Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels.

In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity's consequences in multilevel models is clarified in novel ways.

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis.

INTRODUCTION: Childhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey. METHOD: The current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association. RESULT: The prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran's I = 0.40, p<0.001). significant hotspot areas of stunting were identified in the west and south Afar, Tigray, Amhara and east SNNPR regions. In the local model, no maternal education, poverty, child age 6-23 months and male headed household were predictors associated with spatial variation of stunting among under five children in Ethiopia. In the multivariable multilevel robust Poisson regression the prevalence of stunting among children whose mother's age is >40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6-23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting. CONCLUSION: In Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

Multilevel modeling in single-case studies with zero-inflated and overdispersed count data.

Count outcomes are frequently encountered in single-case experimental designs (SCEDs). Generalized linear mixed models (GLMMs) have shown promise in handling overdispersed count data. However, the presence of excessive zeros in the baseline phase of SCEDs introduces a more complex issue known as zero-inflation, often overlooked by researchers. This study aimed to deal with zero-inflated and overdispersed count data within a multiple-baseline design (MBD) in single-case studies. It examined the performance of various GLMMs (Poisson, negative binomial [NB], zero-inflated Poisson [ZIP], and zero-inflated negative binomial [ZINB] models) in estimating treatment effects and generating inferential statistics. Additionally, a real example was used to demonstrate the analysis of zero-inflated and overdispersed count data. The simulation results indicated that the ZINB model provided accurate estimates for treatment effects, while the other three models yielded biased estimates. The inferential statistics obtained from the ZINB model were reliable when the baseline rate was low. However, when the data were overdispersed but not zero-inflated, both the ZINB and ZIP models exhibited poor performance in accurately estimating treatment effects. These findings contribute to our understanding of using GLMMs to handle zero-inflated and overdispersed count data in SCEDs. The implications, limitations, and future research directions are also discussed.

Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure.

Propensity score (PS) analyses are increasingly popular in behavioral sciences. Two issues often add complexities to PS analyses, including missing data in observed covariates and clustered data structure. In previous research, methods for conducting PS analyses with considering either issue alone were examined. In practice, the two issues often co-occur; but the performance of methods for PS analyses in the presence of both issues has not been evaluated previously. In this study, we consider PS weighting analysis when data are clustered and observed covariates have missing values. A simulation study is conducted to evaluate the performance of different missing data handling methods (complete-case, single-level imputation, or multilevel imputation) combined with different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting or the clustered weighting, weighted single-level or multilevel outcome models). The results suggest that the bias in average treatment effect estimation can be reduced, by better accounting for clustering in both the missing data handling stage (such as with the multilevel imputation) and the PS analysis stage (such as with the fixed-effects PS model, clustered weighting, and weighted multilevel outcome model). A real-data example is provided for illustration.

Detecting Cohort Effects in Accelerated Longitudinal Designs Using Multilevel Models.

Accelerated longitudinal designs allow researchers to efficiently collect longitudinal data covering a time span much longer than the study duration. One important assumption of these designs is that each cohort (a group defined by their age of entry into the study) shares the same longitudinal trajectory. Although previous research has examined the impact of violating this assumption when each cohort is defined by a single age of entry, it is possible that each cohort is instead defined by a range of ages, such as groups that experience a particular historical event. In this paper we examined how including cohort membership in linear and quadratic multilevel models performed in detecting and controlling for cohort effects in this scenario. Using a Monte Carlo simulation study, we assessed the performance of this approach under conditions related to the number of cohorts, the overlap between cohorts, the strength of the cohort effect, the number of affected parameters, and the sample size. Our results indicate that models including a proxy variable for cohort membership based on age at study entry performed comparably to using true cohort membership in detecting cohort effects accurately and returning unbiased parameter estimates. This indicates that researchers can control for cohort effects even when true cohort membership is unknown.

Practice of people towards COVID-19 infection prevention strategies in Benishangul Gumuz Region, North-West Ethiopia: Multilevel analysis.

