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.

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.

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).

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.

Predictors of unmet need for contraception among adolescent girls and young women in selected high fertility countries in sub-Saharan Africa: A multilevel mixed effects analysis.

INTRODUCTION: Despite the desire of adolescent girls and young women (AGYW) in sub-Saharan Africa (SSA) to use contraceptives, the majority of them have challenges with access to contraceptive services. This is more evident in high fertility countries in SSA. The purpose of this study was to examine the predictors of unmet need for contraception among AGYW in selected high fertility countries in SSA. MATERIALS AND METHODS: Data from current Demographic and Health Surveys (DHS) carried out between 2010 and 2018 in 10 countries in SSA were analysed. A sample size of 24,898 AGYW who were either married or cohabiting was used. Unmet need for contraception was the outcome variable in this study. The explanatory variables were age, marital status, occupation, educational level, frequency of reading newspaper/magazine, frequency of listening to radio, frequency of watching television and parity (individual level variables) and wealth quintile, sex of household head, place of residence and decision-maker in healthcare (household/community level variables). Descriptive and multilevel logistic regression analyses were carried out. The results of the multilevel logistic regression analyses were reported using adjusted odds ratios at 95% confidence interval. RESULTS: The prevalence of unmet need for contraception in all the countries considered in this study was 24.9%, with Angola, recording the highest prevalence of 42.6% while Niger had the lowest prevalence of 17.8%. In terms of the individual level predictors, the likelihood of unmet need for contraception was low among AGYW aged 20-24 [aOR = 0.82; 95% CI = 0.76-0.88], those with primary [aOR = 1.22; 95% CI = 1.13-1.31] and secondary/higher levels of formal education [aOR = 1.18; 95% CI = 1.08-1.28, p < 0.001], cohabiting AGYW [aOR = 1.52; 95% CI = 1.42-1.63] and AGYW with three or more births [aOR = 3.41; 95% CI = 3.02-3.85]. At the household/community level, the odds of unmet need for contraception was highest among poorer AGYW [aOR = 1.36; 95% CI = 1.21-1.53], AGYW in female-headed households [aOR = 1.22; 95% CI = 1.13-1.33], urban AGYW [aOR = 1.21; 95% CI = 1.11-1.32] and AGYW who took healthcare decisions alone [aOR = 1.10; 95% CI = 1.01-1.21]. CONCLUSION: This study has identified disparities in unmet need for contraception among AGYW in high fertility countries in SSA, with AGYW in Angola having the highest prevalence. Both individual and household/community level factors predicted unmet need for contraception among AGYW in this study. However, based on the ICC values, household/community level factors prevailed the individual level factors. Enhancing access to contraception among poorer AGYW, those in female-headed households, those in urban areas and those who take healthcare decisions alone by both governmental and non-governmental organisations in high fertility countries is recommended.

Métodos y desafíos en la medición de desigualdades sociales en salud de Cuba / Methods and challenges in the measurement of social inequalities in health of Cuba

Introducción: Los individuos en posiciones de desventaja socioeconómica presentan un mayor riesgo de sufrir enfermedades. En Cuba, las ciencias sociales investigan sobre la inequidad social desde enfoques diferentes, en los que prevalecen las técnicas cualitativas con poca divulgación de los métodos cuantitativos que permitan la localización de desigualdades. Objetivo: Proponer un procedimiento para medir las desigualdades sociales en salud en el contexto cubano con el empleo de métodos cuantitativos. Métodos: Revisión bibliográfica sobre las técnicas y sus fundamentos. Se compararon los métodos según desafíos metodológicos, estructura de la información de entrada, ventajas y limitantes, interpretación de los resultados, posibilidades para capturar la desigualdad y software disponible para cada técnica. Se propusieron etapas para la medición de las desigualdades sociales en salud de acuerdo con la comparación realizada, los desafíos metodológicos planteados en las investigaciones, las alternativas metodológicas propuestas y la experticia de los investigadores. Conclusiones: Entre las limitantes de los métodos clásicos está la necesidad de tener datos hasta el nivel mínimo de análisis. El agrupamiento tiene como desafío metodológico el diseño de una extracción de rasgos. El análisis multinivel asume que los efectos contextuales son los mismos para todos los individuos dentro de los grupos a lo largo del tiempo. Esta dificultad es resuelta por el análisis de secuencias sociales. El requerimiento de datos longitudinales es el mayor impedimento de esta técnica para su utilización en Cuba(AU)

