We investigate state-level age-specific mortality trends based on the United States Mortality Database (USMDB) published by the Human Mortality Database. In tandem with looking at the longevity experience across the 51 states, we also consider a collection of socio-demographic, economic and educational covariates that correlate with mortality trends. To obtain smoothed mortality surfaces for each state, we implement the machine learning framework of Multi-Output Gaussian Process regression (Huynh \& Ludkovski 2021) on targeted groupings of 3--6 states. Our detailed exploratory analysis shows that the mortality experience is highly inhomogeneous across states in terms of respective Age structures. We moreover document multiple divergent trends between best and worst states, between Females and Males, and between younger and older Ages. The comparisons across the 50+ fitted models offer opportunities for rich insights about drivers of mortality in the U.S. and are visualized through numerous figures and an online interactive dashboard.

翻译：我们基于人类死亡率数据库发布的美国死亡率数据库（USMDB），研究了州级年龄别死亡率趋势。在考察全美51个州的长寿经验的同时，我们还考虑了一系列与死亡率趋势相关的社会人口、经济和受教育程度协变量。为获取各州的平滑死亡率曲面，我们采用多输出高斯过程回归的机器学习框架（Huynh & Ludkovski 2021），对3至6个州的目标分组进行建模。详细的探索性分析表明，各州在年龄结构方面的死亡率经验存在高度异质性。此外，我们还记录了最优州与最差州、女性与男性、低龄与高龄群体之间的多种分化趋势。基于50余个拟合模型的比较分析，为深入理解美国死亡率驱动因素提供了丰富洞见，相关结果通过大量图表及在线交互式仪表盘进行可视化呈现。