INTRODUCTION: Coronavirus 2019 (COVID- 19) is an acute respiratory viral infectious disease in human being caused by RNA virus that belonged to the family of corona virus. The incidence of this disease was growing exponentially and affects millions of the world population that leads to expose thousands of peoples for death. Thus, this study was targeted to assess the practice of people on COVID-19 infections prevention strategies in the region. METHODS: A community based cross sectional study design was conducted in Benishangul Gumuz Region from May 25 -June 15, 2020. Multistage sampling technique was carried out to select 21 kebeles/ketena and 422 study participants. Data were collected by face to face interview using semi-structured questionnaires. The collected data were entered, cleaned and analyzed using STATA software version 14.0. Descriptive, bi-variable and multivariable multilevel models were applied. Variables with p value < 0.25 in bi-variable analysis were selected as candidates for multivariable analysis. Finally, the variables with p-value < 0.5 were considered as statistically significant, then variables with odds ratio, 95% CI were used to interpret the effect of association. RESULTS: The magnitude of good practice on prevention strategies of COVID- 19 infections was 62.1%. The most frequently practiced prevention strategies for COVID-19 infections were hand washing with water and soap (80.7%), alcohol-based hand rub (68.8%), maintaining social/physical distance (74.2%) and avoiding touching eyes. Individual and community level factors that affecting practice of COVID- 19 infection prevention strategies were discovered. Hence, community level factor was place of origin (AOR = 0.1; 95%CI: 0.03, 0.35) whereas individual level factors were able to read and write (AOR = 0.18; 95%CI: 0.04, 0.81) and being merchant (AOR = 2.07; 95%CI: 1.01, 4.28). CONCLUSION: The level of practice of community towards COVID-19 infections prevention strategies were low as compared with the expected outcome. Individual and community level factors were identified. This implies that social mobilization and community engagement was not effective. Thus, designing appropriate strategies to improve of practice prevention strategies are strongly recommend.

Geographic weighted regression analysis of hot spots of anemia and its associated factors among children aged 6-59 months in Ethiopia: A geographic weighted regression analysis and multilevel robust Poisson regression analysis.

INTRODUCTION: Anemia among children aged 6-59 months remains a major public health problem in low-and high-income countries including Ethiopia. Anemia is associated with significant consequences on the health of children such as under-five morbidity and mortality, increased risk of infection, and poor academic performance. The prevalence of anemia in Ethiopia has varied across areas. Therefore, this study aimed to investigate the geographic weighted regression analysis of anemia and its associated factors among children aged 6-59 months in Ethiopia. METHODS: This study was based on the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total weighted sample of 8482 children aged 6-59 months was included. For the spatial analysis, Arc-GIS version 10.7 and SaTScan version 9.6 statistical software were used. Spatial regression was done to identify factors associated with the hotspots of anemia and model comparison was based on adjusted R2 and Corrected Akaike Information Criteria (AICc). For the associated factors, the multilevel robust Poisson regression was fitted since the prevalence of anemia was greater than 10%. Variables with a p-value < 0.2 in the bi-variable analysis were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval was reported to declare the statistical significance and strength of association. RESULTS: The prevalence of anemia among children aged 6-59 months was 57.56% (95%CI: 56.50%, 58.61%) with significant spatial variation across regions in Ethiopia. The significant hot spot areas of anemia among children aged 6-59 months were detected in the central, west, and east Afar, Somali, Dire Dawa, Harari, and northwest Gambella regions. Mothers who had anemia, a child aged 23-59 months, mothers aged 15-19 years, and coming from a household with a poorer or poorest household were significant predictors of the spatial variations of anemia among children aged 6-59 months. In the multilevel robust Poisson analysis, born to mothers aged 30-39 (APR = 0.84, 95% CI: 0.76, 0.92) and 40-49 years (APR = 0.73, 95% CI: 0.65, 0.83), mothers who didn't have formal education (APR = 1.10, 95% CI: 1.00, 1.20), Children in the poorest household wealth index (APR = 1.17, 95% CI: 1.06, 1.29), being 4-6 (APR = 1.08, 95% CI: 1.02, 1.13) and above 6 order of birth (APR = 1.15, 95% CI: 1.07, 1.23), children born to anemic mothers (APR = 1.24, 95% CI: 1.19, 1.29), children aged 24-59 months (APR = 0.70, 95% CI: 0.68, 0.73), stunted children (APR = 1.09, 95% CI: 1.04, 1.13) and underweight children (APR = 1.07, 95% CI: 1.03, 1.13) were significantly associated with anemia among children aged 6-59 months. CONCLUSION AND RECOMMENDATION: Anemia is still a public health problem for children in Ethiopia. Residing in a geographic area where a high proportion of children born to mothers aged 15-19 years, a child aged 6-23 months, coming from a household with poorer or poorest wealth index, and mothers with anemia increased the risk of experiencing anemia among children aged 6-59 months. Maternal education, maternal age, child age, household wealth, stunting, underweight, birth order, and maternal anemia were significant predictors of anemia among children. The detailed map of anemia hot spots among children aged 6-59 months and its predictors could assist program planners and decision-makers to design targeted public health interventions.

Individual and community-level determinants of knowledge of ovulatory cycle among women of childbearing age in Ethiopia: A multilevel analysis based on 2016 Ethiopian Demographic and Health Survey.

