so that household features would arise as significant predictors of long-term airborne endotoxin levels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictors of stroke in patients with impaired glucose tolerance: results from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research trial.

PubMed

Preiss, David; Giles, Thomas D; Thomas, Laine E; Sun, Jie-Lena; Haffner, Steven M; Holman, Rury R; Standl, Eberhard; Mazzone, Theodore; Rutten, Guy E; Tognoni, Gianni; Chiang, Fu-Tien; McMurray, John J V; Califf, Robert M

2013-09-01

Risk factors for stroke are well-established in general populations but sparsely studied in individuals with impaired glucose tolerance. We identified predictors of stroke among participants with impaired glucose tolerance in the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial. Cox proportional-hazard regression models were constructed using baseline variables, including the 2 medications studied, valsartan and nateglinide. Among 9306 participants, 237 experienced a stroke over 6.4 years. Predictors of stroke included classical risk factors such as existing cerebrovascular and coronary heart disease, higher pulse pressure, higher low-density lipoprotein cholesterol, older age, and atrial fibrillation. Other factors, including previous venous thromboembolism, higher waist circumference, lower estimated glomerular filtration rate, lower heart rate, and lower body mass index, provided additional important predictive information, yielding a C-index of 0.72. Glycemic measures were not predictive of stroke. Variables associated with stroke were similar in participants with no prior history of cerebrovascular disease at baseline. The most powerful predictors of stroke in patients with impaired glucose tolerance included a combination of established risk factors and novel variables, such as previous venous thromboembolism and elevated waist circumference, allowing moderately effective identification of high-risk individuals.

Predictors of intelligence at the age of 5: family, pregnancy and birth characteristics, postnatal influences, and postnatal growth.

PubMed

Eriksen, Hanne-Lise Falgreen; Kesmodel, Ulrik Schiøler; Underbjerg, Mette; Kilburn, Tina Røndrup; Bertrand, Jacquelyn; Mortensen, Erik Lykke

2013-01-01

Parental education and maternal intelligence are well-known predictors of child IQ. However, the literature regarding other factors that may contribute to individual differences in IQ is inconclusive. The aim of this study was to examine the contribution of a number of variables whose predictive status remain unclarified, in a sample of basically healthy children with a low rate of pre- and postnatal complications. 1,782 5-year-old children sampled from the Danish National Birth Cohort (2003-2007) were assessed with a short form of the Wechsler Preschool and Primary Scale of Intelligence - Revised. Information on parental characteristics, pregnancy and birth factors, postnatal influences, and postnatal growth was collected during pregnancy and at follow-up. A model including study design variables and child's sex explained 7% of the variance in IQ, while parental education and maternal IQ increased the explained variance to 24%. Other predictors were parity, maternal BMI, birth weight, breastfeeding, and the child's head circumference and height at follow-up. These variables, however, only increased the explained variance to 29%. The results suggest that parental education and maternal IQ are major predictors of IQ and should be included routinely in studies of cognitive development. Obstetrical and postnatal factors also predict IQ, but their contribution may be of comparatively limited magnitude.

Preinjury somatization symptoms contribute to clinical recovery after sport-related concussion.

PubMed

Nelson, Lindsay D; Tarima, Sergey; LaRoche, Ashley A; Hammeke, Thomas A; Barr, William B; Guskiewicz, Kevin; Randolph, Christopher; McCrea, Michael A

2016-05-17

To determine the degree to which preinjury and acute postinjury psychosocial and injury-related variables predict symptom duration following sport-related concussion. A total of 2,055 high school and collegiate athletes completed preseason evaluations. Concussed athletes (n = 127) repeated assessments serially (<24 hours and days 8, 15, and 45) postinjury. Cox proportional hazard modeling was used to predict concussive symptom duration (in days). Predictors considered included demographic and history variables; baseline psychological, neurocognitive, and balance functioning; acute injury characteristics; and postinjury clinical measures. Preinjury somatic symptom score (Brief Symptom Inventory-18 somatization scale) was the strongest premorbid predictor of symptom duration. Acute (24-hour) postconcussive symptom burden (Sport Concussion Assessment Tool-3 symptom severity) was the best injury-related predictor of recovery. These 2 predictors were moderately correlated (r = 0.51). Path analyses indicated that the relationship between preinjury somatization symptoms and symptom recovery was mediated by postinjury concussive symptoms. Preinjury somatization symptoms contribute to reported postconcussive symptom recovery via their influence on acute postconcussive symptoms. The findings highlight the relevance of premorbid psychological factors in postconcussive recovery, even in a healthy athlete sample relatively free of psychopathology or medical comorbidities. Future research should elucidate the neurobiopsychosocial mechanisms that explain the role of this individual difference variable in outcome following concussive injury. © 2016 American Academy of Neurology.

