The Transition of Sociodemographic and Substance Abuse Characteristics, Pairwise Co-occurrences and Factors Associated with Polysubstance Use Among US Adolescents and Young Adults.

Background: Substance abuse by adolescents and young adults is a major public health issue. This study aimed to (i) show the transition of sociodemographic and substance abuse characteristics from 1992 to 2017 among US adolescents and young adults, (ii) evaluate the likelihood of co-occurrence of substances, and (iii) identify significant sociodemographic characteristics in association with polysubstance abuse. Methods: This study extracted data for adolescents and young adults from 1992 and 2017 Treatment Episode Data Set-Admission (TEDS-A) datasets. The extracted sample included 337858 admissions in 1992 and 333322 in 2017. Findings: Both years experienced significant admissions. A significant transition in 2017 compared to 1992 was evident in education, living status, and ethnicity. Substance-specific transition showed alcohol was dominant in 1992, while marijuana/ hashish was dominant in 2017. Also, heroin, other opiates/synthetics, and methamphetamine experienced an increase, while cocaine/crack decreased. The pairwise co-occurrences exhibited a considerable variation in the likelihood of using one substance given another one. The odds ratios (ORs) obtained from generalized ordered logit models showed significantly higher odds of one or more substances with age, while education showed the opposite scenario. A mixed effect of gender was evident in 1992, whereas females were significantly less likely with one or more substances than males in 2017. Other significant vulnerable groups were those not in the labor force, homeless, white, and Mexican Americans. Conclusion: The findings may help to understand the overall changes between 1992 and 2017 and take necessary measures to reduce the burden of this public health problem.

Evaluación métrica de caninos permanentes para la estimación sexual en la Colección “Prof. Dr. Rómulo Lambre”, Buenos Aires, Argentina / Metric evaluation of permanent canines for sexual estimation in the Collection “Prof. Dr. Romulo Lambre”, Buenos Aires, Argentina

Introducción la estimación del sexo es un aspecto fundamental de la labor forense, ya que constituye un paso obligatorio para la identificación de restos humanos de procedencia desconocida. El análisis metodológico de la dentición como estimador sexual reviste importancia debido al elevado grado de preservación de los dientes. Considerando la necesidad de contar con información concreta respecto del potencial de la dentición en la estimación del sexo en casos locales de Argentina, el objetivo del presente estudio es evaluar la propuesta previamente desarrollada por Luna (2019) en una muestra local de restos esqueléticos humanos. Materiales y métodos se seleccionó una muestra de 152 caninos permanentes pertenecientes a 98 individuos de ambos sexos que forman parte de la colección osteológica Profesor Dr. Rómulo Lambre (La Plata, Argentina). Posteriormente se aplicó la propuesta de Luna (2019) para la estimación del sexo a partir de la métrica de la corona y del cuello de los caninos, la cual considera las medidas directas y los diferentes tipos de funciones discriminantes y regresiones logísticas. Resultados de las medidas directas consideradas, solo el diámetro mesiodistal cervical ofreció resultados aceptables (>75%) para la estimación sexual. Asimismo, únicamente la función discriminante 1 presentó probabilidades a posteriori de clasificaciones correctas superiores a 0,75 y las regresiones logísticas 1 y 3 exhibieron resultados generales satisfactorios. Conclusiones esta propuesta basada en el estudio métrico de caninos permanentes constituye una alternativa metodológica adecuada en situaciones en las cuales los elementos óseos diagnósticos del sexo se encuentran deteriorados o ausentes. (AU)

Introduction Sex estimation is a fundamental aspect of forensic work as a mandatory step for the identification of human remains of unknown origin. The methodological analysis of the dentition as a sexual estimator is important due to its high degree of preservation. Considering the need for specific information regarding the potential of dentition for sex estimation in forensic cases from Argentina, the aim of this study is to evaluate the proposal previously developed by Luna (2019) in a local sample of human skeletal remains. Materials and methods A sample of 152 permanent canines belonging to 98 individuals of both sexes was selected- The individuals belong to the Prof. Dr. Rómulo Lambre osteological collection (La Plata, Argentina). Luna's proposal (2019) was applied to estimate sex from canine crown and neck metrics, which considers direct measurements and different types of discriminant functions and logistic regressions. Results Only the cervical mesiodistal diameter showed acceptable results (>75%) for sex estimation. Moreover, discriminant function 1 showed a posteriori probabilities of correct classifications greater than 0.75 and logistic regressions 1 and 3 offered acceptable overall results. Conclusions This proposal based on the metric recording of permanent canines constitutes an adequate methodological alternative in situations in which the diagnostic bone elements of sex are deteriorated or absent. (AU)

Artificial Intelligence in Hepatology- Ready for the Primetime.

Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.

Factors influencing alcohol consumption among university students in Southern Thailand.

Background: Underage drinkers are the primary cause of death and illness worldwide. Initiation of drinking at younger ages and levels of drinking during young adulthood may also shape future public health by influencing alcohol consumption. From this situation, it is necessary to study various factors to provide sufficient information to reduce adolescent alcohol consumption. Objective: This study aimed to examine the prevalence and factors that influenced alcohol consumption of first-year students in a university network in Southern Thailand. Material and methods: A total participant 685 of 1,100 first-year students from 12 universities in southern Thailand were randomized and recruited using eligible criteria. The instrument was an online questionnaire based on the preceding model that consisted of 9 parts with 93 items. For descriptive analysis, percentages were used to describe the characteristics and alcohol consumption behaviours of participants. In addition, logistic regressions were used to determine the factors influencing. Results: The results showed 62.3% of participants responded to the online questionnaire. During the past six months, 36% reported consuming alcohol. Males reported drinking more (45.3%) than females. The most popular drink was beer (57.7%). There were 8.16 standard drinks, (82.3%) consumed at night, (70.2%) drank at their place, and consumed with friends (83.6%). The results of multiple logistic regression showed significant factors influencing drinking alcohol. The lower attitude was 2.56 times more likely to consume alcohol than a high level (AOR: 2.56, 95%CI: 1.53-4.28). Reversely, the higher marketing perception was more likely to consume alcohol than a low level (AOR: 5.35, 95%CI: 1.94-14.58). In addition, students with mother drinker, lover drinker, and close friend drinker were more likely to consume alcohol (AOR: 2.35, 95%CI: 1.07-5.16), (AOR: 3.60, 95%CI: 1.99-6.50), and (AOR: 5.29, 95%CI: 3.31-8.45) respectively. Conclusion: In conclusion, attitude, marketing factors, and social factors were associated with alcohol consumption among Thai university students that were revealed as positive predictors regarding binge drinking. The study shows how healthcare providers may reduce binge drinking by designing effective prevention programs.

Penalized logistic regressions with technical indicators predict up and down trends.

Correctly predicting up and down trends for stock prices is of immense important in the financial market. To further improve the prediction performance, in this paper we introduce five penalties: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation and minimax concave penalty to logistic regressions with 19 technical indicators, and propose the five penalized logistic regressions to predict up and down trends for stock prices. Firstly, we translate the five penalized logistic log-likelihood functions into the five penalized weighted least squares functions and combine them with the tenfold cross-validation method to calculate the solution path to parameter estimators. Secondly, we combine the binomial deviation with cross-validation error as a risk measure to choose an appropriate tuning parameter for the penalty functions and apply the training set and the coordinate descent algorithm to obtain parameter estimators and probability estimators. Thirdly, we employ the testing set and the chosen optimal thresholds to construct two-class confusion matrices and receiver operating characteristic curves to assess the prediction performances to the five regressions. Finally, we compare the proposed five penalized logistic regressions with logistic regression, support vector machine and artificial neural network and found that the minimax concave penalty logistic regression performs the best in terms of the prediction performance to up and down trends for Google's stock prices. Therefore, in this paper we propose the five new prediction methods to improve the prediction accuracy of stock returns and bring economic benefits for investors.

Emergency department visits following total joint arthroplasty: do revisions present a higher burden?

