Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

PubMed

Chatzis, Sotirios P; Andreou, Andreas S

2015-11-01

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

Determinants of The Grade A Embryos in Infertile Women; Zero-Inflated Regression Model.

PubMed

Almasi-Hashiani, Amir; Ghaheri, Azadeh; Omani Samani, Reza

2017-10-01

In assisted reproductive technology, it is important to choose high quality embryos for embryo transfer. The aim of the present study was to determine the grade A embryo count and factors related to it in infertile women. This historical cohort study included 996 infertile women. The main outcome was the number of grade A embryos. Zero-Inflated Poisson (ZIP) regression and Zero-Inflated Negative Binomial (ZINB) regression were used to model the count data as it contained excessive zeros. Stata software, version 13 (Stata Corp, College Station, TX, USA) was used for all statistical analyses. After adjusting for potential confounders, results from the ZINB model show that for each unit increase in the number 2 pronuclear (2PN) zygotes, we get an increase of 1.45 times as incidence rate ratio (95% confidence interval (CI): 1.23-1.69, P=0.001) in the expected grade A embryo count number, and for each increase in the cleavage day we get a decrease 0.35 times (95% CI: 0.20-0.61, P=0.001) in expected grade A embryo count. There is a significant association between both the number of 2PN zygotes and cleavage day with the number of grade A embryos in both ZINB and ZIP regression models. The estimated coefficients are more plausible than values found in earlier studies using less relevant models. Copyright© by Royan Institute. All rights reserved.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

Analyzing hospitalization data: potential limitations of Poisson regression.

PubMed

Weaver, Colin G; Ravani, Pietro; Oliver, Matthew J; Austin, Peter C; Quinn, Robert R

2015-08-01

Poisson regression is commonly used to analyze hospitalization data when outcomes are expressed as counts (e.g. number of days in hospital). However, data often violate the assumptions on which Poisson regression is based. More appropriate extensions of this model, while available, are rarely used. We compared hospitalization data between 206 patients treated with hemodialysis (HD) and 107 treated with peritoneal dialysis (PD) using Poisson regression and compared results from standard Poisson regression with those obtained using three other approaches for modeling count data: negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression. We examined the appropriateness of each model and compared the results obtained with each approach. During a mean 1.9 years of follow-up, 183 of 313 patients (58%) were never hospitalized (indicating an excess of 'zeros'). The data also displayed overdispersion (variance greater than mean), violating another assumption of the Poisson model. Using four criteria, we determined that the NB and ZINB models performed best. According to these two models, patients treated with HD experienced similar hospitalization rates as those receiving PD {NB rate ratio (RR): 1.04 [bootstrapped 95% confidence interval (CI): 0.49-2.20]; ZINB summary RR: 1.21 (bootstrapped 95% CI 0.60-2.46)}. Poisson and ZIP models fit the data poorly and had much larger point estimates than the NB and ZINB models [Poisson RR: 1.93 (bootstrapped 95% CI 0.88-4.23); ZIP summary RR: 1.84 (bootstrapped 95% CI 0.88-3.84)]. We found substantially different results when modeling hospitalization data, depending on the approach used. Our results argue strongly for a sound model selection process and improved reporting around statistical methods used for modeling count data. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Multilevel Models for Binary Data

ERIC Educational Resources Information Center

Powers, Daniel A.

2012-01-01

The methods and models for categorical data analysis cover considerable ground, ranging from regression-type models for binary and binomial data, count data, to ordered and unordered polytomous variables, as well as regression models that mix qualitative and continuous data. This article focuses on methods for binary or binomial data, which are…

Evaluation of the Use of Zero-Augmented Regression Techniques to Model Incidence of Campylobacter Infections in FoodNet.

PubMed

Tremblay, Marlène; Crim, Stacy M; Cole, Dana J; Hoekstra, Robert M; Henao, Olga L; Döpfer, Dörte

2017-10-01

The Foodborne Diseases Active Surveillance Network (FoodNet) is currently using a negative binomial (NB) regression model to estimate temporal changes in the incidence of Campylobacter infection. FoodNet active surveillance in 483 counties collected data on 40,212 Campylobacter cases between years 2004 and 2011. We explored models that disaggregated these data to allow us to account for demographic, geographic, and seasonal factors when examining changes in incidence of Campylobacter infection. We hypothesized that modeling structural zeros and including demographic variables would increase the fit of FoodNet's Campylobacter incidence regression models. Five different models were compared: NB without demographic covariates, NB with demographic covariates, hurdle NB with covariates in the count component only, hurdle NB with covariates in both zero and count components, and zero-inflated NB with covariates in the count component only. Of the models evaluated, the nonzero-augmented NB model with demographic variables provided the best fit. Results suggest that even though zero inflation was not present at this level, individualizing the level of aggregation and using different model structures and predictors per site might be required to correctly distinguish between structural and observational zeros and account for risk factors that vary geographically.

Antibiotic Resistances in Livestock: A Comparative Approach to Identify an Appropriate Regression Model for Count Data

PubMed Central

Hüls, Anke; Frömke, Cornelia; Ickstadt, Katja; Hille, Katja; Hering, Johanna; von Münchhausen, Christiane; Hartmann, Maria; Kreienbrock, Lothar

2017-01-01

Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i) to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model) and (ii) to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate model. PMID:28620609

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

NASA Technical Reports Server (NTRS)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

C-reactive protein, platelets, and patent ductus arteriosus.

PubMed

Meinarde, Leonardo; Hillman, Macarena; Rizzotti, Alina; Basquiera, Ana Lisa; Tabares, Aldo; Cuestas, Eduardo

2016-12-01

The association between inflammation, platelets, and patent ductus arteriosus (PDA) has not been studied so far. The purpose of this study was to evaluate whether C-reactive protein (CRP) is related to low platelet count and PDA. This was a retrospective study of 88 infants with a birth weight ≤1500 g and a gestational age ≤30 weeks. Platelet count, CRP, and an echocardiogram were assessed in all infants. The subjects were matched by sex, gestational age, and birth weight. Differences were compared using the χ 2 , t-test, or Mann-Whitney U-test, as appropriate. Significant variables were entered into a logistic regression model. The association between CRP and platelets was evaluated by correlation and regression analysis. Platelet count (167 000 vs. 213 000 µl -1 , p = 0.015) was lower and the CRP (0.45 vs. 0.20 mg/dl, p = 0.002) was higher, and the platelet count correlated inversely with CRP (r = -0.145, p = 0.049) in the infants with vs. without PDA. Only CRP was independently associated with PDA in a logistic regression model (OR 64.1, 95% confidence interval 1.4-2941, p = 0.033).

Regression Discontinuity Designs in Epidemiology

PubMed Central

Moscoe, Ellen; Mutevedzi, Portia; Newell, Marie-Louise; Bärnighausen, Till

2014-01-01

When patients receive an intervention based on whether they score below or above some threshold value on a continuously measured random variable, the intervention will be randomly assigned for patients close to the threshold. The regression discontinuity design exploits this fact to estimate causal treatment effects. In spite of its recent proliferation in economics, the regression discontinuity design has not been widely adopted in epidemiology. We describe regression discontinuity, its implementation, and the assumptions required for causal inference. We show that regression discontinuity is generalizable to the survival and nonlinear models that are mainstays of epidemiologic analysis. We then present an application of regression discontinuity to the much-debated epidemiologic question of when to start HIV patients on antiretroviral therapy. Using data from a large South African cohort (2007–2011), we estimate the causal effect of early versus deferred treatment eligibility on mortality. Patients whose first CD4 count was just below the 200 cells/μL CD4 count threshold had a 35% lower hazard of death (hazard ratio = 0.65 [95% confidence interval = 0.45–0.94]) than patients presenting with CD4 counts just above the threshold. We close by discussing the strengths and limitations of regression discontinuity designs for epidemiology. PMID:25061922

Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data

PubMed Central

Austin, Peter C.; Stryhn, Henrik; Leckie, George; Merlo, Juan

2017-01-01

Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster‐specific random effects that allow one to partition the total variation in the outcome into between‐cluster variation and between‐individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time‐to‐event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time‐to‐event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure. PMID:29114926

A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

NASA Astrophysics Data System (ADS)

Hapugoda, J. C.; Sooriyarachchi, M. R.

2017-09-01

Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

A flexible count data regression model for risk analysis.

PubMed

Guikema, Seth D; Coffelt, Jeremy P; Goffelt, Jeremy P

2008-02-01

In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression.

PubMed

Martina, R; Kay, R; van Maanen, R; Ridder, A

2015-01-01

Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well. Copyright © 2014 John Wiley & Sons, Ltd.

Simple to complex modeling of breathing volume using a motion sensor.

PubMed

John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-06-01

To compare simple and complex modeling techniques to estimate categories of low, medium, and high ventilation (VE) from ActiGraph™ activity counts. Vertical axis ActiGraph™ GT1M activity counts, oxygen consumption and VE were measured during treadmill walking and running, sports, household chores and labor-intensive employment activities. Categories of low (<19.3 l/min), medium (19.3 to 35.4 l/min) and high (>35.4 l/min) VEs were derived from activity intensity classifications (light <2.9 METs, moderate 3.0 to 5.9 METs and vigorous >6.0 METs). We examined the accuracy of two simple techniques (multiple regression and activity count cut-point analyses) and one complex (random forest technique) modeling technique in predicting VE from activity counts. Prediction accuracy of the complex random forest technique was marginally better than the simple multiple regression method. Both techniques accurately predicted VE categories almost 80% of the time. The multiple regression and random forest techniques were more accurate (85 to 88%) in predicting medium VE. Both techniques predicted the high VE (70 to 73%) with greater accuracy than low VE (57 to 60%). Actigraph™ cut-points for light, medium and high VEs were <1381, 1381 to 3660 and >3660 cpm. There were minor differences in prediction accuracy between the multiple regression and the random forest technique. This study provides methods to objectively estimate VE categories using activity monitors that can easily be deployed in the field. Objective estimates of VE should provide a better understanding of the dose-response relationship between internal exposure to pollutants and disease. Copyright © 2013 Elsevier B.V. All rights reserved.

Estimated allele substitution effects underlying genomic evaluation models depend on the scaling of allele counts.

PubMed

Bouwman, Aniek C; Hayes, Ben J; Calus, Mario P L

2017-10-30

Genomic evaluation is used to predict direct genomic values (DGV) for selection candidates in breeding programs, but also to estimate allele substitution effects (ASE) of single nucleotide polymorphisms (SNPs). Scaling of allele counts influences the estimated ASE, because scaling of allele counts results in less shrinkage towards the mean for low minor allele frequency (MAF) variants. Scaling may become relevant for estimating ASE as more low MAF variants will be used in genomic evaluations. We show the impact of scaling on estimates of ASE using real data and a theoretical framework, and in terms of power, model fit and predictive performance. In a dairy cattle dataset with 630 K SNP genotypes, the correlation between DGV for stature from a random regression model using centered allele counts (RRc) and centered and scaled allele counts (RRcs) was 0.9988, whereas the overall correlation between ASE using RRc and RRcs was 0.27. The main difference in ASE between both methods was found for SNPs with a MAF lower than 0.01. Both the ratio (ASE from RRcs/ASE from RRc) and the regression coefficient (regression of ASE from RRcs on ASE from RRc) were much higher than 1 for low MAF SNPs. Derived equations showed that scenarios with a high heritability, a large number of individuals and a small number of variants have lower ratios between ASE from RRc and RRcs. We also investigated the optimal scaling parameter [from - 1 (RRcs) to 0 (RRc) in steps of 0.1] in the bovine stature dataset. We found that the log-likelihood was maximized with a scaling parameter of - 0.8, while the mean squared error of prediction was minimized with a scaling parameter of - 1, i.e., RRcs. Large differences in estimated ASE were observed for low MAF SNPs when allele counts were scaled or not scaled because there is less shrinkage towards the mean for scaled allele counts. We derived a theoretical framework that shows that the difference in ASE due to shrinkage is heavily influenced by the power of the data. Increasing the power results in smaller differences in ASE whether allele counts are scaled or not.

Traffic effects on bird counts on North American Breeding Bird Survey routes

USGS Publications Warehouse

Griffith, Emily H.; Sauer, John R.; Royle, J. Andrew

2010-01-01

The North American Breeding Bird Survey (BBS) is an annual roadside survey used to estimate population change in >420 species of birds that breed in North America. Roadside sampling has been criticized, in part because traffic noise can interfere with bird counts. Since 1997, data have been collected on the numbers of vehicles that pass during counts at each stop. We assessed the effect of traffic by modeling total vehicles as a covariate of counts in hierarchical Poisson regression models used to estimate population change. We selected species for analysis that represent birds detected at low and high abundance and birds with songs of low and high frequencies. Increases in vehicle counts were associated with decreases in bird counts in most of the species examined. The size and direction of these effects remained relatively constant between two alternative models that we analyzed. Although this analysis indicated only a small effect of incorporating traffic effects when modeling roadside counts of birds, we suggest that continued evaluation of changes in traffic at BBS stops should be a component of future BBS analyses.

Evaluation of treatment outcomes for patients on first-line regimens in US President's Emergency Plan for AIDS Relief (PEPFAR) clinics in Uganda: predictors of virological and immunological response from RV288 analyses.

PubMed

Crawford, K W; Wakabi, S; Magala, F; Kibuuka, H; Liu, M; Hamm, T E

2015-02-01

Viral load (VL) monitoring is recommended, but seldom performed, in resource-constrained countries. RV288 is a US President's Emergency Plan for AIDS Relief (PEPFAR) basic programme evaluation to determine the proportion of patients on treatment who are virologically suppressed and to identify predictors of virological suppression and recovery of CD4 cell count. Analyses from Uganda are presented here. In this cross-sectional, observational study, patients on first-line antiretroviral therapy (ART) (efavirenz or nevirapine+zidovudine/lamivudine) from Kayunga District Hospital and Kagulamira Health Center were randomly selected for a study visit that included determination of viral load (HIV-1 RNA), CD4 cell count and clinical chemistry tests. Subjects were recruited by time on treatment: 6-12, 13-24 or >24 months. Logistic regression modelling identified predictors of virological suppression. Linear regression modelling identified predictors of CD4 cell count recovery on ART. We found that 85.2% of 325 subjects were virologically suppressed (viral load<47 HIV-1 RNA copies/ml). There was no difference in the proportion of virologically suppressed subjects by time on treatment, yet CD4 counts were higher in each successive stratum. Women had higher median CD4 counts than men overall (406 vs. 294 cells/μL, respectively; P<0.0001) and in each time-on-treatment stratum. In a multivariate logistic regression model, predictors of virological suppression included efavirenz use [odds ratio (OR) 0.47; 95% confidence interval (CI) 0.22-1.02; P=0.057], lower cost of clinic visits (OR 0.815; 95% CI 0.66-1.00; P=0.05), improvement in CD4 percentage (OR 1.06; 95% CI 1.014-1.107; P=0.009), and care at Kayunga vs. Kangulamira (OR 0.47; 95% CI 0.23-0.92; P=0.035). In a multivariate linear regression model of covariates associated with CD4 count recovery, time on highly active antiretroviral therapy (ART) (P<0.0001), patient satisfaction with care (P=0.038), improvements in total lymphocyte count (P<0.0001) and haemoglobin concentration (P=0.05) were positively associated, whereas age at start of ART (P=0.0045) was negatively associated with this outcome. High virological suppression rates are achievable on first-line ART in Uganda. The odds of virological suppression were positively associated with efavirenz use and improvements in CD4 cell percentage and total lymphocyte count and negatively associated with the cost of travel to the clinic. CD4 cell reconstitution was positively associated with CD4 count at study visit, time on ART, satisfaction with care at clinic, haemoglobin concentration and total lymphocyte count and negatively associated with age. © 2014 British HIV Association.

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts.

PubMed

Preisser, John S; Long, D Leann; Stamm, John W

2017-01-01

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-inflated negative binomial, and marginalized zero-inflated negative binomial models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the randomized clinical trial were similar despite their distinctive interpretations. The choice of statistical model class should match the study's purpose, while accounting for the broad decline in children's caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts. © 2017 S. Karger AG, Basel.

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

PubMed Central

Preisser, John S.; Long, D. Leann; Stamm, John W.

2017-01-01

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two datasets, one consisting of fictional dmft counts in two groups and the other on DMFS among schoolchildren from a randomized clinical trial (RCT) comparing three toothpaste formulations to prevent incident dental caries, are analysed with negative binomial hurdle (NBH), zero-inflated negative binomial (ZINB), and marginalized zero-inflated negative binomial (MZINB) models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the RCT were similar despite their distinctive interpretations. Choice of statistical model class should match the study’s purpose, while accounting for the broad decline in children’s caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts. PMID:28291962

Characterizing the performance of the Conway-Maxwell Poisson generalized linear model.

PubMed

Francis, Royce A; Geedipally, Srinivas Reddy; Guikema, Seth D; Dhavala, Soma Sekhar; Lord, Dominique; LaRocca, Sarah

2012-01-01

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway-Maxwell Poisson (COM-Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM-Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM-Poisson GLM, and (2) estimate the prediction accuracy of the COM-Poisson GLM using simulated data sets. The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values. The results also show that the COM-Poisson GLM yields accurate parameter estimates. The COM-Poisson GLM provides a promising and flexible approach for performing count data regression. © 2011 Society for Risk Analysis.

Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts

USGS Publications Warehouse

Muller, Benjamin J.; Cade, Brian S.; Schwarzkoph, Lin

2018-01-01

Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.

Temporal trends in sperm count: a systematic review and meta-regression analysis.

PubMed

Levine, Hagai; Jørgensen, Niels; Martino-Andrade, Anderson; Mendiola, Jaime; Weksler-Derri, Dan; Mindlis, Irina; Pinotti, Rachel; Swan, Shanna H

2017-11-01

Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality. To provide a systematic review and meta-regression analysis of recent trends in sperm counts as measured by sperm concentration (SC) and total sperm count (TSC), and their modification by fertility and geographic group. PubMed/MEDLINE and EMBASE were searched for English language studies of human SC published in 1981-2013. Following a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years of sample collection and covariates [fertility group ('Unselected by fertility' versus 'Fertile'), geographic group ('Western', including North America, Europe Australia and New Zealand versus 'Other', including South America, Asia and Africa), age, ejaculation abstinence time, semen collection method, method of measuring SC and semen volume, exclusion criteria and indicators of completeness of covariate data]. The slopes of SC and TSC were estimated as functions of sample collection year using both simple linear regression and weighted meta-regression models and the latter were adjusted for pre-determined covariates and modification by fertility and geographic group. Assumptions were examined using multiple sensitivity analyses and nonlinear models. SC declined significantly between 1973 and 2011 (slope in unadjusted simple regression models -0.70 million/ml/year; 95% CI: -0.72 to -0.69; P < 0.001; slope in adjusted meta-regression models = -0.64; -1.06 to -0.22; P = 0.003). The slopes in the meta-regression model were modified by fertility (P for interaction = 0.064) and geographic group (P for interaction = 0.027). There was a significant decline in SC between 1973 and 2011 among Unselected Western (-1.38; -2.02 to -0.74; P < 0.001) and among Fertile Western (-0.68; -1.31 to -0.05; P = 0.033), while no significant trends were seen among Unselected Other and Fertile Other. Among Unselected Western studies, the mean SC declined, on average, 1.4% per year with an overall decline of 52.4% between 1973 and 2011. Trends for TSC and SC were similar, with a steep decline among Unselected Western (-5.33 million/year, -7.56 to -3.11; P < 0.001), corresponding to an average decline in mean TSC of 1.6% per year and overall decline of 59.3%. Results changed minimally in multiple sensitivity analyses, and there was no statistical support for the use of a nonlinear model. In a model restricted to data post-1995, the slope both for SC and TSC among Unselected Western was similar to that for the entire period (-2.06 million/ml, -3.38 to -0.74; P = 0.004 and -8.12 million, -13.73 to -2.51, P = 0.006, respectively). This comprehensive meta-regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because of the significant public health implications of these results, research on the causes of this continuing decline is urgently needed. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Regression Analysis of Mixed Recurrent-Event and Panel-Count Data with Additive Rate Models

PubMed Central

Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L.

2015-01-01

Summary Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007; Zhao et al., 2011). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013). In this paper, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. PMID:25345405

Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

PubMed

Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

2017-02-01

The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism for the effects on RBC and WBC while no interactions were proved for the joint effect on PLT count. These results confirm that the assessment of interactions between chemicals in the mixture greatly depends on the concept or method used for this evaluation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model

NASA Astrophysics Data System (ADS)

Vazifedan, Turaj; Shitan, Mahendran

Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.

Remote sensing measurements of sea surface temperature as an indicator of Vibrio parahaemolyticus in oyster meat and human illnesses.

PubMed

Konrad, Stephanie; Paduraru, Peggy; Romero-Barrios, Pablo; Henderson, Sarah B; Galanis, Eleni

2017-08-31

Vibrio parahaemolyticus (Vp) is a naturally occurring bacterium found in marine environments worldwide. It can cause gastrointestinal illness in humans, primarily through raw oyster consumption. Water temperatures, and potentially other environmental factors, play an important role in the growth and proliferation of Vp in the environment. Quantifying the relationships between environmental variables and indicators or incidence of Vp illness is valuable for public health surveillance to inform and enable suitable preventative measures. This study aimed to assess the relationship between environmental parameters and Vp in British Columbia (BC), Canada. The study used Vp counts in oyster meat from 2002-2015 and laboratory confirmed Vp illnesses from 2011-2015 for the province of BC. The data were matched to environmental parameters from publicly available sources, including remote sensing measurements of nighttime sea surface temperature (SST) obtained from satellite readings at a spatial resolution of 1 km. Using three separate models, this paper assessed the relationship between (1) daily SST and Vp counts in oyster meat, (2) weekly mean Vp counts in oysters and weekly Vp illnesses, and (3) weekly mean SST and weekly Vp illnesses. The effects of salinity and chlorophyll a were also evaluated. Linear regression was used to quantify the relationship between SST and Vp, and piecewise regression was used to identify SST thresholds of concern. A total of 2327 oyster samples and 293 laboratory confirmed illnesses were included. In model 1, both SST and salinity were significant predictors of log(Vp) counts in oyster meat. In model 2, the mean log(Vp) count in oyster meat was a significant predictor of Vp illnesses. In model 3, weekly mean SST was a significant predictor of weekly Vp illnesses. The piecewise regression models identified a SST threshold of approximately 14 o C for both model 1 and 3, indicating increased risk of Vp in oyster meat and Vp illnesses at higher temperatures. Monitoring of SST, particularly through readily accessible remote sensing data, could serve as a warning signal for Vp and help inform the introduction and cessation of preventative or control measures.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Two-Part and Related Regression Models for Longitudinal Data

PubMed Central

Farewell, V.T.; Long, D.L.; Tom, B.D.M.; Yiu, S.; Su, L.

2017-01-01

Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution. PMID:28890906

Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models.

PubMed

Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I

2018-01-01

Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

Regression analysis of mixed recurrent-event and panel-count data with additive rate models.

PubMed

Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L

2015-03-01

Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. © 2014, The International Biometric Society.

Marginalized zero-altered models for longitudinal count data.

PubMed

Tabb, Loni Philip; Tchetgen, Eric J Tchetgen; Wellenius, Greg A; Coull, Brent A

2016-10-01

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias.

Marginalized zero-altered models for longitudinal count data

PubMed Central

Tabb, Loni Philip; Tchetgen, Eric J. Tchetgen; Wellenius, Greg A.; Coull, Brent A.

2015-01-01

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias. PMID:27867423

Thrombocytopenia following implantation of the stentless biological sorin freedom SOLO valve.

PubMed

Gersak, Borut; Gartner, Urska; Antonic, Miha

2011-07-01

Stentless biological valves have proven advantages in hemodynamic performance and left ventricular function compared to stented biological valves. Following a marked postoperative fall in the platelet count of patients after implantation of the Freedom SOLO valve, the study aim was to confirm clinical observations that this effect was more severe in patients receiving Freedom SOLO valves than in those receiving St. Jude Medical (SJM) mechanical aortic valves. Preoperative and postoperative platelet counts were compared in two groups of patients who underwent aortic valve replacement (AVR) without any concomitant procedures between January and December 2007. Patients received either a Freedom SOLO valve (n = 28) or a SJM mechanical valve (n = 41). Mean values of platelet counts were compared using three multiple linear regression models. Platelet counts were significantly lower in the Freedom SOLO group than in the SJM group from the first postoperative day (POD 1) up to POD 6 (p <0.001). In three patients of the Freedom SOLO group the platelet count fell below 30x10(9)/l, while the lowest level in the SJM group was 75x10(9)/l. Based on multiple linear regression models, the type of valve implanted had a statistically significant influence on postoperative platelet counts on POD 1, POD 3, and POD 5 (p <0.001). Whilst the reason for this phenomenon is unknown, the use of consistent monitoring should prevent severe falls in platelet count from becoming dangerous for the patient. Further studies are required to investigate the phenomenon since, despite a shorter cardiopulmonary bypass time, the fall in platelet count was more profound in the Freedom SOLO group.

Mental health status and healthcare utilization among community dwelling older adults.

PubMed

Adepoju, Omolola; Lin, Szu-Hsuan; Mileski, Michael; Kruse, Clemens Scott; Mask, Andrew

2018-04-27

Shifts in mental health utilization patterns are necessary to allow for meaningful access to care for vulnerable populations. There have been long standing issues in how mental health is provided, which has caused problems in that care being efficacious for those seeking it. To assess the relationship between mental health status and healthcare utilization among adults ≥65 years. A negative binomial regression model was used to assess the relationship between mental health status and healthcare utilization related to office-based physician visits, while a two-part model, consisting of logistic regression and negative binomial regression, was used to separately model emergency visits and inpatient services. The receipt of care in office-based settings were marginally higher for subjects with mental health difficulties. Both probabilities and counts of inpatient hospitalizations were similar across mental health categories. The count of ER visits was similar across mental health categories; however, the probability of having an emergency department visit was marginally higher for older adults who reported mental health difficulties in 2012. These findings are encouraging and lend promise to the recent initiatives on addressing gaps in mental healthcare services.

Valuation of National Park System Visitation: The Efficient Use of Count Data Models, Meta-Analysis, and Secondary Visitor Survey Data

NASA Astrophysics Data System (ADS)

Neher, Christopher; Duffield, John; Patterson, David

2013-09-01

The National Park Service (NPS) currently manages a large and diverse system of park units nationwide which received an estimated 279 million recreational visits in 2011. This article uses park visitor data collected by the NPS Visitor Services Project to estimate a consistent set of count data travel cost models of park visitor willingness to pay (WTP). Models were estimated using 58 different park unit survey datasets. WTP estimates for these 58 park surveys were used within a meta-regression analysis model to predict average and total WTP for NPS recreational visitation system-wide. Estimated WTP per NPS visit in 2011 averaged 102 system-wide, and ranged across park units from 67 to 288. Total 2011 visitor WTP for the NPS system is estimated at 28.5 billion with a 95% confidence interval of 19.7-43.1 billion. The estimation of a meta-regression model using consistently collected data and identical specification of visitor WTP models greatly reduces problems common to meta-regression models, including sample selection bias, primary data heterogeneity, and heteroskedasticity, as well as some aspects of panel effects. The article provides the first estimate of total annual NPS visitor WTP within the literature directly based on NPS visitor survey data.

Methods for analyzing matched designs with double controls: excess risk is easily estimated and misinterpreted when evaluating traffic deaths.

PubMed

Redelmeier, Donald A; Tibshirani, Robert J

2018-06-01

To demonstrate analytic approaches for matched studies where two controls are linked to each case and events are accumulating counts rather than binary outcomes. A secondary intent is to clarify the distinction between total risk and excess risk (unmatched vs. matched perspectives). We review past research testing whether elections can lead to increased traffic risks. The results are reinterpreted by analyzing both the total count of individuals in fatal crashes and the excess count of individuals in fatal crashes, each time accounting for the matched double controls. Overall, 1,546 individuals were in fatal crashes on the 10 election days (average = 155/d), and 2,593 individuals were in fatal crashes on the 20 control days (average = 130/d). Poisson regression of total counts yielded a relative risk of 1.19 (95% confidence interval: 1.12-1.27). Poisson regression of excess counts yielded a relative risk of 3.22 (95% confidence interval: 2.72-3.80). The discrepancy between analyses of total counts and excess counts replicated with alternative statistical models and was visualized in graphical displays. Available approaches provide methods for analyzing count data in matched designs with double controls and help clarify the distinction between increases in total risk and increases in excess risk. Copyright © 2018 Elsevier Inc. All rights reserved.

A review of spatio-temporal modelling of quadrat count data with application to striga occurrence in a pearl millet field

NASA Astrophysics Data System (ADS)

Hess, Dale; van Lieshout, Marie-Colette; Payne, Bill; Stein, Alfred

This paper describes how spatial statistical techniques may be used to analyse weed occurrence in tropical fields. Quadrat counts of weed numbers are available over a series of years, as well as data on explanatory variables, and the aim is to smooth the data and assess spatial and temporal trends. We review a range of models for correlated count data. As an illustration, we consider data on striga infestation of a 60 × 24 m 2 millet field in Niger collected from 1985 until 1991, modelled by independent Poisson counts and a prior auto regression term enforcing spatial coherence. The smoothed fields show the presence of a seed bank, the estimated model parameters indicate a decay in the striga numbers over time, as well as a clear correlation with the amount of rainfall in 15 consecutive days following the sowing date. Such results could contribute to precision agriculture as a guide to more cost-effective striga control strategies.

Recent trends in counts of migrant hawks from northeastern North America

USGS Publications Warehouse

Titus, K.; Fuller, M.R.

1990-01-01

Using simple regression, pooled-sites route-regression, and nonparametric rank-trend analyses, we evaluated trends in counts of hawks migrating past 6 eastern hawk lookouts from 1972 to 1987. The indexing variable was the total count for a season. Bald eagle (Haliaeetus leucocephalus), peregrine falcon (Falco peregrinus), merlin (F. columbarius), osprey (Pandion haliaetus), and Cooper's hawk (Accipiter cooperii) counts increased using route-regression and nonparametric methods (P 0.10). We found no consistent trends (P > 0.10) in counts of sharp-shinned hawks (A. striatus), northern goshawks (A. gentilis) red-shouldered hawks (Buteo lineatus), red-tailed hawks (B. jamaicensis), rough-legged hawsk (B. lagopus), and American kestrels (F. sparverius). Broad-winged hawk (B. platypterus) counts declined (P < 0.05) based on the route-regression method. Empirical comparisons of our results with those for well-studied species such as the peregrine falcon, bald eagle, and osprey indicated agreement with nesting surveys. We suggest that counts of migrant hawks are a useful and economical method for detecting long-term trends in species across regions, particularly for species that otherwise cannot be easily surveyed.

Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis.

PubMed

Sentürk, Damla; Dalrymple, Lorien S; Nguyen, Danh V

2014-11-30

We propose functional linear models for zero-inflated count data with a focus on the functional hurdle and functional zero-inflated Poisson (ZIP) models. Although the hurdle model assumes the counts come from a mixture of a degenerate distribution at zero and a zero-truncated Poisson distribution, the ZIP model considers a mixture of a degenerate distribution at zero and a standard Poisson distribution. We extend the generalized functional linear model framework with a functional predictor and multiple cross-sectional predictors to model counts generated by a mixture distribution. We propose an estimation procedure for functional hurdle and ZIP models, called penalized reconstruction, geared towards error-prone and sparsely observed longitudinal functional predictors. The approach relies on dimension reduction and pooling of information across subjects involving basis expansions and penalized maximum likelihood techniques. The developed functional hurdle model is applied to modeling hospitalizations within the first 2 years from initiation of dialysis, with a high percentage of zeros, in the Comprehensive Dialysis Study participants. Hospitalization counts are modeled as a function of sparse longitudinal measurements of serum albumin concentrations, patient demographics, and comorbidities. Simulation studies are used to study finite sample properties of the proposed method and include comparisons with an adaptation of standard principal components regression. Copyright © 2014 John Wiley & Sons, Ltd.

The value of daily platelet counts for predicting dengue shock syndrome: Results from a prospective observational study of 2301 Vietnamese children with dengue.

PubMed

Lam, Phung Khanh; Ngoc, Tran Van; Thu Thuy, Truong Thi; Hong Van, Nguyen Thi; Nhu Thuy, Tran Thi; Hoai Tam, Dong Thi; Dung, Nguyen Minh; Hanh Tien, Nguyen Thi; Thanh Kieu, Nguyen Tan; Simmons, Cameron; Wills, Bridget; Wolbers, Marcel

2017-04-01

Dengue is the most important mosquito-borne viral infection to affect humans. Although it usually manifests as a self-limited febrile illness, complications may occur as the fever subsides. A systemic vascular leak syndrome that sometimes progresses to life-threatening hypovolaemic shock is the most serious complication seen in children, typically accompanied by haemoconcentration and thrombocytopenia. Robust evidence on risk factors, especially features present early in the illness course, for progression to dengue shock syndrome (DSS) is lacking. Moreover, the potential value of incorporating serial haematocrit and platelet measurements in prediction models has never been assessed. We analyzed data from a prospective observational study of Vietnamese children aged 5-15 years admitted with clinically suspected dengue to the Hospital for Tropical Diseases in Ho Chi Minh City between 2001 and 2009. The analysis population comprised all children with laboratory-confirmed dengue enrolled between days 1-4 of illness. Logistic regression was the main statistical model for all univariate and multivariable analyses. The prognostic value of daily haematocrit levels and platelet counts were assessed using graphs and separate regression models fitted on each day of illness. Among the 2301 children included in the analysis, 143 (6%) progressed to DSS. Significant baseline risk factors for DSS included a history of vomiting, higher temperature, a palpable liver, and a lower platelet count. Prediction models that included serial daily platelet counts demonstrated better ability to discriminate patients who developed DSS from others, than models based on enrolment information only. However inclusion of daily haematocrit values did not improve prediction of DSS. Daily monitoring of platelet counts is important to help identify patients at high risk of DSS. Development of dynamic prediction models that incorporate signs, symptoms, and daily laboratory measurements, could improve DSS prediction and thereby reduce the burden on health services in endemic areas.

Detecting isotopic ratio outliers

NASA Astrophysics Data System (ADS)

Bayne, C. K.; Smith, D. H.

An alternative method is proposed for improving isotopic ratio estimates. This method mathematically models pulse-count data and uses iterative reweighted Poisson regression to estimate model parameters to calculate the isotopic ratios. This computer-oriented approach provides theoretically better methods than conventional techniques to establish error limits and to identify outliers.

Poisson Regression Analysis of Illness and Injury Surveillance Data

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

Frome E.L., Watkins J.P., Ellis E.D.

2012-12-12

The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences duemore » to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra-Poisson variation. The R open source software environment for statistical computing and graphics is used for analysis. Additional details about R and the data that were used in this report are provided in an Appendix. Information on how to obtain R and utility functions that can be used to duplicate results in this report are provided.« less

Use of non-linear mixed-effects modelling and regression analysis to predict the number of somatic coliphages by plaque enumeration after 3 hours of incubation.

PubMed

Mendez, Javier; Monleon-Getino, Antonio; Jofre, Juan; Lucena, Francisco

2017-10-01

The present study aimed to establish the kinetics of the appearance of coliphage plaques using the double agar layer titration technique to evaluate the feasibility of using traditional coliphage plaque forming unit (PFU) enumeration as a rapid quantification method. Repeated measurements of the appearance of plaques of coliphages titrated according to ISO 10705-2 at different times were analysed using non-linear mixed-effects regression to determine the most suitable model of their appearance kinetics. Although this model is adequate, to simplify its applicability two linear models were developed to predict the numbers of coliphages reliably, using the PFU counts as determined by the ISO after only 3 hours of incubation. One linear model, when the number of plaques detected was between 4 and 26 PFU after 3 hours, had a linear fit of: (1.48 × Counts 3 h + 1.97); and the other, values >26 PFU, had a fit of (1.18 × Counts 3 h + 2.95). If the number of plaques detected was <4 PFU after 3 hours, we recommend incubation for (18 ± 3) hours. The study indicates that the traditional coliphage plating technique has a reasonable potential to provide results in a single working day without the need to invest in additional laboratory equipment.

Not in My Back Yard: A Comparative Analysis of Crime Around Publicly Funded Drug Treatment Centers, Liquor Stores, Convenience Stores, and Corner Stores in One Mid-Atlantic City.

PubMed

Furr-Holden, C Debra M; Milam, Adam J; Nesoff, Elizabeth D; Johnson, Renee M; Fakunle, David O; Jennings, Jacky M; Thorpe, Roland J

2016-01-01

This research examined whether publicly funded drug treatment centers (DTCs) were associated with violent crime in excess of the violence happening around other commercial businesses. Violent crime data and locations of community entities were geocoded and mapped. DTCs and other retail outlets were matched based on a Neighborhood Disadvantage score at the census tract level. Street network buffers ranging from 100 to 1,400 feet were placed around each location. Negative binomial regression models were used to estimate the relationship between the count of violent crimes and the distance from each business type. Compared with the mean count of violent crime around drug treatment centers, the mean count of violent crime (M = 2.87) was significantly higher around liquor stores (M = 3.98; t test; p < .01) and corner stores (M = 3.78; t test; p < .01), and there was no statistically significant difference between the count around convenience stores (M = 2.65; t test; p = .32). In the adjusted negative binomial regression models, there was a negative and significant relationship between the count of violent crime and the distance from drug treatment centers (β = -.069, p < .01), liquor stores (β = -.081, p < .01), corner stores (β = -.116, p < .01), and convenience stores (β = -.154, p < .01). Violent crime associated with drug treatment centers is similar to that associated with liquor stores and is less frequent than that associated with convenience stores and corner stores.

A tutorial on count regression and zero-altered count models for longitudinal substance use data

PubMed Central

Atkins, David C.; Baldwin, Scott A.; Zheng, Cheng; Gallop, Robert J.; Neighbors, Clayton

2012-01-01

Critical research questions in the study of addictive behaviors concern how these behaviors change over time - either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website. PMID:22905895

MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

EPA Science Inventory

Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

Pick Your Poisson: A Tutorial on Analyzing Counts of Student Victimization Data

ERIC Educational Resources Information Center

Huang, Francis L.; Cornell, Dewey G.

2012-01-01

School violence research is often concerned with infrequently occurring events such as counts of the number of bullying incidents or fights a student may experience. Analyzing count data using ordinary least squares regression may produce improbable predicted values, and as a result of regression assumption violations, result in higher Type I…

Marginal regression models for clustered count data based on zero-inflated Conway-Maxwell-Poisson distribution with applications.

PubMed

Choo-Wosoba, Hyoyoung; Levy, Steven M; Datta, Somnath

2016-06-01

Community water fluoridation is an important public health measure to prevent dental caries, but it continues to be somewhat controversial. The Iowa Fluoride Study (IFS) is a longitudinal study on a cohort of Iowa children that began in 1991. The main purposes of this study (http://www.dentistry.uiowa.edu/preventive-fluoride-study) were to quantify fluoride exposures from both dietary and nondietary sources and to associate longitudinal fluoride exposures with dental fluorosis (spots on teeth) and dental caries (cavities). We analyze a subset of the IFS data by a marginal regression model with a zero-inflated version of the Conway-Maxwell-Poisson distribution for count data exhibiting excessive zeros and a wide range of dispersion patterns. In general, we introduce two estimation methods for fitting a ZICMP marginal regression model. Finite sample behaviors of the estimators and the resulting confidence intervals are studied using extensive simulation studies. We apply our methodologies to the dental caries data. Our novel modeling incorporating zero inflation, clustering, and overdispersion sheds some new light on the effect of community water fluoridation and other factors. We also include a second application of our methodology to a genomic (next-generation sequencing) dataset that exhibits underdispersion. © 2015, The International Biometric Society.

A 30-day-ahead forecast model for grass pollen in north London, United Kingdom.

PubMed

Smith, Matt; Emberlin, Jean

2006-03-01

A 30-day-ahead forecast method has been developed for grass pollen in north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21 May to 8 August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961 to 1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of 1 to 4; the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of 1 to 4, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002, respectively, when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.

Prediction of Poor Ovarian response by Biochemical and Biophysical Markers: A Logistic Regression Model.

PubMed

Jaiswar, S P; Natu, S M; Sujata; Sankhwar, P L; Manjari, Gupta

2015-12-01

To study correlation between ovarian reserve with biophysical markers (antral follicle count and ovarian volume) and biochemical markers (S. FSH, S. Inhibin B, and S. AMH) and use these markers to predict poor ovarian response to ovarian induction. This is a prospective observational study. One hundred infertile women attending the Obst & Gynae Dept, KGMU were recruited. Blood samples were collected on day 2/day 3 for assessment of S. FSH, S. Inhibin B, and S. AMH and TVS were done for antral follicle count and ovarian volume. Clomephene citrate 100 mg 1OD was given from day 2 to 6, and patients were followed up with serial USG measurements. The numbers of dominant follicles (> or = 14 mm) at the time of hCG administration were counted. Patients with <3 follicles in the 1st cycle were subjected to the 2nd cycle of clomephene 100 mg 1OD from day 2 to day 6 with Inj HMG 150 IU given i.m. starting from day 8 and every alternate day until at least one leading follicle attained ≥18 mm. Development of <3 follicles at end of the 2nd cycle was considered as poor response. Univariate analyses showed that s. inhibin B presented the highest (ROCAUC = 0.862) discriminating potential for predicting poor ovarian response, In multivariate logistic regression model, the variables age, FSH, AMH, INHIBIN B, and AFC remained significant, and the resulting model showed a predicted accuracy of 84.4 %. A derived multimarker computation by a logistic regression model for predicting poor ovarian response was obtained through this study. Thus, potential poor responders could be identified easily, and appropriate ovarian stimulation protocol could be devised for such pts.

Associations of Genetically Determined Continental Ancestry with CD4+ Count and Plasma HIV-1 RNA beyond Self-Reported Race and Ethnicity

PubMed Central

Brummel, Sean S; Singh, Kumud K; Maihofer, Adam X.; Farhad, Mona; Qin, Min; Fenton, Terry; Nievergelt, Caroline M.; Spector, Stephen A.

2015-01-01

Background Ancestry informative markers (AIMs) measure genetic admixtures within an individual beyond self-reported racial/ethnic (SRR) groups. Here, we used genetically determined ancestry (GDA) across SRR groups and examine associations between GDA and HIV-1 RNA and CD4+ counts in HIV-positive children in the US. Methods 41 AIMs, developed to distinguish 7 continental regions, were detected by real-time-PCR in 994 HIV-positive, antiretroviral naïve children. GDA was estimated comparing each individual’s genotypes to allele frequencies found in a large set of reference individuals originating from global populations using STRUCTURE. The means of GDA were calculated for each category of SRR. Linear regression was used to model GDA on CD4+ count and log10 RNA, adjusting for SRR and age. Results Subjects were 61% Black, 25% Hispanic, 13% White and 1.3% Unknown. The mean age was 2.3 years (45% male), mean CD4+ count 981 cells/mm3, and mean log10 RNA 5.11. Marked heterogeneity was found for all SRR groups with high admixture for Hispanics. In adjusted linear regression models, subjects with 100% European ancestry were estimated to have 0.33 higher log10 RNA levels (95% CI: (0.03, 0.62), p=0.028) and 253 CD4+ cells /mm3 lower (95% CI: (−517, 11), p = 0.06) in CD4+ count, compared to subjects with 100% African ancestry. Conclusion Marked continental admixture was found among this cohort of HIV-infected children from the US. GDA contributed to differences in RNA and CD4+ counts beyond SRR, and should be considered when outcomes associated with HIV infection are likely to have a genetic component. PMID:26536313

Associations of Genetically Determined Continental Ancestry With CD4+ Count and Plasma HIV-1 RNA Beyond Self-Reported Race and Ethnicity.

PubMed

Brummel, Sean S; Singh, Kumud K; Maihofer, Adam X; Farhad, Mona; Qin, Min; Fenton, Terry; Nievergelt, Caroline M; Spector, Stephen A

2016-04-15

Ancestry informative markers (AIMs) measure genetic admixtures within an individual beyond self-reported racial/ethnic (SRR) groups. Here, we used genetically determined ancestry (GDA) across SRR groups and examine associations between GDA and HIV-1 RNA and CD4 counts in HIV-positive children in the United States. Forty-one AIMs, developed to distinguish 7 continental regions, were detected by real-time PCR in 994 HIV-positive, antiretroviral naive children. GDA was estimated comparing each individual's genotypes to allele frequencies found in a large set of reference individuals originating from global populations using STRUCTURE. The means of GDA were calculated for each category of SRR. Linear regression was used to model GDA on CD4 count and log10 RNA, adjusting for SRR and age. Subjects were 61% black, 25% Hispanic, 13% white, and 1.3% Unknown. The mean age was 2.3 years (45% male), mean CD4 count of 981 cells per cubic millimeter, and mean log10 RNA of 5.11. Marked heterogeneity was found for all SRR groups with high admixture for Hispanics. In adjusted linear regression models, subjects with 100% European ancestry were estimated to have 0.33 higher log10 RNA levels (95% CI: 0.03 to 0.62, P = 0.028) and 253 CD4 cells per cubic millimeter lower (95% CI: -517 to 11, P = 0.06) in CD4 count, compared to subjects with 100% African ancestry. Marked continental admixture was found among this cohort of HIV-infected children from the United States. GDA contributed to differences in RNA and CD4 counts beyond SRR and should be considered when outcomes associated with HIV infection are likely to have a genetic component.

Time series regression studies in environmental epidemiology.

PubMed

Bhaskaran, Krishnan; Gasparrini, Antonio; Hajat, Shakoor; Smeeth, Liam; Armstrong, Ben

2013-08-01

Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed ('lagged') associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.

Trend estimation in populations with imperfect detection

USGS Publications Warehouse

Kery, Marc; Dorazio, Robert M.; Soldaat, Leo; Van Strien, Arco; Zuiderwijk, Annie; Royle, J. Andrew

2009-01-01

1. Trends of animal populations are of great interest in ecology but cannot be directly observed owing to imperfect detection. Binomial mixture models use replicated counts to estimate abundance, corrected for detection, in demographically closed populations. Here, we extend these models to open populations and illustrate them using sand lizard Lacerta agilis counts from the national Dutch reptile monitoring scheme. 2. Our model requires replicated counts from multiple sites in each of several periods, within which population closure is assumed. Counts are described by a hierarchical generalized linear model, where the state model deals with spatio-temporal patterns in true abundance and the observation model with imperfect counts, given that true state. We used WinBUGS to fit the model to lizard counts from 208 transects with 1–10 (mean 3) replicate surveys during each spring 1994–2005. 3. Our state model for abundance contained two independent log-linear Poisson regressions on year for coastal and inland sites, and random site effects to account for unexplained heterogeneity. The observation model for detection of an individual lizard contained effects of region, survey date, temperature, observer experience and random survey effects. 4. Lizard populations increased in both regions but more steeply on the coast. Detectability increased over the first few years of the study, was greater on the coast and for the most experienced observers, and highest around 1 June. Interestingly, the population increase inland was not detectable when the observed counts were analysed without account of detectability. The proportional increase between 1994 and 2005 in total lizard abundance across all sites was estimated at 86% (95% CRI 35–151). 5. Synthesis and applications. Open-population binomial mixture models are attractive for studying true population dynamics while explicitly accounting for the observation process, i.e. imperfect detection. We emphasize the important conceptual benefit provided by temporal replicate observations in terms of the interpretability of animal counts.

Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events.

PubMed

Chan, Kwun Chuen Gary; Wang, Mei-Cheng

2017-01-01

Recurrent event processes with marker measurements are mostly and largely studied with forward time models starting from an initial event. Interestingly, the processes could exhibit important terminal behavior during a time period before occurrence of the failure event. A natural and direct way to study recurrent events prior to a failure event is to align the processes using the failure event as the time origin and to examine the terminal behavior by a backward time model. This paper studies regression models for backward recurrent marker processes by counting time backward from the failure event. A three-level semiparametric regression model is proposed for jointly modeling the time to a failure event, the backward recurrent event process, and the marker observed at the time of each backward recurrent event. The first level is a proportional hazards model for the failure time, the second level is a proportional rate model for the recurrent events occurring before the failure event, and the third level is a proportional mean model for the marker given the occurrence of a recurrent event backward in time. By jointly modeling the three components, estimating equations can be constructed for marked counting processes to estimate the target parameters in the three-level regression models. Large sample properties of the proposed estimators are studied and established. The proposed models and methods are illustrated by a community-based AIDS clinical trial to examine the terminal behavior of frequencies and severities of opportunistic infections among HIV infected individuals in the last six months of life.

Leucocyte count in young adults with first-ever ischaemic stroke: associated factors and association on prognosis.

PubMed

Heikinheimo, Terttu; Putaala, Jukka; Haapaniemi, Elena; Kaste, Markku; Tatlisumak, Turgut

2015-02-01

Limited data exist on the associated factors and correlation of leucocyte count to outcome in young adults with first-ever ischaemic stroke. Our objectives were to investigate factors associated with elevated leucocyte count and whether there is correlation between leucocyte count and short- and long-term outcomes. Of our database of 1008 consecutive patients aged 15 to 49, we included those with leucocyte count measured within the first two days from stroke onset. Outcomes were three-month and long-term disability, death, and vascular events. Linear regression was used to explore baseline variables associated with leucocyte count. Logistic regression and Cox proportional models studied the association between leucocyte count and clinical outcomes. In our study cohort of 781 patients (61.7% males; mean age 41.4 years), mean leucocyte count was high: 8.8 ± 3.1 × 10(9) cells/L (Reference range: 3.4-8.2 × 10(9) cells/L). Higher leucocyte levels were associated with dyslipidaemia, smoking, peripheral arterial disease, stroke severity, and lesion size. After adjustment for age, gender, relevant risk factors, both continuous leucocyte count and the highest quartile of leucocyte count were independently associated with unfavourable three-month outcome. Regarding events in the long-term (follow-up 8.1 ± 4.2 years in survivors), no association between leucocyte count and the event risks appeared. Among young stroke patients, high leucocyte count was a common finding. It was associated with vascular disease and its risk factors as well as severity of stroke, but it was also independently associated with unfavourable three-month outcome in these patients. There was no association with the long-term outcome. [Correction added on 31 October 2013 after first online publication: In the Results section of the Abstract, the cohort of 797 patients in this study was corrected to 781 patients.]. © 2013 The Authors. International Journal of Stroke © 2013 World Stroke Organization.

Not in My Back Yard: A Comparative Analysis of Crime Around Publicly Funded Drug Treatment Centers, Liquor Stores, Convenience Stores, and Corner Stores in One Mid-Atlantic City

PubMed Central

Furr-Holden, C. Debra M.; Milam, Adam J.; Nesoff, Elizabeth D.; Johnson, Renee M.; Fakunle, David O.; Jennings, Jacky M.; Thorpe, Roland J.

2016-01-01

Objective: This research examined whether publicly funded drug treatment centers (DTCs) were associated with violent crime in excess of the violence happening around other commercial businesses. Method: Violent crime data and locations of community entities were geocoded and mapped. DTCs and other retail outlets were matched based on a Neighborhood Disadvantage score at the census tract level. Street network buffers ranging from 100 to 1,400 feet were placed around each location. Negative binomial regression models were used to estimate the relationship between the count of violent crimes and the distance from each business type. Results: Compared with the mean count of violent crime around drug treatment centers, the mean count of violent crime (M = 2.87) was significantly higher around liquor stores (M = 3.98; t test; p < .01) and corner stores (M = 3.78; t test; p < .01), and there was no statistically significant difference between the count around convenience stores (M = 2.65; t test; p = .32). In the adjusted negative binomial regression models, there was a negative and significant relationship between the count of violent crime and the distance from drug treatment centers (β = -.069, p < .01), liquor stores (β = -.081, p < .01), corner stores (β = -.116, p < .01), and convenience stores (β = -.154, p < .01). Conclusions: Violent crime associated with drug treatment centers is similar to that associated with liquor stores and is less frequent than that associated with convenience stores and corner stores. PMID:26751351

A Predictive Model for Microbial Counts on Beaches where Intertidal Sand is the Primary Source

PubMed Central

Feng, Zhixuan; Reniers, Ad; Haus, Brian K.; Solo-Gabriele, Helena M.; Wang, John D.; Fleming, Lora E.

2015-01-01

Human health protection at recreational beaches requires accurate and timely information on microbiological conditions to issue advisories. The objective of this study was to develop a new numerical mass balance model for enterococci levels on nonpoint source beaches. The significant advantage of this model is its easy implementation, and it provides a detailed description of the cross-shore distribution of enterococci that is useful for beach management purposes. The performance of the balance model was evaluated by comparing predicted exceedances of a beach advisory threshold value to field data, and to a traditional regression model. Both the balance model and regression equation predicted approximately 70% the advisories correctly at the knee depth and over 90% at the waist depth. The balance model has the advantage over the regression equation in its ability to simulate spatiotemporal variations of microbial levels, and it is recommended for making more informed management decisions. PMID:25840869

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

PubMed

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P < 0.0001) based on testing by the Lagrangemultiplier. Therefore, the over-dispersion dispersed data using a modified Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

A hierarchical spatial model of avian abundance with application to Cerulean Warblers

USGS Publications Warehouse

Thogmartin, Wayne E.; Sauer, John R.; Knutson, Melinda G.

2004-01-01

Surveys collecting count data are the primary means by which abundance is indexed for birds. These counts are confounded, however, by nuisance effects including observer effects and spatial correlation between counts. Current methods poorly accommodate both observer and spatial effects because modeling these spatially autocorrelated counts within a hierarchical framework is not practical using standard statistical approaches. We propose a Bayesian approach to this problem and provide as an example of its implementation a spatial model of predicted abundance for the Cerulean Warbler (Dendroica cerulea) in the Prairie-Hardwood Transition of the upper midwestern United States. We used an overdispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods. We used 21 years of North American Breeding Bird Survey counts as the response in a loglinear function of explanatory variables describing habitat, spatial relatedness, year effects, and observer effects. The model included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land cover composition and configuration, climate, terrain heterogeneity, and human influence. The inherent hierarchy in the model was from counts occurring, in part, as a function of observers within survey routes within years. We found that the percentage of forested wetlands, an index of wetness potential, and an interaction between mean annual precipitation and deciduous forest patch size best described Cerulean Warbler abundance. Based on a map of relative abundance derived from the posterior parameter estimates, we estimated that only 15% of the species' population occurred on federal land, necessitating active engagement of public landowners and state agencies in the conservation of the breeding habitat for this species. Models of this type can be applied to any data in which the response is counts, such as animal counts, activity (e.g.,nest) counts, or species richness. The most noteworthy practical application of this spatial modeling approach is the ability to map relative species abundance. The functional relationships that we elucidated for the Cerulean Warbler provide a basis for the development of management programs and may serve to focus management and monitoring on areas and habitat variables important to Cerulean Warblers.

Marginalized zero-inflated Poisson models with missing covariates.

PubMed

Benecha, Habtamu K; Preisser, John S; Divaris, Kimon; Herring, Amy H; Das, Kalyan

2018-05-11

Unlike zero-inflated Poisson regression, marginalized zero-inflated Poisson (MZIP) models for counts with excess zeros provide estimates with direct interpretations for the overall effects of covariates on the marginal mean. In the presence of missing covariates, MZIP and many other count data models are ordinarily fitted using complete case analysis methods due to lack of appropriate statistical methods and software. This article presents an estimation method for MZIP models with missing covariates. The method, which is applicable to other missing data problems, is illustrated and compared with complete case analysis by using simulations and dental data on the caries preventive effects of a school-based fluoride mouthrinse program. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

EM Adaptive LASSO—A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes

PubMed Central

Mallick, Himel; Tiwari, Hemant K.

2016-01-01

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice. PMID:27066062

EM Adaptive LASSO-A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes.

PubMed

Mallick, Himel; Tiwari, Hemant K

2016-01-01

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice.

Modeling bias and variation in the stochastic processes of small RNA sequencing

PubMed Central

Etheridge, Alton; Sakhanenko, Nikita; Galas, David

2017-01-01

Abstract The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data. PMID:28369495

Preliminary evidence for school-based physical activity policy needs in Washington, DC.

PubMed

Goodman, Emily; Evans, W Douglas; DiPietro, Loretta

2012-01-01

The school setting could be a primary venue for promoting physical activity among inner-city children due to the structured natured of the school day. We examined differences in step counts between structured school days (SSD) and weekend days (WED) among a sample of public school children in Washington, DC. Subjects (N = 29) were third- to sixth-grade students enrolled in government-funded, extended-day enrichment programs. Step counts were measured using a pedometer (Bodytronics) over 2 SSD and 2 WED. Differences in mean step counts between SSD and WED were determined using multivariable linear regression, with adjustments for age, sex, and reported distance between house and school (miles). Recorded step counts were low on both SSD and WED (7735 ± 3540 and 8339 ± 5314 steps/day). Boys tended to record more steps on SSD compared with girls (8080 ± 3141 vs. 7491 ± 3872 steps/day, respectively), whereas girls recorded more steps on the WED compared with boys (9292 ± 6381 vs. 7194 ± 3669 steps/day). Parameter estimates from the regression modeling suggest distance from school (P < .01) to be the strongest predictor of daily step counts, independent of day (SSD/WED), sex, and age. Among inner-city school children, a safe walking route to and from school may provide an important opportunity for daily physical activity.

Bibliometric Analysis of Manuscript Characteristics That Influence Citations: A Comparison of Six Major Radiology Journals.

PubMed

Shekhani, Haris Naseem; Shariff, Shoaib; Bhulani, Nizar; Khosa, Faisal; Hanna, Tarek Noel

2017-12-01

The objective of our study was to investigate radiology manuscript characteristics that influence citation rate, capturing features of manuscript construction that are discrete from study design. Consecutive articles published from January 2004 to June 2004 were collected from the six major radiology journals with the highest impact factors: Radiology (impact factor, 5.076), Investigative Radiology (2.320), American Journal of Neuroradiology (AJNR) (2.384), RadioGraphics (2.494), European Radiology (2.364), and American Journal of Roentgenology (2.406). The citation count for these articles was retrieved from the Web of Science, and 29 article characteristics were tabulated manually. A point-biserial correlation, Spearman rank-order correlation, and multiple regression model were performed to predict citation number from the collected variables. A total of 703 articles-211 published in Radiology, 48 in Investigative Radiology, 106 in AJNR, 52 in RadioGraphics, 129 in European Radiology, and 157 in AJR-were evaluated. Punctuation was included in the title in 55% of the articles and had the highest statistically significant positive correlation to citation rate (point-biserial correlation coefficient [r pb ] = 0.85, p < 0.05). Open access status provided a low-magnitude, but significant, correlation to citation rate (r pb = 0.140, p < 0.001). The following variables created a significant multiple regression model to predict citation count (p < 0.005, R 2 = 0.186): study findings in the title, abstract word count, abstract character count, total number of words, country of origin, and all authors in the field of radiology. Using bibliometric knowledge, authors can craft a title, abstract, and text that may enhance visibility and citation count over what they would otherwise experience.

Modelling the variation in skin-test tuberculin reactions, post-mortem lesion counts and case pathology in tuberculosis-exposed cattle: Effects of animal characteristics, histories and co-infection.

PubMed

Byrne, A W; Graham, J; Brown, C; Donaghy, A; Guelbenzu-Gonzalo, M; McNair, J; Skuce, R A; Allen, A; McDowell, S W

2018-06-01

Correctly identifying bovine tuberculosis (bTB) in cattle remains a significant problem in endemic countries. We hypothesized that animal characteristics (sex, age, breed), histories (herd effects, testing, movement) and potential exposure to other pathogens (co-infection; BVDV, liver fluke and Mycobacterium avium reactors) could significantly impact the immune responsiveness detected at skin testing and the variation in post-mortem pathology (confirmation) in bTB-exposed cattle. Three model suites were developed using a retrospective observational data set of 5,698 cattle culled during herd breakdowns in Northern Ireland. A linear regression model suggested that antemortem tuberculin reaction size (difference in purified protein derivative avium [PPDa] and bovine [PPDb] reactions) was significantly positively associated with post-mortem maximum lesion size and the number of lesions found. This indicated that reaction size could be considered a predictor of both the extent (number of lesions/tissues) and the pathological progression of infection (maximum lesion size). Tuberculin reaction size was related to age class, and younger animals (<2.85 years) displayed larger reaction sizes than older animals. Tuberculin reaction size was also associated with breed and animal movement and increased with the time between the penultimate and disclosing tests. A negative binomial random-effects model indicated a significant increase in lesion counts for animals with M. avium reactions (PPDb-PPDa < 0) relative to non-reactors (PPDb-PPDa = 0). Lesion counts were significantly increased in animals with previous positive severe interpretation skin-test results. Animals with increased movement histories, young animals and non-dairy breed animals also had significantly increased lesion counts. Animals from herds that had BVDV-positive cattle had significantly lower lesion counts than animals from herds without evidence of BVDV infection. Restricting the data set to only animals with a bTB visible lesion at slaughter (n = 2471), an ordinal regression model indicated that liver fluke-infected animals disclosed smaller lesions, relative to liver fluke-negative animals, and larger lesions were disclosed in animals with increased movement histories. © 2018 Blackwell Verlag GmbH.

Multilevel joint competing risk models

NASA Astrophysics Data System (ADS)

Karunarathna, G. H. S.; Sooriyarachchi, M. R.

2017-09-01

Joint modeling approaches are often encountered for different outcomes of competing risk time to event and count in many biomedical and epidemiology studies in the presence of cluster effect. Hospital length of stay (LOS) has been the widely used outcome measure in hospital utilization due to the benchmark measurement for measuring multiple terminations such as discharge, transferred, dead and patients who have not completed the event of interest at the follow up period (censored) during hospitalizations. Competing risk models provide a method of addressing such multiple destinations since classical time to event models yield biased results when there are multiple events. In this study, the concept of joint modeling has been applied to the dengue epidemiology in Sri Lanka, 2006-2008 to assess the relationship between different outcomes of LOS and platelet count of dengue patients with the district cluster effect. Two key approaches have been applied to build up the joint scenario. In the first approach, modeling each competing risk separately using the binary logistic model, treating all other events as censored under the multilevel discrete time to event model, while the platelet counts are assumed to follow a lognormal regression model. The second approach is based on the endogeneity effect in the multilevel competing risks and count model. Model parameters were estimated using maximum likelihood based on the Laplace approximation. Moreover, the study reveals that joint modeling approach yield more precise results compared to fitting two separate univariate models, in terms of AIC (Akaike Information Criterion).

Urinary symptoms following external beam radiotherapy of the prostate: Dose-symptom correlates with multiple-event and event-count models.

PubMed

Yahya, Noorazrul; Ebert, Martin A; Bulsara, Max; House, Michael J; Kennedy, Angel; Joseph, David J; Denham, James W

2015-11-01

This study aimed to compare urinary dose-symptom correlates after external beam radiotherapy of the prostate using commonly utilised peak-symptom models to multiple-event and event-count models which account for repeated events. Urinary symptoms (dysuria, haematuria, incontinence and frequency) from 754 participants from TROG 03.04-RADAR trial were analysed. Relative (R1-R75 Gy) and absolute (A60-A75Gy) bladder dose-surface area receiving more than a threshold dose and equivalent uniform dose using exponent a (range: a ∈[1 … 100]) were derived. The dose-symptom correlates were analysed using; peak-symptom (logistic), multiple-event (generalised estimating equation) and event-count (negative binomial regression) models. Stronger dose-symptom correlates were found for incontinence and frequency using multiple-event and/or event-count models. For dysuria and haematuria, similar or better relationships were found using peak-symptom models. Dysuria, haematuria and high grade (⩾ 2) incontinence were associated to high dose (R61-R71 Gy). Frequency and low grade (⩾ 1) incontinence were associated to low and intermediate dose-surface parameters (R13-R41Gy). Frequency showed a parallel behaviour (a=1) while dysuria, haematuria and incontinence showed a more serial behaviour (a=4 to a ⩾ 100). Relative dose-surface showed stronger dose-symptom associations. For certain endpoints, the multiple-event and event-count models provide stronger correlates over peak-symptom models. Accounting for multiple events may be advantageous for a more complete understanding of urinary dose-symptom relationships. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape

PubMed Central

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for ‘difficult’ variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables. PMID:29713298

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape.

PubMed

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables.

The monocyte counts to HDL cholesterol ratio in obese and lean patients with polycystic ovary syndrome.

PubMed

Usta, Akin; Avci, Eyup; Bulbul, Cagla Bahar; Kadi, Hasan; Adali, Ertan

2018-04-10

Women with polycystic ovary syndrome are more likely to suffer from obesity, insulin resistance, and chronic low-grade inflammation. In fact, the excessive activation of monocytes exacerbates oxidative stress and inflammation. However, high-density lipoprotein cholesterol neutralizes the pro-inflammatory and pro-oxidant effects of monocytes. The aim of this study is to investigate whether monocyte counts to high-density lipoprotein cholesterol ratio can predict the inflammatory condition in patients with polycystic ovary syndrome. In this cross-sectional study, a total of 124 women (61 of them with polycystic ovary syndrome and 63 age-matched healthy volunteers) were included in the study population. Obese polycystic ovary syndrome patients (n = 30) with a body mass index of ≥25 kg/m 2 and lean polycystic ovary syndrome patients (n = 31) with a body mass index of < 25 kg/m 2 were compared to age-and body mass index-matched healthy subjects (30 obese and 33 non-obese). The monocyte counts to high density lipoprotein cholesterol values in women with polycystic ovary syndrome were significantly higher than in control subjects (p = 0.0018). Moreover, a regression analysis revealed that body mass index, the homeostasis model assessment of insulin resistance and the high sensitivity C-reactive protein levels were confounding factors that affected the monocyte counts to high density lipoprotein cholesterol values. Additionally, a univariate and multivariate logistic regression analysis demonstrated that the increased monocyte counts to high density lipoprotein cholesterol values were more sensitive than the other known risk factors (such as increased body mass index, homeostasis model assessment of insulin resistance and high sensitive C-reactive protein levels) in the prediction of the inflammation in patients with polycystic ovary syndrome. The present study demonstrated that the monocyte count to high density lipoprotein cholesterol may be a novel and useful predictor of the presence of polycystic ovary syndrome.

Posttraumatic Growth and HIV Disease Progression

ERIC Educational Resources Information Center

Milam, Joel

2006-01-01

The relationship between posttraumatic growth (PTG; perceiving positive changes since diagnosis) and disease status, determined by changes in viral load and CD4 count over time, was examined among 412 people living with HIV. In controlled multiple regression models, PTG was not associated with disease status over time for the entire sample.…

Quantification of Covariance in Tropical Cyclone Activity across Teleconnected Basins

NASA Astrophysics Data System (ADS)

Tolwinski-Ward, S. E.; Wang, D.

2015-12-01

Rigorous statistical quantification of natural hazard covariance across regions has important implications for risk management, and is also of fundamental scientific interest. We present a multivariate Bayesian Poisson regression model for inferring the covariance in tropical cyclone (TC) counts across multiple ocean basins and across Saffir-Simpson intensity categories. Such covariability results from the influence of large-scale modes of climate variability on local environments that can alternately suppress or enhance TC genesis and intensification, and our model also simultaneously quantifies the covariance of TC counts with various climatic modes in order to deduce the source of inter-basin TC covariability. The model explicitly treats the time-dependent uncertainty in observed maximum sustained wind data, and hence the nominal intensity category of each TC. Differences in annual TC counts as measured by different agencies are also formally addressed. The probabilistic output of the model can be probed for probabilistic answers to such questions as: - Does the relationship between different categories of TCs differ statistically by basin? - Which climatic predictors have significant relationships with TC activity in each basin? - Are the relationships between counts in different basins conditionally independent given the climatic predictors, or are there other factors at play affecting inter-basin covariability? - How can a portfolio of insured property be optimized across space to minimize risk? Although we present results of our model applied to TCs, the framework is generalizable to covariance estimation between multivariate counts of natural hazards across regions and/or across peril types.

QSAR, docking and ADMET studies of artemisinin derivatives for antimalarial activity targeting plasmepsin II, a hemoglobin-degrading enzyme from P. falciparum.

PubMed

Qidwai, Tabish; Yadav, Dharmendra K; Khan, Feroz; Dhawan, Sangeeta; Bhakuni, R S

2012-01-01

This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54, A62 and A64 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r(2)) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV(2)) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 was chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice. The experimental results obtained agreed well with the predicted values.

Censored Hurdle Negative Binomial Regression (Case Study: Neonatorum Tetanus Case in Indonesia)

NASA Astrophysics Data System (ADS)

Yuli Rusdiana, Riza; Zain, Ismaini; Wulan Purnami, Santi

2017-06-01

Hurdle negative binomial model regression is a method that can be used for discreate dependent variable, excess zero and under- and overdispersion. It uses two parts approach. The first part estimates zero elements from dependent variable is zero hurdle model and the second part estimates not zero elements (non-negative integer) from dependent variable is called truncated negative binomial models. The discrete dependent variable in such cases is censored for some values. The type of censor that will be studied in this research is right censored. This study aims to obtain the parameter estimator hurdle negative binomial regression for right censored dependent variable. In the assessment of parameter estimation methods used Maximum Likelihood Estimator (MLE). Hurdle negative binomial model regression for right censored dependent variable is applied on the number of neonatorum tetanus cases in Indonesia. The type data is count data which contains zero values in some observations and other variety value. This study also aims to obtain the parameter estimator and test statistic censored hurdle negative binomial model. Based on the regression results, the factors that influence neonatorum tetanus case in Indonesia is the percentage of baby health care coverage and neonatal visits.

Can the big five factors of personality predict lymphocyte counts?

PubMed

Ožura, Ana; Ihan, Alojz; Musek, Janek

2012-03-01

Psychological stress is known to affect the immune system. The Limbic Hypothalamic Pituitary Adrenal (LHPA) axis has been identified as the principal path of the bidirectional communication between the immune system and the central nervous system with significant psychological activators. Personality traits acted as moderators of the relationship between life conflicts and psychological distress. This study focuses on the relationship between the Big Five factors of personality and immune regulation as indicated by Lymphocyte counts. Our study included 32 professional soldiers from the Slovenian Army that completed the Big Five questionnaire (Goldberg IPIP-300). We also assessed their white blood cell counts with a detailed lymphocyte analysis using flow cytometry. The correlations between personality variables and immune system parameters were calculated. Furthermore, regression analyses were performed using personality variables as predictors and immune parameters as criteria. The results demonstrated that the model using the Big Five factors as predictors of Lymphocyte counts is significant in predicting the variance in NK and B cell counts. Agreeableness showed the strongest predictive function. The results offer support for the theoretical models that stressed the essential links between personality and immune regulation. Further studies with larger samples examining the Big five factors and immune system parameters are needed.

Comparative evaluation of human heat stress indices on selected hospital admissions in Sydney, Australia.

PubMed

Goldie, James; Alexander, Lisa; Lewis, Sophie C; Sherwood, Steven

2017-08-01

To find appropriate regression model specifications for counts of the daily hospital admissions of a Sydney cohort and determine which human heat stress indices best improve the models' fit. We built parent models of eight daily counts of admission records using weather station observations, census population estimates and public holiday data. We added heat stress indices; models with lower Akaike Information Criterion scores were judged a better fit. Five of the eight parent models demonstrated adequate fit. Daily maximum Simplified Wet Bulb Globe Temperature (sWBGT) consistently improved fit more than most other indices; temperature and heatwave indices also modelled some health outcomes well. Humidity and heat-humidity indices better fit counts of patients who died following admission. Maximum sWBGT is an ideal measure of heat stress for these types of Sydney hospital admissions. Simple temperature indices are a good fallback where a narrower range of conditions is investigated. Implications for public health: This study confirms the importance of selecting appropriate heat stress indices for modelling. Epidemiologists projecting Sydney hospital admissions should use maximum sWBGT as a common measure of heat stress. Health organisations interested in short-range forecasting may prefer simple temperature indices. © 2017 The Authors.

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

PubMed

Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara

2017-01-01

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

A biometeorological model of an encephalitis vector

NASA Astrophysics Data System (ADS)

Raddatz, R. L.

1986-01-01

Multiple linear regression techniques and seven years of data were used to build a biometeorological model of Winnipeg's mean daily levels of Culex tarsalis Coquillett. An eighth year of data was used to test the model. Hydrologic accounting of precipitation, evapotranspiration and runoff provided estimates of wetness while the warmness of the season was gauged in terms of the average temperature difference from normal and a threshold antecedent temperature regime. These factors were found to be highly correlated with the time-series of Cx. tarsalis counts. The impact of mosquito adulticiding measures was included in the model via a control effectiveness parameter. An activity-level adjustment, based on mean daily temperatures, was also made to the counts. This model can, by monitoring the weather, provide forecasts of Cx. tarsalis populations for Winnipeg with a lead-time of three weeks, thereby, contributing to an early warning of an impending Western Equine Encephalitis outbreak.

Short-term effects of airborne pollens on asthma attacks as seen by general practitioners in the Greater Paris area, 2003-2007.

PubMed

Huynh, Bich Tram; Tual, Séverine; Turbelin, Clément; Pelat, Camille; Cecchi, Lorenzo; D'Amato, Gennaro; Blanchon, Thierry; Annesi-Maesano, Isabella

2010-09-01

To investigate for the first time the short-term effects of airborne pollen counts on general practitioner (GP) consultations for asthma attacks in the Greater Paris area between 2003-2007. Counts were available for common pollens (Betula, Cupressa, Fraxinus and Poaceae). Weekly data on GP visits for asthma attacks were obtained from the French GP Sentinel Network. A quasi-Poisson regression with generalised additive models was implemented. Short-term effects of pollen counts were assessed using single and multi-pollen models after adjustment for air pollution and influenza. A mean weekly incidence rate of 25.4 cases of asthma attacks per 100,000 inhabitants was estimated during the study period. The strongest significant association between asthma attacks and pollen counts was registered for grass (Poaceae) in the same week of asthma attacks, with a slight reduction of the effect observed in the multi-pollen model. Adjusted relative risk for Poaceae was 1.54 (95% CI: 1.33-1.79) with an inter-quartile range increase of 17.6 grains/m3 during the pollen season. For the first time, a significant short-term association was observed between Poaceae pollen counts and consultations for asthma attacks as seen by GPs. These findings need to be confirmed by more consistent time-series and investigations on a daily basis.

Toward a Value for Guided Rafting on Southern Rivers

Treesearch

J. Michael Bowker; Donald B.K. English; Jason A. Donovan

1996-01-01

This study examines per trip consumer surplus associated with guided whitewater rafting on two southern rivers. First, household recreation demand functions are estimated based on the individual travel cost model using truncated count data regression methods and alternative price specifications. Findings show mean per trip consumer surplus point estimates between $89...

The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

PubMed

Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

2017-11-01

Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

Bayesian Correction for Misclassification in Multilevel Count Data Models.

PubMed

Nelson, Tyler; Song, Joon Jin; Chin, Yoo-Mi; Stamey, James D

2018-01-01

Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.

Nickel and blood counts in workers exposed to urban stressors.

PubMed

Rosati, Maria Valeria; Casale, Teodorico; Ciarrocca, Manuela; Weiderpass, Elisabete; Capozzella, Assunta; Schifano, Maria Pia; Tomei, Francesco; Nieto, Hector Alberto; Marrocco, Mariasilvia; Tomei, Gianfranco; Caciari, Tiziana; Sancini, Angela

2016-06-01

Nickel (Ni) and Ni compounds are widely present in the urban air. The purpose of this study is to estimate exposure of individuals to Ni and the correlation between this exposure and the values of blood counts in outdoor workers. This study focused on a sample of 101 outdoor workers (55 male and 46 female; 65 nonsmokers and 36 smokers), all employed in the municipal police in a large Italian city. The personal levels of exposure to Ni were assessed through (a) environmental monitoring of Ni present in the urban air obtained from individual samples and (b) biological monitoring of urinary and blood Ni. The blood count parameters were obtained from the hemochromocytometric tests. Pearson correlation coefficients (r) were calculated to assess the association between the blood and urinary Ni and the complete blood count. Multiple linear regression models were used to examine the associations between the complete blood count and the independent variables (age, gender, years of work for current tasks, cigarette smoking habit (current and never smoker), values of airborne Ni, and blood and urinary Ni). Multiple linear regression analysis performed on the total group of 101 subjects confirms the association among the red blood cells count, the hematocrit, and the urinary Ni (R(2) = 0.520, p = 0.025 and R(2) = 0.530, p = 0.030). These results should lead to further studies on the effects of Ni in working populations exposed to urban pollutants. The possibility that the associations found in our study may be partially explained by other urban pollutants (such as benzene, toluene, and other heavy metals) not taken into consideration in this study cannot be ruled out. © The Author(s) 2014.