BACKGROUND: Knowledge of the ovulatory cycle (KOC) aids women to refrain and engage in sexual intercourse to avoid and to get pregnancy, respectively. The effect of community-level factors on KOC was not yet known in Ethiopia. Therefore, we aimed to investigate the community- and individual-level determinants of KOC among women of childbearing age. METHODS: We used the 2016 Ethiopian Demographic and Health Survey, and total weighted samples of 15,683 women were included. Intra-class correlation, median odds ratio, and deviance were executed for model comparison in which a model with the lowest deviance was the best model i.e. model III in this case. A multivariable multilevel logistic regression model was employed to identify community- and individual-level factors of correct KOC. In the ultimate model, an adjusted odds ratio (AOR) with a 95% confidence interval was reported and variables with a p<0.05 were considered as statistically significant. RESULTS: In this study, 3,698 [23.58% (95% CI; 22.92-24.25)] participants had correct KOC. Women's age in years, i.e. 20-24 (AOR = 1.46;1.28-1.68) 25-29 (AOR = 1.72; 1.49-1.99), 30-34 (AOR = 2.21; 1.89-2.58), 35-39 (AOR = 1.78; 1.51-2.09), 40-44 (AOR = 1.97; 1.65-2.37), and 45-49 (AOR = 1.78; 1.44-2.19), knowledge of contraceptive methods (AOR = 3.08; 2.07-4.58), increased women's educational level, i.e. higher (AOR = 4.24; 3.54-5.07), secondary (AOR = 2.89; 2.48-3.36), and primary (AOR = 1.57; 1.39-1.78), higher household's wealth index, i.e. richest (AOR = 1.71; 1.35-2.16), richer (AOR = 1.42; 1.16-1.72), middle (AOR = 1.29; 1.07-1.56), and poorer (AOR = 1.24; 1.03-1.48), current contraceptive use (AOR = 1.26; 1.13-1.39), menstruating in the last six weeks (AOR = 1.13; 1.03-1.24), women's media exposure (AOR = 1.20; 1.07-1.35), and being in the community with a high level of media exposure (AOR = 1.53; 1.24-1.88) were statistically significant with KOC. CONCLUSIONS: Knowledge of the ovulatory cycle was low in this study, which demands health education for women of childbearing age. Special attention should be given to teenagers, those with lower educational, and lower economic status. Besides, the strengthening of media campaigns could increase women's KOC, which is crucial for preventing unintended pregnancy.

Elucidation of global and national genomic epidemiology of Salmonella enterica serovar Enteritidis through multilevel genome typing.

Salmonella enterica serovar Enteritidis is a major cause of foodborne Salmonella infections and outbreaks in humans. Effective surveillance and timely outbreak detection are essential for public health control. Multilevel genome typing (MGT) with multiple levels of resolution has been previously demonstrated as a promising tool for this purpose. In this study, we developed MGT with nine levels for S. Enteritidis and characterised the genomic epidemiology of S. Enteritidis in detail. We examined 26 670 publicly available S. Enteritidis genome sequences from isolates spanning 101 years from 86 countries to reveal their spatial and temporal distributions. Using the lower resolution MGT levels, globally prevalent and regionally restricted sequence types (STs) were identified; avian associated MGT4-STs were found that were common in human cases in the USA; temporal trends were observed in the UK with MGT5-STs from 2014 to 2018 revealing both long lived endemic STs and the rapid expansion of new STs. Using MGT3 to MGT6, we identified multidrug resistance (MDR) associated STs at various MGT levels, which improves precision of detection and global tracking of MDR clones. We also found that the majority of the global S. Enteritidis population fell within two predominant lineages, which had significantly different propensity of causing large scale outbreaks. An online open MGT database has been established for unified international surveillance of S. Enteritidis. We demonstrated that MGT provides a flexible and high-resolution genome typing tool for S. Enteritidis surveillance and outbreak detection.

Resources, methods, and data infrastructure to promote research in dementia care, caregiving, and services.

BACKGROUND: The National Institute on Aging, in conjunction with the Department of Health and Human Services as part of the National Alzheimer's Project Act, hosted a 2020 Dementia Care, Caregiving, and Services Research Summit Virtual Meeting Series on August 13, 2020. This article reflects three presentations related to Theme 6: Research Resources, Methods, and Data Infrastructure. Dr. Bynum discussed the challenges of identifying people for population- and healthcare-based research, including how definitions of dementia have changed over time, the opportunities and challenges inherent in the use of electronic data sources, and the need to fit data collection strategies to research goals and questions. Dr. Travison provided an overview on the growing use of embedded pragmatic clinical trials (ePCTs) and how to enhance their impact in dementia research. Dr. Wendler presented on the ethical considerations relevant to consent for dementia research, including assessment of decisional capacity and the role of decisional surrogates. CONCLUSIONS: The availability of claims data, electronic health records, and other sources of "existing" data has made the use and development of ePCTs both easier and more appealing. Among other things, they offer advantages in terms of lower cost and generalizability to real-world settings. This is turn has necessitated the use of informatic and analytic approaches to account for some of the limitations and complexities of such data, including multilevel clustering and the need to link and jointly analyze data from the person with dementia and those of their care partner. As part of this process, it will be important to broaden the scope of who is assessed for decisional capacity, make those assessments more study specific, and assist surrogates in making decisions based on what the individual would have chosen for themselves if capacitated (i.e., substituted judgment).