An assessment of environmental literacy and analysis of predictors of responsible environmental behavior held by secondary teachers in Hualien County of Taiwan

NASA Astrophysics Data System (ADS)

Hsu, Shih-Jang

The major purpose of this study was to determine the relative contribution of nine variables in predicting teachers' responsible environmental behavior (REB). The theoretic framework of this study was based on the Hines model, the Hungerford and Volk model, and the environmental literacy framework proposed by Environmental Literacy Assessment Consortium. A nine-page instrument was administered by mailed questionnaire to 300 randomly selected secondary teachers in Hualien County of Taiwan with a 78.7% response rate. Correlation and stepwise multiple regression analyses were conducted. The following conclusions were drawn: (1) For all the respondents, all the nine environmental literacy variables were significant correlates of REB. These correlates included: perceived knowledge of environmental action strategies (KNOW; r =.46), intention to act (IA; r =.46), perceived skill in using environmental action strategies (SKILL; r =.45), perceived knowledge of environmental problems and issues (KISSU; r =.34), environmental sensitivity (r =.28), environmental responsibility (r =.27), perceived knowledge of ecology and environmental science (r =.27), locus of control (r =.27), and environmental attitudes (r =.21). (2) When only the nine environmental literacy variables were considered, the most parsimonious set of predictors of REB for all the teachers included: (a) KNOW, (Rsp2 =.2116); (b) IA, (Rsp2 =.0916); and (c) SKILL, (Rsp2 =.0205). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.2559); (b) SKILL (Rsp2.0926); and (c) environmental responsibility (Rsp2 =.0219). For the rural teachers, the most parsimonious set of predictors included: (a) KNOW (Rsp2 =.1872); (b) IA (Rsp2 =.0816); and (c) KISSU (Rsp2 =.0318). (3) When the environmental literacy variables as well as demographic and experience variables were considered, the most parsimonious set of predictors for all the teachers included: (a) KNOW, (Rsp2 =.2834); (b) IA, (Rsp2 =.0696); (c) area of residence, (Rsp2 =.0174); and (d) SKILL, (Rsp2 =.0163). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.3199); (b) SKILL (Rsp2 =.0840); (c) major sources of environmental information (Rsp2 =.0432); and (d) membership in environmental organizations, (Rsp2 =.0240). Implications for environmental education program development and instructional practice were presented. Recommendations for further research were also provided.

Impression management and achievement motivation: Investigating substantive links.

PubMed

Elliot, Andrew J; Aldhobaiban, Nawal; Murayama, Kou; Kobeisy, Ahmed; Gocłowska, Małgorzata A; Khyat, Aber

2018-02-01

In this research, we investigate impression management (IM) as a substantive personality variable by linking it to differentiated achievement motivation constructs, namely achievement motives (workmastery, competitiveness, fear of failure) and achievement goals (mastery-approach, mastery-avoidance, performance-approach, performance-avoidance). Study 1 revealed that IM was a positive predictor of workmastery and a negative predictor of competitiveness (with and without self-deceptive enhancement (SDE) controlled). Studies 2a and 2b revealed that IM was a positive predictor of mastery-approach goals and mastery-avoidance goals (without and, in Study 2b, with SDE controlled). These findings highlight the value of conceptualising and utilising IM as a personality variable in its own right and shed light on the nature of the achievement motive and achievement goal constructs. © 2016 International Union of Psychological Science.

Predictors of Outcomes in Autism Early Intervention: Why Don’t We Know More?

PubMed Central

Vivanti, Giacomo; Prior, Margot; Williams, Katrina; Dissanayake, Cheryl

2014-01-01

Response to early intervention programs in autism is variable. However, the factors associated with positive versus poor treatment outcomes remain unknown. Hence the issue of which intervention/s should be chosen for an individual child remains a common dilemma. We argue that lack of knowledge on “what works for whom and why” in autism reflects a number of issues in current approaches to outcomes research, and we provide recommendations to address these limitations. These include: a theory-driven selection of putative predictors; the inclusion of proximal measures that are directly relevant to the learning mechanisms demanded by the specific educational strategies; the consideration of family characteristics. Moreover, all data on associations between predictor and outcome variables should be reported in treatment studies. PMID:24999470

THE COMPETITION BETWEEN METHYLMERCURY RISKS AND OMEGA-3 POLYUNSATURATED FATTY ACID BENEFITS: A REVIEW OF CONFLICTING EVIDENCE ON FISH CONSUMPTION AND CARDIOVASCULAR HEALTH.

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

LIPFERT, F.W.; SULLIVAN, T.M.

2006-10-31

The health concerns of methylmercury (MeHg) contamination of seafood have recently been extended to include cardiovascular effects, especially premature mortality. Although the fatty acids (fish oils) found in most species are thought to confer a wide range of health benefits, especially to the cardiovascular system, some epidemiological studies have suggested that such benefits may be offset by adverse effects of MeHg. This comprehensive review is based on searches of the NIH MEDLINE database and compares and contrasts 145 published studies involving cardiovascular effects and exposures to mercury and other fish contaminants, intake of fatty acids including dietary supplements of fishmore » oils, and rates of seafood consumption. Since few of these studies include adequate simultaneous measurements of all of these potential predictor variables, we summarized their effects separately, across the available studies of each, and then drew conclusions based on the aggregated findings. It is important to realize that studies of seafood consumption encompass the net effects of all of these predictor variables, but that seafood intake studies are rarely supported by human biomarker measurements that reflect the actual uptake of harmful as well as beneficial fish ingredients. As a result, exposure measurement error is an issue when comparing studies and predictor variables. It is also possible that the observed benefits of eating fish may relate more to the characteristics of the consumers than to those of the fish. We found the evidence for adverse cardiovascular effects of MeHg to be sparse and unconvincing. Studies of cardiovascular mortality show net benefits, and the findings of adverse effects are mainly limited to studies Finland at high mercury exposure levels. By contrast, a very consistent picture of beneficial effects is seen for fatty acids, after recognizing the effects of exposure uncertainties and the presence of threshold effects. Studies based on measured biomarker levels are seen to be the most reliable and present a convincing picture of strong beneficial effects, especially for those causes of death involving cardiac arrhythmia. This conclusion also extends to studies of fish-oil supplementation. Studies based on fish consumption show mainly benefits from increased consumption. This finding is supported by an ecological study at the national population level, for which the lifestyle effects that might be correlated with fish consumption within a given population would be expected to ''average out'' across nations. Finally, the net survival benefits resulting from eating fish are consistent with studies involving complete diets, although benefits are also seen to accrue from reduced consumption of red meat and saturated fats.« less

Performance Variability as a Predictor of Response to Aphasia Treatment.

