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使用 NHANES 进行的 COVID-19 死亡率的社会决定因素模拟研究。

Social determinants of mortality from COVID-19: A simulation study using NHANES.

机构信息

New England Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, Massachusetts, United States of America.

Harvard Medical School, Boston, Massachusetts, United States of America.

出版信息

PLoS Med. 2021 Jan 11;18(1):e1003490. doi: 10.1371/journal.pmed.1003490. eCollection 2021 Jan.

Abstract

BACKGROUND

The COVID-19 epidemic in the United States is widespread, with more than 200,000 deaths reported as of September 23, 2020. While ecological studies show higher burdens of COVID-19 mortality in areas with higher rates of poverty, little is known about social determinants of COVID-19 mortality at the individual level.

METHODS AND FINDINGS

We estimated the proportions of COVID-19 deaths by age, sex, race/ethnicity, and comorbid conditions using their reported univariate proportions among COVID-19 deaths and correlations among these variables in the general population from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used these proportions to randomly sample individuals from NHANES. We analyzed the distributions of COVID-19 deaths by race/ethnicity, income, education level, and veteran status. We analyzed the association of these characteristics with mortality by logistic regression. Summary demographics of deaths include mean age 71.6 years, 45.9% female, and 45.1% non-Hispanic white. We found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity (54.8% of deaths, 95% CI 49.0%-59.6%, p < 0.001), individuals with income below the median (67.5%, 95% CI 63.4%-71.5%, p < 0.001), individuals with less than a high school level of education (25.6%, 95% CI 23.4% -27.9%, p < 0.001), and veterans (19.5%, 95% CI 15.8%-23.4%, p < 0.001). Except for veteran status, these characteristics are significantly associated with COVID-19 mortality in multiple logistic regression. Limitations include the lack of institutionalized people in the sample (e.g., nursing home residents and incarcerated persons), the need to use comorbidity data collected from outside the US, and the assumption of the same correlations among variables for the noninstitutionalized population and COVID-19 decedents.

CONCLUSIONS

Substantial inequalities in COVID-19 mortality are likely, with disproportionate burdens falling on those who are of racial/ethnic minorities, are poor, have less education, and are veterans. Healthcare systems must ensure adequate access to these groups. Public health measures should specifically reach these groups, and data on social determinants should be systematically collected from people with COVID-19.

摘要

背景

截至 2020 年 9 月 23 日,美国新冠肺炎疫情广泛蔓延,报告死亡病例超过 20 万例。尽管生态研究表明,贫困率较高的地区 COVID-19 死亡率更高,但关于个体层面 COVID-19 死亡率的社会决定因素知之甚少。

方法和发现

我们使用 2017-2018 年全国健康和营养检查调查(NHANES)中 COVID-19 死亡病例的报告单变量比例及其在普通人群中这些变量之间的相关性,估计了年龄、性别、种族/族裔和合并症的 COVID-19 死亡比例。我们使用这些比例从 NHANES 中随机抽样个体。我们分析了种族/族裔、收入、教育水平和退伍军人身份的 COVID-19 死亡分布。我们分析了这些特征与死亡率的关系,采用逻辑回归进行分析。死亡的综合人口统计学特征包括平均年龄 71.6 岁,女性占 45.9%,非西班牙裔白人占 45.1%。我们发现,非白种人(死亡的 54.8%,95%CI 49.0%-59.6%,p<0.001)、收入低于中位数(67.5%,95%CI 63.4%-71.5%,p<0.001)、受教育程度低于高中学历(25.6%,95%CI 23.4%-27.9%,p<0.001)和退伍军人(19.5%,95%CI 15.8%-23.4%,p<0.001)的个体中不成比例地出现死亡。除了退伍军人身份外,这些特征在多变量逻辑回归中与 COVID-19 死亡率显著相关。局限性包括样本中没有机构化人群(例如,养老院居民和被监禁人员),需要使用美国以外收集的合并症数据,以及假设非机构化人群和 COVID-19 死者之间的变量具有相同的相关性。

结论

COVID-19 死亡率存在显著的不平等现象,少数族裔、贫困人口、受教育程度较低和退伍军人的负担不成比例。医疗保健系统必须确保这些人群能够获得足够的医疗服务。公共卫生措施应特别针对这些群体,并从患有 COVID-19 的人群中系统地收集社会决定因素数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf65/7799807/1c3e369eeb95/pmed.1003490.g001.jpg

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