Variable selection for distribution-free models for longitudinal zero-inflated count responses.

PubMed

Chen, Tian; Wu, Pan; Tang, Wan; Zhang, Hui; Feng, Changyong; Kowalski, Jeanne; Tu, Xin M

2016-07-20

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. Like most parametric models, they are quite sensitive to departures from assumed distributions. Recently, new approaches have been proposed to provide distribution-free, or semi-parametric, alternatives. These methods extend the generalized estimating equations to provide robust inference for population mixtures defined by zero-inflated count outcomes. In this paper, we propose methods to extend smoothly clipped absolute deviation (SCAD)-based variable selection methods to these new models. Variable selection has been gaining popularity in modern clinical research studies, as determining differential treatment effects of interventions for different subgroups has become the norm, rather the exception, in the era of patent-centered outcome research. Such moderation analysis in general creates many explanatory variables in regression analysis, and the advantages of SCAD-based methods over their traditional counterparts render them a great choice for addressing this important and timely issues in clinical research. We illustrate the proposed approach with both simulated and real study data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Nutsedge Counts Predict Meloidogyne incognita Juvenile Counts in an Integrated Management System.

PubMed

Ou, Zhining; Murray, Leigh; Thomas, Stephen H; Schroeder, Jill; Libbin, James

2008-06-01

The southern root-knot nematode (Meloidogyne incognita), yellow nutsedge (Cyperus esculentus) and purple nutsedge (Cyperus rotundus) are important pests in crops grown in the southern US. Management of the individual pests rather than the pest complex is often unsuccessful due to mutually beneficial pest interactions. In an integrated pest management scheme using alfalfa to suppress nutsedges and M. incognita, we evaluated quadratic polynomial regression models for prediction of the number of M. incognita J2 in soil samples as a function of yellow and purple nutsedge plant counts, squares of nutsedge counts and the cross-product between nutsedge counts . In May 2005, purple nutsedge plant count was a significant predictor of M. incognita count. In July and September 2005, counts of both nutsedges and the cross-product were significant predictors. In 2006, the second year of the alfalfa rotation, counts of all three species were reduced. As a likely consequence, the predictive relationship between nutsedges and M. incognita was not significant for May and July. In September 2006, purple nutsedge was a significant predictor of M. incognita. These results lead us to conclude that nutsedge plant counts in a field infested with the M. incognita-nutsedge pest complex can be used as a visual predictor of M. incognita J2 populations, unless the numbers of nutsedge plants and M. incognita are all very low.

Nutsedge Counts Predict Meloidogyne incognita Juvenile Counts in an Integrated Management System

PubMed Central

Ou, Zhining; Murray, Leigh; Thomas, Stephen H.; Schroeder, Jill; Libbin, James

2008-01-01

The southern root-knot nematode (Meloidogyne incognita), yellow nutsedge (Cyperus esculentus) and purple nutsedge (Cyperus rotundus) are important pests in crops grown in the southern US. Management of the individual pests rather than the pest complex is often unsuccessful due to mutually beneficial pest interactions. In an integrated pest management scheme using alfalfa to suppress nutsedges and M. incognita, we evaluated quadratic polynomial regression models for prediction of the number of M. incognita J2 in soil samples as a function of yellow and purple nutsedge plant counts, squares of nutsedge counts and the cross-product between nutsedge counts . In May 2005, purple nutsedge plant count was a significant predictor of M. incognita count. In July and September 2005, counts of both nutsedges and the cross-product were significant predictors. In 2006, the second year of the alfalfa rotation, counts of all three species were reduced. As a likely consequence, the predictive relationship between nutsedges and M. incognita was not significant for May and July. In September 2006, purple nutsedge was a significant predictor of M. incognita. These results lead us to conclude that nutsedge plant counts in a field infested with the M. incognita-nutsedge pest complex can be used as a visual predictor of M. incognita J2 populations, unless the numbers of nutsedge plants and M. incognita are all very low. PMID:19259526

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

Detecting trends in raptor counts: power and type I error rates of various statistical tests

USGS Publications Warehouse

Hatfield, J.S.; Gould, W.R.; Hoover, B.A.; Fuller, M.R.; Lindquist, E.L.

1996-01-01

We conducted simulations that estimated power and type I error rates of statistical tests for detecting trends in raptor population count data collected from a single monitoring site. Results of the simulations were used to help analyze count data of bald eagles (Haliaeetus leucocephalus) from 7 national forests in Michigan, Minnesota, and Wisconsin during 1980-1989. Seven statistical tests were evaluated, including simple linear regression on the log scale and linear regression with a permutation test. Using 1,000 replications each, we simulated n = 10 and n = 50 years of count data and trends ranging from -5 to 5% change/year. We evaluated the tests at 3 critical levels (alpha = 0.01, 0.05, and 0.10) for both upper- and lower-tailed tests. Exponential count data were simulated by adding sampling error with a coefficient of variation of 40% from either a log-normal or autocorrelated log-normal distribution. Not surprisingly, tests performed with 50 years of data were much more powerful than tests with 10 years of data. Positive autocorrelation inflated alpha-levels upward from their nominal levels, making the tests less conservative and more likely to reject the null hypothesis of no trend. Of the tests studied, Cox and Stuart's test and Pollard's test clearly had lower power than the others. Surprisingly, the linear regression t-test, Collins' linear regression permutation test, and the nonparametric Lehmann's and Mann's tests all had similar power in our simulations. Analyses of the count data suggested that bald eagles had increasing trends on at least 2 of the 7 national forests during 1980-1989.

Modeling and mapping abundance of American Woodcock across the Midwestern and Northeastern United States

USGS Publications Warehouse

Thogmartin, W.E.; Sauer, J.R.; Knutson, M.G.

2007-01-01

We used an over-dispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods, to model population spatial patterns of relative abundance of American woodcock (Scolopax minor) across its breeding range in the United States. We predicted North American woodcock Singing Ground Survey counts with a log-linear function of explanatory variables describing habitat, year effects, and observer effects. The model also included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land-cover composition, climate, terrain heterogeneity, and human influence. Woodcock counts were higher in landscapes with more forest, especially aspen (Populus tremuloides) and birch (Betula spp.) forest, and in locations with a high degree of interspersion among forest, shrubs, and grasslands. Woodcock counts were lower in landscapes with a high degree of human development. The most noteworthy practical application of this spatial modeling approach was the ability to map predicted relative abundance. Based on a map of predicted relative abundance derived from the posterior parameter estimates, we identified major concentrations of woodcock abundance in east-central Minnesota, USA, the intersection of Vermont, USA, New York, USA, and Ontario, Canada, the upper peninsula of Michigan, USA, and St. Lawrence County, New York. The functional relations we elucidated for the American woodcock provide a basis for the development of management programs and the model and map may serve to focus management and monitoring on areas and habitat features important to American woodcock.

Short-Term Clinical Disease Progression in HIV-Infected Patients Receiving Combination Antiretroviral Therapy: Results from the TREAT Asia HIV Observational Database

PubMed Central

Srasuebkul, Preeyaporn; Lim, Poh Lian; Lee, Man Po; Kumarasamy, Nagalingeswaran; Zhou, Jialun; Sirisanthana, Thira; Li, Patrick C. K.; Kamarulzaman, Adeeba; Oka, Shinichi; Phanuphak, Praphan; Vonthanak, Saphonn; Merati, Tuti P.; Chen, Yi-Ming A.; Sungkanuparph, Somnuek; Tau, Goa; Zhang, Fujie; Lee, Christopher K. C.; Ditangco, Rossana; Pujari, Sanjay; Choi, Jun Y.; Smith, Jeffery; Law, Matthew G.

2009-01-01

Objective The aim of our study was to develop, on the basis of simple clinical data, predictive short-term risk equations for AIDS or death in Asian patients infected with human immunodeficiency virus (HIV) who were included in the TREAT Asia HIV Observational Database. Methods Inclusion criteria were highly active antiretroviral therapy initiation and completion of required laboratory tests. Predictors of short-term AIDS or death were assessed using Poisson regression. Three different models were developed: a clinical model, a CD4 cell count model, and a CD4 cell count and HIV RNA level model. We separated patients into low-risk, high-risk, and very high-risk groups according to the key risk factors Identified. Results In the clinical model, patients with severe anemia or a body mass index (BMI; calculated as the weight in kilograms divided by the square of the height in meters) ≤18 were at very high risk, and patients who were aged <40 years or were male and had mild anemia were at high risk. In the CD4 cell count model, patients with a CD4 cell count <50 cells/µL, severe anemia, or a BMI ≤18 were at very high risk, and patients who had a CD4 cell count of 51–200 cells/µL, were aged <40 years, or were male and had mild anemia were at high risk. In the CD4 cell count and HIV RNA level model, patients with a CD4 cell count <50 cells/µL, a detectable viral load, severe anemia, or a BMI ≤18 were at very high risk, and patients with a CD4 cell count of 51–200 cells/µL and mild anemia were at high risk. The incidence of new AIDS or death in the clinical model was 1.3, 4.9, and 15.6 events per 100 person-years in the low-risk, high-risk, and very high-risk groups, respectively. In the CD4 cell count model the respective incidences were 0.9, 2.7, and 16.02 events per 100 person-years; in the CD4 cell count and HIV RNA level model, the respective incidences were 0.8, 1.8, and 6.2 events per 100 person-years. Conclusions These models are simple enough for widespread use in busy clinics and should allow clinicians to identify patients who are at high risk of AIDS or death in Asia and the Pacific region and in resource-poor settings. PMID:19226231

Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers

USDA-ARS?s Scientific Manuscript database

Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults but not in preschool-aged children. Because the relationships between accelerometer counts (ACs), HR, and EE are confounded by growth and ma...

Decreasing annual nest counts in a globally important loggerhead sea turtle population.

PubMed

Witherington, Blair; Kubilis, Paul; Brost, Beth; Meylan, Anne

2009-01-01

The loggerhead sea turtle (Caretta caretta) nests on sand beaches, has both oceanic and neritic life stages, and migrates internationally. We analyzed an 18-year time series of Index Nesting Beach Survey (Index) nest-count data to describe spatial and temporal trends in loggerhead nesting on Florida (USA) beaches. The Index data were highly resolved: 368 fixed zones (mean length 0.88 km) were surveyed daily during annual 109-day survey seasons. Spatial and seasonal coverage averaged 69% of estimated total nesting by loggerheads in the state. We carried out trend analyses on both annual survey-region nest-count totals (N = 18) and annual zone-level nest densities (N = 18 x 368 = 6624). In both analyses, negative binomial regression models were used to fit restricted cubic spline curves to aggregated nest counts. Between 1989 and 2006, loggerhead nest counts on Florida Index beaches increased and then declined, with a net decrease over the 18-year period. This pattern was evident in both a trend model of annual survey-region nest-count totals and a mixed-effect, "single-region" trend model of annual zone-level nest densities that took into account both spatial and temporal correlation between counts. We also saw this pattern in a zone-level model that allowed trend line shapes to vary between six coastal subregions. Annual mean zone-level nest density declined significantly (-28%; 95% CI: -34% to -21%) between 1989 and 2006 and declined steeply (-43%; 95% CI: -48% to -39%) during 1998-2006. Rates of change in annual mean nest density varied more between coastal subregions during the "mostly increasing" period prior to 1998 than during the "steeply declining" period after 1998. The excellent fits (observed vs. expected count R2 > 0.91) of the mixed-effect zone-level models confirmed the presence of strong, positive, within-zone autocorrelation (R > 0.93) between annual counts, indicating a remarkable year-to-year consistency in the longshore spatial distribution of nests over the survey region. We argue that the decline in annual loggerhead nest counts in peninsular Florida can best be explained by a decline in the number of adult female loggerheads in the population. Causes of this decline are explored.

Total nucleated cell and leukocyte differential counts in canine pleural and peritoneal fluid and equine synovial fluid samples: comparison of automated and manual methods.

PubMed

Brudvig, Jean M; Swenson, Cheryl L

2015-12-01

Rapid and precise measurement of total and differential nucleated cell counts is a crucial diagnostic component of cavitary and synovial fluid analyses. The objectives of this study included (1) evaluation of reliability and precision of canine and equine fluid total nucleated cell count (TNCC) determined by the benchtop Abaxis VetScan HM5, in comparison with the automated reference instruments ADVIA 120 and the scil Vet abc, respectively, and (2) comparison of automated with manual canine differential nucleated cell counts. The TNCC and differential counts in canine pleural and peritoneal, and equine synovial fluids were determined on the Abaxis VetScan HM5 and compared with the ADVIA 120 and Vet abc analyzer, respectively. Statistical analyses included correlation, least squares fit linear regression, Passing-Bablok regression, and Bland-Altman difference plots. In addition, precision of the total cell count generated by the VetScan HM5 was determined. Agreement was excellent without significant constant or proportional bias for canine cavitary fluid TNCC. Automated and manual differential counts had R(2)  < .5 for individual cell types (least squares fit linear regression). Equine synovial fluid TNCC agreed but with some bias due to the VetScan HM5 overestimating TNCC compared to the Vet abc. Intra-assay precision of the VetScan HM5 in 3 fluid samples was 2-31%. The Abaxis VetScan HM5 provided rapid, reliable TNCC for canine and equine fluid samples. The differential nucleated cell count should be verified microscopically as counts from the VetScan HM5 and also from the ADVIA 120 were often incorrect in canine fluid samples. © 2015 American Society for Veterinary Clinical Pathology.

Some considerations for excess zeroes in substance abuse research.

PubMed

Bandyopadhyay, Dipankar; DeSantis, Stacia M; Korte, Jeffrey E; Brady, Kathleen T

2011-09-01

Count data collected in substance abuse research often come with an excess of "zeroes," which are typically handled using zero-inflated regression models. However, there is a need to consider the design aspects of those studies before using such a statistical model to ascertain the sources of zeroes. We sought to illustrate hurdle models as alternatives to zero-inflated models to validate a two-stage decision-making process in situations of "excess zeroes." We use data from a study of 45 cocaine-dependent subjects where the primary scientific question was to evaluate whether study participation influences drug-seeking behavior. The outcome, "the frequency (count) of cocaine use days per week," is bounded (ranging from 0 to 7). We fit and compare binomial, Poisson, negative binomial, and the hurdle version of these models to study the effect of gender, age, time, and study participation on cocaine use. The hurdle binomial model provides the best fit. Gender and time are not predictive of use. Higher odds of use versus no use are associated with age; however once use is experienced, odds of further use decrease with increase in age. Participation was associated with higher odds of no-cocaine use; once there is use, participation reduced the odds of further use. Age and study participation are significantly predictive of cocaine-use behavior. The two-stage decision process as modeled by a hurdle binomial model (appropriate for bounded count data with excess zeroes) provides interesting insights into the study of covariate effects on count responses of substance use, when all enrolled subjects are believed to be "at-risk" of use.

Post-precipitation bias in band-tailed pigeon surveys conducted at mineral sites

USGS Publications Warehouse

Overton, C.T.; Schmitz, R.A.; Casazza, Michael L.

2005-01-01

Many animal surveys to estimate populations or index trends include protocol prohibiting counts during rain but fail to address effects of rainfall preceding the count. Prior research on Pacific Coast band-tailed pigeons (Patagioenas fasciata monilis) documented declines in use of mineral sites during rainfall. We hypothesized that prior precipitation was associated with a short-term increase in use of mineral sites following rain. We conducted weekly counts of band-tailed pigeons at 19 Pacific Northwest mineral sites in 2001 and 20 sites in 2002. Results from regression analysis indicated higher counts ???2 days after rain (11.31??5.00% [x????SE]) compared to ???3 days. Individual index counts conducted ???2 days after rain were biased high, resulting in reduced ability to accurately estimate population trends. Models of band-tailed pigeon visitation rates throughout the summer showed increased mineral-site counts during both June and August migration periods, relative to the July breeding period. Our research supported previous studies recommending that mineral-site counts used to index the band-tailed pigeon population be conducted during July. We further recommend conducting counts >3 days after rain to avoid weather-related bias in index estimation. The design of other population sampling strategies that rely on annual counts should consider the influence of aberrant weather not only coincident with but also preceding surveys if weather patterns are thought to influence behavior or detection probability of target species.

Macro-level vulnerable road users crash analysis: A Bayesian joint modeling approach of frequency and proportion.

PubMed

Cai, Qing; Abdel-Aty, Mohamed; Lee, Jaeyoung

2017-10-01

This study aims at contributing to the literature on pedestrian and bicyclist safety by building on the conventional count regression models to explore exogenous factors affecting pedestrian and bicyclist crashes at the macroscopic level. In the traditional count models, effects of exogenous factors on non-motorist crashes were investigated directly. However, the vulnerable road users' crashes are collisions between vehicles and non-motorists. Thus, the exogenous factors can affect the non-motorist crashes through the non-motorists and vehicle drivers. To accommodate for the potentially different impact of exogenous factors we convert the non-motorist crash counts as the product of total crash counts and proportion of non-motorist crashes and formulate a joint model of the negative binomial (NB) model and the logit model to deal with the two parts, respectively. The formulated joint model is estimated using non-motorist crash data based on the Traffic Analysis Districts (TADs) in Florida. Meanwhile, the traditional NB model is also estimated and compared with the joint model. The result indicates that the joint model provides better data fit and can identify more significant variables. Subsequently, a novel joint screening method is suggested based on the proposed model to identify hot zones for non-motorist crashes. The hot zones of non-motorist crashes are identified and divided into three types: hot zones with more dangerous driving environment only, hot zones with more hazardous walking and cycling conditions only, and hot zones with both. It is expected that the joint model and screening method can help decision makers, transportation officials, and community planners to make more efficient treatments to proactively improve pedestrian and bicyclist safety. Published by Elsevier Ltd.

A Semiparametric Change-Point Regression Model for Longitudinal Observations.

PubMed

Xing, Haipeng; Ying, Zhiliang

2012-12-01

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpret approach is to introduce change-points in regression coefficients. In this connection, we propose a semiparametric change-point regression model, in which the error process (stochastic component) is nonparametric and the baseline mean function (functional part) is completely unspecified, the observation times are allowed to be subject-specific, and the number, locations and magnitudes of change-points are unknown and need to be estimated. We further develop an estimation procedure which combines the recent advance in semiparametric analysis based on counting process argument and multiple change-points inference, and discuss its large sample properties, including consistency and asymptotic normality, under suitable regularity conditions. Simulation results show that the proposed methods work well under a variety of scenarios. An application to a real data set is also given.

Meta-analytical synthesis of regression coefficients under different categorization scheme of continuous covariates.

PubMed

Yoneoka, Daisuke; Henmi, Masayuki

2017-11-30

Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts. Copyright © 2017 John Wiley & Sons, Ltd.

Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis

NASA Astrophysics Data System (ADS)

de Weger, Letty A.; Beerthuizen, Thijs; Hiemstra, Pieter S.; Sont, Jacob K.

2014-08-01

One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature ( R 2 = 0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures ( R 2 = 0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.

Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis.

PubMed

de Weger, Letty A; Beerthuizen, Thijs; Hiemstra, Pieter S; Sont, Jacob K

2014-08-01

One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature (R (2)=0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures (R (2)=0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.

Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa

PubMed Central

Bor, Jacob; Tanser, Frank; Bärnighausen, Till

2017-01-01

Background Loss to follow-up is high among HIV patients not yet receiving antiretroviral therapy (ART). Clinical trials have demonstrated the clinical efficacy of early ART; however, these trials may miss an important real-world consequence of providing ART at diagnosis: its impact on retention in care. Methods and findings We examined the effect of immediate (versus deferred) ART on retention in care using a regression discontinuity design. The analysis included all patients (N = 11,306) entering clinical HIV care with a first CD4 count between 12 August 2011 and 31 December 2012 in a public-sector HIV care and treatment program in rural South Africa. Patients were assigned to immediate versus deferred ART eligibility, as determined by a CD4 count < 350 cells/μl, per South African national guidelines. Patients referred to pre-ART care were instructed to return every 6 months for CD4 monitoring. Patients initiated on ART were instructed to return at 6 and 12 months post-initiation and annually thereafter for CD4 and viral load monitoring. We assessed retention in HIV care at 12 months, as measured by the presence of a clinic visit, lab test, or ART initiation 6 to 18 months after initial CD4 test. Differences in retention between patients presenting with CD4 counts just above versus just below the 350-cells/μl threshold were estimated using local linear regression models with a data-driven bandwidth and with the algorithm for selecting the bandwidth chosen ex ante. Among patients with CD4 counts close to the 350-cells/μl threshold, having an ART-eligible CD4 count (<350 cells/μl) was associated with higher 12-month retention than not having an ART-eligible CD4 count (50% versus 32%), an intention-to-treat risk difference of 18 percentage points (95% CI 11 to 23; p < 0.001). The decision to start ART was determined by CD4 count for one in four patients (25%) presenting close to the eligibility threshold (95% CI 20% to 31%; p < 0.001). In this subpopulation, having an ART-eligible CD4 count was associated with higher 12-month retention than not having an ART-eligible CD4 count (91% versus 21%), a complier causal risk difference of 70 percentage points (95% CI 42 to 98; p < 0.001). The major limitations of the study are the potential for limited generalizability, the potential for outcome misclassification, and the absence of data on longer-term health outcomes. Conclusions Patients who were eligible for immediate ART had dramatically higher retention in HIV care than patients who just missed the CD4-count eligibility cutoff. The clinical and population health benefits of offering immediate ART regardless of CD4 count may be larger than suggested by clinical trials. PMID:29182641

Trends in CD4 counts in HIV-infected patients with HIV viral load monitoring while on combination antiretroviral treatment: results from The TREAT Asia HIV Observational Database

PubMed Central

2010-01-01

Background The aim of this study was to examine the relationship between trends in CD4 counts (slope) and HIV viral load (VL) after initiation of combination antiretroviral treatment (cART) in Asian patients in The TREAT Asia HIV Observational Database (TAHOD). Methods Treatment-naive HIV-infected patients who started cART with three or more and had three or more CD4 count and HIV VL tests were included. CD4 count slopes were expressed as changes of cells per microliter per year. Predictors of CD4 count slopes from 6 months after initiation were assessed by random-effects linear regression models. Results A total of 1676 patients (74% male) were included. The median time on cART was 4.2 years (IQR 2.5-5.8 years). In the final model, CD4 count slope was associated with age, concurrent HIV VL and CD4 count, disease stage, hepatitis B or C co-infection, and time since cART initiation. CD4 count continues to increase with HIV VL up to 20 000 copies/mL during 6-12 months after cART initiation. However, the HIV VL has to be controlled below 5 000, 4 000 and 500 copies/mL for the CD4 count slope to remain above 20 cells/microliter per year during 12-18, 18-24, and beyond 24 months after cART initiation. Conclusions After cART initiation, CD4 counts continued to increase even when the concurrent HIV VL was detectable. However, HIV VL needed to be controlled at a lower level to maintain a positive CD4 count slope when cART continues. The effect on long-term outcomes through the possible development of HIV drug resistance remains uncertain. PMID:21182796

Quantitative structure-activity relationship of the curcumin-related compounds using various regression methods

NASA Astrophysics Data System (ADS)

Khazaei, Ardeshir; Sarmasti, Negin; Seyf, Jaber Yousefi

2016-03-01

Quantitative structure activity relationship were used to study a series of curcumin-related compounds with inhibitory effect on prostate cancer PC-3 cells, pancreas cancer Panc-1 cells, and colon cancer HT-29 cells. Sphere exclusion method was used to split data set in two categories of train and test set. Multiple linear regression, principal component regression and partial least squares were used as the regression methods. In other hand, to investigate the effect of feature selection methods, stepwise, Genetic algorithm, and simulated annealing were used. In two cases (PC-3 cells and Panc-1 cells), the best models were generated by a combination of multiple linear regression and stepwise (PC-3 cells: r2 = 0.86, q2 = 0.82, pred_r2 = 0.93, and r2m (test) = 0.43, Panc-1 cells: r2 = 0.85, q2 = 0.80, pred_r2 = 0.71, and r2m (test) = 0.68). For the HT-29 cells, principal component regression with stepwise (r2 = 0.69, q2 = 0.62, pred_r2 = 0.54, and r2m (test) = 0.41) is the best method. The QSAR study reveals descriptors which have crucial role in the inhibitory property of curcumin-like compounds. 6ChainCount, T_C_C_1, and T_O_O_7 are the most important descriptors that have the greatest effect. With a specific end goal to design and optimization of novel efficient curcumin-related compounds it is useful to introduce heteroatoms such as nitrogen, oxygen, and sulfur atoms in the chemical structure (reduce the contribution of T_C_C_1 descriptor) and increase the contribution of 6ChainCount and T_O_O_7 descriptors. Models can be useful in the better design of some novel curcumin-related compounds that can be used in the treatment of prostate, pancreas, and colon cancers.

Long-term exposure to ambient particulate matter (PM2.5) is associated with platelet counts in adults.

PubMed

Zhang, Zilong; Chan, Ta-Chien; Guo, Cui; Chang, Ly-Yun; Lin, Changqing; Chuang, Yuan Chieh; Jiang, Wun Kai; Ho, Kin Fai; Tam, Tony; Woo, Kam S; Lau, Alexis K H; Lao, Xiang Qian

2018-05-09

The prothrombotic effects of particulate matter (PM) may underlie the association of air pollution with increased risks of cardiovascular disease. This study aimed to investigate the association between long-term exposure to PM with an aerodynamic diameter ≤2.5 μm (PM 2.5 ) and platelet counts, a marker of coagulation profiles. The study participants were from a cohort consisting of 362,396 Taiwanese adults who participated in a standard medical examination program between 2001 and 2014. Platelet counts were measured through Complete Blood Count tests. A satellite-based spatio-temporal model was used to estimate 2-year average ambient PM 2.5 concentration at each participant's address. Mixed-effects linear regression models were used to investigate the association between PM 2.5 exposure and platelet counts. This analysis included 175,959 men with 396,248 observations and 186,437 women with 397,877 observations. Every 10-μg/m 3 increment in the 2-year average PM 2.5 was associated with increases of 0.42% (95% CI: 0.38%, 0.47%) and 0.49% (95% CI: 0.44%, 0.54%) in platelet counts in men and women, respectively. A series of sensitivity analyses, including an analysis in participants free of cardiometabolic disorders, confirmed the robustness of the observed associations. Baseline data analyses showed that every 10-μg/m 3 increment in PM 2.5 was associated with higher risk of 17% and 14% of having elevated platelet counts (≥90th percentile) in men and women, respectively. Long-term exposure to PM 2.5 appears to be associated with increased platelet counts, indicating potential adverse effects on blood coagulability. Copyright © 2018 Elsevier Ltd. All rights reserved.

Bayesian hierarchical modelling of continuous non‐negative longitudinal data with a spike at zero: An application to a study of birds visiting gardens in winter

PubMed Central

Buckland, Stephen T.; King, Ruth; Toms, Mike P.

2015-01-01

The development of methods for dealing with continuous data with a spike at zero has lagged behind those for overdispersed or zero‐inflated count data. We consider longitudinal ecological data corresponding to an annual average of 26 weekly maximum counts of birds, and are hence effectively continuous, bounded below by zero but also with a discrete mass at zero. We develop a Bayesian hierarchical Tweedie regression model that can directly accommodate the excess number of zeros common to this type of data, whilst accounting for both spatial and temporal correlation. Implementation of the model is conducted in a Markov chain Monte Carlo (MCMC) framework, using reversible jump MCMC to explore uncertainty across both parameter and model spaces. This regression modelling framework is very flexible and removes the need to make strong assumptions about mean‐variance relationships a priori. It can also directly account for the spike at zero, whilst being easily applicable to other types of data and other model formulations. Whilst a correlative study such as this cannot prove causation, our results suggest that an increase in an avian predator may have led to an overall decrease in the number of one of its prey species visiting garden feeding stations in the United Kingdom. This may reflect a change in behaviour of house sparrows to avoid feeding stations frequented by sparrowhawks, or a reduction in house sparrow population size as a result of sparrowhawk increase. PMID:25737026

Zero-Inflated Poisson Modeling of Fall Risk Factors in Community-Dwelling Older Adults.

PubMed

Jung, Dukyoo; Kang, Younhee; Kim, Mi Young; Ma, Rye-Won; Bhandari, Pratibha

2016-02-01

The aim of this study was to identify risk factors for falls among community-dwelling older adults. The study used a cross-sectional descriptive design. Self-report questionnaires were used to collect data from 658 community-dwelling older adults and were analyzed using logistic and zero-inflated Poisson (ZIP) regression. Perceived health status was a significant factor in the count model, and fall efficacy emerged as a significant predictor in the logistic models. The findings suggest that fall efficacy is important for predicting not only faller and nonfaller status but also fall counts in older adults who may or may not have experienced a previous fall. The fall predictors identified in this study--perceived health status and fall efficacy--indicate the need for fall-prevention programs tailored to address both the physical and psychological issues unique to older adults. © The Author(s) 2014.

Pseudo and conditional score approach to joint analysis of current count and current status data.

PubMed

Wen, Chi-Chung; Chen, Yi-Hau

2018-04-17

We develop a joint analysis approach for recurrent and nonrecurrent event processes subject to case I interval censorship, which are also known in literature as current count and current status data, respectively. We use a shared frailty to link the recurrent and nonrecurrent event processes, while leaving the distribution of the frailty fully unspecified. Conditional on the frailty, the recurrent event is assumed to follow a nonhomogeneous Poisson process, and the mean function of the recurrent event and the survival function of the nonrecurrent event are assumed to follow some general form of semiparametric transformation models. Estimation of the models is based on the pseudo-likelihood and the conditional score techniques. The resulting estimators for the regression parameters and the unspecified baseline functions are shown to be consistent with rates of square and cubic roots of the sample size, respectively. Asymptotic normality with closed-form asymptotic variance is derived for the estimator of the regression parameters. We apply the proposed method to a fracture-osteoporosis survey data to identify risk factors jointly for fracture and osteoporosis in elders, while accounting for association between the two events within a subject. © 2018, The International Biometric Society.

On state-of-charge determination for lithium-ion batteries

NASA Astrophysics Data System (ADS)

Li, Zhe; Huang, Jun; Liaw, Bor Yann; Zhang, Jianbo

2017-04-01

Accurate estimation of state-of-charge (SOC) of a battery through its life remains challenging in battery research. Although improved precisions continue to be reported at times, almost all are based on regression methods empirically, while the accuracy is often not properly addressed. Here, a comprehensive review is set to address such issues, from fundamental principles that are supposed to define SOC to methodologies to estimate SOC for practical use. It covers topics from calibration, regression (including modeling methods) to validation in terms of precision and accuracy. At the end, we intend to answer the following questions: 1) can SOC estimation be self-adaptive without bias? 2) Why Ah-counting is a necessity in almost all battery-model-assisted regression methods? 3) How to establish a consistent framework of coupling in multi-physics battery models? 4) To assess the accuracy in SOC estimation, statistical methods should be employed to analyze factors that contribute to the uncertainty. We hope, through this proper discussion of the principles, accurate SOC estimation can be widely achieved.

On Models for Binomial Data with Random Numbers of Trials

PubMed Central

Comulada, W. Scott; Weiss, Robert E.

2010-01-01

Summary A binomial outcome is a count s of the number of successes out of the total number of independent trials n = s + f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability π of success that cannot be directly incorporated by the logistic regression model. Observations where n = 0 are excluded from the binomial analysis yet may be important to understanding how π is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study. PMID:17688514

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time series regression model for infectious disease and weather.

PubMed

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Nondestructive detection of total viable count changes of chilled pork in high oxygen storage condition based on hyperspectral technology

NASA Astrophysics Data System (ADS)

Zheng, Xiaochun; Peng, Yankun; Li, Yongyu; Chao, Kuanglin; Qin, Jianwei

2017-05-01

The plate count method is commonly used to detect the total viable count (TVC) of bacteria in pork, which is timeconsuming and destructive. It has also been used to study the changes of the TVC in pork under different storage conditions. In recent years, many scholars have explored the non-destructive methods on detecting TVC by using visible near infrared (VIS/NIR) technology and hyperspectral technology. The TVC in chilled pork was monitored under high oxygen condition in this study by using hyperspectral technology in order to evaluate the changes of total bacterial count during storage, and then evaluate advantages and disadvantages of the storage condition. The VIS/NIR hyperspectral images of samples stored in high oxygen condition was acquired by a hyperspectral system in range of 400 1100nm. The actual reference value of total bacteria was measured by standard plate count method, and the results were obtained in 48 hours. The reflection spectra of the samples are extracted and used for the establishment of prediction model for TVC. The spectral preprocessing methods of standard normal variate transformation (SNV), multiple scatter correction (MSC) and derivation was conducted to the original reflectance spectra of samples. Partial least squares regression (PLSR) of TVC was performed and optimized to be the prediction model. The results show that the near infrared hyperspectral technology based on 400-1100nm combined with PLSR model can describe the growth pattern of the total bacteria count of the chilled pork under the condition of high oxygen very vividly and rapidly. The results obtained in this study demonstrate that the nondestructive method of TVC based on NIR hyperspectral has great potential in monitoring of edible safety in processing and storage of meat.

House Dust Endotoxin and Peripheral Leukocyte Counts: Results from Two Large Epidemiologic Studies.

PubMed

Fessler, Michael B; Carnes, Megan U; Salo, Päivi M; Wilkerson, Jesse; Cohn, Richard D; King, Debra; Hoppin, Jane A; Sandler, Dale P; Travlos, Greg; London, Stephanie; Thorne, Peter; Zeldin, Darryl

2017-05-31

The peripheral leukocyte count is a biomarker of inflammation and is associated with human all-cause mortality. Although causes of acute leukocytosis are well-described, chronic environmental determinants of leukocyte number are less well understood. We investigated the relationship between house dust endotoxin concentration and peripheral leukocyte counts in human subjects. The endotoxin–leukocyte relationship was evaluated by linear regression in the National Health and Nutrition Examination Survey (NHANES) 2005–2006 (n=6,254) and the Agricultural Lung Health Study (ALHS; n=1,708). In the ALHS, we tested for a gene [Toll-like Receptor 4 ( TLR4 ), encoding the endotoxin receptor]-by-environment interaction in the endotoxin–leukocyte relationship using regression models with an interaction term. There is a statistically significant, positive association between endotoxin concentration and total leukocyte number [estimated change, 0.186×10 3 /μL (95% CI: 0.070, 0.301×10 3 /μL) per 10-fold change in endotoxin; p=0.004) in the NHANES. Similar positive associations were found for monocytes, lymphocytes, and neutrophils. Stratified analyses revealed possible effect modification by asthma and chronic obstructive pulmonary disease. We observed similar associations in the ALHS. For total leukocytes, there was suggestive evidence in the ALHS of a gene-by-environment interaction for minor allele carrier status at the TLR4 haplotype defined by rs4986790 and rs4986791 (interaction p=0.15). This is, to our knowledge, the first report of an association between house dust endotoxin and leukocyte count in a national survey. The finding was replicated in a farming population. Peripheral leukocyte count may be influenced by residential endotoxin exposure in diverse settings. https://doi.org/10.1289/EHP661.

House Dust Endotoxin and Peripheral Leukocyte Counts: Results from Two Large Epidemiologic Studies

PubMed Central

Carnes, Megan U.; Salo, Päivi M.; Wilkerson, Jesse; Cohn, Richard D.; King, Debra; Hoppin, Jane A.; Sandler, Dale P.; Travlos, Greg; London, Stephanie J.; Thorne, Peter S.; Zeldin, Darryl C.

2017-01-01

Background: The peripheral leukocyte count is a biomarker of inflammation and is associated with human all-cause mortality. Although causes of acute leukocytosis are well-described, chronic environmental determinants of leukocyte number are less well understood. Objectives: We investigated the relationship between house dust endotoxin concentration and peripheral leukocyte counts in human subjects. Methods: The endotoxin–leukocyte relationship was evaluated by linear regression in the National Health and Nutrition Examination Survey (NHANES) 2005–2006 (n=6,254) and the Agricultural Lung Health Study (ALHS; n=1,708). In the ALHS, we tested for a gene [Toll-like Receptor 4 (TLR4), encoding the endotoxin receptor]-by-environment interaction in the endotoxin–leukocyte relationship using regression models with an interaction term. Results: There is a statistically significant, positive association between endotoxin concentration and total leukocyte number [estimated change, 0.186×103/μL (95% CI: 0.070, 0.301×103/μL) per 10-fold change in endotoxin; p=0.004) in the NHANES. Similar positive associations were found for monocytes, lymphocytes, and neutrophils. Stratified analyses revealed possible effect modification by asthma and chronic obstructive pulmonary disease. We observed similar associations in the ALHS. For total leukocytes, there was suggestive evidence in the ALHS of a gene-by-environment interaction for minor allele carrier status at the TLR4 haplotype defined by rs4986790 and rs4986791 (interaction p=0.15). Conclusions: This is, to our knowledge, the first report of an association between house dust endotoxin and leukocyte count in a national survey. The finding was replicated in a farming population. Peripheral leukocyte count may be influenced by residential endotoxin exposure in diverse settings. https://doi.org/10.1289/EHP661 PMID:28599265

Regular exercise and related factors in patients with Parkinson's disease: Applying zero-inflated negative binomial modeling of exercise count data.

PubMed

Lee, JuHee; Park, Chang Gi; Choi, Moonki

2016-05-01

This study was conducted to identify risk factors that influence regular exercise among patients with Parkinson's disease in Korea. Parkinson's disease is prevalent in the elderly, and may lead to a sedentary lifestyle. Exercise can enhance physical and psychological health. However, patients with Parkinson's disease are less likely to exercise than are other populations due to physical disability. A secondary data analysis and cross-sectional descriptive study were conducted. A convenience sample of 106 patients with Parkinson's disease was recruited at an outpatient neurology clinic of a tertiary hospital in Korea. Demographic characteristics, disease-related characteristics (including disease duration and motor symptoms), self-efficacy for exercise, balance, and exercise level were investigated. Negative binomial regression and zero-inflated negative binomial regression for exercise count data were utilized to determine factors involved in exercise. The mean age of participants was 65.85 ± 8.77 years, and the mean duration of Parkinson's disease was 7.23 ± 6.02 years. Most participants indicated that they engaged in regular exercise (80.19%). Approximately half of participants exercised at least 5 days per week for 30 min, as recommended (51.9%). Motor symptoms were a significant predictor of exercise in the count model, and self-efficacy for exercise was a significant predictor of exercise in the zero model. Severity of motor symptoms was related to frequency of exercise. Self-efficacy contributed to the probability of exercise. Symptom management and improvement of self-efficacy for exercise are important to encourage regular exercise in patients with Parkinson's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

Regression analysis of mixed recurrent-event and panel-count data

PubMed Central

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L.

2014-01-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20, 1–42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. PMID:24648408

Low CD1c + myeloid dendritic cell counts correlated with a high risk of rapid disease progression during early HIV-1 infection.

PubMed

Diao, Yingying; Geng, Wenqing; Fan, Xuejie; Cui, Hualu; Sun, Hong; Jiang, Yongjun; Wang, Yanan; Sun, Amy; Shang, Hong

2015-08-19

During early HIV-1 infection (EHI), the interaction between the immune response and the virus determines disease progression. Although CD1c + myeloid dendritic cells (mDCs) can trigger the immune response, the relationship between CD1c + mDC alteration and disease progression has not yet been defined. EHI changes in CD1c + mDC counts, surface marker (CD40, CD86, CD83) expression, and IL-12 secretion were assessed by flow cytometry in 29 patients. When compared with the normal controls, patients with EHI displayed significantly lower CD1c + mDC counts and IL-12 secretion and increased surface markers. CD1c + mDC counts were positively correlated with CD4+ T cell counts and inversely associated with viral loads. IL-12 secretion was only positively associated with CD4+ T cell counts. Rapid progressors had lower counts, CD86 expression, and IL-12 secretion of CD1c + mDCs comparing with typical progressors. Kaplan-Meier analysis and Cox regression models suggested patients with low CD1c + mDC counts (<10 cells/μL) had a 4-fold higher risk of rapid disease progression than those with high CD1c + mDC counts. However, no relationship was found between surface markers or IL-12 secretion and disease progression. During EHI, patients with low CD1c + mDC counts were more likely to experience rapid disease progression than those with high CD1c + mDC counts.

Predicting survival in AIDS: refining the model.

PubMed

Hutchinson, S J; Brettle, R P; Gore, S M

1997-11-01

We tested the validity of a previously-published AIDS staging system by examining AIDS-defining diseases (ADDs) and CD4 counts as prognostic factors for survival of the 248 AIDS patients in the Edinburgh City Hospital Cohort, of whom 56% were injecting drug-users (IDUs). Cox regression was used to model the proportionality of risk of death as the CD4 count declined and more ADDs were experienced, and dependence upon post-AIDS treatment. Using the system of Mocroft et al. (Lancet 1995; 346:12-17) to grade severity, our data were well enough modelled, but we suggest: (i) regrading of HIV dementia (RR 3.9, 95% CI 2.5-6.0), mainly attributed to the drug users, to a very severe ADD; (ii) reduction in risk from zidovudine (RR 0.7, 95% CI 0.5-1.0) during AIDS follow-up for patients starting treatment at or after AIDS diagnosis; (iii) improved management of first mild ADDs (from 1987-89 to 1994-95: 40% reduction in IDUs appearing with mild index diseases, and an approximate three-fold reduction in risk associated with a mild ADD). This study supports previous findings on the significance of ADDs and lowest CD4 count in predicting the lifetime of AIDS patients.

Nucleated red blood cells in growth-restricted fetuses: associations with short-term neonatal outcome.

PubMed

Minior, V K; Bernstein, P S; Divon, M Y

2000-01-01

To determine the utility of the neonatal nucleated red blood cell (NRBC) count as an independent predictor of short-term perinatal outcome in growth-restricted fetuses. Hospital charts of neonates with a discharge diagnosis indicating a birth weight <10th percentile were reviewed for perinatal outcome. We studied all eligible neonates who had a complete blood count on the first day of life. After multiple gestations, anomalous fetuses and diabetic pregnancies were excluded; 73 neonates comprised the study group. Statistical analysis included ANOVA, simple and stepwise regression. Elevated NRBC counts were significantly associated with cesarean section for non-reassuring fetal status, neonatal intensive care unit admission and duration of neonatal intensive care unit stay, respiratory distress and intubation, thrombocytopenia, hyperbilirubinemia, intraventricular hemorrhage and neonatal death. Stepwise regression analysis including gestational age at birth, birth weight and NRBC count demonstrated that in growth-restricted fetuses, NRBC count was the strongest predictor of neonatal intraventricular hemorrhage, neonatal respiratory distress and neonatal death. An elevated NRBC count independently predicts adverse perinatal outcome in growth-restricted fetuses. Copyright 2000 S. Karger AG, Basel.

Prevalence and Evolution of Renal Impairment in People Living With HIV in Rural Tanzania.

PubMed

Mapesi, Herry; Kalinjuma, Aneth V; Ngerecha, Alphonce; Franzeck, Fabian; Hatz, Christoph; Tanner, Marcel; Mayr, Michael; Furrer, Hansjakob; Battegay, Manuel; Letang, Emilio; Weisser, Maja; Glass, Tracy R

2018-04-01

We assessed the prevalence, incidence, and predictors of renal impairment among people living with HIV (PLWHIV) in rural Tanzania. In a cohort of PLWHIV aged ≥15 years enrolled from January 2013 to June 2016, we assessed the association between renal impairment (estimated glomerural filtration rate < 90 mL/min/1.73 m 2 ) at enrollment and during follow-up with demographic and clinical characteristcis using logistic regression and Cox proportional hazards models. Of 1093 PLWHIV, 172 (15.7%) had renal impairment at enrollment. Of 921 patients with normal renal function at baseline, 117 (12.7%) developed renal impairment during a median follow-up (interquartile range) of 6.2 (0.4-14.7) months. The incidence of renal impairment was 110 cases per 1000 person-years (95% confidence interval [CI], 92-132). At enrollment, logistic regression identified older age (adjusted odds ratio [aOR], 1.79; 95% CI, 1.52-2.11), hypertension (aOR, 1.84; 95% CI, 1.08-3.15), CD4 count <200 cells/mm 3 (aOR, 1.80; 95% CI, 1.23-2.65), and World Health Organization (WHO) stage III/IV (aOR, 3.00; 95% CI, 1.96-4.58) as risk factors for renal impairment. Cox regression model confirmed older age (adjusted hazard ratio [aHR], 1.85; 95% CI, 1.56-2.20) and CD4 count <200 cells/mm 3 (aHR, 2.05; 95% CI, 1.36-3.09) to be associated with the development of renal impairment. Our study found a low prevalence of renal impairment among PLWHIV despite high usage of tenofovir and its association with age, hypertension, low CD4 count, and advanced WHO stage. These important and reassuring safety data stress the significance of noncommunicable disease surveillance in aging HIV populations in sub-Saharan Africa.

The isoform A of reticulon-4 (Nogo-A) in cerebrospinal fluid of primary brain tumor patients: influencing factors.

PubMed

Koper, Olga Martyna; Kamińska, Joanna; Milewska, Anna; Sawicki, Karol; Mariak, Zenon; Kemona, Halina; Matowicka-Karna, Joanna

2018-05-18

The influence of isoform A of reticulon-4 (Nogo-A), also known as neurite outgrowth inhibitor, on primary brain tumor development was reported. Therefore the aim was the evaluation of Nogo-A concentrations in cerebrospinal fluid (CSF) and serum of brain tumor patients compared with non-tumoral individuals. All serum results, except for two cases, obtained both in brain tumors and non-tumoral individuals, were below the lower limit of ELISA detection. Cerebrospinal fluid Nogo-A concentrations were significantly lower in primary brain tumor patients compared to non-tumoral individuals. The univariate linear regression analysis found that if white blood cell count increases by 1 × 10 3 /μL, the mean cerebrospinal fluid Nogo-A concentration value decreases 1.12 times. In the model of multiple linear regression analysis predictor variables influencing cerebrospinal fluid Nogo-A concentrations included: diagnosis, sex, and sodium level. The mean cerebrospinal fluid Nogo-A concentration value was 1.9 times higher for women in comparison to men. In the astrocytic brain tumor group higher sodium level occurs with lower cerebrospinal fluid Nogo-A concentrations. We found the opposite situation in non-tumoral individuals. Univariate linear regression analysis revealed, that cerebrospinal fluid Nogo-A concentrations change in relation to white blood cell count. In the created model of multiple linear regression analysis we found, that within predictor variables influencing CSF Nogo-A concentrations were diagnosis, sex, and sodium level. Results may be relevant to the search for cerebrospinal fluid biomarkers and potential therapeutic targets in primary brain tumor patients. Nogo-A concentrations were tested by means of enzyme-linked immunosorbent assay (ELISA).

Association between climate factors, pollen counts, and childhood hay fever prevalence in the United States.

PubMed

Silverberg, Jonathan I; Braunstein, Marc; Lee-Wong, Mary

2015-02-01

Climate factors and pollen counts may play a role in hay fever. We sought to determine the impact of specific climate factors and pollen counts on the US prevalence of hay fever and statewide variation in prevalence. We used a merged analysis of the 2007 National Survey of Children's Health from a representative sample of 91,642 children aged 0 to 17 years and the 2006-2007 National Climate Data Center and Weather Service measurements of relative humidity (%), indoor heating degree days, precipitation, Palmer Hydrological Drought Index, clear sky and issued ultraviolet indices, stratospheric ozone levels, and outdoor air temperature and National Allergy Bureau total pollen counts. Multivariate survey logistic regression models controlled for sex, race/ethnicity, age, household income, and birthplace. The US prevalence of hay fever in childhood was 18.0% (95% CI, 17.7% to 18.2%), with the highest prevalence in southeastern and southern states. Hay fever prevalence was significantly lower with second and third quartile mean annual relative humidity (logistic regression, P ≤ .01 for both), fourth quartile mean annual Palmer Hydrological Drought Index (P = .02), third and fourth quartile mean annual heating degree days (P < .0001 for both), and third and fourth quartile mean annual stratospheric ozone levels but increased with second, third, and fourth quartile mean annual temperature (P ≤ .02 for both), fourth quartile mean annual precipitation (P = .0007), mean total pollen counts (P = .01), and second, third, and fourth quartile issued ultraviolet index (P ≤ .0001 for all). Principal-component analysis was also used to determine the combined effects of correlated climate variables and pollen counts. This study provides evidence of the influence of climate on the US prevalence of childhood hay fever. Copyright © 2014 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Hypertension and hematologic parameters in a community near a uranium processing facility

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

Wagner, Sara E., E-mail: swagner@uga.edu; Burch, James B.; South Carolina Statewide Cancer Prevention and Control Program, Columbia, SC

Background: Environmental uranium exposure originating as a byproduct of uranium processing can impact human health. The Fernald Feed Materials Production Center functioned as a uranium processing facility from 1951 to 1989, and potential health effects among residents living near this plant were investigated via the Fernald Medical Monitoring Program (FMMP). Methods: Data from 8216 adult FMMP participants were used to test the hypothesis that elevated uranium exposure was associated with indicators of hypertension or changes in hematologic parameters at entry into the program. A cumulative uranium exposure estimate, developed by FMMP investigators, was used to classify exposure. Systolic and diastolicmore » blood pressure and physician diagnoses were used to assess hypertension; and red blood cells, platelets, and white blood cell differential counts were used to characterize hematology. The relationship between uranium exposure and hypertension or hematologic parameters was evaluated using generalized linear models and quantile regression for continuous outcomes, and logistic regression or ordinal logistic regression for categorical outcomes, after adjustment for potential confounding factors. Results: Of 8216 adult FMMP participants 4187 (51%) had low cumulative uranium exposure, 1273 (15%) had moderate exposure, and 2756 (34%) were in the high (>0.50 Sievert) cumulative lifetime uranium exposure category. Participants with elevated uranium exposure had decreased white blood cell and lymphocyte counts and increased eosinophil counts. Female participants with higher uranium exposures had elevated systolic blood pressure compared to women with lower exposures. However, no exposure-related changes were observed in diastolic blood pressure or hypertension diagnoses among female or male participants. Conclusions: Results from this investigation suggest that residents in the vicinity of the Fernald plant with elevated exposure to uranium primarily via inhalation exhibited decreases in white blood cell counts, and small, though statistically significant, gender-specific alterations in systolic blood pressure at entry into the FMMP.« less

Predicting county-level cancer incidence rates and counts in the United States

PubMed Central

Yu, Binbing

2018-01-01

Many countries, including the United States, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model has been adopted by the American Cancer Society to estimate the number of new cancer cases in the United States and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties and FIPS code regions is of increasing interest by local policymakers. The natural extension is to directly apply the joinpoint model to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects joinpoint model for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard joinpoint model and the proposed method were compared through a validation study. The proposed method out-performed the standard joinpoint model for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut. PMID:23670947

Prediction of gastrointestinal disease with over-the-counter diarrheal remedy sales records in the San Francisco Bay Area.

PubMed

Kirian, Michelle L; Weintraub, June M

2010-07-20

Water utilities continue to be interested in implementing syndromic surveillance for the enhanced detection of waterborne disease outbreaks. The authors evaluated the ability of sales of over-the-counter diarrheal remedies available from the National Retail Data Monitor to predict endemic and epidemic gastrointestinal disease in the San Francisco Bay Area. Time series models were fit to weekly diarrheal remedy sales and diarrheal illness case counts. Cross-correlations between the pre-whitened residual series were calculated. Diarrheal remedy sales model residuals were regressed on the number of weekly outbreaks and outbreak-associated cases. Diarrheal remedy sales models were used to auto-forecast one week-ahead sales. The sensitivity and specificity of signals, generated by observed diarrheal remedy sales exceeding the upper 95% forecast confidence interval, in predicting weekly outbreaks were calculated. No significant correlations were identified between weekly diarrheal remedy sales and diarrhea illness case counts, outbreak counts, or the number of outbreak-associated cases. Signals generated by forecasting with the diarrheal remedy sales model did not coincide with outbreak weeks more reliably than signals chosen randomly. This work does not support the implementation of syndromic surveillance for gastrointestinal disease with data available though the National Retail Data Monitor.

A global goodness-of-fit statistic for Cox regression models.

PubMed

Parzen, M; Lipsitz, S R

1999-06-01

In this paper, a global goodness-of-fit test statistic for a Cox regression model, which has an approximate chi-squared distribution when the model has been correctly specified, is proposed. Our goodness-of-fit statistic is global and has power to detect if interactions or higher order powers of covariates in the model are needed. The proposed statistic is similar to the Hosmer and Lemeshow (1980, Communications in Statistics A10, 1043-1069) goodness-of-fit statistic for binary data as well as Schoenfeld's (1980, Biometrika 67, 145-153) statistic for the Cox model. The methods are illustrated using data from a Mayo Clinic trial in primary billiary cirrhosis of the liver (Fleming and Harrington, 1991, Counting Processes and Survival Analysis), in which the outcome is the time until liver transplantation or death. The are 17 possible covariates. Two Cox proportional hazards models are fit to the data, and the proposed goodness-of-fit statistic is applied to the fitted models.

Hydrological modeling of geophysical parameters of arboviral and protozoan disease vectors in Internally Displaced People camps in Gulu, Uganda.

PubMed

Jacob, Benjamin G; Muturi, Ephantus J; Caamano, Erick X; Gunter, James T; Mpanga, Enoch; Ayine, Robert; Okelloonen, Joseph; Nyeko, Jack Pen-Mogi; Shililu, Josephat I; Githure, John I; Regens, James L; Novak, Robert J; Kakoma, Ibulaimu

2008-03-14

The aim of this study was to determine if remotely sensed data and Digital Elevation Model (DEM) can test relationships between Culex quinquefasciatus and Anopheles gambiae s.l. larval habitats and environmental parameters within Internally Displaced People (IDP) campgrounds in Gulu, Uganda. A total of 65 georeferenced aquatic habitats in various IDP camps were studied to compare the larval abundance of Cx. quinquefasciatus and An. gambiae s.l. The aquatic habitat dataset were overlaid onto Land Use Land Cover (LULC) maps retrieved from Landsat imagery with 150 m x 150 m grid cells stratified by levels of drainage. The LULC change was estimated over a period of 14 years. Poisson regression analyses and Moran's I statistics were used to model relationships between larval abundance and environmental predictors. Individual larval habitat data were further evaluated in terms of their covariations with spatial autocorrelation by regressing them on candidate spatial filter eigenvectors. Multispectral QuickBird imagery classification and DEM-based GIS methods were generated to evaluate stream flow direction and accumulation for identification of immature Cx. quinquefasciatus and An. gambiae s.l. and abundance. The main LULC change in urban Gulu IDP camps was non-urban to urban, which included about 71.5 % of the land cover. The regression models indicate that counts of An. gambiae s.l. larvae were associated with shade while Cx. quinquefasciatus were associated with floating vegetation. Moran's I and the General G statistics for mosquito density by species and instars, identified significant clusters of high densities of Anopheles; larvae, however, Culex are not consistently clustered. A stepwise negative binomial regression decomposed the immature An. gambiae s.l. data into empirical orthogonal bases. The data suggest the presence of roughly 11% to 28 % redundant information in the larval count samples. The DEM suggest a positive correlation for Culex (0.24) while for Anopheles there was a negative correlation (-0.23) for a local model distance to stream. These data demonstrate that optical remote sensing; geostatistics and DEMs can be used to identify parameters associated with Culex and Anopheles aquatic habitats.

Hydrological modeling of geophysical parameters of arboviral and protozoan disease vectors in Internally Displaced People camps in Gulu, Uganda

PubMed Central

Jacob, Benjamin G; Muturi, Ephantus J; Caamano, Erick X; Gunter, James T; Mpanga, Enoch; Ayine, Robert; Okelloonen, Joseph; Nyeko, Jack Pen-Mogi; Shililu, Josephat I; Githure, John I; Regens, James L; Novak, Robert J; Kakoma, Ibulaimu

2008-01-01

Background The aim of this study was to determine if remotely sensed data and Digital Elevation Model (DEM) can test relationships between Culex quinquefasciatus and Anopheles gambiae s.l. larval habitats and environmental parameters within Internally Displaced People (IDP) campgrounds in Gulu, Uganda. A total of 65 georeferenced aquatic habitats in various IDP camps were studied to compare the larval abundance of Cx. quinquefasciatus and An. gambiae s.l. The aquatic habitat dataset were overlaid onto Land Use Land Cover (LULC) maps retrieved from Landsat imagery with 150 m × 150 m grid cells stratified by levels of drainage. The LULC change was estimated over a period of 14 years. Poisson regression analyses and Moran's I statistics were used to model relationships between larval abundance and environmental predictors. Individual larval habitat data were further evaluated in terms of their covariations with spatial autocorrelation by regressing them on candidate spatial filter eigenvectors. Multispectral QuickBird imagery classification and DEM-based GIS methods were generated to evaluate stream flow direction and accumulation for identification of immature Cx. quinquefasciatus and An. gambiae s.l. and abundance. Results The main LULC change in urban Gulu IDP camps was non-urban to urban, which included about 71.5 % of the land cover. The regression models indicate that counts of An. gambiae s.l. larvae were associated with shade while Cx. quinquefasciatus were associated with floating vegetation. Moran's I and the General G statistics for mosquito density by species and instars, identified significant clusters of high densities of Anopheles; larvae, however, Culex are not consistently clustered. A stepwise negative binomial regression decomposed the immature An. gambiae s.l. data into empirical orthogonal bases. The data suggest the presence of roughly 11% to 28 % redundant information in the larval count samples. The DEM suggest a positive correlation for Culex (0.24) while for Anopheles there was a negative correlation (-0.23) for a local model distance to stream. Conclusion These data demonstrate that optical remote sensing; geostatistics and DEMs can be used to identify parameters associated with Culex and Anopheles aquatic habitats. PMID:18341699

Food insecurity, CD4 counts, and incomplete viral suppression among HIV+ patients from Texas Children's Hospital: A pilot study

USDA-ARS?s Scientific Manuscript database

Our goal was to determine the relationship between food insecurity and CD4 counts and viral suppression among pediatric HIV-positive patients. Food insecurity was assessed by validated survey. CD4 counts and viral load were abstracted from patients’ charts. We used linear regression for the dependen...

Epidemiology, Seasonality, and Predictors of Outcome of AIDS-Associated Penicillium marneffei Infection in Ho Chi Minh City, Viet Nam

PubMed Central

Wolbers, Marcel; Quang, Vo Minh; Chinh, Nguyen Tran; Huong Lan, Nguyen Phu; Lam, Pham Si; Kozal, Michael J.; Shikuma, Cecilia M.; Day, Jeremy N.; Farrar, Jeremy

2011-01-01

Background. Penicillium marneffei is an important human immunodeficiency virus (HIV)–associated opportunistic pathogen in Southeast Asia. The epidemiology and the predictors of penicilliosis outcome are poorly understood. Methods. We performed a retrospective study of culture-confirmed incident penicilliosis admissions during 1996–2009 at the Hospital for Tropical Diseases in Ho Chi Minh City, Viet Nam. Seasonality of penicilliosis was assessed using cosinor models. Logistic regression was used to assess predictors of death or worsening disease based on 10 predefined covariates, and Cox regression was performed to model time-to-antifungal initiation. Results. A total of 795 patients were identified; hospital charts were obtainable for 513 patients (65%). Cases increased exponentially and peaked in 2007 (156 cases), mirroring the trends in AIDS admissions during the study period. A highly significant seasonality for penicilliosis (P < .001) but not for cryptococcosis (P = .63) or AIDS admissions (P = .83) was observed, with a 27% (95% confidence interval, 14%–41%) increase in incidence during rainy months. All patients were HIV infected; the median CD4 cell count (62 patients) was 7 cells/μL (interquartile range, 4–24 cells/μL). Hospital outcome was an improvement in 347 (68%), death in 101 (20%), worsening in 42 (8%), and nonassessable in 23 (5%) cases. Injection drug use, shorter history, absence of fever or skin lesions, elevated respiratory rates, higher lymphocyte count, and lower platelet count independently predicted poor outcome in both complete-case and multiple-imputation analyses. Time-to-treatment initiation was shorter for patients with skin lesions (hazard ratio, 3.78; 95% confidence interval, 2.96–4.84; P < .001). Conclusions. Penicilliosis incidence correlates with the HIV/AIDS epidemic in Viet nam. The number of cases increases during rainy months. Injection drug use, shorter history, absence of fever or skin lesions, respiratory difficulty, higher lymphocyte count, and lower platelet count predict poor in-hospital outcome. PMID:21427403

The quest for conditional independence in prospectivity modeling: weights-of-evidence, boost weights-of-evidence, and logistic regression

NASA Astrophysics Data System (ADS)

Schaeben, Helmut; Semmler, Georg

2016-09-01

The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes 0,1 classification of T. A special case of logistic regression called weights-of-evidence (WofE) is geologists' favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regression view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence (BoostWofE) was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the (initial) purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to "validate" this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking conditional independence whatever the consecutively processing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly compensate violations of joint conditional independence if the predictors are indicators.

The relevance of intestinal dysbiosis in liver transplant candidates.

PubMed

Grąt, M; Hołówko, W; Wronka, K M; Grąt, K; Lewandowski, Z; Kosińska, I; Krasnodębski, M; Wasilewicz, M; Gałęcka, M; Szachta, P; Zborowska, H; Patkowski, W; Krawczyk, M

2015-04-01

The gut microbial ecosystem plays an important role in the pathogenesis of liver diseases. However, the association of microbial community structure with the severity of liver dysfunction is not completely understood. Fecal microflora was assessed in 40 patients with liver cirrhosis listed for primary liver transplantation (LT). Independent associations between fecal microbial counts and serum bilirubin, serum creatinine, international normalized ratio (INR), and the Model for End-stage Liver Disease (MELD) score were established in multiple linear regression models. Bifidobacterium (standardized regression coefficient [sβ] = -0.549; P < 0.001), Enterococcus (sβ = 0.369; P = 0.004), and yeast (sβ = 0.315; P = 0.018) numbers were independently associated with serum bilirubin, while Escherichia coli counts (sβ = 0.318; P = 0.046) correlated with INR, and Bifidobacterium counts (sβ = 0.410; P = 0.009) with serum creatinine. Only Bifidobacterium (sβ = -0.468; P = 0.003) and Enterococcus (sβ = 0.331; P = 0.029) counts were independent predictors of the MELD score. Bifidobacterium/Enterococcus ratio, proposed as a measure of pre-LT gut dysbiosis, was significantly related to the MELD score following the adjustment for the absolute Bifidobacterium (sβ = -0.333; P = 0.029) and Enterococcus (sβ = -0.966; P = 0.003) numbers. This pre-transplant dysbiosis ratio (PTDR) was significantly correlated with Enterococcus (R = -0.897; P < 0.001) but not with Bifidobacterium (R = 0.098; P = 0.546) counts. Among the other components of gut microflora, only hydrogen peroxide (H2 O2 )-producing Lactobacillus strains significantly influenced Enterococcus counts (sβ = 0.349; P = 0.028) and PTDR (sβ = -0.318; P = 0.046). While the abundance of both Bifidobacterium and Enterococcus is related to liver dysfunction, the size of the Enterococcus population seems to be the most important determinant of pre-LT gut dysbiosis in cirrhotic patients. The H2 O2 -producing Lactobacillus strains potentially ameliorate this dysbiotic state. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

An Experimental Study in Determining Energy Expenditure from Treadmill Walking using Hip-Worn Inertial Sensors

PubMed Central

Vathsangam, Harshvardhan; Emken, Adar; Schroeder, E. Todd; Spruijt-Metz, Donna; Sukhatme, Gaurav S.

2011-01-01

This paper describes an experimental study in estimating energy expenditure from treadmill walking using a single hip-mounted triaxial inertial sensor comprised of a triaxial accelerometer and a triaxial gyroscope. Typical physical activity characterization using accelerometer generated counts suffers from two drawbacks - imprecison (due to proprietary counts) and incompleteness (due to incomplete movement description). We address these problems in the context of steady state walking by directly estimating energy expenditure with data from a hip-mounted inertial sensor. We represent the cyclic nature of walking with a Fourier transform of sensor streams and show how one can map this representation to energy expenditure (as measured by V O2 consumption, mL/min) using three regression techniques - Least Squares Regression (LSR), Bayesian Linear Regression (BLR) and Gaussian Process Regression (GPR). We perform a comparative analysis of the accuracy of sensor streams in predicting energy expenditure (measured by RMS prediction accuracy). Triaxial information is more accurate than uniaxial information. LSR based approaches are prone to outlier sensitivity and overfitting. Gyroscopic information showed equivalent if not better prediction accuracy as compared to accelerometers. Combining accelerometer and gyroscopic information provided better accuracy than using either sensor alone. We also analyze the best algorithmic approach among linear and nonlinear methods as measured by RMS prediction accuracy and run time. Nonlinear regression methods showed better prediction accuracy but required an order of magnitude of run time. This paper emphasizes the role of probabilistic techniques in conjunction with joint modeling of triaxial accelerations and rotational rates to improve energy expenditure prediction for steady-state treadmill walking. PMID:21690001

Box–Cox Transformation and Random Regression Models for Fecal egg Count Data

PubMed Central

da Silva, Marcos Vinícius Gualberto Barbosa; Van Tassell, Curtis P.; Sonstegard, Tad S.; Cobuci, Jaime Araujo; Gasbarre, Louis C.

2012-01-01

Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. In ruminants, fecal egg count (FEC) is commonly used to measure resistance to nematodes. FEC values are not normally distributed and logarithmic transformations have been used in an effort to achieve normality before analysis. However, the transformed data are often still not normally distributed, especially when data are extremely skewed. A series of repeated FEC measurements may provide information about the population dynamics of a group or individual. A total of 6375 FEC measures were obtained for 410 animals between 1992 and 2003 from the Beltsville Agricultural Research Center Angus herd. Original data were transformed using an extension of the Box–Cox transformation to approach normality and to estimate (co)variance components. We also proposed using random regression models (RRM) for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM and restricted maximum likelihood. Within the different orders of Legendre polynomials used, those with more parameters (order 4) adjusted FEC data best. Results indicated that the transformation of FEC data utilizing the Box–Cox transformation family was effective in reducing the skewness and kurtosis, and dramatically increased estimates of heritability, and measurements of FEC obtained in the period between 12 and 26 weeks in a 26-week experimental challenge period are genetically correlated. PMID:22303406

Box-Cox Transformation and Random Regression Models for Fecal egg Count Data.

PubMed

da Silva, Marcos Vinícius Gualberto Barbosa; Van Tassell, Curtis P; Sonstegard, Tad S; Cobuci, Jaime Araujo; Gasbarre, Louis C

2011-01-01

Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. In ruminants, fecal egg count (FEC) is commonly used to measure resistance to nematodes. FEC values are not normally distributed and logarithmic transformations have been used in an effort to achieve normality before analysis. However, the transformed data are often still not normally distributed, especially when data are extremely skewed. A series of repeated FEC measurements may provide information about the population dynamics of a group or individual. A total of 6375 FEC measures were obtained for 410 animals between 1992 and 2003 from the Beltsville Agricultural Research Center Angus herd. Original data were transformed using an extension of the Box-Cox transformation to approach normality and to estimate (co)variance components. We also proposed using random regression models (RRM) for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM and restricted maximum likelihood. Within the different orders of Legendre polynomials used, those with more parameters (order 4) adjusted FEC data best. Results indicated that the transformation of FEC data utilizing the Box-Cox transformation family was effective in reducing the skewness and kurtosis, and dramatically increased estimates of heritability, and measurements of FEC obtained in the period between 12 and 26 weeks in a 26-week experimental challenge period are genetically correlated.

Bayesian hierarchical modelling of continuous non-negative longitudinal data with a spike at zero: An application to a study of birds visiting gardens in winter.

PubMed

Swallow, Ben; Buckland, Stephen T; King, Ruth; Toms, Mike P

2016-03-01

The development of methods for dealing with continuous data with a spike at zero has lagged behind those for overdispersed or zero-inflated count data. We consider longitudinal ecological data corresponding to an annual average of 26 weekly maximum counts of birds, and are hence effectively continuous, bounded below by zero but also with a discrete mass at zero. We develop a Bayesian hierarchical Tweedie regression model that can directly accommodate the excess number of zeros common to this type of data, whilst accounting for both spatial and temporal correlation. Implementation of the model is conducted in a Markov chain Monte Carlo (MCMC) framework, using reversible jump MCMC to explore uncertainty across both parameter and model spaces. This regression modelling framework is very flexible and removes the need to make strong assumptions about mean-variance relationships a priori. It can also directly account for the spike at zero, whilst being easily applicable to other types of data and other model formulations. Whilst a correlative study such as this cannot prove causation, our results suggest that an increase in an avian predator may have led to an overall decrease in the number of one of its prey species visiting garden feeding stations in the United Kingdom. This may reflect a change in behaviour of house sparrows to avoid feeding stations frequented by sparrowhawks, or a reduction in house sparrow population size as a result of sparrowhawk increase. © 2015 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Regression analysis of mixed recurrent-event and panel-count data.

PubMed

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L

2014-07-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Determinants of personal exposure to PM2.5, ultrafine particle counts, and CO in a transport microenvironment.

PubMed

Kaur, S; Nieuwenhuijsen, M J

2009-07-01

Short-term human exposure concentrations to PM2.5, ultrafine particle counts (particle range: 0.02-1 microm), and carbon monoxide (CO) were investigated at and around a street canyon intersection in Central London, UK. During a four week field campaign, groups of four volunteers collected samples at three timings (morning, lunch, and afternoon), along two different routes (a heavily trafficked route and a backstreet route) via five modes of transport (walking, cycling, bus, car, and taxi). This was followed by an investigation into the determinants of exposure using a regression technique which incorporated the site-specific traffic counts, meteorological variables (wind speed and temperature) and the mode of transport used. The analyses explained 9, 62, and 43% of the variability observed in the exposure concentrations to PM2.5, ultrafine particle counts, and CO in this study, respectively. The mode of transport was a statistically significant determinant of personal exposure to PM2.5, ultrafine particle counts, and CO, and for PM2.5 and ultrafine particle counts it was the most important determinant. Traffic count explained little of the variability in the PM2.5 concentrations, but it had a greater influence on ultrafine particle count and CO concentrations. The analyses showed that temperature had a statistically significant impact on ultrafine particle count and CO concentrations. Wind speed also had a statistically significant effect but smaller. The small proportion in variability explained in PM2.5 by the model compared to the largest proportion in ultrafine particle counts and CO may be due to the effect of long-range transboundary sources, whereas for ultrafine particle counts and CO, local traffic is the main source.

High-sensitivity serum C-reactive protein levels in subjects with or without myocardial infarction or periodontitis.

PubMed

Persson, G Rutger; Pettersson, Thomas; Ohlsson, Ola; Renvert, Stefan

2005-03-01

Serum high-sensitivity C-reactive protein (hsC-rp) is a non-specific marker of inflammation. Elevated hsC-rp levels are found in subjects with cardiovascular diseases (CVDs). Periodontitis may influence hsC-rp levels. To assess periodontal status and hsC-rp serum levels in consecutive subjects hospitalized and diagnosed with acute myocardial infarction (AMI) (n=85) and in a group of carefully matched subjects (gender, age social, ethnic, and smoking habits) without clinical evidence of CVD (n=63). hsC-rp levels, other routine serum values, and clinical periodontal conditions were studied. Subjects with AMI had higher hsC-rp levels than control subjects (p<0.001, Mann-Whitney U-test). The odds that subjects in the control group with periodontitis (30% or more sites with>4.0 mm loss of alveolar bone) had serum hsC-rp>1.8 mg/l was 1.5 (95% CI: 1.1-7.3, p<0.05). Stepwise linear regression analysis failed to include periodontal parameters in an explanatory model to hsC-rp values. Only the serum leucocyte (white blood cell (WBC)) counts were explanatory to hsC-rp values (beta standard coefficient=0.45, t=3.2, p<0.001). Serum WBC counts were significantly higher in control subjects with periodontitis (p<0.03) but not in subjects in the AMI group (p<0.57). (1) As expected, elevated serum hsC-rp concentration and serum WBC counts are associated with acute coronary heart disease. (2) Elevated serum hsC-rp values are associated with radiographically defined periodontitis in subjects with no evidence of CVD. (3) Periodontal parameters are not explanatory to elevated serum hsC-rp values if serum WBC and low-density lipoprotein counts are included in the regression model. Copyright 2005 Blackwell Munksgaard.

Estimating animal populations and body sizes from burrows: Marine ecologists have their heads buried in the sand

NASA Astrophysics Data System (ADS)

Schlacher, Thomas A.; Lucrezi, Serena; Peterson, Charles H.; Connolly, Rod M.; Olds, Andrew D.; Althaus, Franziska; Hyndes, Glenn A.; Maslo, Brooke; Gilby, Ben L.; Leon, Javier X.; Weston, Michael A.; Lastra, Mariano; Williams, Alan; Schoeman, David S.

2016-06-01

Most ecological studies require knowledge of animal abundance, but it can be challenging and destructive of habitat to obtain accurate density estimates for cryptic species, such as crustaceans that tunnel deeply into the seafloor, beaches, or mudflats. Such fossorial species are, however, widely used in environmental impact assessments, requiring sampling techniques that are reliable, efficient, and environmentally benign for these species and environments. Counting and measuring the entrances of burrows made by cryptic species is commonly employed to index population and body sizes of individuals. The fundamental premise is that burrow metrics consistently predict density and size. Here we review the evidence for this premise. We also review criteria for selecting among sampling methods: burrow counts, visual censuses, and physical collections. A simple 1:1 correspondence between the number of holes and population size cannot be assumed. Occupancy rates, indexed by the slope of regression models, vary widely between species and among sites for the same species. Thus, 'average' or 'typical' occupancy rates should not be extrapolated from site- or species specific field validations and then be used as conversion factors in other situations. Predictions of organism density made from burrow counts often have large uncertainty, being double to half of the predicted mean value. Whether such prediction uncertainty is 'acceptable' depends on investigators' judgements regarding the desired detectable effect sizes. Regression models predicting body size from burrow entrance dimensions are more precise, but parameter estimates of most models are specific to species and subject to site-to-site variation within species. These results emphasise the need to undertake thorough field validations of indirect census techniques that include tests of how sensitive predictive models are to changes in habitat conditions or human impacts. In addition, new technologies (e.g. drones, thermal-, acoustic- or chemical sensors) should be used to enhance visual census techniques of burrows and surface-active animals.

Statistical power for detecting trends with applications to seabird monitoring

USGS Publications Warehouse

Hatch, Shyla A.

2003-01-01

Power analysis is helpful in defining goals for ecological monitoring and evaluating the performance of ongoing efforts. I examined detection standards proposed for population monitoring of seabirds using two programs (MONITOR and TRENDS) specially designed for power analysis of trend data. Neither program models within- and among-years components of variance explicitly and independently, thus an error term that incorporates both components is an essential input. Residual variation in seabird counts consisted of day-to-day variation within years and unexplained variation among years in approximately equal parts. The appropriate measure of error for power analysis is the standard error of estimation (S.E.est) from a regression of annual means against year. Replicate counts within years are helpful in minimizing S.E.est but should not be treated as independent samples for estimating power to detect trends. Other issues include a choice of assumptions about variance structure and selection of an exponential or linear model of population change. Seabird count data are characterized by strong correlations between S.D. and mean, thus a constant CV model is appropriate for power calculations. Time series were fit about equally well with exponential or linear models, but log transformation ensures equal variances over time, a basic assumption of regression analysis. Using sample data from seabird monitoring in Alaska, I computed the number of years required (with annual censusing) to detect trends of -1.4% per year (50% decline in 50 years) and -2.7% per year (50% decline in 25 years). At ??=0.05 and a desired power of 0.9, estimated study intervals ranged from 11 to 69 years depending on species, trend, software, and study design. Power to detect a negative trend of 6.7% per year (50% decline in 10 years) is suggested as an alternative standard for seabird monitoring that achieves a reasonable match between statistical and biological significance.

