Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA 90095, USA.
Int J Environ Res Public Health. 2022 Aug 27;19(17):10672. doi: 10.3390/ijerph191710672.
Life expectancy (LE) is a core measure of population health. Studies have confirmed the predictive importance of modifiable determinants on LE, but less is known about their association with LE change over time at the US county level. In addition, we explore the predictive association of LE change with COVID-19 mortality. We used a linear regression model to calculate county-level annual LE change from 2011 to 2016, and categorized LE change (≤-0.1 years change per year as decreasing, ≥0.1 years as increasing, otherwise no change). A multinomial regression model was used to determine the association between modifiable determinants of health indicators from the County Health Rankings and LE change. A Poisson regression model was used to evaluate the relationship between change in life expectancy and COVID-19 mortality through September 2021. Among 2943 counties, several modifiable determinants of health were significantly associated with odds of being in increasing LE or decreasing LE counties, including adult smoking, obesity, unemployment, and proportion of children in poverty. The presence of an increasing LE in 2011-2016, as compared to no change, was significantly associated with a 5% decrease in COVID-19 mortality between 2019 and 2021 (β = 0.953, 95% CI: 0.943, 0.963). We demonstrated that change in LE at the county level is a useful metric for tracking public health progress, measuring the impact of public health initiatives, and gauging preparedness and vulnerability for future public health emergencies.
预期寿命 (LE) 是衡量人口健康的核心指标。研究证实了可改变的决定因素对 LE 的预测重要性，但在美县一级，关于它们与 LE 随时间变化的关系知之甚少。此外，我们还探讨了 LE 变化与 COVID-19 死亡率的预测关联。我们使用线性回归模型计算了 2011 年至 2016 年县一级 LE 的年度变化，并对 LE 变化（每年变化≤-0.1 年为减少，≥0.1 年为增加，否则不变）进行了分类。使用多项回归模型来确定来自县健康排名的健康指标的可改变决定因素与 LE 变化之间的关联。使用泊松回归模型来评估截至 2021 年 9 月期间，LE 变化与 COVID-19 死亡率之间的关系。在 2943 个县中，一些健康的可改变决定因素与 LE 增加或减少的县的几率显著相关，包括成年人吸烟、肥胖、失业和贫困儿童比例。与没有变化相比，2011-2016 年 LE 增加与 2019 年至 2021 年 COVID-19 死亡率降低 5%显著相关（β=0.953，95%置信区间：0.943，0.963）。我们表明，县级 LE 变化是跟踪公共卫生进展、衡量公共卫生举措影响以及衡量未来公共卫生紧急情况的准备和脆弱性的有用指标。