PubMed

Duncan, E Susan; Schmah, Tanya; Small, Steven L

2016-10-01

Performance variability in individuals with aphasia is typically regarded as a nuisance factor complicating assessment and treatment. We present the alternative hypothesis that intraindividual variability represents a fundamental characteristic of an individual's functioning and an important biomarker for therapeutic selection and prognosis. A total of 19 individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement both on overall language functioning and repetition ability. Furthermore, we determined which pretreatment variables best predicted improvement on the repetition test. Significant gains were made on the Western Aphasia Battery-Revised (WAB) Aphasia Quotient, Cortical Quotient, and 2 subtests as well as on a separate repetition test. Using stepwise regression, we found that pretreatment intraindividual variability was the only predictor of improvement in performance on the repetition test, with greater pretreatment variability predicting greater improvement. Furthermore, the degree of reduction in this variability over the course of treatment was positively correlated with the degree of improvement. Intraindividual variability may be indicative of potential for improvement on a given task, with more uniform performance suggesting functioning at or near peak potential. © The Author(s) 2016.

A SIGNIFICANCE TEST FOR THE LASSO1

PubMed Central

Lockhart, Richard; Taylor, Jonathan; Tibshirani, Ryan J.; Tibshirani, Robert

2014-01-01

In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of models visited along the lasso solution path. We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an Exp(1) asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in the current lasso model). Our proof of this result for the special case of the first predictor to enter the model (i.e., testing for a single significant predictor variable against the global null) requires only weak assumptions on the predictor matrix X. On the other hand, our proof for a general step in the lasso path places further technical assumptions on X and the generative model, but still allows for the important high-dimensional case p > n, and does not necessarily require that the current lasso model achieves perfect recovery of the truly active variables. Of course, for testing the significance of an additional variable between two nested linear models, one typically uses the chi-squared test, comparing the drop in residual sum of squares (RSS) to a χ12 distribution. But when this additional variable is not fixed, and has been chosen adaptively or greedily, this test is no longer appropriate: adaptivity makes the drop in RSS stochastically much larger than χ12 under the null hypothesis. Our analysis explicitly accounts for adaptivity, as it must, since the lasso builds an adaptive sequence of linear models as the tuning parameter λ decreases. In this analysis, shrinkage plays a key role: though additional variables are chosen adaptively, the coefficients of lasso active variables are shrunken due to the l1 penalty. Therefore, the test statistic (which is based on lasso fitted values) is in a sense balanced by these two opposing properties—adaptivity and shrinkage—and its null distribution is tractable and asymptotically Exp(1). PMID:25574062

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in the model making them more difficult to interpret but highlighting the usefulness of the non-linear machine learning method. 2D interaction plots show probability of anoxic groundwater conditions largely control estimated nitrate concentrations compared to the other predictors.

Economic burden of malaria and predictors of cost variability to rural households in south-central Ethiopia

PubMed Central

Lindtjørn, Bernt; Deressa, Wakgari; Gari, Taye; Loha, Eskindir; Robberstad, Bjarne

2017-01-01

Background While recognizing the recent remarkable achievement in the global malaria reduction, the disease remains a challenge to the malaria endemic countries in Africa. Beyond the huge health consequence of malaria, policymakers need to be informed about the economic burden of the disease to the households. However, evidence on the economic burden of malaria in Ethiopia is scanty. The aims of this study were to estimate the economic burden of malaria episode and to identify predictors of cost variability to the rural households. Methods A prospective costing approach from a household perspective was employed. A total of 190 malaria patients were enrolled to the study from three health centers and nine health posts in Adami Tullu district in south-central Ethiopia, in 2015. Primary data were collected on expenditures due to malaria, forgone working days because of illness, socioeconomic and demographic situation, and households’ assets. Quantile regression was applied to predict factors associated with the cost variation. Socioeconomic related inequality was measured using concentration index and concentration curve. Results The median cost of malaria per episode to the household was USD 5.06 (IQR: 2.98–8.10). The direct cost accounted for 39%, while the indirect counterpart accounted for 61%. The history of malaria in the last six months and the level of the facility visited in the health system predominantly influenced the direct cost. The indirect cost was mainly influenced by the availability of antimalarial drugs in the health facility. The concentration curve and the concentration index for direct cost indicate significant pro-rich inequality. Plasmodium falciparum is significantly more costly for households compared to Plasmodium vivax. Conclusion The economic burden of malaria to the rural households in Ethiopia was substantial—mainly to the poor—indicating that reducing malaria burden could contribute to the poverty reduction as well. PMID:29020063

Foster children's attachment behavior and representation: Influence of children's pre-placement experiences and foster caregiver's sensitivity.

