Accurate mortality modeling is central to actuarial science and public health, especially as mental health emerges as a significant factor in population outcomes. This paper develops and applies a Bayesian hierarchical model to analyze U.S. county-level mortality and suicide rates from 2010 to 2023. Applying a conditional autoregressive (CAR) structure to each combination of sex and age grouping, the model captures spatial and temporal trends while incorporating mental health surveillance data and socio-economic indicators. We first assess socio-economic covariates in predicting suicide. While the results vary considerably by age and sex, we find that the county-wide levels of educational attainment, housing prices, marriage rates, racial composition, household size, and poor mental health days all have significant relationships with suicide rates. We next consider the impact of various mental health indicators on all-cause and suicide-specific mortality and find that the strongest effects are observed in younger populations. The spatial and temporal correlation structures reveal substantial regional clustering and time-consistent trends in both all-cause mortality and suicide rates, supporting the use of spatio-temporal methods. Our findings highlight the value of integrating mental health surveillance data into mortality models to better identify emerging risk areas and vulnerable populations. This approach has the potential to inform public health policy, resource allocation, and targeted interventions aimed at reducing disparities in mortality and suicide across U.S. communities.

翻译：精确的死亡率建模是精算科学与公共卫生的核心，尤其在心理健康日益成为人口结果重要影响因素的背景下。本文开发并应用贝叶斯分层模型，分析了2010年至2023年美国县级死亡率与自杀率数据。通过对性别与年龄分组的每种组合施加条件自回归结构，该模型在纳入心理健康监测数据与社会经济指标的同时，捕捉了空间与时间趋势。我们首先评估了社会经济协变量对自杀的预测作用。结果显示，尽管不同年龄与性别群体间存在显著差异，但县级教育水平、住房价格、结婚率、种族构成、家庭规模及心理健康不良天数均与自杀率存在显著关联。随后，我们考察了多种心理健康指标对全因死亡率及自杀特异性死亡率的影响，发现最显著的影响出现在年轻群体中。空间与时间相关性结构揭示了全因死亡率与自杀率均存在明显的区域聚集性和时间一致性趋势，这支持了时空建模方法的应用。我们的研究结果突显了将心理健康监测数据整合到死亡率模型中的价值，有助于更准确地识别新兴风险区域与脆弱人群。该方法有望为公共卫生政策制定、资源分配及针对性干预措施提供依据，以降低美国不同社区间的死亡率与自杀率差异。