Am J Epidemiol. 2021 Aug 1;190(8):1447-1451. doi: 10.1093/aje/kwab058.
In their commentary, Zalla et al. (Am J Epidemiol. 2021;190(8):1439-1446) argue that the approach taken by the Centers for Disease Control and Prevention, comparing the proportion of coronavirus disease 2019 (COVID-19) deaths by race/ethnicity with a weighted population distribution, ignores how systemic racism structures the composition of places. While the Centers for Disease Control and Prevention have abandoned their measure, they did so because of the changing geographic distribution of COVID-19, not because the measure underestimates racial disparities. We further Zalla et al.'s argument, advocating for a relational approach to estimating COVID-19 racial inequities that integrates the reciprocal relationship between context and composition through the interaction of places and people over time. To support our argument, we present a series of figures exploring the heterogeneous relationships between places, people, and time, using publicly available, US county-level COVID-19 mortality data from February to December 2020 from Johns Hopkins University. Longitudinal and more geographically granular data that allows for disaggregation by person, place, and time will improve our estimation and understanding of inequities in COVID-19.
在评论中,Zalla 等人(Am J Epidemiol. 2021;190(8):1439-1446)认为,疾病预防控制中心采取的方法,即将冠状病毒病 2019(COVID-19)死亡比例按种族/族裔与加权人口分布进行比较,忽略了系统性种族主义如何构建地方构成。虽然疾病预防控制中心已经放弃了他们的措施,但这是因为 COVID-19 的地理分布不断变化,而不是因为该措施低估了种族差异。我们进一步推进了 Zalla 等人的观点,主张采用一种关系方法来估计 COVID-19 的种族不平等,该方法通过随着时间的推移,通过地方和人员的相互作用,将背景和构成之间的相互关系整合在一起。为了支持我们的观点,我们使用约翰霍普金斯大学提供的 2020 年 2 月至 12 月的美国县级 COVID-19 死亡率的公开、可用数据,展示了一系列探索地方、人员和时间之间异质关系的图表。纵向和更具地理粒度的数据,允许按人员、地点和时间进行细分,将提高我们对 COVID-19 中不平等现象的估计和理解。