AIMS: Although readmission has historically been of primary interest, emergency department (ED) visits are increasingly a point of focus and can serve as a potentially unnecessary gateway to readmission. This study aims to analyze the difference between primary and revision total joint arthroplasty (TJA) cases in terms of the rate and reasons associated with 90-day ED visits. METHODS: We retrospectively reviewed all patients who underwent TJA from 2011 to 2021 at a single, large, tertiary urban institution. Patients were separated into two cohorts based on whether they underwent primary or revision TJA (rTJA). Outcomes of interest included ED visit within 90-days of surgery, as well as reasons for ED visit and readmission rate. Multivariable logistic regressions were performed to compare the two groups while accounting for all statistically significant demographic variables. RESULTS: Overall, 28,033 patients were included, of whom 24,930 (89%) underwent primary and 3,103 (11%) underwent rTJA. The overall rate of 90-day ED visits was significantly lower for patients who underwent primary TJA in comparison to those who underwent rTJA (3.9% vs 7.0%; p < 0.001). Among those who presented to the ED, the readmission rate was statistically lower for patients who underwent primary TJA compared to rTJA (23.5% vs 32.1%; p < 0.001). CONCLUSION: ED visits present a significant burden to the healthcare system. Patients who undergo rTJA are more likely to present to the ED within 90 days following surgery compared to primary TJA patients. However, among patients in both cohorts who visited the ED, three-quarters did not require readmission. Future efforts should aim to develop cost-effective and patient-centred interventions that can aid in reducing preventable ED visits following TJA. Cite this article: Bone Jt Open 2022;3(7):543-548.

A Cross-Sectional Survey of Risky Travel and Leisure Behaviors During the COVID-19 Pandemic.

PURPOSE: COVID-19 is largely spread through close contact with infected people in indoor spaces. Avoiding these spaces is one of the most effective ways to slow the spread. This study assessed who had engaged in risky travel and leisure behaviors before the availability of vaccines. DESIGN: National cross-sectional on-line survey collected in November and December 2020. Setting: United States; Participants: 2589 adults representative by gender and race/ethnicity to the US population; Measures: The survey assessed if people had resumed 11 risky behaviors during the pandemic, prior to vaccines. Independent variables included age, race/ethnicity, region of the country, education, income, preexisting conditions, perceived severity and susceptibility, positive COVID diagnosis, and political ideology. ANALYSIS: Univariate analysis and logistic regressions were used to assess demographic and psychological factors of those resuming these behaviors. Results: Most (60.3%) of people had resumed at least 1 behavior with eating inside of restaurants (33.2%) and visiting family and friends (37.5%) being the most prevalent. In the multivariate analyses, perceived susceptibility was significant across all behaviors. Young people, fiscal conservatives, and people with higher perceived severity were more likely to perform several of the behaviors. Preexisting conditions did not predict any of the behaviors. CONCLUSIONS: Travel and leisure behaviors vary by type of risk and may need specific tailored, prevention messages to promote risk reduction during future pandemics.

Quality of Life, Health and the Great Recession in Spain: Why Older People Matter?

The financial crisis of 2008 precipitated the "Great Recession". In this scenario, we took Spain as a country of study, because although it experienced significant negative shocks associated with macroeconomic variables (GDP or unemployment), its welfare indicators have been marked by limited changes. This study used data from waves 2 and 4 (years 2006-2007 and 2010-2012, respectively) of the Survey on Health, Aging and Retirement in Europe (SHARE). Specifically, through logistic regressions we have analysed the effects of socioeconomic, demographic, health and "Great Recession" factors on the quality of life (QoL) of elders in Spain. Although QoL did not change too much during the "Great Recession", the results confirmed the importance of several factors (such as chronicity) that affect the satisfaction with the QoL among the older people. In this regard, statistically significant effects were obtained for individual exposure to recession. Therefore, a decrease in household income in the crisis period with respect to the pre-crisis period would increase by 44% the probability of reporting a low QoL (OR = 1.44; 95% CI: 1.00-2.07). Furthermore, gender differences were observed. Health and socioeconomic variables are the most significant when determining individual QoL. Therefore, when creating policies, establishing multidisciplinary collaborations is essential.

Structure estimation of binary graphical models on stratified data: Application to the description of injury tables for victims of road accidents.

