Quantifying and explaining variation in life expectancy at census tract, county, and state levels in the United States
Quantifying and explaining variation in life expectancy at census tract, county, and state levels in the United States
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DOI：
10.1073/pnas.2003719117
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发表时间：
2020-07-28
影响因子：
11.1
通讯作者：
Subramanian, S. V.
中科院分区：
文献类型：
--
作者：
Boing, Antonio Fernando;Boing, Alexandra Crispim;Subramanian, S. V.
在美国，关于预期寿命的地域不平等的研究仅集中在州或县一级的统计数据的单水平分析上。这项研究开发了一个多层次的视角，通过同时在人口普查区域(CT)、县和州的水平上模拟地理变化来理解预期寿命的变化。我们分析了65,662个CT的数据，这些CT嵌套在3020个县和48个州(加上哥伦比亚特区)。因变量是在每个CT中观察到的特定年龄的预期寿命。我们还考虑了以下CT水平的社会经济和人口特征作为独立变量：人口密度；黑人、单亲父母、低于联邦贫困线的人口比例、25岁或以上拥有学士学位或更高学历的人口比例；以及家庭收入中位数。在出生时预期寿命的总体地理差异中，70.4%的差异可归因于CT，其次是州19.0%和县10.7%。CT对老年人预期寿命的相对重要性更大(70.4%至96.8%)。CT水平的自变量解释了5%到76.6%的州间差异，11.1%到58.6%的县间差异，0.7%到44.9%的不同年龄段的CT间预期寿命差异。我们的研究结果表明，美国长寿方面的人口不平等主要是一种局部现象。在旨在减少美国健康不平等的公共政策讨论中，有必要提高地方地理制图的精确度和针对性。
Studies on geographic inequalities in life expectancy in the United States have exclusively focused on single-level analyses of aggre-gated data at state or county level. This study develops a multi-level perspective to understanding variation in life expectancy by simultaneously modeling the geographic variation at the levels of census tracts (CTs), counties, and states. We analyzed data from 65,662 CTs, nested within 3,020 counties and 48 states (plus Dis-trict of Columbia). The dependent variable was age-specific life expectancy observed in each of the CTs. We also considered the following CT-level socioeconomic and demographic characteristics as independent variables: population density; proportions of pop-ulation who are black, who are single parents, who are below the federal poverty line, and who are aged 25 or older who have a bachelor's degree or higher; and median household income. Of the total geographic variation in life expectancy at birth, 70.4% of the variation was attributed to CTs, followed by 19.0% for states and 10.7% for counties. The relative importance of CTs was greater for life expectancy at older ages (70.4 to 96.8%). The CT-level inde-pendent variables explained 5 to 76.6% of between-state varia-tion, 11.1 to 58.6% of between-county variation, and 0.7 to 44.9% of between-CT variation in life expectancy across different age groups. Our findings indicate that population inequalities in lon-gevity in the United States are primarily a local phenomenon. There is a need for greater precision and targeting of local geog-raphies in public policy discourse aimed at reducing health inequal-ities in the United States.