估算未知：在考虑缺失种族和族裔数据后，COVID-19 负担中的更大种族和族裔差异。
Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data.
机构信息
From the Department of Epidemiology, Rollins School of Public Health, Emory University.
Department of Global Health, Rollins School of Public Health, Emory University.
出版信息
Epidemiology. 2021 Mar 1;32(2):157-161. doi: 10.1097/EDE.0000000000001314.
Abstract
BACKGROUND
Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. However, the magnitude of the disparity is unclear because race/ethnicity information is often missing in surveillance data.
METHODS
We quantified the burden of SARS-CoV-2 notification, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias analysis for misclassification.
RESULTS
The ratio of the absolute racial/ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold for persons classified Black and 1.6-fold for those classified Hispanic, in reference to classified White persons.
CONCLUSIONS
These results highlight that complete case analyses may underestimate absolute disparities in notification rates. Complete reporting of race/ethnicity information is necessary for health equity. When data are missing, quantitative bias analysis methods may improve estimates of racial/ethnic disparities in the COVID-19 burden.
摘要
背景
由于持续存在的社会不平等，美国的黑人和西班牙裔以及原住民感染 SARS-CoV-2 病毒和 COVID-19 死亡的风险增加。然而，由于监测数据中经常缺少种族/民族信息，因此尚不清楚这种差异的程度。
方法
我们通过种族/民族合并填补和定量偏倚分析对分类错误进行校正，按种族/民族群体量化了一个城市县的 SARS-CoV-2 通知、住院和病死率负担。
结果
与完整病例分析相比，偏倚校正后通知率的绝对种族/民族差异的比值，黑人增加了 1.3 倍，西班牙裔增加了 1.6 倍，而白人则为参照。
结论
这些结果表明，完整病例分析可能低估了通知率方面的绝对差异。完整报告种族/民族信息对于健康公平至关重要。当数据缺失时，定量偏倚分析方法可以提高 COVID-19 负担中种族/民族差异的估计值。
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