Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data.
Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data.
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DOI：
10.1097/ede.0000000000001314
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发表时间：
2021-03-01
期刊：
影响因子：
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通讯作者：
Collin LJ
中科院分区：
文献类型：
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作者：
Labgold K;Hamid S;Shah S;Gandhi NR;Chamberlain A;Khan F;Khan S;Smith S;Williams S;Lash TL;Collin LJ
由于持续存在的社会不平等，美国黑人、西班牙裔和原住民感染 SARS-CoV-2 和死于 COVID-19 的风险增加。然而，差异的严重程度尚不清楚，因为监测数据中经常缺少种族/民族信息。我们采用种族/民族综合插补和错误分类定量偏差分析，按种族/族裔群体量化了城市县的 SARS-CoV-2 通报负担、住院率和病死率。与完整案例分析相比，偏倚调整后通知率中的绝对种族/族裔差异比率，分类黑人和西班牙裔人相对于分类白人分别增加了 1.3 倍和 1.6 倍。这些结果强调，完整的案例分析可能会低估通知率的绝对差异。完整报告种族/民族信息对于健康公平而言是必要的。当数据缺失时，定量偏差分析方法可能会改善对 COVID-19 负担中种族/民族差异的估计。
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. Yet the magnitude of the disparity is unclear because race/ethnicity information is often missing in surveillance data. 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. The ratio of the absolute racial/ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold and 1.6-fold for classified Black and Hispanic persons in reference to classified White persons, respectively. 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.