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Producing subnational estimates of the under-five mortality rate (U5MR) is avital goal for the United Nations to reduce inequalities in mortality andwell-being across the globe. There is a great disparity in U5MR betweenhigh-income and Low-and-Middle Income Countries (LMICs). Current methods formodelling U5MR in LMICs use smoothing methods to reduce uncertainty inestimates caused by data sparsity. This paper includes cohort alongside age andperiod in a novel application of an Age-Period-Cohort model for U5MR. In thiscontext, current methods only use age and period (and not cohort) forsmoothing. With data from the Kenyan Demographic and Health Surveys (DHS) weuse a Bayesian hierarchical model with terms to smooth over temporal andspatial components whilst fully accounting for the complex stratified,multi-staged cluster design of the DHS. Our results show that the use of cohortmay be useful in the context of subnational estimates of U5MR. We validate ourresults at the subnational level by comparing our results against directestimates.

Estimating Subnational Under-Five Mortality Rates Using a Spatio-Temporal Age-Period-Cohort Model