Application of the Extended Health Control Belief Model to Predict Hepatitis A and B Vaccinations.

PubMed

Reynolds, Grace L; Nguyen, Hannah H; Singh-Carlson, Savitri; Fisher, Dennis G; Odell, Anne; Xandre, Pamela

2016-09-01

Adult vaccination compliance rates vary according to sample and type of vaccine administered (influenza, pneumococcal). This study looked at vaccination of a community sample of low-income, minority adults. Nurses offered free vaccination for hepatitis A and B in the form of the combined Twinrix vaccine to adults on a walk-in basis. In addition to dosing information, participants completed the Risk Behavior Assessment, the Coping Strategies Indicator and the Cardiovascular Risk Assessment. Skaff's extended Health Belief Model was used as the theoretical framework. Count regression was used to model receipt of one, two, or three doses. The majority of participants were male with a mean age of 40 years. The distribution of doses was: 173 individuals (27.6%) received one dose only, 261 (41.7%) received two doses, and 191 (30.5%) received three doses of vaccine. The multivariate count regression model including being male, having previously been told by a health care provider that one has syphilis, having severe negative emotions, and perceived social support were associated with participants' receiving fewer doses of hepatitis vaccine. A greater problem-solving score was associated with a higher number of vaccine doses received. Despite free vaccinations offered in an easily accessible community setting, the majority of participants failed to complete the hepatitis vaccine series. More effort is needed to get adult men to participate in hepatitis vaccination clinics. Additional research is necessary to understand barriers other than cost to adults receiving vaccination. © 2016 Wiley Periodicals, Inc.

A prognostic pollen emissions model for climate models (PECM1.0)

NASA Astrophysics Data System (ADS)

Wozniak, Matthew C.; Steiner, Allison L.

2017-11-01

We develop a prognostic model called Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1) a taxa-specific land cover database, phenology, and emission potential, and (2) a plant functional type (PFT) land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

Mean Platelet Volume (MPV), Platelet Distribution Width (PDW), Platelet Count and Plateletcrit (PCT) as predictors of in-hospital paediatric mortality: a case-control Study.

PubMed

Golwala, Zainab Mohammedi; Shah, Hardik; Gupta, Neeraj; Sreenivas, V; Puliyel, Jacob M

2016-06-01

Thrombocytopenia has been shown to predict mortality. We hypothesize that platelet indices may be more useful prognostic indicators. Our study subjects were children one month to 14 years old admitted to our hospital. To determine whether platelet count, plateletcrit (PCT), mean platelet volume (MPV) and platelet distribution width (PDW) and their ratios can predict mortality in hospitalised children. Children who died during hospital stay were the cases. Controls were age matched children admitted contemporaneously. The first blood sample after admission was used for analysis. Receiver operating characteristic (ROC) curve was used to identify the best threshold for measured variables and the ratios studied. Multiple regression analysis was done to identify independent predictors of mortality. Forty cases and forty controls were studied. Platelet count, PCT and the ratios of MPV/Platelet count, MPV/PCT, PDW/Platelet count, PDW/PCT and MPV × PDW/Platelet count × PCT were significantly different among children who survived compared to those who died. On multiple regression analysis the ratio of MPV/PCT, PDW/Platelet count and MPV/Platelet count were risk factors for mortality with an odds ratio of 4.31(95% CI, 1.69-10.99), 3.86 (95% CI, 1.53-9.75), 3.45 (95% CI, 1.38-8.64) respectively. In 67% of the patients who died MPV/PCT ratio was above 41.8 and PDW/Platelet count was above 3.86. In 65% of patients who died MPV/Platelet count was above 3.45. The MPV/PCT, PDW/Platelet count and MPV/Platelet count, in the first sample after admission in this case control study were predictors of mortality and could predict 65% to 67% of deaths accurately.

KIDS COUNT Data Book, 2009: State Profiles of Child Well-Being

ERIC Educational Resources Information Center

Annie E. Casey Foundation, 2009

2009-01-01

The broad array of data presented each year in the "KIDS COUNT Data Book" is intended to illuminate the status of America's children and to assess trends in their well-being. By updating the assessment every year, KIDS COUNT provides ongoing benchmarks that can be used to see how states have advanced or regressed over time. Readers can…

KIDS COUNT Data Book, 2008: State Profiles of Child Well-Being

ERIC Educational Resources Information Center

Annie E. Casey Foundation, 2008

2008-01-01

The broad array of data we present each year in the "KIDS COUNT Data Book" is intended to illuminate the status of America's children and to assess trends in their well-being. By updating the assessment every year, KIDS COUNT provides ongoing benchmarks that can be used to see how states have advanced or regressed over time. Readers can…

Spatial analysis of soybean canopy response to soybean cyst nematodes (Heterodera glycines) in eastern Arkansas: An approach to future precision agriculture technology application

NASA Astrophysics Data System (ADS)

Kulkarni, Subodh

2008-10-01

Heterodera glycines Ichinohe, commonly known as soybean cyst nematode (SCN) is a serious widespread pathogen of soybean in the US. Present research primarily investigated feasibility of detecting SCN infestation in the field using aerial images and ground level spectrometric sensing. Non-spatial and spatial linear regression analyses were performed to correlate SCN population densities with Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) derived from soybean canopy spectra. Field data were obtained from two fields; Field A and B under different nematode control strategies in 2003 and 2004. Analysis of aerial image data from July 18, 2004 from the Field A showed a significant relationship between SCN population at planting and the GNDVI (R2=0.17 at p=0.0006). Linear regression analysis revealed that SCN had a little effect on yield (R2 =0.14, at p=0.0001, RMSEP=1052.42 kg ha-1) and GNDVI (R 2=0.17 at p=0.0006, RMSEP=0.087) derived from the aerial imagery on a single date. However, the spatial regression analysis based on spherical semivariogram showed that the RMSEP was 0.037 for the GNDVI on July 18, 2004 and 427.32 kg ha-1 for yield on October 14, 2003 indicating better model performance. For July 18, 2004 data from Field B, a relationship between NDVI and the cyst counts at planting was significant (R2=0.5 at p=0.0468). Non-spatial analyses of the ground level spectrometric data for the first field showed that NDVI and GNDVI were correlated with cyst counts at planting (R 2=0.34 and 0.27 at p=0.0015 and 0.0127, respectively), and GNDVI was correlated with eggs count at planting (R2= 0.27 at p=0.0118). Both NDVI and GNDVI were correlated with egg counts at flowering (R 2=0.34 and 0.27 at p=0.0013 and 0.0018, respectively). However, paired T test to validate the above relationships showed that, predicted values of NDVI and GNDVI were significantly different. The statistical evidences suggested that variability in vegetation indices was caused by SCN infestation. Comparison of estimators such as -2 RLL, AIC, and BIC of non-spatial and spatial models affirmed that incorporating spatial covariance structure of observations improved model performances. These results demonstrated a limited potential of aerial imaging and ground level spectrometry for detecting nematode infestation in the field. However, it is strongly recommended that more multisite-multiyear trials must be performed to establish and validate empirical models to quantify SCN population densities and their impact on soybean canopy reflectance.

White Blood Cells, Neutrophils, and Reactive Oxygen Metabolites among Asymptomatic Subjects.

PubMed

Kotani, Kazuhiko; Sakane, Naoki

2012-06-01

Chronic inflammation and oxidative stress are associated with health and the disease status. The objective of the present study was to investigate the association among white blood cell (WBC) counts, neutrophil counts as a WBC subpopulation, and diacron reactive oxygen metabolites (d-ROMs) levels in an asymptomatic population. The clinical data, including general cardiovascular risk variables and high-sensitivity C-reactive protein (hs-CRP), were collected from 100 female subjects (mean age, 62 years) in outpatient clinics. The correlation of the d-ROMs with hs-CRP, WBC, and neutrophil counts was examined. The mean/median levels were WBC counts 5.9 × 10(9)/L, neutrophil counts 3.6 × 10(9)/L, hs-CRP 0.06 mg/dL, and d-ROMs 359 CURR U. A simple correlation analysis showed a significant positive correlation of the d-ROMs with the WBC counts, neutrophil counts, or hs-CRP levels. The correlation between d-ROMs and neutrophil counts (β = 0.22, P < 0.05), as well as that between d-ROMs and hs-CRP (β = 0.28, P < 0.01), remained significant and independent in a multiple linear regression analysis adjusted for other variables. A multiple linear regression analysis showed that WBC counts had only a positive correlation tendency to the d-ROMs. Neutrophils may be slightly but more involved in the oxidative stress status, as assessed by d-ROMs, in comparison to the overall WBC. Further studies are needed to clarify the biologic mechanism(s) of the observed relationship.

Environmental, Spatial, and Sociodemographic Factors Associated with Nonfatal Injuries in Indonesia.

PubMed

Irianti, Sri; Prasetyoputra, Puguh

2017-01-01

Background . The determinants of injuries and their reoccurrence in Indonesia are not well understood, despite their importance in the prevention of injuries. Therefore, this study seeks to investigate the environmental, spatial, and sociodemographic factors associated with the reoccurrence of injuries among Indonesian people. Methods . Data from the 2013 round of the Indonesia Baseline Health Research (IBHR 2013) were analysed using a two-part hurdle regression model. A logit regression model was chosen for the zero-hurdle part , while a zero-truncated negative binomial regression model was selected for the counts part . Odds ratio (OR) and incidence rate ratio (IRR) were the measures of association, respectively. Results . The results suggest that living in a household with distant drinking water source, residing in slum areas, residing in Eastern Indonesia, having low educational attainment, being men, and being poorer are positively related to the likelihood of experiencing injury. Moreover, being a farmer or fishermen, having low educational attainment, and being men are positively associated with the frequency of injuries. Conclusion . This study would be useful to prioritise injury prevention programs in Indonesia based on the environmental, spatial, and sociodemographic characteristics.

Fibrinogen, viscosity, and white blood cell count are major risk factors for ischemic heart disease. The Caerphilly and Speedwell collaborative heart disease studies.

PubMed

Yarnell, J W; Baker, I A; Sweetnam, P M; Bainton, D; O'Brien, J R; Whitehead, P J; Elwood, P C

1991-03-01

Recent studies have suggested that hemostatic factors and white blood cell count are predictive of ischemic heart disease (IHD). The relations of fibrinogen, viscosity, and white blood cell count to the incidence of IHD in the Caerphilly and Speedwell prospective studies are described. The two studies have a common core protocol and are based on a combined cohort of 4,860 middle-aged men from the general population. The first follow-up was at a nearly constant interval of 5.1 years in Caerphilly and 3.2 years in Speedwell; 251 major IHD events had occurred. Age-adjusted relative odds of IHD for men in the top 20% of the distribution compared with the bottom 20% were 4.1 (95% confidence interval, 2.6-6.5) for fibrinogen, 4.5 (95% confidence interval, 2.8-7.4) for viscosity, and 3.2 (95% confidence interval, 2.0-4.9) for white blood cell count. Associations with IHD were similar in men who had never smoked, exsmokers, and current smokers, and the results suggest that at least part of the effect of smoking on IHD is mediated through fibrinogen, viscosity, and white blood cell count. Multivariate analysis shows that white blood cell count is an independent risk factor for IHD as is either fibrinogen or viscosity, or possibly both. Jointly, these three variables significantly improve the fit of a logistic regression model containing all the main conventional risk factors. Further, a model including age, smoking habits, fibrinogen, viscosity, and white blood cell count predicts IHD as well as one in which the three hemostatic/rheological variables are replaced by total cholesterol, diastolic pressure, and body mass index. Jointly, fibrinogen, viscosity, and white blood cell count are important risk factors for IHD.

Examining the influence of link function misspecification in conventional regression models for developing crash modification factors.

PubMed

Wu, Lingtao; Lord, Dominique

2017-05-01

This study further examined the use of regression models for developing crash modification factors (CMFs), specifically focusing on the misspecification in the link function. The primary objectives were to validate the accuracy of CMFs derived from the commonly used regression models (i.e., generalized linear models or GLMs with additive linear link functions) when some of the variables have nonlinear relationships and quantify the amount of bias as a function of the nonlinearity. Using the concept of artificial realistic data, various linear and nonlinear crash modification functions (CM-Functions) were assumed for three variables. Crash counts were randomly generated based on these CM-Functions. CMFs were then derived from regression models for three different scenarios. The results were compared with the assumed true values. The main findings are summarized as follows: (1) when some variables have nonlinear relationships with crash risk, the CMFs for these variables derived from the commonly used GLMs are all biased, especially around areas away from the baseline conditions (e.g., boundary areas); (2) with the increase in nonlinearity (i.e., nonlinear relationship becomes stronger), the bias becomes more significant; (3) the quality of CMFs for other variables having linear relationships can be influenced when mixed with those having nonlinear relationships, but the accuracy may still be acceptable; and (4) the misuse of the link function for one or more variables can also lead to biased estimates for other parameters. This study raised the importance of the link function when using regression models for developing CMFs. Copyright © 2017 Elsevier Ltd. All rights reserved.

New variable selection methods for zero-inflated count data with applications to the substance abuse field

PubMed Central

Buu, Anne; Johnson, Norman J.; Li, Runze; Tan, Xianming

2011-01-01

Zero-inflated count data are very common in health surveys. This study develops new variable selection methods for the zero-inflated Poisson regression model. Our simulations demonstrate the negative consequences which arise from the ignorance of zero-inflation. Among the competing methods, the one-step SCAD method is recommended because it has the highest specificity, sensitivity, exact fit, and lowest estimation error. The design of the simulations is based on the special features of two large national databases commonly used in the alcoholism and substance abuse field so that our findings can be easily generalized to the real settings. Applications of the methodology are demonstrated by empirical analyses on the data from a well-known alcohol study. PMID:21563207

Considerations for monitoring raptor population trends based on counts of migrants

USGS Publications Warehouse

Titus, K.; Fuller, M.R.; Ruos, J.L.; Meyburg, B-U.; Chancellor, R.D.

1989-01-01

Various problems were identified with standardized hawk count data as annually collected at six sites. Some of the hawk lookouts increased their hours of observation from 1979-1985, thereby confounding the total counts. Data recording and missing data hamper coding of data and their use with modern analytical techniques. Coefficients of variation among years in counts averaged about 40%. The advantages and disadvantages of various analytical techniques are discussed including regression, non-parametric rank correlation trend analysis, and moving averages.

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.

PubMed

Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C

2015-12-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (c) 2016 APA, all rights reserved).

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data

PubMed Central

Holsclaw, Tracy; Hallgren, Kevin A.; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.

2015-01-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non-normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased type-I and type-II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally-technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in supplementary materials. PMID:26098126

In vivo macular pigment measurements: a comparison of resonance Raman spectroscopy and heterochromatic flicker photometry

PubMed Central

Hogg, R E; Anderson, R S; Stevenson, M R; Zlatkova, M B; Chakravarthy, U

2007-01-01

Aim To investigate whether two methods of measuring macular pigment—namely, heterochromatic flicker photometry (HFP) and resonance Raman spectroscopy (RRS)—yield comparable data. Methods Macular pigment was measured using HFP and RRS in the right eye of 107 participants aged 20–79 years. Correlations between methods were sought and regression models generated. RRS was recorded as Raman counts and HFP as macular pigment optical density (MPOD). The average of the top three of five Raman counts was compared with MPOD obtained at 0.5° eccentricity, and an integrated measure (spatial profile; MPODsp) computed from four stimulus sizes on HFP. Results The coefficient of variation was 12.0% for MPODsp and 13.5% for Raman counts. MPODsp exhibited significant correlations with Raman counts (r = 0.260, p = 0.012), whereas MPOD at 0.5° did not correlate significantly (r = 0.163, p = 0.118). MPODsp was not significantly correlated with age (p = 0.062), whereas MPOD at 0.5° was positively correlated (p = 0.011). Raman counts showed a significant decrease with age (p = 0.002) and were significantly lower when pupil size was smaller (p = 0.015). Conclusions Despite a statistically significant correlation, the correlations were weak, with those in excess of 90% of the variance between MPODsp and Raman counts remaining unexplained, meriting further research. PMID:16825281

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.

PubMed

Lord, Dominique; Washington, Simon P; Ivan, John N

2005-01-01

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.

Novel Phenotype Issues Raised in Cross-National Epidemiological Research on Drug Dependence

PubMed Central

Anthony, James C.

2010-01-01

Stage-transition models based on the American Diagnostic and Statistical Manual (DSM) generally are applied in epidemiology and genetics research on drug dependence syndromes associated with cannabis, cocaine, and other internationally regulated drugs (IRD). Difficulties with DSM stage-transition models have surfaced during cross-national research intended to provide a truly global perspective, such as the work of the World Mental Health Surveys (WMHS) Consortium. Alternative simpler dependence-related phenotypes are possible, including population-level count process models for steps early and before coalescence of clinical features into a coherent syndrome (e.g., zero-inflated Poisson regression). Selected findings are reviewed, based on ZIP modeling of alcohol, tobacco, and IRD count processes, with an illustration that may stimulate new research on genetic susceptibility traits. The annual National Surveys on Drug Use and Health can be readily modified for this purpose, along the lines of a truly anonymous research approach that can help make NSDUH-type cross-national epidemiological surveys more useful in the context of subsequent genome wide association (GWAS) research and post-GWAS investigations with a truly global health perspective. PMID:20201862

Factors associated with change in objectively measured physical activity in older people - data from the physical activity cohort Scotland study.

PubMed

Clarke, Clare L; Sniehotta, Falko F; Vadiveloo, Thenmalar; Argo, Ishbel S; Donnan, Peter T; McMurdo, Marion E T; Witham, Miles D

2017-08-14

Cross-sectional relationships between physical activity and health have been explored extensively, but less is known about how physical activity changes with time in older people. The aim of this study was to assess baseline predictors of how objectively measured physical activity changes with time in older people. Longitudinal cohort study using data from the Physical Activity Cohort Scotland. A sample of community-dwelling older people aged 65 and over were recruited in 2009-2011, then followed up 2-3 years later. Physical activity was measured using Stayhealthy RT3 accelerometers over 7 days. Other data collected included baseline comorbidity, health-related quality of life (SF-36), extended Theory of Planned Behaviour Questionnaire and Social Capital Module of the General Household Survey. Associations between follow-up accelerometer counts and baseline predictors were analysed using a series of linear regression models, adjusting for baseline activity levels and follow-up time. Follow up data were available for 339 of the original 584 participants. The mean age was 77 years, 185 (55%) were female and mean follow up time was 26 months. Mean activity counts fell by between 2% per year (age < =80, deprivation decile 5-10) and 12% per year (age > 80, deprivation decile 5-10) from baseline values. In univariate analysis age, sex, deprivation decile, most SF-36 domains, most measures of social connectedness, most measures from the extended Theory of Planned Behaviour, hypertension, diabetes mellitus, chronic pain and depression score were significantly associated with adjusted activity counts at follow-up. In multivariate regression age, satisfactory friend network, SF-36 physical function score, and the presence of diabetes mellitus were independent predictors of activity counts at follow up after adjustment for baseline count and duration of follow up. Health status and social connectedness, but not extended Theory of Planned Behaviour measures, independently predicted changes in physical activity in community dwelling older people.

Kernel Regression Estimation of Fiber Orientation Mixtures in Diffusion MRI

PubMed Central

Cabeen, Ryan P.; Bastin, Mark E.; Laidlaw, David H.

2016-01-01

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework’s data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III. PMID:26691524

Modeling energy expenditure in children and adolescents using quantile regression

PubMed Central

Yang, Yunwen; Adolph, Anne L.; Puyau, Maurice R.; Vohra, Firoz A.; Zakeri, Issa F.

2013-01-01

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the covariate effects of weight, PA, and HR across the conditional EE distribution. QR and ordinary least squares (OLS) regressions are estimated in 109 children, aged 5–18 yr. QR modeling of EE outperformed OLS regression for both nonobese and obese populations. Average prediction errors for QR compared with OLS were not only smaller at the median τ = 0.5 (18.6 vs. 21.4%), but also substantially smaller at the tails of the distribution (10.2 vs. 39.2% at τ = 0.1 and 8.7 vs. 19.8% at τ = 0.9). Covariate effects of weight, PA, and HR on EE for the nonobese and obese children differed across quantiles (P < 0.05). The associations (linear and quadratic) between PA and HR with EE were stronger for the obese than nonobese population (P < 0.05). In conclusion, QR provided more accurate predictions of EE compared with conventional OLS regression, especially at the tails of the distribution, and revealed substantially different covariate effects of weight, PA, and HR on EE in nonobese and obese children. PMID:23640591

Factors associated with excessive bleeding after cardiac surgery: A prospective cohort study.

PubMed

Lopes, Camila Takao; Brunori, Evelise Fadini Reis; Cavalcante, Agueda Maria Ruiz Zimmer; Moorhead, Sue Ann; Swanson, Elizabeth; Lopes, Juliana de Lima; de Barros, Alba Lucia Bottura Leite

2016-01-01

To identify factors associated with excessive bleeding (ExB) after cardiac surgery in adults. Excessive bleeding after cardiac surgery must be anticipated for implementation of timely interventions. A prospective cohort study with 323 adults requiring open-chest cardiac surgery. Potential factors associated with ExB were investigated through univariate analysis and logistic regression. The accuracy of the relationship between the independent variables and the outcome was depicted through the receiver-operating characteristic (ROC) curve. The factors associated with ExB included gender, body mass index (BMI), preoperative platelet count, intraoperative heparin doses and intraoperative platelet transfusion. The ROC curve cut-off points were 26.35 for the BMI; 214,000 for the preoperative platelet count, and 6.25 for intraoperative heparin dose. This model had an accuracy = 77.3%, a sensitivity = 81%, and a specificity = 62%. Male gender, BMI, preoperative platelet count, dose of intraoperative heparin >312.5 mg without subsequent platelet transfusion, are factors associated with ExB. Copyright © 2016 Elsevier Inc. All rights reserved.

Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry.

PubMed

Garnotel, M; Bastian, T; Romero-Ugalde, H M; Maire, A; Dugas, J; Zahariev, A; Doron, M; Jallon, P; Charpentier, G; Franc, S; Blanc, S; Bonnet, S; Simon, C

2018-03-01

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions. NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated. Here we developed and validated an activity-specific model which, coupled with an automatic activity-recognition algorithm, improved the variance explained by the predictions from accelerometry counts by 43% of daily PAEE compared with models relying on a simple relationship between accelerometry counts and EE.

Evaluation of nonpoint-source contamination, Wisconsin; selected streamwater-quality data, land-use and best-management practices inventory, and quality assurance and quality control, water year 1993

USGS Publications Warehouse

Corsi, Steven R.; Walker, John F.; Graczyk, D.J.; Greb, S.R.; Owens, D.W.; Rappold, K.F.

1995-01-01

A special study was done to determine the effect of holding time on fecal coliform colony counts. A linear regression indicated that the mean decrease in colony counts over 72 hours was 8.2 percent per day. Results after 24 hours showed that colony counts increased in some samples and decreased in others.

Can cover data be used as a surrogate for seedling counts in regeneration stocking evaluations in northern hardwood forests?

Treesearch

Todd E. Ristau; Susan L. Stout

2014-01-01

Assessment of regeneration can be time-consuming and costly. Often, foresters look for ways to minimize the cost of doing inventories. One potential method to reduce time required on a plot is use of percent cover data rather than seedling count data to determine stocking. Robust linear regression analysis was used in this report to predict seedling count data from...

Do classic blood biomarkers of JSLE identify active lupus nephritis? Evidence from the UK JSLE Cohort Study.

PubMed

Smith, E M D; Jorgensen, A L; Beresford, M W

2017-10-01

Background Lupus nephritis (LN) affects up to 80% of juvenile-onset systemic lupus erythematosus (JSLE) patients. The value of commonly available biomarkers, such as anti-dsDNA antibodies, complement (C3/C4), ESR and full blood count parameters in the identification of active LN remains uncertain. Methods Participants from the UK JSLE Cohort Study, aged <16 years at diagnosis, were categorized as having active or inactive LN according to the renal domain of the British Isles Lupus Assessment Group score. Classic biomarkers: anti-dsDNA, C3, C4, ESR, CRP, haemoglobin, total white cells, neutrophils, lymphocytes, platelets and immunoglobulins were assessed for their ability to identify active LN using binary logistic regression modeling, with stepAIC function applied to select a final model. Receiver-operating curve analysis was used to assess diagnostic accuracy. Results A total of 370 patients were recruited; 191 (52%) had active LN and 179 (48%) had inactive LN. Binary logistic regression modeling demonstrated a combination of ESR, C3, white cell count, neutrophils, lymphocytes and IgG to be best for the identification of active LN (area under the curve 0.724). Conclusions At best, combining common classic blood biomarkers of lupus activity using multivariate analysis provides a 'fair' ability to identify active LN. Urine biomarkers were not included in these analyses. These results add to the concern that classic blood biomarkers are limited in monitoring discrete JSLE manifestations such as LN.

The Relationship Between Caffeine Intake and Immunological and Virological Markers of HIV Disease Progression in Miami Adult Studies on HIV Cohort.

PubMed

Ramamoorthy, Venkataraghavan; Campa, Adriana; Rubens, Muni; Martinez, Sabrina S; Fleetwood, Christina; Stewart, Tiffanie; Liuzzi, Juan P; George, Florence; Khan, Hafiz; Li, Yinghui; Baum, Marianna K

2017-05-01

Although there are many studies on adverse health effects of substance use and HIV disease progression, similar studies about caffeine consumption are few. In this study, we investigated the effects of caffeine on immunological and virological markers of HIV disease progression. A convenience sample of 130 clinically stable people living with HIV/AIDS on antiretroviral therapy (65 consuming ≤250 mg/day and 65 consuming >250 mg/day of caffeine) were recruited from the Miami Adult Studies on HIV (MASH) cohort. This study included a baseline and 3-month follow-up visit. Demographics, body composition measures, substance use, Modified Caffeine Consumption Questionnaire (MCCQ), and CD4 count and HIV viral load were obtained for all participants. Multivariable linear regression and Linear Mixed Models (LMMs) were used to understand the effect of caffeine consumption on CD4 count and HIV viral load. The mean age of the cohort was 47.9 ± 6.4 years, 60.8% were men and 75.4% were African Americans. All participants were on ART during both the visits. Mean caffeine intake at baseline was 337.6 ± 305.0 mg/day and did not change significantly at the 3-month follow-up visit. Multivariable linear regressions after adjustment for covariates showed significant association between caffeine consumption and higher CD4 count (β = 1.532, p = 0.049) and lower HIV viral load (β = -1.067, p = 0.048). LMM after adjustment for covariates showed that the relationship between caffeine and CD4 count (β = 1.720, p = 0.042) and HIV viral load (β = -1.389, p = 0.033) continued over time in a dose-response manner. Higher caffeine consumption was associated with higher CD4 cell counts and lower HIV viral loads indicating beneficial effects on HIV disease progression. Further studies examining biochemical effects of caffeine on CD4 cell counts and viral replication need to be done in the future.

Ensemble-based methods for forecasting census in hospital units

PubMed Central

2013-01-01

Background The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. Methods In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. Results Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. Conclusions Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts. PMID:23721123

Ensemble-based methods for forecasting census in hospital units.

PubMed

Koestler, Devin C; Ombao, Hernando; Bender, Jesse

2013-05-30

The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

Association of psychological stress response of fatigue with white blood cell count in male daytime workers.

PubMed

Nishitani, Naoko; Sakakibara, Hisataka

2014-01-01

Relationships between work-related psychological and physical stress responses and counts of white blood cells (WBCs), neutrophils, and lymphocytes were investigated in 101 daytime workers. Counts of WBCs and neutrophils were positively associated with smoking and inversely correlated with high density lipoprotein (HDL)-cholesterol levels. Additionally, general fatigue score as measured by the profile of mood state was positively correlated with WBC and neutrophil counts whereas lymphocyte counts was not significantly associated with fatigue score. Multiple regression analysis showed that WBC count was significantly related to general fatigue, age, and HDL-cholesterol levels. Neutrophil count was significantly related to HDL-cholesterol levels and fatigue score. Among various psychological stress response variables, general fatigue may be a key determinant of low-grade inflammation as represented by increases of WBC and neutrophil counts.

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

Herd-level risk factors for Campylobacter fetus infection, Brucella seropositivity and within-herd seroprevalence of brucellosis in cattle in northern Nigeria.

PubMed

Mai, H M; Irons, P C; Kabir, J; Thompson, P N

2013-09-01

Brucellosis and campylobacteriosis are economically important diseases affecting bovine reproductive efficiency in Nigeria. A questionnaire-based survey was conducted in 271 cattle herds in Adamawa, Kaduna and Kano states of northern Nigeria using multistage cluster sampling. Serum from 4745 mature animals was tested for Brucella antibodies using the Rose-Bengal plate test and positives were confirmed in series-testing protocol using competitive enzyme-linked immunosorbent assay. Preputial scrapings from 602 bulls were tested using culture and identification for Campylobacter fetus. For each disease, a herd was classified as positive if one or more animals tested positive. For each herd, information on potential managemental and environmental risk factors was collected through a questionnaire administered during an interview with the manager, owner or herdsman. Multiple logistic regression models were used to model the odds of herd infection for each disease. A zero-inflated Poisson model was used to model the count of Brucella-positive animals within herds, with the number tested as an exposure variable. The presence of small ruminants (sheep and/or goats) on the same farm, and buying-in of >3 new animals in the previous year or failure to practice quarantine were associated with increased odds of herd-level campylobacteriosis and brucellosis, as well as increased within-herd counts of Brucella-positive animals. In addition, high rainfall, initial acquisition of animals from markets, practice of gynaecological examination and failure to practice herd prophylactic measures were positively associated with the odds of C. fetus infection in the herd. Herd size of >15, pastoral management system and presence of handling facility on the farm were associated with increased odds, and gynaecological examination with reduced odds of herd-level Brucella seropositivity. Furthermore, the zero-inflated Poisson model showed that borrowing or sharing of bulls was associated with higher counts, and provision of mineral supplement with lower counts of Brucella-positive cattle within herds. Identification of risk factors for bovine campylobacteriosis and brucellosis can help to identify appropriate control measures, and the use of zero-inflated count model can provide more specific information on these risk factors. Copyright © 2013 Elsevier B.V. All rights reserved.

Long-term mortality in HIV-positive individuals virally suppressed for >3 years with incomplete CD4 recovery.

PubMed

Engsig, Frederik N; Zangerle, Robert; Katsarou, Olga; Dabis, Francois; Reiss, Peter; Gill, John; Porter, Kholoud; Sabin, Caroline; Riordan, Andrew; Fätkenheuer, Gerd; Gutiérrez, Félix; Raffi, Francois; Kirk, Ole; Mary-Krause, Murielle; Stephan, Christoph; de Olalla, Patricia Garcia; Guest, Jodie; Samji, Hasina; Castagna, Antonella; d'Arminio Monforte, Antonella; Skaletz-Rorowski, Adriane; Ramos, Jose; Lapadula, Giuseppe; Mussini, Cristina; Force, Lluís; Meyer, Laurence; Lampe, Fiona; Boufassa, Faroudy; Bucher, Heiner C; De Wit, Stéphane; Burkholder, Greer A; Teira, Ramon; Justice, Amy C; Sterling, Tim R; M Crane, Heidi; Gerstoft, Jan; Grarup, Jesper; May, Margaret; Chêne, Geneviève; Ingle, Suzanne M; Sterne, Jonathan; Obel, Niels

2014-05-01

Some human immunodeficiency virus (HIV)-infected individuals initiating combination antiretroviral therapy (cART) with low CD4 counts achieve viral suppression but not CD4 cell recovery. We aimed to identify (1) risk factors for failure to achieve CD4 count >200 cells/µL after 3 years of sustained viral suppression and (2) the association of the achieved CD4 count with subsequent mortality. We included treated HIV-infected adults from 2 large international HIV cohorts, who had viral suppression (≤500 HIV type 1 RNA copies/mL) for >3 years with CD4 count ≤200 cells/µL at start of the suppressed period. Logistic regression was used to identify risk factors for incomplete CD4 recovery (≤200 cells/µL) and Cox regression to identify associations with mortality. Of 5550 eligible individuals, 835 (15%) did not reach a CD4 count >200 cells/µL after 3 years of suppression. Increasing age, lower initial CD4 count, male heterosexual and injection drug use transmission, cART initiation after 1998, and longer time from initiation of cART to start of the virally suppressed period were risk factors for not achieving a CD4 count >200 cells/µL. Individuals with CD4 ≤200 cells/µL after 3 years of viral suppression had substantially increased mortality (adjusted hazard ratio, 2.60; 95% confidence interval, 1.86-3.61) compared with those who achieved CD4 count >200 cells/µL. The increased mortality was seen across different patient groups and for all causes of death. Virally suppressed HIV-positive individuals on cART who do not achieve a CD4 count >200 cells/µL have substantially increased long-term mortality.

Modeling to predict growth/no growth boundaries and kinetic behavior of Salmonella on cutting board surfaces.

PubMed

Yoon, Hyunjoo; Lee, Joo-Yeon; Suk, Hee-Jin; Lee, Sunah; Lee, Heeyoung; Lee, Soomin; Yoon, Yohan

2012-12-01

This study developed models to predict the growth probabilities and kinetic behavior of Salmonella enterica strains on cutting boards. Polyethylene coupons (3 by 5 cm) were rubbed with pork belly, and pork purge was then sprayed on the coupon surface, followed by inoculation of a five-strain Salmonella mixture onto the surface of the coupons. These coupons were stored at 13 to 35°C for 12 h, and total bacterial and Salmonella cell counts were enumerated on tryptic soy agar and xylose lysine deoxycholate (XLD) agar, respectively, every 2 h, which produced 56 combinations. The combinations that had growth of ≥0.5 log CFU/cm(2) of Salmonella bacteria recovered on XLD agar were given the value 1 (growth), and the combinations that had growth of <0.5 log CFU/cm(2) were assigned the value 0 (no growth). These growth response data from XLD agar were analyzed by logistic regression for producing growth/no growth interfaces of Salmonella bacteria. In addition, a linear model was fitted to the Salmonella cell counts to calculate the growth rate (log CFU per square centimeter per hour) and initial cell count (log CFU per square centimeter), following secondary modeling with the square root model. All of the models developed were validated with observed data, which were not used for model development. Growth of total bacteria and Salmonella cells was observed at 28, 30, 33, and 35°C, but there was no growth detected below 20°C within the time frame investigated. Moreover, various indices indicated that the performance of the developed models was acceptable. The results suggest that the models developed in this study may be useful in predicting the growth/no growth interface and kinetic behavior of Salmonella bacteria on polyethylene cutting boards.

GATE based Monte Carlo simulation of planar scintigraphy to estimate the nodular dose in radioiodine therapy for autonomous thyroid adenoma.

PubMed

Hammes, Jochen; Pietrzyk, Uwe; Schmidt, Matthias; Schicha, Harald; Eschner, Wolfgang

2011-12-01

The recommended target dose in radioiodine therapy of solitary hyperfunctioning thyroid nodules is 300-400Gy and therefore higher than in other radiotherapies. This is due to the fact that an unknown, yet significant portion of the activity is stored in extranodular areas but is neglected in the calculatory dosimetry. We investigate the feasibility of determining the ratio of nodular and extranodular activity concentrations (uptakes) from post-therapeutically acquired planar scintigrams with Monte Carlo simulations in GATE. The geometry of a gamma camera with a high energy collimator was emulated in GATE (Version 5). A geometrical thyroid-neck phantom (GP) and the ICRP reference voxel phantoms "Adult Female" (AF, 16ml thyroid) and "Adult Male" (AM, 19ml thyroid) were used as source regions. Nodules of 1ml and 3ml volume were placed in the phantoms. For each phantom and each nodule 200 scintigraphic acquisitions were simulated. Uptake ratios of nodule and rest of thyroid ranging from 1 to 20 could be created by summation. Quantitative image analysis was performed by investigating the number of simulated counts in regions of interest (ROIs). ROIs were created by perpendicular projection of the phantom onto the camera plane to avoid a user dependant bias. The ratio of count densities in ROIs over the nodule and over the contralateral lobe, which should be least affected by nodular activity, was taken to be the best available measure for the uptake ratios. However, the predefined uptake ratios are underestimated by these count density ratios: For an uptake ratio of 20 the count ratios range from 4.5 (AF, 1ml nodule) to 15.3 (AM, 3ml nodule). Furthermore, the contralateral ROI is more strongly affected by nodular activity than expected: For an uptake ratio of 20 between nodule and rest of thyroid up to 29% of total counts in the ROI over the contralateral lobe are caused by decays in the nodule (AF 3 ml). In the case of the 1ml nodules this effect is smaller: 9-11% (AF) respectively 7-8% (AM). For each phantom, the dependency of count density ratios upon uptake ratios can be modeled well by both linear and quadratic regression (quadratic: r(2)>0.99), yielding sets of parameters which in reverse allow the computation of uptake ratios (and thus dose) from count density ratios. A single regression model obtained by fitting the data of all simulations simultaneously did not provide satisfactory results except for GP, while underestimating the true uptake ratios in AF and overestimating them in AM. The scintigraphic count density ratios depend upon the uptake ratios between nodule and rest of thyroid, upon their volumes, and their respective position in a non-trivial way. Further investigations are required to derive a comprehensive rule to calculate the uptake or dose ratios based on post-therapeutic scintigraphy. Copyright © 2011. Published by Elsevier GmbH.

Relationship between high white blood cell count and insulin resistance (HOMA-IR) in Korean children and adolescents: Korean National Health and Nutrition Examination Survey 2008-2010.

PubMed

Park, J-M; Lee, D-C; Lee, Y-J

2017-05-01

Increasing evidence has indicated that insulin resistance is associated with inflammation. However, few studies have investigated the association between white blood cell (WBC) count and insulin resistance, as measured by a homeostasis model assessment of insulin resistance (HOMA-IR) in a general pediatric population. This study aimed to examine the association between WBC count and insulin resistance as measured by HOMA-IR in a nationally representative sample of children and adolescents. In total, 2761 participants (1479 boys and 1282 girls) aged 10-18 years were selected from the 2008-2010 Korean National Health and Nutrition Examination Survey. Insulin resistance was defined as a HOMA-IR value greater than the 90th percentile. The odds ratios and 95% confidence intervals for insulin resistance were determined using multiple logistic regression analysis. The mean values of most cardiometabolic variables tended to increase proportionally with WBC count quartiles. The prevalence of insulin resistance significantly increased in accordance with WBC count quartiles in both boys and girls. Compared to individuals in the lowest WBC count quartile, the odds ratio for insulin resistance for individuals in the highest quartile was 2.84 in boys and 3.20 in girls, after adjusting for age, systolic blood pressure, body mass index, and waist circumference. A higher WBC count was positively associated with an increased risk of insulin resistance in Korean children and adolescents. This study suggests that WBC count could facilitate the identification of children and adolescents with insulin resistance. Copyright © 2017 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

Experimental validation of a coupled neutron-photon inverse radiation transport solver

NASA Astrophysics Data System (ADS)

Mattingly, John; Mitchell, Dean J.; Harding, Lee T.

2011-10-01

Sandia National Laboratories has developed an inverse radiation transport solver that applies nonlinear regression to coupled neutron-photon deterministic transport models. The inverse solver uses nonlinear regression to fit a radiation transport model to gamma spectrometry and neutron multiplicity counting measurements. The subject of this paper is the experimental validation of that solver. This paper describes a series of experiments conducted with a 4.5 kg sphere of α-phase, weapons-grade plutonium. The source was measured bare and reflected by high-density polyethylene (HDPE) spherical shells with total thicknesses between 1.27 and 15.24 cm. Neutron and photon emissions from the source were measured using three instruments: a gross neutron counter, a portable neutron multiplicity counter, and a high-resolution gamma spectrometer. These measurements were used as input to the inverse radiation transport solver to evaluate the solver's ability to correctly infer the configuration of the source from its measured radiation signatures.

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

A Count Model to Study the Correlates of 60 Min of Daily Physical Activity in Portuguese Children

PubMed Central

Borges, Alessandra; Gomes, Thayse Natacha; Santos, Daniel; Pereira, Sara; dos Santos, Fernanda K.; Chaves, Raquel; Katzmarzyk, Peter T.; Maia, José

2015-01-01

This study aimed to present data on Portuguese children (aged 9–11 years) complying with moderate-to-vigorous physical activity (MVPA) guidelines, and to identify the importance of correlates from multiple domains associated with meeting the guidelines. Physical activity (PA) was objectively assessed by accelerometry throughout seven days on 777 children. A count model using Poisson regression was used to identify the best set of correlates that predicts the variability in meeting the guidelines. Only 3.1% of children met the recommended daily 60 min of MVPA for all seven days of the week. Further, the Cochrane–Armitage chi-square test indicated a linear and negative trend (p < 0.001) from none to all seven days of children complying with the guidelines. The count model explained 22% of the variance in meeting MVPA guidelines daily. Being a girl, having a higher BMI, belonging to families with higher income, sleeping more and taking greater time walking from home to a sporting venue significantly reduced the probability of meeting daily recommended MVPA across the seven days. Furthermore, compared to girls, increasing sleep time in boys increased their chances of compliance with the MVPA recommendations. These results reinforce the relevance of considering different covariates’ roles on PA compliance when designing efficient intervention strategies to promote healthy and active lifestyles in children. PMID:25730296

A count model to study the correlates of 60 min of daily physical activity in Portuguese children.

PubMed

Borges, Alessandra; Gomes, Thayse Natacha; Santos, Daniel; Pereira, Sara; dos Santos, Fernanda K; Chaves, Raquel; Katzmarzyk, Peter T; Maia, José

2015-02-26

This study aimed to present data on Portuguese children (aged 9-11 years) complying with moderate-to-vigorous physical activity (MVPA) guidelines, and to identify the importance of correlates from multiple domains associated with meeting the guidelines. Physical activity (PA) was objectively assessed by accelerometry throughout seven days on 777 children. A count model using Poisson regression was used to identify the best set of correlates that predicts the variability in meeting the guidelines. Only 3.1% of children met the recommended daily 60 min of MVPA for all seven days of the week. Further, the Cochrane-Armitage chi-square test indicated a linear and negative trend (p<0.001) from none to all seven days of children complying with the guidelines. The count model explained 22% of the variance in meeting MVPA guidelines daily. Being a girl, having a higher BMI, belonging to families with higher income, sleeping more and taking greater time walking from home to a sporting venue significantly reduced the probability of meeting daily recommended MVPA across the seven days. Furthermore, compared to girls, increasing sleep time in boys increased their chances of compliance with the MVPA recommendations. These results reinforce the relevance of considering different covariates' roles on PA compliance when designing efficient intervention strategies to promote healthy and active lifestyles in children.

Evaluation of age determination techniques for gray wolves

USGS Publications Warehouse

Landon, D.B.; Waite, C.A.; Peterson, R.O.; Mech, L.D.

1998-01-01

We evaluated tooth wear, cranial suture fusion, closure of the canine pulp cavity, and cementum annuli as methods of age determination for known- and unknown-age gray wolves (Canis lupus) from Alaska, Minnesota, Ontario, and Isle Royale, Michigan. We developed age classes for cranial suture closure and tooth wear. We used measurement data obtained from known-age captive and wild wolves to generate a regression equation to predict age based on the degree of closure of the canine pulp cavity. Cementum annuli were studied in known- and unknown-age animals, and calcified, unstained thin sections were found to provide clear annulus patterns under polarized transmitted light. Annuli counts varied among observers, partly because of variation in the pattern of annuli in different regions of the cementum. This variation emphasizes the need for standardized models of cementum analysis. Cranial suture fusion is of limited utility in age determination, while tooth wear can be used to estimate age of adult wolves within 4 years. Wolves lt 7 years old could be aged to within 13 years with the regression equation for closure of the canine pulp cavity. Although inaccuracy remains a problem, cementum-annulus counts were the most promising means of estimating age for gray wolves.

[Survival time of HIV/AIDS cases and related factors in Beijing, 1995-2015].

PubMed

Li, Y; Wang, J; He, S F; Chen, J; Lu, H Y

2017-11-10

Objective: To analyze the survival time of HIV/AIDS cases and related factors in Beijing from 1995 to 2015. Methods: A retrospective cohort study was conducted to analyze the data of 12 874 HIV/AIDS cases. The data were collected from Chinese HIV/AIDS Comprehensive Information Management System. Life table method was applied to calculate the survival proportion, and Cox proportion hazard regression model were used to identify the factors related with survival time. Results: Among 12 874 HIV/AIDS cases, 303 (2.4%) died of AIDS related diseases; 9 346 (72.6%) received antiretroviral therapy. The average survival time was 226.5 months (95 %CI : 223.0-230.1), and the survival rates of 1, 5, 10, and 15 years were 98.2%, 96.4%, 93.2%, and 91.9% respectively. Multivariate Cox proportion hazard regression model showed that AIDS phase ( HR =1.439, 95 %CI : 1.041-1.989), heterosexual transmission ( HR =1.646, 95 %CI : 1.184-2.289), being married ( HR =2.186, 95 %CI : 1.510-3.164); older age (≥60 years) at diagnosis ( HR =6.608, 95 %CI : 3.546-12.316); lower CD(4)(+)T cell counts at diagnosis (<350 cells/μl) ( HR =8.711, 95 %CI : 5.757-13.181); receiving no antiretroviral therapy (ART) ( HR =18.223, 95 %CI : 13.317-24.937) were the high risk factors influencing the survival of AIDS patients compared with HIV phase, homosexual transmission, being unmarried, younger age (≤30 years), higher CD(4)(+)T cell count (≥350 cell/μl) and receiving ART. Conclusion: The average survival time of HIV/AIDS cases was 226.5 months after diagnoses. Receiving ART, higher CD(4)(+)T cell counts at the first test, HIV phase, younger age, being unmarried and the homosexual transmission were related to the longer survival time of HIV/AIDS cases. Receiving no ART, the lower CD(4)(+)T cell counts at the first test, AIDS phase, older age, being married and heterosexual transmission indicated higher risk of death due to AIDS.

Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks.

PubMed

Hajmeer, M; Basheer, I; Cliver, D O

2006-09-01

Using artificial neural networks (ANNs), a highly accurate model was developed to simulate survival curves of Listeria monocytogenes in chorizos as affected by the initial water activity (a(w0)) of the sausage formulation, temperature (T), and air inflow velocity (F) where the sausages are stored. The ANN-based survival model (R(2)=0.970) outperformed the regression-based cubic model (R(2)=0.851), and as such was used to derive other models (using regression) that allow prediction of the times needed to drop count by 1, 2, 3, and 4 logs (i.e., nD-values, n=1, 2, 3, 4). The nD-value regression models almost perfectly predicted the various times derived from a number of simulated survival curves exhibiting a wide variety of the operating conditions (R(2)=0.990-0.995). The nD-values were found to decrease with decreasing a(w0), and increasing T and F. The influence of a(w0) on nD-values seems to become more significant at some critical value of a(w0), below which the variation is negligible (0.93 for 1D-value, 0.90 for 2D-value, and <0.85 for 3D- and 4D-values). There is greater influence of storage T and F on 3D- and 4D-values than on 1D- and 2D-values.

A prospective study of marine phytoplankton and reported ...

EPA Pesticide Factsheets

BACKGROUND: Blooms of marine phytoplankton may adversely affect human health. The potential public health impact of low-level exposures is not well established, and few prospective cohort studies of recreational exposures to marine phytoplankton have been conducted.OBJECTIVE: We evaluated the association between phytoplankton cell counts and subsequent illness among recreational beachgoers.METHODS:We recruited beachgoers at Boquer6n Beach, Puerto Rico, during the summer of 2009. We conducted interviews at three time points to assess baseline health, water activities, and subsequent illness. Daily water samples were quantitatively assayed for phytoplankton cell count. Logistic regression models, adjusted for age and sex, were used to assess the association between exposure to three categories of phytoplankton concentration and subsequent illness.RESULTS: During 26 study days, 15,726 individuals successfully completed all three interviews. Daily total phytoplankton cell counts ranged from 346 to 2,012 cells/ml (median, 712 cells/ml). The category with the highest (≥75th percentile) total phytoplankton cell count was associated with eye irritation [adjusted odds ratio (OR) = 1.30; 95% confidence interval (Cl): 1.01, 1.66], rash (OR = 1.27; 95% Cl: 1.02, 1.57), and earache (OR = 1.25; 95% Cl: 0.88, 1.77). In phytoplankton group-specific analyses, the category with the highest Cyanobacteria counts was associated with respiratory illness (OR = 1.37; 95% Cl: 1.12, 1

Effects of high CD4 cell counts on death and attrition among HIV patients receiving antiretroviral treatment: an observational cohort study.

PubMed

Tang, Zhenzhu; Pan, Stephen W; Ruan, Yuhua; Liu, Xuanhua; Su, Jinming; Zhu, Qiuying; Shen, Zhiyong; Zhang, Heng; Chen, Yi; Lan, Guanghua; Xing, Hui; Liao, Lingjie; Feng, Yi; Shao, Yiming

2017-06-09

Current WHO guidelines recommend initiating ART regardless of CD4+ cell count. In response, we conducted an observational cohort study to assess the effects of pre-ART CD4+ cell count levels on death, attrition, and death or attrition in HIV treated patients. This large HIV treatment cohort study (n = 49,155) from 2010 to 2015 was conducted in Guangxi, China. We used a Cox regression model to analyze associations between pre-ART CD4+ cell counts and death, attrition, and death or attrition. The average mortality and ART attrition rates among all treated patients were 2.63 deaths and 5.32 attritions per 100 person-years, respectively. Compared to HIV patients with <350 CD4+ cells/mm 3 at ART initiation, HIV patients with >500 CD4+ cells/mm 3 at ART initiation had a significantly lower mortality rate (Adjusted hazard ratio: 0.56, 95% CI: 0.40-0.79), but significantly higher ART attrition rate (AHR: 1.17, 95% CI: 1.03-1.33). Results from this study suggest that HIV patients with high CD4+ cell counts at the time of ART initiation may be at greater risk of treatment attrition. To further reduce ART attrition, it is imperative that patient education and healthcare provider training on ART adherence be enhanced and account for CD4 levels at ART initiation.

Walking as a Contributor to Physical Activity in Healthy Older Adults: 2 Week Longitudinal Study Using Accelerometry and the Doubly Labeled Water Method

PubMed Central

Bonomi, Alberto G; Westerterp, Klaas R

2016-01-01

Background Physical activity is recommended to promote healthy aging. Defining the importance of activities such as walking in achieving higher levels of physical activity might provide indications for interventions. Objective To describe the importance of walking in achieving higher levels of physical activity in older adults. Methods The study included 42 healthy subjects aged between 51 and 84 years (mean body mass index 25.6 kg/m2 [SD 2.6]). Physical activity, walking, and nonwalking activity were monitored with an accelerometer for 2 weeks. Physical activity was quantified by accelerometer-derived activity counts. An algorithm based on template matching and signal power was developed to classify activity counts into nonwalking counts, short walk counts, and long walk counts. Additionally, in a subgroup of 31 subjects energy expenditure was measured using doubly labeled water to derive physical activity level (PAL). Results Subjects had a mean PAL of 1.84 (SD 0.19, range 1.43-2.36). About 20% of the activity time (21% [SD 8]) was spent walking, which accounted for about 40% of the total counts (43% [SD 11]). Short bouts composed 83% (SD 9) of walking time, providing 81% (SD 11) of walking counts. A stepwise regression model to predict PAL included nonwalking counts and short walk counts, explaining 58% of the variance of PAL (standard error of the estimate=0.12). Walking activities produced more counts per minute than nonwalking activities (P<.001). Long walks produced more counts per minute than short walks (P=.001). Nonwalking counts were independent of walking counts (r=−.05, P=.38). Conclusions Walking activities are a major contributor to physical activity in older adults. Walking activities occur at higher intensities than nonwalking activities, which might prevent individuals from engaging in more walking activity. Finally, subjects who engage in more walking activities do not tend to compensate by limiting nonwalking activities. Trial Registration ClinicalTrials.gov NCT01609764; https://clinicaltrials.gov/ct2/show/NCT01609764 (Archived by WebCite at http://www.webcitation.org/6grls0wAp) PMID:27268471

Effects of youth, price, and audience size on alcohol advertising in magazines.

PubMed

Nelson, Jon P; Young, Douglas J

2008-04-01

In this paper, we study the effects of youth readership, price of advertisements, and audience size on alcohol advertising in 35 major magazines. The regressions also account for readership demographics (adult reader age, income, gender, race), magazine characteristics (newsstand sales, number of annual issues), and type of beverage (beer, wine, spirits). Using count data models, the results indicate significant effects for price, audience size, and adult demographics, but fail to support claims that alcohol advertisers target adolescent readers.

Terminal Duct Lobular Unit Involution of the Normal Breast: Implications for Breast Cancer Etiology

PubMed Central

Pfeiffer, Ruth M.; Patel, Deesha A.; Linville, Laura; Brinton, Louise A.; Gierach, Gretchen L.; Yang, Xiaohong R.; Papathomas, Daphne; Visscher, Daniel; Mies, Carolyn; Degnim, Amy C.; Anderson, William F.; Hewitt, Stephen; Khodr, Zeina G.; Clare, Susan E.; Storniolo, Anna Maria; Sherman, Mark E.

2014-01-01

Background Greater degrees of terminal duct lobular unit (TDLU) involution have been linked to lower breast cancer risk; however, factors that influence this process are poorly characterized. Methods To study this question, we developed three reproducible measures that are inversely associated with TDLU involution: TDLU counts, median TDLU span, and median acini counts/TDLU. We determined factors associated with TDLU involution using normal breast tissues from 1938 participants (1369 premenopausal and 569 postmenopausal) ages 18 to 75 years in the Susan G. Komen Tissue Bank at the Indiana University Simon Cancer Center. Multivariable zero-inflated Poisson models were used to estimate relative risks (RRs) and 95% confidence intervals (95% CIs) for factors associated with TDLU counts, and multivariable ordinal logistic regression models were used to estimate odds ratios (ORs) and 95% CIs for factors associated with categories of median TDLU span and acini counts/TDLU. Results All TDLU measures started declining in the third age decade (all measures, two-sided P trend ≤ .001); and all metrics were statistically significantly lower among postmenopausal women. Nulliparous women demonstrated lower TDLU counts compared with uniparous women (among premenopausal women, RR = 0.79, 95% CI = 0.73 to 0.85; among postmenopausal, RR = 0.67, 95% CI = 0.56 to 0.79); however, rates of age-related TDLU decline were faster among parous women. Other factors were related to specific measures of TDLU involution. Conclusion Morphometric analysis of TDLU involution warrants further evaluation to understand the pathogenesis of breast cancer and assessing its role as a progression marker for women with benign biopsies or as an intermediate endpoint in prevention studies. PMID:25274491

Program Implementation of Option B+ at a President's Emergency Plan for AIDS Relief-Supported HIV Clinic Improves Clinical Indicators But Not Retention in Care in Mbarara, Uganda.

PubMed

Miller, Kathleen; Muyindike, Winnie; Matthews, Lynn T; Kanyesigye, Michael; Siedner, Mark J

2017-08-01

2013 WHO guidelines for prevention of mother to child transmission recommend combination antiretroviral therapy (ART) for all pregnant women, regardless of CD4 count (Option B/B+). We conducted a retrospective analysis of data from a government-operated HIV clinic in Mbarara, Uganda before and after implementation of Option B+ to assess the impact on retention in care. We limited our analysis to women not on ART at the time of their first reported pregnancy with CD4 count >350. We fit regression models to estimate relationships between calendar period (Option A vs. Option B+) and the primary outcome of interest, retention in care. One thousand and sixty-two women were included in the analysis. Women were more likely to start ART within 6 months of pregnancy in the Option B+ period (68% vs. 7%, p < 0.0001) and had significantly greater increases in CD4 count 1 year after pregnancy (+172 vs. -5 cells, p < 0.001). However, there was no difference in the proportion of women retained in care 1 year after pregnancy (73% vs. 70%, p = 0.34). In models adjusted for age, distance to clinic, marital status, and CD4 count, Option B+ was associated with a nonsignificant 30% increased odds of retention in care at 1 year [adjusted odds ratio (AOR) = 1.30, 95% CI 0.98-1.73, p = 0.06]. After transition to an Option B+ program, pregnant women with CD4 count >350 were more likely to initiate combination therapy; however, interventions to mitigate losses from HIV care during pregnancy are needed to improve the health of women, children, and families.

Circulating Tumor Cell Counts Are Prognostic of Overall Survival in SWOG S0421: A Phase III Trial of Docetaxel With or Without Atrasentan for Metastatic Castration-Resistant Prostate Cancer

PubMed Central

Goldkorn, Amir; Ely, Benjamin; Quinn, David I.; Tangen, Catherine M.; Fink, Louis M.; Xu, Tong; Twardowski, Przemyslaw; Van Veldhuizen, Peter J.; Agarwal, Neeraj; Carducci, Michael A.; Monk, J. Paul; Datar, Ram H.; Garzotto, Mark; Mack, Philip C.; Lara, Primo; Higano, Celestia S.; Hussain, Maha; Thompson, Ian Murchie; Cote, Richard J.; Vogelzang, Nicholas J.

2014-01-01

Purpose Circulating tumor cell (CTC) enumeration has not been prospectively validated in standard first-line docetaxel treatment for metastatic castration-resistant prostate cancer. We assessed the prognostic value of CTCs for overall survival (OS) and disease response in S0421, a phase III trial of docetaxel plus prednisone with or without atrasentan. Patients and Methods CTCs were enumerated at baseline (day 0) and before cycle two (day 21) using CellSearch. Baseline counts and changes in counts from day 0 to 21 were evaluated for association with OS, prostate-specific antigen (PSA), and RECIST response using Cox regression as well as receiver operator characteristic (ROC) curves, integrated discrimination improvement (IDI) analysis, and regression trees. Results Median day-0 CTC count was five cells per 7.5 mL, and CTCs < versus ≥ five per 7.5 mL were significantly associated with baseline PSA, bone pain, liver disease, hemoglobin, alkaline phosphatase, and subsequent PSA and RECIST response. Median OS was 26 months for < five versus 13 months for ≥ five CTCs per 7.5 mL at day 0 (hazard ratio [HR], 2.74 [adjusting for covariates]). ROC curves had higher areas under the curve for day-0 CTCs than for PSA, and IDI analysis showed that adding day-0 CTCs to baseline PSA and other covariates increased predictive accuracy for survival by 8% to 10%. Regression trees yielded new prognostic subgroups, and rising CTC count from day 0 to 21 was associated with shorter OS (HR, 2.55). Conclusion These data validate the prognostic utility of CTC enumeration in a large docetaxel-based prospective cohort. Baseline CTC counts were prognostic, and rising CTCs at 3 weeks heralded significantly worse OS, potentially serving as an early metric to help redirect and optimize therapy in this clinical setting. PMID:24616308

Circulating tumor cell counts are prognostic of overall survival in SWOG S0421: a phase III trial of docetaxel with or without atrasentan for metastatic castration-resistant prostate cancer.

PubMed

Goldkorn, Amir; Ely, Benjamin; Quinn, David I; Tangen, Catherine M; Fink, Louis M; Xu, Tong; Twardowski, Przemyslaw; Van Veldhuizen, Peter J; Agarwal, Neeraj; Carducci, Michael A; Monk, J Paul; Datar, Ram H; Garzotto, Mark; Mack, Philip C; Lara, Primo; Higano, Celestia S; Hussain, Maha; Thompson, Ian Murchie; Cote, Richard J; Vogelzang, Nicholas J

2014-04-10

Circulating tumor cell (CTC) enumeration has not been prospectively validated in standard first-line docetaxel treatment for metastatic castration-resistant prostate cancer. We assessed the prognostic value of CTCs for overall survival (OS) and disease response in S0421, a phase III trial of docetaxel plus prednisone with or without atrasentan. CTCs were enumerated at baseline (day 0) and before cycle two (day 21) using CellSearch. Baseline counts and changes in counts from day 0 to 21 were evaluated for association with OS, prostate-specific antigen (PSA), and RECIST response using Cox regression as well as receiver operator characteristic (ROC) curves, integrated discrimination improvement (IDI) analysis, and regression trees. Median day-0 CTC count was five cells per 7.5 mL, and CTCs < versus ≥ five per 7.5 mL were significantly associated with baseline PSA, bone pain, liver disease, hemoglobin, alkaline phosphatase, and subsequent PSA and RECIST response. Median OS was 26 months for < five versus 13 months for ≥ five CTCs per 7.5 mL at day 0 (hazard ratio [HR], 2.74 [adjusting for covariates]). ROC curves had higher areas under the curve for day-0 CTCs than for PSA, and IDI analysis showed that adding day-0 CTCs to baseline PSA and other covariates increased predictive accuracy for survival by 8% to 10%. Regression trees yielded new prognostic subgroups, and rising CTC count from day 0 to 21 was associated with shorter OS (HR, 2.55). These data validate the prognostic utility of CTC enumeration in a large docetaxel-based prospective cohort. Baseline CTC counts were prognostic, and rising CTCs at 3 weeks heralded significantly worse OS, potentially serving as an early metric to help redirect and optimize therapy in this clinical setting.

Association Between Bone Marrow Dosimetric Parameters and Acute Hematologic Toxicity in Anal Cancer Patients Treated With Concurrent Chemotherapy and Intensity-Modulated Radiotherapy

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

Mell, Loren K.; Schomas, David A.; Salama, Joseph K.

Purpose: To test the hypothesis that the volume of pelvic bone marrow (PBM) receiving 10 and 20 Gy or more (PBM-V{sub 10} and PBM-V{sub 20}) is associated with acute hematologic toxicity (HT) in anal cancer patients treated with concurrent chemoradiotherapy. Methods and Materials: We analyzed 48 consecutive anal cancer patients treated with concurrent chemotherapy and intensity-modulated radiation therapy. The median radiation dose to gross tumor and regional lymph nodes was 50.4 and 45 Gy, respectively. Pelvic bone marrow was defined as the region extending from the iliac crests to the ischial tuberosities, including the os coxae, lumbosacral spine, and proximalmore » femora. Endpoints included the white blood cell count (WBC), absolute neutrophil count (ANC), hemoglobin, and platelet count nadirs. Regression models with multiple independent predictors were used to test associations between dosimetric parameters and HT. Results: Twenty patients (42%) had Stage T3-4 disease; 15 patients (31%) were node positive. Overall, 27 (56%), 24 (50%), 4 (8%), and 13 (27%) experienced acute Grade 3-4 leukopenia, neutropenia, anemia, and thrombocytopenia, respectively. On multiple regression analysis, increased PBM-V{sub 5}, V{sub 10}, V{sub 15}, and V{sub 20} were significantly associated with decreased WBC and ANC nadirs, as were female gender, decreased body mass index, and increased lumbosacral bone marrow V{sub 10}, V{sub 15}, and V{sub 20} (p < 0.05 for each association). Lymph node positivity was significantly associated with a decreased WBC nadir on multiple regression analysis (p < 0.05). Conclusion: This analysis supports the hypothesis that increased low-dose radiation to PBM is associated with acute HT during chemoradiotherapy for anal cancer. Techniques to limit bone marrow irradiation may reduce HT in anal cancer patients.« less

Population Census of a Large Common Tern Colony with a Small Unmanned Aircraft

PubMed Central

Chabot, Dominique; Craik, Shawn R.; Bird, David M.

2015-01-01

Small unmanned aircraft systems (UAS) may be useful for conducting high-precision, low-disturbance waterbird surveys, but limited data exist on their effectiveness. We evaluated the capacity of a small UAS to census a large (>6,000 nests) coastal Common tern (Sterna hirundo) colony of which ground surveys are particularly disruptive and time-consuming. We compared aerial photographic tern counts to ground nest counts in 45 plots (5-m radius) throughout the colony at three intervals over a nine-day period in order to identify sources of variation and establish a coefficient to estimate nest numbers from UAS surveys. We also compared a full colony ground count to full counts from two UAS surveys conducted the following day. Finally, we compared colony disturbance levels over the course of UAS flights to matched control periods. Linear regressions between aerial and ground counts in plots had very strong correlations in all three comparison periods (R 2 = 0.972–0.989, P < 0.001) and regression coefficients ranged from 0.928–0.977 terns/nest. Full colony aerial counts were 93.6% and 94.0%, respectively, of the ground count. Varying visibility of terns with ground cover, weather conditions and image quality, and changing nest attendance rates throughout incubation were likely sources of variation in aerial detection rates. Optimally timed UAS surveys of Common tern colonies following our method should yield population estimates in the 93–96% range of ground counts. Although the terns were initially disturbed by the UAS flying overhead, they rapidly habituated to it. Overall, we found no evidence of sustained disturbance to the colony by the UAS. We encourage colonial waterbird researchers and managers to consider taking advantage of this burgeoning technology. PMID:25874997

A Time Series Analysis: Weather Factors, Human Migration and Malaria Cases in Endemic Area of Purworejo, Indonesia, 2005–2014

PubMed Central

REJEKI, Dwi Sarwani Sri; NURHAYATI, Nunung; AJI, Budi; MURHANDARWATI, E. Elsa Herdiana; KUSNANTO, Hari

2018-01-01

Background: Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005–2014. Methods: This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. Results: The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. Conclusion: Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection. PMID:29900134

Regression Analysis of Mixed Panel Count Data with Dependent Terminal Events

PubMed Central

Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L.

2017-01-01

Event history studies are commonly conducted in many fields and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data above, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally the methodology is applied to a childhood cancer study that motivated this study. PMID:28098397

Factors Associated with Dental Caries in a Group of American Indian Children at age 36 Months

PubMed Central

Warren, John J.; Blanchette, Derek; Dawson, Deborah V.; Marshall, Teresa A.; Phipps, Kathy R.; Starr, Delores; Drake, David R.

2015-01-01

Objectives Early childhood caries (ECC) is rampant among American Indian children, but there has been relatively little study of this problem. This paper reports on risk factors for caries for a group of American Indian children at age 36 months as part of a longitudinal study. Methods Pregnant women from a Northern Plains Tribal community were recruited to participate in a longitudinal study of caries and caries risk factors. Standardized dental examinations were completed on children and questionnaires were completed by mothers at baseline and when children were 4, 8, 12, 16, 22, 28 and 36 months of age. Examinations were surface-specific for dental caries, and the questionnaires collected data on demographic, dietary and behavioral factors. Non-parametric bivariate tests and logistic regression models were used to identify risk factors for caries at 36 months, and negative binomial regression was used to identify factors related to caries severity (dmf counts). Results Among the 232 children, and caries prevalence for cavitated lesions was 80%, with an additional 15% having only non-cavitated lesions. The mean dmfs was 9.6, and of the total dmfs, nearly 62% of affected surfaces were decayed, 31% were missing, and 7% were filled. Logistic regression identified higher added sugar beverage consumption, younger maternal age at baseline, higher maternal DMFS at baseline, and greater number of people in the household as significant (p<0.05) risk factors. Negative binomial regression found that only maternal DMFS was associated with child dmf counts. Conclusions By the age of 36 months, dental caries is nearly universal in this population of American Indian children. Caries risk factors included sugared beverage consumption, greater household size and maternal factors, but further analyses are needed to better understand caries in this population. PMID:26544674

Construction of a pathological risk model of occult lymph node metastases for prognostication by semi-automated image analysis of tumor budding in early-stage oral squamous cell carcinoma

PubMed Central

Pedersen, Nicklas Juel; Jensen, David Hebbelstrup; Lelkaitis, Giedrius; Kiss, Katalin; Charabi, Birgitte; Specht, Lena; von Buchwald, Christian

2017-01-01

It is challenging to identify at diagnosis those patients with early oral squamous cell carcinoma (OSCC), who have a poor prognosis and those that have a high risk of harboring occult lymph node metastases. The aim of this study was to develop a standardized and objective digital scoring method to evaluate the predictive value of tumor budding. We developed a semi-automated image-analysis algorithm, Digital Tumor Bud Count (DTBC), to evaluate tumor budding. The algorithm was tested in 222 consecutive patients with early-stage OSCC and major endpoints were overall (OS) and progression free survival (PFS). We subsequently constructed and cross-validated a binary logistic regression model and evaluated its clinical utility by decision curve analysis. A high DTBC was an independent predictor of both poor OS and PFS in a multivariate Cox regression model. The logistic regression model was able to identify patients with occult lymph node metastases with an area under the curve (AUC) of 0.83 (95% CI: 0.78–0.89, P <0.001) and a 10-fold cross-validated AUC of 0.79. Compared to other known histopathological risk factors, the DTBC had a higher diagnostic accuracy. The proposed, novel risk model could be used as a guide to identify patients who would benefit from an up-front neck dissection. PMID:28212555

Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression

NASA Astrophysics Data System (ADS)

Winahju, W. S.; Mukarromah, A.; Putri, S.

2015-03-01

Leprosy is a chronic infectious disease caused by bacteria of leprosy (Mycobacterium leprae). Leprosy has become an important thing in Indonesia because its morbidity is quite high. Based on WHO data in 2014, in 2012 Indonesia has the highest number of new leprosy patients after India and Brazil with a contribution of 18.994 people (8.7% of the world). This number makes Indonesia automatically placed as the country with the highest number of leprosy morbidity of ASEAN countries. The province that most contributes to the number of leprosy patients in Indonesia is East Java. There are two kind of leprosy. They consist of pausibacillary and multibacillary. The morbidity of multibacillary leprosy is higher than pausibacillary leprosy. This paper will discuss modeling both of the number of multibacillary and pausibacillary leprosy patients as responses variables. These responses are count variables, so modeling will be conducted by using bivariate poisson regression method. Unit experiment used is in East Java, and predictors involved are: environment, demography, and poverty. The model uses data in 2012, and the result indicates that all predictors influence significantly.

Which Preschool Mathematics Competencies Are Most Predictive of Fifth Grade Achievement?

PubMed

Nguyen, Tutrang; Watts, Tyler W; Duncan, Greg J; Clements, Douglas H; Sarama, Julie S; Wolfe, Christopher; Spitler, Mary Elaine

In an effort to promote best practices regarding mathematics teaching and learning at the preschool level, national advisory panels and organizations have emphasized the importance of children's emergent counting and related competencies, such as the ability to verbally count, maintain one-to-one correspondence, count with cardinality, subitize, and count forward or backward from a given number. However, little research has investigated whether the kind of mathematical knowledge promoted by the various standards documents actually predict later mathematics achievement. The present study uses longitudinal data from a primarily low-income and minority sample of children to examine the extent to which preschool mathematical competencies, specifically basic and advanced counting, predict fifth grade mathematics achievement. Using regression analyses, we find early numeracy abilities to be the strongest predictors of later mathematics achievement, with advanced counting competencies more predictive than basic counting competencies. Our results highlight the significance of preschool mathematics knowledge for future academic achievement.

Enhancing HIV Testing and Treatment among Men Who Have Sex with Men in China: A Pilot Model with Two-Rapid Tests, Single Blood Draw Session, and Intensified Case Management in Six Cities in 2013.

PubMed

Zhang, Dapeng; Lu, Hongyan; Zhuang, Minghua; Wu, Guohui; Yan, Hongjing; Xu, Jun; Wei, Xiaoli; Li, Chengmei; Meng, Sining; Fu, Xiaojing; Qi, Jinlei; Wang, Peng; Luo, Mei; Dai, Min; Yip, Ray; Sun, Jiangping; Wu, Zunyou

2016-01-01

To explore models to improve HIV testing, linkage to care and treatment among men who have sex with men (MSM) in cooperation with community-based organizations (CBOs) in China. We introduced a new model for HIV testing services targeting MSM in six cities in 2013.These models introduced provision of rapid HIV testing by CBO staff and streamlined processes for HIV screening, confirmation of initial reactive screening results, and linkage to care among diagnosed people. We monitored attrition along each step of the continuum of care from screening to treatment and compared program performance between 2012 and 2013. According to the providers of two rapid tests (HIV screening), four different services delivery models were examined in 2013: Model A = first screen at CDC, second at CDC (Model A = CDC+CDC), Model B = first and second screens at CBOs (Model B = CBO+CBO), Model C = first screen at CBO, second at Hospital (Model C = CBO+Hosp), and Model D = first screen at CBO, second at CDC (Model D = CBO+CDC). Logistic regressions were performed to assess advantages of different screening models of case finding and case management. Compared to 2012, the number of HIV screening tests performed for MSM increased 35.8% in 2013 (72,577 in 2013 vs. 53,455 in 2012). We observed a 5.6% increase in proportion of cases screened reactive receiving HIV confirmatory tests (93.9% in 2013 vs. 89.2% in 2012, χ2 = 48.52, p<0.001) and 65% reduction in loss to CD4 cell count tests (15% in 2013 vs. 43% in 2012, χ2 = 628.85, p<0.001). Regarding linkage to care and treatment, the 2013 pilot showed that the Model D had the highest rate of loss between screening reactive and confirmatory test among the four models, with 18.1% fewer receiving a second screening test and a further 5.9% loss among those receiving HIV confirmatory tests. The Model B and the Model C showed lower losses (0.8% and 1.3%) for newly diagnosed HIV positives receiving CD4 cell count tests, and higher rates of HIV positives referred to designated ART hospitals (88.0% and 93.3%) than the Model A and Model D (4.6% and 5.7% for CD4 cell count test, and 68.9% and 64.4% for referring to designated ART hospitals). The proportion of cases where the screening test was reactive that were commenced on ART was highest in Model C; 52.8% of cases commenced on ART compared to 38.9%, 34.2% and 21.1% in Models A, B and D respectively. Using Model A as a reference group, the multivariate logistic regression results also showed the advantages of Models B, C and D, which increased CD4 cell count test, referral to designated ART hospitals and initiation of ART, when controlling for program city and other factors. This study has demonstrated that involvement of CBOs in HIV rapid testing provision, streamlining testing and care procedures and early hospital case management can improve testing, linkage to, and retention in care and treatment among MSM in China.

Health care resource utilization and characteristics of patients with eosinophilic asthma in secondary health care in Finland.

PubMed

Mäkelä, Mika J; Christensen, Helene Nordahl; Karlsson, Antti; Rastogi, Sarang; Kettunen, Kirsi

2018-01-01

Background : Eosinophilic airway inflammation is common in asthma patients and appears to be associated with severe exacerbations and loss of asthma control. Objective : To describe the resource utilization and clinical characteristics of patients with eosinophilic asthma. Design : Asthma patients ≥18 years with ≥1 blood eosinophil count in secondary care (South West Finland) during 2003‒2013 were included. Clinical characteristics (age, lung function, body mass index, and comorbidities) and asthma-related resource utilization (hospital admissions, outpatient visits, and emergency room [ER] visits) were retrieved. Resource utilization rates were compared for patients with blood eosinophil ≤ or >300 cells/μL, using adjusted negative binomial regression models. Results : Overall, 4,357 eligible patients were identified (mean age 60 years, females 68%), of which 1,927 (44%) had >300 eosinophil cells/μL blood. Patients with ≤300 and >300 eosinophil counts, exhibited similar clinical characteristics, including advanced age, poor lung function, and overweight. Comorbidities such as pneumonia, sinusitis, and nasal polyps, were more frequent among those with >300 eosinophil cells/μL blood compared with patients with lower counts. Eosinophil counts >300 cells/μL were associated with greater hospital admissions (rate ratio [RR] [95% confidence interval CI]: 1.13 [1.02;1.24]) and outpatient visits (RR [95% CI]: 1.11 [1.03;1.20]) compared with patients with lower eosinophil counts. Rates of ER visits were similar between the patient groups (RR [95% CI]: 0.99 [0.87;1.12]). Conclusions : Hospital admissions and outpatient visits occurred more often for patients with eosinophil counts >300 cells/µL, than for patients with lower eosinophil counts. Routine blood eosinophil screening might be useful to identify patients with an eosinophilic phenotype eligible for more targeted treatments.

Calibration methods influence quantitative material decomposition in photon-counting spectral CT

NASA Astrophysics Data System (ADS)

Curtis, Tyler E.; Roeder, Ryan K.