Graphical models are used in many applications such as medical diagnostics and computer security. Increasingly often, the estimation of such models has to be performed on several predefined strata of the whole population. For instance, in epidemiology and clinical research, strata are often defined according to age, gender, treatment, or disease type. In this article, we propose new approaches dedicated to the estimation of binary graphical models on such strata. These approaches are implemented by combining well-known methods that have been developed in the context of a single binary graphical model, with penalties encouraging structured sparsity, which have recently been shown to be appropriate when dealing with stratified data. Empirical comparisons on synthetic data highlight that our approaches generally outperform its competitors. We present an application of the approach to study associations among the injuries suffered by victims of road accidents according to road user type.

Factors predictive of a fetal alcohol spectrum disorder diagnosis: Parent and teacher ratings.

Diagnostic assessment in Fetal Alcohol Spectrum Disorder (FASD) is informed by multidisciplinary assessment incorporating objective (i.e., test measures) and subjective means, such as parent and teacher behavior ratings. The purpose of this study was to extend our previous neuropsychological test findings by identifying parent and teacher ratings of academic achievement, attention, executive functioning, and adaptive functioning as predictors of an FASD diagnosis. The charts of 315 children and adolescents with prenatal alcohol exposure (PAE) who underwent assessment for FASD were retrospectively reviewed. Direct logistic regressions analyzed the contribution of different ratings on the likelihood of an FASD diagnosis. The results suggest that a number of rating measures do contribute toward accurately differentiating those with FASD from within a PAE population, including teacher ratings of learning problems, inattention, and adaptive skills. The classification accuracy for each regression was clinically significant (59.1-70.8%). Children with worse ratings on these variables are approximately 1.5 to 2 times more likely to receive an FASD diagnosis. Only teacher ratings (not parent) significantly contributed to whether a diagnosis was made, suggesting that teacher observational rating scales are a critical component of an FASD assessment. Together with our previous research examining neuropsychological evaluation and FASD diagnostic assessment, this study helps to further guide decisions to streamline care in multidisciplinary assessment and intervention planning.

Frequency and predictors of health services use by Native Hawaiians and Pacific Islanders: evidence from the U.S. National Health Interview Survey.

BACKGROUND: Native Hawaiians and Pacific Islanders (NHPIs) are one of the fasting growing racial groups in the United States (US). NHPIs have a significantly higher disease burden than the US population as a whole, yet they remain underrepresented in research. The purpose of this study is to examine factors associated with health care utilization among NHPIs. METHODS: Drawing from the 2014 NHPI-National Health Interview Survey, we used stereotype logistic regressions to examine utilization of emergency department (ED) and outpatient services among 2172 individuals aged 18 and older. RESULTS: NHPIs with chronic diseases were twice as likely to be multiple ED users and nearly four times as likely to be frequent-users of outpatient services. Social support played a protective role in preventing multiple use of ED. Having a usual source of care made it more than eight times as likely to be a frequent-user of outpatient services. Use of eHealth information increased the odds of using ED and outpatient services. Ability to afford health care increased the odds of using outpatient services. There was no association between health insurance coverage and use of ED and outpatient services among NHPIs. CONCLUSIONS: This research provides the first available national estimates of health services use by NHPIs. Efforts to improve appropriate use of health services should consider leveraging the protective factors of social support to reduce the odds of frequent ED use, and having a usual source of care to increase use of outpatient services.

Efecto de atributos paisajísticos en los patrones de presencia de Ateles fusciceps en el noroccidente ecuatoriano / Landscape attributes effects on Ateles fusciceps presence patterns in northwestern Ecuador

Este trabajo evalúa el efecto que tienen ciertas variables paisajísticas (ríos, tierras agropecuarias, áreas antrópicas y bosque nativo) en los patrones de presencia del mono araña, Ateles fusciceps, en el noroccidente ecuatoriano. Se utilizaron registros geográficos de Ateles fusciceps tomados en campo y de estudios previos. Se evaluó el efecto de la proximidad de cada variable por medio de la prueba T de Student. Posteriormente mediante regresiones logísticas y por medio del Criterio de Información de Akaike (AIC) se seleccionaron los mejores modelos y se identificaron las variables más importantes. Se observó que tierras agropecuarias y zonas antrópicas tienen un efecto negativo para este primate, pues los puntos de presencia se encontraron alejados de éstas. Se evidencia también que Ateles fusciceps prefiere sitios cercanos a bosque, resultado que corrobora investigaciones previas, sin embargo también se encuentra una asociación con ríos, resultado que no ha sido reportado en estudios anteriores. Se encontraron dos modelos importantes para predecir patrones de presencia de este primate, el primero compuesto por: bosque nativo, ríos y zonas antrópicas (AICw=0.48), mientras que el segundo abarca: bosque nativo, ríos, zonas antrópicas y tierras agropecuarias (AICw=0.34). Estos resultados servirán de base para futuros análisis, dirigidos a la conservación de A. fusciceps.