PubMed

Bovenschen, Ina; Lang, Katrin; Zimmermann, Janin; Förthner, Judith; Nowacki, Katja; Roland, Inga; Spangler, Gottfried

2016-01-01

Although the majority of foster children have been exposed to early adversity in their biological families and have experienced one or more disruptions of attachment relationships, most studies surprisingly found foster children to be as securely attached as children in low-risk samples. However, attention has been paid almost exclusively to attachment formation in young children up to two years of age, and the majority of studies solely investigated attachment behavior whereas few is known about foster children's representations about attachment relationships. To extend findings on attachment in foster children and its predictors, our study examined both attachment behavior and representations in foster children aged between 3 and 8 years. Diverse potential predictors including child variables, birth parents' variables, pre-placement experiences, and foster caregiver's behavior were included in the analyses. Results revealed that foster children showed both lower attachment security and higher disorganization scores than children in low-risk samples. Attachment behavior and representation were found to be widely independent from each other. Different factors contributed to attachment behavior and representation: whereas foster children's attachment behavior was mainly influenced by foster parents' behavior, pre-placement experiences did predict hyperactivation and disorganization on the representational level. The results indicate that, when intervening with foster families, it seems crucial to focus not exclusively on the promotion of secure attachment behavior but also to develop interventions enhancing secure and organized attachment representations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting tobacco sales in community pharmacies using population demographics and pharmacy type.

PubMed

Hickey, Lisa M; Farris, Karen B; Peterson, N Andrew; Aquilino, Mary L

2006-01-01

To determine whether the population demographics of the location of pharmacies were associated with tobacco sales in pharmacies, when controlling for pharmacy type. Retrospective analysis. Iowa. All retailers in Iowa that obtained tobacco licenses and all pharmacies registered with the Iowa Board of Pharmacy in 2003. MAIN OUTCOME MEASURE AND INTERVENTIONS: Percentage of pharmacies selling tobacco (examined by pharmacy type using chi-square analysis); median income and distribution of race/ethnicity in the county for pharmacies that did or did not sell tobacco (t tests); predictors of whether a pharmacy sold tobacco (logistic regression using the independent variables county-level demographic variables and pharmacy characteristics). County gender composition, race/ethnicity make-up, and income levels were different for tobacco-selling and -nonselling pharmacies. Logistic regression showed that whether a pharmacy sold tobacco was strongly dependent on the type of pharmacy; compared with independent pharmacies (of which only 5% sold tobacco products), chain pharmacies were 34 times more likely to sell tobacco products, mass merchandiser outlets were 47 times more likely to stock these goods, and grocery stores were 378 times more likely to do so. Pharmacies selling tobacco were more likely to be located in counties with significantly higher numbers of multiracial groups. The best predictor of whether an Iowa pharmacy sells tobacco products is type of pharmacy. In multivariable analyses, population demographics of the county in which pharmacies were located were generally not predictive of whether a pharmacy sold tobacco.

Medical school dropout--testing at admission versus selection by highest grades as predictors.

PubMed

O'Neill, Lotte; Hartvigsen, Jan; Wallstedt, Birgitta; Korsholm, Lars; Eika, Berit

2011-11-01

Very few studies have reported on the effect of admission tests on medical school dropout. The main aim of this study was to evaluate the predictive validity of non-grade-based admission testing versus grade-based admission relative to subsequent dropout. This prospective cohort study followed six cohorts of medical students admitted to the medical school at the University of Southern Denmark during 2002-2007 (n=1544). Half of the students were admitted based on their prior achievement of highest grades (Strategy 1) and the other half took a composite non-grade-based admission test (Strategy 2). Educational as well as social predictor variables (doctor-parent, origin, parenthood, parents living together, parent on benefit, university-educated parents) were also examined. The outcome of interest was students' dropout status at 2 years after admission. Multivariate logistic regression analysis was used to model dropout. Strategy 2 (admission test) students had a lower relative risk for dropping out of medical school within 2 years of admission (odds ratio 0.56, 95% confidence interval 0.39-0.80). Only the admission strategy, the type of qualifying examination and the priority given to the programme on the national application forms contributed significantly to the dropout model. Social variables did not predict dropout and neither did Strategy 2 admission test scores. Selection by admission testing appeared to have an independent, protective effect on dropout in this setting. © Blackwell Publishing Ltd 2011.

Risk factors for psychological maladjustment of parents of children with cancer.

PubMed

Hoekstra-Weebers, J E; Jaspers, J P; Kamps, W A; Klip, E C

1999-12-01

To examine risk variables for future, more immediate, and persistent psychological distress of parents of pediatric cancer patients. Parents (n = 128) completed questionnaires at the time of diagnosis (T1) and 12 months later (T2). Multiple regression analyses were performed using the following as predictors: demographics, illness-related variables, other life events, personality, coping styles, and social support. Trait anxiety was the strongest predictor of both fathers' and mothers' future distress. Changes in trait anxiety during the year also accompanied changes in both parents' levels of distress. Additional prospective predictors for fathers were the coping style "social support-seeking" and dissatisfaction with support. Dissatisfaction with support also had short-term effects for fathers. An additional prospective predictor for mothers was the number of pleasant events they had experienced prior to diagnosis, while a short-term effect was found for performance in assertiveness. No predictors for the persistence of distress were found. These results underscore the importance of personality anxiety in predicting parents' risk for adjustment difficulties associated with the experience of cancer in one's child. An additional risk factor for fathers was social support. For mothers, previously experienced life events and the frequency of assertive behavior were additional risk factors.