Prognostic factors affecting the short-term efficacy of non-surgical treatment of chronic periodontitis: a multilevel modeling analysis.

BACKGROUND: This study is aimed to analyze the prognostic factors affecting the short-term efficacy of non-surgical treatment of patients in periodontitis from stage II to stage IV by the multilevel modeling analysis. MATERIALS AND METHODS: A total of 58 patients with chronic periodontitis were included in this study. Patients were clinically explored before and 3 months after the treatment and the difference in probing depth was determined [Reduction of probing depth (Δ PD) = baseline PD - finial probing depth (FPD)] which is considered as the therapeutic evaluation. Three different levels were analyzed: patients, teeth and sites to construct a multi-layer linear model. RESULTS: Probing depth (PD) improved significantly compared with that before treatment (p < 0.05), in which FPD was (3.90 ± 1.39) mm, and the ΔPD was (1.79 ± 0.97) mm. Compared with the mesial sites and distal sites of the multi-rooted teeth, the number of PD ≥ 5 mm or PD < 5 mm after the treatment was significantly different (P < 0.05), and the proportion of PD < 5 mm was higher in mesial sites. The null model showed that Δ PD varied greatly between groups at various levels (P < 0.001), with prediction variable of site level, tooth level, and patient level accounted for 66%, 18%, and 16% of the overall difference, respectively. The complete model showed that the Δ PD of smokers was significantly lower than that of non-smokers (P < 0.001). The Δ PD of the mesial and distal sites was larger than that of the buccolingual central site (P < 0.001). The Δ PD of single-rooted teeth was larger than that of multi-rooted teeth (P < 0.001). The baseline PD, tooth mobility (TM), bleeding index (BI), clinical attachment loss (CAL) were significantly negatively correlated with Δ PD (P < 0.001). CONCLUSIONS: Patients with periodontitis from stage II to stage IV, who were non-smoking, have good compliance, good awareness of oral health, and low percentage sites with PD ≥ 5 mm at baseline, single-rooted teeth with hypomobility, less clinical attachment loss and lower bleeding index and sites of mesial or distal can obtain an ideal short-term efficacy of non-surgical treatment.

Multidimensional characteristics of young Brazilian volleyball players: A Bayesian multilevel analysis.

Brazil has been the benchmark for volleyball performance for at least two decades, providing a unique context to examine expertise development. This study examined the variation in body size, functional capacities, motivation for achievement, competitiveness, and deliberate practice of youth volleyball players associated with differences in biological maturity status, chronological age, and accumulated deliberate volleyball practice, adopting a Bayesian multilevel modeling approach. We considered 68 female and 94 male adolescent players (14.2 years, 90% confidence interval: 12.7 to 16.0). Players were grouped by the onset of deliberate volleyball practice as related to biologic maturation milestones [pre-puberty deliberate practice onset (12% of the sample), mid-puberty deliberate practice onset (51% of the sample), and late-puberty deliberate practice onset (37% of the sample). There was substantial variation in body dimensions and functional performance by gender. There was no variation by gender for motivation for deliberate practice and motivation for achievement and competitiveness. The young volleyball players appeared to be highly motivated and committed to deliberate practice, achievement, and competitiveness. Alignment of chronological age, biological maturation, and accumulated training experience allow more in-depth insights into young volleyball players' development, providing sounder support for coaches´ decisions.

Peer influence in adolescent drinking behavior: A meta-analysis of stochastic actor-based modeling studies.