In this work, I investigate the effect of some landscape variables (rivers, agricultural lands, anthropic areas and native forest) on the presence patterns of spider monkey, Ateles fusciceps, in the northwestern Ecuador. Geographical records collected in field and others from previous studies were used to conduct this study. Effects of proximity of each variable to presence of A. fusciceps were assessed with Student T tests. The best model and the most important variables were identified using logistic regressions and the Akaike Information Criterion (AIC). The results showed that agricultural lands and anthropic zones were from the primate presence points, suggesting a negative effect on A. fusciceps. I also found that A. fusciceps prefers sites near rivers, this observation has not been reported in previous studies. Two important models were found to predict the presence of A. fusciceps, the first one was composed by three variables: native forest, rivers and anthropic areas (AICw= 0.48) and the second model was composed by the four variables: native forest, rivers, anthropic areas and agricultural lands (AICw= 0.34). The results of this work will contribute a basis for future studies aimed on A. fusciceps conservation.

[Detection and prevention in later life: risk profiles for physical, psychological, social and environmental frailty.] / Detectie en preventie van kwetsbaarheid: Op zoek naar risicoprofielen voor fysieke, psychische, sociale en omgevingskwetsbaarheid.

In order to provide proactive care and support for older people attention is needed for the prevention of frailty among older adults. Subsequently, accurate case finding of those who are more at risk of becoming frail is crucial to undertake specific preventive actions. This study investigates frailty and risk profiles of frailty among older people in order to support proactive detection. Hereby, frailty is conceived not only as a physical problem, but also refers to emotional, social, and environmental hazards. Using data generated from the Belgian Ageing Studies (N = 21,664 home-dwelling older people), a multinomial logistic regression model was tested which included socio-demographic and socio-economic indicators as well as the four dimensions of frailty (physical, social, psychological and environmental). Findings indicate that for both men and women having moved in the previous 10 years and having a lower household income are risk factors of becoming multidimensional frail. However, studying the different frailty domains, several risk profiles arise (e. g. marital status is important for psychological frailty), and gender-specific risk groups are detected (e. g. non-married men). This paper elaborates on practical implications and formulates a number of future research recommendations to tackle frailty in an ageing society.

[Formula: see text]Factors predictive of a fetal alcohol spectrum disorder: Neuropsychological assessment.

A variety of neurodevelopmental impairments related to fetal alcohol spectrum disorder (FASD) diagnoses have been consistently documented. However, it is not clear whether such variables are predictive of a diagnosis. The purpose of the present study is to use logistic regressions to identify predictors of FASD in neuropsychological assessment. Charts of 180 children and adolescents with prenatal alcohol exposure (PAE) who underwent psychological and diagnostic assessment for FASD were retrospectively reviewed. A total of 107 received an FASD diagnosis (the PAE-FASD group) and 73 did not (the PAE group). Following preliminary analyses, direct logistic regressions were performed to assess the contribution of different neuropsychological testing measures on the likelihood of a child or adolescent receiving an FASD diagnosis. The results indicate that the classification accuracy of the PAE-FASD and PAE groups is clinically significant across models of intelligence, academic achievement, memory, and executive functioning. Classification rates across the various models range from 67.1% to 75.5%, with models incorporating 10 intelligence subtests or 3 academic subtests emerging as superior to those using broad indices of intelligence and/or individual subtests of memory or executive functioning. A "test battery" model incorporating verbal intelligence, verbal/auditory working memory (digit span), basic reading and spelling skills, math calculations, delayed story recall, and spatial planning and problem-solving yielded a classification rate of 74.7%. These results suggest that neuropsychological testing is a critical component of FASD assessment and help guide decisions to maximize the efficiency and efficacy of the diagnostic process and treatment recommendations.

