Association of Social and Economic Inequality With Coronavirus Disease 2019 Incidence and Mortality Across US Counties.
Association of Social and Economic Inequality With Coronavirus Disease 2019 Incidence and Mortality Across US Counties.
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DOI：
10.1001/jamanetworkopen.2020.34578
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发表时间：
2021-01-04
影响因子：
13.8
通讯作者：
De Maio F
中科院分区：
文献类型：
--
作者：
Liao TF;De Maio F
种族/族裔人口构成和经济不平等是否与冠状病毒病2019年(新冠肺炎)的发病率和死亡率有关？这项对美国3141县新冠肺炎大流行前200天累计发病率和死亡率的横断面生态分析证实了发病率和死亡率与种族/民族构成和收入不平等以及发病率和死亡率与这两个结构性因素的联合关联。这项研究建议，新冠肺炎监控系统应该考虑县级收入不平等，以更好地了解新冠肺炎的社会格局。这一横断面生态分析量化了经济不平等、种族/民族构成、政治因素和州医疗保健政策与美国县级冠状病毒病2019年(新冠肺炎)相关的发病率和死亡负担之间的关联。现在已经确定，在整个美国，小规模人口承担了2019年冠状病毒病不成比例的负担(新冠肺炎)。然而，人们对一个县的种族/民族组成、收入不平等程度、政治因素以及人口中的新冠肺炎结果之间的相互作用知之甚少。量化经济不平等、种族/民族构成、政治因素和国家医疗保健政策与新冠肺炎相关的发病率和死亡负担之间的关联。这项横断面研究使用了来自美国50个州和华盛顿特区3142个县的数据。从美国疾病控制和预防中心、美国人口普查局、美国社区调查中心、GitHub、凯撒家庭基金会、州政府委员会和全国州长协会收集了从2020年1月22日美国首例确诊病例到2020年8月8日新冠肺炎大流行前200天的数据。种族/民族构成是根据黑人或西班牙裔人口的百分比确定的；收入不平等，使用基尼指数；州长的政治、政治背景和性别，州长任期限制，以及2016年投票给共和党的县人口百分比；以及州医疗政策，根据《平价医疗法案》扩大医疗补助的参与。评估了另外六个协变量。在大流行的前200天内，美国各州的累积新冠肺炎发病率和死亡率。主要衡量指标包括黑人和西班牙裔人口构成的百分比、收入不平等以及一系列额外的协变量。这项研究涵盖了美国3142个县中的3141个。黑人人口平均为9.365%(范围0-86.593%)；西班牙裔人口平均为9.754%(范围为0.648%-96.353%)；平均基尼比为44.538(范围为25.670-66.470)；各州内实施医疗补助计划扩大的县比例为0.577(范围为0-1)；新冠肺炎确诊病例的平均每10万 人口为1093.882(范围为0-14 019.852)；每10万 人口中与新冠肺炎相关的死亡人数平均为26.173人(范围为0-413.858)。一个县的收入不平等程度每增加1.0个百分点，新冠肺炎发病率的调整风险比(RR)为1.020(95%CI，1.012-1.027)，新冠肺炎死亡率的调整风险比(RR)为1.030(95%CI，1.012-1.047)。不平等通过相互作用加剧了种族/族裔构成之间的联系，收入不平等越严重，发病率曲线RR的截距增加了1.041倍(95%CI，1.031-1.051)，死亡率曲线RR的截距增加了1.068倍(95%CI，1.042-1.094)，但它们的曲率略有降低，尤其是西班牙裔。在控制了州一级的特殊性后，没有任何州政治因素与新冠肺炎的发病率或死亡率相关。然而，在实施了医疗补助扩大的州的县，发病率RR将减少0.678(95%CI，0.501-0.918)。这一县级生态分析建议，新冠肺炎监测系统应该考虑县级收入不平等，以更好地了解新冠肺炎发病率和死亡率的社会模式。无论种族/族裔构成如何，收入高度不平等都可能损害人口健康。
Are racial/ethnic population composition and economic inequality associated with coronavirus disease 2019 (COVID-19) incidence and mortality? This cross-sectional ecological analysis of cumulative COVID-19 incidence and mortality rates for the first 200 days of the pandemic in 3141 US counties confirmed positive associations of incidence and mortality rates with racial/ethnic composition and with income inequality well as a joint association of incidence and mortality with both structural factors. This study suggests that COVID-19 surveillance systems should take into account county-level income inequality to better understand the social patterning of COVID-19. This cross-sectional ecological analysis quantifies the association of economic inequality, racial/ethnic composition, political factors, and state health care policy with the incidence and mortality burden associated with coronavirus disease 2019 (COVID-19) at the US county level. It is now established that across the United States, minoritized populations have borne a disproportionate burden from coronavirus disease 2019 (COVID-19). However, little is known about the interaction among a county’s racial/ethnic composition, its level of income inequality, political factors, and COVID-19 outcomes in the population. To quantify the association of economic inequality, racial/ethnic composition, political factors, and state health care policy with the incidence and mortality burden associated with COVID-19. This cross-sectional study used data from the 3142 counties in