Department of Epidemiology and Biostatistics, School of Medicine, University of California, 550 16th St 2nd floor, San Francisco, CA 94158, USA.
Department of Psychiatry, School of Medicine, University of California, 1001 Potrero Ave, San Francisco, CA 94110, USA.
Int J Environ Res Public Health. 2020 Nov 23;17(22):8695. doi: 10.3390/ijerph17228695.
Mounting evidence reveals considerable racial inequities in coronavirus disease 2019 (COVID-19) outcomes in the United States (US). Area-level racial bias has been associated with multiple adverse health outcomes, but its association with COVID-19 is yet unexplored. Combining county-level data from Project Implicit on implicit and explicit anti-Black bias among non-Hispanic Whites, Johns Hopkins Coronavirus Resource Center, and , we used adjusted linear regressions to estimate overall COVID-19 incidence and mortality rates through 01 July 2020, Black and White incidence rates through 28 May 2020, and Black-White incidence rate gaps on average area-level implicit and explicit racial bias. Across 2994 counties, the average COVID-19 mortality rate (standard deviation) was 1.7/10,000 people (3.3) and average cumulative COVID-19 incidence rate was 52.1/10,000 (77.2). Higher racial bias was associated with higher overall mortality rates (per 1 standard deviation higher implicit bias b = 0.65/10,000 (95% confidence interval: 0.39, 0.91); explicit bias b = 0.49/10,000 (0.27, 0.70)) and higher overall incidence (implicit bias b = 8.42/10,000 (4.64, 12.20); explicit bias b = 8.83/10,000 (5.32, 12.35)). In 957 counties with race-specific data, higher racial bias predicted higher White and Black incidence rates, and larger Black-White incidence rate gaps. Anti-Black bias among Whites predicts worse COVID-19 outcomes and greater inequities. Area-level interventions may ameliorate health inequities.
越来越多的证据表明，美国（US）在 2019 年冠状病毒病（COVID-19）的结果方面存在相当大的种族不平等。区域层面的种族偏见与多种不良健康结果有关，但与 COVID-19 的关联尚未得到探索。我们结合了 Project Implicit 关于非西班牙裔白人的隐性和显性反黑偏见的县级数据、约翰霍普金斯冠状病毒资源中心（Johns Hopkins Coronavirus Resource Center）和 的数据，使用调整后的线性回归来估计截至 2020 年 7 月 1 日的 COVID-19 总发病率和死亡率、截至 2020 年 5 月 28 日的黑人和白人发病率以及平均区域水平的隐性和显性种族偏见的黑白发病率差距。在 2994 个县中，COVID-19 死亡率（标准差）平均为每 10000 人 1.7（3.3），累计 COVID-19 发病率平均为每 10000 人 52.1（77.2）。更高的种族偏见与更高的总死亡率（每 1 个标准差的隐性偏见 b = 0.65/10000（95%置信区间：0.39，0.91）；显性偏见 b = 0.49/10000（0.27，0.70））和更高的总发病率（隐性偏见 b = 8.42/10000（4.64，12.20）；显性偏见 b = 8.83/10000（5.32，12.35））相关。在有特定种族数据的 957 个县中，更高的种族偏见预示着更高的白人和黑人发病率以及更大的黑白发病率差距。白人的反黑偏见预示着更糟糕的 COVID-19 结果和更大的不平等。区域层面的干预措施可能会减轻健康不平等。
