|Modelling children's anthropometric status using Bayesian distributional regression merging socio-economic and remote sensed data from South Asia and sub-Saharan Africa|
||Johannes Seiler, Kenneth Harttgen, Thomas Kneib, and Stefan Lang
||Economics and Human Biology, Volume 40; DOI: https://doi.org/10.1016/j.ehb.2020.100950
Body Mass Index (BMI)
More than one region
||A history of insufficient nutritional intake is reflected by low anthropometric measures and can lead to growth failures, limited mental development, poor health outcomes and a higher risk of dying. Children below five years are among those most vulnerable and, while improvements in the share of children affected by insufficient nutritional intake has been observed, both sub-Saharan Africa and South Asia have a disproportionately high share of growth failures and large disparities at national and sub-national levels. In this study, we use a Bayesian distributional regression approach to develop models for the standard anthropometric measures, stunting and wasting. This approach allows us to model both the mean and the standard deviation of the underlying response distribution. Accordingly, the whole distribution of the anthropometric measures can be evaluated. This is of particular importance, considering the fact that (severe) growth failures of children are defined having a z-score below -2 (-3), emphasising the need to extend the analysis beyond the conditional mean. In addition, we merge individual data taken from the Demographic and Health Surveys with remote sensed data for a large sample of 38 countries located in sub-Saharan Africa and South Asia for the period 1990–2016, in order to combine individual and household specific characteristics with geophysical and environmental characteristics, and to allow for a comparison over time. Our results show besides gender differences across space, and strong non-linear effects of included socio-economic characteristics, in particular for maternal education and the wealth of the household that, surprisingly, in the presence of socio-economic characteristics, remote sensed data does not contribute to variations in growth failures, and including a pure spatial effect excluding remote sensed data leads to even better results. Further, while all regions showed improvements towards the target of the Sustainable Development Goals (SDGs), our analysis identifies hotspots of growth failures at sub-national levels within India, Nigeria, Niger, and Madagascar, emphasising the need to accelerate progress to reach the target set by the SDGs.