2017-03-01

Photon-counting detectors and nanoparticle contrast agents can potentially enable molecular imaging and material decomposition in computed tomography (CT). Material decomposition has been investigated using both simulated and acquired data sets. However, the effect of calibration methods on material decomposition has not been systematically investigated. Therefore, the objective of this study was to investigate the influence of the range and number of contrast agent concentrations within a modular calibration phantom on quantitative material decomposition. A commerciallyavailable photon-counting spectral micro-CT (MARS Bioimaging) was used to acquire images with five energy bins selected to normalize photon counts and leverage the contrast agent k-edge. Material basis matrix values were determined using multiple linear regression models and material decomposition was performed using a maximum a posteriori estimator. The accuracy of quantitative material decomposition was evaluated by the root mean squared error (RMSE), specificity, sensitivity, and area under the curve (AUC). An increased maximum concentration (range) in the calibration significantly improved RMSE, specificity and AUC. The effects of an increased number of concentrations in the calibration were not statistically significant for the conditions in this study. The overall results demonstrated that the accuracy of quantitative material decomposition in spectral CT is significantly influenced by calibration methods, which must therefore be carefully considered for the intended diagnostic imaging application.

Phenotypic effects of subclinical paratuberculosis (Johne's disease) in dairy cattle.

PubMed

Pritchard, Tracey C; Coffey, Mike P; Bond, Karen S; Hutchings, Mike R; Wall, Eileen

2017-01-01

The effect of subclinical paratuberculosis (or Johne's disease) risk status on performance, health, and fertility was studied in 58,096 UK Holstein-Friesian cows with 156,837 lactations across lactations 1 to 3. Low-, medium-, and high-risk group categories were allocated to cows determined by a minimum of 4 ELISA milk tests taken at any time during their lactating life. Lactation curves of daily milk, protein, and fat yields and protein and fat percentage, together with log e -transformed somatic cell count, were estimated using a random regression model to quantify differences between risk groups. The effect of subclinical paratuberculosis risk groups on fertility, lactation-average somatic cell count, and mastitis were analyzed using linear regression fitting risk group as a fixed effect. Milk yield losses associated with high-risk cows compared with low-risk cows in lactations 1, 2, and 3 for mean daily yield were 0.34, 1.05, and 1.61kg; likewise, accumulated 305-d yields were 103, 316, and 485kg, respectively. The total loss was 904kg over the first 3 lactations. Protein and fat yield losses associated with high-risk cows were significant, but primarily a feature of decreasing milk yield. Similar trends were observed for both test-day and lactation-average somatic cell count measures with higher somatic cell counts from medium- and high-risk cows compared with low-risk cows, and differences were in almost all cases significant. Likewise, mastitis incidence was significantly higher in high-risk cows compared with low-risk cows in lactations 2 and 3. Whereas the few significant differences between risk groups among fertility traits were inconsistent with no clear trend. These results are expected to be conservative, as some animals that were considered negative may become positive after the timeframe of this study, particularly if the animal was tested when relatively young. However, the magnitude of milk yield losses together with higher somatic cell counts and an increase in mastitis incidence should motivate farmers to implement the appropriate control measures to reduce the spread of the disease. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Effectiveness of an Integrated Approach to HIV and Hypertension Care in Rural South Africa: Controlled Interrupted Time-Series Analysis.

PubMed

Ameh, Soter; Klipstein-Grobusch, Kerstin; Musenge, Eustasius; Kahn, Kathleen; Tollman, Stephen; Gómez-Olivé, Francesc Xavier

2017-08-01

South Africa faces a dual burden of HIV/AIDS and noncommunicable diseases. In 2011, a pilot integrated chronic disease management (ICDM) model was introduced by the National Health Department into selected primary health care (PHC) facilities. The objective of this study was to assess the effectiveness of the ICDM model in controlling patients' CD4 counts (>350 cells/mm) and blood pressure [BP (<140/90 mm Hg)] in PHC facilities in the Bushbuckridge municipality, South Africa. A controlled interrupted time-series study was conducted using the data from patients' clinical records collected multiple times before and after the ICDM model was initiated in PHC facilities in Bushbuckridge. Patients ≥18 years were recruited by proportionate sampling from the pilot (n = 435) and comparing (n = 443) PHC facilities from 2011 to 2013. Health outcomes for patients were retrieved from facility records for 30 months. We performed controlled segmented regression to model the monthly averages of individuals' propensity scores using autoregressive moving average model at 5% significance level. The pilot facilities had 6% greater likelihood of controlling patients' CD4 counts than the comparison facilities (coefficient = 0.057; 95% confidence interval: 0.056 to 0.058; P < 0.001). Compared with the comparison facilities, the pilot facilities had 1.0% greater likelihood of controlling patients' BP (coefficient = 0.010; 95% confidence interval: 0.003 to 0.016; P = 0.002). Application of the model had a small effect in controlling patients' CD4 counts and BP, but showed no overall clinical benefit for the patients; hence, the need to more extensively leverage the HIV program for hypertension treatment.

SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.

PubMed

Wang, Tianyu; Nabavi, Sheida

2018-04-24

Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a data imputation approach, a logistic regression model and a nonparametric method based on the Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model and data imputation are used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated datasets and real datasets to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity. Copyright © 2018 Elsevier Inc. All rights reserved.

Association of BPD and IVH with early neutrophil and white counts in VLBW neonates with gestational age <32 weeks

PubMed Central

Palta, Mari; Sadek-Badawi, Mona; Carlton, David P

2008-01-01

Objectives To investigate associations between early low neutrophil count from routine blood samples, white blood count (WBC), pregnancy complications and neonatal outcomes for very low birth weight infants (VLBW ≤1500g) with gestational age <32 weeks. Patients and Methods Information was abstracted on all infants admitted to level III NICUs in Wisconsin 2003-2004. 1002 (78%) had differential and corrected total white counts within 2 ½ hours of birth. Data analyses included frequency tables, binary logistic, ordinal logistc and ordinary regression. Results Low neutrophil count (<1000/μL) was strongly associated with low WBC, pregnancy complications and antenatal steroids. Low neutrophil count predicted bronchopulmonary dysplasia severity level (BPD) (OR: 1.7, 95% CI: 1.1-2.7) and intraventricular hemorrhage (IVH) grade (OR: 2.2, 95% CI: 1.3-3.8). Conclusions Early neutrophil counts may have multiple causes interfering with their routine use as an inflammatory marker. Nonetheless, low neutrophil count has consistent independent associations with outcomes. PMID:18563166

Seasonality and Coronary Heart Disease Deaths in United States Firefighters

PubMed Central

Mbanu, Ibeawuchi; Wellenius, Gregory A.; Mittleman, Murray A.; Peeples, Lynne; Stallings, Leonard A.; Kales, Stefanos N.

2013-01-01

United States firefighters have a high on-duty fatality rate and coronary heart disease is the leading cause. Seasonality affects the incidence of cardiovascular events in the general population, but its effects on firefighters are unknown. We statistically examined the seasonal and annual variation of all on-duty coronary heart disease deaths among US firefighters between 1994 and 2004 using the chi-square distribution and Poisson regression model of the monthly fatality counts. We also examined the effect of ambient temperature (apparent as well as wind chill temperature) on coronary heart disease fatalities during the study span using a time-stratified, case-crossover study design. When grouped by season, we observed the distribution of the 449 coronary heart disease fatalities to show a relative peak in winter (32%) and relative nadir in spring (21%). This pattern was significantly different (p=0.005) from the expected distribution under the null hypothesis where season has no effect. The pattern persisted in additional analyses, stratifying the deaths by the type of duty in which the firefighters were engaged at the time of their deaths. In the Poisson regression model of the monthly fatality counts, the overall goodness-of-fit between the actual and predicted case counts was excellent ( χ42 = 16.63; p = 0.002). Two distinct peaks were detected, one in January-February and the other in August-September. Overall, temperature was not associated with increased risk of on-duty death. After allowing for different effects of temperature in mild/hot versus cold periods, a 1°C increase was not protective in cold weather, nor did it increase the risk of death in warmer weather. The findings of this study reveal statistical evidence for excess coronary heart disease deaths among firefighters during winter; however, the temporal pattern coronary heart disease deaths was not linked to temperature variation. We also found the seasonal pattern to be independent of duty-related risks. PMID:17701682

Unobtrusive measurement of indoor energy expenditure using an infrared sensor-based activity monitoring system.

PubMed

Hwang, Bosun; Han, Jonghee; Choi, Jong Min; Park, Kwang Suk

2008-11-01

The purpose of this study was to develop an unobtrusive energy expenditure (EE) measurement system using an infrared (IR) sensor-based activity monitoring system to measure indoor activities and to estimate individual quantitative EE. IR-sensor activation counts were measured with a Bluetooth-based monitoring system and the standard EE was calculated using an established regression equation. Ten male subjects participated in the experiment and three different EE measurement systems (gas analyzer, accelerometer, IR sensor) were used simultaneously in order to determine the regression equation and evaluate the performance. As a standard measurement, oxygen consumption was simultaneously measured by a portable metabolic system (Metamax 3X, Cortex, Germany). A single room experiment was performed to develop a regression model of the standard EE measurement from the proposed IR sensor-based measurement system. In addition, correlation and regression analyses were done to compare the performance of the IR system with that of the Actigraph system. We determined that our proposed IR-based EE measurement system shows a similar correlation to the Actigraph system with the standard measurement system.

The Calibration of AVHRR/3 Visible Dual Gain Using Meteosat-8 as a MODIS Calibration Transfer Medium

NASA Technical Reports Server (NTRS)

Avey, Lance; Garber, Donald; Nguyen, Louis; Minnis, Patrick

2007-01-01

This viewgraph presentation reviews the NOAA-17 AVHRR visible channels calibrated against MET-8/MODIS using dual gain regression methods. The topics include: 1) Motivation; 2) Methodology; 3) Dual Gain Regression Methods; 4) Examples of Regression methods; 5) AVHRR/3 Regression Strategy; 6) Cross-Calibration Method; 7) Spectral Response Functions; 8) MET8/NOAA-17; 9) Example of gain ratio adjustment; 10) Effect of mixed low/high count FOV; 11) Monitor dual gains over time; and 12) Conclusions

The Use of Linear Instrumental Variables Methods in Health Services Research and Health Economics: A Cautionary Note

PubMed Central

Terza, Joseph V; Bradford, W David; Dismuke, Clara E

2008-01-01

Objective To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings. Data Sources Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data. Study Design Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor. Principle Findings The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods. Conclusions We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity. PMID:18546544

Species associations and habitat influence the range-wide distribution of breeding Canada Geese (Branta canadensis interior) on Western Hudson Bay

USGS Publications Warehouse

Reiter, Matthew E.; Andersen, David E.; Raedeke, Andrew H.; Humburg, Dale D.

2017-01-01

Inter- and intra-specific interactions are potentially important factors influencing the distribution of populations. Aerial survey data, collected during range-wide breeding population surveys for Eastern Prairie Population (EPP) Canada Geese (Branta canadensis interior), 1987–2008, were evaluated to assess factors influencing their nesting distribution. Specifically, associations between nesting Lesser Snow Geese (Chen caerulescens caerulescens) and EPP Canada Geese were quantified; and changes in the spatial distribution of EPP Canada Geese were identified. Mixed-effects Poisson regression models of EPP Canada Goose nest counts were evaluated within a cross-validation framework. The total count of EPP Canada Goose nests varied moderately among years between 1987 and 2008 with no long-term trend; however, the total count of nesting Lesser Snow Geese generally increased. Three models containing factors related to previous EPP Canada Goose nest density (representing recruitment), distance to Hudson Bay (representing brood-habitat), nesting habitat type, and Lesser Snow Goose nest density (inter-specific associations) were the most accurate, improving prediction accuracy by 45% when compared to intercept-only models. EPP Canada Goose nest density varied by habitat type, was negatively associated with distance to coastal brood-rearing areas, and suggested density-dependent intra-specific effects on recruitment. However, a non-linear relationship between Lesser Snow and EPP Canada Goose nest density suggests that as nesting Lesser Snow Geese increase, EPP Canada Geese locally decline and subsequently the spatial distribution of EPP Canada Geese on western Hudson Bay has changed.

Trends in baseline CD4 cell counts and risk factors for late antiretroviral therapy initiation among HIV-positive patients in Shanghai, a retrospective cross-sectional study.

PubMed

Sun, Jianjun; Liu, Li; Shen, Jiayin; Chen, Panpan; Lu, Hongzhou

2017-04-19

There are few studies focus on the factors underlying the late initiation of ART in China. We analyzed the trends in the median CD4 cell counts among different patient groups over time and the risk factors for the late initiation of ART in Shanghai, China. A retrospective cross-sectional survey was made in the Department of Infectious Disease of Shanghai Public Health Clinical Center which is a designated diagnosis and treatment center for HIV-positive patients in Shanghai during the period of January 1st, 2008--June 30th, 2014. Late ART initiation was defined as a CD4 cell count <200 cells/mm 3 or having a clinical AIDS diagnosis prior to ART initiation. Trends in the median CD4 cell count at ART initiation and the proportion of late ART initiation by year were evaluated using Spearman's correlations and Chi-squared methods, respectively. We used a logistic regression model to analyze the risk factors for late ART initiation. The related factors collected in the multivariate model were the patient's age, gender, infection routes and marital status. A total of 3796 patients were analyzed in this study, with a median baseline CD4 cell count of 205 cells/mm 3 [interquartile range: 75-287]. The median CD4 cell counts of patients initiating ART late increased from 76 cells/mm 3 in 2008 to 103 cells/mm 3 in 2014 (p < 0.001), and the proportion of late ART initiation decreased from 80% to 45% (p < 0.001). The risk factors for late ART initiation were male gender, heterosexual transmission and older age (>30 years) (p < 0.001). Notable improvements were made in the median CD4 cell count at ART initiation and the proportion of late ART initiation from 2008 to 2014. However, persons with high risk of HIV exposure who are male, older even heterosexual orientation should be given more opportunities to receive frequently screening, earlier diagnoses and timely treatment.

Evaluating abundance and trends in a Hawaiian avian community using state-space analysis

USGS Publications Warehouse

Camp, Richard J.; Brinck, Kevin W.; Gorresen, P.M.; Paxton, Eben H.

2016-01-01

Estimating population abundances and patterns of change over time are important in both ecology and conservation. Trend assessment typically entails fitting a regression to a time series of abundances to estimate population trajectory. However, changes in abundance estimates from year-to-year across time are due to both true variation in population size (process variation) and variation due to imperfect sampling and model fit. State-space models are a relatively new method that can be used to partition the error components and quantify trends based only on process variation. We compare a state-space modelling approach with a more traditional linear regression approach to assess trends in uncorrected raw counts and detection-corrected abundance estimates of forest birds at Hakalau Forest National Wildlife Refuge, Hawai‘i. Most species demonstrated similar trends using either method. In general, evidence for trends using state-space models was less strong than for linear regression, as measured by estimates of precision. However, while the state-space models may sacrifice precision, the expectation is that these estimates provide a better representation of the real world biological processes of interest because they are partitioning process variation (environmental and demographic variation) and observation variation (sampling and model variation). The state-space approach also provides annual estimates of abundance which can be used by managers to set conservation strategies, and can be linked to factors that vary by year, such as climate, to better understand processes that drive population trends.

Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama.

PubMed

Jacob, Benjamin G; Burkett-Cadena, Nathan D; Luvall, Jeffrey C; Parcak, Sarah H; McClure, Christopher J W; Estep, Laura K; Hill, Geoffrey E; Cupp, Eddie W; Novak, Robert J; Unnasch, Thomas R

2010-02-24

A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2 from QuickBird data encompassing visible and near-infrared (NIR) band information (0.45 to 0.72 microm) acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4 was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that Culex erracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2. For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation (R2 = -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation (R2 = -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationship between total sampled Cx. erracticus data and elevation (R2 = -.4711; p < .0001), with a SD of 11.16, and the total sampled Northern Cardinal data and elevation (R2 = -.5831; p < .0001), SD of 11.42. These data demonstrate that GIS/remote sensing models and spatial statistics can capture space-varying functional relationships between field-sampled mosquito and bird parameters for determining risk for EEEV transmission.

Liquid scintillation counting for /sup 14/C uptake of single algal cells isolated from natural samples

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

Rivkin, R.B.; Seliger, H.H.

1981-07-01

Short term rates of /sup 14/C uptake for single cells and small numbers of isolated algal cells of five phytoplankton species from natural populations were measured by liquid scintillation counting. Regression analysis of uptake rates per cell for cells isolated from unialgal cultures of seven species of dinoflagellates, ranging in volume from ca. 10/sup 3/ to 10/sup 7/ ..mu..m/sup 3/, gave results identical to uptake rates per cell measured by conventional /sup 14/C techniques. Relative standard errors or regression coefficients ranged between 3 and 10%, indicating that for any species there was little variation in photosynthesis per cell.

A statistical model to estimate the impact of a hepatitis A vaccination programme.

PubMed

Oviedo, Manuel; Pilar Muñoz, M; Domínguez, Angela; Borras, Eva; Carmona, Gloria

2008-11-11

A program of routine hepatitis A+B vaccination in preadolescents was introduced in 1998 in Catalonia, a region situated in the northeast of Spain. The objective of this study was to quantify the reduction in the incidence of hepatitis A in order to differentiate the natural reduction of the incidence of hepatitis A from that produced due to the vaccination programme and to predict the evolution of the disease in forthcoming years. A generalized linear model (GLM) using negative binomial regression was used to estimate the incidence rates of hepatitis A in Catalonia by year, age group and vaccination. Introduction of the vaccine reduced cases by 5.5 by year (p-value<0.001), but there was a significant interaction between the year of report and vaccination that smoothed this reduction (p-value<0.001). The reduction was not equal in all age groups, being greater in the 12-18 years age group, which fell from a mean rate of 8.15 per 100,000 person/years in the pre-vaccination period (1992-1998) to 1.4 in the vaccination period (1999-2005). The model predicts the evolution accurately for the group of vaccinated subjects. Negative binomial regression is more appropriate than Poisson regression when observed variance exceeds the observed mean (overdispersed count data), can cause a variable apparently contribute more on the model of what really makes it.

ENSO-based probabilistic forecasts of March-May U.S. tornado and hail activity

NASA Astrophysics Data System (ADS)

Lepore, Chiara; Tippett, Michael K.; Allen, John T.

2017-09-01

Extended logistic regression is used to predict March-May severe convective storm (SCS) activity based on the preceding December-February (DJF) El Niño-Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross-validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state.

Health information exchange and healthcare utilization.

PubMed

Vest, Joshua R

2009-06-01

Health information exchange (HIE) makes previously inaccessible data available to clinicians, resulting in more complete information. This study tested the hypotheses that HIE information access reduced emergency room visits and inpatient hospitalizations for ambulatory care sensitive conditions among medically indigent adults. HIE access was quantified by how frequently system users' accessed patients' data. Encounter counts were modeled using zero inflated binomial regression. HIE was not accessed for 43% of individuals. Patient factors associated with accessed data included: prior utilization, chronic conditions, and age. Higher levels of information access were significantly associated with increased counts of all encounter types. Results indicate system users were more likely to access HIE for patients for whom the information might be considered most beneficial. Ultimately, these results imply that HIE information access did not transform care in the ways many would expect. Expectations in utilization reductions, however logical, may have to be reevaluated or postponed.

Statistical analysis of mixed recurrent event data with application to cancer survivor study

PubMed Central

Zhu, Liang; Tong, Xingwei; Zhao, Hui; Sun, Jianguo; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L.

2014-01-01

Event history studies occur in many fields including economics, medical studies and social science. In such studies concerning some recurrent events, two types of data have been extensively discussed in the literature. One is recurrent event data that arise if study subjects are monitored or observed continuously. In this case, the observed information provides the times of all occurrences of the recurrent events of interest. The other is panel count data, which occur if the subjects are monitored or observed only periodically. This can happen if the continuous observation is too expensive or not practical and in this case, only the numbers of occurrences of the events between subsequent observation times are available. In this paper, we discuss a third type of data, which is a mixture of recurrent event and panel count data and for which there exists little literature. For regression analysis of such data, a marginal mean model is presented and we propose an estimating equation-based approach for estimation of regression parameters. A simulation study is conducted to assess the finite sample performance of the proposed methodology and indicates that it works well for practical situations. Finally it is applied to a motivating study on childhood cancer survivors. PMID:23139023

Development and validation of prognostic models in metastatic breast cancer: a GOCS study.

PubMed

Rabinovich, M; Vallejo, C; Bianco, A; Perez, J; Machiavelli, M; Leone, B; Romero, A; Rodriguez, R; Cuevas, M; Dansky, C

1992-01-01

The significance of several prognostic factors and the magnitude of their influence on response rate and survival were assessed by means of uni- and multivariate analyses in 362 patients with stage IV (UICC) breast carcinoma receiving combination chemotherapy as first systemic treatment over an 8-year period. Univariate analyses identified performance status and prior adjuvant radiotherapy as predictors of objective regression (OR), whereas the performance status, prior chemotherapy and radiotherapy (adjuvants), white blood cells count, SGOT and SGPT levels, and metastatic pattern were significantly correlated to survival. In multivariate analyses favorable characteristics associated to OR were prior adjuvant radiotherapy, no prior chemotherapy and postmenopausal status. Regarding survival, the performance status and visceral involvement were selected by the Cox model. The predictive accuracy of the logistic and the proportional hazards models was retrospectively tested in the training sample, and prospectively in a new population of 126 patients also receiving combined chemotherapy as first treatment for metastatic breast cancer. A certain overfitting to data in the training sample was observed with the regression model for response. However, the discriminative ability of the Cox model for survival was clearly confirmed.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

Sociodemographic, perceived and objective need indicators of mental health treatment use and treatment-seeking intentions among primary care medical patients.

PubMed

Elhai, Jon D; Voorhees, Summer; Ford, Julian D; Min, Kyeong Sam; Frueh, B Christopher

2009-01-30

We explored sociodemographic and illness/need associations with both recent mental healthcare utilization intensity and self-reported behavioral intentions to seek treatment. Data were examined from a community sample of 201 participants presenting for medical appointments at a Midwestern U.S. primary care clinic, in a cross-sectional survey study. Using non-linear regression analyses accounting for the excess of zero values in treatment visit counts, we found that both sociodemographic and illness/need models were significantly predictive of both recent treatment utilization intensity and intentions to seek treatment. Need models added substantial variance in prediction, above and beyond sociodemographic models. Variables with the greatest predictive role in explaining past treatment utilization intensity were greater depression severity, perceived need for treatment, older age, and lower income. Robust variables in predicting intentions to seek treatment were greater depression severity, perceived need for treatment, and more positive treatment attitudes. This study extends research findings on mental health treatment utilization, specifically addressing medical patients and using statistical methods appropriate to examining treatment visit counts, and demonstrates the importance of both objective and subjective illness/need variables in predicting recent service use intensity and intended future utilization.

Maximum ikelihood estimation for the double-count method with independent observers

USGS Publications Warehouse

Manly, Bryan F.J.; McDonald, Lyman L.; Garner, Gerald W.

1996-01-01

Data collected under a double-count protocol during line transect surveys were analyzed using new maximum likelihood methods combined with Akaike's information criterion to provide estimates of the abundance of polar bear (Ursus maritimus Phipps) in a pilot study off the coast of Alaska. Visibility biases were corrected by modeling the detection probabilities using logistic regression functions. Independent variables that influenced the detection probabilities included perpendicular distance of bear groups from the flight line and the number of individuals in the groups. A series of models were considered which vary from (1) the simplest, where the probability of detection was the same for both observers and was not affected by either distance from the flight line or group size, to (2) models where probability of detection is different for the two observers and depends on both distance from the transect and group size. Estimation procedures are developed for the case when additional variables may affect detection probabilities. The methods are illustrated using data from the pilot polar bear survey and some recommendations are given for design of a survey over the larger Chukchi Sea between Russia and the United States.

"Let Me Count the Ways:" Fostering Reasons for Living among Low-Income, Suicidal, African American Women

ERIC Educational Resources Information Center

West, Lindsey M.; Davis, Telsie A.; Thompson, Martie P.; Kaslow, Nadine J.

2011-01-01

Protective factors for fostering reasons for living were examined among low-income, suicidal, African American women. Bivariate logistic regressions revealed that higher levels of optimism, spiritual well-being, and family social support predicted reasons for living. Multivariate logistic regressions indicated that spiritual well-being showed…

Regression estimators for late-instar gypsy moth larvae at low pupulation densities

Treesearch

W.E. Wallnr; A.S. Devito; Stanley J. Zarnoch

1989-01-01

Two regression estimators were developed for determining densities of late-instar gypsy moth, Lymantria dispar (Lepidoptera: Lymantriidae), larvae from burlap band and pyrethrin spray counts on oak trees in Vermont, Massachusetts, Connecticut, and New York. Studies were conducted by marking larvae on individual burlap banded trees within 15...

Regression analysis of mixed panel count data with dependent terminal events.

PubMed

Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L

2017-05-10

Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

QSAR and docking based semi-synthesis and in vitro evaluation of 18 β-glycyrrhetinic acid derivatives against human lung cancer cell line A-549.

PubMed

Yadav, Dharmendra Kumar; Kalani, Komal; Khan, Feroz; Srivastava, Santosh Kumar

2013-12-01

For the prediction of anticancer activity of glycyrrhetinic acid (GA-1) analogs against the human lung cancer cell line (A-549), a QSAR model was developed by forward stepwise multiple linear regression methodology. The regression coefficient (r(2)) and prediction accuracy (rCV(2)) of the QSAR model were taken 0.94 and 0.82, respectively in terms of correlation. The QSAR study indicates that the dipole moments, size of smallest ring, amine counts, hydroxyl and nitro functional groups are correlated well with cytotoxic activity. The docking studies showed high binding affinity of the predicted active compounds against the lung cancer target EGFR. These active glycyrrhetinic acid derivatives were then semi-synthesized, characterized and in-vitro tested for anticancer activity. The experimental results were in agreement with the predicted values and the ethyl oxalyl derivative of GA-1 (GA-3) showed equal cytotoxic activity to that of standard anticancer drug paclitaxel.

Building a Multivariable Linear Regression Model of On-road Traffic for Creation of High Resolution Emission Inventories

NASA Astrophysics Data System (ADS)

Powell, James Eckhardt

Emissions inventories are an important tool, often built by governments, and used to manage emissions. To build an inventory of urban CO2 emissions and other fossil fuel combustion products in the urban atmosphere, an inventory of on-road traffic is required. In particular, a high resolution inventory is necessary to capture the local characteristics of transport emissions. These emissions vary widely due to the local nature of the fleet, fuel, and roads. Here we show a new model of ADT for the Portland, OR metropolitan region. The backbone is traffic counter recordings made by the Portland Bureau of Transportation at 7,767 sites over 21 years (1986-2006), augmented with PORTAL (The Portland Regional Transportation Archive Listing) freeway traffic count data. We constructed a regression model to fill in traffic network gaps using GIS data such as road class and population density. An EPA-supplied emissions factor was used to estimate transportation CO2 emissions, which is compared to several other estimates for the city's CO2 footprint.

Prediction of Mass Spectral Response Factors from Predicted Chemometric Data for Druglike Molecules

NASA Astrophysics Data System (ADS)

Cramer, Christopher J.; Johnson, Joshua L.; Kamel, Amin M.

2017-02-01

A method is developed for the prediction of mass spectral ion counts of drug-like molecules using in silico calculated chemometric data. Various chemometric data, including polar and molecular surface areas, aqueous solvation free energies, and gas-phase and aqueous proton affinities were computed, and a statistically significant relationship between measured mass spectral ion counts and the combination of aqueous proton affinity and total molecular surface area was identified. In particular, through multilinear regression of ion counts on predicted chemometric data, we find that log10(MS ion counts) = -4.824 + c 1•PA + c 2•SA, where PA is the aqueous proton affinity of the molecule computed at the SMD(aq)/M06-L/MIDI!//M06-L/MIDI! level of electronic structure theory, SA is the total surface area of the molecule in its conjugate base form, and c 1 and c 2 have values of -3.912 × 10-2 mol kcal-1 and 3.682 × 10-3 Å-2. On a 66-molecule training set, this regression exhibits a multiple R value of 0.791 with p values for the intercept, c 1, and c 2 of 1.4 × 10-3, 4.3 × 10-10, and 2.5 × 10-6, respectively. Application of this regression to an 11-molecule test set provides a good correlation of prediction with experiment ( R = 0.905) albeit with a systematic underestimation of about 0.2 log units. This method may prove useful for semiquantitative analysis of drug metabolites for which MS response factors or authentic standards are not readily available.

Social media responses to heat waves.

PubMed

Jung, Jihoon; Uejio, Christopher K

2017-07-01

Social network services (SNSs) may benefit public health by augmenting surveillance and distributing information to the public. In this study, we collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, we examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets suggests that most jurisdictions provide heating instead of cooling energy assistance.

The Impact of Paternal and Maternal Smoking on Semen Quality of Adolescent Men.

PubMed

Axelsson, Jonatan; Rylander, Lars; Rignell-Hydbom, Anna; Silfver, Karl Ågren; Stenqvist, Amelie; Giwercman, Aleksander

2013-01-01

Maternal smoking during pregnancy has been reported to negatively impact sperm counts of the sons. Sufficient data on the effect of paternal smoking is lacking. We wished to elucidate the impact of maternal and paternal smoking during pregnancy and current own smoking on reproductive function of the male offspring. Semen parameters including sperm DNA integrity were analyzed in 295 adolescents from the general population close to Malmö, Sweden, recruited for the study during 2008-2010. Information on maternal smoking was obtained from the Swedish Medical Birth Register, and regarding own and paternal smoking from questionnaires. The impacts of maternal, paternal and own smoking were evaluated in a multivariate regression model and by use of models including interaction terms. Totally, three exposures and five outcomes were evaluated. In maternally unexposed men, paternal smoking was associated with 46% lower total sperm count (95%CI: 21%, 64%) in maternally unexposed men. Both paternal and maternal smoking were associated with a lower sperm concentration (mean differences: 35%; 95%CI: 8.1%, 55% and 36%; 95%CI: 3.9%, 57%, respectively) if the other parent was a non-smoker. No statistically significant impact of own smoking on semen parameters was seen. Prenatal both maternal and paternal smoking were separately associated with some decrease in sperm count in men of whom the other parent was not reported to smoke.

Social media responses to heat waves

NASA Astrophysics Data System (ADS)

Jung, Jihoon; Uejio, Christopher K.

2017-07-01

Social network services (SNSs) may benefit public health by augmenting surveillance and distributing information to the public. In this study, we collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, we examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets suggests that most jurisdictions provide heating instead of cooling energy assistance.

Real-time ArcGIS and heterotrophic plate count based chloramine disinfectant control in water distribution system.

PubMed

Bai, Xiaohui; Zhi, Xinghua; Zhu, Huifeng; Meng, Mingqun; Zhang, Mingde

2015-01-01

This study investigates the effect of chloramine residual on bacteria growth and regrowth and the relationship between heterotrophic plate counts (HPCs) and the concentration of chloramine residual in the Shanghai drinking water distribution system (DWDS). In this study, models to control HPCs in the water distribution system and consumer taps are also developed. Real-time ArcGIS was applied to show the distribution and changed results of the chloramine residual concentration in the pipe system by using these models. Residual regression analysis was used to get a reasonable range of the threshold values that allows the chloramine residual to efficiently inhibit bacteria growth in the Shanghai DWDS; the threshold values should be between 0.45 and 0.5 mg/L in pipe water and 0.2 and 0.25 mg/L in tap water. The low residual chloramine value (0.05 mg/L) of the Chinese drinking water quality standard may pose a potential health risk for microorganisms that should be improved. Disinfection by-products (DBPs) were detected, but no health risk was identified.

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.

Predictive factors of early moderate/severe ovarian hyperstimulation syndrome in non-polycystic ovarian syndrome patients: a statistical model.

PubMed

Ashrafi, Mahnaz; Bahmanabadi, Akram; Akhond, Mohammad Reza; Arabipoor, Arezoo

2015-11-01

To evaluate demographic, medical history and clinical cycle characteristics of infertile non-polycystic ovary syndrome (NPCOS) women with the purpose of investigating their associations with the prevalence of moderate-to-severe OHSS. In this retrospective study, among 7073 in vitro fertilization and/or intracytoplasmic sperm injection (IVF/ICSI) cycles, 86 cases of NPCO patients who developed moderate-to-severe OHSS while being treated with IVF/ICSI cycles were analyzed during the period of January 2008 to December 2010 at Royan Institute. To review the OHSS risk factors, 172 NPCOS patients without developing OHSS, treated at the same period of time, were selected randomly by computer as control group. We used multiple logistic regression in a backward manner to build a prediction model. The regression analysis revealed that the variables, including age [odds ratio (OR) 0.9, confidence interval (CI) 0.81-0.99], antral follicles count (OR 4.3, CI 2.7-6.9), infertility cause (tubal factor, OR 11.5, CI 1.1-51.3), hypothyroidism (OR 3.8, CI 1.5-9.4) and positive history of ovarian surgery (OR 0.2, CI 0.05-0.9) were the most important predictors of OHSS. The regression model had an area under curve of 0.94, presenting an allowable discriminative performance that was equal with two strong predictive variables, including the number of follicles and serum estradiol level on human chorionic gonadotropin day. The predictive regression model based on primary characteristics of NPCOS patients had equal specificity in comparison with two mentioned strong predictive variables. Therefore, it may be beneficial to apply this model before the beginning of ovarian stimulation protocol.

Which Preschool Mathematics Competencies Are Most Predictive of Fifth Grade Achievement?

PubMed Central

Nguyen, Tutrang; Watts, Tyler W.; Duncan, Greg J.; Clements, Douglas H.; Sarama, Julie S.; Wolfe, Christopher; Spitler, Mary Elaine

2016-01-01

In an effort to promote best practices regarding mathematics teaching and learning at the preschool level, national advisory panels and organizations have emphasized the importance of children’s emergent counting and related competencies, such as the ability to verbally count, maintain one-to-one correspondence, count with cardinality, subitize, and count forward or backward from a given number. However, little research has investigated whether the kind of mathematical knowledge promoted by the various standards documents actually predict later mathematics achievement. The present study uses longitudinal data from a primarily low-income and minority sample of children to examine the extent to which preschool mathematical competencies, specifically basic and advanced counting, predict fifth grade mathematics achievement. Using regression analyses, we find early numeracy abilities to be the strongest predictors of later mathematics achievement, with advanced counting competencies more predictive than basic counting competencies. Our results highlight the significance of preschool mathematics knowledge for future academic achievement. PMID:27057084

Association Between White Blood Cell Count Following Radiation Therapy With Radiation Pneumonitis in Non-Small Cell Lung Cancer

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

Tang, Chad; Gomez, Daniel R.; Wang, Hongmei

Purpose: Radiation pneumonitis (RP) is an inflammatory response to radiation therapy (RT). We assessed the association between RP and white blood cell (WBC) count, an established metric of systemic inflammation, after RT for non-small cell lung cancer. Methods and Materials: We retrospectively analyzed 366 patients with non-small cell lung cancer who received ≥60 Gy as definitive therapy. The primary endpoint was whether WBC count after RT (defined as 2 weeks through 3 months after RT completion) was associated with grade ≥3 or grade ≥2 RP. Median lung volume receiving ≥20 Gy (V{sub 20}) was 31%, and post-RT WBC counts rangedmore » from 1.7 to 21.2 × 10{sup 3} WBCs/μL. Odds ratios (ORs) associating clinical variables and post-RT WBC counts with RP were calculated via logistic regression. A recursive-partitioning algorithm was used to define optimal post-RT WBC count cut points. Results: Post-RT WBC counts were significantly higher in patients with grade ≥3 RP than without (P<.05). Optimal cut points for post-RT WBC count were found to be 7.4 and 8.0 × 10{sup 3}/μL for grade ≥3 and ≥2 RP, respectively. Univariate analysis revealed significant associations between post-RT WBC count and grade ≥3 (n=46, OR=2.6, 95% confidence interval [CI] 1.4‒4.9, P=.003) and grade ≥2 RP (n=164, OR=2.0, 95% CI 1.2‒3.4, P=.01). This association held in a stepwise multivariate regression. Of note, V{sub 20} was found to be significantly associated with grade ≥2 RP (OR=2.2, 95% CI 1.2‒3.4, P=.01) and trended toward significance for grade ≥3 RP (OR=1.9, 95% CI 1.0-3.5, P=.06). Conclusions: Post-RT WBC counts were significantly and independently associated with RP and have potential utility as a diagnostic or predictive marker for this toxicity.« less

Estimating equations estimates of trends

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1994-01-01

The North American Breeding Bird Survey monitors changes in bird populations through time using annual counts at fixed survey sites. The usual method of estimating trends has been to use the logarithm of the counts in a regression analysis. It is contended that this procedure is reasonably satisfactory for more abundant species, but produces biased estimates for less abundant species. An alternative estimation procedure based on estimating equations is presented.

Zero-inflated Poisson regression models for QTL mapping applied to tick-resistance in a Gyr × Holstein F2 population

PubMed Central

Silva, Fabyano Fonseca; Tunin, Karen P.; Rosa, Guilherme J.M.; da Silva, Marcos V.B.; Azevedo, Ana Luisa Souza; da Silva Verneque, Rui; Machado, Marco Antonio; Packer, Irineu Umberto

2011-01-01

Now a days, an important and interesting alternative in the control of tick-infestation in cattle is to select resistant animals, and identify the respective quantitative trait loci (QTLs) and DNA markers, for posterior use in breeding programs. The number of ticks/animal is characterized as a discrete-counting trait, which could potentially follow Poisson distribution. However, in the case of an excess of zeros, due to the occurrence of several noninfected animals, zero-inflated Poisson and generalized zero-inflated distribution (GZIP) may provide a better description of the data. Thus, the objective here was to compare through simulation, Poisson and ZIP models (simple and generalized) with classical approaches, for QTL mapping with counting phenotypes under different scenarios, and to apply these approaches to a QTL study of tick resistance in an F2 cattle (Gyr × Holstein) population. It was concluded that, when working with zero-inflated data, it is recommendable to use the generalized and simple ZIP model for analysis. On the other hand, when working with data with zeros, but not zero-inflated, the Poisson model or a data-transformation-approach, such as square-root or Box-Cox transformation, are applicable. PMID:22215960

Using heterogeneity in the population structure of U.S. swine farms to compare transmission models for porcine epidemic diarrhoea

PubMed Central

O’Dea, Eamon B.; Snelson, Harry; Bansal, Shweta

2016-01-01

In 2013, U.S. swine producers were confronted with the disruptive emergence of porcine epidemic diarrhoea (PED). Movement of animals among farms is hypothesised to have played a role in the spread of PED among farms. Via this or other mechanisms, the rate of spread may also depend on the geographic density of farms and climate. To evaluate such effects on a large scale, we analyse state-level counts of outbreaks with variables describing the distribution of farm sizes and types, aggregate flows of animals among farms, and an index of climate. Our first main finding is that it is possible for a correlation analysis to be sensitive to transmission model parameters. This finding is based on a global sensitivity analysis of correlations on simulated data that included a biased and noisy observation model based on the available PED data. Our second main finding is that flows are significantly associated with the reports of PED outbreaks. This finding is based on correlations of pairwise relationships and regression modeling of total and weekly outbreak counts. These findings illustrate how variation in population structure may be employed along with observational data to improve understanding of disease spread. PMID:26947420

Association between breast cancer genetic susceptibility variants and terminal duct lobular unit involution of the breast

PubMed Central

Bodelon, Clara; Oh, Hannah; Chatterjee, Nilanjan; Garcia-Closas, Montserrat; Palakal, Maya; Sherman, Mark E.; Pfeiffer, Ruth M.; Geller, Berta; Vacek, Pamela; Weaver, Donald L.; Chicoine, Rachael; Papathomas, Daphne; Xiang, Jackie; Patel, Deesha A.; Khodr, Zeina G.; Linville, Laura; Clare, Susan E.; Visscher, Daniel W.; Mies, Carolyn; Hewitt, Stephen M.; Brinton, Louise A.; Storniolo, Anna Maria V.; He, Chunyan; Chanock, Stephen J.

2016-01-01

Terminal duct lobular units (TDLUs) are the predominant source of future breast cancers, and lack of TDLU involution (higher TDLU counts, higher acini count per TDLU and the product of the two) is a breast cancer risk factor. Numerous breast cancer susceptibility single nucleotide polymorphisms (SNPs) have been identified, but whether they are associated with TDLU involution is unknown. In a pooled analysis of 872 women from two studies, we investigated 62 established breast cancer SNPs and relationships with TDLU involution. Poisson regression models with robust variance were used to calculate adjusted per-allele relative risks (with the non-breast cancer risk allele as the referent) and 95% confidence intervals between TDLU measures and each SNP. All statistical tests were two-sided; P<0.05 was considered statistically significant. Overall, 36 SNPs (58.1%) were related to higher TDLU counts although this was not statistically significant (P=0.25). Six of the 62 SNPs (9.7%) were nominally associated with at least one TDLU measure: rs616488 (PEX14), rs11242675 (FOXQ1) and rs6001930 (MKL1) were associated with higher TDLU count (P=0.047, 0.045 and 0.031, respectively); rs1353747 (PDE4D) and rs6472903 (8q21.11) were associated with higher acini count per TDLU (P=0.007 and 0.027, respectively); and rs1353747 (PDE4D) and rs204247 (RANBP9) were associated with the product of TDLU and acini counts (P=0.024 and 0.017, respectively). Our findings suggest breast cancer SNPs may not strongly influence TDLU involution. Agnostic genome-wide association studies of TDLU involution may provide new insights on its biologic underpinnings and breast cancer susceptibility. PMID:27859137

AZT Impairs Immunological Recovery on First-line ART: Collaborative analysis of cohort studies in Southern Africa

PubMed Central

WANDELER, Gilles; GSPONER, Thomas; MULENGA, Lloyd; GARONE, Daniela; WOOD, Robin; MASKEW, Mhairi; PROZESKY, Hans; HOFFMANN, Christopher; EHMER, Jochen; DICKINSON, Diana; DAVIES, Mary-Ann; EGGER, Matthias; KEISER, Olivia

2013-01-01

Objectives Zidovudine (AZT) is recommended for first-line antiretroviral therapy (ART) in resource limited settings. AZT may, however, lead to anemia and impaired immunological response. We compared CD4 counts over 5 years between patients starting ART with and without AZT in Southern Africa. Design Cohort study Methods Patients aged ≥16 years who started first-line ART in South Africa, Botswana, Zambia or Lesotho were included. We used linear mixed-effect models to compare CD4 cell count trajectories between patients on AZT-containing regimens and patients on other regimens, censoring follow-up at first treatment change. Impaired immunological recovery, defined as a CD4 count below 100 cells/μl at 1 year, was assessed in logistic regression. Analyses were adjusted for baseline CD4 count and haemoglobin level, age, gender, type of regimen, viral load monitoring and calendar year. Results 72,597 patients starting ART, including 19,758 (27.2%) on AZT, were analysed. Patients on AZT had higher CD4 cell counts (150 vs.128 cells/μl) and haemoglobin level (12.0 vs. 11.0 g/dl) at baseline, and were less likely to be female than those on other regimens. Adjusted differences in CD4 counts between regimens containing and not containing AZT were −16 cells/μl (95% CI −18 to −14) at 1 year and −56 cells/μl (95% CI −59 to −52) at 5 years. Impaired immunological recovery was more likely with AZT compared to other regimens (odds ratio 1.40, 95% CI 1.22–1.61). Conclusions In Southern Africa AZT is associated with inferior immunological recovery compared to other backbones. Replacing AZT with another NRTI could avoid unnecessary switches to second-line ART. PMID:23660577

Highly active antiretroviral therapy including protease inhibitors does not confer a unique CD4 cell benefit. The AVANTI and INCAS Study Groups.

PubMed

2000-07-07

To determine if triple combination therapy, particularly including HIV protease inhibitors (PI), confers an unique immunological benefit that is independent of reductions of plasma viral load (pVL). The correlation between changes from baseline in CD4 cell count and pVL was examined at all time points up to 52 weeks in three randomized clinical trials (AVANTI-2, AVANTI-3 and INCAS) that compared dual nucleoside therapy with triple combination therapy. Individual pVL and CD4 cell counts changes from baseline were entered into multivariate linear regression models for patients receiving double therapy and for those receiving triple therapy including a PI and/or a non-nucleoside reverse transcriptase inhibitor (NNRTI), and the null hypothesis was tested. After 52 weeks of therapy, the relationship between changes from baseline CD4 cell count and pVL was independent of whether patients were assigned double or triple therapy (P = 0.23 and 0.69 for intercept and slope, respectively), or whether patients were assigned triple therapy including a PI or triple therapy including an NNRTI (P = 0.92 and 0.95, respectively). Less than 5% of patients ever had 'discordant' increases in both CD4 cell count and pVL compared with baseline, and this proportion was unrelated to the class of therapy used. 'Discordant' decreases from baseline in both parameters were observed in up to 35% of individuals. The correlation between pVL and CD4 cell count changes from baseline improved over time on therapy, regardless of the therapeutic regimen involved. The data provide no evidence for a CD4 cell count benefit of highly active antiretroviral therapy (HAART) unique to triple therapy or PI-containing regimens.

Mortality in members of HIV-1 serodiscordant couples in Africa and implications for antiretroviral therapy initiation: Results of analyses from a multicenter randomized trial

PubMed Central

2012-01-01

Background The risk of HIV-1 related mortality is strongly related to CD4 count. Guidance on optimal timing for initiation of antiretroviral therapy (ART) is still evolving, but the contribution of HIV-1 infection to excess mortality at CD4 cell counts above thresholds for HIV-1 treatment has not been fully described, especially in resource-poor settings. To compare mortality among HIV-1 infected and uninfected members of HIV-1 serodiscordant couples followed for up to 24 months, we conducted a secondary data analysis examining mortality among HIV-1 serodiscordant couples participating in a multicenter, randomized controlled trial at 14 sites in seven sub-Saharan African countries. Methods Predictors of death were examined using Cox regression and excess mortality by CD4 count and plasma HIV-1 RNA was computed using Poisson regression for correlated data. Results Among 3295 HIV serodiscordant couples, we observed 109 deaths from any cause (74 deaths among HIV-1 infected and 25 among HIV-1 uninfected persons). Among HIV-1 infected persons, the risk of death increased with lower CD4 count and higher plasma viral levels. HIV-1 infected persons had excess mortality due to medical causes of 15.2 deaths/1000 person years at CD4 counts of 250 – 349 cells/μl and 8.9 deaths at CD4 counts of 350 – 499 cells/μl. Above a CD4 count of 500 cells/μl, mortality was comparable among HIV-1 infected and uninfected persons. Conclusions Among African serodiscordant couples, there is a high rate of mortality attributable to HIV-1 infection at CD4 counts above the current threshold (200 – 350 cells/μl) for ART initiation in many African countries. These data indicate that earlier initiation of treatment is likely to provide clinical benefit if further expansion of ART access can be achieved. Trial Registration Clinicaltrials.gov (NCT00194519) PMID:23130818

PSHREG: A SAS macro for proportional and nonproportional subdistribution hazards regression

PubMed Central

Kohl, Maria; Plischke, Max; Leffondré, Karen; Heinze, Georg

2015-01-01

We present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. Our macro first modifies the input data set appropriately and then applies SAS's standard Cox regression procedure, PROC PHREG, using weights and counting-process style of specifying survival times to the modified data set. The modified data set can also be used to estimate cumulative incidence curves for the event of interest. The application of PROC PHREG has several advantages, e.g., it directly enables the user to apply the Firth correction, which has been proposed as a solution to the problem of undefined (infinite) maximum likelihood estimates in Cox regression, frequently encountered in small sample analyses. Deviation from proportional subdistribution hazards can be detected by both inspecting Schoenfeld-type residuals and testing correlation of these residuals with time, or by including interactions of covariates with functions of time. We illustrate application of these extended methods for competing risk regression using our macro, which is freely available at: http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/pshreg, by means of analysis of a real chronic kidney disease study. We discuss differences in features and capabilities of %pshreg and the recent (January 2014) SAS PROC PHREG implementation of proportional subdistribution hazards modelling. PMID:25572709

A multicenter mortality prediction model for patients receiving prolonged mechanical ventilation

PubMed Central

Carson, Shannon S.; Kahn, Jeremy M.; Hough, Catherine L.; Seeley, Eric J.; White, Douglas B.; Douglas, Ivor S.; Cox, Christopher E.; Caldwell, Ellen; Bangdiwala, Shrikant I.; Garrett, Joanne M.; Rubenfeld, Gordon D.

2012-01-01

Objective Significant deficiencies exist in the communication of prognosis for patients requiring prolonged mechanical ventilation after acute illness, in part because of clinician uncertainty about long-term outcomes. We sought to refine a mortality prediction model for patients requiring prolonged ventilation using a multicentered study design. Design Cohort study. Setting Five geographically diverse tertiary care medical centers in the United States (California, Colorado, North Carolina, Pennsylvania, Washington). Patients Two hundred sixty adult patients who received at least 21 days of mechanical ventilation after acute illness. Interventions None. Measurements and Main Results For the probability model, we included age, platelet count, and requirement for vasopressors and/or hemodialysis, each measured on day 21 of mechanical ventilation, in a logistic regression model with 1-yr mortality as the outcome variable. We subsequently modified a simplified prognostic scoring rule (ProVent score) by categorizing the risk variables (age 18–49, 50–64, and >65 yrs; platelet count 0–150 and >150; vasopressors; hemodialysis) in another logistic regression model and assigning points to variables according to β coefficient values. Overall mortality at 1 yr was 48%. The area under the curve of the receiver operator characteristic curve for the primary ProVent probability model was 0.79 (95% confidence interval, 0.75–0.81), and the p value for the Hosmer-Lemeshow goodness-of-fit statistic was .89. The area under the curve for the categorical model was 0.77, and the p value for the goodness-of-fit statistic was .34. The area under the curve for the ProVent score was 0.76, and the p value for the Hosmer-Lemeshow goodness-of-fit statistic was .60. For the 50 patients with a ProVent score >2, only one patient was able to be discharged directly home, and 1-yr mortality was 86%. Conclusion The ProVent probability model is a simple and reproducible model that can accurately identify patients requiring prolonged mechanical ventilation who are at high risk of 1-yr mortality. PMID:22080643

The Relationships between Weather-Related Factors and Daily Outdoor Physical Activity Counts on an Urban Greenway

PubMed Central

Wolff, Dana; Fitzhugh, Eugene C.

2011-01-01

The purpose of this study was to examine relationships between weather and outdoor physical activity (PA). An online weather source was used to obtain daily max temperature [DMT], precipitation, and wind speed. An infra-red trail counter provided data on daily trail use along a greenway, over a 2-year period. Multiple regression analysis was used to examine associations between PA and weather, while controlling for day of the week and month of the year. The overall regression model explained 77.0% of the variance in daily PA (p < 0.001). DMT (b = 10.5), max temp-squared (b = −4.0), precipitation (b = −70.0), and max wind speed (b = 1.9) contributed significantly. Conclusion: Aggregated daily data can detect relationships between weather and outdoor PA. PMID:21556205

Homophily in coauthorship networks of East European sociologists

NASA Astrophysics Data System (ADS)

Hâncean, Marian-Gabriel; Perc, Matjaž

2016-10-01

We study to what degree and how homophily and network properties affect individual citation counts of researchers in the sociology departments of three East European countries, namely Poland, Romania, and Slovenia. We built first-order personal coauthorship networks out of the Web of Science publication records. Each sociologist is assigned as a focal node or ego, while her coauthors are alters. We analyze the data using structural measurements methods, hierarchical regression models, and we make visualizations based on the clustered graph technique. For all three populations, our results indicate that the mean score of the citations of alters substantially predicts the citation counts of egos. In particular, citation similarity increases the chances for coauthorship ties. Evidence for the impact of network properties on the citation levels of egos is mixed. For Poland, normalized ego-betweenness shows a negative effect on citation counts, while network density displays a positive one. For Romania and Slovenia, network characteristics have only a minor impact. Even if the visual summarization of the personal networks uncovers a wide palette of coauthorship patterns, homophily appears to be pervasive. These results are relevant for domestic policy makers who aim to improve the aggregated research performance in East European countries.

Homophily in coauthorship networks of East European sociologists

PubMed Central

Hâncean, Marian-Gabriel; Perc, Matjaž

2016-01-01

We study to what degree and how homophily and network properties affect individual citation counts of researchers in the sociology departments of three East European countries, namely Poland, Romania, and Slovenia. We built first-order personal coauthorship networks out of the Web of Science publication records. Each sociologist is assigned as a focal node or ego, while her coauthors are alters. We analyze the data using structural measurements methods, hierarchical regression models, and we make visualizations based on the clustered graph technique. For all three populations, our results indicate that the mean score of the citations of alters substantially predicts the citation counts of egos. In particular, citation similarity increases the chances for coauthorship ties. Evidence for the impact of network properties on the citation levels of egos is mixed. For Poland, normalized ego-betweenness shows a negative effect on citation counts, while network density displays a positive one. For Romania and Slovenia, network characteristics have only a minor impact. Even if the visual summarization of the personal networks uncovers a wide palette of coauthorship patterns, homophily appears to be pervasive. These results are relevant for domestic policy makers who aim to improve the aggregated research performance in East European countries. PMID:27786271

Comparative study of airborne Alternaria conidia levels in two cities in Castilla-La Mancha (central Spain), and correlations with weather-related variables.

PubMed

Sabariego, Silvia; Bouso, Veronica; Pérez-Badia, Rosa

2012-01-01

Alternaria conidia are among the airborne biological particles known to trigger allergic respiratory diseases. The presented paper reports on a study of seasonal variations in airborne Alternaria conidia concentrations in 2 cities in the central Spanish region of Castilla-La Mancha, Albacete and Toledo. The influence of weather-related variables on airborne conidia levels and distribution was also analysed. Sampling was carried out from 2008-2010 using a Hirst sampler, following the methodology established by the Spanish Aerobiology Network. Annual airborne Alternaria conidia counts were higher in Toledo (annual mean 3,936 conidia) than in Albacete (annual mean 2,268 conidia). Conidia were detected in the air throughout the year, but levels peaked between May-September. Considerable year-on-year variations were recorded both in total annual counts and in seasonal distribution. A significant positive correlation was generally found between mean daily Alternaria counts and both temperature and hours of sunlight, while a significant negative correlation was recorded for relative humidity, daily and cumulative rainfall, and wind speed. Regression models indicated that between 31%-52% of the variation in airborne Alternaria conidia concentrations could be explained by weather-related variables.

HIV-1 disease progression in immune-competent HIV-1-infected and breastfeeding mothers participating in the ANRS 12174 clinical trial in Burkina Faso, South Africa, Uganda and Zambia: a cohort study

PubMed Central

Engebretsen, Ingunn M S; Nagot, Nicolas; Meda, Nicolas Yelbomkan; Vallo, Roselyne; Kankasa, Chipepo; Tumwine, James K; Singata-Madliki, Mandisa; Harper, Kim; Hofmeyr, G Justus; Van de Perre, Philippe; Tylleskär, Thorkild

2018-01-01

Objective We have assessed HIV-1 disease progression among HIV-1-positive mothers in relation to duration of any or exclusive breast feeding in the context of ANRS 12174 trial. Methods The analysis was completed on 203, 212, 272 and 529 HIV-1-positive and lactating mothers with CD4 count >350 cells/µL from Burkina Faso, South Africa, Uganda and Zambia, respectively. The trial compared lamivudine and lopinavir/ritonavir as a peri-exposure prophylaxis during a 50-week follow-up time. A multiple logistic regression model was run with the mothers’ weight, CD4 count and HIV-1 viral load as separate dependent variables, then combined into a dependent composite endpoint called HIV-1 disease progression where HIV-1 viral load was replaced by the HIV-1 clinical stage. Exclusive or predominant breast feeding (EPBF) and any breastfeeding duration were the key explanatory variables. Results In the adjusted model, the associations between EPBF duration and weight change, CD4 cell count and the HIV-1 viral load were consistently insignificant. The CD4 cell count was associated with a significantly higher mothers’ body mass index (BMI; a mean increase of 4.9 (95% CI 2.1 to 7.7) CD4 cells/µL per each additional kilogram per square metre of BMI) and haemoglobin concentration (19.4 (95% CI 11.4 to 27.4) CD4 cells/µL per each additional gram per decilitre of haemoglobin concentration). There was no significant association between EPBF duration and HIV-1 disease progression. A higher education level was a factor associated with a slower HIV-1 disease progression. Conclusion Breast feeding was not a risk factor for a faster progression of HIV-1 disease in mothers of this cohort with a baseline CD4 cell count >350 cells/µL. Trial registration number NCT0064026; Post-results. PMID:29626043

Synergistic and singular effects of river discharge and lunar illumination on dam passage of upstream migrant yellow-phase American eels

USGS Publications Warehouse

Welsh, Stuart A.; Aldinger, Joni L.; Braham, Melissa A.; Zimmerman, Jennifer L.

2016-01-01

Monitoring of dam passage can be useful for management and conservation assessments of American eel, particularly if passage counts can be examined over multiple years. During a 7-year study (2007–2013) of upstream migration of American eels within the lower Shenandoah River (Potomac River drainage), we counted and measured American eels at the Millville Dam eel pass, where annual study periods were determined by the timing of the eel pass installation during spring or summer and removal during fall. Daily American eel counts were analysed with negative binomial regression models, with and without a year (YR) effect, and with the following time-varying environmental covariates: river discharge of the Shenandoah River at Millville (RDM) and of the Potomac River at Point of Rocks, lunar illumination (LI), water temperature, and cloud cover. A total of 17 161 yellow-phase American eels used the pass during the seven annual periods, and length measurements were obtained from 9213 individuals (mean = 294 mm TL, s.e. = 0.49, range 183–594 mm). Data on passage counts of American eels supported an additive-effects model (YR + LI + RDM) where parameter estimates were positive for river discharge (β = 7.3, s.e. = 0.01) and negative for LI (β = −1.9, s.e. = 0.34). Interestingly, RDM and LI acted synergistically and singularly as correlates of upstream migration of American eels, but the highest daily counts and multiple-day passage events were associated with increased RDM. Annual installation of the eel pass during late spring or summer prevented an early spring assessment, a period with higher RDM relative to those values obtained during sampling periods. Because increases in river discharge are climatically controlled events, upstream migration events of American eels within the Potomac River drainage are likely linked to the influence of climate variability on flow regime.

Fine Particulate Air Pollution and Mortality in Nine California Counties: Results from CALFINE

PubMed Central

Ostro, Bart; Broadwin, Rachel; Green, Shelley; Feng, Wen-Ying; Lipsett, Michael

2006-01-01

Many epidemiologic studies provide evidence of an association between daily counts of mortality and ambient particulate matter < 10 μm in diameter (PM10). Relatively few studies, however, have investigated the relationship of mortality with fine particles [PM < 2.5 μm in diameter (PM2.5)], especially in a multicity setting. We examined associations between PM2.5 and daily mortality in nine heavily populated California counties using data from 1999 through 2002. We considered daily counts of all-cause mortality and several cause-specific subcategories (respiratory, cardiovascular, ischemic heart disease, and diabetes). We also examined these associations among several subpopulations, including the elderly (> 65 years of age), males, females, non-high school graduates, whites, and Hispanics. We used Poisson multiple regression models incorporating natural or penalized splines to control for covariates that could affect daily counts of mortality, including time, seasonality, temperature, humidity, and day of the week. We used meta-analyses using random-effects models to pool the observations in all nine counties. The analysis revealed associations of PM2.5 levels with several mortality categories. Specifically, a 10-μg/m3 change in 2-day average PM2.5 concentration corresponded to a 0.6% (95% confidence interval, 0.2–1.0%) increase in all-cause mortality, with similar or greater effect estimates for several other subpopulations and mortality subcategories, including respiratory disease, cardiovascular disease, diabetes, age > 65 years, females, deaths out of the hospital, and non-high school graduates. Results were generally insensitive to model specification and the type of spline model used. This analysis adds to the growing body of evidence linking PM2.5 with daily mortality. PMID:16393654

Neighborhood walkability and particulate air pollution in a nationwide cohort of women.

PubMed

James, Peter; Hart, Jaime E; Laden, Francine

2015-10-01

Features of neighborhoods associated with walkability (i.e., connectivity, accessibility, and density) may also be correlated with levels of ambient air pollution, which would attenuate the health benefits of walkability. We examined the relationship between neighborhood walkability and ambient air pollution in a cross-sectional analysis of a cohort study spanning the entire United States using residence-level exposure assessment for ambient air pollution and the built environment. Using data from the Nurses' Health Study, we used linear regression to estimate the association between a neighborhood walkability index, combining neighborhood intersection count, business count, and population density (defined from Census data, infoUSA business data, and StreetMap USA data), and air pollution, defined from a GIS-based spatiotemporal PM2.5 model. After adjustment for Census tract median income, median home value, and percent with no high school education, the highest tertile of walkability index, intersection count, business count, and population density was associated with a with 1.58 (95% CI 1.54, 1.62), 1.20 (95% CI 1.16, 1.24), 1.31 (95% CI 1.27, 1.35), and 1.84 (95% CI 1.80, 1.88) µg/m(3) higher level of PM2.5 respectively, compared to the lowest tertile. Results varied somewhat by neighborhood socioeconomic status and greatly by region. This nationwide analysis showed a positive relationship between neighborhood walkability and modeled air pollution levels, which were consistent after adjustment for neighborhood-level socioeconomic status. Regional differences in the air pollution-walkability relationship demonstrate that there are factors that vary from region to region that allow for walkable neighborhoods with low levels of air pollution. Copyright © 2015 Elsevier Inc. All rights reserved.

Exposures to Walkability and Particulate Air Pollution in a Nationwide Cohort of Women

PubMed Central

James, Peter; Hart, Jaime E.; Laden, Francine

2015-01-01

Background Features of neighborhoods associated with walkability (i.e., connectivity, accessibility, and density) may also be correlated with levels of ambient air pollution, which would attenuate the health benefits of walkability. Objectives We examined the relationship between neighborhood walkability and ambient air pollution in a cross-sectional analysis of a cohort study spanning the entire United States using residence-level exposure assessment for ambient air pollution and the built environment. Methods Using data from the Nurses’ Health Study, we used linear regression to estimate the association between a neighborhood walkability index, combining neighborhood intersection count, business count, and population density (defined from Census data, infoUSA business data, and StreetMap USA data), and air pollution, defined from a GIS-based spatiotemporal PM2.5 model. Results After adjustment for Census tract median income, median home value, and percent with no high school education, the highest tertile of walkability index, intersection count, business count, and population density was associated with a with 1.58 (95% CI 1.54, 1.62), 1.20 (95% CI 1.16, 1.24), 1.31 (95% CI 1.27, 1.35), and 1.84 (95% CI 1.80, 1.88) μg/m3 higher level of PM2.5 respectively, compared to the lowest tertile. Results varied somewhat by neighborhood socioeconomic status and greatly by region. Conclusions This nationwide analysis showed a positive relationship between neighborhood walkability and modeled air pollution levels, which were consistent after adjustment for neighborhood-level socioeconomic status. Regional differences in the air pollution-walkability relationship demonstrate that there are factors that vary across region that allow for walkable neighborhoods with low levels of air pollution. PMID:26397775

A statistical model for Windstorm Variability over the British Isles based on Large-scale Atmospheric and Oceanic Mechanisms

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Befort, Daniel J.; Wild, Simon B.; Ulbrich, Uwe; Leckebusch, Gregor C.

2016-04-01

Time-clustered winter storms are responsible for a majority of the wind-induced losses in Europe. Over last years, different atmospheric and oceanic large-scale mechanisms as the North Atlantic Oscillation (NAO) or the Meridional Overturning Circulation (MOC) have been proven to drive some significant portion of the windstorm variability over Europe. In this work we systematically investigate the influence of different large-scale natural variability modes: more than 20 indices related to those mechanisms with proven or potential influence on the windstorm frequency variability over Europe - mostly SST- or pressure-based - are derived by means of ECMWF ERA-20C reanalysis during the last century (1902-2009), and compared to the windstorm variability for the European winter (DJF). Windstorms are defined and tracked as in Leckebusch et al. (2008). The derived indices are then employed to develop a statistical procedure including a stepwise Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN), aiming to hindcast the inter-annual (DJF) regional windstorm frequency variability in a case study for the British Isles. This case study reveals 13 indices with a statistically significant coupling with seasonal windstorm counts. The Scandinavian Pattern (SCA) showed the strongest correlation (0.61), followed by the NAO (0.48) and the Polar/Eurasia Pattern (0.46). The obtained indices (standard-normalised) are selected as predictors for a windstorm variability hindcast model applied for the British Isles. First, a stepwise linear regression is performed, to identify which mechanisms can explain windstorm variability best. Finally, the indices retained by the stepwise regression are used to develop a multlayer perceptron-based ANN that hindcasted seasonal windstorm frequency and clustering. Eight indices (SCA, NAO, EA, PDO, W.NAtl.SST, AMO (unsmoothed), EA/WR and Trop.N.Atl SST) are retained by the stepwise regression. Among them, SCA showed the highest linear coefficient, followed by SST in western Atlantic, AMO and NAO. The explanatory regression model (considering all time steps) provided a Coefficient of Determination (R^2) of 0.75. A predictive version of the linear model applying a leave-one-out cross-validation (LOOCV) shows an R2 of 0.56 and a relative RMSE of 4.67 counts/season. An ANN-based nonlinear hindcast model for the seasonal windstorm frequency is developed with the aim to improve the stepwise hindcast ability and thus better predict a time-clustered season over the case study. A 7 node-hidden layer perceptron is set, and the LOOCV procedure reveals a R2 of 0.71. In comparison to the stepwise MLR the RMSE is reduced a 20%. This work shows that for the British Isles case study, most of the interannual variability can be explained by certain large-scale mechanisms, considering also nonlinear effects (ANN). This allows to discern a time-clustered season from a non-clustered one - a key issue for applications e.g., in the (re)insurance industry.

[Factors affecting the DAPI fluorescence direct count in the tidal river sediment].

PubMed

Chen, Chen; Huang, Shan; Wu, Qun-he; Li, Rui-yi; Zhang, Ren-duo

2010-08-01

The factors affecting the DAPI (4', 6-diamidino-2-phenylidole) fluorescence direct count in the tidal river sediment were examined. Sediment samples were collected from the Guangzhou section of the Pearl River. Besides sediment texture and organic matter, an improved staining procedure and the involved parameters were analyzed. Results showed that the procedure with the sediment with 2000 fold dilution and ultrasonic water bath for 10 min, and with a final DAPI concentration of 10 microg x mL(-1) and staining time for more than 30 min produced the optimum results of DAPI direct count in the sediment. The total bacterial number was correlated to the proportion of the non-nucleoid-containing cells to the total bacterial number (r = 0.587, p = 0.004). The organic matter content also correlated to the ration. The clay content had a strong correlation with the organic matter, through which the clay content also affected the ratio. A multiple regression analysis between the ration versus the organic matter, the total bacterial number, and the clay content showed that the regression equation fit the measure values satisfactorily (r = 0.694). These results indicated that the above factors needed to be considered in the applications of the DAPI fluorescence direct counting method to the tidal river sediment.

When to initiate highly active antiretroviral therapy in low-resource settings: the Moroccan experience.

PubMed

Loubiere, Sandrine; el Filal, Kamal Marhoum; Sodqi, Mustapha; Loundou, Anderson; Luchini, Stéphane; Cleary, Susan; Moatti, Jean-Paul; Himmich, Hakima

2008-01-01

The aim of this study was to assess the cost-effectiveness of HIV treatment alternatives - with and without highly active antiretroviral therapy (HAART) - within alternative strata based on the CD4+ T-cell count at the initiation of treatment in a low-resource setting. A retrospective observational study was conducted following 286 HIV-positive individuals admitted to the principal teaching hospital in Casablanca, Morocco, between 1995 and 2002. Patients were stratified by CD4+ T-cell count and regression models were fitted to determine risk of opportunistic infection. Data on healthcare resource use were derived from patient records and were evaluated from the hospital perspective. HAART led to a significant reduction in the number of HIV-related opportunistic infections (P<0.0001), extended survival (61.3 versus 55.2 months; P<0.0001) and reduced hospital stays (P<0.0001) in comparison with care in the absence of HAART. When medical care and drug costs were considered together, HAART was more costly than providing treatment for opportunistic infections. The incremental cost-effectiveness ratio was lower than gross domestic product (GDP) per capita for patients starting HAART with a CD4+ T-cell count <200 cells/mm3, but this increased to nearly three times GDP per capita when HAART was initiated at CD4+ T-cell counts above this threshold. HAART is more cost-effective than treating HIV-related opportunistic infections and, contrary to conclusions drawn in developed countries, HAART is more cost-effective when the CD4+ T-cell count drops to <200 cells/mm3.

White blood cell counts, insulin resistance, vitamin D levels and sarcopenia in Korean elderly men.

PubMed

Kim, Sang-Hwan; Kwon, Hyun Seok; Hwang, Hee-Jin

2017-05-01

Sarcopenia is a major determinant of frailty, disability and mortality in the elderly. Whether low-grade inflammation, insulin resistance and vitamin D are independently associated with sarcopenia remains unclear. In our study, sarcopenia was defined as an appendicular skeletal muscle mass divided by height squared (ASM/Ht 2 ) that was <2 SD below the normal means for young adults. Insulin resistance was estimated using the homeostasis model assessment of insulin resistance (HOMA-IR) index [(insulin (IU/mL) × fasting glucose (mg/dL)/18)/22.5]. Data of white blood cell counts and serum 25-hydroxyvitamin D (25-(OH)D) levels were collected in the second and third year (2008-2009) of Fourth Korean National Health and Nutrition Examination Survey (KNHANES IV). The results showed that the prevalence of sarcopenia in Korean elderly men aged more than 65 years was 11.2%. ASM/Ht 2 were positively associated with vitamin D levels, but negatively associated with white blood cell counts and HOMA-IR by multiple regression analysis. After adjustment for covariables, sarcopenia was associated with the highest quartile of WBC counts (OR = 2.93, 95% CI = 1.21-7.14) and the highest quartile of serum vitamin D levels (OR = 0.38, 95% CI = 0.15-0.95). In conclusion, the study findings suggest that higher WBC counts and lower vitamin D levels are independently associated with the presence of sarcopenia in community-dwelling elderly men. They also provide a basis for further studies of the complex immune-endocrine network in sarcopenia.

Robust inference in the negative binomial regression model with an application to falls data.

PubMed

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

Inter-rater reliability of malaria parasite counts and comparison of methods

PubMed Central

2009-01-01

Background The introduction of artemesinin-based treatment for falciparum malaria has led to a shift away from symptom-based diagnosis. Diagnosis may be achieved by using rapid non-microscopic diagnostic tests (RDTs), of which there are many available. Light microscopy, however, has a central role in parasite identification and quantification and remains the main method of parasite-based diagnosis in clinic and hospital settings and is necessary for monitoring the accuracy of RDTs. The World Health Organization has prepared a proficiency testing panel containing a range of malaria-positive blood samples of known parasitaemia, to be used for the assessment of commercially available malaria RDTs. Different blood film and counting methods may be used for this purpose, which raises questions regarding accuracy and reproducibility. A comparison was made of the established methods for parasitaemia estimation to determine which would give the least inter-rater and inter-method variation Methods Experienced malaria microscopists counted asexual parasitaemia on different slides using three methods; the thin film method using the total erythrocyte count, the thick film method using the total white cell count and the Earle and Perez method. All the slides were stained using Giemsa pH 7.2. Analysis of variance (ANOVA) models were used to find the inter-rater reliability for the different methods. The paired t-test was used to assess any systematic bias between the two methods, and a regression analysis was used to see if there was a changing bias with parasite count level. Results The thin blood film gave parasite counts around 30% higher than those obtained by the thick film and Earle and Perez methods, but exhibited a loss of sensitivity with low parasitaemia. The thick film and Earle and Perez methods showed little or no bias in counts between the two methods, however, estimated inter-rater reliability was slightly better for the thick film method. Conclusion The thin film method gave results closer to the true parasite count but is not feasible at a parasitaemia below 500 parasites per microlitre. The thick film method was both reproducible and practical for this project. The determination of malarial parasitaemia must be applied by skilled operators using standardized techniques. PMID:19939271

Metal ion levels and lymphocyte counts: ASR hip resurfacing prosthesis vs. standard THA

PubMed Central

2013-01-01

Background and purpose Wear particles from metal–on–metal arthroplasties are under suspicion for adverse effects both locally and systemically, and the DePuy ASR Hip Resurfacing System (RHA) has above–average failure rates. We compared lymphocyte counts in RHA and total hip arthroplasty (THA) and investigated whether cobalt and chromium ions affected the lymphocyte counts. Method In a randomized controlled trial, we followed 19 RHA patients and 19 THA patients. Lymphocyte subsets and chromium and cobalt ion concentrations were measured at baseline, at 8 weeks, at 6 months, and at 1 and 2 years. Results The T–lymphocyte counts for both implant types declined over the 2–year period. This decline was statistically significant for CD3+CD8+ in the THA group, with a regression coefficient of –0.04 × 109cells/year (95% CI: –0.08 to –0.01). Regression analysis indicated a depressive effect of cobalt ions in particular on T–cells with 2–year whole–blood cobalt regression coefficients for CD3+ of –0.10 (95% CI: –0.16 to –0.04) × 109 cells/parts per billion (ppb), for CD3+CD4+ of –0.06 (–0.09 to –0.03) × 109 cells/ppb, and for CD3+CD8+ of –0.02 (–0.03 to –0.00) × 109 cells/ppb. Interpretation Circulating T–lymphocyte levels may decline after surgery, regardless of implant type. Metal ions—particularly cobalt—may have a general depressive effect on T– and B–lymphocyte levels. Registered with ClinicalTrials.gov under # NCT01113762 PMID:23597114

A new biodegradation prediction model specific to petroleum hydrocarbons.

PubMed

Howard, Philip; Meylan, William; Aronson, Dallas; Stiteler, William; Tunkel, Jay; Comber, Michael; Parkerton, Thomas F

2005-08-01

A new predictive model for determining quantitative primary biodegradation half-lives of individual petroleum hydrocarbons has been developed. This model uses a fragment-based approach similar to that of several other biodegradation models, such as those within the Biodegradation Probability Program (BIOWIN) estimation program. In the present study, a half-life in days is estimated using multiple linear regression against counts of 31 distinct molecular fragments. The model was developed using a data set consisting of 175 compounds with environmentally relevant experimental data that was divided into training and validation sets. The original fragments from the Ministry of International Trade and Industry BIOWIN model were used initially as structural descriptors and additional fragments were then added to better describe the ring systems found in petroleum hydrocarbons and to adjust for nonlinearity within the experimental data. The training and validation sets had r2 values of 0.91 and 0.81, respectively.

Hierarchical species distribution models

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.

2016-01-01

Determining the distribution pattern of a species is important to increase scientific knowledge, inform management decisions, and conserve biodiversity. To infer spatial and temporal patterns, species distribution models have been developed for use with many sampling designs and types of data. Recently, it has been shown that count, presence-absence, and presence-only data can be conceptualized as arising from a point process distribution. Therefore, it is important to understand properties of the point process distribution. We examine how the hierarchical species distribution modeling framework has been used to incorporate a wide array of regression and theory-based components while accounting for the data collection process and making use of auxiliary information. The hierarchical modeling framework allows us to demonstrate how several commonly used species distribution models can be derived from the point process distribution, highlight areas of potential overlap between different models, and suggest areas where further research is needed.

Visual attention based bag-of-words model for image classification

NASA Astrophysics Data System (ADS)

Wang, Qiwei; Wan, Shouhong; Yue, Lihua; Wang, Che

2014-04-01

Bag-of-words is a classical method for image classification. The core problem is how to count the frequency of the visual words and what visual words to select. In this paper, we propose a visual attention based bag-of-words model (VABOW model) for image classification task. The VABOW model utilizes visual attention method to generate a saliency map, and uses the saliency map as a weighted matrix to instruct the statistic process for the frequency of the visual words. On the other hand, the VABOW model combines shape, color and texture cues and uses L1 regularization logistic regression method to select the most relevant and most efficient features. We compare our approach with traditional bag-of-words based method on two datasets, and the result shows that our VABOW model outperforms the state-of-the-art method for image classification.