Introduction: Individuals in positions of socio-economic disadvantage are at increased risk for diseases. In Cuba, the social sciences research on social inequity from different approaches, in which prevail the qualitative techniques with little disclosure of quantitative methods that allow the location of inequalities. Objective: To propose a procedure for measuring social inequalities in health in the Cuban context with the use of quantitative methods. Methods: Bibliographical review on the techniques and their fundamentals. The methods were compared according to the methodological challenges, the structure of the entry information, advantages and limiting factors, interpretation of the results, possibility to capture inequality, and software available for each technique. There were proposed stages for measuring the social inequalities in health according with the comparison made, the methodological challenges posed in the researches, the methodological alternatives proposed and the expertise of the researchers. Conclusions: Among the limitations of the classical methods is the need to have data up to the minimum level of analysis. Grouping has as methodological challenge the design of a removal of features. Multilevel analysis assumes that the contextual effects are the same for all individuals within groups over time. This difficulty is solved by the analysis of social scripts. The requirement of longitudinal data is the biggest handicap of this technique for its use in Cuba(AU)

Multilevel Heterogeneous Factor Analysis and Application to Ecological Momentary Assessment.

Ansari et al. (Psychometrika 67:49-77, 2002) applied a multilevel heterogeneous model for confirmatory factor analysis to repeated measurements on individuals. While the mean and factor loadings in this model vary across individuals, its factor structure is invariant. Allowing the individual-level residuals to be correlated is an important means to alleviate the restriction imposed by configural invariance. We relax the diagonality assumption of residual covariance matrix and estimate it using a formal Bayesian Lasso method. The approach improves goodness of fit and avoids ad hoc one-at-a-time manipulation of entries in the covariance matrix via modification indexes. We illustrate the approach using simulation studies and real data from an ecological momentary assessment.

Math versus meaning in MAIHDA: A commentary on multilevel statistical models for quantitative intersectionality.

RATIONALE: Intersectionality has been increasingly adopted as a theoretical framework within quantitative research, raising questions about the congruence between theory and statistical methodology. Which methods best map onto intersectionality theory, with regard to their assumptions and the results they produce? Which methods are best positioned to provide information on health inequalities and direction for their remediation? One method, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), has been argued to provide statistical efficiency for high-dimensional intersectional analysis along with valid intersection-specific predictions and tests of interactions. However, the method has not been thoroughly tested in scenarios where ground truth is known. METHOD: We perform a simulation analysis using plausible data generating scenarios where intersectional effects are present. We apply variants of MAIHDA and ordinary least squares regression to each, and we observe how the effects are reflected in the estimates that the methods produce. RESULTS: The first-order fixed effects estimated by MAIHDA can be interpreted neither as effects on mean outcome when interacting variables are set to zero (as in a correctly-specified linear regression model), nor as effects on mean outcome averaged over the individuals in the population (as in a misspecified linear regression model), but rather as effects on mean outcome averaged over an artificial population where all intersections are of equal size. Furthermore, the values of the random effects do not reflect advantage or disadvantage of different intersectional groups. CONCLUSIONS: Because first-order fixed effects estimates are the reference point for interpreting random effects as intersectional effects in MAIHDA analyses, the random effects alone do not provide meaningful estimates of intersectional advantage or disadvantage. Rather, the fixed and random parts of the model must be combined for their estimates to be meaningful. We therefore advise caution when interpreting the results of MAIHDA in quantitative intersectional analyses.

Intrinsic and external determinants of antibiotic prescribing: a multi-level path analysis of primary care prescriptions in Hubei, China.

