1999-2001 年至 2015-2017 年美国县级工龄死亡率趋势分解》(Decomposing County-Level Working-Age Mortality Trends in the United States Between 1999-2001 and 2015-2017)。

IF 1.1 Q3 DEMOGRAPHY Spatial Demography Pub Date : 2022-04-01 Epub Date: 2021-08-24 DOI:10.1007/s40980-021-00095-6
Nick Graetz, Irma T Elo
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引用次数: 0

摘要

研究记录了近几十年来美国死亡率的显著地域差异。然而,很少有研究探讨县一级的死亡率趋势在多大程度上可以被国家、州和大都市一级的趋势所解释,以及哪些县的特定因素导致了剩余的差异。结合有关死亡的生命统计数据和人口普查数据以及随时间变化的县级背景特征,我们使用空间明确的贝叶斯分层模型来分析 2000 年至 2017 年间工作年龄死亡率、州、大都市地位和县级社会经济条件、家庭特征、劳动力市场条件、健康行为和人口特征之间的关联。此外,我们还采用沙普利分解法来说明在 1999-2001 年和 2015-2017 年期间,县级特征的每种变化对观察到的美国各县死亡率变化的叠加贡献超过了国家、州和大都市-非大都市死亡率趋势。死亡率趋势因州和大都市状况而异,县级特征的贡献也不尽相同。与居住州相比,大都市地位更能预测县级死亡率的变化。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有助于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率的下降则减缓了这些改善,在许多非大都市地区,这些变化足以压倒保护性因素的积极贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Decomposing County-Level Working-Age Mortality Trends in the United States Between 1999-2001 and 2015-2017.

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.

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Spatial Demography
Spatial Demography DEMOGRAPHY-
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期刊介绍: Spatial Demography focuses on understanding the spatial and spatiotemporal dimension of demographic processes.  More specifically, the journal is interested in submissions that include the innovative use and adoption of spatial concepts, geospatial data, spatial technologies, and spatial analytic methods that further our understanding of demographic and policy-related related questions. The journal publishes both substantive and methodological papers from across the discipline of demography and its related fields (including economics, geography, sociology, anthropology, environmental science) and in applications ranging from local to global scale. In addition to research articles the journal will consider for publication review essays, book reviews, and reports/reviews on data, software, and instructional resources.
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