Factors associated with dental caries in a group of American Indian children at age 36 months.

PubMed

Warren, John J; Blanchette, Derek; Dawson, Deborah V; Marshall, Teresa A; Phipps, Kathy R; Starr, Delores; Drake, David R

2016-04-01

Early childhood caries (ECC) is rampant among American Indian children, but there has been relatively little study of this problem. This article reports on risk factors for caries for a group of American Indian children at age 36 months as part of a longitudinal study. Pregnant women from a Northern Plains Tribal community were recruited to participate in a longitudinal study of caries and caries risk factors. Standardized dental examinations were completed on children, and questionnaires were completed by mothers at baseline and when children were 4, 8, 12, 16, 22, 28, and 36 months of age. Examinations were surface-specific for dental caries, and the questionnaires collected data on demographic, dietary, and behavioral factors. Nonparametric bivariate tests and logistic regression models were used to identify risk factors for caries at 36 months, and negative binomial regression was used to identify factors related to caries severity (dmf counts). Among the 232 children, and caries prevalence for cavitated lesions was 80%, with an additional 15% having only noncavitated lesions. The mean dmfs was 9.6, and of the total dmfs, nearly 62% of affected surfaces were decayed, 31% were missing, and 7% were filled. Logistic regression identified higher added-sugar beverage consumption, younger maternal age at baseline, higher maternal DMFS at baseline, and greater number of people in the household as significant (P < 0.05) risk factors. Negative binomial regression found that only maternal DMFS was associated with child dmf counts. By the age of 36 months, dental caries is nearly universal in this population of American Indian children. Caries risk factors included sugared beverage consumption, greater household size, and maternal factors, but further analyses are needed to better understand caries in this population. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Surveying woodland hawks with broadcasts of great horned owl vocalization

USGS Publications Warehouse

Mosher, James A.; Fuller, Mark R.

1996-01-01

Pre-recorded vocalizations of great horned owls (Bubo virginianus) broadcast into predominantly wooded habitat along roadside survey routes resulted in as many detections of resident red-shouldered hawks (Buteo lineatus) and Cooper's hawks (Accipiter cooperii) as broadcasts of each conspecific calls. Survey results for 3 species, expressed as average number of contacts/route, were directly related to the number of resident pairs located during systematic searches conducted on foot across the study area. Regression models based on road-transect counts were significant for predicting abundance of red-shouldered hawks, broad-winged hawks (Buteo platypterus), and Cooper's hawks from our study areas.

Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis.

PubMed

Dong, Chunjiao; Clarke, David B; Richards, Stephen H; Huang, Baoshan

2014-01-01

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

A hierarchical model for estimating change in American Woodcock populations

USGS Publications Warehouse

Sauer, J.R.; Link, W.A.; Kendall, W.L.; Kelley, J.R.; Niven, D.K.

2008-01-01

The Singing-Ground Survey (SGS) is a primary source of information on population change for American woodcock (Scolopax minor). We analyzed the SGS using a hierarchical log-linear model and compared the estimates of change and annual indices of abundance to a route regression analysis of SGS data. We also grouped SGS routes into Bird Conservation Regions (BCRs) and estimated population change and annual indices using BCRs within states and provinces as strata. Based on the hierarchical model?based estimates, we concluded that woodcock populations were declining in North America between 1968 and 2006 (trend = -0.9%/yr, 95% credible interval: -1.2, -0.5). Singing-Ground Survey results are generally similar between analytical approaches, but the hierarchical model has several important advantages over the route regression. Hierarchical models better accommodate changes in survey efficiency over time and space by treating strata, years, and observers as random effects in the context of a log-linear model, providing trend estimates that are derived directly from the annual indices. We also conducted a hierarchical model analysis of woodcock data from the Christmas Bird Count and the North American Breeding Bird Survey. All surveys showed general consistency in patterns of population change, but the SGS had the shortest credible intervals. We suggest that population management and conservation planning for woodcock involving interpretation of the SGS use estimates provided by the hierarchical model.

Linking land cover and water quality in New York City's water supply watersheds.

PubMed

Mehaffey, M H; Nash, M S; Wade, T G; Ebert, D W; Jones, K B; Rager, A

2005-08-01

The Catskill/Delaware reservoirs supply 90% of New York City's drinking water. The City has implemented a series of watershed protection measures, including land acquisition, aimed at preserving water quality in the Catskill/Delaware watersheds. The objective of this study was to examine how relationships between landscape and surface water measurements change between years. Thirty-two drainage areas delineated from surface water sample points (total nitrogen, total phosphorus, and fecal coliform bacteria concentrations) were used in step-wise regression analyses to test landscape and surface-water quality relationships. Two measurements of land use, percent agriculture and percent urban development, were positively related to water quality and consistently present in all regression models. Together these two land uses explained 25 to 75% of the regression model variation. However, the contribution of agriculture to water quality condition showed a decreasing trend with time as overall agricultural land cover decreased. Results from this study demonstrate that relationships between land cover and surface water concentrations of total nitrogen, total phosphorus, and fecal coliform bacteria counts over a large area can be evaluated using a relatively simple geographic information system method. Land managers may find this method useful for targeting resources in relation to a particular water quality concern, focusing best management efforts, and maximizing benefits to water quality with minimal costs.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots.

PubMed

Washington, Simon; Haque, Md Mazharul; Oh, Jutaek; Lee, Dongmin

2014-05-01

Hot spot identification (HSID) aims to identify potential sites-roadway segments, intersections, crosswalks, interchanges, ramps, etc.-with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset. Copyright © 2014 Elsevier Ltd. All rights reserved.

Oral candidiasis in systemic lupus erythematosus.

PubMed

Fangtham, M; Magder, L S; Petri, M A

2014-06-01

We assessed the frequency of oral candidiasis and the association between demographic variables, disease-related variables, corticosteroid treatment, other treatments and the occurrence of oral candidiasis in the Hopkins Lupus Cohort. In this large prospective cohort study of 2258 patients with systemic lupus erythematosus (SLE), demographic and clinical associates of oral candidiasis were estimated by univariate, multivariate and within-person regression models. There were 53,548 cohort visits. Oral candidiasis was diagnosed at 675 visits (1.25%) in 325 (14%) of the patients. In the multivariate analyses, oral candidiasis was associated with African-American ethnicity, SELENA-SLEDAI disease activity, high white blood cell count, a history of bacterial infection, prednisone use and immunosuppressive use. The urine protein by urine dip stick was higher in SLE patients with oral candidiasis. Considering only patients who had candidiasis at some visits in a 'within-person' analysis, candidiasis was more frequent in visits with higher SELENA-SLEDAI disease activity, high white blood cell count, proteinuria by urine dip stick, a history of bacterial infection and prednisone use. The use of hydroxychloroquine was associated with a lower risk of oral candidiasis, but was not statistically significant (p = 0.50) in the within-person analysis models. This study identified multiple risk factors for oral candidiasis in SLE. Inspection of the oral cavity for signs of oral candidiasis is recommended especially in SLE patients with active disease, proteinuria, high white blood cell count, taking prednisone, immunosuppressive drugs or antibiotics. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets.

PubMed

Tichy, Diana; Pickl, Julia Maria Anna; Benner, Axel; Sültmann, Holger

2017-03-31

The identification of microRNA (miRNA) target genes is crucial for understanding miRNA function. Many methods for the genome-wide miRNA target identification have been developed in recent years; however, they have several limitations including the dependence on low-confident prediction programs and artificial miRNA manipulations. Ago-RNA immunoprecipitation combined with high-throughput sequencing (Ago-RIP-Seq) is a promising alternative. However, appropriate statistical data analysis algorithms taking into account the experimental design and the inherent noise of such experiments are largely lacking.Here, we investigate the experimental design for Ago-RIP-Seq and examine biostatistical methods to identify de novo miRNA target genes. Statistical approaches considered are either based on a negative binomial model fit to the read count data or applied to transformed data using a normal distribution-based generalized linear model. We compare them by a real data simulation study using plasmode data sets and evaluate the suitability of the approaches to detect true miRNA targets by sensitivity and false discovery rates. Our results suggest that simple approaches like linear regression models on (appropriately) transformed read count data are preferable. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Does "Enhanced Support" for Offenders Effectively Reduce Custodial Violence and Disruption? An Evaluation of the Enhanced Support Service Pilot.

PubMed

Camp, Jake; Joy, Kerry; Freestone, Mark

2018-01-01

This study aimed to examine the effectiveness of The Enhanced Support Service (ESS) pilot in reducing custodial violence and disruption, and the associated costs, by observing the behavioural change of the 35 service users who participated in ESS intervention within its first 22 months of operation. Frequencies of recorded incidents of aggressive behaviours, self-harming behaviours, noncompliance, and positive behaviours were counted from routine administrative systems using a coding structure developed in previous studies. The count data were analysed using nonparametric tests and Poisson regression models to derive an Incident Rate Ratio (IRR). Findings suggest the ESS is associated with a reduction in aggressive behaviours and noncompliance, with medium to large effect sizes ( r = .31-.53); however, it was not associated with a reduction in deliberate self-harm or increased positive behaviours. The Poisson models revealed that levels of pre-intervention behaviour, intervention length, intervention completion, and service location had varying effects on postintervention behaviour, with those who completed intervention demonstrating more favourable outcomes. The ESS service model was associated with a reduction in behaviour that challenges, which has implications for the reduction in associated social, economic, and political costs-as well as the commissioning of interventions and future research in this area.

Associations of White Blood Cell Count,Alanine Aminotransferase,and Aspartate Aminotransferase in the First Trimester withGestational Diabetes Mellitus.

PubMed

Zhao, Li-li; Li, Wei; Ping, Fan; Ma, Liang-kun; Nie, Min

2016-06-10

Objective To explore the associations of white blood cell (WBC) count,alanine aminotransferase (ALT),and aspartate aminotransferase(AST) in the first trimester of pregnancy with gestational diabetes mellitus (GDM). Methods Totally 725 GDM women and 935 women who remained euglycemic throughout pregnancy were enrolled in this study. Pre-pregnancy weight/height were recorded. WBC,ALT,and AST levels were detected between 8 and 12 weeks of pregnancy.At 24 to 28 weeks of pregnancy,the glucose and insulin levels were measured. The WBC,ALT,and AST levels were compared between two groups,and the associations of WBC,ALT,and AST levels with the blood glucose and insulin levels were retrospectively analyzed. Meanwhile,the potential associations of those factors with the occurrence of GDM were analzyed. Results WBC count [9.41(8.15,10.84)?10(9)/L vs. 9.04 (7.64,10.37)?10(9)/L,P=1.0?10(-5)] and ALT levels [18.00(12.00,30.00)U/L vs. 16.00 (11.00,26.00)U/L,P=0.004] in the first trimester of pregnancy were significantly increased in GDM subjects than in normal glucose tolerance(NGT)subjects;however,the AST level showed no significant difference between these two groups [41.00 (26.00,43.00)U/L vs. 41.00 (23.00,43.00)U/L,P=0.588]. Logistic regression analysis illustrated that elevated WBC count was an independent risk factor for GDM after adjustment for age,pre-pregnancy body mass index,blood pressure,and family history of diabetes(OR=1.119,P=0.001). The ROC curve revealed that threshold of WBC count was 7.965?10(9)/L(AUC=0.566,P=1?10(-5)),which had a sensitivity of 79.4% and a specificity of 31.3%. Multivariate linear regression analysis showed that homeostasis model assessment of insulin resistance was positively correlated with WBC count(B=0.051,P=0.022,R(2)=0.083);1-hour blood glucose after oral 50 grams of sugar (B=0.044,P=0.001,R(2)=0.044) and fasting plasma true insulin(B=0.214,P=0.032,R(2)=0.066) were positively correlated with WBC count;1-hour true insulin after 100 grams oral glucose to lerance test(OGTT) was positively correlated with AST (B=0.616,P=1.85?10(-5),R(2)=0.052);2-hour true insulin after 100 grams OGTT was positively correlated with ALT (B=0.148,P=0.027)and AST(B=0.936,P=3.71?10(-8),R(2)=0.077);and 3-hour true insulin after 100 grams oral glucose tolerance test(OGTT) was positively correlated with ALT (B=0.189,P=0.002) and AST (B=0.688,P=7.25?10(-6),R(2)=0.067).Conclusions The WBC count in the first trimester of pregnancy can increase the risk of GDM. Thus,WBC count may be a useful predictors of GDM.

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

PubMed

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have similar performances reaching AUC values 0.783 and 0.779 for traditional Lasso and Tree-Lasso, respectfully. However, information loss of Lasso models is 0.35 bits higher compared to Tree-Lasso model. We propose a method for building predictive models applicable for the detection of readmission risk based on Electronic Health records. Integration of domain knowledge (in the form of ICD-9-CM taxonomy) and a data-driven, sparse predictive algorithm (Tree-Lasso Logistic Regression) resulted in an increase of interpretability of the resulting model. The models are interpreted for the readmission prediction problem in general pediatric population in California, as well as several important subpopulations, and the interpretations of models comply with existing medical understanding of pediatric readmission. Finally, quantitative assessment of the interpretability of the models is given, that is beyond simple counts of selected low-level features. Copyright © 2016 Elsevier B.V. All rights reserved.

A novel Acute Retroviral Syndrome Severity Score predicts the key surrogate markers for HIV-1 disease progression.

PubMed

Braun, Dominique L; Kouyos, Roger; Oberle, Corinna; Grube, Christina; Joos, Beda; Fellay, Jacques; McLaren, Paul J; Kuster, Herbert; Günthard, Huldrych F

2014-01-01

Best long-term practice in primary HIV-1 infection (PHI) remains unknown for the individual. A risk-based scoring system associated with surrogate markers of HIV-1 disease progression could be helpful to stratify patients with PHI at highest risk for HIV-1 disease progression. We prospectively enrolled 290 individuals with well-documented PHI in the Zurich Primary HIV-1 Infection Study, an open-label, non-randomized, observational, single-center study. Patients could choose to undergo early antiretroviral treatment (eART) and stop it after one year of undetectable viremia, to go on with treatment indefinitely, or to defer treatment. For each patient we calculated an a priori defined "Acute Retroviral Syndrome Severity Score" (ARSSS), consisting of clinical and basic laboratory variables, ranging from zero to ten points. We used linear regression models to assess the association between ARSSS and log baseline viral load (VL), baseline CD4+ cell count, and log viral setpoint (sVL) (i.e. VL measured ≥90 days after infection or treatment interruption). Mean ARSSS was 2.89. CD4+ cell count at baseline was negatively correlated with ARSSS (p = 0.03, n = 289), whereas HIV-RNA levels at baseline showed a strong positive correlation with ARSSS (p<0.001, n = 290). In the regression models, a 1-point increase in the score corresponded to a 0.10 log increase in baseline VL and a CD4+ cell count decline of 12/µl, respectively. In patients with PHI and not undergoing eART, higher ARSSS were significantly associated with higher sVL (p = 0.029, n = 64). In contrast, in patients undergoing eART with subsequent structured treatment interruption, no correlation was found between sVL and ARSSS (p = 0.28, n = 40). The ARSSS is a simple clinical score that correlates with the best-validated surrogate markers of HIV-1 disease progression. In regions where ART is not universally available and eART is not standard this score may help identifying patients who will profit the most from early antiretroviral therapy.

Comparisons of citations in Web of Science, Scopus, and Google Scholar for articles published in general medical journals.

PubMed

Kulkarni, Abhaya V; Aziz, Brittany; Shams, Iffat; Busse, Jason W

2009-09-09

Until recently, Web of Science was the only database available to track citation counts for published articles. Other databases are now available, but their relative performance has not been established. To compare the citation count profiles of articles published in general medical journals among the citation databases of Web of Science, Scopus, and Google Scholar. Cohort study of 328 articles published in JAMA, Lancet, or the New England Journal of Medicine between October 1, 1999, and March 31, 2000. Total citation counts for each article up to June 2008 were retrieved from Web of Science, Scopus, and Google Scholar. Article characteristics were analyzed in linear regression models to determine interaction with the databases. Number of citations received by an article since publication and article characteristics associated with citation in databases. Google Scholar and Scopus retrieved more citations per article with a median of 160 (interquartile range [IQR], 83 to 324) and 149 (IQR, 78 to 289), respectively, than Web of Science (median, 122; IQR, 66 to 241) (P < .001 for both comparisons). Compared with Web of Science, Scopus retrieved more citations from non-English-language sources (median, 10.2% vs 4.1%) and reviews (30.8% vs 18.2%), and fewer citations from articles (57.2% vs 70.5%), editorials (2.1% vs 5.9%), and letters (0.8% vs 2.6%) (all P < .001). On a log(10)-transformed scale, fewer citations were found in Google Scholar to articles with declared industry funding (nonstandardized regression coefficient, -0.09; 95% confidence interval [CI], -0.15 to -0.03), reporting a study of a drug or medical device (-0.05; 95% CI, -0.11 to 0.01), or with group authorship (-0.29; 95% CI, -0.35 to -0.23). In multivariable analysis, group authorship was the only characteristic that differed among the databases; Google Scholar had significantly fewer citations to group-authored articles (-0.30; 95% CI, -0.36 to -0.23) compared with Web of Science. Web of Science, Scopus, and Google Scholar produced quantitatively and qualitatively different citation counts for articles published in 3 general medical journals.

Geographically weighted poisson regression semiparametric on modeling of the number of tuberculosis cases (Case study: Bandung city)

NASA Astrophysics Data System (ADS)

Octavianty, Toharudin, Toni; Jaya, I. G. N. Mindra

2017-03-01

Tuberculosis (TB) is a disease caused by a bacterium, called Mycobacterium tuberculosis, which typically attacks the lungs but can also affect the kidney, spine, and brain (Centers for Disease Control and Prevention). Indonesia had the largest number of TB cases after India (Global Tuberculosis Report 2015 by WHO). The distribution of Mycobacterium tuberculosis genotypes in Indonesia showed the high genetic diversity and tended to vary by geographic regions. For instance, in Bandung city, the prevalence rate of TB morbidity is quite high. A number of TB patients belong to the counted data. To determine the factors that significantly influence the number of tuberculosis patients in each location of the observations can be used statistical analysis tool that is Geographically Weighted Poisson Regression Semiparametric (GWPRS). GWPRS is an extension of the Poisson regression and GWPR that is influenced by geographical factors, and there is also variables that influence globally and locally. Using the TB Data in Bandung city (in 2015), the results show that the global and local variables that influence the number of tuberculosis patients in every sub-district.

Impulsivity and emotion dysregulation as predictors of food addiction.

PubMed

Pivarunas, Bernadette; Conner, Bradley T

2015-12-01

Food addiction is the clinical occurrence in which individuals develop physical and psychological dependencies on high fat, high sugar, and highly palatable foods. Past research has demonstrated a number of similarities between food addiction and drug use disorders including the activation of specific brain regions and neurotransmitters, disrupted neuronal circuitry, and behavioral indicators of addiction such as continued use despite negative consequences. The present study examined the role of impulsivity and emotion dysregulation in food addiction as both play salient roles in drug use disorders. Poisson regression analyses using data from 878 undergraduate students revealed negative urgency, the tendency to act impulsively when under distress, and emotion dysregulation positively predicted symptom count on the Yale Food Addiction Scale (Gearhardt, Corbin, & Brownell, 2009) whereas a lack of premeditation negatively predicted symptom count (all ps<0.05). Future research is needed to confirm precursors to eating episodes in food addiction, elucidate causal mechanisms, and support an explanatory model of food addiction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mothers of Obese Children Use More Direct Imperatives to Restrict Eating.

PubMed

Pesch, Megan H; Miller, Alison L; Appugliese, Danielle P; Rosenblum, Katherine L; Lumeng, Julie C

2018-04-01

To examine the association of mother and child characteristics with use of direct imperatives to restrict eating. A total of 237 mother-child dyads (mean child age, 70.9 months) participated in a video-recorded, laboratory-standardized eating protocol with 2 large portions of cupcakes. Videos were reliably coded for counts of maternal direct imperatives to restrict children's eating. Anthropometrics were measured. Regression models tested the association of participant characteristics with counts of direct imperatives. Child obese weight status and maternal white non-Hispanic race/ethnicity were associated with greater levels of direct imperatives to restrict eating (p = .0001 and .0004, respectively). Mothers of obese children may be using more direct imperatives to restrict eating so as to achieve behavioral compliance to decrease their child's food intake. Future work should consider the effects direct imperatives have on children's short- and long-term eating behaviors and weight gain trajectories. Copyright © 2017 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Quantitative Relationship Between AUEC of Absolute Neutrophil Count and Duration of Severe Neutropenia for G-CSF in Breast Cancer Patients.

PubMed

Li, Liang; Ma, Lian; Schrieber, Sarah J; Rahman, Nam Atiqur; Deisseroth, Albert; Farrell, Ann T; Wang, Yaning; Sinha, Vikram; Marathe, Anshu

2018-02-02

The aim of the study was to evaluate the quantitative relationship between duration of severe neutropenia (DSN, the efficacy endpoint) and area under effect curve of absolute neutrophil counts (ANC-AUEC, the pharmacodynamic endpoint), based on data from filgrastim products, a human granulocyte colony-stimulating factor (G-CSF). Clinical data from filgrastim product comparator and test arms of two randomized, parallel-group, phase III studies in breast cancer patients treated with myelosuppressive chemotherapy were utilized. A zero-inflated Poisson regression model best described the negative correlation between DSN and ANC-AUEC. The models predicted that with 10 × 10 9 day/L of increase in ANC-AUEC, the mean DSN would decrease from 1.1 days to 0.93 day in Trial 1 and from 1.2 days to 1.0 day in Trial 2. The findings of the analysis provide useful information regarding the relationship between ANC and DSN that can be used for dose selection and optimization of clinical trial design for G-CSF. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

The effects of age on associations between markers of HIV progression and markers of metabolic function including albumin, haemoglobin and lipid concentrations.

PubMed

Samuel, M; Jose, S; Winston, A; Nelson, M; Johnson, M; Chadwick, D; Fisher, M; Leen, C; Gompels, M; Gilson, R; Post, F A; Hay, P; Sabin, C A

2014-05-01

We investigated whether age modified associations between markers of HIV progression, CD4 T lymphocyte count and HIV RNA viral load (VL), and the following markers of metabolic function: albumin, haemoglobin, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC). A retrospective analysis of data from the United Kingdom Collaborative HIV Cohort was carried out. Analyses were limited to antiretroviral-naïve subjects to focus on the impact of HIV disease itself. A total of 16670 subjects were included in the analysis. Multilevel linear regression models assessed associations between CD4 count/VL and each of the outcomes. Statistical tests for interactions assessed whether associations differed among age groups. After adjustment for gender and ethnicity, there was evidence that lower CD4 count and higher VL were associated with lower TC, LDL-C, haemoglobin and albumin concentrations but higher triglyceride concentrations. Age modified associations between CD4 count and albumin (P < 0.001) and haemoglobin (P = 0.001), but not between CD4 count and HDL-C, LDL-C and TC, or VL and any outcome. Among participants aged < 30, 30-50 and > 50 years, a 50 cells/μL lower CD4 count correlated with a 2.4 [95% confidence interval (CI) 1.7-3.0], 3.6 (95% CI 3.2-4.0) and 5.1 (95% CI 4.0-6.1) g/L lower haemoglobin concentration and a 0.09 (95% CI 0.07-0.11), 0.12 (95% CI 0.11-0.13) and 0.16 (95% CI 0.13-0.19) g/L lower albumin concentration, respectively. We present evidence that age modifies associations between CD4 count and plasma albumin and haemoglobin levels. A given reduction in CD4 count was associated with a greater reduction in haemoglobin and albumin concentrations among older people living with HIV. These findings increase our understanding of how the metabolic impact of HIV is influenced by age. © 2013 The Authors. HIV Medicine published by John Wiley & Sons Ltd on behalf of British HIV Association.

Temporal trends in postseroconversion CD4 cell count and HIV load: the Concerted Action on Seroconversion to AIDS and Death in Europe Collaboration, 1985-2002.

PubMed

Dorrucci, Maria; Rezza, Giovanni; Porter, Kholoud; Phillips, Andrew

2007-02-15

To determine whether early postseroconversion CD4 cell counts and human immunodeficiency virus (HIV) loads have changed over time. Our analysis was based on 22 cohorts of people with known dates of seroconversion from Europe, Australia, and Canada (Concerted Action on Seroconversion to AIDS and Death in Europe Collaboration). We focused on individuals seroconverting between 1985 and 2002 who had the first CD4 cell count (n=3687) or HIV load (n=1584) measured within 2 years of seroconversion and before antiretroviral use. Linear regression models were used to assess time trends in postseroconversion CD4 cell count and HIV load. Trends in time to key thresholds were also assessed, using survival analysis. The overall median initial CD4 cell count was 570 cells/ microL (interquartile range [IQR], 413-780 cells/ microL). The median initial HIV load was 35,542 copies/mL (IQR, 7600-153,050 copies/mL; on log(10) scale, 3.9-5.2 log(10) copies/mL). The postseroconversion CD4 cell count changed by an average of -6.33 cells/ microL/year (95% confidence interval [CI], -8.47 to -4.20 cells/ microL/year; P<.001), whereas an increase was observed in log(10) HIV load (+0.044 log(10) copies/mL/year; 95% CI, +0.034 to +0.053 log(10) copies/mL/year). These trends remained after adjusting for potential confounders. The probability of progressing to a CD4 cell count of <500 cells/ microL by 24 months from seroconversion increased from 0.66 (95% CI, 0.63-0.69) for individuals who seroconverted before 1991 to 0.80 (95% CI, 0.75-0.84) for those who seroconverted during 1999-2002. These data suggest that, in Europe, there has been a trend of decrease in the early CD4 cell count and of increase in the early HIV load. Additional research will be necessary to determine whether similar trends exist in other geographical areas.

Trends in CD4 cell count response to first-line antiretroviral treatment in HIV-positive patients from Asia, 2003-2013: TREAT Asia HIV Observational Database Low Intensity Transfer.

PubMed

De La Mata, Nicole L; Ly, Penh S; Ng, Oon T; Nguyen, Kinh V; Merati, Tuti P; Pham, Thuy T; Lee, Man P; Choi, Jun Y; Sohn, Annette H; Law, Matthew G; Kumarasamy, Nagalingeswaran

2017-11-01

Antiretroviral treatment (ART) guidelines have changed over the past decade, recommending earlier initiation and more tolerable regimens. The study objective was to examine the CD4 response to ART, depending on the year of ART initiation, in HIV-positive patients in the Asia-Pacific. We included HIV-positive adult patients who initiated ART between 2003 and 2013 in our regional cohort from eight urban referral centres in seven countries within Asia. We used mixed-effects linear regression models to evaluate differences in CD4 response by year of ART initiation during 36 months of follow-up, adjusted a priori for other covariates. Overall, 16,962 patients were included. Patients initiating in 2006-9 and 2010-13 had an estimated mean CD4 cell count increase of 8 and 15 cells/µl, respectively, at any given time during the 36-month follow-up, compared to those in 2003-5. The median CD4 cell count at ART initiation also increased from 96 cells/µl in 2003-5 to 173 cells/µl in 2010-13. Our results suggest that the CD4 response to ART is modestly higher for those initiating ART in more recent years. Moreover, fewer patients are presenting with lower absolute CD4 cell counts over time. This is likely to reduce their risk of opportunistic infections and future non-AIDS defining cancers.

Factors influencing platelet clumping during peripheral blood hematopoietic stem cell collection

PubMed Central

Mathur, Gagan; Bell, Sarah L.; Collins, Laura; Nelson, Gail A.; Knudson, C. Michael; Schlueter, Annette J.

2018-01-01

BACKGROUND Platelet clumping is a common occurrence during peripheral blood hematopoietic stem cell (HSC) collection using the Spectra Optia mononuclear cell (MNC) protocol. If clumping persists, it may prevent continuation of the collection and interfere with proper MNC separation. This study is the first to report the incidence of clumping, identify precollection factors associated with platelet clumping, and describe the degree to which platelet clumping interferes with HSC product yield. STUDY DESIGN AND METHODS In total, 258 HSC collections performed on 116 patients using the Optia MNC protocol were reviewed. Collections utilized heparin in anticoagulant citrate dextrose to facilitate large-volume leukapheresis. Linear and logistic regression models were utilized to determine which precollection factors were predictive of platelet clumping and whether clumping was associated with product yield or collection efficiency. RESULTS Platelet clumping was observed in 63% of collections. Multivariable analysis revealed that a lower white blood cell count was an independent predictor of clumping occurrence. Chemotherapy mobilization and a lower peripheral blood CD34+ cell count were predictors of the degree of clumping. Procedures with clumping had higher collection efficiency but lower blood volume processed on average, resulting in no difference in collection yields. Citrate toxicity did not correlate with clumping. CONCLUSION Although platelet clumping is a common technical problem seen during HSC collection, the total CD34+ cell-collection yields were not affected by clumping. WBC count, mobilization approach, and peripheral blood CD34+ cell count can help predict clumping and potentially drive interventions to proactively manage clumping. PMID:28150319

Standardized measures of lobular involution and subsequent breast cancer risk among women with benign breast disease: a nested case-control study.

PubMed

Figueroa, Jonine D; Pfeiffer, Ruth M; Brinton, Louise A; Palakal, Maya M; Degnim, Amy C; Radisky, Derek; Hartmann, Lynn C; Frost, Marlene H; Stallings Mann, Melody L; Papathomas, Daphne; Gierach, Gretchen L; Hewitt, Stephen M; Duggan, Maire A; Visscher, Daniel; Sherman, Mark E

2016-08-01

Lesser degrees of terminal duct-lobular unit (TDLU) involution predict higher breast cancer risk; however, standardized measures to quantitate levels of TDLU involution have only recently been developed. We assessed whether three standardized measures of TDLU involution, with high intra/inter pathologist reproducibility in normal breast tissue, predict subsequent breast cancer risk among women in the Mayo benign breast disease (BBD) cohort. We performed a masked evaluation of biopsies from 99 women with BBD who subsequently developed breast cancer (cases) after a median of 16.9 years and 145 age-matched controls. We assessed three metrics inversely related to TDLU involution: TDLU count/mm(2), median TDLU span (microns, which approximates acini content), and median category of acini counts/TDLU (0-10; 11-20; 21-30; 31-50; >50). Associations with subsequent breast cancer risk for quartiles (or categories of acini counts) of each of these measures were assessed with multivariable conditional logistic regression to estimate odds ratios (ORs) and 95 % confidence intervals (CI). In multivariable models, women in the highest quartile compared to the lowest quartiles of TDLU counts and TDLU span measures were significantly associated with subsequent breast cancer diagnoses; TDLU counts quartile4 versus quartile1, OR = 2.44, 95 %CI 0.96-6.19, p-trend = 0.02; and TDLU spans, quartile4 versus quartile1, OR = 2.83, 95 %CI = 1.13-7.06, p-trend = 0.03. Significant associations with categorical measures of acini counts/TDLU were also observed: compared to women with median category of <10 acini/TDLU, women with >25 acini counts/TDLU were at significantly higher risk, OR = 3.40, 95 %CI 1.03-11.17, p-trend = 0.032. Women with TDLU spans and TDLU count measures above the median were at further increased risk, OR = 3.75 (95 %CI 1.40-10.00, p-trend = 0.008), compared with women below the median for both of these metrics. Similar results were observed for combinatorial metrics of TDLU acini counts/TDLU, and TDLU count. Standardized quantitative measures of TDLU counts and acini counts approximated by TDLU span measures or visually assessed in categories are independently associated with breast cancer risk. Visual assessment of TDLU numbers and acini content, which are highly reproducible between pathologists, could help identify women at high risk for subsequent breast cancer among the million women diagnosed annually with BBD in the US.

Standardized measures of lobular involution and subsequent breast cancer risk among women with benign breast disease: a nested case–control study

PubMed Central

Figueroa, Jonine D.; Pfeiffer, Ruth M.; Brinton, Louise A.; Palakal, Maya M.; Degnim, Amy C.; Radisky, Derek; Hartmann, Lynn C.; Frost, Marlene H.; Mann, Melody L. Stallings; Papathomas, Daphne; Gierach, Gretchen L.; Hewitt, Stephen M.; Duggan, Maire A.; Visscher, Daniel; Sherman, Mark E.

2016-01-01

Lesser degrees of terminal duct-lobular unit (TDLU) involution predict higher breast cancer risk; however, standardized measures to quantitate levels of TDLU involution have only recently been developed. We assessed whether three standardized measures of TDLU involution, with high intra/inter pathologist reproducibility in normal breast tissue, predict subsequent breast cancer risk among women in the Mayo benign breast disease (BBD) cohort. We performed a masked evaluation of biopsies from 99 women with BBD who subsequently developed breast cancer (cases) after a median of 16.9 years and 145 age-matched controls. We assessed three metrics inversely related to TDLU involution: TDLU count/mm2, median TDLU span (microns, which approximates acini content), and median category of acini counts/TDLU (0–10; 11–20; 21–30; 31–50; >50). Associations with subsequent breast cancer risk for quartiles (or categories of acini counts) of each of these measures were assessed with multivariable conditional logistic regression to estimate odds ratios (ORs) and 95 % confidence intervals (CI). In multivariable models, women in the highest quartile compared to the lowest quartiles of TDLU counts and TDLU span measures were significantly associated with subsequent breast cancer diagnoses; TDLU counts quartile4 versus quartile1, OR = 2.44, 95 %CI 0.96–6.19, p-trend = 0.02; and TDLU spans, quartile4 versus quartile1, OR = 2.83, 95 %CI = 1.13–7.06, p-trend = 0.03. Significant associations with categorical measures of acini counts/TDLU were also observed: compared to women with median category of <10 acini/TDLU, women with >25 acini counts/TDLU were at significantly higher risk, OR = 3.40, 95 %CI 1.03–11.17, p-trend = 0.032. Women with TDLU spans and TDLU count measures above the median were at further increased risk, OR = 3.75 (95 %CI 1.40–10.00, p-trend = 0.008), compared with women below the median for both of these metrics. Similar results were observed for combinatorial metrics of TDLU acini counts/TDLU, and TDLU count. Standardized quantitative measures of TDLU counts and acini counts approximated by TDLU span measures or visually assessed in categories are independently associated with breast cancer risk. Visual assessment of TDLU numbers and acini content, which are highly reproducible between pathologists, could help identify women at high risk for subsequent breast cancer among the million women diagnosed annually with BBD in the US. PMID:27488681

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

Roundness variation in JPEG images affects the automated process of nuclear immunohistochemical quantification: correction with a linear regression model.

PubMed

López, Carlos; Jaén Martinez, Joaquín; Lejeune, Marylène; Escrivà, Patricia; Salvadó, Maria T; Pons, Lluis E; Alvaro, Tomás; Baucells, Jordi; García-Rojo, Marcial; Cugat, Xavier; Bosch, Ramón

2009-10-01

The volume of digital image (DI) storage continues to be an important problem in computer-assisted pathology. DI compression enables the size of files to be reduced but with the disadvantage of loss of quality. Previous results indicated that the efficiency of computer-assisted quantification of immunohistochemically stained cell nuclei may be significantly reduced when compressed DIs are used. This study attempts to show, with respect to immunohistochemically stained nuclei, which morphometric parameters may be altered by the different levels of JPEG compression, and the implications of these alterations for automated nuclear counts, and further, develops a method for correcting this discrepancy in the nuclear count. For this purpose, 47 DIs from different tissues were captured in uncompressed TIFF format and converted to 1:3, 1:23 and 1:46 compression JPEG images. Sixty-five positive objects were selected from these images, and six morphological parameters were measured and compared for each object in TIFF images and those of the different compression levels using a set of previously developed and tested macros. Roundness proved to be the only morphological parameter that was significantly affected by image compression. Factors to correct the discrepancy in the roundness estimate were derived from linear regression models for each compression level, thereby eliminating the statistically significant differences between measurements in the equivalent images. These correction factors were incorporated in the automated macros, where they reduced the nuclear quantification differences arising from image compression. Our results demonstrate that it is possible to carry out unbiased automated immunohistochemical nuclear quantification in compressed DIs with a methodology that could be easily incorporated in different systems of digital image analysis.

[Sagittal otolith morphology and the relationship between its mass and the age of Liza haematocheila in the Yangtze Estuary, China].

PubMed

Ji, Yan; Zhao, Feng; Yang, Qin; Ma, Rong Rong; Yang, Gang; Zhang, Tao; Zhuang, Ping

2018-03-01

To examine the relationship of morphological characters of sagittal otolith and the age of Liza haematocheila in the Yangtze Estuary, we analyzed the morphological parameters of 324 pairs of otoliths extracted from 358 L. haematocheila specimens from the Yangtze Estuary in February to June of 2017. The results showed that sagittal otolith had rostrum, antirostrum and obvious central notch. The size and shape of sagittal otolith significantly changed with their growth, from regular melon seeds shape outline to long narrow leaf shape and increasing irregular wavy outline. The average density of sagittal otolith was 1.52 mg·mm -2 . The average rectangularity was 0.68. The length of sagittal otolith was 0.021%-0.047% of entire body length (BL), the width was 0.009%-0.021% of entire BL, and the mass was 0.045‰-0.731‰ of the entire body mass (BM). Otolith length (OL), otolith width (OW) and otolith mass (OM) were all significantly related to the BL, with the determination coefficient for OW and OM model being the highest (R 2 =0.928). The relationship between OM and BL was described best by exponential regression: OM=0.0009BL 1.8737 (R 2 =0.967). The relationships between OM and age (A), BL and A were well fitted by multinomial regressions, respectively: OM=2.9262A 2 +4.8437A+2.1894 (R 2 =0.847), BL=-3.2248A 2 +102.54A+38.373 (R 2 =0.858). In addition, OM was linearly correlated with A. The estimated otolith's ages from the model did not significantly variate from the real ages counting from annulus counts. Therefore, OM could be an effective parameter for the age estimation of L. haematocheila.

Magnitude of efficacy measurements in grass allergy immunotherapy trials is highly dependent on pollen exposure.

PubMed

Durham, S R; Nelson, H S; Nolte, H; Bernstein, D I; Creticos, P S; Li, Z; Andersen, J S

2014-05-01

The objective was to evaluate the association between grass pollen exposure, allergy symptoms and impact on measured treatment effect after grass sublingual immunotherapy (SLIT)-tablet treatment. The association between grass pollen counts and total combined rhinoconjunctivitis symptom and medication score (TCS) was based on a post hoc analysis of data collected over six trials and seven grass pollen seasons across North America and Europe, including 2363 subjects treated with grass SLIT-tablet or placebo. Daily pollen counts were obtained from centralized pollen databases. The effect of treatment on the relationship between the TCS and pollen counts was investigated, and the relative difference between grass SLIT-tablet and placebo as a function of average grass pollen counts was modelled by linear regression. The magnitude of treatment effect based on TCS was greater with higher pollen exposure (P < 0.001). The relative treatment effect in terms of TCS for each trial was correlated with the average grass pollen exposure during the first period of the season, with predicted reduction in TCS = 12% + 0.35% × pollen count (slope significantly different from 0, P = 0.003; R(2)  = 0.66). Corresponding correlations to the entire grass pollen season and to the peak season were equally good, whereas there was a poor correlation between difference in measured efficacy and pollen exposure during the last part of the season. In seasonal allergy trials with grass SLIT-tablet, the observed treatment effect is highly dependent on pollen exposure with the magnitude being greater with higher pollen exposure. This is an important relationship to consider when interpreting individual clinical trial results. © 2014 The Authors. Allergy Published by John Wiley & Sons Ltd.

Magnitude of efficacy measurements in grass allergy immunotherapy trials is highly dependent on pollen exposure

PubMed Central

Durham, S R; Nelson, H S; Nolte, H; Bernstein, D I; Creticos, P S; Li, Z; Andersen, J S

2014-01-01

Background The objective was to evaluate the association between grass pollen exposure, allergy symptoms and impact on measured treatment effect after grass sublingual immunotherapy (SLIT)-tablet treatment. Methods The association between grass pollen counts and total combined rhinoconjunctivitis symptom and medication score (TCS) was based on a post hoc analysis of data collected over six trials and seven grass pollen seasons across North America and Europe, including 2363 subjects treated with grass SLIT-tablet or placebo. Daily pollen counts were obtained from centralized pollen databases. The effect of treatment on the relationship between the TCS and pollen counts was investigated, and the relative difference between grass SLIT-tablet and placebo as a function of average grass pollen counts was modelled by linear regression. Results The magnitude of treatment effect based on TCS was greater with higher pollen exposure (P < 0.001). The relative treatment effect in terms of TCS for each trial was correlated with the average grass pollen exposure during the first period of the season, with predicted reduction in TCS = 12% + 0.35% × pollen count (slope significantly different from 0, P = 0.003; R2 = 0.66). Corresponding correlations to the entire grass pollen season and to the peak season were equally good, whereas there was a poor correlation between difference in measured efficacy and pollen exposure during the last part of the season. Conclusions In seasonal allergy trials with grass SLIT-tablet, the observed treatment effect is highly dependent on pollen exposure with the magnitude being greater with higher pollen exposure. This is an important relationship to consider when interpreting individual clinical trial results. PMID:24605984

Continuous Activity Monitoring During Concurrent Chemoradiotherapy

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

Ohri, Nitin, E-mail: ohri.nitin@gmail.com; Kabarriti, Rafi; Bodner, William R.

Purpose: To perform a prospective trial testing the feasibility and utility of acquiring activity data as a measure of health status during concurrent chemoradiotherapy. Methods and Materials: Ambulatory patients who were planned for treatment with concurrent chemoradiotherapy with curative intent for cancers of the head and neck, lung, or gastrointestinal tract were provided with activity monitors before treatment initiation. Patients were asked to wear the devices continuously throughout the radiation therapy course. Step count data were downloaded weekly during radiation therapy and 2 and 4 weeks after radiation therapy completion. The primary objective was to demonstrate feasibility, defined as collection ofmore » step counts for 80% of the days during study subjects' radiation therapy courses. Secondary objectives included establishing step count as a dynamic predictor of unplanned hospitalization risk. Results: Thirty-eight enrolled patients were treated with concurrent chemoradiotherapy. Primary diagnoses included head and neck cancer (n=11), lung cancer (n=13), and a variety of gastrointestinal cancers (n=14). Step data were collected for 1524 of 1613 days (94%) during patients' radiation therapy courses. Fourteen patients were hospitalized during radiation therapy or within 4 weeks of radiation therapy completion. Cox regression modeling demonstrated a significant association between recent step counts (3-day average) and hospitalization risk, with a 38% reduction in the risk of hospitalization for every 1000 steps taken each day (hazard ratio 0.62, 95% confidence interval 0.46-0.83, P=.002). Inferior quality of life scores and impaired performance status were not associated with increased hospitalization risk. Conclusion: Continuous activity monitoring during concurrent chemoradiotherapy is feasible and well-tolerated. Step counts may serve as powerful, objective, and dynamic indicators of hospitalization risk.« less

Increased rate of positive penicillin skin tests among patients with glioma: insights into the association between allergies and glioma risk.

PubMed

Han, Sheng; Huang, Yanming; Wang, Zixun; Li, Zhonghua; Qin, Xiaofei; Wu, Anhua

2014-11-01

Allergy and immunoglobulin E levels are inversely associated with glioma risk. Previous studies have focused on respiratory and food allergies, and little information is available regarding drug allergies. This study evaluated the rate of positive penicillin skin tests (PenSTs) and blood eosinophil counts in a large population of patients with glioma compared with nontumor controls to provide evidence for the relationship between drug allergies and glioma risk. A retrospective case-control study was conducted in patients diagnosed with glioma (n = 913) between January 2004 and June 2013. The study patients were matched with nontumor controls (n = 1091) for age, sex, and date of admission to the hospital. Preoperative results of the PenST and eosinophil counts were obtained, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using conditional logistic regression models, while a Kaplan-Meier analysis was used to assess overall survival. The percentage of positive PenSTs was higher among patients with glioma than in control subjects. The age-, sex-, and admission date-adjusted OR for positive versus negative PenSTs was 2.392 (95% CI 1.891-3.026). Eosinophil counts were also higher in glioma cases than in controls: the OR for eosinophil > 0.06 × 10(9)/L versus ≤ 0.06 × 10(9)/L was 1.923 (95% CI 1.608-2.301). There was no association between positive PenST/eosinophil counts and glioma grade or patient survival (n = 105). In contrast to previously reported relationships between allergy and glioma, in the present study a significantly higher rate of positive PenSTs and higher eosinophil counts were found in patients with glioma than in nontumor controls. These results suggest a complex relationship between allergies and glioma development.

Osteocalcin is not a strong determinant of serum testosterone and sperm count in men from infertile couples.

PubMed

Schwetz, V; Gumpold, R; Graupp, M; Hacker, N; Schweighofer, N; Trummer, O; Pieber, T R; Ballon, M; Lerchbaum, E; Obermayer-Pietsch, B

2013-07-01

Osteocalcin (OC) - released by osteoblasts and known as a marker of bone turnover - has been suggested to influence male fertility in murine models by enhancing testosterone production and sperm count. Results from clinical studies are scarce, however. The aim of this cross-sectional study was to investigate the proposed association of OC, undercarboxylated osteocalcin (ucOC) or carboxylated osteocalcin (cOC) with testosterone and sperm count in a cohort of 159 young male adults from infertile couples. Semen analysis was performed. Testosterone, free testosterone, LH, OC and ucOC were measured in serum samples after an overnight fast. cOC and OC correlated weakly but significantly with testosterone (OC: r = 0.165, p = 0.040, cOC: r = 0.193, p = 0.017), but not after adjusting for age and body mass index (BMI) or waist-hip ratio (WHR). %ucOC (ucOC levels expressed as percentage of total OC) correlated inversely with LH (r = -0.184, p = 0.023) and remained significant after the same adjustment. No significant correlations were observed between OC, cOC, ucOC, %ucOC and sperm count, semen volume and number of vital spermatozoa. In binary logistic regression analyses, none of the parameters of OC were predictors of oligozoospermia after adjusting for age and BMI or WHR. The weak association between %ucOC and LH has marginal clinical importance because of the lack of associations of parameters of OC with testosterone and sperm count. The current data thus cannot support the notion that OC is associated with male fertility in young men from infertile couples. © 2013 American Society of Andrology and European Academy of Andrology.

Acute Effects of Fine Particulate Air Pollution on Cardiac Arrhythmia: The APACR Study

PubMed Central

He, Fan; Shaffer, Michele L.; Rodriguez-Colon, Sol; Yanosky, Jeff D.; Bixler, Edward; Cascio, Wayne E.

2011-01-01

Background: The mechanisms underlying the relationship between particulate matter (PM) air pollution and cardiac disease are not fully understood. Objectives: We examined the effects and time course of exposure to fine PM [aerodynamic diameter ≤ 2.5 μm (PM2.5)] on cardiac arrhythmia in 105 middle-age community-dwelling healthy nonsmokers in central Pennsylvania. Methods: The 24-hr beat-to-beat electrocardiography data were obtained using a high-resolution Holter system. After visually identifying and removing artifacts, we summarized the total number of premature ventricular contractions (PVCs) and premature atrial contractions (PACs) for each 30-min segment. A personal PM2.5 nephelometer was used to measure individual-level real-time PM2.5 exposures for 24 hr. We averaged these data to obtain 30-min average time–specific PM2.5 exposures. Distributed lag models under the framework of negative binomial regression and generalized estimating equations were used to estimate the rate ratio between 10-μg/m3 increases in average PM2.5 over 30-min intervals and ectopy counts. Results: The mean ± SD age of participants was 56 ± 8 years, with 40% male and 73% non-Hispanic white. The 30-min mean ± SD for PM2.5 exposure was 13 ± 22 μg/m3, and PAC and PVC counts were 0.92 ± 4.94 and 1.22 ± 7.18. Increases of 10 μg/m3 in average PM2.5 concentrations during the same 30 min or the previous 30 min were associated with 8% and 3% increases in average PVC counts, respectively. PM2.5 was not significantly associated with PAC count. Conclusion: PM2.5 exposure within approximately 60 min was associated with increased PVC counts in healthy individuals. PMID:21398201

QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model.

PubMed

Liu, Lian; Zhang, Shao-Wu; Huang, Yufei; Meng, Jia

2017-08-31

As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m 1 A-Seq, Par-CLIP, RIP-Seq, etc.

Evaluation of aerial survey methods for Dall's sheep

USGS Publications Warehouse

Udevitz, Mark S.; Shults, Brad S.; Adams, Layne G.; Kleckner, Christopher

2006-01-01

Most Dall's sheep (Ovis dalli dalli) population-monitoring efforts use intensive aerial surveys with no attempt to estimate variance or adjust for potential sightability bias. We used radiocollared sheep to assess factors that could affect sightability of Dall's sheep in standard fixed-wing and helicopter surveys and to evaluate feasibility of methods that might account for sightability bias. Work was conducted in conjunction with annual aerial surveys of Dall's sheep in the western Baird Mountains, Alaska, USA, in 2000–2003. Overall sightability was relatively high compared with other aerial wildlife surveys, with 88% of the available, marked sheep detected in our fixed-wing surveys. Total counts from helicopter surveys were not consistently larger than counts from fixed-wing surveys of the same units, and detection probabilities did not differ for the 2 aircraft types. Our results suggest that total counts from helicopter surveys cannot be used to obtain reliable estimates of detection probabilities for fixed-wing surveys. Groups containing radiocollared sheep often changed in size and composition before they could be observed by a second crew in units that were double-surveyed. Double-observer methods that require determination of which groups were detected by each observer will be infeasible unless survey procedures can be modified so that groups remain more stable between observations. Mean group sizes increased during our study period, and our logistic regression sightability model indicated that detection probabilities increased with group size. Mark–resight estimates of annual population sizes were similar to sightability-model estimates, and confidence intervals overlapped broadly. We recommend the sightability-model approach as the most effective and feasible of the alternatives we considered for monitoring Dall's sheep populations.

Life-course origins of social inequalities in adult immune cell markers of inflammation in a developing southern Chinese population: the Guangzhou Biobank Cohort Study.

PubMed

West, Douglas A; Leung, Gabriel M; Jiang, Chao Q; Elwell-Sutton, Timothy M; Zhang, Wei S; Lam, Tai H; Cheng, Kar K; Schooling, C Mary

2012-04-03

Socioeconomic position (SEP) throughout life is associated with cardiovascular disease, though the mechanisms linking these two are unclear. It is also unclear whether there are critical periods in the life course when exposure to better socioeconomic conditions confers advantages or whether SEP exposures accumulate across the whole life course. Inflammation may be a mechanism linking socioeconomic position (SEP) with cardiovascular disease. In a large sample of older residents of Guangzhou, in southern China, we examined the association of life course SEP with inflammation. In baseline data on 9,981 adults (≥ 50 years old) from the Guangzhou Biobank Cohort Study (2006-08), we used multivariable linear regression and model fit to assess the associations of life course SEP at four stages (childhood, early adult, late adult and current) with white blood, granulocyte and lymphocyte cell counts. A model including SEP at all four life stages best explained the association of life course SEP with white blood and granulocyte cell count for men and women, with early adult SEP (education) making the largest contribution. A critical period model best explained the association of life course SEP with lymphocyte count, with sex-specific associations. Early adult SEP was negatively associated with lymphocytes for women. Low SEP throughout life may negatively impact late adult immune-inflammatory status. However, some aspects of immune-inflammatory status may be sensitive to earlier exposures, with sex-specific associations. The findings were compatible with the hypothesis that in a developing population, upregulation of the gonadotropic axis with economic development may obscure the normally protective effects of social advantage for men.

The effect of public awareness campaigns on suicides: evidence from Nagoya, Japan.

PubMed

Matsubayashi, Tetsuya; Ueda, Michiko; Sawada, Yasuyuki

2014-01-01

Public awareness campaigns about depression and suicide have been viewed as highly effective strategies in preventing suicide, yet their effectiveness has not been established in previous studies. This study evaluates the effectiveness of a public-awareness campaign by comparing suicide counts before and after a city-wide campaign in Nagoya, Japan, where the city government distributed promotional materials that were aimed to stimulate public awareness of depression and promote care-seeking behavior during the period of 2010-2012. In each of the sixteen wards of the city of Nagoya, we count the number of times that the promotional materials were distributed per month and then examine the association between the suicide counts and the frequency of distributions in the months following such distributions. We run a Poisson regression model that controls for the effects of ward-specific observed and unobserved heterogeneities and temporal shocks. Our analysis indicates that more frequent distribution of the campaign material is associated with a decrease in the number of suicides in the subsequent months. The campaign was estimated to have been especially effective for the male residents of the city. The underlying mechanism of how the campaign reduced suicides remains to be unclear. Public awareness campaigns can be an effective strategy in preventing suicide. © 2013 Elsevier B.V. All rights reserved.

Statistical modeling of dental unit water bacterial test kit performance.

PubMed

Cohen, Mark E; Harte, Jennifer A; Stone, Mark E; O'Connor, Karen H; Coen, Michael L; Cullum, Malford E

2007-01-01

While it is important to monitor dental water quality, it is unclear whether in-office test kits provide bacterial counts comparable to the gold standard method (R2A). Studies were conducted on specimens with known bacterial concentrations, and from dental units, to evaluate test kit accuracy across a range of bacterial types and loads. Colony forming units (CFU) were counted for samples from each source, using R2A and two types of test kits, and conformity to Poisson distribution expectations was evaluated. Poisson regression was used to test for effects of source and device, and to estimate rate ratios for kits relative to R2A. For all devices, distributions were Poisson for low CFU/mL when only beige-pigmented bacteria were considered. For higher counts, R2A remained Poisson, but kits exhibited over-dispersion. Both kits undercounted relative to R2A, but the degree of undercounting was reasonably stable. Kits did not grow pink-pigmented bacteria from dental-unit water identified as Methylobacterium rhodesianum. Only one of the test kits provided results with adequate reliability at higher bacterial concentrations. Undercount bias could be estimated for this device and used to adjust test kit results. Insensitivity to methylobacteria spp. is problematic.

The influence of a consumer-wearable activity tracker on sedentary time and prolonged sedentary bouts: secondary analysis of a randomized controlled trial.

PubMed

Sloan, Robert A; Kim, Youngdeok; Sahasranaman, Aarti; Müller-Riemenschneider, Falk; Biddle, Stuart J H; Finkelstein, Eric A

2018-03-22

A recent meta-analysis surmised pedometers were a useful panacea to independently reduce sedentary time (ST). To further test and expand on this deduction, we analyzed the ability of a consumer-wearable activity tracker to reduce ST and prolonged sedentary bouts (PSB). We originally conducted a 12-month randomized control trial where 800 employees from 13 organizations were assigned to control, activity tracker, or one of two activity tracker plus incentive groups designed to increase step count. The primary outcome was accelerometer measured moderate-to-vigorous physical activity. We conducted a secondary analysis on accelerometer measured daily ST and PSB bouts. A general linear mixed model was used to examine changes in ST and prolonged sedentary bouts, followed by between-group pairwise comparisons. Regression analyses were conducted to examine the association of changes in step counts with ST and PSB. The changes in ST and PSB were not statistically significant and not different between the groups (P < 0.05). Increases in step counts were concomitantly associated with decreases in ST and PSB, regardless of intervention (P < 0.05). Caution should be taken when considering consumer-wearable activity trackers as a means to reduce sedentary behavior. Trial registration NCT01855776 Registered: August 8, 2012.

Energy metabolism and hematology of white-tailed deer fawns

USGS Publications Warehouse

Rawson, R.E.; DelGiudice, G.D.; Dziuk, H.E.; Mech, L.D.

1992-01-01

Resting metabolic rates, weight gains and hematologic profiles of six newborn, captive white-tailed deer (Odocoileus virginianus) fawns (four females, two males) were determined during the first 3 mo of life. Estimated mean daily weight gain of fawns was 0.2 kg. The regression equation for metabolic rate was: Metabolic rate (kcal/kg0.75/day) = 56.1 +/- 1.3 (age in days), r = 0.65, P less than 0.001). Regression equations were also used to relate age to red blood cell count (RBC), hemoglobin concentration (Hb), packed cell volume, white blood cell count, mean corpuscular volume, mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular hemoglobin. The age relationships of Hb, MCHC, and smaller RBC's were indicative of an increasing and more efficient oxygen-carrying and exchange capacity to fulfill the increasing metabolic demands for oxygen associated with increasing body size.

Whole body counter calibration using Monte Carlo modeling with an array of phantom sizes based on national anthropometric reference data

NASA Astrophysics Data System (ADS)

Shypailo, R. J.; Ellis, K. J.

2011-05-01

During construction of the whole body counter (WBC) at the Children's Nutrition Research Center (CNRC), efficiency calibration was needed to translate acquired counts of 40K to actual grams of potassium for measurement of total body potassium (TBK) in a diverse subject population. The MCNP Monte Carlo n-particle simulation program was used to describe the WBC (54 detectors plus shielding), test individual detector counting response, and create a series of virtual anthropomorphic phantoms based on national reference anthropometric data. Each phantom included an outer layer of adipose tissue and an inner core of lean tissue. Phantoms were designed for both genders representing ages 3.5 to 18.5 years with body sizes from the 5th to the 95th percentile based on body weight. In addition, a spherical surface source surrounding the WBC was modeled in order to measure the effects of subject mass on room background interference. Individual detector measurements showed good agreement with the MCNP model. The background source model came close to agreement with empirical measurements, but showed a trend deviating from unity with increasing subject size. Results from the MCNP simulation of the CNRC WBC agreed well with empirical measurements using BOMAB phantoms. Individual detector efficiency corrections were used to improve the accuracy of the model. Nonlinear multiple regression efficiency calibration equations were derived for each gender. Room background correction is critical in improving the accuracy of the WBC calibration.

Plasma n-6 Fatty Acid Levels Are Associated With CD4 Cell Counts, Hospitalization, and Mortality in HIV-Infected Patients.

PubMed

Kabagambe, Edmond K; Ezeamama, Amara E; Guwatudde, David; Campos, Hannia; Fawzi, Wafaie

2016-12-15

Fatty acids, including n-6 series, modulate immune function, but their effect on CD4 cell counts, death, or hospitalization in HIV-infected patients on antiretroviral therapy is unknown. In a randomized trial for effects of multivitamins in HIV-infected patients in Uganda, we used gas chromatography to measure plasma n-6 fatty acids at baseline; determined CD4 counts at baseline, 3, 6, 12, and 18 months; and recorded hospitalization or death events. The associations of fatty acids with CD4 counts and events were analyzed using repeated-measures analysis of variance and Cox regression, respectively. Among 297 patients with fatty acids measurements, 16 patients died and 69 were hospitalized within 18 months. Except for linoleic acid, n-6 fatty acids levels were positively associated with CD4 counts at baseline but not during follow-up. In models that included all 5 major n-6 fatty acids, age; sex; body mass index; anemia status; use of antiretroviral therapy, multivitamin supplements, and alcohol; and the risk of death or hospitalization decreased significantly with an increase in linoleic acid and gamma-linolenic acid levels, whereas associations for dihomo-gamma-linolenic acid, arachidonic acid, and aolrenic acid were null. The hazard ratios (95% confidence intervals) per 1 SD increase in linoleic acid and gamma-linolenic acid were 0.73 (0.56-0.94) and 0.51 (0.36-0.72), respectively. Gamma-linolenic acid remained significant (hazard ratio = 0.51; 95% confidence interval: 0.35 to 0.68) after further adjustment for other plasma fatty acids. Lower levels of gamma-linolenic acid are associated with lower CD4 counts and an increased risk of death or hospitalization. These results suggest a potential for using n-6 fatty acids to improve outcomes from antiretroviral therapy.

Failure to prescribe pneumocystis prophylaxis is associated with increased mortality, even in the cART era: results from the Treat Asia HIV observational database

PubMed Central

2012-01-01

Background Pneumocystis jiroveci pneumonia (PCP) prophylaxis is recommended for patients with CD4 counts of less than 200 cells/mm3. This study examines the proportion of patients in the TREAT Asia HIV Observational Database (TAHOD) receiving PCP prophylaxis, and its effect on PCP and mortality. Methods TAHOD patients with prospective follow up had data extracted for prophylaxis using co-trimoxazole, dapsone or pentamidine. The proportion of patients on prophylaxis was calculated for each calendar year since 2003 among patients with CD4 counts of less than 200 cells/mm3. The effect of prophylaxis on PCP and survival were assessed using random-effect Poisson regression models. Results There were a total of 4050 patients on prospective follow up, and 90% of them were receiving combination antiretroviral therapy. Of those with CD4 counts of less than 200 cells/mm3, 58% to 72% in any given year received PCP prophylaxis, predominantly co-trimoxazole. During follow up, 62 patients developed PCP (0.5 per 100 person-years) and 169 died from all causes (1.36/100 person-years). After stratifying by site and adjusting for age, CD4 count, CDC stage and antiretroviral treatment, those without prophylaxis had no higher risk of PCP, but had a significantly higher risk of death (incident rate ratio 10.8, p < 0.001). PCP prophylaxis had greatest absolute benefit in patients with CD4 counts of less than 50 cells/mm3, lowering mortality rates from 33.5 to 6.3 per 100 person-years. Conclusions Approximately two-thirds of TAHOD patients with CD4 counts of less than 200 cells/mm3 received PCP prophylaxis. Patients without prophylaxis had significantly higher mortality, even in the era of combination ART. Although PCP may be under-diagnosed, these data suggest that prophylaxis is associated with important survival benefits. PMID:22281054

Thrombocytopenia in neonates with polycythemia: incidence, risk factors and clinical outcome.

PubMed

Vlug, Roos D; Lopriore, Enrico; Janssen, Marleen; Middeldorp, Johanna M; Rath, Mirjam E A; Smits-Wintjens, Vivianne E H J

2015-02-01

Polycythemia occurs in 1 to 5% of neonates and is associated with complications, including an increased risk of thrombocytopenia. To evaluate incidence, risk factors, management and outcome of thrombocytopenia in neonates with polycythemia. All neonates with polycythemia admitted to our neonatal intensive care unit between 2006 and 2013 were included in this retrospective study. We evaluated the incidence of thrombocytopenia (platelet count <150 × 10(9)/l) and severe thrombocytopenia (platelet count <50 × 10(9)/l) and the correlation between platelet counts and hematocrit values. The incidence of thrombocytopenia and severe thrombocytopenia was 51 (71/140) and 9% (13/140), respectively. Platelet count was negatively correlated with hematocrit (spearman correlation coefficient -0.233, p = 0.007). After multiple regression analysis, we found an independent association between thrombocytopenia and being small for gestational age (OR: 10.0; 95%; CI: 1.2-81.7; p = 0.031). Thrombocytopenia occurs in 51% of neonates with polycythemia and is independently associated with growth restriction. Increased hematocrit is associated with decreased platelet count.

Statistical mapping of count survey data

USGS Publications Warehouse

Royle, J. Andrew; Link, W.A.; Sauer, J.R.; Scott, J. Michael; Heglund, Patricia J.; Morrison, Michael L.; Haufler, Jonathan B.; Wall, William A.

2002-01-01

We apply a Poisson mixed model to the problem of mapping (or predicting) bird relative abundance from counts collected from the North American Breeding Bird Survey (BBS). The model expresses the logarithm of the Poisson mean as a sum of a fixed term (which may depend on habitat variables) and a random effect which accounts for remaining unexplained variation. The random effect is assumed to be spatially correlated, thus providing a more general model than the traditional Poisson regression approach. Consequently, the model is capable of improved prediction when data are autocorrelated. Moreover, formulation of the mapping problem in terms of a statistical model facilitates a wide variety of inference problems which are cumbersome or even impossible using standard methods of mapping. For example, assessment of prediction uncertainty, including the formal comparison of predictions at different locations, or through time, using the model-based prediction variance is straightforward under the Poisson model (not so with many nominally model-free methods). Also, ecologists may generally be interested in quantifying the response of a species to particular habitat covariates or other landscape attributes. Proper accounting for the uncertainty in these estimated effects is crucially dependent on specification of a meaningful statistical model. Finally, the model may be used to aid in sampling design, by modifying the existing sampling plan in a manner which minimizes some variance-based criterion. Model fitting under this model is carried out using a simulation technique known as Markov Chain Monte Carlo. Application of the model is illustrated using Mourning Dove (Zenaida macroura) counts from Pennsylvania BBS routes. We produce both a model-based map depicting relative abundance, and the corresponding map of prediction uncertainty. We briefly address the issue of spatial sampling design under this model. Finally, we close with some discussion of mapping in relation to habitat structure. Although our models were fit in the absence of habitat information, the resulting predictions show a strong inverse relation with a map of forest cover in the state, as expected. Consequently, the results suggest that the correlated random effect in the model is broadly representing ecological variation, and that BBS data may be generally useful for studying bird-habitat relationships, even in the presence of observer errors and other widely recognized deficiencies of the BBS.

One-Year Mortality in Older Patients with Cancer: Development and External Validation of an MNA-Based Prognostic Score.

PubMed

Bourdel-Marchasson, Isabelle; Diallo, Abou; Bellera, Carine; Blanc-Bisson, Christelle; Durrieu, Jessica; Germain, Christine; Mathoulin-Pélissier, Simone; Soubeyran, Pierre; Rainfray, Muriel; Fonck, Mariane; Doussau, Adelaïde

2016-01-01

The MNA (Mini Nutritional Assessment) is known as a prognosis factor in older population. We analyzed the prognostic value for one-year mortality of MNA items in older patients with cancer treated with chemotherapy as the basis of a simplified prognostic score. The prospective derivation cohort included 606 patients older than 70 years with an indication of chemotherapy for cancers. The endpoint to predict was one-year mortality. The 18 items of the Full MNA, age, gender, weight loss, cancer origin, TNM, performance status and lymphocyte count were considered to construct the prognostic model. MNA items were analyzed with a backward step-by-step multivariate logistic regression and other items were added in a forward step-by-step regression. External validation was performed on an independent cohort of 229 patients. At one year 266 deaths had occurred. Decreased dietary intake (p = 0.0002), decreased protein-rich food intake (p = 0.025), 3 or more prescribed drugs (p = 0.023), calf circumference <31 cm (p = 0.0002), tumor origin (p<0.0001), metastatic status (p = 0.0007) and lymphocyte count <1500/mm3 (0.029) were found to be associated with 1-year mortality in the final model and were used to construct a prognostic score. The area under curve (AUC) of the score was 0.793, which was higher than the Full MNA AUC (0.706). The AUC of the score in validation cohort (229 subjects, 137 deaths) was 0.698. Key predictors of one-year mortality included cancer cachexia clinical features, comorbidities, the origin and the advanced status of the tumor. The prognostic value of this model combining a subset of MNA items and cancer related items was better than the full MNA, thus providing a simple score to predict 1-year mortality in older patients with an indication of chemotherapy.

Modeling and simulation of count data.

PubMed

Plan, E L

2014-08-13

Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity.

On the importance of controlling for effort in analysis of count survey data: Modeling population change from Christmas Bird Count data

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.; Helbig, Andreas J.; Flade, Martin

1999-01-01

Count survey data are commonly used for estimating temporal and spatial patterns of population change. Since count surveys are not censuses, counts can be influenced by 'nuisance factors' related to the probability of detecting animals but unrelated to the actual population size. The effects of systematic changes in these factors can be confounded with patterns of population change. Thus, valid analysis of count survey data requires the identification of nuisance factors and flexible models for their effects. We illustrate using data from the Christmas Bird Count (CBC), a midwinter survey of bird populations in North America. CBC survey effort has substantially increased in recent years, suggesting that unadjusted counts may overstate population growth (or understate declines). We describe a flexible family of models for the effect of effort, that includes models in which increasing effort leads to diminishing returns in terms of the number of birds counted.

Impact of donor- and collection-related variables on product quality in ex utero cord blood banking.

PubMed

Askari, Sabeen; Miller, John; Chrysler, Gayl; McCullough, Jeffrey

2005-02-01

Optimizing product quality is a current focus in cord blood banking. This study evaluates the role of selected donor- and collection-related variables. Retrospective review was performed of cord blood units (CBUs) collected ex utero between February 1, 2000, and February 28, 2002. Preprocessing volume and total nucleated cell (TNC) counts and postprocessing CD34 cell counts were used as product quality indicators. Of 2084 CBUs, volume determinations and TNC counts were performed on 1628 and CD34+ counts on 1124 CBUs. Mean volume and TNC and CD34+ counts were 85.2 mL, 118.9 x 10(7), and 5.2 x 10(6), respectively. In univariate analysis, placental weight of greater than 500 g and meconium in amniotic fluid correlated with better volume and TNC and CD34+ counts. Greater than 40 weeks' gestation predicted enhanced volume and TNC count. Cesarean section, two- versus one-person collection, and not greater than 5 minutes between placental delivery and collection produced superior volume. Increased TNC count was also seen in Caucasian women, primigravidae, female newborns, and collection duration of more than 5 minutes. A time between delivery of newborn and placenta of not greater than 10 minutes predicted better volume and CD34+ count. By regression analysis, collection within not greater than 5 minutes of placental delivery produced superior volume and TNC count. Donor selection and collection technique modifications may improve product quality. TNC count appears to be more affected by different variables than CD34+ count.

Zero adjusted models with applications to analysing helminths count data.

PubMed

Chipeta, Michael G; Ngwira, Bagrey M; Simoonga, Christopher; Kazembe, Lawrence N

2014-11-27

It is common in public health and epidemiology that the outcome of interest is counts of events occurrence. Analysing these data using classical linear models is mostly inappropriate, even after transformation of outcome variables due to overdispersion. Zero-adjusted mixture count models such as zero-inflated and hurdle count models are applied to count data when over-dispersion and excess zeros exist. Main objective of the current paper is to apply such models to analyse risk factors associated with human helminths (S. haematobium) particularly in a case where there's a high proportion of zero counts. The data were collected during a community-based randomised control trial assessing the impact of mass drug administration (MDA) with praziquantel in Malawi, and a school-based cross sectional epidemiology survey in Zambia. Count data models including traditional (Poisson and negative binomial) models, zero modified models (zero inflated Poisson and zero inflated negative binomial) and hurdle models (Poisson logit hurdle and negative binomial logit hurdle) were fitted and compared. Using Akaike information criteria (AIC), the negative binomial logit hurdle (NBLH) and zero inflated negative binomial (ZINB) showed best performance in both datasets. With regards to zero count capturing, these models performed better than other models. This paper showed that zero modified NBLH and ZINB models are more appropriate methods for the analysis of data with excess zeros. The choice between the hurdle and zero-inflated models should be based on the aim and endpoints of the study.

Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: an example of smoking cessation.

PubMed

Xie, Haiyi; Tao, Jill; McHugo, Gregory J; Drake, Robert E

2013-07-01

Count data with skewness and many zeros are common in substance abuse and addiction research. Zero-adjusting models, especially zero-inflated models, have become increasingly popular in analyzing this type of data. This paper reviews and compares five mixed-effects Poisson family models commonly used to analyze count data with a high proportion of zeros by analyzing a longitudinal outcome: number of smoking quit attempts from the New Hampshire Dual Disorders Study. The findings of our study indicated that count data with many zeros do not necessarily require zero-inflated or other zero-adjusting models. For rare event counts or count data with small means, a simpler model such as the negative binomial model may provide a better fit. Copyright © 2013 Elsevier Inc. All rights reserved.

No significant effect of cannabis use on the count and percentage of circulating CD4 T-cells in HIV-HCV co-infected patients (ANRS CO13-HEPAVIH French cohort).

PubMed

Marcellin, Fabienne; Lions, Caroline; Rosenthal, Eric; Roux, Perrine; Sogni, Philippe; Wittkop, Linda; Protopopescu, Camelia; Spire, Bruno; Salmon-Ceron, Dominique; Dabis, François; Carrieri, Maria Patrizia

2017-03-01

Despite cannabis use being very common in patients co-infected with HIV and hepatitis C virus (HCV), its effect on these patients' immune systems remains undocumented. Documenting the potential effect of cannabis use on HIV immunological markers would help caregivers make more targeted health recommendations to co-infected patients. We performed a longitudinal analysis of the relationship between cannabis use and peripheral blood CD4 T-cell measures in co-infected patients receiving antiretroviral therapy. Cannabis use was assessed using annual self-administered questionnaires in 955 patients (2386 visits) enrolled in the ANRS CO13-HEPAVIH cohort. The effect of cannabis use on circulating CD4 T-cell count and percentage was estimated using multivariate linear regression models with generalised estimating equations. Sensitivity analyses were conducted after excluding visits where (i) tobacco use and (ii) smoking >=10 tobacco cigarettes/day were reported. At the first visit, 48% of patients reported cannabis use during the previous four weeks, and 58% of these patients also smoked ≥10 tobacco cigarettes/day. After multiple adjustment, cannabis use was not significantly associated with either circulating CD4 T-cell count [model coefficient (95% confidence interval): 0.27 (-0.07; 0.62), P = 0.12] or percentage [-0.04 (-0.45; 0.36), P = 0.83]. Sensitivity analyses confirmed these results. Findings show no evidence for a negative effect of cannabis use on circulating CD4 T-cell counts/percentages in HIV-HCV co-infected patients. In-depth immunological studies are needed to document whether cannabis has a harmful effect on CD4 levels in lungs and on cells' functional properties. [Marcellin F, Lions C, Rosenthal E, Roux P, Sogni P, Wittkop L, Protopopescu C, Spire B, Salmon-Ceron D, Dabis F, Carrieri MP, HEPAVIH ANRS CO13 Study Group. No significant effect of cannabis use on the count and percentage of circulating CD4 T-cells in HIV-HCV co-infected patients (ANRSCO13-HEPAVIH French cohort). Drug Alcohol Rev 2017;36:227-238]. © 2016 Australasian Professional Society on Alcohol and other Drugs.

Alcohol use disorders among people living with HIV/AIDS in Southern Brazil: prevalence, risk factors and biological markers outcomes.

PubMed

da Silva, Cláudio Moss; Mendoza-Sassi, Raúl Andrés; da Mota, Luisa Dias; Nader, Maíba Mikhael; de Martinez, Ana Maria Barral

2017-04-11

Alcohol abuse is an important public health problem, frequently unrecognized among people living with HIV/AIDS (PLWHA), and requires investigation and intervention. It is usually associated with lower adherence to highly active antiretroviral therapy (HAART). It can also produce adverse clinical outcomes, such as changes in certain HIV markers, particularly CD4 cell counts and HIV viral loads (VLs). Thus, this study aimed to evaluate the prevalence of alcohol abuse among PLWHA, its associated risk factors and effects on CD4 cell counts and HIV VLs in southern Brazil. Between December 2012 and July 2013, 343 patients were interviewed at a reference hospital in southern Brazil. The instrument used was the Alcohol Use Disorder Identification Test (AUDIT), and a cutoff of eight points or more was applied. Socioeconomic, demographic, clinical and laboratory data were also collected. The statistical analysis included a Poisson regression to evaluate the factors associated with alcohol use disorder, and a linear regression was performed to assess the relationship between AUDIT scores and CD4 cell counts and HIV VLs. Alcohol abuse was present in 28.6% of the respondents, and possible dependence was present in 5%. The risk factors identified included being male, mixed or black skin color, low education and the use of intravenous or inhaled drugs. A higher AUDIT score was associated with a lower CD4 cell count but was not associated with higher HIV VL values. Our results show the importance of screening for alcohol abuse in this group. The prevalence of alcohol abuse was high, and it was associated with socioeconomic factors and the use of illicit drugs. Moreover, AUDIT score negatively affected CD4 cell counts as well.

The impact of article length on the number of future citations: a bibliometric analysis of general medicine journals.

