Graetz Nick, Elo Irma T
Population Studies Center, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA 19104, USA.
Department of Sociology, Population Aging Research Center, University of Pennsylvania, Philadelphia, USA.
Spat Demogr. 2022 Apr;10(1):33-74. doi: 10.1007/s40980-021-00095-6. Epub 2021 Aug 24.
Studies have documented significant geographic divergence in U.S. mortality in recent decades. However, few studies have examined the extent to which county-level trends in mortality can be explained by national, state, and metropolitan-level trends, and which county-specific factors contribute to remaining variation. Combining vital statistics data on deaths and Census data with time-varying county-level contextual characteristics, we use a spatially explicit Bayesian hierarchical model to analyze the associations between working-age mortality, state, metropolitan status and county-level socioeconomic conditions, family characteristics, labor market conditions, health behaviors, and population characteristics between 2000 and 2017. Additionally, we employ a Shapley decomposition to illustrate the additive contributions of each changing county-level characteristic to the observed mortality change in U.S. counties between 1999-2001 and 2015-2017 over and above national, state, and metropolitan-nonmetropolitan mortality trends. Mortality trends varied by state and metropolitan status as did the contribution of county-level characteristics. Metropolitan status predicted more of the county-level variance in mortality than state of residence. Of the county-level characteristics, changes in percent college-graduates, smoking prevalence and the percent of foreign-born population contributed to a decline in all-cause mortality over this period, whereas increasing levels of poverty, unemployment, and single-parent families and declines manufacturing employment slowed down these improvements, and in many nonmetropolitan areas were large enough to overpower the positive contributions of the protective factors.
研究表明,近几十年来美国死亡率存在显著的地理差异。然而,很少有研究考察县级死亡率趋势在多大程度上可以由国家、州和大都市层面的趋势来解释,以及哪些县级特定因素导致了剩余的差异。我们将死亡人口的生命统计数据和人口普查数据与随时间变化的县级背景特征相结合,使用空间明确的贝叶斯层次模型来分析2000年至2017年期间工作年龄死亡率、州、大都市地位与县级社会经济状况、家庭特征、劳动力市场状况、健康行为和人口特征之间的关联。此外,我们采用夏普利分解法来说明在1999 - 2001年至2015 - 2017年期间,除国家、州和大都市 - 非大都市死亡率趋势之外,每个变化的县级特征对美国各县观察到的死亡率变化的累加贡献。死亡率趋势因州和大都市地位而异,县级特征的贡献也是如此。大都市地位比居住州预测了更多的县级死亡率差异。在县级特征中,大学毕业生百分比、吸烟率和外国出生人口百分比的变化导致了这一时期全因死亡率的下降,而贫困、失业和单亲家庭水平的上升以及制造业就业的下降减缓了这些改善,并且在许多非大都市地区,这些因素的影响大到足以抵消保护因素的积极贡献。