OBJECTIVES: To measure the effects of peer influence and peer selection on drinking behavior in adolescence through a rigorous statistical approach designed to unravel these interrelated processes. METHODS: We conducted systematic searches of electronic databases, thesis collections and conference proceedings to identify studies that used longitudinal network design and stochastic actor-oriented modeling to analyze drinking behavior in adolescents. Parameter estimates collected from individual studies were analyzed using multilevel random-effects models. RESULTS: We identified 26 articles eligible for meta-analysis. Meta-analyses for different specifications of the peer influence effect were conducted separately. The peer influence effect was positive for every specification: for average similarity (avSim) mean log odds ratio was 1.27 with 95% confidence interval [0.04; 2.49]; for total similarity (totSim) 0.46 (95% CI = [0.44; 0.48]), and for average alter (avAlt) 0.70 (95% CI = [-0.01; 1.41]). The peer selection effect (simX) was also positive: 0.46 (95% CI = [0.28; 0.63]). Conversion log odds ratio values to Cohen's d gives estimates from 0.25 to 0.70, which is considered as medium to large effect. CONCLUSIONS: Advances in methodology for social network analysis have made it possible to accurately estimate peer influence effects free from peer selection effects. More research is necessary to clarify the roles of age, gender, and individual susceptibility on the changing behavior of adolescents under the influence of their peers. Understanding the effects of peer influence should inform practitioners and policy makers to design and deliver more effective prevention programs.

Do mothers who delivered at health facilities return to health facilities for postnatal care follow-up? A multilevel analysis of the 2016 Ethiopian Demographic and Health Survey.

INTRODUCTION: Returning to health facility for postnatal care (PNC) use after giving birth at health facility could reflect the health seeking behavior of mothers. However, such studies are rare though they are critically important to develop vigorous strategies to improve PNC service utilization. Therefore, this study aimed to determine the magnitude and factors associated with returning to health facilities for PNC among mothers who delivered in Ethiopian health facilities after they were discharged. METHODS: This cross-sectional study used 2016 Ethiopian Demographic and Health Survey data. A total of 2405mothers who gave birth in a health facility were included in this study. Multilevel mixed-effect logistic regression model was fitted to estimate both independent (fixed) effects of the explanatory variables and community-level (random) effects on return for PNC utilization. Variable with p-value of ≤ 0.25 from unadjusted multilevel logistic regression were selected to develop three models and p-value of ≤0.05 was used to declare significance of the explanatory variables on the outcome variable in the final (adjusted) model. Analysis was done using IBM SPSS statistics version 21. RESULT: In this analysis, from the total 2405 participants, 14.3% ((95%CI: 12.1-16.8), (n = 344)) of them returned to health facilities for PNC use after they gave birth at a health facility. From the multilevel logistic regression analysis, being employed (AOR = 1.51, 95%CI: 1.04-2.19), receiving eight and above antenatal care visits (AOR = 2.90, 95%CI: 1.05-8.00), caesarean section delivery (AOR = 2.53, 95%CI: 1.40-4.58) and rural residence (AOR = 0.56, 95%CI: 0.36-0.88) were found significantly associated with return to health facilities for PNC use among women who gave birth at health facility. CONCLUSION: Facility-based PNC utilization among mothers who delivered at health facilities is low in Ethiopia. Both individual and community level variables were determined women to return to health facilities for PNC use. Thus, adopting context-specific strategies/policies could improve PNC utilization and should be paid a due focus.

Multilevel Twin Models: Geographical Region as a Third Level Variable.

The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children's height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children's height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.

Multilevel Modeling in Classical Twin and Modern Molecular Behavior Genetics.

For more than a decade, it has been known that many common behavior genetics models for a single phenotype can be estimated as multilevel models (e.g., van den Oord 2001; Guo and Wang 2002; McArdle and Prescott 2005; Rabe-Hesketh et al. 2007). This paper extends the current knowledge to (1) multiple phenotypes such that the method is completely general to the variance structure hypothesized, and (2) both higher and lower levels of nesting. The multi-phenotype method also allows extended relationships to be considered (see also, Bard et al. 2012; Hadfield and Nakagawa 2010). The extended relationship model can then be continuously expanded to merge with the case typically seen in the molecular genetics analyses of unrelated individuals (e.g., Yang et al. 2011). We use the multilevel form of behavior genetics models to fit a multivariate three level model that allows for (1) child level variation from unique environments and additive genetics, (2) family level variation from additive genetics and common environments, and (3) neighborhood level variation from broader geographic contexts. Finally, we provide R (R Development Core Team 2020) functions and code for multilevel specification of several common behavior genetics models using OpenMx (Neale et al. 2016).

