Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling

• Published in: BMC public health 2022-05, Vol.22 (1), p.1008-21, Article 1008
• Main Authors: Das, Sumonkanti, Baffour, Bernard, Richardson, Alice
• Format: Article
• Language: English
• Subjects: Age; Bangladesh - epidemiology; Bayes Theorem; Bayesian analysis; Care and treatment; Censuses; Child; Child Nutrition Disorders - epidemiology; Children & youth; Cross-Sectional Studies; Demographics; Demography; Distribution; Domains; Economic aspects; Estimates; Growth Disorders - epidemiology; Health care disparities; Hierarchies; Humans; Indicators; Inequalities; Infant; Malnutrition; Malnutrition - epidemiology; Malnutrition in children; Mathematical models; Nutrition; Patient outcomes; Prevalence; Public health; Rural-urban disparities; Sex; Small area estimation; Social aspects; Spatial and cross-sectional correlations; Stunting; Sustainable development; Thinness - epidemiology; Undernutrition; Underweight; Wasting
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/s12889-022-13170-4

Micro-level statistics on child undernutrition are highly prioritized by stakeholders for measuring and monitoring progress on the sustainable development goals. In this regard district-representative data were collected in the Bangladesh Multiple Indicator Cluster Survey 2019 for identifying localised disparities. However, district-level estimates of undernutrition indicators - stunting, wasting and underweight - remain largely unexplored. This study aims to estimate district-level prevalence of these indicators as well as to explore their disparities at sub-national (division) and district level spatio-demographic domains cross-classified by children sex, age-groups, and place of residence. Bayesian multilevel models are developed at the sex-age-residence-district level, accounting for cross-sectional, spatial and spatio-demographic variations. The detailed domain-level predictions are aggregated to higher aggregation levels, which results in numerically consistent and reasonable estimates when compared to the design-based direct estimates. Spatio-demographic distributions of undernutrition indicators indicate south-western districts have lower vulnerability to undernutrition than north-eastern districts, and indicate significant inequalities within and between administrative hierarchies, attributable to child age and place of residence. These disparities in undernutrition at both aggregated and disaggregated spatio-demographic domains can aid policymakers in the social inclusion of the most vulnerable to meet the sustainable development goals by 2030.