Understanding patterns in mortality across subpopulations is essential for local health policy decision making. One of the key challenges of subnational mortality rate estimation is the presence of small populations and zero or near zero death counts. When studying differences between subpopulations, this challenge is compounded as the small populations are further divided along socioeconomic or demographic lines. In this paper, we build on principal component-based Bayesian hierarchical approaches for subnational mortality rate estimation to model correlations across subpopulations. The principal components identify structural differences between subpopulations, and coefficient and error models track the correlations between subpopulations over time. We illustrate the use of the model in a simulation study as well as on county-level sex-specific US mortality data. We find that results from the model are reasonable and that it successfully extracts meaningful patterns in US sex-specific mortality. Additionally, we show that ancillary correlation parameters are a useful tool for studying the convergence and divergence of mortality patterns over time.

翻译：理解亚人群死亡率模式对于地方卫生政策决策至关重要。次国家级死亡率估算的主要挑战之一在于存在小规模人口及零或接近零的死亡计数。当研究亚人群间差异时，这一挑战更为严峻，因为小规模人口还需按社会经济或人口学特征进一步细分。本文基于主成分贝叶斯层次方法，构建了次国家级死亡率估算模型，以刻画亚人群间的相关性。主成分识别亚人群间的结构性差异，而系数与误差模型则追踪亚人群相关性随时间的变化趋势。我们通过模拟研究及美国县级性别特异性死亡率数据验证了模型的应用效果。结果表明模型结果合理，且成功提取了美国性别特异性死亡率中的有意义模式。此外，我们证实附属相关性参数是研究死亡率模式随时间收敛与发散的有效工具。