key: cord-0757386-2xs8rfz8
authors: Mody, Aaloke; Pfeifauf, Kristin; Bradley, Cory; Fox, Branson; Hlatshwayo, Matifadza G; Ross, Will; Sanders-Thompson, Vetta; Joynt, Karen; Reidhead, Mat; Schootman, Mario; Powderly, William G; Geng, Elvin H
title: Understanding Drivers of COVID-19 Racial Disparities: A Population-Level Analysis of COVID-19 Testing among Black and White Populations
date: 2020-12-14
journal: Clin Infect Dis
DOI: 10.1093/cid/ciaa1848
sha: ae14a765b1e4b55c8b8a46dbaf25453d285a9472
doc_id: 757386
cord_uid: 2xs8rfz8

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.

The impact of the coronavirus disease 2019 (COVID-19) pandemic has mirrored existing racial health disparities in the U.S. [1] [2] [3] [4] and, even if not entirely unsurprising, demands additional explanation and urgent remedy. Over the last six months, epidemiologic studies have repeatedly shown greater burden of COVID-19 cases, hospitalizations, and mortality in minority communities [2] [3] [4] [5] [6] [7] [8] . This disparate impact likely results, in part, from social and economic inequities deeply embedded in American society. For example, overrepresentation of minorities in lower-wage service and essential occupations means greater exposure risks and less access to protective measures (e.g., no guaranteed sick leave) for many Black individuals. However, better understanding of the contribution of health systems behavior to COVID-19 disparities [4] [5] [6] [7] [8] [9] [10] can reveal immediately modifiable mechanisms and redirect ongoing public health efforts.

COVID-19 testing, in particular, is one of the most essential components of an effective COVID-19 public health response and represents an important potential mechanism for disparities [1, 2] . Adequate testing is essential for epidemic control as it facilitates early case detection, self-isolation, and prevention of onward transmission [11] [12] [13] [14] [15] [16] .

Furthermore, it enables accurate recognition of disease burden in communities, thereby contributing to appropriate responses from both the public health system and individuals (e.g., mask wearing and social distancing) [17] . Though inequitable COVID-19 testing in already marginalized populations can magnify their risk for poor outcomes, few studies have examined the extent of disparities in COVID-19 testing [11-14, 18, 19] relative to the burden of disease.

We seek to deepen our understanding of health disparities in COVID-19 testing by examining testing equities explicitly in relation to disease burden over time and geography in the St. Louis and Kansas City regions in Missouri. We use, in part, a tool from economicsthe Lorenz curve-which is commonly used to visualize and quantify wealth and incomebased inequality in a population [20] . This novel application of an established methodology A c c e p t e d M a n u s c r i p t 4 will enable quantification of the underlying inequities in COVID-19 testing to directly inform health policy solutions [20] .

We sought to assess disparities in COVID- 19 

Our analyses are based on the premise that an equitable testing strategy is defined by a relative balance between the number of tests done and the overall disease burden in a community, rather than simply an equal number of tests done per person (i.e., equal testing).

Decreased testing rates relative to the number of cases identified generally indicates that testing is only occurring among symptomatic patients with a higher probability of disease. In contrast, higher test rates per case indicates testing is sufficiently widespread to be effective at also capturing asymptomatic and mild cases of COVID-19, which are a major driver of the pandemic [11] [12] [13] [14] [15] 21] . The WHO suggests that adequate testing levels are indicated by at least 10, and ideally 30, tests for every diagnosed case [11] [12] [13] [14] [15] . Based on these principles, we sought to assess disparities in COVID-19 testing and disease burden in several ways.

A c c e p t e d M a n u s c r i p t 5   First, we estimated new COVID-19 tests and cases per day and the rate of COVID-19 testing per diagnosed case among Black and White individuals over time. Second, we generated modified versions of Lorenz curves to assess the relative equity in the distribution of COVID-19 testing and disease burden across zip codes. Lorenz curves-originally developed by economists to graphically represent income equality-have more recently been leveraged as a tool for public health [20, 22, 23] . A Lorenz curve is generated by plotting the cumulative proportion of the total population against the cumulative proportion of a resource or burden of disease after sorting values in ascending order. If the resource or burden is equitably distributed across the population, the curve will follow a straight line at a 45-degree angle. The curve becomes more convex with increasing inequity.

We adapted this method to examine disparities in: 1) the number of COVID-19 tests performed relative to the burden of diagnosed COVID-19 cases, and 2) the gap between existing and adequate testing levels, which we define as the number of additional negative tests needed to achieve 20 tests per diagnosed case (based on WHO guidance) [14, 24, 25] .

We used zip codes as the unit of analysis and generated Lorenz curves for both the early (March 14 to June 15) and later (June 15 to September 15) phases of the pandemic. We also calculated Gini coefficients-a measure of equality/inequality between 0 and 1, with 0 indicating perfect equality and 1 indicating perfect inequality-and Hoover indices-a metric that indicates what percent of the resource would need to be reallocated in order to achieve an equitable distribution [26] . Lastly, we grouped zip codes into quartiles based on their position on Lorenz curves and assessed differences in zip code-level sociodemographic and socioeconomic characteristics using Kruskal-Wallis tests.

