Flexible statistical modeling of childhood malnutrition in Malawi.

Abstract

Childhood malnutrition is one of the most significant health problems affecting public health departments, mainly in developing countries. The development of proper assessment of malnutrition is one of the challenges faced by policy makers in many countries across the globe. Therefore, the current study was undertaken with the primary objective of assessing and determining all possible determinants of malnutrition in Malawi, using the Demographic and Health Survey (DHS) data 2015/16. Different types of statistical models were adopted to allow variety in methodology and to find the most accurate results among the models used. As a point of departure, the study utilized Generalized Linear Models (GLM) to account for the ordering of the outcome variable (severe, moderate and nourished). Furthermore, we noticed that it would be substantial to extend the ordinal logistic regression to include random effects and therefore to consider the variability between the primary sampling units or villages. Furthermore, we adopted a class of models that allows flexible functional dependence of an outcome variable on covariates by using nonparametric regression. Hence, the use of the generalized additive mixed model (GAMM), which relaxes the assumption of normality and linearity inherent in linear regressions. Analyses of childhood stunting have mainly used mean regression, yet modelling using quantile regression is more appropriate than using mean regression in that the former provides flexibility to study the impact of predictors on different desired quantiles of the response distribution, whereas the latter allows only studying the impact of predictors on the mean of the response variable. Therefore, quantile regression models were adopted for the provision of a complete picture of the relationship between the outcome variable (stunting) and the predictor variables on different desired quantiles of the response distribution. This study fitted a Bayesian additive quantile regression model with structural spatial effects for childhood stunting in Malawi, using 2015/16 DHS data. Inference was fully Bayesian, using the new integrated nested Laplace approximation (INLA), purely because of its much faster computation as compared to Markov chain Monte Carlo (MCMC). Furthervii Abstract more, different types of quantile regression models were fitted and compared according to each Deviance Information Criteria (DIC) for determination of the best model among them. Each of these models has inherent strengths and weaknesses. The choice of one depends on what the research is trying to accomplish and the type of data one has. In this study, we combined the results from different models, mainly from our quantile regression models. The significant determinants of childhood stunting in Malawi were found to be the age of the child, the education level of parents (mother and father), the family’s place of residence, gender of the child, incidence of recent fever, incidence of recent diarrhoea, multiple births, mother’s age at the birth, body mass index of the mother, wealth index of the family, source of drinking water and districts. Furthermore, from the spatial quantile regression model, a map was generated showing the distribution of malnutrition in a district level of Malawi. This map gave us an overview on how stunting is distributed in Malawi and from the map we were able to visualize and assess affected districts.