Labgold Katie, Hamid Sarah, Shah Sarita, Gandhi Neel R, Chamberlain Allison, Khan Fazle, Khan Shamimul, Smith Sasha, Williams Steve, Lash Timothy L, Collin Lindsay J
Department of Epidemiology, Rollins School of Public Health, Emory University.
Department of Global Health, Rollins School of Public Health, Emory University.
medRxiv. 2020 Oct 2:2020.09.30.20203315. doi: 10.1101/2020.09.30.20203315.
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. The magnitude of the disparity is unclear, however, because race/ethnicity information is often missing in surveillance data. In this study, we quantified the burden of SARS-CoV-2 infection, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias-adjustment for misclassification. After bias-adjustment, the magnitude of the absolute racial/ethnic disparity, measured as the difference in infection rates between classified Black and Hispanic persons compared to classified White persons, increased 1.3-fold and 1.6-fold respectively. These results highlight that complete case analyses may underestimate absolute disparities in infection rates. Collecting race/ethnicity information at time of testing is optimal. However, when data are missing, combined imputation and bias-adjustment improves estimates of the racial/ethnic disparities in the COVID-19 burden.
由于长期存在的社会不平等现象，美国的黑人、西班牙裔和原住民感染新冠病毒以及因新冠疫情死亡的风险更高。然而，由于监测数据中常常缺少种族/族裔信息，这种差异的程度尚不清楚。在本研究中，我们通过种族/族裔组合插补和误分类的定量偏差调整，按种族/族裔群体对一个城市县的新冠病毒感染、住院和病死率负担进行了量化。经过偏差调整后，以分类的黑人和西班牙裔人群与分类的白人人群之间的感染率差异衡量的绝对种族/族裔差异幅度分别增加了1.3倍和1.6倍。这些结果表明，完整病例分析可能会低估感染率方面的绝对差异。在检测时收集种族/族裔信息是最佳做法。然而，当数据缺失时，组合插补和偏差调整可改善对新冠疫情负担中种族/族裔差异的估计。