Background: Irrational use of antibiotics is a major driver of development of antibiotic resistance, which heavily threatens population health worldwide. Understanding the mechanism of physician's antibiotic prescribing decisions is increasingly highlighted to promote prudent use of antibiotics. Therefore, the current study aimed to fill the gap, modelling physician's antibiotic prescribing and identifying the potential intrinsic and external determinants of antibiotic prescribing in primary care. Methods: A total of 428,475 prescriptions from 499 prescribers in 65 primary care facilities in Hubei of China were audited. Teixeira Antibiotic Prescribing Behavioral Model (TAPBM) was referred as theoretical basis to identify intrinsic and external predictors of antibiotic prescriptions. A questionnaire survey was conducted, covering potential physician's intrinsic determinants (knowledge, attitudes and individual characteristics) and external factors both in individual level (patient pressure, time pressure and financial incentives) and institutional level (setting and local socio-economic development). A two-level path analysis was performed linking potential determinants of antibiotic use with physician's actual practices. Results: About 44.28% of the prescriptions contained antibiotics, with 9.28% containing two or more antibiotics. The multi-level path analysis revealed that knowledge was a significant predictor of attitudes (ß = 0.154, p < 0.05), but higher knowledge and attitudes failed to translate into antibiotic prescribing practices ((ß = - 0.076 - 0.039, p > 0.05). Instead, external factors played a more important role and physician's antibiotic use was significantly associated with patient pressure (ß = 0.102, p = 0.022), time pressure (ß = - 0.164, p = 0.002), financial incentives (ß = - 0.133- - 0.155, p = 0.027) and institutional environments (rural area, ß = 0.408, p = 0.002; and high socioeconomic setting, ß = - 0.641 - -0.578, p < 0.001 ). The prescribers who were male (ß = - 0.168, p = 0.007) or had lower qualification (ß = - 0.114, p = 0.028) were also more likely to prescribe antibiotics than others. Conclusion: Antibiotic prescribing practices are complex process and associated with both intrinsic (prescriber) and external (patients and institutional environment) factors. A systematic approach is required to curb over-prescription of antibiotics. Apart from educating prescribers, it is equally important, if not more, to educate patients, break incentives and nurture professional culture within organization to reduce the overuse of antibiotics.

Can a Good Life Be Unsatisfying? Within-Person Dynamics of Life Satisfaction and Psychological Well-Being in Late Midlife.

Psychological investigations into the structure of well-being have been largely cross-sectional. However, longitudinal models are needed as Western societies work to improve individual well-being. The current multilevel-modeling study examined within-person dynamics of well-being over 8 years. I asked two questions: (a) How do life satisfaction and psychological well-being (measures drawn from two well-being research traditions) relate over time? and (b) do these relationships vary on the basis of individuals' extraversion or neuroticism? Measures of life satisfaction and psychological well-being were collected in 8 consecutive years from 159 American adults in late midlife. A dispositional-life-satisfaction set point and yearly variation in life satisfaction both related to higher yearly psychological well-being. Neuroticism, but not extraversion, predicted a stronger within-person relationship between life satisfaction and psychological well-being. For participants with very low neuroticism, life satisfaction and psychological well-being varied independently. In sum, experiences of life satisfaction and psychological well-being converged for more neurotic individuals and diverged for more emotionally stable participants.

Evaluating fit indices in a multilevel latent growth curve model: A Monte Carlo study.

The multilevel latent growth curve model (MLGCM), which is subsumed by the multilevel structural equation modeling framework, has been advocated as a means of investigating individual and cluster trajectories. Still, how to evaluate the goodness of fit of MLGCMs has not been well addressed. The purpose of this study was to conduct a systematic Monte Carlo simulation to carefully investigate the effectiveness of (a) level-specific fit indices and (b) target-specific fit indices in an MLGCM, in terms of their independence from the sample size's influence and their sensitivity to misspecification in the MLGCM that occurs in either the between-covariance, between-mean, or within-covariance structure. The design factors included the number of clusters, the cluster size, and the model specification. We used Mplus 7.4 to generate simulated replications and estimate each of the models. We appropriately controlled the severity of misspecification when we generated the simulated replications. The simulation results suggested that applying RMSEAT_S_COV, TLIT _ S _ COV, and SRMRB maximizes the capacity to detect misspecifications in the between-covariance structure. Moreover, the use of RMSEAPS _ B, CFIPS _ B, and TLIPS _ B is recommended for evaluating the fit of the between-mean structure. Finally, we found that evaluation of the within-covariance structure turned out to be unexpectedly challenging, because none of the within-level-specific fit indices (RMSEAPS _ W, CFIPS _ W, TLIPS _ W, and SRMRW) had a practically significant sensitivity.

Individual- and state-level factors associated with functional limitation prevalence among Colombian elderly: a multilevel analysis.