Third, we generated bubble plots to compare rates of COVID-19 testing for Black versus White residents living in the same zip code. For this analysis, we only considered zip codes whose populations were at least 1% Black and 1% White to avoid identifying extreme outliers from small denominators.

A c c e p t e d M a n u s c r i p t 6 Lastly, we performed univariate and multivariable mixed-effects Poisson regression to identify individual (e.g., race, age) and zip-code level (e.g., racial makeup, health insurance coverage) factors independently associated with having a positive COVID-19 test.

Zip code was included as random effect. We also assessed for an interaction between race and age, stratifying by time period. The effect of race and racism on health outcomes is mediated by (as opposed to confounded by) ecological structural factors such socioeconomic status; thus, unadjusted analyses assess the overall association with race and racism while adjusted analyses can be thought to assess the contribution of systemic racism that still remains even when adjusting away the mediating effects of the measured ecological factors [1, 2, 27] .

To account for missingness in race, patient zip code, and age variables, we performed multiple imputation using multivariate normal imputation methods (n=50 imputations) and adaptive rounding of categorical variables [28] [29] [30] . Missingness was highly dependent on the test date, test result, and performing lab, and thus the missing at random assumption (i.e., that missingness was random conditional on all the variables included in the imputation model [test date, test result, performing lab, race, zip code, age, and mortality]) required for unbiased imputation was very plausible in our setting [28] [29] [30] .

All analyses were conducted using Stata MP 16.1 and R 3.2.4. P-values less than or equal to 0.05 were considered statistically significant. A c c e p t e d M a n u s c r i p t 7

In both regions, the number of COVID-19 tests per diagnosed case steadily increased until mid-June, but began to decline after a new surge in cases beginning in mid-July ( Figure 1 ). The rate of tests per case in the Black population consistently remained about half that of the White population until August. Even though the overall number of tests expanded steadily over time, it increased more rapidly among the White as opposed to the Black population ( Figure 1 ). 

Black individuals also had consistently lower rates of COVID-19 testing per case compared to White individuals residing in the same zip codes (Figure 4 ). This pattern was largely irrespective of the overall racial makeup of a zip code (i.e., whether the zip codes were predominately White or Black). Only 13 of 173 zip codes had a testing rate of greater than 20 tests per case among Black residents, but 30 zip codes had this rate among its White residents.

In multivariable mixed-effects Poisson regression, Black race was one of the strongest factors associated with testing positive for COVID-19 (aRR 1.60 [95% CI 1.52- consistently associated with lower rates of testing per case across age strata and time periods, but were lowest for older Black individuals in the earlier phases of the pandemic (p<0.001 for interaction for both periods) ( Figure 5 ). A key premise of our analysis is that an equitable testing strategy is essential for a successful COVID-19 response and requires that testing be scaled up in proportion to the disease burden in an area, which is also in line with current WHO guidance [14, 24, 25] .

Increases in the overall disease burden also affect this metric, but public health programs failing to adapt testing to meet this threshold will still run the risk of identifying only the most severe and symptomatic cases in a community while systematically missing more mild and asymptomatic cases. This ultimately has immense implications for disease control as transmission from asymptomatic individuals is a major driver of the pandemic [11] [12] [13] [14] [15] 21] .

We find that, though the burden of COVID-19 disease has disproportionately affected Black communities more, rates of COVID-19 testing have also not been correspondingly scaled up relative to this increased disease burden. This finding remained consistent over time, across regions, and even within geographical areas.

First, despite overall expansion of testing, rates of COVID-19 tests per case among Black individuals consistently remained half that of White individuals for most of the pandemic, a finding that has also been demonstrated in other regions of the country [13, 18, 19] . Moreover, overall testing numbers actually increased more rapidly among White compared to Black individuals, with testing in Black populations always being far from the target necessary for optimizing infection control. Though disparities were reduced in later phases of the pandemic, this was driven more by increased case counts among the White population rather than any increased testing in the Black population. Second, using modified Lorenz curves, the majority of zip codes with a higher proportion of Black residents and lower health insurance coverage also had the lowest rates of testing per case and higher gaps between existing and adequate levels of testing as opposed to the zip codes with higher rates of testing, which were overwhelmingly White. Third, Black residents were more likely to have lower rates of tests per case even compared to White residents within the same zip code and this was irrespective of the overall racial make-up of that zip code-level.

A c c e p t e d M a n u s c r i p t 10 mediation of ecologic zip code-level characteristics, being Black was associated with a higher risk of testing positive for COVID-19 and thus having lower rates of tests per case.

The lowest testing rates occurring in the most at-risk group: older Black individuals in the early pandemic phases when there was less knowledge about transmission prevention, limited access to testing, and no evidence-based treatments. Thus, our analyses demonstrate a pattern of COVID-19 testing disparities that, though changing, was pervasive regardless of time or geography and reflects aspects of both structurally-and individuallymediated racism [1] . Ultimately, these disparities may also be an important driver of the disparities in actual disease burden, a point of national concern.

