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Child malnutrition is one of the major causes of child morbidity and mortality around the globe especially in developing countries. The current study attempts to investigate the factors that contribute to malnutrition captured through WAZ (weight for age) among children in the Cholistan desert area of Punjab, Pakistan. Out of 900 households surveyed, 584 households were identified, having a sample of 1059 children aged between 0 and 59 months. The logit, multilevel logit, generalized linear mixed model, and generalized linear latent and mixed model approaches were employed to analyze the collected data. The findings reveal that the wealth index of households, mother’s age at birth of a child, birth order of the child, duration of breastfeeding, distance to the basic health unit, and use of protected/clean water significantly affect children for being underweight. The policy recommendations are made in line with the study findings to suggest ways that can reduce prevalance of underweight children in the area.

Distinguishing between longitudinal dependence due to the effects of previous responses on subsequent responses and dependence due to unobserved heterogeneity is important in many disciplines. For example, wheezing is an inflammatory reaction that may 'remodel' a child's airway structure and thereby affect the probability of future wheezing (state dependence). Alternatively, children could vary in their susceptibilities because of unobserved covariates such as genes (unobserved heterogeneity). For binary responses, distinguishing between state dependence and unobserved heterogeneity is typically accomplished by using dynamic/transition models that include both a lagged response and a random intercept. Naive maximum likelihood estimators can be severely inconsistent because of two kinds of endogeneity problem: lack of independence of the initial response and the random intercept (the initial conditions problem) and lack of independence of the covariates and the random intercept (the endogenous covariates problem). We clarify and unify previous work on handling these problems in the disconnected literatures of statistics and econometrics, suggest improved methods, investigate the asymptotic performance of competing methods and provide practical recommendations. The recommended methods are applied to longitudinal data on children's wheezing, where we investigate the extent of state dependence and unobserved heterogeneity and whether there is an effect of maternal smoking.

Multilevel modelling is sometimes used for data from complex surveys involving multistage sampling, unequal sampling probabilities and stratification. We consider generalized linear mixed models and particularly the case of dichotomous responses. A pseudolikelihood approach for accommodating inverse probability weights in multilevel models with an arbitrary number of levels is implemented by using adaptive quadrature. A sandwich estimator is used to obtain standard errors that account for stratification and clustering. When level 1 weights are used that vary between elementary units in clusters, the scaling of the weights becomes important. We point out that not only variance components but also regression coefficients can be severely biased when the response is dichotomous. The pseudolikelihood methodology is applied to complex survey data on reading proficiency from the American sample of the 'Program for international student assessment' 2000 study, using the Stata program gllamm which can estimate a wide range of multilevel and latent variable models. Performance of pseudo-maximum-likelihood with different methods for handling level 1 weights is investigated in a Monte Carlo experiment. Pseudo-maximum-likelihood estimators of (conditional) regression coefficients perform well for large cluster sizes but are biased for small cluster sizes. In contrast, estimators of marginal effects perform well in both situations. We conclude that caution must be exercised in pseudo-maximum-likelihood estimation for small cluster sizes when level 1 weights are used.

The study examines the impact of non-communicable diseases (NCDs) and employment status in South Africa utilising the National Income Dynamics Study longitudinal data from 2008 to 2017. The Generalized Linear Latent and Mixed Methods (GLLAMM) were employed to fit the multinomial logit model with correlated random intercept over panel multinomial logit without random effects to control for unobserved heterogeneity between individuals or intercepts. The empirical results indicate that the significant impact of NCDs on employment status differs by gender. NCDs were found to be most threatening to women employment status. The odds of women being economically inactive in the labour market are highly associated with NCDs. Further, having multiple NCDs also significantly increases the women's probability of being economically inactive population relative to being employed. The results highlight the necessity for undertaking a massive awareness campaign regarding the prevention and control of NCDs, especially among women.

We discuss prediction of random effects and of expected responses in multilevel generalized linear models. Prediction of random effects is useful for instance in small area estimation and disease mapping, effectiveness studies and model diagnostics. Prediction of expected responses is useful for planning, model interpretation and diagnostics. For prediction of random effects, we concentrate on empirical Bayes prediction and discuss three different kinds of standard errors; the posterior standard deviation and the marginal prediction error standard deviation (comparative standard errors) and the marginal sampling standard deviation (diagnostic standard error). Analytical expressions are available only for linear models and are provided in an appendix . For other multilevel generalized linear models we present approximations and suggest using parametric bootstrapping to obtain standard errors. We also discuss prediction of expectations of responses or probabilities for a new unit in a hypothetical cluster, or in a new (randomly sampled) cluster or in an existing cluster. The methods are implemented in gllamm and illustrated by applying them to survey data on reading proficiency of children nested in schools. Simulations are used to assess the performance of various predictions and associated standard errors for logistic random-intercept models under a range of conditions.

This paper analyzes the main factors that motivate the inter-firm and inter-regional mobility of workers in the Brazilian formal labor market. Using micro-data from Labor Ministry of Brazil between 1995 and 2002 (RAIS-Migra), we verify the determinants of formal labor mobility in Brazil by sectors to different levels of technological intensity. Based on Generalized Linear Latent and Mixed Model (GLLAMM), we find that the mobility is positively related to the wage level and personal characteristics, such as high educational level and male gender. On the other hand, the seniority level of worker is negatively related to the mobility level. Futhermore, all these results may be different according to the spatial extension considered.