Predictors of Early Termination in a University Counseling Training Clinic

ERIC Educational Resources Information Center

Lampropoulos, Georgios K.; Schneider, Mercedes K.; Spengler, Paul M.

2009-01-01

Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and…

Verbal and Nonverbal Predictors of Spelling Performance

ERIC Educational Resources Information Center

Sadoski, Mark; Willson, Victor L.; Holcomb, Angelia; Boulware-Gooden, Regina

2005-01-01

Verbal and nonverbal predictors of spelling performance in Grades 1-12 were investigated using the national norming data from a standardized spelling test. Verbal variables included number of letters, phonemes, syllables, digraphs, blends, silent markers, r-controlled vowels, and the proportion of grapheme-phoneme correspondence. The nonverbal…

Predictors of Secondary Traumatic Stress among Children's Advocacy Center Forensic Interviewers

ERIC Educational Resources Information Center

Bonach, Kathryn; Heckert, Alex

2012-01-01

This study examined various predictor variables that were hypothesized to impact secondary traumatic stress in forensic interviewers (n = 257) from children's advocacy centers across the United States. Data were examined to investigate the relationship between organizational satisfaction, organizational buffers, and job support with secondary…

Pharmacogenetic Predictors of Methylphenidate Dose-Response in Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Froehlich, Tanya E.; Epstein, Jeffery N.; Nick, Todd G.; Melguizo Castro, Maria S.; Stein, Mark A.; Brinkman, William B.; Graham, Amanda J.; Langberg, Joshua M.; Kahn, Robert S.

2011-01-01

Objective: Because of significant individual variability in attention-deficit/hyperactivity disorder (ADHD) medication response, there is increasing interest in identifying genetic predictors of treatment effects. This study examined the role of four catecholamine-related candidate genes in moderating methylphenidate (MPH) dose-response. Method:…

Relationship between Graphical Device Comprehension and Overall Text Comprehension for Third-Grade Children

ERIC Educational Resources Information Center

Roberts, Kathryn L.; Norman, Rebecca R.; Cocco, Jaime

2015-01-01

This study examined relationships between reading comprehension, known predictors of reading comprehension (i.e., cognitive flexibility, fluency, reading motivation and attitude, vocabulary), and graphical device comprehension. One-hundred fifty-six third graders completed assessments of known predictor variables and an assessment tapping…

A Study of Predictors of Environmental Behaviour using U.S. Samples.

ERIC Educational Resources Information Center

Sia, Archibald P.; And Others

1986-01-01

Reports on a study done with the intentions of determining the relative contribution of eight variables in predicting environmental behavior. Concluded that the major predictors were skill in using environmental action strategies, level of environmental sensitivity, and percieved knowledge of environmental action strategies. (TW)

Predictors of Life Satisfaction in Individuals with Intellectual Disabilities

ERIC Educational Resources Information Center

Miller, S. M.; Chan, F.

2008-01-01

Background: The purpose of this study was to examine factors that predict life satisfaction in individuals with intellectual disabilities (ID). Two groups of variables were studied: life skills (interpersonal, instrumental and leisure) and higher-order predictors (social support, self-determination and productivity). Method: Fifty-six participants…

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically significant differences being found between the two AUCs (p =0.079). The four-category proposal for PCO2 was ≤ 43;(43,52];(52,65];> 65, for which the following values were obtained: AIC=258.1 and AUC=0.81. No statistically significant differences were found between the AUC of the four-category option and that of the continuous predictor, which yielded an AIC of 250.3 and an AUC of 0.825 (p =0.115). Conclusions Our proposed method provides clinicians with the number and location of cut points for categorising variables, and performs as successfully as the original continuous predictor when it comes to developing clinical prediction rules. PMID:23802742

Recovery of Work-Related Stress: Complaint Reduction and Work-Resumption are Relatively Independent Processes.

PubMed

de Vente, Wieke; Kamphuis, Jan Henk; Blonk, Roland W B; Emmelkamp, Paul M G

2015-09-01

The process of recovery from work-related stress, consisting of complaint reduction and work-resumption, is not yet fully understood. The aim of this study was to investigate predictors of complaint reduction and work-resumption, as well as testing complaint reduction as a mediator in the association between predictors and work-resumption. Seventy-one patients on sickness-leave because of work-related stress complaints were followed over a period of 13 months. Predictors comprised personal (demographics, coping, cognitions), work-related (job-characteristics, social support), and illness-related (complaint duration, absence duration) variables. Dependent variables were distress complaints, burnout complaints, and work-resumption. Complaints reduced considerably over time to borderline clinical levels and work-resumption increased to 68% at 13 months. Predictors of stronger reduction of distress complaints were male gender, less working hours, less decision authority, more co-worker support, and shorter absence duration. Predictors of stronger reduction of burnout complaints were male gender, lower age, high education, less avoidant coping, less decision authority, more job security, and more co-worker support. Predictors of work-resumption were lower age and stronger reduction of burnout complaints. No indication for a mediating role of burnout complaints between the predictor age and work-resumption was found. Complaint reduction and work-resumption are relatively independent processes. Symptom reduction is influenced by individual and work-related characteristics, which holds promise for a multidisciplinary treatment approach for work-related stress.