[Factors Associated with Overweight and Obesity in Schoolchildren from 8 to 9 Years Old. Barcelona, Spain]. / Factores asociados al sobrepeso y la obesidad en escolares de 8 a 9 años de Barcelona.

OBJECTIVE: Childhood overweight and obesity have increased progressively in the last decades, especially in countries of Southern Europe. The aim of this study was to identify the prevalence of overweight, obesity and its determinants in schoolchildren between 8-9 years old from Barcelona. METHODS: Cross-sectional study of a representative sample of 3,262 schoolchildren in 2011. Body Mass Index (BMI) was calculated following the criteria established by the World Health Organization (z-scores). Variables on eating behaviour, physical activity and use of new technologies were studied through 2 questionnaires. Logistic regression models were adjusted, obtaining adjusted odds ratio and their confidence intervals (95%). RESULTS: The prevalence of overweight was 24.0% and 12.7% for obesity. Obesity was significantly higher in boys than in girls (14.8% vs 10.8%.). No statistically significant differences were observed in the compliance of recommendations of physical activity practice and use of new technologies according to BMI. Factors associated with obesity in boys were to attend a school located in a neighbourhood of disadvantaged socio-economic status [ORa=1.88 (1.35-2.63)], to belong to an immigrant family [ORa=1.57 (1.12-2.20)], to do not eat at school [ORa=1.76 (1.20-2.59)] and to have some meal alone [ORa=1.95 (1.27-3.00)]. In girls associated factors were to belong to a single-parent family [ORa=1.58 (1.06-2.34)] and to an immigrant family [ORa=1.53 (1.07-2.18)]. CONCLUSIONS: The prevalence of childhood obesity in Barcelona is high. It is more common in boys, being the social determinants most relevant associated factors.

[Aggressions towards Primary Health Care Workers in Madrid, Spain, 2011-2012]. / Agresiones sufridas por las personas que trabajan en atención primaria de la Comunidad de Madrid, 2011-2012.

OBJECTIVE: The number of aggressions towards health care professionals has risen over the past few years. There are no previous studies in primary care covering an entire region and to all professional categories. The aim of this study was to characterize aggressions in Primary Care in the Community of Madrid. METHODS: Multicenter cross-sectional study. Analysis of a Registration System that reports any type of aggression suffered by Primary Care workers, in the Community of Madrid. The study variables included sociodemographic characteristics of the aggressor and the victim, the type of aggression (verbal or physical abuse), its causes and consequences. We described median, intercuartilic range and frequencies. Logistic regression was performed calculating odds ratio and their 95% confidence intervals. RESULTS: 1,157 assaults were reported, 53.07% suffered by doctors. Physical assault occurred in 4.7% of the cases. The main reason was dissatisfaction with the care (36.1%). The non-medical staff showed less risk of being physically assaulted (OR: 0.38; CI95%: 0.17-0.86). The perpetrator profile was male (56.8%), aged between 31-40 (26.8%) years. Health care victim profile was female (84%), aged between 45-60 years. 10% of professionals reported some form of aggression, 5,9% of aggression were submitted to court. CONCLUSIONS: The risk of assault is higher in health personnel, particularly physicians. There were significant differences by gender and age, both in the profile of the aggressor and the victim.

A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits.

In association studies of complex traits, fixed-effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance-component tests based on mixed models were developed for region-based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C). Although there are some comparison studies to evaluate the performance of mixed and fixed models, there is no systematic analysis to determine when the mixed models perform better and when the fixed models perform better. Here we evaluated, based on extensive simulations, the performance of the fixed and mixed model statistics, using genetic variants located in 3, 6, 9, 12, and 15 kb simulated regions. We compared the performance of three models: (i) mixed models that lead to SKAT, SKAT-O, and SKAT-C, (ii) traditional fixed-effect additive models, and (iii) fixed-effect functional regression models. To evaluate the type I error rates of the tests of fixed models, we generated genotype data by two methods: (i) using all variants, (ii) using only rare variants. We found that the fixed-effect tests accurately control or have low false positive rates. We performed simulation analyses to compare power for two scenarios: (i) all causal variants are rare, (ii) some causal variants are rare and some are common. Either one or both of the fixed-effect models performed better than or similar to the mixed models except when (1) the region sizes are 12 and 15 kb and (2) effect sizes are small. Therefore, the assumption of mixed models could be satisfied and SKAT/SKAT-O/SKAT-C could perform better if the number of causal variants is large and each causal variant contributes a small amount to the traits (i.e., polygenes). In major gene association studies, we argue that the fixed-effect models perform better or similarly to mixed models in most cases because some variants should affect the traits relatively large. In practice, it makes sense to perform analysis by both the fixed and mixed effect models and to make a comparison, and this can be readily done using our R codes and the SKAT packages.