This study aimed to identify the main regional factors associated with variations in the prevalence of functional limitation on the older adult in Colombia adjusted by individual characteristics. This multilevel study used cross-sectional data from 23,694 adults over 60 years of age in the SABE, Colombia nationwide survey. State-level factors (poverty, development, inequity, violence, health coverage, and access to improved water sources), as well as individual health related, socioeconomic and demographic characteristics, were analyzed. The overall prevalence of functional impairment for the basic activities of daily living (ADL) was 22%. The presence of comorbidities, low educational level, physical inactivity, no participation in social groups, mistreatment and being over 75 years old were associated with functional limitation. At the group level, the analysis showed significant differences in the functional limitation prevalence across states, particularly regarding the socioeconomic status measured according to the Human Development Index (median OR = 1.22; 95%CI: 1.13-1.30; p = 0.011). This study provides evidence on the impact of socioeconomic variation across states on FL prevalence in the Colombian elderly once adjusted for individual characteristics. The findings of this study, through a multilevel approach methodology, provide information to effectively address the conditions that affect the functionality in this population through the identification and prioritization of public health care in groups with economic and health vulnerability.

Individual- and community-level determinants of child immunization in the Democratic Republic of Congo: A multilevel analysis.

Understanding modifiable determinants of full immunization of children provide a valuable contribution to immunization programs and help reduce disease, disability, and death. This study is aimed to assess the individual and community-level determinants of full immunization coverage among children in the Democratic Republic of Congo. This study used data from the Demographic and Health Survey 2013-14 from the Democratic Republic of Congo. Data regarding total 3,366 children between 12 and 23 months of age were used in this study. Children who were immunized with one dose of BCG, three doses of polio, three doses of DPT, and a dose of measles vaccine was considered fully immunized. Descriptive statistics were calculated for the prevalence and distribution of full immunization coverage. Two-level multilevel logistic regression analysis, with individual-level (level 1) characteristics nested within community-level (level 2) characteristics, was used to assess the individual- and community-level determinants of full immunization coverage. This study found that about 45.3% [95%CI: 42.02, 48.52] of children aged 12-23 months were fully immunized in the DRC. The results confirmed immunization coverage varied and ranged between 5.8% in Mongala province to 70.6% in Nord-Kivu province. Results from multilevel analysis revealed that, four Antenatal Care (ANC) visits [AOR: 1.64; 95%CI: 1.23, 2.18], institutional delivery [AOR: 2.37; 95%CI: 1.52, 3.72], and Postnatal Care (PNC) service utilization [AOR: 1.43; 95%CI: 1.04, 1.95] were statistically significantly associated with the full immunization coverage. Similarly, children of mothers with secondary or higher education [AOR: 1.32; 95%CI: 1.00, 1.81] and from the richest wealth quintile [AOR: 1.96; 95%CI: 1.18, 3.27] had significantly higher odds of being fully immunized compared to their counterparts whose mothers were relatively poorer and less educated. Among the community-level characteristics, residents of the community with a higher rate of institutional delivery [AOR: 2.36; 95%CI: 1.59, 3.51] were found to be positively associated with the full immunization coverage. Also, the random effect result found about 35% of the variation in immunization coverage among the communities was attributed to community-level factors.The Democratic Republic of Congo has a noteworthy gap in full immunization coverage. Modifiable factors-particularly health service utilization including four ANC visits, institutional delivery, and postnatal visits-had a strong positive effect on full immunization coverage. The study underlines the importance of promoting immunization programs tailored to the poor and women with little education.

Analyzing individual growth with clustered longitudinal data: A comparison between model-based and design-based multilevel approaches.

To prevent biased estimates of intraindividual growth and interindividual variability when working with clustered longitudinal data (e.g., repeated measures nested within students; students nested within schools), individual dependency should be considered. A Monte Carlo study was conducted to examine to what extent two model-based approaches (multilevel latent growth curve model - MLGCM, and maximum model - MM) and one design-based approach (design-based latent growth curve model - D-LGCM) could produce unbiased and efficient parameter estimates of intraindividual growth and interindividual variability given clustered longitudinal data. The solutions of a single-level latent growth curve model (SLGCM) were also provided to demonstrate the consequences of ignoring individual dependency. Design factors considered in the present simulation study were as follows: number of clusters (NC = 10, 30, 50, 100, 150, 200, and 500) and cluster size (CS = 5, 10, and 20). According to our results, when intraindividual growth is of interest, researchers are free to implement MLGCM, MM, or D-LGCM. With regard to interindividual variability, MLGCM and MM were capable of producing accurate parameter estimates and SEs. However, when D-LGCM and SLGCM were applied, parameter estimates of interindividual variability were not comprised exclusively of the variability in individual (e.g., students) growth but instead were the combined variability of individual and cluster (e.g., school) growth, which cannot be interpreted. The take-home message is that D-LGCM does not qualify as an alternative approach to analyzing clustered longitudinal data if interindividual variability is of interest.