Species-environment relationships and potential for distribution modelling in coastal waters

NASA Astrophysics Data System (ADS)

Snickars, M.; Gullström, M.; Sundblad, G.; Bergström, U.; Downie, A.-L.; Lindegarth, M.; Mattila, J.

2014-01-01

Due to increasing pressure on the marine environment there is a growing need to understand species-environment relationships. To provide background for prioritising among variables (predictors) for use in distribution models, the relevance of predictors for benthic species was reviewed using the coastal Baltic Sea as a case-study area. Significant relationships for three response groups (fish, macroinvertebrates, macrovegetation) and six predictor categories (bottom topography, biotic features, hydrography, wave exposure, substrate and spatiotemporal variability) were extracted from 145 queried peer-reviewed field-studies covering three decades and six subregions. In addition, the occurrence of interaction among predictors was analysed. Hydrography was most often found in significant relationships, had low level of interaction with other predictors, but also had the most non-significant relationships. Depth and wave exposure were important in all subregions and are readily available, increasing their applicability for cross-regional modelling efforts. Otherwise, effort to model species distributions may prove challenging at larger scale as the relevance of predictors differed among both response groups and regions. Fish and hard bottom macrovegetation have the largest modelling potential, as they are structured by a set of predictors that at the same time are accurately mapped. A general importance of biotic features implies that these need to be accounted for in distribution modelling, but the mapping of most biotic features is challenging, which currently lowers the applicability. The presence of interactions suggests that predictive methods allowing for interactive effects are preferable. Detailing these complexities is important for future distribution modelling.

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.

PubMed

Heddam, Salim; Kisi, Ozgur

2017-07-01

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Theory-Based Predictors of Intention to Engage in Precautionary Sexual Behavior among Puerto Rican High School Adolescents

ERIC Educational Resources Information Center

Collazo, Andres A.

2004-01-01

Predictors of intention to abstain from sexual intercourse or use condoms consistently with both main and other partners were investigated in 431 Puerto Rican high school students. The basis for this study was the theories of reasoned action (TRA) and planned behavior (TPB), and two predictors from the theory of interpersonal behavior (TIB). As…

Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth

NASA Astrophysics Data System (ADS)

Olivas Saunders, Rolando

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 mum (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5 . These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn't account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level.

Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction

NASA Technical Reports Server (NTRS)

Huikuri, H. V.; Makikallio, T. H.; Peng, C. K.; Goldberger, A. L.; Hintze, U.; Moller, M.

2000-01-01

BACKGROUND: Preliminary data suggest that the analysis of R-R interval variability by fractal analysis methods may provide clinically useful information on patients with heart failure. The purpose of this study was to compare the prognostic power of new fractal and traditional measures of R-R interval variability as predictors of death after acute myocardial infarction. METHODS AND RESULTS: Time and frequency domain heart rate (HR) variability measures, along with short- and long-term correlation (fractal) properties of R-R intervals (exponents alpha(1) and alpha(2)) and power-law scaling of the power spectra (exponent beta), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Variability of North Atlantic Hurricane Frequency in a Large Ensemble of High-Resolution Climate Simulations

NASA Astrophysics Data System (ADS)

Mei, W.; Kamae, Y.; Xie, S. P.

2017-12-01

Forced and internal variability of North Atlantic hurricane frequency during 1951-2010 is studied using a large ensemble of climate simulations by a 60-km atmospheric general circulation model that is forced by observed sea surface temperatures (SSTs). The simulations well capture the interannual-to-decadal variability of hurricane frequency in best track data, and further suggest a possible underestimate of hurricane counts in the current best track data prior to 1966 when satellite measurements were unavailable. A genesis potential index (GPI) averaged over the Main Development Region (MDR) accounts for more than 80% of the forced variations in hurricane frequency, with potential intensity and vertical wind shear being the dominant factors. In line with previous studies, the difference between MDR SST and tropical mean SST is a simple but useful predictor; a one-degree increase in this SST difference produces 7.1±1.4 more hurricanes. The hurricane frequency also exhibits internal variability that is comparable in magnitude to the interannual variability. The 100-member ensemble allows us to address the following important questions: (1) Are the observations equivalent to one realization of such a large ensemble? (2) How many ensemble members are needed to reproduce the variability in observations and in the forced component of the simulations? The sources of the internal variability in hurricane frequency will be identified and discussed. The results provide an explanation for the relatively week correlation ( 0.6) between MDR GPI and hurricane frequency on interannual timescales in observations.

Predictors of post-traumatic stress symptoms in Oklahoma City: exposure, social support, peri-traumatic responses.

PubMed

Tucker, P; Pfefferbaum, B; Nixon, S J; Dickson, W

2000-11-01

Eighty-five adults seeking mental health assistance six months after the Oklahoma City bombing were assessed to determine which of three groups of variables (exposure, peri-traumatic responses, and social support) predicted development of post-traumatic stress disorder (PTSD) symptoms. Variables most highly associated with subsequent PTSD symptoms included having been injured (among exposure variables), feeling nervous or afraid (peri-traumatic responses), and responding that counseling helped (support variables). Combining primary predictors in the three areas, PTSD symptoms were more likely to occur in those reporting counseling to help and those feeling nervous or afraid at the time of the bombing. Implications of these findings are discussed for behavioral health administrators and clinicians planning service delivery to groups of victims seeking mental health intervention after terrorist attacks and other disasters.