Binary regression with differentially misclassified response and exposure variables.

Misclassification is a long-standing statistical problem in epidemiology. In many real studies, either an exposure or a response variable or both may be misclassified. As such, potential threats to the validity of the analytic results (e.g., estimates of odds ratios) that stem from misclassification are widely discussed in the literature. Much of the discussion has been restricted to the nondifferential case, in which misclassification rates for a particular variable are assumed not to depend on other variables. However, complex differential misclassification patterns are common in practice, as we illustrate here using bacterial vaginosis and Trichomoniasis data from the HIV Epidemiology Research Study (HERS). Therefore, clear illustrations of valid and accessible methods that deal with complex misclassification are still in high demand. We formulate a maximum likelihood (ML) framework that allows flexible modeling of misclassification in both the response and a key binary exposure variable, while adjusting for other covariates via logistic regression. The approach emphasizes the use of internal validation data in order to evaluate the underlying misclassification mechanisms. Data-driven simulations show that the proposed ML analysis outperforms less flexible approaches that fail to appropriately account for complex misclassification patterns. The value and validity of the method are further demonstrated through a comprehensive analysis of the HERS example data.

Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses.

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables is a systemic issue in multivariate regression analyses and is likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counterproductive conservation measures. Using simulated data sets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance-partitioning procedure that was recently introduced in the field of ecology, can be used to deal with nonindependence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimization, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses.

Health care expenditure associated with overweight/obesity: a study among urban married women in Delhi, India.

BACKGROUND: Obesity is a multifaceted problem with wide-reaching medical, social and economic consequences. While health consequences are much known, but due to paucity of data, economic consequences are less known in India. The prevalence for excessive weight particularly among women population has been increasing dramatically in India in the last decades. We examined the economic burden on individual and households due to overweight and obesity among women in the national capital territory of India, Delhi. We particularly examined the health expenditure pattern in absolute amount as well as a proportion to their household expenditure among women according to their level of body mass index (BMI). METHODS: A population based follow-up survey of 325 ever-married women aged 20-54 years residing in the national capital territory of Delhi in India, systematically selected from the second round of National Family Health Survey (NFHS-2, 1998-99) samples who were re-interviewed after four years in 2003. Women's expenditure on health has been seen as a gross and as a ratio of total household expenditure. Anthropometric measurements were obtained from women to compute their current body mass index. Multiple logistic regression analysis was used to estimate the odds ratios adjusting for various socio demographic confounders. RESULTS: A significantly (p<0.0001) higher monthly gross health expenditure as well as proportion of total household expenditure was found according to the women's level of BMI. Average monthly health expenditure was Rs. 132 among overweight women, Rs 143 among obese women which further increased to Rs. 224 among morbidly obese women compared to only Rs 68 among normal weight women. Almost, 15% overweight, 16% obese and 21% morbidly obese women (p<0.0001) had economic burden which accounts for more than 5% of their total household expenditure on their health compared to only 10% normal weight women. Significantly, obese and morbidly obese women were more than two times more likely to spend higher amount on their health (OR 2.29 95% CI: 1.07-4.90; p=0.033) than normal weight women. Also overweight women were significantly two times more likely to spend high proportion on their health with respect to total household expenditure (OR 2.11; 95% CI: 1.03-4.35; p=0.042) than normal weight women. CONCLUSIONS: There is substantial economic burden of obesity for individuals as well as for the households which calls for urgent intervention in the obesity awareness and health promotion among Indian women who faced the greatest burden of increasing body weight in the last decade. Prevention is obviously more cost effective than treatment, both in terms of healthcare and personal costs. Health care providers and policy makers need to critically understand the issue of obesity and develop effective policies and programs for its prevention among Indian women.