Understanding Drivers of Coronavirus Disease 2019 (COVID-19) Racial Disparities: A Population-Level Analysis of COVID-19 Testing Among Black and White Populations

Abstract

Background Disparities in COVID-19 testing—the pandemic’s most critical but limited resource—may be an important but modifiable driver of COVID-19 inequities. Methods We analyzed data from the Missouri State Department Health and Senior Services on all COVID-19 tests conducted in the St. Louis and Kansas City regions. We adapted a well-established tool for measuring inequity—the Lorenz curve—to compare COVID-19 testing rates per diagnosed case among Black and White populations. Results Between 3/14/2020 and 9/15/2020, 606,725 and 328,204 COVID-19 tests were conducted in the St. Louis and Kansas City regions, respectively. Over time, Black individuals consistently had approximately half the rate of testing per case compared to White individuals. In the early period (3/14/2020 to 6/15/2020), zip codes in the lowest quartile of testing rates accounted for only 12.1% and 8.8% of all tests in the St. Louis and Kansas City regions, respectively, even though they accounted for 25% of all cases each region. These zip codes had higher proportions of residents who were Black, without insurance, and with lower median incomes. These disparities were reduced but still persisted during later phases of the pandemic (6/16/2020 to 9/15/2020). Lastly, even within the same zip code, Black residents had lower rates of tests per case compared to White residents. Conclusions Black populations had consistently lower COVID-19 testing rates per diagnosed case compared to White populations in two Missouri regions. Public health strategies should proactively focus on addressing equity gaps in COVID-19 testing to improve equity of the overall response.

SARS-CoV-2 active infection prevalence and seroprevalence in the adult population of St. Louis County

“…In particular, in the first year of the pandemic clinical manifestations of disease revealed a remarkable spectrum of clinical severity (as many as 50% of infected people are asymptomatic, and 90% are not severely ill) [3] , [4] , [5] , routine public health surveillance based on diagnosed cases in routine care vastly underestimate both the prevalence of active infection at any given time, and the cumulative incidence over time. The limited supply of tests (early in the pandemic) [6] , [7] , [8] , [9] and the variation in limitations by geography [10] , time, and racial and socio-economic groups [11] , [12] , [13] all make it inappropriate to use any single “correction” factor. Without an estimate of true incidence and prevalence of infection, however, the actual burden of disease, and differences in burden by geography, racial groups, and socio-economic status remain incompletely known.…”

Large-scale infectious disease testing programs have little consideration for equity: findings from a scoping review

“…It is largely recognized that targeted actions towards specific communities or groups imply labelling, potentially leading to stigma, and increases the risk of missing numerous infections in particularly vulnerable population groups (11,12,35,36). The current pandemic is exacerbating health inequities and screening programs need to be designed accordingly to address these inequities, which are also central to mitigating disease spread (37)(38)(39). An example of an approach that could be used in combination with existing screening infrastructure to increase equitable access to COVID-19 screening consists of the deployment of rapid antigen testing kits for in-home testing for those with faced with mobility or geographic restrictions or work and/or family obligations that do not easily coincide with testing program schedules (3).…”

“…Most included articles were prevalence, descriptive, or cross-sectional studies (n= 69, 80%) (1-69*), seven were cohort studies (8%) (70-76*), the remaining studies included a modified stepped wedge (77*), a case report (78*), two commentaries (79,80*), an editorial (81*), a narrative review (82*), a protocol (83*), a retrospective single center report (84*), an RCT (85*), and one prospective clustered randomized trial (86*) (Table 1). The most common disease was COVID-19 (n= 69, 80%) (1,3,(6)(7)(8)(9)(10)(11)(13)(14)(15)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)28,29,(32)(33)(34)(35)(36)(37)(39)(40)(41)(42)(43)(44)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(57)(58)(59)…”

Closing the health equity gap: A role for implementation science?

“…Disproportionate burdens of poor health outcomes on vulnerable communities in the wake of the COVID-19 pandemic have exacerbated health inequities. In the US alone, racial and socioeconomic inequities, established drivers of health inequity, have a demonstrable link to disparate COVID-19–related health outcomes, from testing rates to mortality [ 6 ]. The COVID-19 pandemic has laid bare the cost of health inequities—a cost that is heavily borne by the most vulnerable.…”