PubMed

Falagas, Matthew E; Zarkali, Angeliki; Karageorgopoulos, Drosos E; Bardakas, Vangelis; Mavros, Michael N

2013-01-01

The number of citations received is considered an index of study quality and impact. We aimed to examine the factors associated with the number of citations of published articles, focusing on the article length. Original human studies published in the first trimester of 2006 in 5 major General Medicine journals were analyzed with regard to the number of authors and of author-affiliated institutions, title and abstract word count, article length (number of print pages), number of bibliographic references, study design, and 2006 journal impact factor (JIF). A multiple linear regression model was employed to identify the variables independently associated with the number of article citations received through January 2012. On univariate analysis the JIF, number of authors, article length, study design (interventional/observational and prospective/retrospective), title and abstract word count, number of author-affiliated institutions, and number of references were all associated with the number of citations received. On multivariate analysis with the logarithm of citations as the dependent variable, only article length [regression coefficient: 14.64 (95% confidence intervals: (5.76-23.50)] and JIF [3.37 (1.80-4.948)] independently predicted the number of citations. The variance of citations explained by these parameters was 51.2%. In a sample of articles published in major General Medicine journals, in addition to journal impact factors, article length and number of authors independently predicted the number of citations. This may reflect a higher complexity level and quality of longer and multi-authored studies.

Water-based condensation particle counters comparison near a major freeway with significant heavy-duty diesel traffic

NASA Astrophysics Data System (ADS)

Lee, Eon S.; Polidori, Andrea; Koch, Michael; Fine, Philip M.; Mehadi, Ahmed; Hammond, Donald; Wright, Jeffery N.; Miguel, Antonio. H.; Ayala, Alberto; Zhu, Yifang

2013-04-01

This study compares the instrumental performance of three TSI water-based condensation particle counter (WCPC) models measuring particle number concentrations in close proximity (15 m) to a major freeway that has a significant level of heavy-duty diesel traffic. The study focuses on examining instrument biases and performance differences by different WCPC models under realistic field operational conditions. Three TSI models (3781, 3783, and 3785) were operated for one month in triplicate (nine units in total) in parallel with two sets of Scanning Mobility Particle Sizer (SMPS) spectrometers for the concurrent measurement of particle size distributions. Inter-model bias under different wind directions were first evaluated using 1-min raw data. Although all three WCPC models agreed well in upwind conditions (lower particle number concentrations, in the range of 103-104 particles cm-3), the three models' responses were significantly different under downwind conditions (higher particle number concentrations, above 104 particles cm-3). In an effort to increase inter-model linear correlations, we evaluated the results of using longer averaging time intervals. An averaging time of at least 15 min was found to achieve R2 values of 0.96 or higher when comparing all three models. Similar results were observed for intra-model comparisons for each of the three models. This strong linear relationship helped identify instrument bias related to particle number concentrations and particle size distributions. The TSI 3783 produced the highest particle counts, followed by TSI 3785, which reported 11% lower during downwind conditions than TSI 3783. TSI 3781 recorded particle number concentrations that were 24% lower than those observed by TSI 3783 during downwind condition. We found that TSI 3781 underestimated particles with a count median diameter less than 45 nm. Although the particle size dependency of instrument performance was found the most significant in TSI 3781, both models 3783 and 3785 showed somewhat size dependency. In addition, within each tested WCPC model, one unit was found to count significantly different and be more sensitive to particle size than the other two. Finally, exponential regression analysis was used to numerically quantify instrumental inter-model bias. Correction equations are proposed to adjust the TSI 3781 and 3785 data to the most recent model TSI 3783.

Maintenance therapy of childhood acute lymphoblastic leukemia revisited-Should drug doses be adjusted by white blood cell, neutrophil, or lymphocyte counts?

PubMed

Schmiegelow, Kjeld; Nersting, Jacob; Nielsen, Stine Nygaard; Heyman, Mats; Wesenberg, Finn; Kristinsson, Jon; Vettenranta, Kim; Schrøeder, Henrik; Weinshilboum, Richard; Jensen, Katrine Lykke; Grell, Kathrine; Rosthoej, Susanne

2016-12-01

6-Mercaptopurine (6MP) and methotrexate (MTX) based maintenance therapy is a critical phase of childhood acute lymphoblastic leukemia treatment. Wide interindividual variations in drug disposition warrant frequent doses adjustments, but there is a lack of international consensus on dose adjustment guidelines. To identify relapse predictors, we collected 28,255 data sets on drug doses and blood counts (median: 47/patient) and analyzed erythrocyte (Ery) levels of cytotoxic 6MP/MTX metabolites in 9,182 blood samples (median: 14 samples/patient) from 532 children on MTX/6MP maintenance therapy targeted to a white blood cell count (WBC) of 1.5-3.5 × 10 9 /l. After a median follow-up of 13.8 years for patients in remission, stepwise Cox regression analysis did not find age, average doses of 6MP and MTX, hemoglobin, absolute lymphocyte counts, thrombocyte counts, or Ery levels of 6-thioguanine nucleotides or MTX (including its polyglutamates) to be significant relapse predictors. The parameters significantly associated with risk of relapse (N = 83) were male sex (hazard ratio [HR] 2.0 [1.3-3.1], P = 0.003), WBC at diagnosis (HR = 1.04 per 10 × 10 9 /l rise [1.00-1.09], P = 0.048), the absolute neutrophil count (ANC; HR = 1.7 per 10 9 /l rise [1.3-2.4], P = 0.0007), and Ery thiopurine methyltransferase activity (HR = 2.7 per IU/ml rise [1.1-6.7], P = 0.03). WBC was significantly related to ANC (Spearman correlation coefficient, r s  = 0.77; P < 0.001), and only a borderline significant risk factor for relapse (HR = 1.28 [95% CI: 1.00-1.64], P = 0.046) when ANC was excluded from the Cox model. This study indicates that a low neutrophil count is likely to be the best hematological target for dose adjustments of maintenance therapy. © 2016 Wiley Periodicals, Inc.

Caffeine and Insomnia in People Living With HIV From the Miami Adult Studies on HIV (MASH) Cohort.

PubMed

Ramamoorthy, Venkataraghavan; Campa, Adriana; Rubens, Muni; Martinez, Sabrina S; Fleetwood, Christina; Stewart, Tiffanie; Liuzzi, Juan P; George, Florence; Khan, Hafiz; Li, Yinghui; Baum, Marianna K

We explored the relationship between caffeine consumption, insomnia, and HIV disease progression (CD4+ T cell counts and HIV viral loads). Caffeine intake and insomnia levels were measured using the Modified Caffeine Consumption Questionnaire and the Pittsburgh Insomnia Rating Scale (PIRS) in 130 clinically stable participants who were living with HIV, taking antiretroviral therapy, and recruited from the Miami Adult Studies on HIV cohort. Linear regressions showed that caffeine consumption was significantly and adversely associated with distress score, quality-of-life score, and global PIRS score. Linear regression analyses also showed that global PIRS score was significantly associated with lower CD4+ T cell counts and higher HIV viral loads. Caffeine could have precipitated insomnia in susceptible people living with HIV, which could be detrimental to their disease progression states. Copyright © 2017 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

Effect of age on the diagnostic efficiency of HbA1c for diabetes in a Chinese middle-aged and elderly population: The Shanghai Changfeng Study.

PubMed

Wu, Li; Lin, Huandong; Gao, Jian; Li, Xiaoming; Xia, Mingfeng; Wang, Dan; Aleteng, Qiqige; Ma, Hui; Pan, Baishen; Gao, Xin

2017-01-01

Glycated hemoglobin A1c (HbA1c) ≥6.5% (or 48mmol/mol) has been recommended as a new diagnostic criterion for diabetes; however, limited literature is available regarding the effect of age on the HbA1c for diagnosing diabetes and the causes for this age effect remain unknown. In this study, we investigated whether and why age affects the diagnostic efficiency of HbA1c for diabetes in a community-based Chinese population. In total, 4325 participants without previously known diabetes were enrolled in this study. Participants were stratified by age. Receiver operating characteristic curve (ROC) was plotted for each age group and the area under the curve (AUC) represented the diagnostic efficiency of HbA1c for diabetes defined by the plasma glucose criteria. The area under the ROC curve in each one-year age group was defined as AUCage. Multiple regression analyses were performed to identify factors inducing the association between age and AUCage based on the changes in the β and P values of age. The current threshold of HbA1c (≥6.5% or 48mmol/mol) showed low sensitivity (35.6%) and high specificity (98.9%) in diagnosing diabetes. ROC curve analyses showed that the diagnostic efficiency of HbA1c in the ≥75 years age group was significantly lower than that in the 45-54 years age group (AUC: 0.755 vs. 0.878; P<0.001). Pearson correlation analysis showed that the AUCage of HbA1c was negatively correlated with age (r = -0.557, P = 0.001). When adjusting the red blood cell (RBC) count in the multiple regression model, the negative association between age and AUCage disappeared, with the regression coefficient of age reversed to 0.001 and the P value increased to 0.856. The diagnostic efficiency of HbA1c for diabetes decreased with aging, and this age effect was induced by the decreasing RBC count with age. HbA1c is unsuitable for diagnosing diabetes in elderly individuals because of their physiologically decreased RBC count.

Effect of age on the diagnostic efficiency of HbA1c for diabetes in a Chinese middle-aged and elderly population: The Shanghai Changfeng Study

PubMed Central

Gao, Jian; Li, Xiaoming; Xia, Mingfeng; Wang, Dan; Aleteng, Qiqige; Ma, Hui; Pan, Baishen

2017-01-01

Background and aims Glycated hemoglobin A1c (HbA1c) ≥6.5% (or 48mmol/mol) has been recommended as a new diagnostic criterion for diabetes; however, limited literature is available regarding the effect of age on the HbA1c for diagnosing diabetes and the causes for this age effect remain unknown. In this study, we investigated whether and why age affects the diagnostic efficiency of HbA1c for diabetes in a community-based Chinese population. Methods In total, 4325 participants without previously known diabetes were enrolled in this study. Participants were stratified by age. Receiver operating characteristic curve (ROC) was plotted for each age group and the area under the curve (AUC) represented the diagnostic efficiency of HbA1c for diabetes defined by the plasma glucose criteria. The area under the ROC curve in each one-year age group was defined as AUCage. Multiple regression analyses were performed to identify factors inducing the association between age and AUCage based on the changes in the β and P values of age. Results The current threshold of HbA1c (≥6.5% or 48mmol/mol) showed low sensitivity (35.6%) and high specificity (98.9%) in diagnosing diabetes. ROC curve analyses showed that the diagnostic efficiency of HbA1c in the ≥75 years age group was significantly lower than that in the 45–54 years age group (AUC: 0.755 vs. 0.878; P<0.001). Pearson correlation analysis showed that the AUCage of HbA1c was negatively correlated with age (r = -0.557, P = 0.001). When adjusting the red blood cell (RBC) count in the multiple regression model, the negative association between age and AUCage disappeared, with the regression coefficient of age reversed to 0.001 and the P value increased to 0.856. Conclusions The diagnostic efficiency of HbA1c for diabetes decreased with aging, and this age effect was induced by the decreasing RBC count with age. HbA1c is unsuitable for diagnosing diabetes in elderly individuals because of their physiologically decreased RBC count. PMID:28886160

Modifications of haematology analyzers to improve cell counting and leukocyte differentiating in cerebrospinal fluid controls of the Joint German Society for Clinical Chemistry and Laboratory Medicine.

PubMed

Kleine, Tilmann O; Nebe, C Thomas; Löwer, Christa; Lehmitz, Reinhard; Kruse, Rolf; Geilenkeuser, Wolf-Jochen; Dorn-Beineke, Alexandra

2009-08-01

Flow cytometry (FCM) is used with haematology analyzers (HAs) to count cells and differentiate leukocytes in cerebrospinal fluid (CSF). To evaluate the FCM techniques of HAs, 10 external DGKL trials with CSF controls were carried out in 2004 to 2008. Eight single platform HAs with and without CSF equipment were evaluated with living blood leukocytes and erythrocytes in CSF like DGKL controls: Coulter (LH750,755), Abbott CD3200, CD3500, CD3700, CD4000, Sapphire, ADVIA 120(R) CSF assay, and Sysmex XE-2100(R). Results were compared with visual counting of native cells in Fuchs-Rosenthal chamber, unstained, and absolute values of leukocyte differentiation, assayed by dual platform analysis with immune-FCM (FACSCalibur, CD45, CD14) and the chamber counts. Reference values X were compared with HA values Y by statistical evaluation with Passing/Bablock (P/B) linear regression analysis to reveal conformity of both methods. The HAs, studied, produced no valid results with DGKL CSF controls, because P/B regression revealed no conformity with the reference values due to:-blank problems with impedance analysis,-leukocyte loss with preanalytical erythrocyte lysis procedures, especially of monocytes,-inaccurate results with ADVIA cell sphering and cell differentiation with algorithms and enzyme activities (e.g., peroxidase). HA techniques have to be improved, e.g., using no erythrocyte lysis and CSF adequate techniques, to examine CSF samples precise and accurate. Copyright 2009 International Society for Advancement of Cytometry.

Criterion and Concurrent Validity of the activPAL™ Professional Physical Activity Monitor in Adolescent Females

PubMed Central

Dowd, Kieran P.; Harrington, Deirdre M.; Donnelly, Alan E.

2012-01-01

Background The activPAL has been identified as an accurate and reliable measure of sedentary behaviour. However, only limited information is available on the accuracy of the activPAL activity count function as a measure of physical activity, while no unit calibration of the activPAL has been completed to date. This study aimed to investigate the criterion validity of the activPAL, examine the concurrent validity of the activPAL, and perform and validate a value calibration of the activPAL in an adolescent female population. The performance of the activPAL in estimating posture was also compared with sedentary thresholds used with the ActiGraph accelerometer. Methodologies Thirty adolescent females (15 developmental; 15 cross-validation) aged 15–18 years performed 5 activities while wearing the activPAL, ActiGraph GT3X, and the Cosmed K4B2. A random coefficient statistics model examined the relationship between metabolic equivalent (MET) values and activPAL counts. Receiver operating characteristic analysis was used to determine activity thresholds and for cross-validation. The random coefficient statistics model showed a concordance correlation coefficient of 0.93 (standard error of the estimate = 1.13). An optimal moderate threshold of 2997 was determined using mixed regression, while an optimal vigorous threshold of 8229 was determined using receiver operating statistics. The activPAL count function demonstrated very high concurrent validity (r = 0.96, p<0.01) with the ActiGraph count function. Levels of agreement for sitting, standing, and stepping between direct observation and the activPAL and ActiGraph were 100%, 98.1%, 99.2% and 100%, 0%, 100%, respectively. Conclusions These findings suggest that the activPAL is a valid, objective measurement tool that can be used for both the measurement of physical activity and sedentary behaviours in an adolescent female population. PMID:23094069

Prediction of citation counts for clinical articles at two years using data available within three weeks of publication: retrospective cohort study

PubMed Central

2008-01-01

Objective To determine if citation counts at two years could be predicted for clinical articles that pass basic criteria for critical appraisal using data within three weeks of publication from external sources and an online article rating service. Design Retrospective cohort study. Setting Online rating service, Canada. Participants 1274 articles from 105 journals published from January to June 2005, randomly divided into a 60:40 split to provide derivation and validation datasets. Main outcome measures 20 article and journal features, including ratings of clinical relevance and newsworthiness, routinely collected by the McMaster online rating of evidence system, compared with citation counts at two years. Results The derivation analysis showed that the regression equation accounted for 60% of the variation (R2=0.60, 95% confidence interval 0.538 to 0.629). This model applied to the validation dataset gave a similar prediction (R2=0.56, 0.476 to 0.596, shrinkage 0.04; shrinkage measures how well the derived equation matches data from the validation dataset). Cited articles in the top half and top third were predicted with 83% and 61% sensitivity and 72% and 82% specificity. Higher citations were predicted by indexing in numerous databases; number of authors; abstraction in synoptic journals; clinical relevance scores; number of cited references; and original, multicentred, and therapy articles from journals with a greater proportion of articles abstracted. Conclusion Citation counts can be reliably predicted at two years using data within three weeks of publication. PMID:18292132

The association between hematological parameters and metabolic syndrome in Iranian men: A single center large-scale study.

PubMed

Ahmadzadeh, Jamal; Mansorian, Behnam; Attari, Mohammad Mirza-Aghazadeh; Mohebbi, Ira; Naz-Avar, Raha; Moghadam, Karaim; Ghareh-Bagh, Seyyed Adel Khoshbou

Some studies have demonstrated that metabolic syndrome is associated with hematological parameters. The present study explores the relationship between hematological parameters and numbers of metabolic syndrome conditions in Iranian men. This cross-sectional study included 11,114 participants who were professional drivers of commercial motor vehicles, and were enrolled in the Iranian Health Surveys between 2014 and 2016. Diagnosis of metabolic syndrome was made according to International Diabetes Federation criteria. Clinical data, including anthropometric measurements and serum parameters, were collected. Odds ratios for hematological parameters and metabolic syndrome were calculated using binary logistic regression models. We found that hemoglobin; platelet, and white blood cell counts increased with increasing numbers of metabolic syndrome components (p<0.05 for all). The odds ratio of metabolic syndrome significantly increased across successive quartiles of platelet (1.00, 1.25, 1.29, and 1.51) and white blood cell counts (1.00, 1.51, 1.79, and 2.11) with the lowest quartile as the referent group. Similar associations for hemoglobin and hematocrit in the top quartile were also observed. We did not observe any significant difference in the mean of neutrophil count, mean platelet volume (MPV), red cell distribution width, or platelet distribution width among participants with or without metabolic syndrome. Our findings indicate that high levels of major hematological parameters such as hemoglobin, hematocrit, as well as platelet and white blood cell counts could be novel indicators for the development of metabolic syndrome. Copyright © 2017 Diabetes India. Published by Elsevier Ltd. All rights reserved.

Zero-state Markov switching count-data models: an empirical assessment.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2010-01-01

In this study, a two-state Markov switching count-data model is proposed as an alternative to zero-inflated models to account for the preponderance of zeros sometimes observed in transportation count data, such as the number of accidents occurring on a roadway segment over some period of time. For this accident-frequency case, zero-inflated models assume the existence of two states: one of the states is a zero-accident count state, which has accident probabilities that are so low that they cannot be statistically distinguished from zero, and the other state is a normal-count state, in which counts can be non-negative integers that are generated by some counting process, for example, a Poisson or negative binomial. While zero-inflated models have come under some criticism with regard to accident-frequency applications - one fact is undeniable - in many applications they provide a statistically superior fit to the data. The Markov switching approach we propose seeks to overcome some of the criticism associated with the zero-accident state of the zero-inflated model by allowing individual roadway segments to switch between zero and normal-count states over time. An important advantage of this Markov switching approach is that it allows for the direct statistical estimation of the specific roadway-segment state (i.e., zero-accident or normal-count state) whereas traditional zero-inflated models do not. To demonstrate the applicability of this approach, a two-state Markov switching negative binomial model (estimated with Bayesian inference) and standard zero-inflated negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. It is shown that the Markov switching model is a viable alternative and results in a superior statistical fit relative to the zero-inflated models.

Factors influencing variation of bulk milk antibiotic residue occurrence, somatic cell count, and total bacterial count in dairy sheep flocks.

PubMed

Gonzalo, C; Carriedo, J A; García-Jimeno, M C; Pérez-Bilbao, M; de la Fuente, L F

2010-04-01

To study the variations of bulk tank milk variables in dairy ewe flocks and to identify the main target practices and flock groups to improve milk quality and safety, a total of 71,228 records of antibiotic residue (AR) and milk yield and 68,781 records of somatic cell count (SCC) and total bacterial count (TBC) were obtained over 5 yr from the same 209 dairy ewe flocks of the Assaf breed belonging to the Consortium for Ovine Promotion of Castilla-León (Spain). Based on a logistic regression model, year, month, semester, SCC, TBC, dry therapy, and milk yield significantly contributed to AR variation. High SCC was associated with increased AR violations. When antibiotic dry therapy was implemented, AR occurrence was higher than when this practice was not used. A polynomial monthly distribution throughout the year was observed for AR occurrence; the highest values were in autumn, coinciding with low milk yields per flock. Yearly occurrences drastically diminished from 2004 (1.36%) to 2008 (0.30%), probably as a result of effective educational programs. The mixed-model ANOVA of factors influencing variation in SCC and TBC indicated that year, month, AR, dry therapy group, milking type, and year interactions were significant variation factors for SCC and TBC; mathematical model accounted for 74.1 and 35.4% of total variance for each variable, respectively. Differences in management and hygiene practice caused significant SCC and TBC variations among flocks and within flocks throughout the 5-yr study. Over time, continuously dry treated flocks showed lower logSCC (5.80) and logTBC (4.92) than untreated (6.10 and 5.18, respectively) or discontinuously dry treated (6.01 and 5.05, respectively) flocks. Continuously dry treated flocks had lower AR occurrences than did discontinuously dry treated flocks. As a whole, AR occurrence and SCC and TBC bulk tank milk variables can be used for monitoring mammary health and milk hygiene and safety in dairy sheep throughout time. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Optimally achieving milk bulk tank somatic cell count thresholds.

PubMed

Troendle, Jason A; Tauer, Loren W; Gröhn, Yrjo T

2017-01-01

High somatic cell count in milk leads to reduced shelf life in fluid milk and lower processed yields in manufactured dairy products. As a result, farmers are often penalized for high bulk tank somatic cell count or paid a premium for low bulk tank somatic cell count. Many countries also require all milk from a farm to be lower than a specified regulated somatic cell count. Thus, farms often cull cows that have high somatic cell count to meet somatic cell count thresholds. Rather than naïvely cull the highest somatic cell count cows, a mathematical programming model was developed that determines the cows to be culled from the herd by maximizing the net present value of the herd, subject to meeting any specified bulk tank somatic cell count level. The model was applied to test-day cows on 2 New York State dairy farms. Results showed that the net present value of the herd was increased by using the model to meet the somatic cell count restriction compared with naïvely culling the highest somatic cell count cows. Implementation of the model would be straightforward in dairy management decision software. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A new approach for handling longitudinal count data with zero-inflation and overdispersion: poisson geometric process model.

PubMed

Wan, Wai-Yin; Chan, Jennifer S K

2009-08-01

For time series of count data, correlated measurements, clustering as well as excessive zeros occur simultaneously in biomedical applications. Ignoring such effects might contribute to misleading treatment outcomes. A generalized mixture Poisson geometric process (GMPGP) model and a zero-altered mixture Poisson geometric process (ZMPGP) model are developed from the geometric process model, which was originally developed for modelling positive continuous data and was extended to handle count data. These models are motivated by evaluating the trend development of new tumour counts for bladder cancer patients as well as by identifying useful covariates which affect the count level. The models are implemented using Bayesian method with Markov chain Monte Carlo (MCMC) algorithms and are assessed using deviance information criterion (DIC).

New Insights into Activity Patterns in Children, Found Using Functional Data Analyses.

PubMed

Goldsmith, Jeff; Liu, Xinyue; Jacobson, Judith S; Rundle, Andrew

2016-09-01

Continuous monitoring of activity using accelerometers and other wearable devices provides objective, unbiased measurement of physical activity in minute-by-minute or finer resolutions. Accelerometers have already been widely deployed in studies of healthy aging, recovery of function after heart surgery, and other outcomes. Although common analyses of accelerometer data focus on single summary variables, such as the total or average activity count, there is growing interest in the determinants of diurnal profiles of activity. We use tools from functional data analysis (FDA), an area with an established statistical literature, to treat complete 24-h diurnal profiles as outcomes in a regression model. We illustrate the use of such models by analyzing data collected in New York City from 420 children participating in a Head Start program. Covariates of interest include season, sex, body mass index z-score, presence of an asthma diagnosis, and mother's birthplace. The FDA model finds several meaningful associations between several covariates and diurnal profiles of activity. In some cases, including shifted activity patterns for children of foreign-born mothers and time-specific effects of asthma on activity, these associations exist for covariates that are not associated with average activity count. FDA provides a useful statistical framework for settings in which the effect of covariates on the timing of activity is of interest. The use of similar models in other applications should be considered, and we make code public to facilitate this process.

Statistical approach to the analysis of olive long-term pollen season trends in southern Spain.

PubMed

García-Mozo, H; Yaezel, L; Oteros, J; Galán, C

2014-03-01

Analysis of long-term airborne pollen counts makes it possible not only to chart pollen-season trends but also to track changing patterns in flowering phenology. Changes in higher plant response over a long interval are considered among the most valuable bioindicators of climate change impact. Phenological-trend models can also provide information regarding crop production and pollen-allergen emission. The interest of this information makes essential the election of the statistical analysis for time series study. We analysed trends and variations in the olive flowering season over a 30-year period (1982-2011) in southern Europe (Córdoba, Spain), focussing on: annual Pollen Index (PI); Pollen Season Start (PSS), Peak Date (PD), Pollen Season End (PSE) and Pollen Season Duration (PSD). Apart from the traditional Linear Regression analysis, a Seasonal-Trend Decomposition procedure based on Loess (STL) and an ARIMA model were performed. Linear regression results indicated a trend toward delayed PSE and earlier PSS and PD, probably influenced by the rise in temperature. These changes are provoking longer flowering periods in the study area. The use of the STL technique provided a clearer picture of phenological behaviour. Data decomposition on pollination dynamics enabled the trend toward an alternate bearing cycle to be distinguished from the influence of other stochastic fluctuations. Results pointed to show a rising trend in pollen production. With a view toward forecasting future phenological trends, ARIMA models were constructed to predict PSD, PSS and PI until 2016. Projections displayed a better goodness of fit than those derived from linear regression. Findings suggest that olive reproductive cycle is changing considerably over the last 30years due to climate change. Further conclusions are that STL improves the effectiveness of traditional linear regression in trend analysis, and ARIMA models can provide reliable trend projections for future years taking into account the internal fluctuations in time series. Copyright © 2013 Elsevier B.V. All rights reserved.

Modelling approach for the rainfall erosivity index in sub-humid urban areas in northern Algeria

NASA Astrophysics Data System (ADS)

Touaibia, I.; Abderrahmane Guenim, N.; Touaibia, B.

2014-09-01

This work presents an approach for storm water erosivity index modelling in the absence of measurement in an urban area, in a sub-humid climate. In torrential storms, floods, loaded with sediments, obstruct storm water drainage. With the aim of estimating the amount of sediment that can be deposited on a stretch of road, adjacent to the study area, the erosivity index is determined from a count of 744 rain showers recorded over a period of 19 years. The Universal Soil Loss Equation (USLE) of Wischmeier and Smith is applied, where only the index of erosivity is calculated; it is based on the intensity of the rain starting the process of erosion in the basin. Functional relations are required between this factor and the explanatory variables. A power type regression model is reached, making it possible to bring a decision-making aid in absences of measurements.

Factors Associated with HIV Viral Load in a Respondent Driven Sample in Los Angeles

PubMed Central

King, WD; Larkins, S; Hucks-Ortiz, C; Wang, J; Gorbach, P; Veniegas, R; Shoptaw, S

2008-01-01

This study used a modified version of the Behavioral Model for Vulnerable Populations to examine the predisposing, enabling, and need factors associated with detectable viral load (VL). HIV status was measured using saliva and confirmed by blood. Of 835 persons enrolled, 193 were HIV positive and provided VL counts. A multistage logistic regression demonstrated that the predisposing factors of homelessness and recent substance abuse, particularly methamphetamine abuse, had a negative association with VL. The negative association of homelessness was lessened with the introduction of enabling and need utilization factors in the model. In contrast, the negative association with recent substance abuse on VL was sustained in the final model. Provision of HIV care and medications attenuated the negative association of homelessness within this sample. Guided policy to address substance abuse among those who are HIV positive is needed to improve biological outcomes. PMID:18064555

Relationship between shift work and peripheral total and differential leukocyte counts in Chinese steel workers.

PubMed

Lu, Li-Fen; Wang, Chao-Ping; Tsai, I-Ting; Hung, Wei-Chin; Yu, Teng-Hung; Wu, Cheng-Ching; Hsu, Chia-Chang; Lu, Yung-Chuan; Chung, Fu-Mei; Jean, Mei-Chu Yen

2016-01-01

Even though shift work has been suspected to be a risk factor for cardiovascular disease, little research has been done to determine the logical underlying inflammation mechanisms. This study investigated the association between shift work and circulating total and differential leukocyte counts among Chinese steel workers. The subjects were 1,654 line workers in a steel plant, who responded to a cross-sectional survey with a questionnaire on basic attributes, life style, and sleep. All workers in the plant received a periodic health checkup. Total and differential leukocytes counts were also examined in the checkup. Shift workers had higher rates of alcohol use, smoking, poor sleep, poor physical exercise, and obesity than daytime workers. In further analysis, we found that the peripheral total WBC, monocyte, neutrophil, and lymphocyte counts were also greater in shift workers than in daytime workers. When subjects were divided into quartiles according to total WBC, neutrophil, monocyte, and lymphocyte counts, increased leukocyte count was associated with shift work. Using stepwise linear regression analysis, smoking, obesity, and shift work were independently associated with total WBC, monocyte, neutrophil, and lymphocyte counts. This study indicates that peripheral total and differential leukocyte counts are significantly higher in shift workers, which suggests that shift work may be a risk factor of cardiovascular disease. Applicable intervention strategies are needed for prevention of cardiovascular disease for shift workers.

Quality control of Kato slide counts for Schistosoma mansoni: a review of 12 years' experience in Kenya.

PubMed Central

Sturrock, R. F.; Ouma, J. H.; Kariuki, H. C.; Thiongo, F. W.; Koech, D. K.; Butterworth, A. E.

1997-01-01

A total of 19 annual or biannual audits were performed over a 12-year period by an independent microscopist on randomized subsamples of Kato slides examined for Schistosoma mansoni eggs by Kenyan microscopists from the Division of Vector-borne Diseases (DVBD). The recounts were invariably lower than the originals owing to some deterioration of the preparations between counts, but the two were strongly correlated: significant regressions of recounts on counts taking up 80-90% of the observed variance. Observer bias differed significantly between microscopists but remained stable over time, whereas repeatability of recounts on counts dropped slightly in periods of maximum work load but did not vary systematically with time. Approximately 7% of the counts and recounts disagreed on the presence or absence of eggs, but less than a third of these were negatives that were found positive on recount. False negatives dropped to 1.3% if duplicate counts were considered. The performance of the Kenyan microscopists was remarkably high and consistent throughout the 12-year period. This form of quality control is suitable for projects where limited funds preclude full-time supervisors using more sophisticated systems. PMID:9447781

Point counts from clustered populations: Lessons from an experiment with Hawaiian crows

USGS Publications Warehouse

Hayward, G.D.; Kepler, C.B.; Scott, J.M.

1991-01-01

We designed an experiment to identify factors contributing most to error in counts of Hawaiian Crow or Alala (Corvus hawaiiensis) groups that are detected aurally. Seven observers failed to detect calling Alala on 197 of 361 3-min point counts on four transects extending from cages with captive Alala. A detection curve describing the relation between frequency of flock detection and distance typified the distribution expected in transect or point counts. Failure to detect calling Alala was affected most by distance, observer, and Alala calling frequency. The number of individual Alala calling was not important in detection rate. Estimates of the number of Alala calling (flock size) were biased and imprecise: average difference between number of Alala calling and number heard was 3.24 (.+-. 0.277). Distance, observer, number of Alala calling, and Alala calling frequency all contributed to errors in estimates of group size (P < 0.0001). Multiple regression suggested that number of Alala calling contributed most to errors. These results suggest that well-designed point counts may be used to estimate the number of Alala flocks but cast doubt on attempts to estimate flock size when individuals are counted aurally.

Neonatal nucleated red blood cells in infants of overweight and obese mothers.

PubMed

Sheffer-Mimouni, Galit; Mimouni, Francis B; Dollberg, Shaul; Mandel, Dror; Deutsch, Varda; Littner, Yoav

2007-06-01

The perinatal outcome of the infant of obese mother is adversely affected and in theory, may involve fetal hypoxia. We hypothesized that an index of fetal hypoxia, the neonatal nucleated red blood cell (NRBC) count, is elevated in infants of overweight and obese mothers. Absolute NRBC counts taken during the first 12 hours of life in 41 infants of overweight and obese mothers were compared to 28 controls. Maternal body mass index and infant birthweight were significantly higher in the overweight and obese group (P < 0.01). Hematocrit, corrected white blood cell and lymphocyte counts did not differ between groups. The absolute NRBC count was higher (P = 0.01), and the platelet count lower (P = 0.05) in infants of overweight and obese mothers than in controls. In stepwise regression analysis, the absolute NRBC count in infants of overweight and obese mothers remained significantly higher even after taking into account birthweight or gestational age and Apgar scores (P < 0.02). Infants of overweight and obese mothers have increased nucleated red blood cells at birth compared with controls. We speculate that even apparently healthy fetuses of overweight and obese mothers are exposed to a subtle hypoxemic environment.

Risk factors associated with low CD4+ lymphocyte count among HIV-positive pregnant women in Nigeria.

PubMed

Abimiku, Alash'le; Villalba-Diebold, Pacha; Dadik, Jelpe; Okolo, Felicia; Mang, Edwina; Charurat, Man

2009-09-01

To determine the risk factors for CD4+ lymphocyte counts of 200 cells/mm(3) or lower in HIV-positive pregnant women in Nigeria. A cross-sectional data analysis from a prospective cohort of 515 HIV-positive women attending a prenatal clinic. Risk of a low CD4+ count was estimated using logistic regression analysis. CD4+ lymphocyte counts of 200 cells/mm(3) or lower (280+/-182 cells/mm(3)) were recorded in 187 (36.3%) out of 515 HIV-positive pregnant women included in the study. Low CD4+ count was associated with older age (adjusted odds ratio [aOR] 10.71; 95% confidence interval [CI], 1.20-95.53), lack of condom use (aOR, 5.16; 95% CI, 1.12-23.8), history of genital ulcers (aOR, 1.78; 95% CI, 1.12-2.82), and history of vaginal discharge (aOR; 1.62; 1.06-2.48). Over 35% of the HIV-positive pregnant women had low CD4+ counts, indicating the need for treatment. The findings underscore the need to integrate prevention of mother-to-child transmission with HIV treatment and care, particularly services for sexually transmitted infections.

Methods to improve traffic flow and noise exposure estimation on minor roads.

PubMed

Morley, David W; Gulliver, John

2016-09-01

Address-level estimates of exposure to road traffic noise for epidemiological studies are dependent on obtaining data on annual average daily traffic (AADT) flows that is both accurate and with good geographical coverage. National agencies often have reliable traffic count data for major roads, but for residential areas served by minor roads, especially at national scale, such information is often not available or incomplete. Here we present a method to predict AADT at the national scale for minor roads, using a routing algorithm within a geographical information system (GIS) to rank roads by importance based on simulated journeys through the road network. From a training set of known minor road AADT, routing importance is used to predict AADT on all UK minor roads in a regression model along with the road class, urban or rural location and AADT on the nearest major road. Validation with both independent traffic counts and noise measurements show that this method gives a considerable improvement in noise prediction capability when compared to models that do not give adequate consideration to minor road variability (Spearman's rho. increases from 0.46 to 0.72). This has significance for epidemiological cohort studies attempting to link noise exposure to adverse health outcomes. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Demographic Deficit? Local Population Aging and Access to Services in Rural America, 1990–2010

PubMed Central

Thiede, Brian; Brown, David L.; Sanders, Scott R.; Glasgow, Nina; Kulcsar, Laszlo J.

2017-01-01

Population aging is being experienced by many rural communities in the U.S., as evidenced by increases in the median age and the high incidence of natural population decrease. The implications of these changes in population structure for the daily lives of the residents in such communities have received little attention. We address this issue in the current study by examining the relationship between population aging and the availability of service-providing establishments in the rural U.S. between 1990 and 2010. Using data mainly from the U.S. Census Bureau and the Bureau of Labor Statistics, we estimate a series of fixed-effects regression models to identify the relationship between median age and establishment counts net of changes in overall population and other factors. We find a significant, but non-linear relationship between county median age and the total number of service-providing establishments, and counts of most specific types of services. We find a positive effect of total population size across all of our models. This total population effect is consistent with other research, but the independent effects of age structure that we observe represent a novel finding and suggest that age structure is a salient factor in local rural development and community wellbeing. PMID:28757660

Timing and magnitude of Broad-winged Hawk migration at Montclair Hawk Lookout, New Jersey, and Hawk Mountain Sanctuary, Pennsylvania

USGS Publications Warehouse

Miller, M.W.; Greenstone, E.M.; Greenstone, W.; Bildstein, K.L.

2002-01-01

The Broad-winged Hawk (Buteo platypterus) breeds in eastern and central Canada and the United States, and winters in Central America and northern and central South America. Birders and ornithologists count migrating Broad-winged Hawks at dozens of traditional watch sites throughout the northeastern United States. We modeled counts of migrating Broad-winged Hawks from two raptor migration watch sites: Montclair Hawk Lookout, New Jersey, and Hawk Mountain Sanctuary, Pennsylvania, to determine whether annual abundance and trend estimates from individual sites within the mid-Atlantic states are representative of the region as a whole. We restricted ourselves to counts made between 10:00 and 16:00 EST during September to standardize count effort between sites. We created one model set for annual counts and another model set for daily counts. When modeling daily counts we incorporated weather and identity of individual observers. Akaike's Information Criteria were used to select the best model from an initial set of competing models. Annual counts declined at both sites during 1979-1998. Broad-winged Hawk migration began, peaked, and ended later at Montclair than at Hawk Mountain, even though Hawk Mountain is 155 km west-southwest of Montclair. Mean annual counts of hawks at Montclair were more than twice those at Hawk Mountain, but were not correlated. Broad-winged Hawks counted at Montclair may not be the same birds as those counted at Hawk Mountain. Rather, the two sites may be monitoring different regional subpopulations. Broad-winged Hawks counted at the two sites may use different migration tactics, with those counted at Hawk Mountain being more likely to slope soar, and those at Montclair more likely to use thermal soaring. A system of multiple watch sites is needed to monitor various breeding populations of this widely dispersed migrant.

Use of an activity monitor to detect response to treatment in dogs with osteoarthritis.

PubMed

Brown, Dorothy Cimino; Boston, Raymond C; Farrar, John T

2010-07-01

To determine whether an activity monitor (AM) could be used to detect changes in activity in dogs with osteoarthritis treated with carprofen or a placebo. Randomized controlled trial. 70 dogs with no clinically important abnormalities other than osteoarthritis for which they were not currently being treated. Dogs wore an AM continuously for 21 days. On days 8 through 21, the dogs were treated with carprofen (n = 35) or a placebo (35). Total activity counts for days 1 through 7 (baseline) were compared with total activity counts for days 15 through 21 (endpoint). The change in total activity count from baseline to endpoint was assessed within each treatment group as well as between groups. Linear regression analysis was performed to test for an association between treatment and percentage change in activity counts while controlling for other variables. For placebo-treated dogs, median baseline total activity count was not significantly different from median endpoint total activity count (1,378,408 vs 1,310,112, respectively). For dogs receiving carprofen, there was a significant increase in median activity count from baseline to endpoint (1,276,427 vs 1,374,133). When age and baseline activity counts were controlled for, dogs in the carpofen-treated group had a 20% increase in activity counts, compared with placebo-treated dogs (95% confidence interval, 10% to 26%). Results suggested that the AM used in the present study may be a valid outcome assessment tool for documenting improved activity associated with treatment in dogs with osteoarthritis.

Wavelet principal component analysis of fetal movement counting data preceding hospital examinations due to decreased fetal movement: a prospective cohort study

PubMed Central

2013-01-01

Background Fetal movement (FM) counting is a simple and widely used method of assessing fetal well-being. However, little is known about what women perceive as decreased fetal movement (DFM) and how maternally perceived DFM is reflected in FM charts. Methods We analyzed FM counting data from 148 DFM events occurring in 137 pregnancies. The women counted FM daily from pregnancy week 24 until birth using a modified count-to-ten procedure. Common temporal patterns for the two weeks preceding hospital examination due to DFM were extracted from the FM charts using wavelet principal component analysis; a statistical methodology particularly developed for modeling temporal data with sudden changes, i.e. spikes that are frequently found in FM data. The association of the extracted temporal patterns with fetal complications was assessed by including the individuals’ scores on the wavelet principal components as explanatory variables in multivariable logistic regression analyses for two outcome measures: (i) complications identified during DFM-related consultations (n = 148) and (ii) fetal compromise at the time of consultation (including relevant information about birth outcome and placental pathology). The latter outcome variable was restricted to the DFM events occurring within 21 days before birth (n = 76). Results Analyzing the 148 and 76 DFM events, the first three main temporal FM counting patterns explained 87.2% and 87.4%, respectively, of all temporal variation in the FM charts. These three temporal patterns represented overall counting times, sudden spikes around the time of DFM events, and an inverted U-shaped pattern, explaining 75.3%, 8.6%, and 3.3% and 72.5%, 9.6%, and 5.3% of variation in the total cohort and subsample, respectively. Neither of the temporal patterns was significantly associated with the two outcome measures. Conclusions Acknowledging that sudden, large changes in fetal activity may be underreported in FM charts, our study showed that the temporal FM counting patterns in the two weeks preceding DFM-related consultation contributed little to identify clinically important changes in perceived FM. It thus provides insufficient information for giving detailed advice to women on when to contact health care providers. The importance of qualitative features of maternally perceived DFM should be further explored. PMID:24007565

Wavelet principal component analysis of fetal movement counting data preceding hospital examinations due to decreased fetal movement: a prospective cohort study.

PubMed

Winje, Brita Askeland; Røislien, Jo; Saastad, Eli; Eide, Jorid; Riley, Christopher Finne; Stray-Pedersen, Babill; Frøen, J Frederik

2013-09-05

Fetal movement (FM) counting is a simple and widely used method of assessing fetal well-being. However, little is known about what women perceive as decreased fetal movement (DFM) and how maternally perceived DFM is reflected in FM charts. We analyzed FM counting data from 148 DFM events occurring in 137 pregnancies. The women counted FM daily from pregnancy week 24 until birth using a modified count-to-ten procedure. Common temporal patterns for the two weeks preceding hospital examination due to DFM were extracted from the FM charts using wavelet principal component analysis; a statistical methodology particularly developed for modeling temporal data with sudden changes, i.e. spikes that are frequently found in FM data. The association of the extracted temporal patterns with fetal complications was assessed by including the individuals' scores on the wavelet principal components as explanatory variables in multivariable logistic regression analyses for two outcome measures: (i) complications identified during DFM-related consultations (n = 148) and (ii) fetal compromise at the time of consultation (including relevant information about birth outcome and placental pathology). The latter outcome variable was restricted to the DFM events occurring within 21 days before birth (n = 76). Analyzing the 148 and 76 DFM events, the first three main temporal FM counting patterns explained 87.2% and 87.4%, respectively, of all temporal variation in the FM charts. These three temporal patterns represented overall counting times, sudden spikes around the time of DFM events, and an inverted U-shaped pattern, explaining 75.3%, 8.6%, and 3.3% and 72.5%, 9.6%, and 5.3% of variation in the total cohort and subsample, respectively. Neither of the temporal patterns was significantly associated with the two outcome measures. Acknowledging that sudden, large changes in fetal activity may be underreported in FM charts, our study showed that the temporal FM counting patterns in the two weeks preceding DFM-related consultation contributed little to identify clinically important changes in perceived FM. It thus provides insufficient information for giving detailed advice to women on when to contact health care providers. The importance of qualitative features of maternally perceived DFM should be further explored.

Pre-school obesity is inversely associated with vegetable intake, grocery stores and outdoor play

PubMed Central

Kepper, M.; Tseng, T.-S.; Volaufova, J.; Scribner, R.; Nuss, H.; Sothern, M.

2016-01-01

Summary The study determined the association between body mass index (BMI) [B-Z] score and fruit and vegetable intake, frequency and ratio of fast food outlets and grocery stores in concentric areas around the child’s residence, outdoor play and total crime index. Data from 78 Louisiana pre-school children were analyzed using Pearson’s correlation and multiple regression analysis. Parental-reported fruit intake was linearly associated with increased number of grocery store counts in concentric areas around the child’s residence (P = 0.0406, P = 0.0281). Vegetable intake was inversely (P = 0.04) and the ratio of fast food outlets to grocery stores in a 2-mile concentric area around the child’s residence was positively (P = 0.05) associated to BMI z score after applying Best Model regression analysis (F = 3.06, P = 0.0346). Children residing in neighbourhoods with greater access to fast foods and lower access to fruits and vegetables may be at higher risk for developing obesity during pre-school years. PMID:26305391

Growth and mortality of larval Myctophum affine (Myctophidae, Teleostei).

PubMed

Namiki, C; Katsuragawa, M; Zani-Teixeira, M L

2015-04-01

The growth and mortality rates of Myctophum affine larvae were analysed based on samples collected during the austral summer and winter of 2002 from south-eastern Brazilian waters. The larvae ranged in size from 2·75 to 14·00 mm standard length (L(S)). Daily increment counts from 82 sagittal otoliths showed that the age of M. affine ranged from 2 to 28 days. Three models were applied to estimate the growth rate: linear regression, exponential model and Laird-Gompertz model. The exponential model best fitted the data, and L(0) values from exponential and Laird-Gompertz models were close to the smallest larva reported in the literature (c. 2·5 mm L(S)). The average growth rate (0·33 mm day(-1)) was intermediate among lanternfishes. The mortality rate (12%) during the larval period was below average compared with other marine fish species but similar to some epipelagic fishes that occur in the area. © 2015 The Fisheries Society of the British Isles.

Prevalence of anaemia among HIV patients in rural China during the HAART era.

PubMed

Jin, Yantao; Li, Qingya; Meng, Xiangle; Xu, Qianlei; Yuan, Jun; Li, Zhengwei; Guo, Huijun; Liu, Zhibin

2017-01-01

The prevalence of anaemia among HIV patients receiving highly active antiretroviral therapy (HAART) in China has not been extensively studied. The purpose of this study was to estimate the prevalence of anaemia among HIV patients receiving HAART in China. This cross-sectional study was conducted based on data in routine record registers. Factors associated with anaemia were evaluated by logistic regression model. Among the 8632 HIV patients in this analysis, the overall prevalence of anaemia was 39.2%, and the prevalence of mild, moderate, and severe anaemia were 27.2%, 10.8%, and 1.2%, respectively. Anaemia was more prevalence among male, older, little time taken HAART and lower CD4 cell count. Patients taken TCM had lower prevalence of anaemia. The prevalence of anaemia among the HIV patients receiving HAART was high in this study. HIV patients with anaemia who are older and have CD4 cells count lower than 200 cells/mL require more attention. Traditional Chinese medicine may be a potential method to lower the frequency of anaemia.

Use of a Digital Health Application for Influenza Surveillance in China.

PubMed

Hswen, Yulin; Brownstein, John S; Liu, Jeremiah; Hawkins, Jared B

2017-07-01

To examine whether a commercial digital health application could support influenza surveillance in China. We retrieved data from the Thermia online and mobile educational tool, which allows parents to monitor their children's fever and infectious febrile illnesses including influenza. We modeled monthly aggregated influenza-like illness case counts from Thermia users over time and compared them against influenza monthly case counts obtained from the National Health and Family Planning Commission of the People's Republic of China by using time series regression analysis. We retrieved 44 999 observations from January 2014 through July 2016 from Thermia China. Thermia appeared to predict influenza outbreaks 1 month earlier than the National Health and Family Planning Commission influenza surveillance system (P = .046). Being younger, not having up-to-date immunizations, and having an underlying health condition were associated with participant-reported influenza-like illness. Digital health applications could supplement traditional influenza surveillance systems in China by providing access to consumers' symptom reporting. Growing popularity and use of commercial digital health applications in China potentially affords opportunities to support disease detection and monitoring and rapid treatment mobilization.

Factors predicting discordant virological and immunological responses to antiretroviral therapy in HIV-1 clade C infected Zulu/Xhosa in South Africa.

PubMed

Julg, Boris; Poole, Danielle; Ghebremichael, Musie; Castilla, Carmen; Altfeld, Marcus; Sunpath, Henry; Murphy, Richard A; Walker, Bruce D

2012-01-01

Factors predicting suboptimal CD4 cell recovery have been studied in HIV clade-B infected US and European populations. It is, however, uncertain to what extent these results are applicable to HIV clade-C infected African populations. Multivariate analysis using logistic regression and longitudinal analyses using mixed models were employed to assess the impact of age, gender, baseline CD4 cell count, hemoglobin, body mass index (BMI), tuberculosis and other opportunistic co-infections, and frequencies of regimen change on CD4 cell recovery at 12 and 30 months and on overtime change in CD4 cells among 442 virologically suppressed South Africans. Despite adequate virological response 37% (95% CI:32%-42%) and 83% (95% CI:79%-86%) of patients on antiretroviral therapy failed to restore CD4 cell counts ≥ 200 cells/mm(3) after 12 and ≥ 500 cells/mm(3) after 30 months, respectively, in this South African cohort. Critical risk factors for inadequate recovery were older age (p = 0.001) and nadir CD4 cell count at ART initiation (p<0.0001), while concurrent TB co-infection, BMI, baseline hemoglobin, gender and antiretroviral regimen were not significant risk factors. These data suggest that greater efforts are needed to identify and treat HAART-eligible patients prior to severe CD4 cell decline or achievement of advanced age.

Improving the diagnosis of pulmonary tuberculosis in HIV-infected individuals in Ho Chi Minh City, Viet Nam.

PubMed

Nguyen, D T M; Hung, N Q; Giang, L T; Dung, N H; Lan, N T N; Lan, N N; Yen, N T B; Bang, N D; Ngoc, D V; Trinh, L T T; Beasley, R P; Ford, C E; Hwang, L-Y; Graviss, E A

2011-11-01

District 6, An Hoa Clinic in Ho Chi Minh City (HCMC), Viet Nam. To evaluate the performance of various algorithms in tuberculosis (TB) screening and diagnosis in a human immunodeficiency virus (HIV) infected population in HCMC, Viet Nam. A cross-sectional study of 397 consecutive HIV-infected patients seeking care at the An Hoa Clinic from August 2009 to June 2010. Data on participant demographics, clinical status, chest radiography (CXR) and laboratory results were collected. A multiple logistic regression model was developed to assess the association of covariates and pulmonary TB (PTB). The prevalence of sputum culture-confirmed PTB, acid-fast bacilli (AFB) positive TB, and multidrugresistant TB among the 397 HIV-infected patients was respectively 7%, 2%, and 0.3%. Adjusted odds ratios for low CD4+ cell count, positive sputum smear, and CXR to positive sputum culture were respectively 3.17, 32.04 and 4.28. Clinical findings alone had poor sensitivity, but combining CD4+ cell count, AFB sputum smear and CXR had a more accurate diagnostic performance. Results suggest that symptom screening had poor clinical performance, and support the routine use of sputum culture to improve the detection of TB disease in HIV-infected individuals in Viet Nam. However, when routine sputum culture is not available, an algorithm combining CD4+ cell count, AFB sputum smear and CXR is recommended for diagnosing PTB.

Semi-automatic assessment of skin capillary density: proof of principle and validation.

PubMed

Gronenschild, E H B M; Muris, D M J; Schram, M T; Karaca, U; Stehouwer, C D A; Houben, A J H M

2013-11-01

Skin capillary density and recruitment have been proven to be relevant measures of microvascular function. Unfortunately, the assessment of skin capillary density from movie files is very time-consuming, since this is done manually. This impedes the use of this technique in large-scale studies. We aimed to develop a (semi-) automated assessment of skin capillary density. CapiAna (Capillary Analysis) is a newly developed semi-automatic image analysis application. The technique involves four steps: 1) movement correction, 2) selection of the frame range and positioning of the region of interest (ROI), 3) automatic detection of capillaries, and 4) manual correction of detected capillaries. To gain insight into the performance of the technique, skin capillary density was measured in twenty participants (ten women; mean age 56.2 [42-72] years). To investigate the agreement between CapiAna and the classic manual counting procedure, we used weighted Deming regression and Bland-Altman analyses. In addition, intra- and inter-observer coefficients of variation (CVs), and differences in analysis time were assessed. We found a good agreement between CapiAna and the classic manual method, with a Pearson's correlation coefficient (r) of 0.95 (P<0.001) and a Deming regression coefficient of 1.01 (95%CI: 0.91; 1.10). In addition, we found no significant differences between the two methods, with an intercept of the Deming regression of 1.75 (-6.04; 9.54), while the Bland-Altman analysis showed a mean difference (bias) of 2.0 (-13.5; 18.4) capillaries/mm(2). The intra- and inter-observer CVs of CapiAna were 2.5% and 5.6% respectively, while for the classic manual counting procedure these were 3.2% and 7.2%, respectively. Finally, the analysis time for CapiAna ranged between 25 and 35min versus 80 and 95min for the manual counting procedure. We have developed a semi-automatic image analysis application (CapiAna) for the assessment of skin capillary density, which agrees well with the classic manual counting procedure, is time-saving, and has a better reproducibility as compared to the classic manual counting procedure. As a result, the use of skin capillaroscopy is feasible in large-scale studies, which importantly extends the possibilities to perform microcirculation research in humans. © 2013.

Routine Laboratory Blood Tests May Diagnose Significant Fibrosis in Liver Transplant Recipients with Chronic Hepatitis C: A 10 Year Experience.

PubMed

Sheen, Victoria; Nguyen, Heajung; Jimenez, Melissa; Agopian, Vatche; Vangala, Sitaram; Elashoff, David; Saab, Sammy

2016-03-28

The aims of our study were to determine whether routine blood tests, the aspartate aminotransferase (AST) to Platelet Ratio Index (APRI) and Fibrosis 4 (Fib-4) scores, were associated with advanced fibrosis and to create a novel model in liver transplant recipients with chronic hepatitis C virus (HCV). We performed a cross sectional study of patients at The University of California at Los Angeles (UCLA) Medical Center who underwent liver transplantation for HCV. We used linear mixed effects models to analyze association between fibrosis severity and individual biochemical markers and mixed effects logistic regression to construct diagnostic models for advanced fibrosis (METAVIR F3-4). Cross-validation was used to estimate a receiving operator characteristic (ROC) curve for the prediction models and to estimate the area under the curve (AUC). The mean (± standard deviation [SD]) age of our cohort was 55 (±7.7) years, and almost three quarter were male. The mean (±SD) time from transplant to liver biopsy was 19.9 (±17.1) months. The mean (±SD) APRI and Fib-4 scores were 3 (±12) and 7 (±14), respectively. Increased fibrosis was associated with lower platelet count and alanine aminotransferase (ALT) values and higher total bilirubin and Fib-4 scores. We developed a model that takes into account age, gender, platelet count, ALT, and total bilirubin, and this model outperformed APRI and Fib-4 with an AUC of 0.68 (p < 0.001). Our novel prediction model diagnosed the presence of advanced fibrosis more reliably than APRI and Fib-4 scores. This noninvasive calculation may be used clinically to identify liver transplant recipients with HCV with significant liver damage.

Measurement of DSM-5 section II personality disorder constructs using the MMPI-2-RF in clinical and forensic samples.

PubMed

Anderson, Jaime L; Sellbom, Martin; Pymont, Carly; Smid, Wineke; De Saeger, Hilde; Kamphuis, Jan H

2015-09-01

In the current study, we evaluated the associations between the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) scale scores and the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013) Section II personality disorder (PD) criterion counts in inpatient and forensic psychiatric samples from The Netherlands using structured clinical interviews to operationalize PDs. The inpatient psychiatric sample included 190 male and female patients and the forensic sample included 162 male psychiatric patients. We conducted correlation and count regression analyses to evaluate the utility of relevant MMPI-2-RF scales in predicting PD criterion count scores. Generally, results from these analyses emerged as conceptually expected and provided evidence that MMPI-2-RF scales can be useful in assessing PDs. At the zero-order level, most hypothesized associations between Section II disorders and MMPI-2-RF scales were supported. Similarly, in the regression analyses, a unique set of predictors emerged for each PD that was generally in line with conceptual expectations. Additionally, the results provided general evidence that PDs can be captured by dimensional psychopathology constructs, which has implications for both DSM-5 Section III specifically and the personality psychopathology literature more broadly. (c) 2015 APA, all rights reserved.

Factors associated with success of telaprevir- and boceprevir-based triple therapy for hepatitis C virus infection

PubMed Central

Bichoupan, Kian; Tandon, Neeta; Martel-Laferriere, Valerie; Patel, Neal M; Sachs, David; Ng, Michel; Schonfeld, Emily A; Pappas, Alexis; Crismale, James; Stivala, Alicia; Khaitova, Viktoriya; Gardenier, Donald; Linderman, Michael; Olson, William; Perumalswami, Ponni V; Schiano, Thomas D; Odin, Joseph A; Liu, Lawrence U; Dieterich, Douglas T; Branch, Andrea D

2017-01-01

AIM To evaluate new therapies for hepatitis C virus (HCV), data about real-world outcomes are needed. METHODS Outcomes of 223 patients with genotype 1 HCV who started telaprevir- or boceprevir-based triple therapy (May 2011-March 2012) at the Mount Sinai Medical Center were analyzed. Human immunodeficiency virus-positive patients and patients who received a liver transplant were excluded. Factors associated with sustained virological response (SVR24) and relapse were analyzed by univariable and multivariable logistic regression as well as classification and regression trees. Fast virological response (FVR) was defined as undetectable HCV RNA at week-4 (telaprevir) or week-8 (boceprevir). RESULTS The median age was 57 years, 18% were black, 44% had advanced fibrosis/cirrhosis (FIB-4 ≥ 3.25). Only 42% (94/223) of patients achieved SVR24 on an intention-to-treat basis. In a model that included platelets, SVR24 was associated with white race [odds ratio (OR) = 5.92, 95% confidence interval (CI): 2.34-14.96], HCV sub-genotype 1b (OR = 2.81, 95%CI: 1.45-5.44), platelet count (OR = 1.10, per x 104 cells/μL, 95%CI: 1.05-1.16), and IL28B CC genotype (OR = 3.54, 95%CI: 1.19-10.53). Platelet counts > 135 x 103/μL were the strongest predictor of SVR by classification and regression tree. Relapse occurred in 25% (27/104) of patients with an end-of-treatment response and was associated with non-FVR (OR = 4.77, 95%CI: 1.68-13.56), HCV sub-genotype 1a (OR = 5.20; 95%CI: 1.40-18.97), and FIB-4 ≥ 3.25 (OR = 2.77; 95%CI: 1.07-7.22). CONCLUSION The SVR rate was 42% with telaprevir- or boceprevir-based triple therapy in real-world practice. Low platelets and advanced fibrosis were associated with treatment failure and relapse. PMID:28469811

Factors associated with success of telaprevir- and boceprevir-based triple therapy for hepatitis C virus infection.

PubMed

Bichoupan, Kian; Tandon, Neeta; Martel-Laferriere, Valerie; Patel, Neal M; Sachs, David; Ng, Michel; Schonfeld, Emily A; Pappas, Alexis; Crismale, James; Stivala, Alicia; Khaitova, Viktoriya; Gardenier, Donald; Linderman, Michael; Olson, William; Perumalswami, Ponni V; Schiano, Thomas D; Odin, Joseph A; Liu, Lawrence U; Dieterich, Douglas T; Branch, Andrea D

2017-04-18

To evaluate new therapies for hepatitis C virus (HCV), data about real-world outcomes are needed. Outcomes of 223 patients with genotype 1 HCV who started telaprevir- or boceprevir-based triple therapy (May 2011-March 2012) at the Mount Sinai Medical Center were analyzed. Human immunodeficiency virus-positive patients and patients who received a liver transplant were excluded. Factors associated with sustained virological response (SVR24) and relapse were analyzed by univariable and multivariable logistic regression as well as classification and regression trees. Fast virological response (FVR) was defined as undetectable HCV RNA at week-4 (telaprevir) or week-8 (boceprevir). The median age was 57 years, 18% were black, 44% had advanced fibrosis/cirrhosis (FIB-4 ≥ 3.25). Only 42% (94/223) of patients achieved SVR24 on an intention-to-treat basis. In a model that included platelets, SVR24 was associated with white race [odds ratio (OR) = 5.92, 95% confidence interval (CI): 2.34-14.96], HCV sub-genotype 1b (OR = 2.81, 95%CI: 1.45-5.44), platelet count (OR = 1.10, per x 10 4 cells/μL, 95%CI: 1.05-1.16), and IL28B CC genotype (OR = 3.54, 95%CI: 1.19-10.53). Platelet counts > 135 x 10 3 /μL were the strongest predictor of SVR by classification and regression tree. Relapse occurred in 25% (27/104) of patients with an end-of-treatment response and was associated with non-FVR (OR = 4.77, 95%CI: 1.68-13.56), HCV sub-genotype 1a (OR = 5.20; 95%CI: 1.40-18.97), and FIB-4 ≥ 3.25 (OR = 2.77; 95%CI: 1.07-7.22). The SVR rate was 42% with telaprevir- or boceprevir-based triple therapy in real-world practice. Low platelets and advanced fibrosis were associated with treatment failure and relapse.

A cascaded model of spectral distortions due to spectral response effects and pulse pileup effects in a photon-counting x-ray detector for CT

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

Cammin, Jochen, E-mail: jcammin1@jhmi.edu, E-mail: ktaguchi@jhmi.edu; Taguchi, Katsuyuki, E-mail: jcammin1@jhmi.edu, E-mail: ktaguchi@jhmi.edu; Xu, Jennifer

Purpose: Energy discriminating, photon-counting detectors (PCDs) are an emerging technology for computed tomography (CT) with various potential benefits for clinical CT. The photon energies measured by PCDs can be distorted due to the interactions of a photon with the detector and the interaction of multiple coincident photons. These effects result in distorted recorded x-ray spectra which may lead to artifacts in reconstructed CT images and inaccuracies in tissue identification. Model-based compensation techniques have the potential to account for the distortion effects. This approach requires only a small number of parameters and is applicable to a wide range of spectra andmore » count rates, but it needs an accurate model of the spectral distortions occurring in PCDs. The purpose of this study was to develop a model of those spectral distortions and to evaluate the model using a PCD (model DXMCT-1; DxRay, Inc., Northridge, CA) and various x-ray spectra in a wide range of count rates. Methods: The authors hypothesize that the complex phenomena of spectral distortions can be modeled by: (1) separating them into count-rate independent factors that we call the spectral response effects (SRE), and count-rate dependent factors that we call the pulse pileup effects (PPE), (2) developing separate models for SRE and PPE, and (3) cascading the SRE and PPE models into a combined SRE+PPE model that describes PCD distortions at both low and high count rates. The SRE model describes the probability distribution of the recorded spectrum, with a photo peak and a continuum tail, given the incident photon energy. Model parameters were obtained from calibration measurements with three radioisotopes and then interpolated linearly for other energies. The PPE model used was developed in the authors’ previous work [K. Taguchi et al., “Modeling the performance of a photon counting x-ray detector for CT: Energy response and pulse pileup effects,” Med. Phys. 38(2), 1089–1102 (2011)]. The agreement between the x-ray spectra calculated by the cascaded SRE+PPE model and the measured spectra was evaluated for various levels of deadtime loss ratios (DLR) and incident spectral shapes, realized using different attenuators, in terms of the weighted coefficient of variation (COV{sub W}), i.e., the root mean square difference weighted by the statistical errors of the data and divided by the mean. Results: At low count rates, when DLR < 10%, the distorted spectra measured by the DXMCT-1 were in agreement with those calculated by SRE only, with COV{sub W}'s less than 4%. At higher count rates, the measured spectra were also in agreement with the ones calculated by the cascaded SRE+PPE model; with PMMA as attenuator, COV{sub W} was 5.6% at a DLR of 22% and as small as 6.7% for a DLR as high as 55%. Conclusions: The x-ray spectra calculated by the proposed model agreed with the measured spectra over a wide range of count rates and spectral shapes. The SRE model predicted the distorted, recorded spectra with low count rates over various types and thicknesses of attenuators. The study also validated the hypothesis that the complex spectral distortions in a PCD can be adequately modeled by cascading the count-rate independent SRE and the count-rate dependent PPE.« less

A cascaded model of spectral distortions due to spectral response effects and pulse pileup effects in a photon-counting x-ray detector for CT

PubMed Central

Cammin, Jochen; Xu, Jennifer; Barber, William C.; Iwanczyk, Jan S.; Hartsough, Neal E.; Taguchi, Katsuyuki

2014-01-01

Purpose: Energy discriminating, photon-counting detectors (PCDs) are an emerging technology for computed tomography (CT) with various potential benefits for clinical CT. The photon energies measured by PCDs can be distorted due to the interactions of a photon with the detector and the interaction of multiple coincident photons. These effects result in distorted recorded x-ray spectra which may lead to artifacts in reconstructed CT images and inaccuracies in tissue identification. Model-based compensation techniques have the potential to account for the distortion effects. This approach requires only a small number of parameters and is applicable to a wide range of spectra and count rates, but it needs an accurate model of the spectral distortions occurring in PCDs. The purpose of this study was to develop a model of those spectral distortions and to evaluate the model using a PCD (model DXMCT-1; DxRay, Inc., Northridge, CA) and various x-ray spectra in a wide range of count rates. Methods: The authors hypothesize that the complex phenomena of spectral distortions can be modeled by: (1) separating them into count-rate independent factors that we call the spectral response effects (SRE), and count-rate dependent factors that we call the pulse pileup effects (PPE), (2) developing separate models for SRE and PPE, and (3) cascading the SRE and PPE models into a combined SRE+PPE model that describes PCD distortions at both low and high count rates. The SRE model describes the probability distribution of the recorded spectrum, with a photo peak and a continuum tail, given the incident photon energy. Model parameters were obtained from calibration measurements with three radioisotopes and then interpolated linearly for other energies. The PPE model used was developed in the authors’ previous work [K. Taguchi , “Modeling the performance of a photon counting x-ray detector for CT: Energy response and pulse pileup effects,” Med. Phys. 38(2), 1089–1102 (2011)]. The agreement between the x-ray spectra calculated by the cascaded SRE+PPE model and the measured spectra was evaluated for various levels of deadtime loss ratios (DLR) and incident spectral shapes, realized using different attenuators, in terms of the weighted coefficient of variation (COVW), i.e., the root mean square difference weighted by the statistical errors of the data and divided by the mean. Results: At low count rates, when DLR < 10%, the distorted spectra measured by the DXMCT-1 were in agreement with those calculated by SRE only, with COVW's less than 4%. At higher count rates, the measured spectra were also in agreement with the ones calculated by the cascaded SRE+PPE model; with PMMA as attenuator, COVW was 5.6% at a DLR of 22% and as small as 6.7% for a DLR as high as 55%. Conclusions: The x-ray spectra calculated by the proposed model agreed with the measured spectra over a wide range of count rates and spectral shapes. The SRE model predicted the distorted, recorded spectra with low count rates over various types and thicknesses of attenuators. The study also validated the hypothesis that the complex spectral distortions in a PCD can be adequately modeled by cascading the count-rate independent SRE and the count-rate dependent PPE. PMID:24694136

A statistical downscaling approach for roadside NO2 concentrations: Application to a WRF-Chem study for Berlin

NASA Astrophysics Data System (ADS)

Kuik, Friderike; Lauer, Axel; von Schneidemesser, Erika; Butler, Tim

2017-04-01

Many European cities continue to struggle with meeting the European air quality limits for NO2. In Berlin, Germany, most of the exceedances in NO2 recorded at monitoring sites near busy roads can be largely attributed to emissions from traffic. In order to assess the impact of changes in traffic emissions on air quality at policy relevant scales, we combine the regional atmosphere-chemistry transport model WRF-Chem at a resolution of 1kmx1km with a statistical downscaling approach. Here, we build on the recently published study evaluating the performance of a WRF-Chem setup in representing observed urban background NO2 concentrations from Kuik et al. (2016) and extend this setup by developing and testing an approach to statistically downscale simulated urban background NO2 concentrations to street level. The approach uses a multilinear regression model to relate roadside NO2 concentrations observed with the municipal monitoring network with observed NO2 concentrations at urban background sites and observed traffic counts. For this, the urban background NO2 concentrations are decomposed into a long term, a synoptic and a diurnal component using the Kolmogorov-Zurbenko filtering method. We estimate the coefficients of the regression model for five different roadside stations in Berlin representing different street types. In a next step we combine the coefficients with simulated urban background concentrations and observed traffic counts, in order to estimate roadside NO2 concentrations based on the results obtained with WRF-Chem at the five selected stations. In a third step, we extrapolate the NO2 concentrations to all major roads in Berlin. The latter is based on available data for Berlin of daily mean traffic counts, diurnal and weekly cycles of traffic as well as simulated urban background NO2 concentrations. We evaluate the NO2 concentrations estimated with this method at street level for Berlin with additional observational data from stationary measurements and mobile measurements conducted during a campaign in summer 2014. The results show that this approach allows us to estimate NO2 concentrations at roadside reasonably well. The approach can be applied when observations show a strong correlation between roadside NO2 concentrations and traffic emissions from a single type of road. The method, however, shows weaknesses for intersections where observed NO2 concentrations are influenced by traffic on several different roads. We then apply this downscaling approach to estimate the impact of different traffic emission scenarios both on urban background and street level NO2 concentrations. References Kuik, F., Lauer, A., Churkina, G., Denier van der Gon, H. A. C., Fenner, D., Mar, K. A., and Butler, T. M.: Air quality modelling in the Berlin-Brandenburg region using WRF-Chem v3.7.1: sensitivity to resolution of model grid and input data, Geosci. Model Dev., 9, 4339-4363, doi:10.5194/gmd-9-4339-2016, 2016.

NaCl nucleation from brine in seeded simulations: Sources of uncertainty in rate estimates.

PubMed

Zimmermann, Nils E R; Vorselaars, Bart; Espinosa, Jorge R; Quigley, David; Smith, William R; Sanz, Eduardo; Vega, Carlos; Peters, Baron

2018-06-14

This work reexamines seeded simulation results for NaCl nucleation from a supersaturated aqueous solution at 298.15 K and 1 bar pressure. We present a linear regression approach for analyzing seeded simulation data that provides both nucleation rates and uncertainty estimates. Our results show that rates obtained from seeded simulations rely critically on a precise driving force for the model system. The driving force vs. solute concentration curve need not exactly reproduce that of the real system, but it should accurately describe the thermodynamic properties of the model system. We also show that rate estimates depend strongly on the nucleus size metric. We show that the rate estimates systematically increase as more stringent local order parameters are used to count members of a cluster and provide tentative suggestions for appropriate clustering criteria.

Comparisons of monthly mean cosmic ray counting rates observes from worldwide network of neutron monitors

NASA Technical Reports Server (NTRS)

Ryu, J. Y.; Wada, M.

1985-01-01

In order to examine the stability of neutron monitor observation, each of the monthly average counting rates of a neutron monitors is correlated to those of Kiel neutron monitor. The regression coefficients thus obtained are compared with the coupling coefficients of isotropic intensity radiation. The results of the comparisons for five year periods during 1963 to 1982, and for whole period are given. The variation spectrum with a single power law with an exponent of -0.75 up to 50 GV is not so unsatisfactory one. More than one half of the stations show correlations with the coefficient greater than 0.9. Some stations have shifted the level of mean counting rates by changing the instrumental characteristics which can be adjusted.

Late HIV Testing in a Cohort of HIV-Infected Patients in Puerto Rico.

PubMed

Tossas-Milligan, Katherine Y; Hunter-Mellado, Robert F; Mayor, Angel M; Fernández-Santos, Diana M; Dworkin, Mark S

2015-09-01

Late HIV testing (LT), defined as receiving an AIDS diagnosis within a year of one's first positive HIV test, is associated with higher HIV transmission, lower HAART effectiveness, and worse outcomes. Latinos represent 36% of LT in the US, yet research concerning LT among HIV cases in Puerto Rico is scarce. Multivariable logistic regression analysis was used to identify factors associated with LT, and a Cochran‒Armitage test was used to determine LT trends in an HIV-infected cohort followed at a clinic in Puerto Rico specialized in the management and treatment of HIV. From 2000 to 2011, 47% of eligible patients were late testers, with lower median CD4 counts (54 vs. 420 cells/mm3) and higher median HIV viral load counts (253,680 vs. 23,700 copies/mL) than non-LT patients. LT prevalence decreased significantly, from 47% in 2000 to 37% in 2011. In a mutually adjusted logistic regression model, males, older age at enrollment and past history of IDU significantly increased LT odds, whereas having a history of amphetamine use decreased LT odds. When the data were stratified by mode of transmission, it became apparent that only the category men who have sex with men (MSM) saw a significant reduction in the proportion of LT, falling from 67% in 2000 to 33% in 2011. These results suggest a gap in early HIV detection in Puerto Rico, a gap that decreased only among MSM. An evaluation of the manner in which current HIV-testing guidelines are implemented on the island is needed.

Leukocytes as risk markers for cardiovascular disease in adolescents: association with birth characteristics, nutritional status and biochemical tests

PubMed Central

do Prado, Pedro Paulo; de Faria, Franciane Rocha; de Faria, Eliane Rodrigues; Franceschini, Sylvia do Carmo Castro; Priore, Silvia Eloiza

2016-01-01

Abstract Objective: To evaluate the correlation between the number of leukocytes and cardiovascular risks associated with birth characteristics, nutritional status and biochemical tests. Methods: Cross-sectional study developed with 475 adolescents, born between 1992 and 2001, in the municipality of Viçosa (MG). Maternal medical records were analyzed in the hospital units, and the following was recorded: birth weight and length, head circumference, chest circumference, Apgar score, gestational age. In adolescents, body mass index, skinfold thickness, body composition, blood count, biochemical tests and clinical variables were also assessed. The statistical analyses was carried out using Statistical Package for Social Sciences (SPSS) version 20.0 and Data Analysis and Statistical Software (STATA) with Kruskal–Wallis, Mann–Whitney, chi-square or Fisher's exact tests and Linear Regression. Significance level was set at α<0.05. The study was approved by the Research Ethics Committee of UFV for studies with human subjects. Results: Weight and birth length, head and chest circumference were higher among boys. In adolescents, the number of leukocytes was higher in individuals with excess weight and body fat and high adiposity index, waist-to-height ratio and waist circumference. Only altered triglycerides showed differences between leukocyte medians. Regardless of the anthropometric variable of the final regression model, the stage of adolescence, number of platelets, eosinophils, monocytes and lymphocytes were associated with the increase in leukocytes. Conclusions: The birth variables were not associated with changes in leukocyte numbers, whereas the anthropometric variables were good indicators for a higher leukocyte count, regardless of the stage of adolescence and gender. PMID:26572104

Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT).

PubMed

Isotani, Shuji; Shimoyama, Hirofumi; Yokota, Isao; Noma, Yasuhiro; Kitamura, Kousuke; China, Toshiyuki; Saito, Keisuke; Hisasue, Shin-ichi; Ide, Hisamitsu; Muto, Satoru; Yamaguchi, Raizo; Ukimura, Osamu; Gill, Inderbir S; Horie, Shigeo

2015-10-01

The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model. Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models. The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (p < 0.01). The significant correlated variables for %eGFR alteration were %RCV preservation (r = 0.58, p < 0.01) and %RPV preservation (r = 0.54, p < 0.01). We developed our regression model as follows: postoperative eGFR = 57.87 - 0.55(age) - 15.01(body surface area) + 0.30(preoperative eGFR) + 52.92(%RCV preservation). Strong correlation was seen between postoperative eGFR and the calculated estimation model (r = 0.83; p < 0.001). The external validation cohort (n = 21) showed our model outperformed previously reported models. Combining MDCT renal volumetry and clinical indices might yield an important tool for predicting postoperative renal function.