Background Ownership of insecticidal mosquito nets has dramatically increased in Ethiopia since 2006, but the proportion of persons with access to such nets who use them has declined. It is important to understand individual level net use factors in the context of the home to modify programmes so as to maximize net use. Methods Generalized linear latent and mixed models (GLLAMM) were used to investigate net use using individual level data from people living in net-owning households from two surveys in Ethiopia: baseline 2006 included 12,678 individuals from 2,468 households and a sub-sample of the Malaria Indicator Survey (MIS) in 2007 included 14,663 individuals from 3,353 households. Individual factors (age, sex, pregnancy); net factors (condition, age, net density); household factors (number of rooms [2006] or sleeping spaces [2007], IRS, women's knowledge and school attendance [2007 only], wealth, altitude); and cluster level factors (rural or urban) were investigated in univariate and multi-variable models for each survey. Results In 2006, increased net use was associated with: age 25-49 years (adjusted (a) OR = 1.4, 95% confidence interval (CI) 1.2-1.7) compared to children U5; female gender (aOR = 1.4; 95% CI 1.2-1.5); fewer nets with holes (Ptrend = 0.002); and increasing net density (Ptrend < 0.001). Reduced net use was associated with: age 5-24 years (aOR = 0.2; 95% CI 0.2-0.3). In 2007, increased net use was associated with: female gender (aOR = 1.3; 95% CI 1.1-1.6); fewer nets with holes (aOR [all nets in HH good] = 1.6; 95% CI 1.2-2.1); increasing net density (Ptrend < 0.001); increased women's malaria knowledge (Ptrend < 0.001); and urban clusters (aOR = 2.5; 95% CI 1.5-4.1). Reduced net use was associated with: age 5-24 years (aOR = 0.3; 95% CI 0.2-0.4); number of sleeping spaces (aOR [per additional space] = 0.6, 95% CI 0.5-0.7); more old nets (aOR [all nets in HH older than 12 months] = 0.5; 95% CI 0.3-0.7); and increasing household altitude (Ptrend < 0.001). Conclusion In both surveys, net use was more likely by women, if nets had fewer holes and were at higher net per person density within households. School-age children and young adults were much less likely to use a net. Increasing availability of nets within households (i.e. increasing net density), and improving net condition while focusing on education and promotion of net use, especially in school-age children and young adults in rural areas, are crucial areas for intervention to ensure maximum net use and consequent reduction of malaria transmission.

Wasting is an immediate, visible, and life-threatening form of undernutrition in children aged <5 years. Within a short time, wasting causes recurrent sickness, delayed physical and mental growth, impatience, poor feeding, and low body weight. The long-term consequences of wasting and undernutrition are stunting, inability to learn, poor health status, and poor work performance. Wasting remains a public health problem in Ethiopia. According to the World Health Organization, countries have to reduce undernutrition including child wasting to below 5% by 2025. Ethiopia is attempting to attain national and international targets of undernutrition while struggling with many problems. This study aimed to identify the prevalence and associated factors of wasting to provide information for further renewing policy commitments. We used community-based, cross-sectional data from the Ethiopian Mini Demographic and Health Survey. The survey was conducted in 9 regions and 2 city administrations. Two-stage cluster sampling was used to recruit study participants. In the first stage, enumerations areas were selected, and 28-35 households per enumeration area were selected in the second stage. Our analysis included 2016 women with children aged <5 years from the 2019 EMDHS data set. We dropped incomplete records and included all women who fulfilled the eligibility criteria. We used multilevel ordinal regression using Generalized Linear Latent and Mixed Models (GLLAMM) and predicted probability with log-likelihood ratio tests. Fulfilling the proportional odds model's assumption during the application of multilevel ordinary logistic regression was a cumbersome task. GLLAMM enabled us to perform the multilevel proportional odds model using an alternative method. In our analysis, wasting was 7.68% (95% CI 6.56%-8.93%). Around 26.82% of mothers never used antenatal care for their current child. Most mothers (52.2%) did not have formal education, and 86.8% did not have postnatal care for their children. Additionally, half (50.93%) of the mothers have ≥6 household members. Wasting was associated with feeding diverse foods (coefficient 4.90, 95% CI 4.90-4.98), female sex of the household head (-40.40, 95% CI -40.41 to -40.32), home delivery (-35.51, 95% CI -35.55 to -35.47), first (16.66, 95% CI, 16.60-16.72) and second (16.65, 95% CI 16.60-16.70) birth order, female child (-12.65, 95% CI -12.69 to -12.62), and household size of 1 to 3 (10.86, 95% CI 10.80-10.92). According to the target set by World Health Organization for reducing undernutrition in children aged <5 years to below 5% by 2025, child wasting of 7.68% in Ethiopia should spark an immediate reaction from the government and stakeholders. Informed policy decisions, technology-based child-feeding education, and food self-sufficiency support could improve the current challenges. Additional effort is important to improve low maternal education, family planning, awareness of sex preferences, women empowerment, and maternal health services.

Biometrical genetic modeling of twin or other family data can be used to decompose the variance of an observed response or 'phenotype' into genetic and environmental components. Convenient parameterizations requiring few random effects are proposed, which allow such models to be estimated using widely available software for linear mixed models (continuous phenotypes) or generalized linear mixed models (categorical phenotypes). We illustrate the proposed approach by modeling family data on the continuous phenotype birth weight and twin data on the dichotomous phenotype depression. The example data sets and commands for Stata and R/S-PLUS are available at the Biometrics website.

Latent variable modelling has gradually become an integral part of mainstream statistics and is currently used for a multitude of applications in different subject areas. Examples of 'traditional' latent variable models include latent class models, item—response models, common factor models, structural equation models, mixed or random effects models and covariate measurement error models. Although latent variables have widely different interpretations in different settings, the models have a very similar mathematical structure. This has been the impetus for the formulation of general modelling frameworks which accommodate a wide range of models. Recent developments include multilevel structural equation models with both continuous and discrete latent variables, multiprocess models and nonlinear latent variable models.