Individual- and state-level factors associated with functional limitation prevalence among Colombian elderly: a multilevel analysis / Factores individuales y departamentales asociados con la prevalencia de limitación funcional entre ancianos colombianos: un análisis multinivel / Fatores individuais e estaduais associados à prevalência de limitação funcional em idosos colombianos: uma análise multinível

Abstract: This study aimed to identify the main regional factors associated with variations in the prevalence of functional limitation on the older adult in Colombia adjusted by individual characteristics. This multilevel study used cross-sectional data from 23,694 adults over 60 years of age in the SABE, Colombia nationwide survey. State-level factors (poverty, development, inequity, violence, health coverage, and access to improved water sources), as well as individual health related, socioeconomic and demographic characteristics, were analyzed. The overall prevalence of functional impairment for the basic activities of daily living (ADL) was 22%. The presence of comorbidities, low educational level, physical inactivity, no participation in social groups, mistreatment and being over 75 years old were associated with functional limitation. At the group level, the analysis showed significant differences in the functional limitation prevalence across states, particularly regarding the socioeconomic status measured according to the Human Development Index (median OR = 1.22; 95%CI: 1.13-1.30; p = 0.011). This study provides evidence on the impact of socioeconomic variation across states on FL prevalence in the Colombian elderly once adjusted for individual characteristics. The findings of this study, through a multilevel approach methodology, provide information to effectively address the conditions that affect the functionality in this population through the identification and prioritization of public health care in groups with economic and health vulnerability.

Resumen: Este estudio tuvo por objetivo identificar los principales factores regionales, asociados con variaciones en la prevalencia de la limitación funcional en adultos mayores en Colombia, ajustados por características individuales. Este estudio multinivel usó datos transversales de 23.694 adultos, con más de 60 años de edad, en el SABE, encuesta nacional colombiana. Los factores nacionales (pobreza, desarrollo, inequidad, violencia, cobertura sanitaria, y acceso a fuentes mejoradas de agua), así como en relación con su salud individual, al igual que se analizaron las características socioeconómicas y demográficas. La prevalencia general de discapacidad funcional para las actividades básicas de la vida diaria (ABVD) fue de un 22%. La presencia de comorbilidades, bajo nivel educacional, inactividad física, la no participación en grupos sociales, maltrato y tener más de 75 años de edad estuvo asociado con la limitación funcional. En el nivel del grupo, el análisis mostró significativas diferencias respecto a la prevalencia de limitación funcional, a través de los diferentes estados, particularmente en lo referente al estatus socioeconómico, medido según el Índice de Desarrollo Humano (OR mediano = 1,22; IC95%: 1,13-1,30; p = 0,011). Este estudio proporciona evidencia sobre el impacto de la variación socioeconómica a través de los estados sobre la prevalencia de limitación funcional en los ancianos colombianos, una vez ajustadas las características individuales. Los resultados de este estudio, mediante una metodología de aproximación multinivel, proporcionan información con el fin de orientar efectivamente sobre las condiciones que afectan la funcionalidad de este tipo de población, mediante la identificación y priorización de los cuidados en la salud pública con grupos vulnerables económicamente y desde la perspectiva de la salud.

Resumo: O estudo teve como objetivo identificar os principais fatores regionais associados a variações na prevalência de limitação funcional na população idosa colombiana, ajustada por fatores individuais. O estudo multinível usou dados transversais de 23.694 adultos com mais de 60 anos de idade do estudo SABE colombiano. Foram analisados fatores de nível estadual (índices de pobreza, desenvolvimento, inequidade, violência, cobertura de saúde e acesso a água potável) e fatores individuais (sociodemográficos e de saúde). A prevalência global de comprometimento funcional nas atividades de vida diária (AVD) foi de 22%. A presença de comorbidades, escolaridade baixa, sedentarismo, falta de participação em grupos sociais, maus tratos e idade acima de 75 anos estiveram associados à limitação funcional. Em nível de grupo, a análise mostrou diferenças significativas na prevalência de limitação funcional entre os estados, particularmente quanto à condição socioeconômica, medida pelo Índice de Desenvolvimento Humano (OR médio = 1,22; IC95%: 1,13-1,30; p = 0,011). O estudo oferece evidências do impacto da variação socioeconômica entre estados na prevalência de limitação funcional nos idosos colombianos depois de ajustar por fatores individuais. Através de uma metodologia multinível, os achados fornecem informações para tratar efetivamente as condições que afetam a funcionalidade dessa população idosa através da identificação e priorização dos cuidados de saúde em grupos com vulnerabilidade econômica e sanitária.