Pulse pileup statistics for energy discriminating photon counting x-ray detectors

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

Wang, Adam S.; Harrison, Daniel; Lobastov, Vladimir

Purpose: Energy discriminating photon counting x-ray detectors can be subject to a wide range of flux rates if applied in clinical settings. Even when the incident rate is a small fraction of the detector's maximum periodic rate N{sub 0}, pulse pileup leads to count rate losses and spectral distortion. Although the deterministic effects can be corrected, the detrimental effect of pileup on image noise is not well understood and may limit the performance of photon counting systems. Therefore, the authors devise a method to determine the detector count statistics and imaging performance. Methods: The detector count statistics are derived analyticallymore » for an idealized pileup model with delta pulses of a nonparalyzable detector. These statistics are then used to compute the performance (e.g., contrast-to-noise ratio) for both single material and material decomposition contrast detection tasks via the Cramer-Rao lower bound (CRLB) as a function of the detector input count rate. With more realistic unipolar and bipolar pulse pileup models of a nonparalyzable detector, the imaging task performance is determined by Monte Carlo simulations and also approximated by a multinomial method based solely on the mean detected output spectrum. Photon counting performance at different count rates is compared with ideal energy integration, which is unaffected by count rate. Results: The authors found that an ideal photon counting detector with perfect energy resolution outperforms energy integration for our contrast detection tasks, but when the input count rate exceeds 20%N{sub 0}, many of these benefits disappear. The benefit with iodine contrast falls rapidly with increased count rate while water contrast is not as sensitive to count rates. The performance with a delta pulse model is overoptimistic when compared to the more realistic bipolar pulse model. The multinomial approximation predicts imaging performance very close to the prediction from Monte Carlo simulations. The monoenergetic image with maximum contrast-to-noise ratio from dual energy imaging with ideal photon counting is only slightly better than with dual kVp energy integration, and with a bipolar pulse model, energy integration outperforms photon counting for this particular metric because of the count rate losses. However, the material resolving capability of photon counting can be superior to energy integration with dual kVp even in the presence of pileup because of the energy information available to photon counting. Conclusions: A computationally efficient multinomial approximation of the count statistics that is based on the mean output spectrum can accurately predict imaging performance. This enables photon counting system designers to directly relate the effect of pileup to its impact on imaging statistics and how to best take advantage of the benefits of energy discriminating photon counting detectors, such as material separation with spectral imaging.« less

Association between study design and citation counts of articles published in the American Journal of Orthodontics and Dentofacial Orthopedics and Angle Orthodontist.

PubMed

Allareddy, Veerasathpurush; Lee, Min Kyeong; Shah, Andrea; Elangovan, Satheesh; Lin, Chin-Yu

2012-01-01

The scientific community views meta-analyses and systematic reviews, in addition to well-designed randomized controlled clinical trials, as the highest echelon in the continuum of hierarchy of evidence. The objective of this study was to examine the association between different study designs and citation counts of articles published in the American Journal of Orthodontics and Dentofacial Orthopedics and Angle Orthodontist. All articles, excluding editorial comments, letters to the editor, commentaries, and special articles, that were published in the American Journal of Orthodontics and Dentofacial Orthopedics and Angle Orthodontist during the years 2004 and 2005 were examined in this study. The number of times an article was cited in the first 24 months after its publication was computed. The PubMed database was used to index the study design of the articles. The association between study design and citation counts was examined using the Kruskal-Wallis test. A multivariable negative binomial regression model was used to examine the association between citation count and study design along with several other confounding variables. A total of 624 articles were selected for analysis. Of these, there were 25 meta-analyses or review articles, 42 randomized clinical trials, 59 clinical trials, 48 animal studies, 64 case reports, and 386 quasiexperimental/miscellaneous study designs. The mean ± SD citation count was 1.04 ± 1.46. Nearly half of the articles (n = 311) were not cited even once during the observation period. Case reports were cited less frequently than meta-analyses or reviews (incident risk ratio, 0.37; 95% confidence interval, 0.19 to 0.72; P = .003), even after adjusting for other independent variables. Among various study designs, meta-analyses and review articles are more likely to be cited in the first 24 months after publication. This study demonstrates the importance of publishing more meta-analyses and review articles for quicker dissemination of research findings.

Associations of Physical Activity, Sports Participation and Active Commuting on Mathematic Performance and Inhibitory Control in Adolescents.

PubMed

Domazet, Sidsel L; Tarp, Jakob; Huang, Tao; Gejl, Anne Kær; Andersen, Lars Bo; Froberg, Karsten; Bugge, Anna

2016-01-01

To examine objectively measured physical activity level, organized sports participation and active commuting to school in relation to mathematic performance and inhibitory control in adolescents. The design was cross-sectional. A convenient sample of 869 sixth and seventh grade students (12-14 years) was invited to participate in the study. A total of 568 students fulfilled the inclusion criteria and comprised the final sample for this study. Mathematic performance was assessed by a customized test and inhibitory control was assessed by a modified Eriksen flanker task. Physical activity was assessed with GT3X and GT3X+ accelerometers presented in sex-specific quartiles of mean counts per minute and mean minutes per day in moderate-to-vigorous physical activity. Active commuting and sports participation was self-reported. Mixed model regression was applied. Total physical activity level was stratified by bicycling status in order to bypass measurement error subject to the accelerometer. Non-cyclists in the 2nd quartile of counts per minute displayed a higher mathematic score, so did cyclists in the 2nd and 3rd quartile of moderate-to-vigorous physical activity relative to the least active quartile. Non-cyclists in the 3rd quartile of counts per minute had an improved reaction time and cyclists in the 2nd quartile of counts per minute and moderate-to-vigorous physical activity displayed an improved accuracy, whereas non-cyclists in the 2nd quartile of counts per minute showed an inferior accuracy relative to the least active quartile. Bicycling to school and organized sports participation were positively associated with mathematic performance. Sports participation and bicycling were positively associated with mathematic performance. Results regarding objectively measured physical activity were mixed. Although, no linear nor dose-response relationship was observed there was no indication of a higher activity level impairing the scholastic or cognitive performance.

The relationship between various exposure metrics for elongate mineral particles (EMP) in the taconite mining and processing industry.

PubMed

Hwang, Jooyeon; Ramachandran, Gurumurthy; Raynor, Peter C; Alexander, Bruce H; Mandel, Jeffrey H

2014-01-01

Different dimensions of elongate mineral particles (EMP) have been proposed as being relevant to respiratory health end-points such as mesothelioma and lung cancer. In this article, a methodology for converting personal EMP exposures measured using the National Institute for Occupational Safety and Health (NIOSH) 7400/7402 methods to exposures based on other size-based definitions has been proposed and illustrated. Area monitoring for EMP in the taconite mines in Minnesota's Mesabi Iron Range was conducted using a Micro Orifice Uniform Deposit Impactor (MOUDI) size-fractionating sampler. EMP on stages of the MOUDI were counted and sized according to each EMP definition using an indirect-transfer transmission electron microscopy (ISO Method 13794). EMP were identified using energy-dispersive x-ray and electron diffraction analysis. Conversion factors between the EMP counts based on different definitions were estimated using (1) a linear regression model across all locations and (2) a location-specific ratio of the count based on each EMP definition to the NIOSH 7400/7402 count. The highest fractions of EMP concentrations were found for EMP that were 1-3 μm in length and 0.2-0.5 μm in width. Therefore, the current standard NIOSH Method 7400, which only counts EMP >5 μm in length and ≥ 3 in aspect ratio, may underestimate amphibole EMP exposures. At the same time, there was a high degree of correlation between the exposures estimated according to the different size-based metrics. Therefore, the various dimensional definitions probably do not result in different dose-response relationships in epidemiological analyses. Given the high degree of correlation between the various metrics, a result consistent with prior research, a more reasonable metric might be the measurement of all EMP irrespective of size. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: figures detailing EMP concentration.].

Associations of Physical Activity, Sports Participation and Active Commuting on Mathematic Performance and Inhibitory Control in Adolescents

PubMed Central

Huang, Tao; Gejl, Anne Kær; Froberg, Karsten

2016-01-01

Objectives To examine objectively measured physical activity level, organized sports participation and active commuting to school in relation to mathematic performance and inhibitory control in adolescents. Methods The design was cross-sectional. A convenient sample of 869 sixth and seventh grade students (12–14 years) was invited to participate in the study. A total of 568 students fulfilled the inclusion criteria and comprised the final sample for this study. Mathematic performance was assessed by a customized test and inhibitory control was assessed by a modified Eriksen flanker task. Physical activity was assessed with GT3X and GT3X+ accelerometers presented in sex-specific quartiles of mean counts per minute and mean minutes per day in moderate-to-vigorous physical activity. Active commuting and sports participation was self-reported. Mixed model regression was applied. Total physical activity level was stratified by bicycling status in order to bypass measurement error subject to the accelerometer. Results Non-cyclists in the 2nd quartile of counts per minute displayed a higher mathematic score, so did cyclists in the 2nd and 3rd quartile of moderate-to-vigorous physical activity relative to the least active quartile. Non-cyclists in the 3rd quartile of counts per minute had an improved reaction time and cyclists in the 2nd quartile of counts per minute and moderate-to-vigorous physical activity displayed an improved accuracy, whereas non-cyclists in the 2nd quartile of counts per minute showed an inferior accuracy relative to the least active quartile. Bicycling to school and organized sports participation were positively associated with mathematic performance. Conclusions Sports participation and bicycling were positively associated with mathematic performance. Results regarding objectively measured physical activity were mixed. Although, no linear nor dose-response relationship was observed there was no indication of a higher activity level impairing the scholastic or cognitive performance. PMID:26727211

Impact of intimate partner violence on clinic attendance, viral suppression and CD4 cell count of women living with HIV in an urban clinic setting.

PubMed

Anderson, Jocelyn C; Campbell, Jacquelyn C; Glass, Nancy E; Decker, Michele R; Perrin, Nancy; Farley, Jason

2018-04-01

The substance abuse, violence and HIV/AIDS (SAVA) syndemic represents a complex set of social determinants of health that impacts the lives of women. Specifically, there is growing evidence that intimate partner violence (IPV) places women at risk for both HIV acquisition and poorer HIV-related outcomes. This study assessed prevalence of IPV in an HIV clinic setting, as well as the associations between IPV, symptoms of depression and PTSD on three HIV-related outcomes-CD4 count, viral load, and missed clinic visits. In total, 239 adult women attending an HIV-specialty clinic were included. Fifty-one percent (95% CI: 45%-58%) reported past year psychological, physical, or sexual intimate partner abuse. In unadjusted models, IPV was associated with having a CD4 count <200 (OR: 3.284, 95% CI: 1.251-8.619, p = 0.016) and having a detectable viral load (OR: 1.842, 95% CI: 1.006-3.371, p = 0.048). IPV was not associated with missing >33% of past year all type clinic visits (OR: 1.535, 95% CI: 0.920-2.560, p = 0.101) or HIV specialty clinic visits (OR: 1.251, 95% CI: 0.732-2.140). In multivariable regression, controlling for substance use, mental health symptoms and demographic covariates, IPV remained associated with CD4 count <200 (OR: 3.536, 95% CI: 1.114-11.224, p = 0.032), but not viral suppression. The association between IPV and lower CD4 counts, but not adherence markers such as viral suppression and missed visits, indicates a need to examine potential physiologic impacts of trauma that may alter the immune functioning of women living with HIV. Incorporating trauma-informed approaches into current HIV care settings is one opportunity that begins to address IPV in this patient population.

The effect of alcohol outlets, sales and trading hours on alcohol-related injuries presenting at emergency departments in Perth, Australia, from 2002 to 2010.

PubMed

Hobday, Michelle; Chikritzhs, Tanya; Liang, Wenbin; Meuleners, Lynn

2015-12-01

Few studies have investigated the combined effects of alcohol sales, outlet numbers and trading hours on alcohol-related harms. This study aimed to test whether associations: (i) exist between alcohol-related emergency department (ED) injuries and alcohol sales and counts of outlets; (ii) vary between on- and off-premises outlets; and (iii) vary by trading hours conditions [extended trading permits (ETP) versus standard hours]. Panel study using 117 postcodes over 8 years (2002-10): 936 data points. Perth, Australia. ED injury presentations, aggregated to postcode-level. Alcohol-related injuries were identified using time-based surrogate measures: night injuries (n=51,241) and weekend night injuries (n=30,682). Measures of alcohol availability included number of outlets with standard and extended trading hours and mean sales per postcode. Negative binomial regression modelling with random effects was used to examine associations between availability and alcohol-related injury, controlling for socio-demographic characteristics. (i) Night injuries were associated significantly with counts of on-premises outlets [incident rate ratio (IRR)=1.046; 95% confidence interval (CI)=1.014-1.078] and sales per off-premises outlet (IRR=1.019; 95% CI=1.004-1.035); (ii) counts of on-premises outlets were positively associated with alcohol-related injury while counts of off-premises outlets indicated a negative association; and (iii) weekend night injuries increased by about 5% per on-premises outlet with an ETP (IRR=1.049; 95% CI=1.015-1.084) and by less than 1% for outlets with standard trading hours (IRR=1.008; 95% CI=1.004-1.013). Regions of Perth, Australia with greater off-premises alcohol sales and counts of on-premises alcohol outlets, particularly those with extended trading hours, appear to have higher levels of alcohol-related injuries. © 2015 Society for the Study of Addiction.

Reflectance of vegetation, soil, and water

NASA Technical Reports Server (NTRS)

Wiegand, C. L. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The Kubelka-Munk model, a regression model, and a combination of these models were used to extract plant, soil, and shadow reflectance components of vegetated surfaces. The combination model was superior to the others; it explained 86% of the variation in band 5 reflectance of corn and sorghum, and 90% of the variation in band 6 reflectance of cotton. A fractional shadow term substantially increased the proportion of the digital count sum of squares explained when plant parameters alone explained 85% or less of the variation. Overall recognition of 94 agricultural fields using simultaneously acquired aircraft and spacecraft MSS data was 61.8 and 62.8%, respectively; recognition of vegetable fields larger than 10 acres and taller than 25 cm, rose to 88.9 and 100% for aircraft and spacecraft, respectively. Agriculture and rangeland, were well discriminated for the entire county but level 2 categories of vegetables, citrus, and idle cropland, except for citrus, were not.

Modeling the distribution of illicit oily discharges detected by aerial surveillance in western Canadian marine waters.

PubMed

Serra-Sogas, Norma; O'Hara, Patrick D; Canessa, Rosaline

2014-10-15

Oily discharges from vessel operations have been documented in Canada's Pacific region by the National Aerial Surveillance Program (NASP) since the early 1990s. We explored a number of regression methods to explain the distribution and counts per grid cell of oily discharges detected from 1998 to 2007 using independent predictor variables, while trying to address the large number of zeros present in the data. Best-fit models indicate that discharges are generally concentrated close to shore typically in association with small harbours, and with major commercial and tourist centers. Oily discharges were also concentrated in Barkley Sound and at the entrance of Juan de Fuca Strait. The identification of important factors associated with discharge patterns, and predicting discharge rates in areas with surveillance effort can be used to inform future surveillance. Model output can also be used as inputs for risk models for existing conditions and as baseline for future scenarios. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Prognostic Impact of Neutrophil/Lymphocyte Ratio, Platelet Count, CRP, and Albumin Levels in Metastatic Colorectal Cancer Patients Treated with FOLFIRI-Bevacizumab.

PubMed

Artaç, Mehmet; Uysal, Mükremin; Karaağaç, Mustafa; Korkmaz, Levent; Er, Zehra; Güler, Tunç; Börüban, Melih Cem; Bozcuk, Hakan

2017-06-01

Metastatic colorectal cancer (mCRC) is a lethal disease and fluorouracil-leucovorin-irinotecan (FOLFIRI) plus bevacizumab (bev) is a standard approach. Hence, there is a strong need for identifying new prognostic factors to show the efficacy of FOLFIRI-bev. This is a retrospective study including patients (n = 90) with mCRC from two centers in Turkey. Neutrophil/lymphocyte (N/L) ratio, platelet count, albumin, and C-reactive protein (CRP) were recorded before FOLFIRI-bev therapy. The efficacy of these factors on progression-free survival (PFS) was analyzed with Kaplan Meier and Cox regression analysis. And the cutoff value of N/L ratio was analyzed with ROC analysis. The median age was 56 years (range 21-80). Forty-seven percent of patients with N/L ratio >2.5 showed progressive disease versus 43 % in patients with N/L ratio <2.5 (p = 0.025). The median PFS was 8.1 months for the patients with N/L ratio >2.5 versus 13.5 months for the patients with N/L ratio <2.5 (p = 0.025). At univariate Cox regression analysis, high baseline neutrophil count, LDH, N/L ratio, and CRP were all significantly associated with poor prognosis. At multivariate Cox regression analysis, CRP was confirmed to be a better independent prognostic factor. CRP variable was divided into above the upper limit of normal (ULN) and normal value. The median PFSs of the patients with normal and above ULN were 11.3 versus 5.8 months, respectively (p = 0.022). CRP and N/L ratio are potential predictors for advanced mCRC treated with FOLFIRI-bev.

Association between Platelet Counts before and during Pharmacological Therapy for Patent Ductus Arteriosus and Treatment Failure in Preterm Infants.

PubMed

Sallmon, Hannes; Weber, Sven C; Dirks, Juliane; Schiffer, Tamara; Klippstein, Tamara; Stein, Anja; Felderhoff-Müser, Ursula; Metze, Boris; Hansmann, Georg; Bührer, Christoph; Cremer, Malte; Koehne, Petra

2018-01-01

The role of platelets for mediating closure of the ductus arteriosus in human preterm infants is controversial. Especially, the effect of low platelet counts on pharmacological treatment failure is still unclear. In this retrospective study of 471 preterm infants [<1,500 g birth weight (BW)], who were treated for a patent ductus arteriosus (PDA) with indomethacin or ibuprofen, we investigated whether platelet counts before or during pharmacological treatment had an impact on the successful closure of a hemodynamically significant PDA. The effects of other factors, such as sepsis, preeclampsia, gestational age, BW, and gender, were also evaluated. Platelet counts before initiation of pharmacological PDA treatment did not differ between infants with later treatment success or failure. However, we found significant associations between low platelet counts during pharmacological PDA therapy and treatment failure ( p  < 0.05). Receiver operating characteristic (ROC) curve analysis showed that platelet counts after the first, and before and after the second cyclooxygenase inhibitor (COXI) cycle were significantly associated with treatment failure (area under the curve of >0.6). However, ROC curve analysis did not reveal a specific platelet cutoff-value that could predict PDA treatment failure. Multivariate logistic regression analysis showed that lower platelet counts, a lower BW, and preeclampsia were independently associated with COXI treatment failure. We provide further evidence for an association between low platelet counts during pharmacological therapy for symptomatic PDA and treatment failure, while platelet counts before initiation of therapy did not affect treatment outcome.

Results from raw milk microbiological tests do not predict the shelf-life performance of commercially pasteurized fluid milk.

PubMed

Martin, N H; Ranieri, M L; Murphy, S C; Ralyea, R D; Wiedmann, M; Boor, K J

2011-03-01

Analytical tools that accurately predict the performance of raw milk following its manufacture into commercial food products are of economic interest to the dairy industry. To evaluate the ability of currently applied raw milk microbiological tests to predict the quality of commercially pasteurized fluid milk products, samples of raw milk and 2% fat pasteurized milk were obtained from 4 New York State fluid milk processors for a 1-yr period. Raw milk samples were examined using a variety of tests commonly applied to raw milk, including somatic cell count, standard plate count, psychrotrophic bacteria count, ropy milk test, coliform count, preliminary incubation count, laboratory pasteurization count, and spore pasteurization count. Differential and selective media were used to identify groups of bacteria present in raw milk. Pasteurized milk samples were held at 6°C for 21 d and evaluated for standard plate count, coliform count, and sensory quality throughout shelf-life. Bacterial isolates from select raw and pasteurized milk tests were identified using 16S ribosomal DNA sequencing. Linear regression analysis of raw milk test results versus results reflecting pasteurized milk quality consistently showed low R(2) values (<0.45); the majority of R(2) values were <0.25, indicating small relationship between the results from the raw milk tests and results from tests used to evaluate pasteurized milk quality. Our findings suggest the need for new raw milk tests that measure the specific biological barriers that limit shelf-life and quality of fluid milk products. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Leukocyte Subtype Counts and Its Association with Vascular Structure and Function in Adults with Intermediate Cardiovascular Risk. MARK Study

PubMed Central

Gomez-Sanchez, Leticia; García-Ortiz, Luis; Recio-Rodríguez, José I.; Patino-Alonso, Maria C.; Agudo-Conde, Cristina; Rigo, Fernando; Ramos, Rafel; Martí, Ruth; Gomez-Marcos, Manuel A.

2015-01-01

Objectives We investigated the relationship between leukocyte subtype counts and vascular structure and function based on carotid intima-media thickness, pulse wave velocity, central augmentation index and cardio-ankle vascular index by gender in intermediate cardiovascular risk patients. Methods This study analyzed 500 subjects who were included in the MARK study, aged 35 to 74 years (mean: 60.3±8.4), 45.6% women. Measurement: Brachial ankle Pulse Wave Velocity (ba-PWV) estimate by equation, Cardio-AnkleVascular Index (CAVI) using the VaSera device and Carotid ultrasound was used to measure carotid Intima Media Thickness (IMT). The Mobil-O-Graph was used to measure the Central Augmentation Index (CAIx). Results Total leukocyte, neutrophil and monocyte counts were positively correlated with IMT (p < 0.01) in men. Monocyte count was positively correlated with CAIx in women (p < 0.01). In a multiple linear regression analysis, the IMT mean maintained a positive association with the neutrophil count (β = 1.500, p = 0.007) in men. CAIx maintained a positive association with the monocyte count (β = 2.445, p = 0.022) in women. Conclusion The results of this study suggest that the relationship between subtype circulating leukocyte counts and vascular structure and function, although small, may be different by gender. In men, the neutrophil count was positively correlated with IMT and in women, the monocyte count with CAIx, in a large sample of intermediate-risk patients. These association were maintained after adjusting for age and other confounders. Trial Registration ClinicalTrials.gov NCT01428934 PMID:25885665

Use of an activity monitor to detect response to treatment in dogs with osteoarthritis

PubMed Central

Brown, Dorothy Cimino; Boston, Raymond C.; Farrar, John T.

2010-01-01

Objective To determine whether an activity monitor (AM) could be used to detect changes in activity in dogs with osteoarthritis treated with carprofen or a placebo. Design Randomized controlled trial. Animals 70 dogs with no clinically important abnormalities other than osteoarthritis for which they were not currently being treated. Procedures Dogs wore an AM continuously for 21 days. On days 8 through 21, the dogs were treated with carprofen (n = 35) or a placebo (35). Total activity counts for days 1 through 7 (baseline) were compared with total activity counts for days 15 through 21 (endpoint). The change in total activity count from baseline to endpoint was assessed within each treatment group as well as between groups. Linear regression analysis was performed to test for an association between treatment and percentage change in activity counts while controlling for other variables. Results For placebo-treated dogs, median baseline total activity count was not significantly different from median endpoint total activity count (1,378,408 vs 1,310,112, respectively). For dogs receiving carprofen, there was a significant increase in median activity count from baseline to endpoint (1,276,427 vs 1,374,133). When age and baseline activity counts were controlled for, dogs in the carpofen-treated group had a 20% increase in activity counts, compared with placebo-treated dogs (95% confidence interval, 10% to 26%). Conclusions and Clinical Relevance Results suggested that the AM used in the present study may be a valid outcome assessment tool for documenting improved activity associated with treatment in dogs with osteoarthritis. PMID:20590496

Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers.

PubMed

Briët, Olivier J T; Amerasinghe, Priyanie H; Vounatsou, Penelope

2013-01-01

With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions' impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during "consolidation" and "pre-elimination" phases. Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low.

Generalized Seasonal Autoregressive Integrated Moving Average Models for Count Data with Application to Malaria Time Series with Low Case Numbers

PubMed Central

Briët, Olivier J. T.; Amerasinghe, Priyanie H.; Vounatsou, Penelope

2013-01-01

Introduction With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during “consolidation” and “pre-elimination” phases. Methods Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. Results The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. Conclusions G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low. PMID:23785448

Phthalate exposure and semen quality in fertile US men.

PubMed

Thurston, S W; Mendiola, J; Bellamy, A R; Levine, H; Wang, C; Sparks, A; Redmon, J B; Drobnis, E Z; Swan, S H

2016-07-01

Several experimental and observational studies have demonstrated the antiandrogenicity of several phthalates. However, there is limited evidence of an association between phthalate exposure in adult life and semen quality. The aim of this study was to examine phthalate exposure during adulthood in relation to semen quality in fertile US men. This multi-center cross-sectional study included 420 partners of pregnant women who attended a prenatal clinic in one of five US cities during 1999-2001. Nine phthalate metabolites [mono (2-ethylhexyl) phthalate (MEHP), mono (2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono (2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono (2-ethyl-5-carboxypentyl) phthalate (MECPP)], as well as mono-n-butyl phthalate (MBP) and mono-isobutyl phthalate (MiBP), mono (three carboxypropyl) phthalate (MCPP), monobenzyl phthalate (MBzP), and monoethyl phthalate (MEP)] were measured in urine collected at the same time as the semen sample. We regressed natural log-transformed (ln) sperm concentration, ln(total sperm count), ln(total motile sperm count), percent motile spermatozoa, and percent spermatozoa with normal morphology on each of the nine natural log-transformed metabolite concentrations and on the molar-weighted sum of DEHP metabolites in separate models. We fit unadjusted models and models that adjusted for confounders determined a priori. In unadjusted models, ln(MiBP) was significantly and positively associated with motility and ln(MBzP) significantly negatively associated with ln(total sperm count). In adjusted linear models, urinary metabolite concentrations of DEHP, DBP, DEP, and DOP were not associated with any semen parameter. We found an inverse association between ln(MBzP) concentrations and sperm motility (β = -1.47, 95% CI: -2.61, -0.33), adjusted for ln(creatinine concentration), geographic location, age, race, smoking status, stress, recent fever, time from sample collection and time to complete analysis. Several sensitivity analyses confirmed the robustness of these associations. This study and the available literature suggest that impacts of adult exposure to phthalates at environmental levels on classical sperm parameters are likely to be small. © 2015 American Society of Andrology and European Academy of Andrology.

Role of Temperature, Humidity and Rainfall on Influenza Transmission in Guatemala, El Salvador and Panama

NASA Technical Reports Server (NTRS)

Soebiyanto, Radina P.; Bonilla, Luis; Jara, Jorge; McCracken, John; Azziz?-Baumgartner, Eduardo; Widdowson, Marc-Alain; Kiang, Richard

2012-01-01

Worldwide, seasonal influenza causes about 500,000 deaths and 5 million severe illnesses per year. The environmental drivers of influenza transmission are poorly understood especially in the tropics. We aimed to identify meteorological factors for influenza transmission in tropical Central America. We gathered laboratory-confirmed influenza case-counts by week from Guatemala City, San Salvador Department (El Salvador) and Panama Province from 2006 to 2010. The average total cases per year were: 390 (Guatemala), 99 (San Salvador) and 129 (Panama). Meteorological factors including daily air temperature, rainfall, relative and absolute humidity (RH, AH) were obtained from ground stations, NASA satellites and land models. For these factors, we computed weekly averages and their deviation from the 5-yr means. We assessed the relationship between the number of influenza case-counts and the meteorological factors, including effects lagged by 1 to 4 weeks, using Poisson regression for each site. Our results showed influenza in San Salvador would increase by 1 case within a week of every 1 day with RH>75% (Relative Risk (RR)= 1.32, p=.001) and every 1C increase in minimum temperature (RR=1.29, p=.007) but it would decrease by 1 case for every 1mm-above mean weekly rainfall (RR=0.93,p<.001) (model pseudo-R2=0.55). Within 2 weeks, influenza in Panama was increased by 1 case for every 1% increase in RH (RR=1.04, p=.003), and it was increased by 2 cases for every 1C increase of minimum temperature (RR=2.01, p<.001) (model pseudo-R2=0.4). Influenza counts in Guatemala had 1 case increase for every 1C increase in minimum temperature in the previous week (RR=1.21, p<.001), and for every 1mm/day-above normal increase of rainfall rate (RR=1.03, p=.03) (model pseudo-R2=0.54). Our findings that cases increase with temperature and humidity differ from some temperate-zone studies. But they indicate that climate parameters such as humidity and temperature could be predictive of influenza activity and should be incorporated into country-specific influenza transmission models

Test of a mosquito eggshell isolation method and subsampling procedure.

PubMed

Turner, P A; Streever, W J

1997-03-01

Production of Aedes vigilax, the common salt-marsh mosquito, can be assessed by determining eggshell densities found in soil. In this study, 14 field-collected eggshell samples were used to test a subsampling technique and compare eggshell counts obtained with a flotation method to those obtained by direct examination of sediment (DES). Relative precision of the subsampling technique was assessed by determining the minimum number of subsamples required to estimate the true mean and confidence interval of a sample at a predetermined confidence level. A regression line was fitted to cube-root transformed eggshell counts obtained from flotation and DES and found to be significant (P < 0.001, r2 = 0.97). The flotation method allowed processing of samples in about one-third of the time required by DES, but recovered an average of 44% of the eggshells present. Eggshells obtained with the flotation method can be used to predict those from DES using the following equation: DES count = [1.386 x (flotation count)0.33 - 0.01]3.

Woodcock singing-ground counts and habitat changes in the northeastern United States

USGS Publications Warehouse

Dwyer, T.J.; McAuley, D.G.; Derleth, E.L.

1983-01-01

Aerial photography from the late 1960's and the late 1970's was used to study habitat changes along 78 American woodcock (Scolopax minor) singing-ground routes in 9 northeastern states. The most noticeable changes were declines in the amount of abandoned field, cropland, shrubland, and field/pasture. The amount of land in the urban/industrial type increased 33.4% from the late 1960's to the late 1970's. We examined relationships between the woodcock call-count index and habitat variables using multiple-regression techniques. The abundance of calling male woodcock was positively correlated with the amount of abandoned field and alder (Alnus sp.) and negatively correlated with the amount of urban/industrial type. However, only the change in the urban/industrial type was significantly (P < 0.05) related to the change in the call-count index. Urban/industrial area increased, whereas the call-count index declined on average in our sample of routes by 1.4 birds/route (40.5%).

Effects of electroacupuncture on luteal regression and steroidogenesis in ovarian hyperstimulation syndrome model rat.

PubMed

Huang, Xuan; Chen, Li; Xia, You-Bing; Xie, Min; Sun, Qin; Yao, Bing

2018-03-15

Electroacupuncture (EA) is an effective and safe therapeutic method widely used for treating clinical diseases. Previously, we found that EA could decrease serum hormones and reduce ovarian size in ovarian hyperstimulation syndrome (OHSS) rat model. Nevertheless, the mechanisms that contribute to these improvements remain unclear. HE staining was used to count the number of corpora lutea (CL) and follicles. Immunohistochemical and ELISA were applied to examine luteal functional and structural regression. Immunoprecipitation was used for analyzing the interaction between NPY (neuropeptide Y) and COX-2; western blotting and qRT-PCR were used to evaluate the expressions of steroidogenic enzymes and PKA/CREB pathway. EA treatment significantly reduced the ovarian weight and the number of CL, also decreased ovarian and serum levels of PGE2 and COX-2 expression; increased ovarian PGF2α levels and PGF2α/PGE2 ratio; decreased PCNA expression and distribution; and increased cyclin regulatory inhibitor p27 expression to have further effect on the luteal formation, and promote luteal functional and structural regression. Moreover, expression of COX-2 in ovaries was possessed interactivity increased expression of NPY. Furthermore, EA treatment lowered the serum hormone levels, inhibited PKA/CREB pathway and decreased the expressions of steroidogenic enzymes. Hence, interaction with COX-2, NPY may affect the levels of PGF2α and PGE2 as well as impact the proliferation of granulosa cells in ovaries, thus further reducing the luteal formation, and promoting luteal structural and functional regression, as well as the ovarian steroidogenesis following EA treatment. EA treatment could be an option for preventing OHSS in ART. Copyright © 2018 Elsevier Inc. All rights reserved.

The association of serum prolactin concentration with inflammatory biomarkers - cross-sectional findings from the population-based Study of Health in Pomerania.

PubMed

Friedrich, Nele; Schneider, Harald J; Spielhagen, Christin; Markus, Marcello Ricardo Paulista; Haring, Robin; Grabe, Hans J; Buchfelder, Michael; Wallaschofski, Henri; Nauck, Matthias

2011-10-01

Prolactin (PRL) is involved in immune regulation and may contribute to an atherogenic phenotype. Previous results on the association of PRL with inflammatory biomarkers have been conflicting and limited by small patient studies. Therefore, we used data from a large population-based sample to assess the cross-sectional associations between serum PRL concentration and high-sensitivity C-reactive protein (hsCRP), fibrinogen, interleukin-6 (IL-6), and white blood cell (WBC) count. From the population-based Study of Health in Pomerania (SHIP), a total of 3744 subjects were available for the present analyses. PRL and inflammatory biomarkers were measured. Linear and logistic regression models adjusted for age, sex, body-mass-index, total cholesterol and glucose were analysed. Multivariable linear regression models revealed a positive association of PRL with WBC. Multivariable logistic regression analyses showed a significant association of PRL with increased IL-6 in non-smokers [highest vs lowest quintile: odds ratio 1·69 (95% confidence interval 1·10-2·58), P = 0·02] and smokers [OR 2·06 (95%-CI 1·10-3·89), P = 0·02]. Similar results were found for WBC in non-smokers [highest vs lowest quintile: OR 2·09 (95%-CI 1·21-3·61), P = 0·01)] but not in smokers. Linear and logistic regression analyses revealed no significant associations of PRL with hsCRP or fibrinogen. Serum PRL concentrations are associated with inflammatory biomarkers including IL-6 and WBC, but not hsCRP or fibrinogen. The suggested role of PRL in inflammation needs further investigation in future prospective studies. © 2011 Blackwell Publishing Ltd.

The impact of accelerometer wear location on the relationship between step counts and arterial stiffness in adults treated for hypertension and diabetes.

PubMed

Cooke, Alexandra B; Daskalopoulou, Stella S; Dasgupta, Kaberi

2018-04-01

Accelerometer placement at the wrist is convenient and increasingly adopted despite less accurate physical activity (PA) measurement than with waist placement. Capitalizing on a study that started with wrist placement and shifted to waist placement, we compared associations between PA measures derived from different accelerometer locations with a responsive arterial health indicator, carotid-femoral pulse wave velocity (cfPWV). Cross-sectional study. We previously demonstrated an inverse association between waist-worn pedometer-assessed step counts (Yamax SW-200, 7 days) and cfPWV (-0.20m/s, 95% CI -0.28, -0.12 per 1000 step/day increment) in 366 adults. Participants concurrently wore accelerometers (ActiGraph GT3X+), most at the waist but the first 46 at the wrist. We matched this subgroup with participants from the 'waist accelerometer' group (sex, age, and pedometer-assessed steps/day) and assessed associations with cfPWV (applanation tonometry, Sphygmocor) separately in each subgroup through linear regression models. Compared to the waist group, wrist group participants had higher step counts (mean difference 3980 steps/day; 95% CI 2517, 5443), energy expenditure (967kcal/day, 95% CI 755, 1179), and moderate-to-vigorous-PA (138min; 95% CI 114, 162). Accelerometer-assessed step counts (waist) suggested an association with cfPWV (-0.28m/s, 95% CI -0.58, 0.01); but no relationship was apparent with wrist-assessed steps (0.02m/s, 95% CI -0.24, 0.27). Waist but not wrist ActiGraph PA measures signal associations between PA and cfPWV. We urge researchers to consider the importance of wear location choice on relationships with health indicators. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Adherence to Early Antiretroviral Therapy: Results From HPTN 052, a Phase III, Multinational Randomized Trial of ART to Prevent HIV-1 Sexual Transmission in Serodiscordant Couples.

PubMed

Safren, Steven A; Mayer, Kenneth H; Ou, San-San; McCauley, Marybeth; Grinsztejn, Beatriz; Hosseinipour, Mina C; Kumarasamy, Nagalingeswaran; Gamble, Theresa; Hoffman, Irving; Celentano, David; Chen, Ying Qing; Cohen, Myron S

2015-06-01

Combination antiretroviral therapy (ART) for HIV-1-infected individuals prevents sexual transmission if viral load is suppressed. Participants were HIV-1-infected partners randomized to early ART (CD4 350-550) in HPTN052 (n = 886, median follow-up = 2.1 years), a clinical trial of early ART to prevent sexual transmission of HIV-1 in serodiscordant couples at 13 sites in 9 countries. Adherence was assessed through pill count (dichotomized at <95%) and through self-report items. Predictors of adherence were mental health and general health perceptions, substance use, binge drinking, social support, sexual behaviors, and demographics. Viral suppression was defined as HIV plasma viral load <400 copies per milliliter. Adherence counseling and couples' counseling about safer sex were provided. Logistic and linear regression models using generalized estimating equation for repeated measurements were used. Through pill count, 82% of participants were adherent at 1 month and 83.3% at 1 year. Mental health was the only psychosocial variable associated with adherence [pill count, odds ratios (OR) = 1.05, 95% confidence intervals (CIs): 1.00 to 1.11; self-report parameter estimate, OR = 0.02, 95% CI: 0.01 to 0.04], although regional differences emerged. Pill count (OR = 1.19, 95% CI: 1.10 to 1.30) and self-report (OR = 1.42, 95% CI: 1.14 to 1.77) adherence were associated with viral suppression. Although adherence was high among individuals in stable relationships taking ART for prevention, mental health and adherence covaried. Assessing and intervening on mental health in the context of promoting adherence to ART as prevention should be explored. Adherence and couples' counseling, feedback about viral suppression, and/or altruism may also help explain the magnitude of adherence observed.

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

Evaluation of nonpoint-source contamination, Wisconsin: Selected data for 1992 water year

USGS Publications Warehouse

Graczyk, D.J.; Walker, J.F.; Greb, S.R.; Corsi, Steven R.; Owens, D.W.

1993-01-01

This report presents the annual results of the U.S. Geological Survey's (USGS) watershed-management evaluation monitoring program in Wisconsin. The overall objective of each individual project in the program is to determine if the water chemistry in the receiving stream has changed as a result of the implementation of land-management practices in the watershed. This is accomplished through monitoring of water chemistry and ancillary variables before best-management practices (BMP's) are installed ('pre-BMP'), during installation ('transitional'), and after ('post-BMP') watershed- management plans have been completely implemented. Fecal-coliform (FC) counts ranged between 10 and 310,00/100 mL. A large range of values occurred within duplicate and triplicate samples as well as over time. The median percentage difference between duplicate and triplicate samples was 17 percent although 4 out of the total 60 duplicate and triplicate samples had differences greater than 100 percent. A decrease in FC counts generally occurred over the duration of the 4-day analyses. Linear regression models of the log-concentration values (dependent variable) with respect to time (independent variable) were calculated for all samples. Negative slopes were found for 14 of the 15 samples. Slopes varied from +0.5 to -38.4 percent gain/loss/day, with a median slope of -8.5 percent/day. A t-test was applied to the data to examine whether or not significant differences in FC counts exist with respect to holding times. Because the T-test only compares two treatments, the test was conducted 3 times (0 versus 24-hr holding time, 0 versus 48-hr holding time, and 0 versus 72-hr holding time). Setting the level of significance at p less than 0.05 and assuming equal variances, 27 percent (all from Bower and Otter Creeks) of the samples demonstrated a significant difference in colony count over the first 24 hr, 40 percent over 48 hr, and 47 percent over 72 hr. All samples that exhibited a significant change in colony count were because of a decrease in colony count of the sample.

Modeling Intersection Crash Counts and Traffic Volume

DOT National Transportation Integrated Search

1998-07-01

This research explored the feasibility of modeling crash counts at intersections with use of available exposure measures. The basic purpose of "exposure" is to serve as a size factor to allow comparison of crash counts among populations of different ...

Prevalence and potential risk factors for the occurrence of cefotaxime resistant Escherichia coli in German fattening pig farms--a cross-sectional study.

PubMed

Hering, Johanna; Hille, Katja; Frömke, Cornelia; von Münchhausen, Christiane; Hartmann, Maria; Schneider, Bettina; Friese, Anika; Roesler, Uwe; Merle, Roswitha; Kreienbrock, Lothar

2014-09-01

A cross-sectional study concerning farm prevalence and risk factors for the count of cefotaxime resistant Escherichia coli (E. coli) (CREC) positive samples per sampling group on German fattening pig farms was performed in 2011 and 2012. Altogether 48 farms in four agricultural regions in the whole of Germany were investigated. Faecal samples, boot swabs and dust samples from two sampling groups per farm were taken and supplemental data were collected using a questionnaire. On 85% of the farms, at least one sample contained cefotaxime resistant E. coli colonies. Positive samples were more frequent in faeces (61%) and boot swabs (54%) than in dust samples (11%). Relevant variables from the questionnaire were analysed in a univariable mixed effect Poisson regression model. Variables that were related to the number (risk) of positive samples per sampling group with a p-value <0.2 were entered in a multivariable model. This model was reduced to statistically significant variables via backward selection. Factors that increased the risk for positive samples involved farm management and hygienic aspects. Farms that had a separate pen for diseased pigs had a 2.8 higher mean count of positive samples (95%-CI [1.71; 4.58], p=0.001) than farms without an extra pen. The mean count was increased on farms with under-floor exhaust ventilation compared to farms with over floor ventilation (2.22 [1.43; 3.46], p=0.001) and more positive samples were observed on farms that controlled flies with toxin compared to farms that did not (1.86 [1.24; 2.78], p=0.003). It can be concluded, that CREC are wide spread on German fattening pig farms. In addition the explorative approach of the present study suggests an influence of management strategies on the occurrence of cefotaxime resistant E. coli. Copyright © 2014 Elsevier B.V. All rights reserved.

Technical note: enumeration of mesophilic aerobes in milk: evaluation of standard official protocols and Petrifilm aerobic count plates.

PubMed

Freitas, R; Nero, L A; Carvalho, A F

2009-07-01

Enumeration of mesophilic aerobes (MA) is the main quality and hygiene parameter for raw and pasteurized milk. High levels of these microorganisms indicate poor conditions in production, storage, and processing of milk, and also the presence of pathogens. Fifteen raw and 15 pasteurized milk samples were submitted for MA enumeration by a conventional plating method (using plate count agar) and Petrifilm Aerobic Count plates (3M, St. Paul, MN), followed by incubation according to 3 official protocols: IDF/ISO (incubation at 30 degrees C for 72 h), American Public Health Association (32 degrees C for 48 h), and Brazilian Ministry of Agriculture (36 degrees C for 48 h). The results were compared by linear regression and ANOVA. Considering the results from conventional methodology, good correlation indices and absence of significant differences between mean counts were observed, independent of type of milk sample (raw or pasteurized) and incubation conditions (IDF/ISO, American Public Health Association, or Ministry of Agriculture). Considering the results from Petrifilm Aerobic Count plates, good correlation indices and absence of significant differences were only observed for raw milk samples. The microbiota of pasteurized milk interfered negatively with the performance of Petrifilm Aerobic Count plates, probably because of the presence of microorganisms that poorly reduce the dye indicator of this system.

Multiple Imputation of Completely Missing Repeated Measures Data within Person from a Complex Sample: Application to Accelerometer Data in the National Health and Nutrition Examination Survey

PubMed Central

Liu, Benmei; Yu, Mandi; Graubard, Barry I; Troiano, Richard P; Schenker, Nathaniel

2016-01-01

The Physical Activity Monitor (PAM) component was introduced into the 2003-2004 National Health and Nutrition Examination Survey (NHANES) to collect objective information on physical activity including both movement intensity counts and ambulatory steps. Due to an error in the accelerometer device initialization process, the steps data were missing for all participants in several primary sampling units (PSUs), typically a single county or group of contiguous counties, who had intensity count data from their accelerometers. To avoid potential bias and loss in efficiency in estimation and inference involving the steps data, we considered methods to accurately impute the missing values for steps collected in the 2003-2004 NHANES. The objective was to come up with an efficient imputation method which minimized model-based assumptions. We adopted a multiple imputation approach based on Additive Regression, Bootstrapping and Predictive mean matching (ARBP) methods. This method fits alternative conditional expectation (ace) models, which use an automated procedure to estimate optimal transformations for both the predictor and response variables. This paper describes the approaches used in this imputation and evaluates the methods by comparing the distributions of the original and the imputed data. A simulation study using the observed data is also conducted as part of the model diagnostics. Finally some real data analyses are performed to compare the before and after imputation results. PMID:27488606

Development of an aerial counting system in oil palm plantations

NASA Astrophysics Data System (ADS)

Zulyma Miserque Castillo, Jhany; Laverde Diaz, Rubbermaid; Rueda Guzmán, Claudia Leonor

2016-07-01

This paper proposes the development of a counting aerial system capable of capturing, process and analyzing images of an oil palm plantation to register the number of cultivated palms. It begins with a study of the available UAV technologies to define the most appropriate model according to the project needs. As result, a DJI Phantom 2 Vision+ is used to capture pictures that are processed by a photogrammetry software to create orthomosaics from the areas of interest, which are handled by the developed software to calculate the number of palms contained in them. The implemented algorithm uses a sliding window technique in image pyramids to generate candidate windows, an LBP descriptor to model the texture of the picture, a logistic regression model to classify the windows and a non-maximum suppression algorithm to refine the decision. The system was tested in different images than the ones used for training and for establishing the set point. As result, the system showed a 95.34% detection rate with a 97.83% precision in mature palms and a 79.26% detection rate with a 97.53% precision in young palms giving an FI score of 0.97 for mature palms and 0.87 for the small ones. The results are satisfactory getting the census and high-quality images from which is possible to get more information from the area of interest. All this, achieved through a low-cost system capable of work even in cloudy conditions.

Predictive value of sperm morphology and progressively motile sperm count for pregnancy outcomes in intrauterine insemination.

PubMed

Lemmens, Louise; Kos, Snjezana; Beijer, Cornelis; Brinkman, Jacoline W; van der Horst, Frans A L; van den Hoven, Leonie; Kieslinger, Dorit C; van Trooyen-van Vrouwerff, Netty J; Wolthuis, Albert; Hendriks, Jan C M; Wetzels, Alex M M

2016-06-01

To investigate the value of sperm parameters to predict an ongoing pregnancy outcome in couples treated with intrauterine insemination (IUI), during a methodologically stable period of time. Retrospective, observational study with logistic regression analyses. University hospital. A total of 1,166 couples visiting the fertility laboratory for their first IUI episode, including 4,251 IUI cycles. None. Sperm morphology, total progressively motile sperm count (TPMSC), and number of inseminated progressively motile spermatozoa (NIPMS); odds ratios (ORs) of the sperm parameters after the first IUI cycle and the first finished IUI episode; discriminatory accuracy of the multivariable model. None of the sperm parameters was of predictive value for pregnancy after the first IUI cycle. In the first finished IUI episode, a positive relationship was found for ≤4% of morphologically normal spermatozoa (OR 1.39) and a moderate NIPMS (5-10 million; OR 1.73). Low NIPMS showed a negative relation (≤1 million; OR 0.42). The TPMSC had no predictive value. The multivariable model (i.e., sperm morphology, NIPMS, female age, male age, and the number of cycles in the episode) had a moderate discriminatory accuracy (area under the curve 0.73). Intrauterine insemination is especially relevant for couples with moderate male factor infertility (sperm morphology ≤4%, NIPMS 5-10 million). In the multivariable model, however, the predictive power of these sperm parameters is rather low. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Neural Networks for Readability Analysis.

ERIC Educational Resources Information Center

McEneaney, John E.

This paper describes and reports on the performance of six related artificial neural networks that have been developed for the purpose of readability analysis. Two networks employ counts of linguistic variables that simulate a traditional regression-based approach to readability. The remaining networks determine readability from "visual…

Do depression and anxiety reduce the likelihood of remission in rheumatoid arthritis and psoriatic arthritis? Data from the prospective multicentre NOR-DMARD study.

PubMed

Michelsen, Brigitte; Kristianslund, Eirik Klami; Sexton, Joseph; Hammer, Hilde Berner; Fagerli, Karen Minde; Lie, Elisabeth; Wierød, Ada; Kalstad, Synøve; Rødevand, Erik; Krøll, Frode; Haugeberg, Glenn; Kvien, Tore K

2017-11-01

To investigate the predictive value of baseline depression/anxiety on the likelihood of achieving joint remission in rheumatoid arthritis (RA) and psoriatic arthritis (PsA) as well as the associations between baseline depression/anxiety and the components of the remission criteria at follow-up. We included 1326 patients with RA and 728 patients with PsA from the prospective observational NOR-DMARD study starting first-time tumour necrosis factor inhibitors or methotrexate. The predictive value of depression/anxiety on remission was explored in prespecified logistic regression models and the associations between baseline depression/anxiety and the components of the remission criteria in prespecified multiple linear regression models. Baseline depression/anxiety according to EuroQoL-5D-3L, Short Form-36 (SF-36) Mental Health subscale ≤56 and SF-36 Mental Component Summary ≤38 negatively predicted 28-joint Disease Activity Score <2.6, Simplified Disease Activity Index ≤3.3, Clinical Disease Activity Index ≤2.8, ACR/EULAR Boolean and Disease Activity Index for Psoriatic Arthritis ≤4 remission after 3 and 6 months treatment in RA (p≤0.008) and partly in PsA (p from 0.001 to 0.73). Baseline depression/anxiety was associated with increased patient's and evaluator's global assessment, tender joint count and joint pain in RA at follow-up, but not with swollen joint count and acute phase reactants. Depression and anxiety may reduce likelihood of joint remission based on composite scores in RA and PsA and should be taken into account in individual patients when making a shared decision on a treatment target. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Relationships between locus of control and paranormal beliefs.

PubMed

Newby, Robert W; Davis, Jessica Boyette

2004-06-01

The present study investigated the associations between scores on paranormal beliefs, locus of control, and certain psychological processes such as affect and cognitions as measured by the Linguistic Inquiry and Word Count. Analysis yielded significant correlations between scores on Locus of Control and two subscales of Tobacyk's (1988) Revised Paranormal Beliefs Scale, New Age Philosophy and Traditional Paranormal Beliefs. A step-wise multiple regression analysis indicated that Locus of Control was significantly related to New Age Philosophy. Other correlations were found between Tobacyk's subscales, Locus of Control, and three processes measured by the Linguistic Inquiry and Word Count.

Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas.

PubMed

Bao, Jie; Liu, Pan; Yu, Hao; Xu, Chengcheng

2017-09-01

The primary objective of this study was to investigate how to incorporate human activity information in spatial analysis of crashes in urban areas using Twitter check-in data. This study used the data collected from the City of Los Angeles in the United States to illustrate the procedure. The following five types of data were collected: crash data, human activity data, traditional traffic exposure variables, road network attributes and social-demographic data. A web crawler by Python was developed to collect the venue type information from the Twitter check-in data automatically. The human activities were classified into seven categories by the obtained venue types. The collected data were aggregated into 896 Traffic Analysis Zones (TAZ). Geographically weighted regression (GWR) models were developed to establish a relationship between the crash counts reported in a TAZ and various contributing factors. Comparative analyses were conducted to compare the performance of GWR models which considered traditional traffic exposure variables only, Twitter-based human activity variables only, and both traditional traffic exposure and Twitter-based human activity variables. The model specification results suggested that human activity variables significantly affected the crash counts in a TAZ. The results of comparative analyses suggested that the models which considered both traditional traffic exposure and human activity variables had the best goodness-of-fit in terms of the highest R 2 and lowest AICc values. The finding seems to confirm the benefits of incorporating human activity information in spatial analysis of crashes using Twitter check-in data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sieve analysis using the number of infecting pathogens.

PubMed

Follmann, Dean; Huang, Chiung-Yu

2017-12-14

Assessment of vaccine efficacy as a function of the similarity of the infecting pathogen to the vaccine is an important scientific goal. Characterization of pathogen strains for which vaccine efficacy is low can increase understanding of the vaccine's mechanism of action and offer targets for vaccine improvement. Traditional sieve analysis estimates differential vaccine efficacy using a single identifiable pathogen for each subject. The similarity between this single entity and the vaccine immunogen is quantified, for example, by exact match or number of mismatched amino acids. With new technology, we can now obtain the actual count of genetically distinct pathogens that infect an individual. Let F be the number of distinct features of a species of pathogen. We assume a log-linear model for the expected number of infecting pathogens with feature "f," f=1,…,F. The model can be used directly in studies with passive surveillance of infections where the count of each type of pathogen is recorded at the end of some interval, or active surveillance where the time of infection is known. For active surveillance, we additionally assume that a proportional intensity model applies to the time of potentially infectious exposures and derive product and weighted estimating equation (WEE) estimators for the regression parameters in the log-linear model. The WEE estimator explicitly allows for waning vaccine efficacy and time-varying distributions of pathogens. We give conditions where sieve parameters have a per-exposure interpretation under passive surveillance. We evaluate the methods by simulation and analyze a phase III trial of a malaria vaccine. © 2017, The International Biometric Society.

Universal test and treat is not associated with sub-optimal antiretroviral therapy adherence in rural South Africa: the ANRS 12249 TasP trial.

PubMed

Iwuji, Collins; McGrath, Nuala; Calmy, Alexandra; Dabis, Francois; Pillay, Deenan; Newell, Marie-Louise; Baisley, Kathy; Porter, Kholoud

2018-06-01

HIV treatment guidelines now recommend antiretroviral therapy (ART) initiation regardless of CD4 count to maximize benefit both for the individual and society. It is unknown whether the initiation of ART at higher CD4 counts would affect adherence levels. We investigated whether initiating ART at higher CD4 counts was associated with sub-optimal adherence (<95%) during the first 12 months of ART. A prospective cohort study nested within a two-arm cluster-randomized trial of universal test and treat was implemented from March 2012 to June 2016 to measure the impact of ART on HIV incidence in rural KwaZulu-Natal. ART was initiated regardless of CD4 count in the intervention arm and according to national guidelines in the control arm. ART adherence was measured monthly using a visual analogue scale (VAS) and pill counts (PC). HIV viral load was measured at ART initiation, three and six months, and six-monthly thereafter. We pooled data from participants in both arms and used random-effects logistic regression models to examine the association between CD4 count at ART initiation and sub-optimal adherence, and assessed if adherence levels were associated with virological suppression. Among 900 individuals who initiated ART ≥12 months before study end, median (IQR) CD4 at ART initiation was 350 cells/mm 3 (234, 503); median age was 34.6 years (IQR 27.4 to 46.4) and 71.7% were female. Adherence was sub-optimal in 14.7% of visits as measured by VAS and 20.7% by PC. In both the crude analyses and after adjusting for potential confounders, adherence was not significantly associated with CD4 count at ART initiation (adjusted OR for linear trend in sub-optimal adherence with every 100 cells/mm 3 increase in CD4 count: 1.00, 95% CI 0.95 to 1.05, for VAS, and 1.03, 95% CI 0.99 to 1.07, for PC). Virological suppression at 12 months was 97%. Optimal adherence by both measures was significantly associated with virological suppression (p < 0.001 for VAS; p = 0.006 for PC). We found no evidence that higher CD4 counts at ART initiation were associated with sub-optimal ART adherence in the first 12 months. Our findings should alleviate concerns about adherence in individuals initiating ART at higher CD4 counts, however long-term outcomes are needed. ClinicalTrials.gov NCT01509508. © 2018 The Authors. Journal of the International AIDS Society published by John Wiley & sons Ltd on behalf of the International AIDS Society.

The piecewise-linear dynamic attenuator reduces the impact of count rate loss with photon-counting detectors

NASA Astrophysics Data System (ADS)

Hsieh, Scott S.; Pelc, Norbert J.

2014-06-01

Photon counting x-ray detectors (PCXDs) offer several advantages compared to standard energy-integrating x-ray detectors, but also face significant challenges. One key challenge is the high count rates required in CT. At high count rates, PCXDs exhibit count rate loss and show reduced detective quantum efficiency in signal-rich (or high flux) measurements. In order to reduce count rate requirements, a dynamic beam-shaping filter can be used to redistribute flux incident on the patient. We study the piecewise-linear attenuator in conjunction with PCXDs without energy discrimination capabilities. We examined three detector models: the classic nonparalyzable and paralyzable detector models, and a ‘hybrid’ detector model which is a weighted average of the two which approximates an existing, real detector (Taguchi et al 2011 Med. Phys. 38 1089-102 ). We derive analytic expressions for the variance of the CT measurements for these detectors. These expressions are used with raw data estimated from DICOM image files of an abdomen and a thorax to estimate variance in reconstructed images for both the dynamic attenuator and a static beam-shaping (‘bowtie’) filter. By redistributing flux, the dynamic attenuator reduces dose by 40% without increasing peak variance for the ideal detector. For non-ideal PCXDs, the impact of count rate loss is also reduced. The nonparalyzable detector shows little impact from count rate loss, but with the paralyzable model, count rate loss leads to noise streaks that can be controlled with the dynamic attenuator. With the hybrid model, the characteristic count rates required before noise streaks dominate the reconstruction are reduced by a factor of 2 to 3. We conclude that the piecewise-linear attenuator can reduce the count rate requirements of the PCXD in addition to improving dose efficiency. The magnitude of this reduction depends on the detector, with paralyzable detectors showing much greater benefit than nonparalyzable detectors.

Modeling the frequency-dependent detective quantum efficiency of photon-counting x-ray detectors.

PubMed

Stierstorfer, Karl

2018-01-01

To find a simple model for the frequency-dependent detective quantum efficiency (DQE) of photon-counting detectors in the low flux limit. Formula for the spatial cross-talk, the noise power spectrum and the DQE of a photon-counting detector working at a given threshold are derived. Parameters are probabilities for types of events like single counts in the central pixel, double counts in the central pixel and a neighboring pixel or single count in a neighboring pixel only. These probabilities can be derived in a simple model by extensive use of Monte Carlo techniques: The Monte Carlo x-ray propagation program MOCASSIM is used to simulate the energy deposition from the x-rays in the detector material. A simple charge cloud model using Gaussian clouds of fixed width is used for the propagation of the electric charge generated by the primary interactions. Both stages are combined in a Monte Carlo simulation randomizing the location of impact which finally produces the required probabilities. The parameters of the charge cloud model are fitted to the spectral response to a polychromatic spectrum measured with our prototype detector. Based on the Monte Carlo model, the DQE of photon-counting detectors as a function of spatial frequency is calculated for various pixel sizes, photon energies, and thresholds. The frequency-dependent DQE of a photon-counting detector in the low flux limit can be described with an equation containing only a small set of probabilities as input. Estimates for the probabilities can be derived from a simple model of the detector physics. © 2017 American Association of Physicists in Medicine.

Linear fitting of multi-threshold counting data with a pixel-array detector for spectral X-ray imaging

PubMed Central

Muir, Ryan D.; Pogranichney, Nicholas R.; Muir, J. Lewis; Sullivan, Shane Z.; Battaile, Kevin P.; Mulichak, Anne M.; Toth, Scott J.; Keefe, Lisa J.; Simpson, Garth J.

2014-01-01

Experiments and modeling are described to perform spectral fitting of multi-threshold counting measurements on a pixel-array detector. An analytical model was developed for describing the probability density function of detected voltage in X-ray photon-counting arrays, utilizing fractional photon counting to account for edge/corner effects from voltage plumes that spread across multiple pixels. Each pixel was mathematically calibrated by fitting the detected voltage distributions to the model at both 13.5 keV and 15.0 keV X-ray energies. The model and established pixel responses were then exploited to statistically recover images of X-ray intensity as a function of X-ray energy in a simulated multi-wavelength and multi-counting threshold experiment. PMID:25178010

Linear fitting of multi-threshold counting data with a pixel-array detector for spectral X-ray imaging.

PubMed

Muir, Ryan D; Pogranichney, Nicholas R; Muir, J Lewis; Sullivan, Shane Z; Battaile, Kevin P; Mulichak, Anne M; Toth, Scott J; Keefe, Lisa J; Simpson, Garth J

2014-09-01

Experiments and modeling are described to perform spectral fitting of multi-threshold counting measurements on a pixel-array detector. An analytical model was developed for describing the probability density function of detected voltage in X-ray photon-counting arrays, utilizing fractional photon counting to account for edge/corner effects from voltage plumes that spread across multiple pixels. Each pixel was mathematically calibrated by fitting the detected voltage distributions to the model at both 13.5 keV and 15.0 keV X-ray energies. The model and established pixel responses were then exploited to statistically recover images of X-ray intensity as a function of X-ray energy in a simulated multi-wavelength and multi-counting threshold experiment.

A crash-prediction model for multilane roads.

PubMed

Caliendo, Ciro; Guida, Maurizio; Parisi, Alessandra

2007-07-01

Considerable research has been carried out in recent years to establish relationships between crashes and traffic flow, geometric infrastructure characteristics and environmental factors for two-lane rural roads. Crash-prediction models focused on multilane rural roads, however, have rarely been investigated. In addition, most research has paid but little attention to the safety effects of variables such as stopping sight distance and pavement surface characteristics. Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent. Finally, as far as the authors are aware, prediction models involving all the above-mentioned factors have still not been developed in Italy for multilane roads, such as motorways. Thus, in this paper crash-prediction models for a four-lane median-divided Italian motorway were set up on the basis of accident data observed during a 5-year monitoring period extending between 1999 and 2003. The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence. Model parameters were estimated by the Maximum Likelihood Method, and the Generalized Likelihood Ratio Test was applied to detect the significant variables to be included in the model equation. Goodness-of-fit was measured by means of both the explained fraction of total variation and the explained fraction of systematic variation. The Cumulative Residuals Method was also used to test the adequacy of a regression model throughout the range of each variable. The candidate set of explanatory variables was: length (L), curvature (1/R), annual average daily traffic (AADT), sight distance (SD), side friction coefficient (SFC), longitudinal slope (LS) and the presence of a junction (J). Separate prediction models for total crashes and for fatal and injury crashes only were considered. For curves it is shown that significant variables are L, 1/R and AADT, whereas for tangents they are L, AADT and junctions. The effect of rain precipitation was analysed on the basis of hourly rainfall data and assumptions about drying time. It is shown that a wet pavement significantly increases the number of crashes. The models developed in this paper for Italian motorways appear to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and pavement improvement, and the predictions of accidents counts when comparing different design options. Thus this research may represent a point of reference for engineers in adjusting or designing multilane roads.

A removal model for estimating detection probabilities from point-count surveys

USGS Publications Warehouse

Farnsworth, G.L.; Pollock, K.H.; Nichols, J.D.; Simons, T.R.; Hines, J.E.; Sauer, J.R.

2002-01-01

Use of point-count surveys is a popular method for collecting data on abundance and distribution of birds. However, analyses of such data often ignore potential differences in detection probability. We adapted a removal model to directly estimate detection probability during point-count surveys. The model assumes that singing frequency is a major factor influencing probability of detection when birds are surveyed using point counts. This may be appropriate for surveys in which most detections are by sound. The model requires counts to be divided into several time intervals. Point counts are often conducted for 10 min, where the number of birds recorded is divided into those first observed in the first 3 min, the subsequent 2 min, and the last 5 min. We developed a maximum-likelihood estimator for the detectability of birds recorded during counts divided into those intervals. This technique can easily be adapted to point counts divided into intervals of any length. We applied this method to unlimited-radius counts conducted in Great Smoky Mountains National Park. We used model selection criteria to identify whether detection probabilities varied among species, throughout the morning, throughout the season, and among different observers. We found differences in detection probability among species. Species that sing frequently such as Winter Wren (Troglodytes troglodytes) and Acadian Flycatcher (Empidonax virescens) had high detection probabilities (∼90%) and species that call infrequently such as Pileated Woodpecker (Dryocopus pileatus) had low detection probability (36%). We also found detection probabilities varied with the time of day for some species (e.g. thrushes) and between observers for other species. We used the same approach to estimate detection probability and density for a subset of the observations with limited-radius point counts.

Social Markers of Mild Cognitive Impairment: Proportion of Word Counts in Free Conversational Speech.

PubMed

Dodge, Hiroko H; Mattek, Nora; Gregor, Mattie; Bowman, Molly; Seelye, Adriana; Ybarra, Oscar; Asgari, Meysam; Kaye, Jeffrey A

2015-01-01

Detecting early signs of Alzheimer's disease (AD) and mild cognitive impairment (MCI) during the pre-symptomatic phase is becoming increasingly important for costeffective clinical trials and also for deriving maximum benefit from currently available treatment strategies. However, distinguishing early signs of MCI from normal cognitive aging is difficult. Biomarkers have been extensively examined as early indicators of the pathological process for AD, but assessing these biomarkers is expensive and challenging to apply widely among pre-symptomatic community dwelling older adults. Here we propose assessment of social markers, which could provide an alternative or complementary and ecologically valid strategy for identifying the pre-symptomatic phase leading to MCI and AD. The data came from a larger randomized controlled clinical trial (RCT), where we examined whether daily conversational interactions using remote video telecommunications software could improve cognitive functions of older adult participants. We assessed the proportion of words generated by participants out of total words produced by both participants and staff interviewers using transcribed conversations during the intervention trial as an indicator of how two people (participants and interviewers) interact with each other in one-on-one conversations. We examined whether the proportion differed between those with intact cognition and MCI, using first, generalized estimating equations with the proportion as outcome, and second, logistic regression models with cognitive status as outcome in order to estimate the area under ROC curve (ROC AUC). Compared to those with normal cognitive function, MCI participants generated a greater proportion of words out of the total number of words during the timed conversation sessions (p=0.01). This difference remained after controlling for participant age, gender, interviewer and time of assessment (p=0.03). The logistic regression models showed the ROC AUC of identifying MCI (vs. normals) was 0.71 (95% Confidence Interval: 0.54 - 0.89) when average proportion of word counts spoken by subjects was included univariately into the model. An ecologically valid social marker such as the proportion of spoken words produced during spontaneous conversations may be sensitive to transitions from normal cognition to MCI.

White blood cell count and the incidence of hyperuricemia: insights from a community-based study.

PubMed

Liu, Jian; Shen, Pingyan; Ma, Xiaobo; Yu, Xialian; Ni, Liyan; Hao, Xu; Wang, Weiming; Chen, Nan

2018-06-23

Hyperuricemia (HUA) is a risk factor for chronic kidney disease (CKD). The relationship between HUA and white blood cell (WBC) count remains unknown. A sampling survey for CKD was conducted in Sanlin community in 2012 and 2014. CKD was defined as proteinuria in at least the microalbuminuric stage or an estimated GFR of 60 mL/(min∙1.73 m 2 ). HUA was defined as serum uric acid > 420 μmol/L in men and > 360 μmol/L in women. This study included 1024 participants. The prevalence of HUAwas 17.77%. Patients with HUA were more likely to have higher levels of WBC count, which was positively associated with HUA prevalence. This association was also observed in participants without CKD, diabetes mellitus, hyperlipidemia, or obesity. Multivariate logistic regression analysis showed that WBC count was independently associated with the risk for HUA in male and female participants. Compared with participants without HUA, inflammatory factors such as high-sensitivity C-reactive protein, tumor necrosis factor-α, and interleukin 6 increased in participants with HUA. Hence, WBC count is positively associated with HUA, and this association is independent of conventional risk factors for CKD.

College Student Cyberbullying: Self-Esteem, Depression, Loneliness, and Attachment

ERIC Educational Resources Information Center

Varghese, Mary E.; Pistole, M. Carole

2017-01-01

In an online survey (N = 338) at a large midwestern university, frequency counts indicated that 51 (15.1%) undergraduate students were cyberbully victims during college, and 27 (8.0%) were cyberbully offenders during college. In simultaneous regressions, maternal attachment anxiety explained unique variance in cybervictimization and…

Partial Least Squares Regression Can Aid in Detecting Differential Abundance of Multiple Features in Sets of Metagenomic Samples

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2015-01-01

It is now feasible to examine the composition and diversity of microbial communities (i.e., “microbiomes”) that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology “Metastats” across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency distributions obtained on a small to moderate number of samples. PMID:26734061

Adenosine Triphosphate Regresses Endometrial Explants in a Rat Model of Endometriosis.

PubMed

Zhang, Chen; Gao, Li; Yi, Yanhong; Han, Hongjing; Cheng, Hongyan; Ye, Xue; Ma, Ruiqiong; Sun, Kunkun; Cui, Heng; Chang, Xiaohong

2016-07-01

The aim of this study was to determine the effects of adenosine triphosphate (ATP) in a rat endometriosis model. After surgical induction of endometriosis, 3 rats were killed, and explants were measured in the remaining 19 rats, which were then randomly assigned to 4 groups. Group 1 (n = 4) received normal saline (2 mL/d intragastric [IG]), group 2 (n = 4) gestrinone (0.5 mg/kg/d IG), group 3 (n = 5) ATP (3.4 mg/kg/d IG), and group 4 (n = 6) ATP (1.0 mg/kg/d; intramuscularly), respectively. Four weeks after medication, they were euthanized to evaluate histological features of explants and eutopic uterine tissues. To test the effect of ATP on the growth of eutopic endometrium stromal cells, proliferation rates of hEM15A cells at 24, 48, and 72 hours after treatment with different concentrations of ATP and vehicle control were detected with the Cell Counting Kit-8 (CCK-8) method. There was a significant difference between pretreatment and posttreatment volumes within group 2 (positive control; P = .048) and group 4 (P = .044). On condition that pretreatment implant size was similar in both groups (P = .516), regression of explants in group 4 was significantly higher than that in group 1 (negative control; P = .035). Epithelial cells were significantly better preserved in group 1 than in group 3 (P = .008) and group 4 (P = .037). The CCK-8 assay showed no significant difference in proliferation among hEM15A cells treated with ATP and controls. These results suggest that ATP regresses endometriotic tissues in a rat endometriosis model but has no impact on the growth of eutopic endometrium stromal cells. © The Author(s) 2016.

Geographic dimensions of heat-related mortality in seven U.S. cities.

PubMed

Hondula, David M; Davis, Robert E; Saha, Michael V; Wegner, Carleigh R; Veazey, Lindsay M

2015-04-01

Spatially targeted interventions may help protect the public when extreme heat occurs. Health outcome data are increasingly being used to map intra-urban variability in heat-health risks, but there has been little effort to compare patterns and risk factors between cities. We sought to identify places within large metropolitan areas where the mortality rate is highest on hot summer days and determine if characteristics of high-risk areas are consistent from one city to another. A Poisson regression model was adapted to quantify temperature-mortality relationships at the postal code scale based on 2.1 million records of daily all-cause mortality counts from seven U.S. cities. Multivariate spatial regression models were then used to determine the demographic and environmental variables most closely associated with intra-city variability in risk. Significant mortality increases on extreme heat days were confined to 12-44% of postal codes comprising each city. Places with greater risk had more developed land, young, elderly, and minority residents, and lower income and educational attainment, but the key explanatory variables varied from one city to another. Regression models accounted for 14-34% of the spatial variability in heat-related mortality. The results emphasize the need for public health plans for heat to be locally tailored and not assume that pre-identified vulnerability indicators are universally applicable. As known risk factors accounted for no more than one third of the spatial variability in heat-health outcomes, consideration of health outcome data is important in efforts to identify and protect residents of the places where the heat-related health risks are the highest. Copyright © 2015 Elsevier Inc. All rights reserved.

Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

PubMed

Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

2017-10-01

Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P < .001). Means of software machine-derived values differed significantly from actual PLT yield, 4.72 × 10 11 vs.6.12 × 10 11 , respectively, (P < .001). The following equation was developed to adjust these values: actual PLT yield= 0.221 + (1.254 × theoretical platelet yield). ROC curve model showed an optimal apheresis device software prediction cut-off of 4.65 × 10 11 to obtain a DP, with a sensitivity of 82.2%, specificity of 93.3%, and an area under the curve (AUC) of 0.909. Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.

Retinal microaneurysm count predicts progression and regression of diabetic retinopathy. Post-hoc results from the DIRECT Programme.

PubMed

Sjølie, A K; Klein, R; Porta, M; Orchard, T; Fuller, J; Parving, H H; Bilous, R; Aldington, S; Chaturvedi, N

2011-03-01

To study the association between baseline retinal microaneurysm score and progression and regression of diabetic retinopathy, and response to treatment with candesartan in people with diabetes. This was a multicenter randomized clinical trial. The progression analysis included 893 patients with Type 1 diabetes and 526 patients with Type 2 diabetes with retinal microaneurysms only at baseline. For regression, 438 with Type 1 and 216 with Type 2 diabetes qualified. Microaneurysms were scored from yearly retinal photographs according to the Early Treatment Diabetic Retinopathy Study (ETDRS) protocol. Retinopathy progression and regression was defined as two or more step change on the ETDRS scale from baseline. Patients were normoalbuminuric, and normotensive with Type 1 and Type 2 diabetes or treated hypertensive with Type 2 diabetes. They were randomized to treatment with candesartan 32 mg daily or placebo and followed for 4.6 years. A higher microaneurysm score at baseline predicted an increased risk of retinopathy progression (HR per microaneurysm score 1.08, P < 0.0001 in Type 1 diabetes; HR 1.07, P = 0.0174 in Type 2 diabetes) and reduced the likelihood of regression (HR 0.79, P < 0.0001 in Type 1 diabetes; HR 0.85, P = 0.0009 in Type 2 diabetes), all adjusted for baseline variables and treatment. Candesartan reduced the risk of microaneurysm score progression. Microaneurysm counts are important prognostic indicators for worsening of retinopathy, thus microaneurysms are not benign. Treatment with renin-angiotensin system inhibitors is effective in the early stages and may improve mild diabetic retinopathy. Microaneurysm scores may be useful surrogate endpoints in clinical trials. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.

Novel Index (Hepatic Receptor: IHR) to Evaluate Hepatic Functional Reserve Using (99m)Tc-GSA Scintigraphy.

PubMed

Hasegawa, Daisuke; Onishi, Hideo; Matsutomo, Norikazu

2016-02-01

This study aimed to evaluate the novel index of hepatic receptor (IHR) on the regression analysis derived from time activity curve of the liver for hepatic functional reserve. Sixty patients had undergone (99m)Tc-galactosyl serum albumin ((99m)Tc-GSA) scintigraphy in the retrospective clinical study. Time activity curves for liver were obtained by region of interest (ROI) on the whole liver. A novel hepatic functional predictor was calculated with multiple regression analysis of time activity curves. In the multiple regression function, the objective variables were the indocyanine green (ICG) retention rate at 15 min, and the explanatory variables were the liver counts in 3-min intervals until end from beginning. Then, this result was defined by IHR, and we analyzed the correlation between IHR and ICG, uptake ratio of the heart at 15 minutes to that at 3 minutes (HH15), uptake ratio of the liver to the liver plus heart at 15 minutes (LHL15), and index of convexity (IOC). Regression function of IHR was derived as follows: IHR=0.025×L(6)-0.052×L(12)+0.027×L(27). The multiple regression analysis indicated that liver counts at 6 min, 12 min, and 27 min were significantly related to objective variables. The correlation coefficient between IHR and ICG was 0.774, and the correlation coefficient between ICG and conventional indices (HH15, LHL15, and IOC) were 0.837, 0.773, and 0.793, respectively. IHR had good correlation with HH15, LHL15, and IOC. The finding results suggested that IHR would provide clinical benefit for hepatic functional assessment in the (99m)Tc-GSA scintigraphy.

Crash data modeling with a generalized estimator.

PubMed

Ye, Zhirui; Xu, Yueru; Lord, Dominique

2018-08-01

The investigation of relationships between traffic crashes and relevant factors is important in traffic safety management. Various methods have been developed for modeling crash data. In real world scenarios, crash data often display the characteristics of over-dispersion. However, on occasions, some crash datasets have exhibited under-dispersion, especially in cases where the data are conditioned upon the mean. The commonly used models (such as the Poisson and the NB regression models) have associated limitations to cope with various degrees of dispersion. In light of this, a generalized event count (GEC) model, which can be generally used to handle over-, equi-, and under-dispersed data, is proposed in this study. This model was first applied to case studies using data from Toronto, characterized by over-dispersion, and then to crash data from railway-highway crossings in Korea, characterized with under-dispersion. The results from the GEC model were compared with those from the Negative binomial and the hyper-Poisson models. The cases studies show that the proposed model provides good performance for crash data characterized with over- and under-dispersion. Moreover, the proposed model simplifies the modeling process and the prediction of crash data. Copyright © 2018 Elsevier Ltd. All rights reserved.

Early warning indicators for first-line virologic failure independent of adherence measures in a South African urban clinic.

