Abstract
AbstractAs many low- and middle-income countries continue making gains towards attaining sustainable development goals in under-five mortality, surveillance of mortality outcomes and indicators at the sub-district level will become important as national- and district-level estimates may mask areas with a high burden. Spatial statistical modelling techniques such as geostatistical prevalence mapping can play a role in identifying hotspots of relatively high under-five mortality. To achieve this objective, it is necessary to combine data from multiple sources with different spatial resolutions to produce maps that reveal these hotspot clusters. We pooled DHS, high-resolution census data, economic vulnerability, and malaria risk data to estimate under-five mortality at a sub-district level in Malawi. Using a Bayesian hierarchical modelling approach, we fitted a binomial generalized linear geostatistical model with local area effects to generate estimates of under-five mortality at a sub-district level in Malawi. Results, in general, showed low mortality rates across the country with pockets of locations mostly in northern Malawi showing elevated risk. Rural locations were associated with higher odds of under-five mortality compared to urban locations. Our study provides a better understanding of progress made after the Millennium Development Goals in 2015 and can help improve surveillance through the application of targeted interventions which can lead to the attainment of sustainable development goals by 2030.
Publisher
Cold Spring Harbor Laboratory