A Comparison of Population-Averaged and Cluster-Specific Approaches in the Context of Unequal Probabilities of Selection.

Sampling designs of large-scale survey studies are typically complex, involving multiple design features such as clustering and unequal probabilities of selection. Single-level (i.e., population-averaged) methods that use adjusted variance estimators and multilevel (i.e., cluster-specific) methods provide two alternatives for modeling clustered data. Although the literature comparing these methods is vast, comparisons have been limited to the context in which all sampling units are selected with equal probabilities (thus circumventing the need for sampling weights). The goal of this study was to determine under what conditions single-level and multilevel estimators outperform one another in the context of a two-stage sampling design with unequal probabilities of selection. Monte Carlo simulation methods were used to evaluate the impact of several factors, including population model, informativeness of the design, distribution of the outcome variable, intraclass correlation coefficient, cluster size, and estimation method. Results indicated that the unweighted estimators performed similarly across conditions, whereas the weighted single-level estimators tended to outperform the weighted multilevel estimators, particularly under nonideal sample conditions. Multilevel weight approximation methods did not perform well when the design was informative. An empirical example is provided to demonstrate how researchers might investigate the implications of the simulation results in practice.

[Multilevel Analysis in Health Services Research in Healthcare Organizations: Benefits, Requirements and Implementation]. / Mehrebenenanalysen in der organisationsbezogenen Versorgungsforschung ­ Nutzen, Voraussetzungen und Durchführung.

Multilevel Analysis (MLA) are still rarely used in Health Services Research in Germany, though hierarchical data, e. g. from patients clustered in hospitals, is often present. MLA provide the valuable opportunity to study the health care context in health care organizations and the associations between context and health care outcomes. This article's aims are to introduce this particular method of data analysis, to discuss its' benefits and its' applicability particularly for Health Services Research focusing on organizational characteristics and to provide a concise guideline for performing the analysis. First, the benefits and the necessity for MLA compared to ordinary correlation analyses in the case of hierarchical data are discussed. Furthermore, the statistical requirements and key decisions for the performance of MLA are illustrated.

Estimation Methods for Mixed Logistic Models with Few Clusters.

For mixed models generally, it is well known that modeling data with few clusters will result in biased estimates, particularly of the variance components and fixed effect standard errors. In linear mixed models, small sample bias is typically addressed through restricted maximum likelihood estimation (REML) and a Kenward-Roger correction. Yet with binary outcomes, there is no direct analog of either procedure. With a larger number of clusters, estimation methods for binary outcomes that approximate the likelihood to circumvent the lack of a closed form solution such as adaptive Gaussian quadrature and the Laplace approximation have been shown to yield less-biased estimates than linearization estimation methods that instead linearly approximate the model. However, adaptive Gaussian quadrature and the Laplace approximation are approximating the full likelihood rather than the restricted likelihood; the full likelihood is known to yield biased estimates with few clusters. On the other hand, linearization methods linearly approximate the model, which allows for restricted maximum likelihood and the Kenward-Roger correction to be applied. Thus, the following question arises: Which is preferable, a better approximation of a biased function or a worse approximation of an unbiased function? We address this question with a simulation and an illustrative empirical analysis.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health.

BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between "specific" (measures of association) and "general" (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health. METHODS: We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics. RESULTS: For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%. CONCLUSION: Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible influence on individual use of psychotropic drugs, but appears to strongly condition individual choice of a private GP. However, the latter was only modestly explained by the socioeconomic circumstances of the neighbourhoods. Our analyses are based on real data and provide useful information for understanding neighbourhood level influences in general and on individual use of psychotropic drugs and choice of GP in particular. However, our primary aim is to illustrate how to perform and interpret a multilevel analysis of individual heterogeneity in social epidemiology and public health. Our study shows that neighbourhood "effects" are not properly quantified by reporting differences between neighbourhood averages but rather by measuring the share of the individual heterogeneity that exists at the neighbourhood level.