the 50 US states and for Washington, DC. Data on the first 200 days of the COVID-19 pandemic, from the first confirmed US case on January 22 to August 8, 2020, were gathered from the Centers for Disease Control and Prevention and USAFacts.org, the US Census Bureau, the American Community Survey, GitHub, the Kaiser Family Foundation, the Council of State Governments, and the National Governors Association. Racial/ethnic composition was determined as percentage of the population that is Black or Hispanic; income inequality, using the Gini index; politics, political affiliation and sex of the state governor, gubernatorial term limits, and percentage of the county’s population that voted Republican in 2016; and state health care policy, participation in the expansion of Medicaid under the Affordable Care Act. Six additional covariates were assessed. Cumulative COVID-19 incidence and mortality rates for US counties during the first 200 days of the pandemic. Main measures include percentage Black and Hispanic population composition, income inequality, and a set of additional covariates. This study included 3141 of 3142 US counties. The mean Black population was 9.365% (range, 0-86.593%); the mean Hispanic population was 9.754% (range, 0.648%-96.353%); the mean Gini ratio was 44.538 (range, 25.670-66.470); the proportion of counties within states that implemented Medicaid expansion was 0.577 (range, 0-1); the mean number of confirmed COVID-19 cases per 100 000 population was 1093.882 (range, 0-14 019.852); and the mean number of COVID-19–related deaths per 100 000 population was 26.173 (range, 0-413.858). A 1.0% increase in a county’s income inequality corresponded to an adjusted risk ratio (RR) of 1.020 (95% CI, 1.012-1.027) for COVID-19 incidence and adjusted RR of 1.030 (95% CI, 1.012-1.047) for COVID-19 mortality. Inequality compounded the association of racial/ethnic composition through interaction, with higher income inequality raising the intercepts of the incidence curve RR by a factor of 1.041 (95% CI, 1.031-1.051) and that of the mortality curve RR by a factor of 1.068 (95% CI, 1.042-1.094) but slightly lowering their curvatures, especially for Hispanic composition. When state-level specificities were controlled, none of the state political factors were associated with COVID-19 incidence or mortality. However, a county in a state with Medicaid expansion implemented would see the incidence rate RR decreased by a multiplicative factor of 0.678 (95% CI, 0.501-0.918). This county-level ecological analysis suggests that COVID-19 surveillance systems should account for county-level income inequality to better understand the social patterning of COVID-19 incidence and mortality. High levels of income inequality may harm population health irrespective of racial/ethnic composition.
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影响因子：
6.3
作者：
Lange-Maia, Brittney S.;De Maio, Fernando;Shah, Raj C.
通讯作者：
Shah, Raj C.
影响因子：
4.4
作者：
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通讯作者：
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影响因子：
5
作者：
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通讯作者：
Timpe, Brenden
影响因子：
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作者：
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通讯作者：
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影响因子：
13.7
作者：
BESLEY, T;CASE, A
通讯作者：
CASE, A