| Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling |
| Authors: |
Sumonkanti Das, Bernard Baffour and Alice Richardson |
| Source: |
BMC Public Health, Volume 22, issue 1008; DOI:https://doi.org/10.1186/s12889-022-13170-4 |
| Topic(s): |
Child feeding
Child height
Modelling
Nutrition
Spatial analysis
|
| Country: |
Asia
Bangladesh
|
| Published: |
MAY 2022 |
| Abstract: |
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. |
| Web: |
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13170-4 |
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