Prevalência e fatores associados aos transtornos mentais e ao acesso aos serviços de saúde mental / Prevalence and factors associated with mental disorders and access to mental health services

Introdução: os transtornos mentais são um grave problema de saúde pública, com alta prevalência no Brasil e em todo mundo. Os transtornos mentais comuns envolvem os transtornos de depressão e ansiedade, acometendo principalmente mulheres. O acesso oportuno aos serviços de saúde mental traz o diagnóstico precoce e o tratamento eficaz, minimizando complicações e diminuindo os números de adoecimento mental. Objetivo: Identificar os fatores associados aos transtornos mentais e ao acesso aos serviços de saúde mental no Brasil e no mundo. Método: Trata-se de um estudo de diferentes métodos. 1) revisão sistemática de estudos transversais sobre a prevalência e fatores associados aos transtornos mentais comuns em mulheres, com buscas nas bases de dados PubMed, Web of Science, Science Direct, Scopus, Cinahl; 2) revisão sistemática de estudos transversais sobre a diferença na prevalência do acesso aos serviços de saúde mental entre mulheres e homens, com buscas nas bases de dados PubMed, Web of Science, Science Direct, Scopus, Cinahl; 3) Estudo transversal com dados da Pesquisa Nacional de Saúde do Brasil do ano 2013, com indivíduos de 18 anos ou mais, que analisou a prevalência dos sintomas do sofrimento mental na população brasileira e a associação entre as características individuais e o contexto social, em uma análise multinível; 4) Estudo transversal com dados da Pesquisa Nacional de Saúde de 2019, incluindo indivíduos de 15 anos ou mais para analisar os fatores associados ao acesso precário aos serviços de saúde para o tratamento da depressão no Brasil. Resultados: Na revisão sistemática sobre prevalência e fatores associados aos transtornos mentais comuns em mulheres, foram incluídos 19 estudos, os principais fatores associados relatados foram o desemprego, dívidas, baixa renda econômica, ser dona de casa, tabagismo, menor nível educacional, baixa autoavaliação em saúde, ser solteira, divorciada ou viúva. O risco de viés dos estudos foi classificado como baixo e moderado. Na segunda revisão sistemática, sobre diferenças de acesso entre homens e mulheres, 11 estudos foram incluídos. A prevalência do acesso aos serviços de saúde mental entre as mulheres variou de 5,2% a 56,5%; entre os homens foi de 2,9% a 47%. Os homens obtiveram maior prevalência de acesso apenas nos serviços para tratamento de uso de álcool e drogas. No primeiro estudo transversal, os pensamentos depressivos estiveram associados a adultos jovens e de meia-idade, do sexo feminino, com baixo nível de instrução, sem companheiro, fumantes ou ex-fumantes e que não possuem plano privado de saúde; pertencer às classes D-E e viver em estados com menor expectativa de anos de estudo se mostrou como fator de proteção. Resultados semelhantes foram encontrados para o desfecho decréscimo da energia vital e sintomas somáticos. Já prevalência do acesso precário aos serviços de saúde para o tratamento da depressão foi de 14,9% (IC95% 13,6-16,2), e foi associado aos indivíduos de 15-29 anos (RP=1,52) e 30-59 anos (RP=1,22), sem instrução (RP=1,43), que avaliam sua saúde como regular/ruim/muito ruim (RP= 1,26), que possuem alguma limitação das atividades habituais por causa da depressão (RP=2,71), que tiveram a última consulta de 6 meses a menos de 2 anos (RP=2,63) e há mais de 2 anos (RP=2,25). Conclusão: é necessário um fortalecimento e redirecionamento das políticas públicas de saúde mental, no intuito de atender às necessidades individuais das pessoas mais vulneráveis e com fatores de risco, ofertando acesso oportuno aos serviços de saúde e diminuindo o sofrimento mental, bem como a prevalência de transtornos mentais no Brasil e no mundo (AU).

