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

翻译：生成国家以下水平的五岁以下儿童死亡率（U5MR）估计值是联合国减少全球死亡率和福祉不平等的重要目标。高收入国家与中低收入国家（LMICs）之间的U5MR存在巨大差异。当前建模中低收入国家U5MR的方法采用平滑技术来降低数据稀疏性导致的估计不确定性。本文在年龄-时期-队列模型的新应用中，将队列与年龄和时期一同纳入U5MR分析。在此背景下，现有方法仅利用年龄和时期（不包括队列）进行平滑处理。基于肯尼亚人口与健康调查（DHS）数据，我们采用贝叶斯层次模型，引入平滑时间与空间分量的项，同时充分考虑DHS复杂的分层、多阶段聚类设计。结果表明，队列信息可能有助于国家以下水平的U5MR估计。我们通过将结果与直接估计值进行比较，在国家以下层级验证了我们的发现。