Variable screening via quantile partial correlation

PubMed Central

Ma, Shujie; Tsai, Chih-Ling

2016-01-01

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented. PMID:28943683

A Cross-Language Study of Acoustic Predictors of Speech Intelligibility in Individuals With Parkinson's Disease

PubMed Central

Choi, Yaelin

2017-01-01

Purpose The present study aimed to compare acoustic models of speech intelligibility in individuals with the same disease (Parkinson's disease [PD]) and presumably similar underlying neuropathologies but with different native languages (American English [AE] and Korean). Method A total of 48 speakers from the 4 speaker groups (AE speakers with PD, Korean speakers with PD, healthy English speakers, and healthy Korean speakers) were asked to read a paragraph in their native languages. Four acoustic variables were analyzed: acoustic vowel space, voice onset time contrast scores, normalized pairwise variability index, and articulation rate. Speech intelligibility scores were obtained from scaled estimates of sentences extracted from the paragraph. Results The findings indicated that the multiple regression models of speech intelligibility were different in Korean and AE, even with the same set of predictor variables and with speakers matched on speech intelligibility across languages. Analysis of the descriptive data for the acoustic variables showed the expected compression of the vowel space in speakers with PD in both languages, lower normalized pairwise variability index scores in Korean compared with AE, and no differences within or across language in articulation rate. Conclusions The results indicate that the basis of an intelligibility deficit in dysarthria is likely to depend on the native language of the speaker and listener. Additional research is required to explore other potential predictor variables, as well as additional language comparisons to pursue cross-linguistic considerations in classification and diagnosis of dysarthria types. PMID:28821018

Variables influencing the presence of subyearling fall Chinook salmon in shoreline habitats of the Hanford Reach, Columbia River

USGS Publications Warehouse

Tiffan, K.F.; Clark, L.O.; Garland, R.D.; Rondorf, D.W.

2006-01-01

Little information currently exists on habitat use by subyearling fall Chinook salmon Oncorhynchus tshawytscha rearing in large, main-stem habitats. We collected habitat use information on subyearlings in the Hanford Reach of the Columbia River during May 1994 and April-May 1995 using point abundance electrofishing. We analyzed measures of physical habitat using logistic regression to predict fish presence and absence in shoreline habitats. The difference between water temperature at the point of sampling and in the main river channel was the most important variable for predicting the presence and absence of subyearlings. Mean water velocities of 45 cm/s or less and habitats with low lateral bank slopes were also associated with a greater likelihood of subyearling presence. Intermediate-sized gravel and cobble substrates were significant predictors of fish presence, but small (<32-mm) and boulder-sized (>256-mm) substrates were not. Our rearing model was accurate at predicting fish presence and absence using jackknifing (80% correct) and classification of observations from an independent data set (76% correct). The habitat requirements of fall Chinook salmon in the Hanford Reach are similar to those reported for juvenile Chinook salmon in smaller systems but are met in functionally different ways in a large river.

Chlorophyll a and inorganic suspended solids in backwaters of the upper Mississippi River system: Backwater lake effects and their associations with selected environmental predictors

USGS Publications Warehouse

Rogala, James T.; Gray, Brian R.

2006-01-01

The Long Term Resource Monitoring Program (LTRMP) uses a stratified random sampling design to obtain water quality statistics within selected study reaches of the Upper Mississippi River System (UMRS). LTRMP sampling strata are based on aquatic area types generally found in large rivers (e.g., main channel, side channel, backwater, and impounded areas). For hydrologically well-mixed strata (i.e., main channel), variance associated with spatial scales smaller than the strata scale is a relatively minor issue for many water quality parameters. However, analysis of LTRMP water quality data has shown that within-strata variability at the strata scale is high in off-channel areas (i.e., backwaters). A portion of that variability may be associated with differences among individual backwater lakes (i.e., small and large backwater regions separated by channels) that cumulatively make up the backwater stratum. The objective of the statistical modeling presented here is to determine if differences among backwater lakes account for a large portion of the variance observed in the backwater stratum for selected parameters. If variance associated with backwater lakes is high, then inclusion of backwater lake effects within statistical models is warranted. Further, lakes themselves may represent natural experimental units where associations of interest to management may be estimated.

Choline Intake, Plasma Riboflavin, and the Phosphatidylethanolamine N-Methyltransferase G5465A Genotype Predict Plasma Homocysteine in Folate-Deplete Mexican-American Men with the Methylenetetrahydrofolate Reductase 677TT Genotype12

PubMed Central

Caudill, Marie A.; Dellschaft, Neele; Solis, Claudia; Hinkis, Sabrina; Ivanov, Alexandre A.; Nash-Barboza, Susan; Randall, Katharine E.; Jackson, Brandi; Solomita, Gina N.; Vermeylen, Francoise