Introduction: mental disorders are a serious public health problem, with high prevalence in Brazil and worldwide. Common mental disorders involve depression and anxiety disorders, mainly affecting women. Timely access to mental health services brings early diagnosis and effective treatment, minimizing complications and reducing the number of mental illnesses. Objective: To identify factors associated with mental disorders and access to mental health services in Brazil and worldwide. Method: This is a study of different methods. 1) systematic review of cross-sectional studies on the prevalence and factors associated with common mental disorders in women, with searches in PubMed, Web of Science, Science Direct, Scopus, Cinahl databases; 2) systematic review of cross-sectional studies on the difference in the prevalence of access to mental health services between women and men, with searches in the PubMed, Web of Science, Science Direct, Scopus, Cinahl databases; 3) Cross-sectional study with data from the 2013 National Health Survey of Brazil, aged 18 years or older, which analyzed the prevalence of symptoms of mental distress in the Brazilian population and the association between individual characteristics and social context, in an analysis multilevel; 4) Crosssectional study with data from the 2019 National Health Survey, including 15 years or more to analyze factors associated with poor access to health services for the treatment of depression in Brazil. Results: In the systematic review on the prevalence and factors associated with common mental disorders in women, 19 studies were included, the main associated factors were unemployment, debt, low economic income, being a housewife, smoking, lower educational level, low self-assessment in health, being single, divorced or widowed. The risk of bias of the studies was classified as low and moderate. In the second systematic review, on access differences between men and women, 11 studies were included. The prevalence of access to mental health services among women ranged from 5.2% to 56.5%; among men it was 2.9% to 47%. Men had a higher prevalence of access only to services for the treatment of alcohol and drug use. In the first cross-sectional study, depressive thoughts were associated with young and middle-aged female adults, with a low level of education, without a partner, accident or extreatment, and without a private health insurance plan; belonging to classes D-E and living in states with lower expectations of years of education if informed as a protective factor. Similar results were found for the outcome of decreased vital energy and somatic symptoms. The prevalence of poor access to health services for the treatment of depression was 14.9% (95%CI 13.6-16.2), and it was associated with individuals aged 15-29 years (PR = 1.52) and 30-59 years old (PR = 1.22), without education (PR = 1.43), who assess their health as regular/poor/very bad (PR = 1.26), who have some limitation in their usual activities because of depression (PR = 2.71), who had the last visit from 6 months to less than 2 years (PR = 2.63) and for more than 2 years (PR = 2.25). Conclusion: it is necessary to strengthen and redirect public mental health policies, not aiming to meet the individual needs of the most vulnerable people with risk factors, offering timely access to health services and reducing mental suffering, as well as prevalence of mental disorders in Brazil and worldwide (AU).

Marcas do racismo! Violências contra adolescentes e jovens brasileiros segundo o quesito raça/cor / Marks of racism! Violence against Brazilian adolescents and youth according to race/color

Introdução: A violência pode resultar em lesão, dano psicológico, deficiência no desenvolvimento e até a morte. No entanto, a violência afeta a população de modo desigual, gerando riscos diferenciados em função de gênero, raça/cor, espaço social e idade, sobretudo nos adolescentes e na juventude. Objetivo: Analisar a discriminação racial percebida e a exposição às diversas violências em adolescentes e jovens brasileiros, segundo as diferenças de raça/cor. Metodologia: Foram realizadas três análises: 1) estudo transversal com dados Pesquisa Nacional de Saúde do Escolar de 2015 (PeNSE) 2015, no qual foi analisada a prevalência da discriminação racial e sua associação com fatores socioeconômicos, de saúde e do contexto escolar em uma Regressão de Poisson; 2) estudo transversal que analisou as prevalências de diversas tipologias de violência segundo raça/cor, em um modelo de Regressão de Poisson multinível ajustado por idade, sexo e escolaridade materna utilizando dados da PeNSE 2015; e 3) estudo ecológico que analisou as taxas de homicídios de jovens de 15 a 29 anos nos municípios brasileiros no período de 2015-2017, por sexo e raça e variáveis contextuais relativas à educação, renda, escolaridade e vulnerabilidade em uma Regressão Linear multinível. Resultados: A prevalência da discriminação racial percebida entre escolares brasileiros foi de 2,58% (IC95%: 2,40-2,78) e associou-se com o sexo masculino (RP 1,69; 1,40-2,04), ter cor da pele preta (RP 7,39; 5,49-9,96), ser indígena (RP 4,95; 3,32- 7,38), com mãe sem escolaridade (RP 1,74; 1,25-2,42) e que possui expectativa de escolaridade até o ensino médio (RP 1,50; 1,24-1,83). Quanto à exposição à violência, os estudantes pretos tiveram maior envolvimento em brigas com armas de fogo e que foram seriamente feridos (RP=1,50; IC95%:1,27-1,76; e RP=1,36;IC95%:1,22-1,52, respectivamente); os indígenas apresentaram maior prevalência de se envolverem em luta física (RP= 1,23; IC95%: 1,09-1,38) e de se envolver em brigas com armas contundentes e perfurocortante (RP= 1,38; IC95%: 1,11-1,73). No modelo multinível, ter a cor preta foi associado a estudar em escola localizada em área de risco e estudar em escola que interrompeu ou suspendeu as aulas por motivo de segurança. Quanto às taxas de mortalidade por homicídios, destaca-se a relação mais acentuada entre os homicídios de jovens negros e as taxas de homicídios na população adulta. Conclusão: os resultados demonstram que existe iniquidade racial na exposição à violência no Brasil, sendo as principais vítimas os adolescentes e jovens pertencentes à população negra e indígena (AU).