PubMed

Marconi, Vincent C; Wu, Baohua; Hampton, Jane; Ordóñez, Claudia E; Johnson, Brent A; Singh, Dinesh; John, Sally; Gordon, Michelle; Hare, Anna; Murphy, Richard; Nachega, Jean; Kuritzkes, Daniel R; del Rio, Carlos; Sunpath, Henry

2013-12-01

We sought to develop individual-level Early Warning Indicators (EWI) of virologic failure (VF) for clinicians to use during routine care complementing WHO population-level EWI. A case-control study was conducted at a Durban clinic. Patients after ≥ 5 months of first-line antiretroviral therapy (ART) were defined as cases if they had VF [HIV-1 viral load (VL)>1000 copies/mL] and controls (2:1) if they had VL ≤ 1000 copies/mL. Pharmacy refills and pill counts were used as adherence measures. Participants responded to a questionnaire including validated psychosocial and symptom scales. Data were also collected from the medical record. Multivariable logistic regression models of VF included factors associated with VF (p<0.05) in univariable analyses. We enrolled 158 cases and 300 controls. In the final multivariable model, male gender, not having an active religious faith, practicing unsafe sex, having a family member with HIV, not being pleased with the clinic experience, symptoms of depression, fatigue, or rash, low CD4 counts, family recommending HIV care, and using a TV/radio as ART reminders (compared to mobile phones) were associated with VF independent of adherence measures. In this setting, we identified several key individual-level EWI associated with VF including novel psychosocial factors independent of adherence measures.

Estimation and correction of visibility bias in aerial surveys of wintering ducks

USGS Publications Warehouse

Pearse, A.T.; Gerard, P.D.; Dinsmore, S.J.; Kaminski, R.M.; Reinecke, K.J.

2008-01-01

Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1?100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36?42%, and associated standard errors increased 38?55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species.

Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations.

PubMed

Deng, Chenhui; Plan, Elodie L; Karlsson, Mats O

2016-06-01

Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.

Critical consciousness, racial and gender discrimination, and HIV disease markers in African American women with HIV.

PubMed

Kelso, Gwendolyn A; Cohen, Mardge H; Weber, Kathleen M; Dale, Sannisha K; Cruise, Ruth C; Brody, Leslie R

2014-07-01

Critical consciousness, the awareness of social oppression, is important to investigate as a buffer against HIV disease progression in HIV-infected African American women in the context of experiences with discrimination. Critical consciousness comprises several dimensions, including social group identification, discontent with distribution of social power, rejection of social system legitimacy, and a collective action orientation. The current study investigated self-reported critical consciousness as a moderator of perceived gender and racial discrimination on HIV viral load and CD4+ cell count in 67 African American HIV-infected women. Higher critical consciousness was found to be related to higher likelihood of having CD4+ counts over 350 and lower likelihood of detectable viral load when perceived racial discrimination was high, as revealed by multiple logistic regressions that controlled for highly active antiretroviral therapy (HAART) adherence. Multiple linear regressions showed that at higher levels of perceived gender and racial discrimination, women endorsing high critical consciousness had a larger positive difference between nadir CD4+ (lowest pre-HAART) and current CD4+ count than women endorsing low critical consciousness. These findings suggest that raising awareness of social oppression to promote joining with others to enact social change may be an important intervention strategy to improve HIV outcomes in African American HIV-infected women who report experiencing high levels of gender and racial discrimination.

The effect of cotrimoxazole prophylactic treatment on malaria, birth outcomes, and postpartum CD4 count in HIV-infected women.

PubMed

Dow, Anna; Kayira, Dumbani; Hudgens, Michael G; Van Rie, Annelies; King, Caroline C; Ellington, Sascha; Chome, Nelecy; Kourtis, Athena; Turner, Abigail Norris; Kacheche, Zebrone; Jamieson, Denise J; Chasela, Charles; van der Horst, Charles

2013-01-01

Limited data exist on cotrimoxazole prophylactic treatment (CPT) in pregnant women, including protection against malaria versus standard intermittent preventive therapy with sulfadoxine-pyrimethamine (IPTp). Using observational data we examined the effect of CPT in HIV-infected pregnant women on malaria during pregnancy, low birth weight and preterm birth using proportional hazards, logistic, and log binomial regression, respectively. We used linear regression to assess effect of CPT on CD4 count. Data from 468 CPT-exposed and 768 CPT-unexposed women were analyzed. CPT was associated with protection against malaria versus IPTp (hazard ratio: 0.35, 95% Confidence Interval (CI): 0.20, 0.60). After adjustment for time period this effect was not statistically significant (adjusted hazard ratio: 0.66, 95% CI: 0.28, 1.52). Among women receiving and not receiving CPT, rates of low birth weight (7.1% versus 7.6%) and preterm birth (23.5% versus 23.6%) were similar. CPT was associated with lower CD4 counts 24 weeks postpartum in women receiving (-77.6 cells/ μ L, 95% CI: -125.2, -30.1) and not receiving antiretrovirals (-33.7 cells/ μ L, 95% CI: -58.6, -8.8). Compared to IPTp, CPT provided comparable protection against malaria in HIV-infected pregnant women and against preterm birth or low birth weight. Possible implications of CPT-associated lower CD4 postpartum warrant further examination.

[Risk factors for patent ductus arteriosus in early preterm infants: a case-control study].

PubMed

Du, Jin-Feng; Liu, Tian-Tian; Wu, Hui

2016-01-01

To investigate the risk factors for the occurrence of patent ductus arteriosus (PDA) and to provide a clinical basis for reducing the occurrence of PDA in early preterm infants. A total of 136 early preterm infants (gestational age≤32 weeks) who were hospitalized between January 2013 and December 2014 and diagnosed with hemodynamicalhy significant PDA (hs-PDA) were enrolled as the case group. Based on the matched case-control principle, 136 early preterm infants without hs-PDA were selected among those who were hospitalized within the same period at a ratio of 1:1 and enrolled as the control group. The two groups were matched for sex and gestational age. The basic information of neonates and maternal conditions during the pregnancy and perinatal periods were collected. Logistic regression analysis was performed to identify the risk factors for the development of PDA. Univariate analysis showed that neonatal infectious diseases, neonatal respiratory distress syndrome, decreased platelet count within 24 hours after birth, and low birth weight were associated with the development of hs-PDA (P<0.05). Multivariate conditional logistic regression analysis revealed that neonatal infectious diseases (OR=2.368) and decreased platelet count within 24 hours after birth (OR=0.996) were independent risk factors for hs-PDA. Neonatal infectious diseases and decreased platelet count within 24 hours after birth increase the risk of hs-PDA in early preterm infants.

Critical Consciousness, Racial and Gender Discrimination, and HIV Disease Markers in African American Women with HIV

PubMed Central

Kelso, Gwendolyn A.; Cohen, Mardge H.; Weber, Kathleen M.; Dale, Sannisha K.; Cruise, Ruth C.; Brody, Leslie R.

2014-01-01

Critical consciousness, the awareness of social oppression, is important to investigate as a buffer against HIV disease progression in HIV-infected African American women in the context of experiences with discrimination. Critical consciousness comprises several dimensions, including social group identification, discontent with distribution of social power, rejection of social system legitimacy, and a collective action orientation. The current study investigated self-reported critical consciousness as a moderator of perceived gender and racial discrimination on HIV viral load and CD4+ cell count in 67 African American HIV-infected women. Higher critical consciousness was found to be related to higher likelihood of having CD4+ counts over 350 and lower likelihood of detectable viral load when perceived racial discrimination was high, as revealed by multiple logistic regressions that controlled for highly active antiretroviral therapy (HAART) adherence. Multiple linear regressions showed that at higher levels of perceived gender and racial discrimination, women endorsing high critical consciousness had a larger positive difference between nadir CD4+ (lowest pre-HAART) and current CD4+ count than women endorsing low critical consciousness. These findings suggest that raising awareness of social oppression to promote joining with others to enact social change may be an important intervention strategy to improve HIV outcomes in African American HIV-infected women who report experiencing high levels of gender and racial discrimination. PMID:24077930

Studying the effect of weather conditions on daily crash counts using a discrete time-series model.

PubMed

Brijs, Tom; Karlis, Dimitris; Wets, Geert

2008-05-01

In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an integer autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.

Comparison of athlete-coach perceptions of internal and external load markers for elite junior tennis training.

PubMed

Murphy, Alistair P; Duffield, Rob; Kellett, Aaron; Reid, Machar

2014-09-01

To investigate the discrepancy between coach and athlete perceptions of internal load and notational analysis of external load in elite junior tennis. Fourteen elite junior tennis players and 6 international coaches were recruited. Ratings of perceived exertion (RPEs) were recorded for individual drills and whole sessions, along with a rating of mental exertion, coach rating of intended session exertion, and athlete heart rate (HR). Furthermore, total stroke count and unforced-error count were notated using video coding after each session, alongside coach and athlete estimations of shots and errors made. Finally, regression analyses explained the variance in the criterion variables of athlete and coach RPE. Repeated-measures analyses of variance and interclass correlation coefficients revealed that coaches significantly (P < .01) underestimated athlete session RPE, with only moderate correlation (r = .59) demonstrated between coach and athlete. However, athlete drill RPE (P = .14; r = .71) and mental exertion (P = .44; r = .68) were comparable and substantially correlated. No significant differences in estimated stroke count were evident between athlete and coach (P = .21), athlete notational analysis (P = .06), or coach notational analysis (P = .49). Coaches estimated significantly greater unforced errors than either athletes or notational analysis (P < .01). Regression analyses found that 54.5% of variance in coach RPE was explained by intended session exertion and coach drill RPE, while drill RPE and peak HR explained 45.3% of the variance in athlete session RPE. Coaches misinterpreted session RPE but not drill RPE, while inaccurately monitoring error counts. Improved understanding of external- and internal-load monitoring may help coach-athlete relationships in individual sports like tennis avoid maladaptive training.

Development of a Nomogram for Predicting Severe Neutropenia Associated With Docetaxel-Based Chemotherapy in Patients With Castration-Resistant Prostate Cancer.

PubMed

Hirasawa, Yosuke; Nakashima, Jun; Sugihara, Toru; Takizawa, Issei; Gondo, Tatsuo; Nakagami, Yoshihiro; Horiguchi, Yutaka; Ohno, Yoshio; Namiki, Kazunori; Ohori, Makoto; Tachibana, Masaaki

2017-02-01

Neutropenia is a major adverse event of docetaxel-based chemotherapy. The present study was undertaken to evaluate the incidence of neutropenia and to develop a nomogram for predicting Grade 4 neutropenia during the first cycle of docetaxel-based chemotherapy in patients with castration-resistant prostate cancer (CRPC). This study included 112 patients with CRPC treated with docetaxel-based systemic chemotherapy. We evaluated the incidence and risk factors for Grade 4 neutropenia in the first cycle of chemotherapy. Sixty-two of 112 patients (55.4%) developed Grade 4 neutropenia in the first cycle of docetaxel-based chemotherapy. There were significant differences in age, baseline white blood cell count, and baseline neutrophil count between patients with non-Grade 4 neutropenia and those with Grade 4 neutropenia in univariate analyses. The serum prostate-specific antigen level, hemoglobin level, creatinine, albumin, Eastern Cooperative Oncology Group performance status, metastatic sites, extent of disease, and history of external beam radiotherapy to the prostate were not significantly different between the 2 groups. Multivariate logistic regression analysis showed that age (odds ratio [OR], 1.08; P = .019) and baseline neutrophil counts (OR, 0.79; P = .045) were significant independent risk factors for severe neutropenia. A nomogram and a calibration plot on the basis of these results were developed from a multivariate logistic regression analysis to predict the probability of Grade 4 neutropenia. Age and baseline neutrophil counts were significant independent risk factors for Grade 4 neutropenia. The nomogram to predict it provides useful information for the management of patients with CRPC treated with docetaxel chemotherapy. Copyright © 2016 Elsevier Inc. All rights reserved.

Detection and Measurement of Unhealthy, Environment-Derived Aerosol Materials in an Emergency Department.

PubMed

Choi, Bryan Y; Kobayashi, Leo; Pathania, Shivany; Miller, Courtney B; Locke, Emma R; Stearns, Branden C; Hudepohl, Nathan J; Patefield, Scott S; Suner, Selim; Williams, Kenneth A; Machan, Jason T; Jay, Gregory D

2015-01-01

To measure unhealthy aerosol materials in an Emergency Department (ED) and identify their sources for mitigation efforts. Based on pilot findings of elevated ED particulate matter (PM) levels, investigators hypothesized that unhealthy aerosol materials derive from exogenous (vehicular) sources at ambulance receiving entrances. The Aerosol Environmental Toxicity in Healthcare-related Exposure and Risk program was conducted as an observational study. Calibrated sensors monitored PM and toxic gases at Ambulance Triage Exterior (ATE), Ambulance Triage Desk (ATD), and control Public Triage Desk (PTD) on a 3/3/3-day cycle. Cassette sampling characterized PM; meteorological and ambulance traffic data were logged. Descriptive and multiple linear regression analyses assessed for interactions between aerosol material levels, location, temporal variables, ambulance activity, and meteorological factors. Sensors acquired 93,682 PM0.3, 90,250 PM2.5, and 93,768 PM5 measurements over 366 days to generate a data set representing at least 85.6% of planned measurements. PM0.3, PM2.5, and PM5 mean counts were lowest in PTD; 56%, 224%, and 223% higher in ATD; and 996%, 200%, and 63% higher in ATE, respectively (all p < .001). Qualitative analyses showed similar PM compositions in ATD and ATE. On multiple linear regression analysis, PM0.3 counts correlated primarily with location; PM2.5 and PM5 counts correlated most strongly with location and ambulance presence. PM < 2.5 and toxic gas concentrations at ATD and PTD patient care areas did not exceed hazard levels; PM0.3 counts did not have formal safety thresholds for comparison. Higher levels of PM were linked with ED ambulance areas, although their health impact is unclear. © The Author(s) 2015.

Activity of invasive slug Limax maximus in relation to climate conditions based on citizen's observations and novel regularization based statistical approaches.

PubMed

Morii, Yuta; Ohkubo, Yusaku; Watanabe, Sanae

2018-05-13

Citizen science is a powerful tool that can be used to resolve the problems of introduced species. An amateur naturalist and author of this paper, S. Watanabe, recorded the total number of Limax maximus (Limacidae, Pulmonata) individuals along a fixed census route almost every day for two years on Hokkaido Island, Japan. L. maximus is an invasive slug considered a pest species of horticultural and agricultural crops. We investigated how weather conditions were correlated to the intensity of slug activity using for the first time in ecology the recently developed statistical analyses, Bayesian regularization regression with comparisons among Laplace, Horseshoe and Horseshoe+ priors for the first time in ecology. The slug counts were compared with meteorological data from 5:00 in the morning on the day of observation (OT- and OD-models) and the day before observation (DBOD-models). The OT- and OD-models were more supported than the DBOD-models based on the WAIC scores, and the meteorological predictors selected in the OT-, OD- and DBOD-models were different. The probability of slug appearance was increased on mornings with higher than 20-year-average humidity (%) and lower than average wind velocity (m/s) and precipitation (mm) values in the OT-models. OD-models showed a pattern similar to OT-models in the probability of slug appearance, but also suggested other meteorological predictors for slug activities; positive effect of solar radiation (MJ) for example. Five meteorological predictors, mean and highest temperature (°C), wind velocity (m/s), precipitation amount (mm) and atmospheric pressure (hPa), were selected as the effective factors for the counts in the DBOD-models. Therefore, the DBOD-models will be valuable for the prediction of slug activity in the future, much like a weather forecast. Copyright © 2018 Elsevier B.V. All rights reserved.

Learning to Predict Combinatorial Structures

NASA Astrophysics Data System (ADS)

Vembu, Shankar

2009-12-01

The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions to ensure efficient, polynomial time estimation of model parameters. For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold. In this thesis, we address the problem of designing learning algorithms for predicting combinatorial structures by introducing two new assumptions: (i) The first assumption is that a particular counting problem can be solved efficiently. The consequence is a generalisation of the classical ridge regression for structured prediction. (ii) The second assumption is that a particular sampling problem can be solved efficiently. The consequence is a new technique for designing and analysing probabilistic structured prediction models. These results can be applied to solve several complex learning problems including but not limited to multi-label classification, multi-category hierarchical classification, and label ranking.

Self-Determination Theory and Outpatient Follow-Up After Psychiatric Hospitalization.

PubMed

Sripada, Rebecca K; Bowersox, Nicholas W; Ganoczy, Dara; Valenstein, Marcia; Pfeiffer, Paul N

2016-08-01

The objective of this study was to assess whether the constructs of self-determination theory-autonomy, competence, and relatedness-are associated with adherence to outpatient follow-up appointments after psychiatric hospitalization. 242 individuals discharged from inpatient psychiatric treatment within the Veterans Health Administration completed surveys assessing self-determination theory constructs as well as measures of depression and barriers to treatment. Medical records were used to count the number of mental health visits and no-shows in the 14 weeks following discharge. Logistic regression models assessed the association between survey items assessing theory constructs and attendance at mental healthcare visits. In multivariate models, none of the self-determination theory factors predicted outpatient follow-up attendance. The constructs of self-determination theory as measured by a single self-report survey may not reliably predict adherence to post-hospital care. Need factors such as depression may be more strongly predictive of treatment adherence.

Brief Report: Decentralizing ART Supply for Stable HIV Patients to Community-Based Distribution Centers: Program Outcomes From an Urban Context in Kinshasa, DRC.

PubMed

Vogt, Florian; Kalenga, Lucien; Lukela, Jean; Salumu, Freddy; Diallo, Ibrahim; Nico, Elena; Lampart, Emmanuel; Van den Bergh, Rafael; Shah, Safieh; Ogundahunsi, Olumide; Zachariah, Rony; Van Griensven, Johan

2017-03-01

Facility-based antiretroviral therapy (ART) provision for stable patients with HIV congests health services in resource-limited countries. We assessed outcomes and risk factors for attrition after decentralization to community-based ART refill centers among 2603 patients with HIV in Kinshasa, Democratic Republic of Congo, using a multilevel Poisson regression model. Death, loss to follow-up, and transfer out were 0.3%, 9.0%, and 0.7%, respectively, at 24 months. Overall attrition was 5.66/100 person-years. Patients with >3 years on ART, >500 cluster of differentiation type-4 count, body mass index >18.5, and receiving nevirapine but not stavudine showed reduced attrition. ART refill centers are a promising task-shifting model in low-prevalence urban settings with high levels of stigma and poor ART coverage.

Long terms trends in CD4+ cell counts, CD8+ cell counts, and the CD4+ : CD8+ ratio

PubMed Central

Hughes, Rachael A.; May, Margaret T.; Tilling, Kate; Taylor, Ninon; Wittkop, Linda; Reiss, Peter; Gill, John; Schommers, Philipp; Costagliola, Dominique; Guest, Jodie L.; Lima, Viviane D.; d’Arminio Monforte, Antonella; Smith, Colette; Cavassini, Matthias; Saag, Michael; Castilho, Jessica L.; Sterne, Jonathan A.C.

2018-01-01

Objective: Model trajectories of CD4+ and CD8+ cell counts after starting combination antiretroviral therapy (ART) and use the model to predict trends in these counts and the CD4+ : CD8+ ratio. Design: Cohort study of antiretroviral-naïve HIV-positive adults who started ART after 1997 (ART Cohort Collaboration) with more than 6 months of follow-up data. Methods: We jointly estimated CD4+ and CD8+ cell count trends and their correlation using a bivariate random effects model, with linear splines describing their population trends, and predicted the CD4+ : CD8+ ratio trend from this model. We assessed whether CD4+ and CD8+ cell count trends and the CD4+ : CD8+ ratio trend varied according to CD4+ cell count at start of ART (baseline), and, whether these trends differed in patients with and without virological failure more than 6 months after starting ART. Results: A total of 39 979 patients were included (median follow-up was 53 months). Among patients with baseline CD4+ cell count at least 50 cells/μl, predicted mean CD8+ cell counts continued to decrease between 3 and 15 years post-ART, partly driving increases in the predicted mean CD4+ : CD8+ ratio. During 15 years of follow-up, normalization of the predicted mean CD4+ : CD8+ ratio (to >1) was only observed among patients with baseline CD4+ cell count at least 200 cells/μl. A higher baseline CD4+ cell count predicted a shorter time to normalization. Conclusion: Declines in CD8+ cell count and increases in CD4+ : CD8+ ratio occurred up to 15 years after starting ART. The likelihood of normalization of the CD4+ : CD8+ ratio is strongly related to baseline CD4+ cell count. PMID:29851663

Death rates in HIV-positive antiretroviral-naive patients with CD4 count greater than 350 cells per microL in Europe and North America: a pooled cohort observational study

PubMed Central

2011-01-01

Background It is unclear whether antiretroviral (ART) naive HIV-positive individuals with high CD4 counts have a raised mortality risk compared with the general population, but this is relevant for considering earlier initiation of antiretroviral therapy. Methods Pooling data from 23 European and North American cohorts, we calculated country-, age-, sex-, and year-standardised mortality ratios (SMRs), stratifying by risk group. Included patients had at least one pre-ART CD4 count above 350 cells/mm3. The association between CD4 count and death rate was evaluated using Poisson regression methods. Findings Of 40,830 patients contributing 80,682 person-years of follow up with CD4 count above 350 cells/mm3, 419 (1.0%) died. The SMRs (95% confidence interval) were 1.30 (1.06-1.58) in homosexual men, and 2.94 (2.28-3.73) and 9.37 (8.13-10.75) in the heterosexual and IDU risk groups respectively. CD4 count above 500 cells/mm3 was associated with a lower death rate than 350-499 cells/mm3: adjusted rate ratios (95% confidence intervals) for 500-699 cells/mm3 and above 700 cells/mm3 were 0.77 (0.61-0.95) and 0.66 (0.52-0.85) respectively. Interpretation In HIV-infected ART-naive patients with high CD4 counts, death rates were raised compared with the general population. In homosexual men this was modest, suggesting that a proportion of the increased risk in other groups is due to confounding by other factors. Even in this high CD4 count range, lower CD4 count was associated with raised mortality. PMID:20638118

Factors Associated With Ambulatory Activity in De Novo Parkinson Disease.

PubMed

Christiansen, Cory; Moore, Charity; Schenkman, Margaret; Kluger, Benzi; Kohrt, Wendy; Delitto, Anthony; Berman, Brian; Hall, Deborah; Josbeno, Deborah; Poon, Cynthia; Robichaud, Julie; Wellington, Toby; Jain, Samay; Comella, Cynthia; Corcos, Daniel; Melanson, Ed

2017-04-01

Objective ambulatory activity during daily living has not been characterized for people with Parkinson disease prior to initiation of dopaminergic medication. Our goal was to characterize ambulatory activity based on average daily step count and examine determinants of step count in nonexercising people with de novo Parkinson disease. We analyzed baseline data from a randomized controlled trial, which excluded people performing regular endurance exercise. Of 128 eligible participants (mean ± SD = 64.3 ± 8.6 years), 113 had complete accelerometer data, which were used to determine daily step count. Multiple linear regression was used to identify factors associated with average daily step count over 10 days. Candidate explanatory variable categories were (1) demographics/anthropometrics, (2) Parkinson disease characteristics, (3) motor symptom severity, (4) nonmotor and behavioral characteristics, (5) comorbidities, and (6) cardiorespiratory fitness. Average daily step count was 5362 ± 2890 steps per day. Five factors explained 24% of daily step count variability, with higher step count associated with higher cardiorespiratory fitness (10%), no fear/worry of falling (5%), lower motor severity examination score (4%), more recent time since Parkinson disease diagnosis (3%), and the presence of a cardiovascular condition (2%). Daily step count in nonexercising people recruited for this intervention trial with de novo Parkinson disease approached sedentary lifestyle levels. Further study is warranted for elucidating factors explaining ambulatory activity, particularly cardiorespiratory fitness, and fear/worry of falling. Clinicians should consider the costs and benefits of exercise and activity behavior interventions immediately after diagnosis of Parkinson disease to attenuate the health consequences of low daily step count.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A170).

Relationship between automated total nucleated cell count and enumeration of cells on direct smears of canine synovial fluid.

PubMed

Dusick, Allison; Young, Karen M; Muir, Peter

2014-12-01

Canine osteoarthritis is a common disorder seen in veterinary clinical practice and causes considerable morbidity in dogs as they age. Synovial fluid analysis is an important tool for diagnosis and treatment of canine joint disease and obtaining a total nucleated cell count (TNCC) is particularly important. However, the low sample volumes obtained during arthrocentesis are often insufficient for performing an automated TNCC, thereby limiting diagnostic interpretation. The aim of the present study was to investigate whether estimation of TNCC in canine synovial fluid could be achieved by performing manual cell counts on direct smears of fluid. Fifty-eight synovial fluid samples, taken by arthrocentesis from 48 dogs, were included in the study. Direct smears of synovial fluid were prepared, and hyaluronidase added before cell counts were obtained using a commercial laser-based instrument. A protocol was established to count nucleated cells in a specific region of the smear, using a serpentine counting pattern; the mean number of nucleated cells per 400 × field was then calculated. There was a positive correlation between the automated TNCC and mean manual cell count, with more variability at higher TNCC. Regression analysis was performed to estimate TNCC from manual counts. By this method, 78% of the samples were correctly predicted to fall into one of three categories (within the reference interval, mildly to moderately increased, or markedly increased) relative to the automated TNCC. Intra-observer and inter-observer agreement was good to excellent. The results of the study suggest that interpretation of canine synovial fluid samples of low volume can be aided by methodical manual counting of cells on direct smears. Copyright © 2014 Elsevier Ltd. All rights reserved.

Agreement between pedometer and accelerometer in measuring physical activity in overweight and obese pregnant women.

PubMed

Kinnunen, Tarja I; Tennant, Peter W G; McParlin, Catherine; Poston, Lucilla; Robson, Stephen C; Bell, Ruth

2011-06-27

Inexpensive, reliable objective methods are needed to measure physical activity (PA) in large scale trials. This study compared the number of pedometer step counts with accelerometer data in pregnant women in free-living conditions to assess agreement between these measures. Pregnant women (n = 58) with body mass index ≥25 kg/m(2) at median 13 weeks' gestation wore a GT1M Actigraph accelerometer and a Yamax Digi-Walker CW-701 pedometer for four consecutive days. The Spearman rank correlation coefficients were determined between pedometer step counts and various accelerometer measures of PA. Total agreement between accelerometer and pedometer step counts was evaluated by determining the 95% limits of agreement estimated using a regression-based method. Agreement between the monitors in categorising participants as active or inactive was assessed by determining Kappa. Pedometer step counts correlated moderately (r = 0.36 to 0.54) with most accelerometer measures of PA. Overall step counts recorded by the pedometer and the accelerometer were not significantly different (medians 5961 vs. 5687 steps/day, p = 0.37). However, the 95% limits of agreement ranged from -2690 to 2656 steps/day for the mean step count value (6026 steps/day) and changed substantially over the range of values. Agreement between the monitors in categorising participants to active and inactive varied from moderate to good depending on the criteria adopted. Despite statistically significant correlations and similar median step counts, the overall agreement between pedometer and accelerometer step counts was poor and varied with activity level. Pedometer and accelerometer steps cannot be used interchangeably in overweight and obese pregnant women.

Open access to journal articles in oncology: current situation and citation impact.

PubMed

Hua, F; Sun, H; Walsh, T; Glenny, A-M; Worthington, H

2017-10-01

Recent years have seen numerous efforts and resources devoted to the development of open access (OA), but the current OA situation of the oncology literature remains unknown. We conducted this cross-sectional study to determine the current share and provision methods of OA in the field of oncology, identify predictors of OA status (OA versus non-OA), and study the association between OA and citation counts. PubMed was searched for oncology-related, peer-reviewed journal articles published in December 2014. Google, Google Scholar, PubMed, ResearchGate, OpenDOAR and OAIster were manually checked to assess the OA status of each included article. Citation data were extracted from Web of Science, Scopus and Google Scholar. Descriptive statistics were used to summarize the OA proportion (primary outcome) and OA provision methods. Multivariable logistic regression and multilevel generalized linear model analyses were performed to study predictors of OA status and the association between OA and citation counts, respectively. In a random sample of 1000 articles, 912 were deemed eligible and therefore included. Of these, the full-texts of 530 articles (58.1%; 95% CI: 54.9-61.3) were freely available online: 314 (34.4%) were available from publishers ('Gold road' to OA), 424 (46.5%) were available via self-archiving ('Green road' to OA). According to multivariable regression analyses, impact factor, publisher type, language, research type, number of authors, continent of origin, and country income were significant predictors of articles' OA status; OA articles received a citation rate 1.24 times the incidence rate for non-OA articles (95% CI: 1.05-1.47; P = 0.012). Based on our sample, in the field of oncology, 42% of recent journal articles are behind the pay-wall (non-OA) 1 year after publication; the 'Green road' of providing OA is more common than the 'Gold road'; OA is associated with higher citation counts. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An observational study of emergency department utilization among enrollees of Minnesota Health Care Programs: financial and non-financial barriers have different associations.

PubMed

Shippee, Nathan D; Shippee, Tetyana P; Hess, Erik P; Beebe, Timothy J

2014-02-08

Emergency department (ED) use is costly, and especially frequent among publicly insured populations in the US, who also disproportionately encounter financial (cost/coverage-related) and non-financial/practical barriers to care. The present study examines the distinct associations financial and non-financial barriers to care have with patterns of ED use among a publicly insured population. This observational study uses linked administrative-survey data for enrollees of Minnesota Health Care Programs to examine patterns in ED use-specifically, enrollee self-report of the ED as usual source of care, and past-year count of 0, 1, or 2+ ED visits from administrative data. Main independent variables included a count of seven enrollee-reported financial concerns about healthcare costs and coverage, and a count of seven enrollee-reported non-financial, practical barriers to access (e.g., limited office hours, problems with childcare). Covariates included health, health care, and demographic measures. In multivariate regression models, only financial concerns were positively associated with reporting ED as usual source of care, but only non-financial barriers were significantly associated with greater ED visits. Regression-adjusted values indicated notable differences in ED visits by number of non-financial barriers: zero non-financial barriers meant an adjusted 78% chance of having zero ED visits (95% C.I.: 70.5%-85.5%), 15.9% chance of 1(95% C.I.: 10.4%-21.3%), and 6.2% chance (95% C.I.: 3.5%-8.8%) of 2+ visits, whereas having all seven non-financial barriers meant a 48.2% adjusted chance of zero visits (95% C.I.: 30.9%-65.6%), 31.8% chance of 1 visit (95% C.I.: 24.2%-39.5%), and 20% chance (95% C.I.: 8.4%-31.6%) of 2+ visits. Financial barriers were associated with identifying the ED as one's usual source of care but non-financial barriers were associated with actual ED visits. Outreach/literacy efforts may help reduce reliance on/perception of ED as usual source of care, whereas improved targeting/availability of covered services may help curb frequent actual visits, among publicly insured individuals.

Linking reproduction and survival can improve model estimates of vital rates derived from limited time-series counts of pinnipeds and other species.

PubMed

Battaile, Brian C; Trites, Andrew W

2013-01-01

We propose a method to model the physiological link between somatic survival and reproductive output that reduces the number of parameters that need to be estimated by models designed to determine combinations of birth and death rates that produce historic counts of animal populations. We applied our Reproduction and Somatic Survival Linked (RSSL) method to the population counts of three species of North Pacific pinnipeds (harbor seals, Phoca vitulina richardii (Gray, 1864); northern fur seals, Callorhinus ursinus (L., 1758); and Steller sea lions, Eumetopias jubatus (Schreber, 1776))--and found our model outperformed traditional models when fitting vital rates to common types of limited datasets, such as those from counts of pups and adults. However, our model did not perform as well when these basic counts of animals were augmented with additional observations of ratios of juveniles to total non-pups. In this case, the failure of the ratios to improve model performance may indicate that the relationship between survival and reproduction is redefined or disassociated as populations change over time or that the ratio of juveniles to total non-pups is not a meaningful index of vital rates. Overall, our RSSL models show advantages to linking survival and reproduction within models to estimate the vital rates of pinnipeds and other species that have limited time-series of counts.

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

PubMed

Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

2013-01-01

In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

Strategy, structure, and patient quality outcomes in ambulatory surgery centers (1997-2004).

PubMed

Chukmaitov, Askar; Devers, Kelly J; Harless, David W; Menachemi, Nir; Brooks, Robert G

2011-04-01

The purpose of this study was to examine potential associations among ambulatory surgery centers' (ASCs) organizational strategy, structure, and quality performance. The authors obtained several large-scale, all-payer claims data sets for the 1997 to 2004 period. The authors operationalized quality performance as unplanned hospitalizations at 30 days after outpatient arthroscopy and colonoscopy procedures. The authors draw on related organizational theory, behavior, and health services research literatures to develop their conceptual framework and hypotheses and fitted fixed and random effects Poisson regression models with the count of unplanned hospitalizations. Consistent with the key hypotheses formulated, the findings suggest that higher levels of specialization and the volume of procedures may be associated with a decrease in unplanned hospitalizations at ASCs.

Food Insecurity is Associated with Poor HIV Outcomes Among Women in the United States.

PubMed

Spinelli, Matthew A; Frongillo, Edward A; Sheira, Lila A; Palar, Kartika; Tien, Phyllis C; Wilson, Tracey; Merenstein, Daniel; Cohen, Mardge; Adedimeji, Adebola; Wentz, Eryka; Adimora, Adaora A; Metsch, Lisa R; Turan, Janet M; Kushel, Margot B; Weiser, Sheri D

2017-12-01

Women in the general population experience more food insecurity than men. Few studies have examined food insecurity's impact on HIV treatment outcomes among women. We examined the association between food insecurity and HIV outcomes in a multi-site sample of HIV-infected women in the United States (n = 1154). Two-fifths (40%) of participants reported food insecurity. In an adjusted multivariable Tobit regression model, food insecurity was associated with 2.08 times higher viral load (95% confidence interval (CI): 1.04, 4.15) and lower CD4+ counts (- 42.10, CI: - 81.16, - 3.03). Integration of food insecurity alleviation into HIV programs may improve HIV outcomes in women.

A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue.

PubMed

Laurinavicius, Arvydas; Plancoulaine, Benoit; Laurinaviciene, Aida; Herlin, Paulette; Meskauskas, Raimundas; Baltrusaityte, Indra; Besusparis, Justinas; Dasevicius, Darius; Elie, Nicolas; Iqbal, Yasir; Bor, Catherine

2014-01-01

Immunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles. We aimed to develop a methodology to ensure and improve accuracy of the digital image analysis (DIA) approach. Tissue microarrays (one 1-mm spot per patient, n = 164) from invasive ductal breast carcinoma were stained for Ki67 and scanned. Criterion standard (Ki67-Count) was obtained by counting positive and negative tumour cell profiles using a stereology grid overlaid on a spot image. DIA was performed with Aperio Genie/Nuclear algorithms. A bias was estimated by ANOVA, correlation and regression analyses. Calibration steps of the DIA by adjusting the algorithm settings were performed: first, by subjective DIA quality assessment (DIA-1), and second, to compensate the bias established (DIA-2). Visual estimate (Ki67-VE) on the same images was performed by five pathologists independently. ANOVA revealed significant underestimation bias (P < 0.05) for DIA-0, DIA-1 and two pathologists' VE, while DIA-2, VE-median and three other VEs were within the same range. Regression analyses revealed best accuracy for the DIA-2 (R-square = 0.90) exceeding that of VE-median, individual VEs and other DIA settings. Bidirectional bias for the DIA-2 with overestimation at low, and underestimation at high ends of the scale was detected. Measurement error correction by inverse regression was applied to improve DIA-2-based prediction of the Ki67-Count, in particularfor the clinically relevant interval of Ki67-Count < 40%. Potential clinical impact of the prediction was tested by dichotomising the cases at the cut-off values of 10, 15, and 20%. Misclassification rate of 5-7% was achieved, compared to that of 11-18% for the VE-median-based prediction. Our experiments provide methodology to achieve accurate Ki67-LI estimation by DIA, based on proper validation, calibration, and measurement error correction procedures, guided by quantified bias from reference values obtained by stereology grid count. This basic validation step is an important prerequisite for high-throughput automated DIA applications to investigate tissue heterogeneity and clinical utility aspects of Ki67 and other immunohistochemistry (IHC) biomarkers.

An evidential example of airborne bacteria in a crowded, underground public concourse in Tokyo

NASA Astrophysics Data System (ADS)

Seino, Kaoruko; Takano, Takehito; Nakamura, Keiko; Watanabe, Masafumi

2005-01-01

We examined airborne bacteria in an underground concourse in Tokyo and investigated conditions that influenced bacterial counts. Airborne bacteria were collected by using an impactor sampler. Colonies on plate count agar (PCA) and Columbia colistin-nalidixic acid agar with 5% sheep blood (CNA agar) were enumerated. The range, geometric mean, and 95% CI of the bacterial counts (CFU m-3) on PCA and CNA agar were 150-1380, 456, 382-550 and 50-990, 237, 182-309, respectively. Bacterial counts on PCA significantly correlated with number of the pedestrians (r=0.89), relative humidity (r=0.70) and airborne dust (PM5.0) (r=0.73). Results of a multiple regression indicated independent positive association between the number of pedestrians and bacterial counts on PCA (p<0.01) after excluding the influence of relative humidity and airborne dust. Similar results were obtained with the statistical analysis for the counts of bacteria on CNA agar. Gram-positive cocci were dominant on PCA and CNA agar. Staphylococcus epidermidis and Micrococcus spp. were dominant among the 11 genera and 19 species identified in the present study. Considering the pattern of identified species and the significant independent association between number of pedestrians and bacterial counts, airborne bacteria in a crowded underground concourse were mostly originated from the pedestrians who were walking in the underground concourse. This study gave an evidential example of bacterial conditions in the air of an underground crowded public space in Tokyo.

Modeling particle number concentrations along Interstate 10 in El Paso, Texas

PubMed Central

Olvera, Hector A.; Jimenez, Omar; Provencio-Vasquez, Elias

2014-01-01

Annual average daily particle number concentrations around a highway were estimated with an atmospheric dispersion model and a land use regression model. The dispersion model was used to estimate particle concentrations along Interstate 10 at 98 locations within El Paso, Texas. This model employed annual averaged wind speed and annual average daily traffic counts as inputs. A land use regression model with vehicle kilometers traveled as the predictor variable was used to estimate local background concentrations away from the highway to adjust the near-highway concentration estimates. Estimated particle number concentrations ranged between 9.8 × 103 particles/cc and 1.3 × 105 particles/cc, and averaged 2.5 × 104 particles/cc (SE 421.0). Estimates were compared against values measured at seven sites located along I10 throughout the region. The average fractional error was 6% and ranged between -1% and -13% across sites. The largest bias of -13% was observed at a semi-rural site where traffic was lowest. The average bias amongst urban sites was 5%. The accuracy of the estimates depended primarily on the emission factor and the adjustment to local background conditions. An emission factor of 1.63 × 1014 particles/veh-km was based on a value proposed in the literature and adjusted with local measurements. The integration of the two modeling techniques ensured that the particle number concentrations estimates captured the impact of traffic along both the highway and arterial roadways. The performance and economical aspects of the two modeling techniques used in this study shows that producing particle concentration surfaces along major roadways would be feasible in urban regions where traffic and meteorological data are readily available. PMID:25313294

Insulin resistance is associated with cognition among HIV-1-infected patients: the Hawaii Aging With HIV cohort.

PubMed

Valcour, Victor G; Sacktor, Ned C; Paul, Robert H; Watters, Michael R; Selnes, Ola A; Shiramizu, Bruce T; Williams, Andrew E; Shikuma, Cecilia M

2006-12-01

To determine if insulin resistance (IR) is associated with lower cognitive performance among HIV-1-infected adults and to determine if advanced age magnifies risk. Cross-sectional analysis within the Hawaii Aging With HIV Cohort. We calculated the homeostasis model assessment of insulin resistance (HOMA-IR) among 145 cohort participants. Values were compared to concurrent neuropsychological test performance and cognitive diagnoses. Hypertension, body mass index (BMI), and non-Caucasian self-identity were directly related to insulin resistance (IR); however, age, CD4 lymphocyte count, and rates of treatment with HAART were not. In logistic regression analyses and stratifying cognition status on a 3-tiered scale (normal, minor cognitive motor disorder (MCMD), and HIV-associated dementia (HAD)), we identified an increased risk of meeting a higher diagnostic category as HOMA-IR increased (OR, 1.12; 95% CI: 1.003 to 1.242 per unit of HOMA-IR, P = 0.044). In linear regression models and among nondiabetic participants, an increasing degree of IR was associated with lower performance on neuropsychological summary scores. IR is associated with cognitive dysfunction in this contemporary HIV-1 cohort enriched with older individuals. Metabolic dysfunction may contribute to the multifactorial pathogenesis of cognitive impairment in the era of HAART.

Introduction

Treesearch

J. Michael Scott; C. John Ralph

1981-01-01

Counting birds has a long tradition. Since early in human history, man has noted and recorded the presence, absence, and abundance of birds. This long, and presumably honorable, pursuit that we all engage in, to a greater or lesser extent, is the common currency of many ornithological studies. These studies range from multiple regression analyses of habitat variables...

Using EDA, ANOVA and Regression to Optimise Some Microbiology Data

ERIC Educational Resources Information Center

Binnie, Neil

2004-01-01

Bacteria are cultured in medical laboratories to identify them so patients can be treated correctly. The tryptone dataset contains measurements of bacteria counts following the culturing of five strains of "Staphylococcus aureus". It also contains the time of incubation, temperature of incubation and concentration of tryptone, a nutrient. The…

Academic Achievement of Girls in Rural Schools in Kenya

ERIC Educational Resources Information Center

Mungai, A. M.

2012-01-01

This study examined the effect of two family factors (financial, social capital) and school factors on students' achievement. One hundred eighty two, seventh-grade female students from nine schools in Muranga district, Kenya, were studied. The statistical procedures included logit regression, cross-tabulations, frequency counting and chi-square…

Immunological and infectious risk factors for lung cancer in US veterans with HIV: a longitudinal cohort study.

PubMed

Sigel, Keith; Wisnivesky, Juan; Crothers, Kristina; Gordon, Kirsha; Brown, Sheldon T; Rimland, David; Rodriguez-Barradas, Maria C; Gibert, Cynthia; Goetz, Matthew Bidwell; Bedimo, Roger; Park, Lesley S; Dubrow, Robert

2017-02-01

HIV infection is independently associated with risk of lung cancer, but few data exist for the relation between longitudinal measurements of immune function and lung-cancer risk in people living with HIV. We followed up participants with HIV from the Veterans Aging Cohort Study for a minimum of 3 years between Jan 1, 1998, and Dec 31, 2012, and used cancer registry data to identify incident cases of lung cancer. The index date for each patient was the later of the date HIV care began or Jan 1, 1998. We excluded patients with less than 3 years' follow-up, prevalent diagnoses of lung cancer, or incomplete laboratory data. We used Cox regression models to investigate the relation between different time-updated lagged and cumulative exposures (CD4 cell count, CD8 cell count, CD4/CD8 ratio, HIV RNA, and bacterial pneumonia) and risk of lung cancer. Models were adjusted for age, race or ethnicity, smoking, hepatitis C virus infection, alcohol use disorders, drug use disorders, and history of chronic obstructive pulmonary disease and occupational lung disease. We identified 277 cases of incident lung cancer in 21 666 participants with HIV. In separate models for each time-updated 12 month lagged, 24 month simple moving average cumulative exposure, increased risk of lung cancer was associated with low CD4 cell count (p trend=0·001), low CD4/CD8 ratio (p trend=0·0001), high HIV RNA concentration (p=0·004), and more cumulative bacterial pneumonia episodes (12 month lag only; p trend=0·0004). In a mutually adjusted model including these factors, CD4/CD8 ratio and cumulative bacterial pneumonia episodes remained significant (p trends 0·003 and 0·004, respectively). In our large HIV cohort in the antiretroviral therapy era, we found evidence that dysfunctional immune activation and chronic inflammation contribute to the development of lung cancer in the setting of HIV infection. These findings could be used to target lung-cancer prevention measures to high-risk groups. US National Institutes of Health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Carotid femoral pulse wave velocity in type 2 diabetes and hypertension: capturing arterial health effects of step counts

PubMed Central

Dasgupta, Kaberi; Rosenberg, Ellen; Joseph, Lawrence; Trudeau, Luc; Garfield, Natasha; Chan, Deborah; Sherman, Mark; Rabasa-Lhoret, Rémi; Daskalopoulou, Stella S.

2017-01-01

Objective: Optimal medication use obscures the impact of physical activity on traditional cardiometabolic risk factors. We evaluated the relationship between step counts and carotid-femoral pulse wave velocity (cfPWV), a summative risk indicator, in patients with type 2 diabetes and/or hypertension. Research design and methods: Three hundred and sixty-nine participants were recruited (outpatient clinics; Montreal, Quebec; 2011–2015). Physical activity (pedometer/accelerometer), cfPWV (applanation tonometry), and risk factors (A1C, Homeostatic Model Assessment–Insulin Resistance, blood pressure, lipid profiles) were evaluated. Linear regression models were constructed to quantify the relationship of steps/day with cfPWV. Results: The study population comprised 191 patients with type 2 diabetes and hypertension, 39 with type 2 diabetes, and 139 with hypertension (mean ± SD: age 59.6 ± 11.2 years; BMI 31.3 ± 4.8 kg/m2; 54.2% women). Blood pressure (125/77 ± 15/9 mmHg), A1C (diabetes: 7.7 ± 1.3%; 61 mmol/mol), and low-density lipoprotein cholesterol (diabetes: 2.19 ± 0.8 mmol/l; without diabetes: 3.13 ± 1.1mmol/l) were close to target. Participants averaged 5125 ± 2722 steps/day. Mean cfPWV was 9.8 ± 2.2 m/s. Steps correlated with cfPWV, but not with other risk factors. A 1000 steps/day increment was associated with a 0.1 m/s cfPWV decrement across adjusted models and in subgroup analysis by diabetes status. In a model adjusted for age, sex, BMI, ethnicity, immigrant status, employment, education, diabetes, hypertension, medication classes, the mean cfPWV decrement was 0.11 m/s (95% confidence interval −0.2, −0.02). Conclusions: cfPWV is responsive to step counts in patients who are well controlled on cardioprotective medications. This ability to capture the ‘added value’ of physical activity supports the emerging role of cfPWV in arterial health monitoring. PMID:28129250

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

PubMed Central

Wang, Zhu; Shuangge, Ma; Wang, Ching-Yun

2017-01-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package mpath. PMID:26059498

NIPTmer: rapid k-mer-based software package for detection of fetal aneuploidies.

PubMed

Sauk, Martin; Žilina, Olga; Kurg, Ants; Ustav, Eva-Liina; Peters, Maire; Paluoja, Priit; Roost, Anne Mari; Teder, Hindrek; Palta, Priit; Brison, Nathalie; Vermeesch, Joris R; Krjutškov, Kaarel; Salumets, Andres; Kaplinski, Lauris

2018-04-04

Non-invasive prenatal testing (NIPT) is a recent and rapidly evolving method for detecting genetic lesions, such as aneuploidies, of a fetus. However, there is a need for faster and cheaper laboratory and analysis methods to make NIPT more widely accessible. We have developed a novel software package for detection of fetal aneuploidies from next-generation low-coverage whole genome sequencing data. Our tool - NIPTmer - is based on counting pre-defined per-chromosome sets of unique k-mers from raw sequencing data, and applying linear regression model on the counts. Additionally, the filtering process used for k-mer list creation allows one to take into account the genetic variance in a specific sample, thus reducing the source of uncertainty. The processing time of one sample is less than 10 CPU-minutes on a high-end workstation. NIPTmer was validated on a cohort of 583 NIPT samples and it correctly predicted 37 non-mosaic fetal aneuploidies. NIPTmer has the potential to reduce significantly the time and complexity of NIPT post-sequencing analysis compared to mapping-based methods. For non-commercial users the software package is freely available at http://bioinfo.ut.ee/NIPTMer/ .

The Crash Intensity Evaluation Using General Centrality Criterions and a Geographically Weighted Regression

NASA Astrophysics Data System (ADS)

Ghadiriyan Arani, M.; Pahlavani, P.; Effati, M.; Noori Alamooti, F.

2017-09-01

Today, one of the social problems influencing on the lives of many people is the road traffic crashes especially the highway ones. In this regard, this paper focuses on highway of capital and the most populous city in the U.S. state of Georgia and the ninth largest metropolitan area in the United States namely Atlanta. Geographically weighted regression and general centrality criteria are the aspects of traffic used for this article. In the first step, in order to estimate of crash intensity, it is needed to extract the dual graph from the status of streets and highways to use general centrality criteria. With the help of the graph produced, the criteria are: Degree, Pageranks, Random walk, Eccentricity, Closeness, Betweenness, Clustering coefficient, Eigenvector, and Straightness. The intensity of crash point is counted for every highway by dividing the number of crashes in that highway to the total number of crashes. Intensity of crash point is calculated for each highway. Then, criteria and crash point were normalized and the correlation between them was calculated to determine the criteria that are not dependent on each other. The proposed hybrid approach is a good way to regression issues because these effective measures result to a more desirable output. R2 values for geographically weighted regression using the Gaussian kernel was 0.539 and also 0.684 was obtained using a triple-core cube. The results showed that the triple-core cube kernel is better for modeling the crash intensity.

Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses

PubMed Central

Xia, Yinglin; Morrison-Beedy, Dianne; Ma, Jingming; Feng, Changyong; Cross, Wendi; Tu, Xin

2012-01-01

Modeling count data from sexual behavioral outcomes involves many challenges, especially when the data exhibit a preponderance of zeros and overdispersion. In particular, the popular Poisson log-linear model is not appropriate for modeling such outcomes. Although alternatives exist for addressing both issues, they are not widely and effectively used in sex health research, especially in HIV prevention intervention and related studies. In this paper, we discuss how to analyze count outcomes distributed with excess of zeros and overdispersion and introduce appropriate model-fit indices for comparing the performance of competing models, using data from a real study on HIV prevention intervention. The in-depth look at these common issues arising from studies involving behavioral outcomes will promote sound statistical analyses and facilitate research in this and other related areas. PMID:22536496

Stochastic modeling of the hypothalamic pulse generator activity.

PubMed

Camproux, A C; Thalabard, J C; Thomas, G

1994-11-01

Luteinizing hormone (LH) is released by the pituitary in discrete pulses. In the monkey, the appearance of LH pulses in the plasma is invariably associated with sharp increases (i.e, volleys) in the frequency of the hypothalamic pulse generator electrical activity, so that continuous monitoring of this activity by telemetry provides a unique means to study the temporal structure of the mechanism generating the pulses. To assess whether the times of occurrence and durations of previous volleys exert significant influence on the timing of the next volley, we used a class of periodic counting process models that specify the stochastic intensity of the process as the product of two factors: 1) a periodic baseline intensity and 2) a stochastic regression function with covariates representing the influence of the past. This approach allows the characterization of circadian modulation and memory range of the process underlying hypothalamic pulse generator activity, as illustrated by fitting the model to experimental data from two ovariectomized rhesus monkeys.

Pre-school obesity is inversely associated with vegetable intake, grocery stores and outdoor play.

PubMed

Kepper, M; Tseng, T-S; Volaufova, J; Scribner, R; Nuss, H; Sothern, M

2016-10-01

The study determined the association between body mass index (BMI) z score and fruit and vegetable intake, frequency and ratio of fast food outlets and grocery stores in concentric areas around the child's residence, outdoor play and total crime index. Data from 78 Louisiana pre-school children were analyzed using Pearson's correlation and multiple regression analysis. Parental-reported fruit intake was linearly associated with increased number of grocery store counts in concentric areas around the child's residence (P = 0.0406, P = 0.0281). Vegetable intake was inversely (P = 0.04) and the ratio of fast food outlets to grocery stores in a 2-mile concentric area around the child's residence was positively (P = 0.05) associated to BMI z score after applying Best Model regression analysis (F = 3.06, P = 0.0346). Children residing in neighbourhoods with greater access to fast foods and lower access to fruits and vegetables may be at higher risk for developing obesity during pre-school years. © 2015 World Obesity.

Developing optimum sample size and multistage sampling plans for Lobesia botrana (Lepidoptera: Tortricidae) larval infestation and injury in northern Greece.

PubMed

Ifoulis, A A; Savopoulou-Soultani, M

2006-10-01

The purpose of this research was to quantify the spatial pattern and develop a sampling program for larvae of Lobesia botrana Denis and Schiffermüller (Lepidoptera: Tortricidae), an important vineyard pest in northern Greece. Taylor's power law and Iwao's patchiness regression were used to model the relationship between the mean and the variance of larval counts. Analysis of covariance was carried out, separately for infestation and injury, with combined second and third generation data, for vine and half-vine sample units. Common regression coefficients were estimated to permit use of the sampling plan over a wide range of conditions. Optimum sample sizes for infestation and injury, at three levels of precision, were developed. An investigation of a multistage sampling plan with a nested analysis of variance showed that if the goal of sampling is focusing on larval infestation, three grape clusters should be sampled in a half-vine; if the goal of sampling is focusing on injury, then two grape clusters per half-vine are recommended.

Use of simulation tools to illustrate the effect of data management practices for low and negative plate counts on the estimated parameters of microbial reduction models.

PubMed

Garcés-Vega, Francisco; Marks, Bradley P

2014-08-01

In the last 20 years, the use of microbial reduction models has expanded significantly, including inactivation (linear and nonlinear), survival, and transfer models. However, a major constraint for model development is the impossibility to directly quantify the number of viable microorganisms below the limit of detection (LOD) for a given study. Different approaches have been used to manage this challenge, including ignoring negative plate counts, using statistical estimations, or applying data transformations. Our objective was to illustrate and quantify the effect of negative plate count data management approaches on parameter estimation for microbial reduction models. Because it is impossible to obtain accurate plate counts below the LOD, we performed simulated experiments to generate synthetic data for both log-linear and Weibull-type microbial reductions. We then applied five different, previously reported data management practices and fit log-linear and Weibull models to the resulting data. The results indicated a significant effect (α = 0.05) of the data management practices on the estimated model parameters and performance indicators. For example, when the negative plate counts were replaced by the LOD for log-linear data sets, the slope of the subsequent log-linear model was, on average, 22% smaller than for the original data, the resulting model underpredicted lethality by up to 2.0 log, and the Weibull model was erroneously selected as the most likely correct model for those data. The results demonstrate that it is important to explicitly report LODs and related data management protocols, which can significantly affect model results, interpretation, and utility. Ultimately, we recommend using only the positive plate counts to estimate model parameters for microbial reduction curves and avoiding any data value substitutions or transformations when managing negative plate counts to yield the most accurate model parameters.

Utility of the serum C-reactive protein for detection of occult bacterial infection in children.

PubMed

Isaacman, Daniel J; Burke, Bonnie L

2002-09-01

To assess the utility of serum C-reactive protein (CRP) as a screen for occult bacterial infection in children. Febrile children ages 3 to 36 months who visited an urban children's hospital emergency department and received a complete blood cell count and blood culture as part of their evaluation were prospectively enrolled from February 2, 2000, through May 30, 2001. Informed consent was obtained for the withdrawal of an additional 1-mL aliquot of blood for use in CRP evaluation. Logistic regression and receiver operator characteristic (ROC) curves were modeled for each predictor to identify optimal test values, and were compared using likelihood ratio tests. Two hundred fifty-six patients were included in the analysis, with a median age of 15.3 months (range, 3.1-35.2 months) and median temperature at triage 40.0 degrees C (range, 39.0 degrees C-41.3 degrees C). Twenty-nine (11.3%) cases of occult bacterial infection (OBI) were identified, including 17 cases of pneumonia, 9 cases of urinary tract infection, and 3 cases of bacteremia. The median white blood cell count in this data set was 12.9 x 10(3)/ micro L [corrected] (range, 3.6-39.1 x10(3)/ micro L) [corrected], the median absolute neutrophil count (ANC) was 7.12 x 10(3)/L [corrected] (range, 0.56-28.16 x10(3)/L) [corrected], and the median CRP level was 1.7 mg/dL (range, 0.2-43.3 mg/dL). The optimal cut-off point for CRP in this data set (4.4 mg/dL) achieved a sensitivity of 63% and a specificity of 81% for detection of OBI in this population. Comparing models using cut-off values from individual laboratory predictors (ANC, white blood cell count, and CRP) that maximized sensitivity and specificity revealed that a model using an ANC of 10.6 x10(3)/L [corrected] (sensitivity, 69%; specificity, 79%) was the best predictive model. Adding CRP to the model insignificantly increased sensitivity to 79%, while significantly decreasing specificity to 50%. Active monitoring of emergency department blood cultures drawn during the study period from children between 3 and 36 months of age showed an overall bacteremia rate of 1.1% during this period. An ANC cut-off point of 10.6 x10(3)/L [corrected] offers the best predictive model for detection of occult bacterial infection using a single test. The addition of CRP to ANC adds little diagnostic utility. Furthermore, the lowered incidence of occult bacteremia in our population supports a decrease in the use of diagnostic screening in this population.

Modeling forest fire occurrences using count-data mixed models in Qiannan autonomous prefecture of Guizhou province in China.

PubMed

Xiao, Yundan; Zhang, Xiongqing; Ji, Ping

2015-01-01

Forest fires can cause catastrophic damage on natural resources. In the meantime, it can also bring serious economic and social impacts. Meteorological factors play a critical role in establishing conditions favorable for a forest fire. Effective prediction of forest fire occurrences could prevent or minimize losses. This paper uses count data models to analyze fire occurrence data which is likely to be dispersed and frequently contain an excess of zero counts (no fire occurrence). Such data have commonly been analyzed using count data models such as a Poisson model, negative binomial model (NB), zero-inflated models, and hurdle models. Data we used in this paper is collected from Qiannan autonomous prefecture of Guizhou province in China. Using the fire occurrence data from January to April (spring fire season) for the years 1996 through 2007, we introduced random effects to the count data models. In this study, the results indicated that the prediction achieved through NB model provided a more compelling and credible inferential basis for fitting actual forest fire occurrence, and mixed-effects model performed better than corresponding fixed-effects model in forest fire forecasting. Besides, among all meteorological factors, we found that relative humidity and wind speed is highly correlated with fire occurrence.

Modeling Forest Fire Occurrences Using Count-Data Mixed Models in Qiannan Autonomous Prefecture of Guizhou Province in China

PubMed Central

Ji, Ping

2015-01-01

Forest fires can cause catastrophic damage on natural resources. In the meantime, it can also bring serious economic and social impacts. Meteorological factors play a critical role in establishing conditions favorable for a forest fire. Effective prediction of forest fire occurrences could prevent or minimize losses. This paper uses count data models to analyze fire occurrence data which is likely to be dispersed and frequently contain an excess of zero counts (no fire occurrence). Such data have commonly been analyzed using count data models such as a Poisson model, negative binomial model (NB), zero-inflated models, and hurdle models. Data we used in this paper is collected from Qiannan autonomous prefecture of Guizhou province in China. Using the fire occurrence data from January to April (spring fire season) for the years 1996 through 2007, we introduced random effects to the count data models. In this study, the results indicated that the prediction achieved through NB model provided a more compelling and credible inferential basis for fitting actual forest fire occurrence, and mixed-effects model performed better than corresponding fixed-effects model in forest fire forecasting. Besides, among all meteorological factors, we found that relative humidity and wind speed is highly correlated with fire occurrence. PMID:25790309

Effects of probiotic drop containing Lactobacillus rhamnosus, Bifidobacterium infantis, and Lactobacillus reuteri on salivary Streptococcus mutans and Lactobacillus levels

PubMed Central

Tehrani, Maryam Hajnorouzali; Akhlaghi, Najmeh; Talebian, Leila; Emami, Jaber; Keyhani, Siamak Etzad

2016-01-01

Aims: The aim of the present study was to evaluate the effect of a probiotic drop containing Lactobacillus rhamnosus, Bifidobacterium infantis, and Lactobacillus reuteri on salivary counts of Streptococcus mutans (SM) and Lactobacillus (LB) in children 3–6 years of age. Settings and Design: Sixty-one healthy children were randomly allocated into two parallel blocks in this double-blind, randomized controlled trial (IRCT2014120320202N1) from May to June 2015. Subjects and Methods: Finally 53 participants consumed five drops of placebo (n = 23) or probiotic (n = 30) every night for 2 weeks. Before intervention and 1 day after completion of the intervention, unstimulated salivary samples were collected, and microbiologic evaluations were carried out. Statistical Analysis: Data were analyzed with descriptive statistical methods Wilcoxon signed ranks, Mann–Whitney, and logistic regression. Results: SM level decreased significantly in probiotic group after intervention (P = 0.045), and there were significant differences in salivary SM counts after intervention between two groups (P = 0.04). In probiotic group, LB counts decreased significantly after intervention (P = 0.048); however, there were no significant differences between two groups (P = 0.216). Conclusions: Use of this probiotic drop decreased salivary counts of SM; however, LB counts did not change. In addition, use of the drop in children with higher salivary counts appeared to be more effective. PMID:27994413

Modelling habitat associations with fingernail clam (Family: Sphaeriidae) counts at multiple spatial scales using hierarchical count models

USGS Publications Warehouse

Gray, B.R.; Haro, R.J.; Rogala, J.T.; Sauer, J.S.

2005-01-01

1. Macroinvertebrate count data often exhibit nested or hierarchical structure. Examples include multiple measurements along each of a set of streams, and multiple synoptic measurements from each of a set of ponds. With data exhibiting hierarchical structure, outcomes at both sampling (e.g. Within stream) and aggregated (e.g. Stream) scales are often of interest. Unfortunately, methods for modelling hierarchical count data have received little attention in the ecological literature. 2. We demonstrate the use of hierarchical count models using fingernail clam (Family: Sphaeriidae) count data and habitat predictors derived from sampling and aggregated spatial scales. The sampling scale corresponded to that of a standard Ponar grab (0.052 m(2)) and the aggregated scale to impounded and backwater regions within 38-197 km reaches of the Upper Mississippi River. Impounded and backwater regions were resampled annually for 10 years. Consequently, measurements on clams were nested within years. Counts were treated as negative binomial random variates, and means from each resampling event as random departures from the impounded and backwater region grand means. 3. Clam models were improved by the addition of covariates that varied at both the sampling and regional scales. Substrate composition varied at the sampling scale and was associated with model improvements, and reductions (for a given mean) in variance at the sampling scale. Inorganic suspended solids (ISS) levels, measured in the summer preceding sampling, also yielded model improvements and were associated with reductions in variances at the regional rather than sampling scales. ISS levels were negatively associated with mean clam counts. 4. Hierarchical models allow hierarchically structured data to be modelled without ignoring information specific to levels of the hierarchy. In addition, information at each hierarchical level may be modelled as functions of covariates that themselves vary by and within levels. As a result, hierarchical models provide researchers and resource managers with a method for modelling hierarchical data that explicitly recognises both the sampling design and the information contained in the corresponding data.

Medication adherence and visit-to-visit variability of systolic blood pressure in African Americans with chronic kidney disease in the AASK trial.

PubMed

Hong, K; Muntner, P; Kronish, I; Shilane, D; Chang, T I

2016-01-01

Lower adherence to antihypertensive medications may increase visit-to-visit variability of blood pressure (VVV of BP), a risk factor for cardiovascular events and death. We used data from the African American Study of Kidney Disease and Hypertension (AASK) trial to examine whether lower medication adherence is associated with higher systolic VVV of BP in African Americans with hypertensive chronic kidney disease (CKD). Determinants of VVV of BP were also explored. AASK participants (n=988) were categorized by self-report or pill count as having perfect (100%), moderately high (75-99%), moderately low (50-74%) or low (<50%) proportion of study visits with high medication adherence over a 1-year follow-up period. We used multinomial logistic regression to examine determinants of medication adherence, and multivariable-adjusted linear regression to examine the association between medication adherence and systolic VVV of BP, defined as the coefficient of variation or the average real variability (ARV). Participants with lower self-reported adherence were generally younger and had a higher prevalence of comorbid conditions. Compared with perfect adherence, moderately high, moderately low and low adherence was associated with 0.65% (±0.31%), 0.99% (±0.31%) and 1.29% (±0.32%) higher systolic VVV of BP (defined as the coefficient of variation) in fully adjusted models. Results were qualitatively similar when using ARV or when using pill counts as the measure of adherence. Lower medication adherence is associated with higher systolic VVV of BP in African Americans with hypertensive CKD; efforts to improve medication adherence in this population may reduce systolic VVV of BP.

Predicting Infected Bile Among Patients Undergoing Percutaneous Cholecystostomy

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

Beardsley, Shannon L.; Shlansky-Goldberg, Richard D.; Patel, Aalpen

2005-04-15

Purpose. Patients may not achieve a clinical benefit after percutaneous cholecystostomy due to the inherent difficulty in identifying patients who truly have infected gallbladders. We attempted to identify imaging and biochemical parameters which would help to predict which patients have infected gallbladders. Methods. A retrospective review was performed of 52 patients undergoing percutaneous cholecystostomy for clinical suspicion of acute cholecystitis in whom bile culture results were available. Multiple imaging and biochemical variables were examined alone and in combination as predictors of infected bile, using logistic regression. Results. Of the 52 patients, 25 (48%) had infected bile. Organisms cultured included Enterococcus,more » Enterobacter, Klebsiella, Pseudomonas, E. coli, Citrobacter and Candida. No biochemical parameters were significantly predictive of infected bile; white blood cell count >15,000 was weakly associated with greater odds of infected bile (odds ratio 2.0, p = NS). The presence of gallstones, sludge, gallbladder wall thickening and pericholecystic fluid by ultrasound or CT were not predictive of infected bile, alone or in combination, although a trend was observed among patients with CT findings of acute cholecystitis toward a higher 30-day mortality. Radionuclide scans were performed in 31% of patients; all were positive and 66% of these patients had infected bile. Since no patient who underwent a radionuclide scan had a negative study, this variable could not be entered into the regression model due to collinearity. Conclusion. No single CT or ultrasound imaging variable was predictive of infected bile, and only a weak association of white blood cell count with infected bile was seen. No other biochemical parameters had any association with infected bile. The ability of radionuclide scanning to predict infected bile was higher than that of ultrasound or CT. This study illustrates the continued challenge to identify bacterial cholecystitis among patients referred for percutaneous cholecystostomy.« less

[Leukocytes as risk markers for cardiovascular disease in adolescents: association with birth characteristics, nutritional status and biochemical tests].

PubMed

do Prado Junior, Pedro Paulo; Faria, Franciane Rocha de; Faria, Eliane Rodrigues de; Franceschini, Sylvia do Carmo Castro; Priore, Silvia Eloiza

2016-01-01

To evaluate the correlation between the number of leukocytes and cardiovascular risks associated with birth characteristics, nutritional status and biochemical tests. Cross-sectional study developed with 475 adolescents, born between 1992 and 2001, in the municipality of Viçosa (MG). Maternal medical records were analyzed in the hospital units, and the following was recorded: birth weight and length, head circumference, chest circumference, Apgar score, gestational age. In adolescents, body mass index, skinfold thickness, body composition, blood count, biochemical tests and clinical variables were also assessed. The statistical analyses was carried out using Statistical Package for Social Sciences (SPSS) version 20.0 and Data Analysis and Statistical Software (STATA) with Kruskal-Wallis, Mann-Whitney, chi-square or Fisher's exact tests and Linear Regression. Significance level was set at α<0.05. The study was approved by the Research Ethics Committee of UFV for studies with human subjects. Weight and birth length, head and chest circumference were higher among boys. In adolescents, the number of leukocytes was higher in individuals with excess weight and body fat and high adiposity index, waist-to-height ratio and waist circumference. Only altered triglycerides showed differences between leukocyte medians. Regardless of the anthropometric variable of the final regression model, the stage of adolescence, number of platelets, eosinophils, monocytes and lymphocytes were associated with the increase in leukocytes. The birth variables were not associated with changes in leukocyte numbers, whereas the anthropometric variables were good indicators for a higher leukocyte count, regardless of the stage of adolescence and gender. Copyright © 2015 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

No Evidence of Reciprocal Associations between Daily Sleep and Physical Activity.

PubMed

Mitchell, Jonathan A; Godbole, Suneeta; Moran, Kevin; Murray, Kate; James, Peter; Laden, Francine; Hipp, J Aaron; Kerr, Jacqueline; Glanz, Karen

2016-10-01

This study aimed to determine whether physical activity patterns are associated with sleep later at night and if nighttime sleep is associated with physical activity patterns the next day among adult women. Women (N = 353) living throughout the United States wore a wrist and a hip accelerometer for 7 d. Total sleep time (TST, hours per night) and sleep efficiency (SE, %) were estimated from the wrist accelerometer, and moderate to vigorous physical activity (MVPA, >1040 counts per minute, h·d) and sedentary behavior (SB, <100 counts per minute, h·d) were estimated from the hip accelerometer. Mixed-effects models adjusted for age, race, body mass index, education, employment, marital status, health status, and hip accelerometer wear time were used to analyze the data. Follow-up analyses using quantile regression were used to investigate associations among women with below average TST and MVPA and above average SB. The average age of our sample was 55.5 yr (SD = 10.2 yr). The majority of participants were White (79%) and married (72%), and half were employed full time (49%). The participants spent on average 8.9 and 1.1 h·d in SB and MVPA, respectively, and 6.8 h per night asleep. No associations were observed between MVPA and SB with nighttime TST or SE. There were no associations between nighttime TST and SE with MVPA or SB the next day. The findings were the same in the quantile regression analyses. In free-living adult women, accelerometry-estimated nighttime sleep and physical activity patterns were not associated with one another. On the basis of our observational study involving a sample of adult women, higher physical activity will not necessarily improve sleep at night on a day-to-day basis (and vice versa).

Homicide mortality rates in Canada, 2000-2009: Youth at increased risk.

PubMed

Basham, C Andrew; Snider, Carolyn

2016-10-20

To estimate and compare Canadian homicide mortality rates (HMRs) and trends in HMRs across age groups, with a focus on trends for youth. Data for the period of 2000 to 2009 were collected from Statistics Canada's CANSIM (Canadian Statistical Information Management) Table 102-0540 with the following ICD-10-CA coded external causes of death: X85 to Y09 (assault) and Y87.1 (sequelae of assault). Annual population counts from 2000 to 2009 were obtained from Statistics Canada's CANSIM Table 051-0001. Both death and population counts were organized into five-year age groups. A random effects negative binomial regression analysis was conducted to estimate age group-specific rates, rate ratios, and trends in homicide mortality. There were 9,878 homicide deaths in Canada during the study period. The increase in the overall homicide mortality rate (HMR) of 0.3% per year was not statistically significant (95% CI: -1.1% to +1.8%). Canadians aged 15-19 years and 20-24 years had the highest HMRs during the study period, and experienced statistically significant annual increases in their HMRs of 3% and 4% respectively (p < 0.05). A general, though not statistically significant, decrease in the HMR was observed for all age groups 50+ years. A fixed effects negative binomial regression model showed that the HMR for males was higher than for females over the study period [RRfemale/male = 0.473 (95% CI: 0.361, 0.621)], but no significant difference in sex-specific trends in the HMR was found. An increasing risk of homicide mortality was identified among Canadian youth, ages 15-24, over the 10-year study period. Research that seeks to understand the reasons for the increased homicide risk facing Canada's youth, and public policy responses to reduce this risk, are warranted.

Spinal cord repair in MS

PubMed Central

Ciccarelli, O.; Altmann, D. R.; McLean, M. A.; Wheeler-Kingshott, C. A.; Wimpey, K.; Miller, D. H.; Thompson, A. J.

2010-01-01

Objective: To investigate the mechanisms of spinal cord repair and their relative contribution to clinical recovery in patients with multiple sclerosis (MS) after a cervical cord relapse, using spinal cord 1H-magnetic resonance spectroscopy (MRS) and volumetric imaging. Methods: Fourteen patients with MS and 13 controls underwent spinal cord imaging at baseline and at 1, 3, and 6 months. N-acetyl-aspartate (NAA) concentration, which reflects axonal count and metabolism in mitochondria, and the cord cross-sectional area, which indicates axonal count, were measured in the affected cervical region. Mixed effect linear regression models investigated the temporal evolution of these measures and their association with clinical changes. Ordinal logistic regressions identified predictors of recovery. Results: Patients who recovered showed a sustained increase in NAA after 1 month. In the whole patient group, a greater increase of NAA after 1 month was associated with greater recovery. Patients showed a significant decline in cord area during follow-up, which did not correlate with clinical changes. A worse recovery was predicted by a longer disease duration at study entry. Conclusions: The partial recovery of N-acetyl-aspartate levels after the acute event, which is concurrent with a decline in cord cross-sectional area, may be driven by increased axonal mitochondrial metabolism. This possible repair mechanism is associated with clinical recovery, and is less efficient in patients with longer disease duration. These insights into the mechanisms of spinal cord repair highlight the need to extend spinal cord magnetic resonance spectroscopy to other spinal cord disorders, and explore therapies that enhance recovery by modulating mitochondrial activity. GLOSSARY CI = confidence interval; EDSS = Expanded Disability Status Scale; FOV = field of view; MR = magnetic resonance; MRS = magnetic resonance spectroscopy; MS = multiple sclerosis; NAA = N-acetyl-aspartate; SC = spinal cord; TE = echo time; TI = inversion time; TR = repetition time. PMID:20107138

Preoperatively staging liver fibrosis using noninvasive method in Hepatitis B virus-infected hepatocellular carcinoma patients

PubMed Central

Gao, Hengyi; Zhu, Feng; Wang, Min; Zhang, Hang; Ye, Dawei; Yang, Jiayin; Jiang, Li; Liu, Chang; Qin, Renyi; Yan, Lunan; Xiao, Guangqin

2017-01-01

Background Advanced liver fibrosis can result in serious complications (even patient’s death) after partial hepatectomy. Preoperatively percutaneous liver biopsy is an invasive and expensive method to assess liver fibrosis. We aim to establish a noninvasive model, on the basis of preoperative biomarkers, to predict liver fibrosis in hepatocellular carcinoma (HCC) patients with hepatitis B virus (HBV) infection. Methods The HBV-infected liver cancer patients who had received hepatectomy were retrospectively and prospectively enrolled in this study. Univariate analysis was used to compare the variables of the patients with mild to moderate liver fibrosis and with severe liver fibrosis. The significant factors were selected into binary logistic regression analysis. Factors determined to be significant were used to establish a noninvasive model. Then the diagnostic accuracy of this novel model was examined based on sensitivity, specificity and area under the receiver-operating characteristic curve (AUC). Results This study included 2,176 HBV-infected HCC patients who had undergone partial hepatectomy (1,682 retrospective subjects and 494 prospective subjects). Regression analysis indicated that total bilirubin and prothrombin time had positive correlation with liver fibrosis. It also demonstrated that blood platelet count and fibrinogen had negative correlation with liver fibrosis. The AUC values of the model based on these four factors for predicting significant fibrosis, advanced fibrosis and cirrhosis were 0.79-0.83, 0.83-0.85 and 0.85-0.88, respectively. Conclusion The results showed that this novel preoperative model was an excellent noninvasive method for assessing liver fibrosis in HBV-infected HCC patients. PMID:28008144

Statistical Aspects of Point Count Sampling

Treesearch

Richard J. Barker; John R. Sauer

1995-01-01

The dominant feature of point counts is that they do not census birds, but instead provide incomplete counts of individuals present within a survey plot. Considering a simple model for point count sampling, we demonstrate that use of these incomplete counts can bias estimators and testing procedures, leading to inappropriate conclusions. A large portion of the...

Statistical aspects of point count sampling

USGS Publications Warehouse

Barker, R.J.; Sauer, J.R.; Ralph, C.J.; Sauer, J.R.; Droege, S.

1995-01-01

The dominant feature of point counts is that they do not census birds, but instead provide incomplete counts of individuals present within a survey plot. Considering a simple model for point count sampling, we demon-strate that use of these incomplete counts can bias estimators and testing procedures, leading to inappropriate conclusions. A large portion of the variability in point counts is caused by the incomplete counting, and this within-count variation can be confounded with ecologically meaningful varia-tion. We recommend caution in the analysis of estimates obtained from point counts. Using; our model, we also consider optimal allocation of sampling effort. The critical step in the optimization process is in determining the goals of the study and methods that will be used to meet these goals. By explicitly defining the constraints on sampling and by estimating the relationship between precision and bias of estimators and time spent counting, we can predict the optimal time at a point for each of several monitoring goals. In general, time spent at a point will differ depending on the goals of the study.