2009-01-01

We previously showed that provision of the folate recommended dietary allowance and either 300, 550, 1100, or 2200 mg/d choline for 12 wk resulted in diminished folate status and a tripling of plasma total homocysteine (tHcy) in men with the methylenetetrahydrofolate reductase (MTHFR) 677TT genotype. However, the substantial variation in tHcy within the 677TT genotype at wk 12 implied that several factors were interacting with this genotype to affect homocysteine. As an extension of this work, the present study sought to identify the main predictors of wk-12 plasma tHcy, alone and together with the MTHFR C677T genotype (29 TT, 31 CC), using linear regression analysis. A basic model explaining 82.5% of the variation (i.e. adjusted R2 = 0.825) was constructed. However, the effects of the variables within this model were dependent upon the MTHFR C677T genotype (P for interaction ≤ 0.021). Within the 677TT genotype, serum folate (P = 0.005) and plasma riboflavin (P = 0.002) were strong negative predictors (inversely related) explaining 12 and 15%, respectively, of the variation in tHcy, whereas choline intake (P = 0.003) and serum creatinine (P < 0.001) were strong positive predictors, explaining 19 and 25% of the variation. None of these variables, except creatinine (P = 0.021), correlated with tHcy within the 677CC genotype. Of the 8 additional polymorphisms tested, none appeared to influence tHcy. However, when creatinine was not in the model, the phosphatidylethanolamine N-methyltransferase 5465G→A variant predicted lower tHcy (P < 0.001); an effect confined to the MTHFR 677TT genotype. Thus, in folate-deplete men, several factors with roles in 1-carbon metabolism interact with the MTHFR C677T genotype to affect plasma tHcy. PMID:19211833

Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemic.

PubMed

Van Boeckel, Thomas P; Thanapongtharm, Weerapong; Robinson, Timothy; Biradar, Chandrashekhar M; Xiao, Xiangming; Gilbert, Marius

2012-01-01

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.

Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic

PubMed Central

Van Boeckel, Thomas P.; Thanapongtharm, Weerapong; Robinson, Timothy; Biradar, Chandrashekhar M.; Xiao, Xiangming; Gilbert, Marius

2012-01-01

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention. PMID:23185352

Upper-Airway Collapsibility and Loop Gain Predict the Response to Oral Appliance Therapy in Patients with Obstructive Sleep Apnea

PubMed Central

Andara, Christopher; Landry, Shane; Sands, Scott A.; Joosten, Simon A.; Owens, Robert L.; White, David P.; Hamilton, Garun S.; Wellman, Andrew

2016-01-01

Rationale: Oral appliances (OAs) are commonly used as an alternative treatment to continuous positive airway pressure for patients with obstructive sleep apnea (OSA). However, OAs have variable success at reducing the apnea–hypopnea index (AHI), and predicting responders is challenging. Understanding this variability may lie with the recognition that OSA is a multifactorial disorder and that OAs may affect more than just upper-airway anatomy/collapsibility. Objectives: The objectives of this study were to determine how OA alters AHI and four phenotypic traits (upper-airway anatomy/collapsibility and muscle function, loop gain, and arousal threshold), and baseline predictors of which patients gain the greatest benefit from therapy. Methods: In a randomized crossover study, 14 patients with OSA attended two sleep studies with and without their OA. Under each condition, AHI and the phenotypic traits were assessed. Multiple linear regression was used to determine independent predictors of the reduction in AHI. Measurements and Main Results: OA therapy reduced the AHI (30 ± 5 vs. 11 ± 2 events/h; P < 0.05), which was driven by improvements in upper-airway anatomy/collapsibility under passive (1.9 ± 0.7 vs. 4.7 ± 0.6 L/min; P < 0.005) and active conditions (2.4 ± 0.9 vs. 6.2 ± 0.4 L/min; P < 0.001). No changes were seen in muscle function, loop gain, or the arousal threshold. Using multivariate analysis, baseline passive upper-airway collapsibility and loop gain were independent predictors of the reduction in AHI (r2 = 0.70; P = 0.001). Conclusions: Our findings suggest that OA therapy improves the upper-airway collapsibility under passive and active conditions. Importantly, a greater response to therapy occurred in those patients with a mild anatomic compromise and a lower loop gain. PMID:27181367

Bullying by Definition: An Examination of Definitional Components of Bullying

ERIC Educational Resources Information Center

Goldsmid, Susan; Howie, Pauline

2014-01-01

Lack of definitional consensus remains an important unresolved issue within bullying research. This study examined the ability of definitional variables to predict overall level of victimisation (distress, power inequity, and provocation as predictors) and bullying (intention to harm, power inequity, and provocation as predictors) in 246…

VR Employment Outcomes of Individuals with Autism Spectrum Disorders: A Decade in the Making

ERIC Educational Resources Information Center

Alverson, Charlotte Y.; Yamamoto, Scott H.

2018-01-01

This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school,…

Oral Reading Fluency in Second Language Reading

ERIC Educational Resources Information Center

Jeon, Eun Hee

2012-01-01

This study investigated the role of oral reading fluency in second language reading. Two hundred and fifty-five high school students in South Korea were assessed on three oral reading fluency (ORF) variables and six other reading predictors. The relationship between ORF and other reading predictors was examined through an exploratory factor…

Predictors of Employment Outcomes for State-Federal Vocational Rehabilitation Consumers with HIV/AIDS

ERIC Educational Resources Information Center

Jung, Youngoh; Schaller, James; Bellini, James

2010-01-01

In this study, the authors investigated the effects of demographic, medical, and vocational rehabilitation service variables on employment outcomes of persons living with HIV/AIDS. Binary logistic regression analyses were conducted to determine predictors of employment outcomes using two groups drawn from Rehabilitation Services Administration…