Introduction: Violence can result in injury, psychological damage, developmental disabilities and even death. However, violence affects the population in an unequal way, generating differentiated risks based on gender, race / color, social space and age, especially among adolescents and youth. Objective: To analyze perceived racial discrimination and exposure to various types of violence in Brazilian adolescents and young people, according to race /color differences. Methodology: Three analyzes were performed: 1) cross-sectional study with data 2015 National Student Health Survey (PeNSE) 2015, in which the prevalence of racial discrimination and its association with socioeconomic, health and school context factors were analyzed in a Poisson regression; 2) cross-sectional study that analyzed the prevalence of different types of violence according to race /color, in a multilevel Poisson Regression model adjusted for age, sex and maternal education using data from PeNSE 2015 and 3) ecological study that analyzed rates homicides of young people aged 15 to 29 years in Brazilian municipalities in the period 2015-2017, by sex and race and contextual variables related to education, income, schooling and vulnerability in a multilevel Linear Regression. Results: The prevalence of racial discrimination perceived among Brazilian schoolchildren was 2.58% (95% CI: 2.40-2.78) and was associated male gender (PR 1.69; 1.40 -2.04), having black skin color (RP 7.39; 5.49-9.96), being indigenous (RP 4.95; 3.32-7.38), with an uneducated mother (RP 1, 74; 1.25-2.42) and who has expected schooling up to high school (RP 1.50; 1.24-1.83). As for exposure to violence, black students were more involved in fighting with firearms and who were seriously injured (PR = 1.50; 95% CI: 1.27-1.76; and PR = 1.36; 95% CI : 1.22-1.52, respectively); indigenous people had a higher prevalence of being involved in physical fighting (PR = 1.23; 95% CI: 1.09-1.38) and of getting involved in fights with blunt weapons and sharps (PR = 1.38; 95% CI: 1.11 - 1.73). In the multilevel model, having black skin color was associated with studying at a school located in a risk area and studying at a school that interrupted or suspended classes for safety reasons. Regarding homicide mortality rates, the most pronounced relationship between homicides among young blacks and homicide rates in the adult population stands out. Conclusion: the results demonstrate that there is racial inequity in the exposure to violence in Brazil, with the main victims being adolescents and young people belonging to the black and indigenous population (AU).

Multilevel and spatial analyses of childhood malnutrition in Uganda: examining individual and contextual factors.

In this study, we examine the concepts of spatial dependence and spatial heterogeneity in the effect of macro-level and micro-level factors on stunting among children aged under five in Uganda. We conducted a cross-sectional analysis of 3624 Ugandan children aged under five, using data from the 2016 Ugandan Demographic and Health Survey. Multilevel mixed-effect analysis, spatial regression methods and multi-scale geographically weight regression (MGWR) analysis were employed to examine the association between our predictors and stunting as well as to analyse spatial dependence and variability in the association. Approximately 28% of children were stunted. In the multilevel analysis, the effect of drought, diurnal temperature and livestock per km2 on stunting was modified by child, parent and household factors. Likewise, the contextual factors had a modifiable effect on the association between child's sex, mother's education and stunting. The results of the spatial regression models indicate a significant spatial error dependence in the residuals. The MGWR suggests rainfall and diurnal temperature had spatial varying associations with stunting. The spatial heterogeneity of rainfall and diurnal temperature as predictors of stunting suggest some areas in Uganda might be more sensitive to variability in these climatic conditions in relation to stunting than others.

Health psychology attendance: A multilevel analysis of patient-level predictors and therapist effects.

The study investigated adult outpatient Health Psychology Services appointment attendance, cancellation, and missed appointments (A/C/M). The first objective was to determine which demographic and process factors predicted the probability of A/C/M. The second objective was to determine whether there remained residual significant differences in A/C/M between therapists (i.e., a "therapist effect"), after controlling for explanatory variables. We conducted a practice-based retrospective 2-year cohort study. Three-level multilevel models were constructed and tested to analyze the probability of A/C/M at (1) assessment appointments (N = 1,175) and (2) follow-up appointments (N = 5,441). After controlling for predictor variables, significant therapist effects were found for attendance (10.0% to 13.0%) and cancellation (4.4%) at follow-up appointments (but not assessments), indicating significantly different attendance rates at follow-up between therapists. Predictors of attendance at follow-up included patient age, pretherapy symptom severity scores (including risk and symptom scores), and completion of intake questionnaires. Early morning follow-up appointments were least likely to be canceled, followed by late afternoon and finally midday appointments. Treatment intensity predicted attendance, but among qualified therapists, qualification type and pay level were nonsignificant. No significant predictors of attendance at assessment were detected. Attendance at Health Psychology Services outpatient appointments varies significantly according to patient, therapist, and appointment factors. Key routinely collected variables are predictive of attendance at follow-up. Clinical implications include the potential to identify patients at risk of nonattendance and target engagement interventions to these patients. Research directions include closer examination of variability in follow-up attendance between therapists. (PsycInfo Database Record (c) 2020 APA, all rights reserved).