The underlying etiologies for these consistent disparities in COVID-19 testing are likely several-fold, but, ultimately, are all manifestations of structural racism in our healthcare system and current society [1] [2] [3] [4] . It is thus to be expected that these existing structural disparities in health care have permeated into the COVID-19 response as well [11, 12] and have only been exacerbated through mechanisms such as access to testing sites or funding allocation during the pandemic [31] [32] [33] [34] [35] . For example, North St. Louis, a predominately Black community that was one of the hardest hit in Missouri, did not have a single testing site several weeks into the pandemic [35] . It is also important to acknowledge that years of experience with structural racism in a historically discriminatory healthcare system has also garnered a significant yet appropriate level of mistrust of the healthcare system, which may lead those in Black communities to have a higher threshold for seeking out testing [4] . These potential drivers of testing disparities are layered onto the inequities that have led to an increased burden of disease in Black communities, which includes higher proportions of essential workers, less paid sick leave, lower ability to work from home, and living in more crowded settings and multigenerational households [4] [5] [6] 10] .

Addressing inequities in testing is an immediately actionable target in the short-term, but will likely require implementing proactive public health responses that move beyond the existing healthcare infrastructure to increase testing access. Our analyses show that, to A c c e p t e d M a n u s c r i p t 11 date, there has been limited evidence of any adaptive or targeted strategies to increase testing in areas with a higher burden of disease. Going forward, however, it is essential for public health officials to consider more deliberate and targeted strategies. Targeted community-based testing campaigns in venues such as community centers, high density residential spaces such as public housing, places of worship, or transportation hubs could improve access to testing, particularly in communities that have suffered neglect by existing public health infrastructures to date [11, 12] . Saliva-based COVID-19 tests, which can easily be administered on a large scale, can make community-based testing campaigns significantly more feasible [36, 37] . Community-based approaches will also be essential for ensuring equitable access to COVID-19 vaccines once they are available. In designing these efforts, it is essential that public health officials actively engage the individual communities themselves in developing plans that take into account the layered levels of trauma that exist in these communities [3, 4] . Focusing on implementing more equitable testing strategies should be an immediate priority, but, ultimately, it must also be anchored by a long-term commitment to actively dismantle the underlying structural racism that gives rise to such health disparities.

This analysis also strongly compels routine monitoring using formal metrics to quantitatively track the equity of their distribution to inform adaptive testing strategies. Such metrics have been lacking, but are a powerful tool because they can be used to identify and prioritize communities in most need, track improvements or worsening over time (particularly in response to interventions), compare different regions, and, ultimately, provide a measure of accountability for healthcare systems' commitments to protecting health equity. Modified uses of Lorenz curves provide a straightforward method to do so. For example, tracking Gini coefficients and Hoover indices over time suggest that testing disparities have been easing in both the St. Louis and Kansas City regions, but that this has more likely been driven by changes in disease patterns rather than strategic changes to testing efforts. They thus present a novel option for assessing inequities not only in testing, but also for disease A c c e p t e d M a n u s c r i p t 12 burden, ability to socially distance, and allocation of COVID-19 vaccines once they become available [4, 38, 39] . Ultimately, they can help develop roadmaps for building a more equitable COVID-19 response by identifying to whom, where, and how particular resources need to be targeted.

There are several limitations to our analysis. First, state reporting of COVID-19 tests was mandatory, but not all variables were reported consistently, race and, to a lesser extent, zip code in particular. Still, as this missingness was highly dependent on the test date, test result, and performing lab, multiple imputation would still yield unbiased results even with higher levels of missingness [28] [29] [30] . Second, we lacked data on hospitalizations from both regions, which may be a better reflection of regional disease burden since it is less affected by limitations in COVID-19 testing. Still, we did identify significant inequities in testing that we would expect to only be further amplified given that diagnosed cases likely underestimates the true number of infections. Third, we had insufficient data to parse between potential drivers of these disparities, such as physical access, insurance coverage, test-seeking behavior, and differences in symptomatic versus asymptomatic testing. Lastly, the premise of our analyses is that equitable and adequate are defined in relation to the burden of disease in area, but we acknowledge that this metric is also affected by the disease burden.

Still, our approach is in line with WHO guidance [14, 24, 25] , and we do believe that our framing of equitable testing yields essential information for understanding how to optimize testing strategies going forward. M a n u s c r i p t 21 Tests per Diagnosed Case. This figure represents age-stratified estimates of COVID-19 testing per case in models adjusted for differences in zip code-level characteristics. Estimates based on mixed-effects Poisson regression stratified by time period assessing an interaction between race and age and adjusted for long-term care residency status, zip codelevel characteristics, and region. For both periods, the p-value for the interaction between race and age strata was <0.001.

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Statistics and Research: Coronavirus (COVID-19) Testing. Available at

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Willingness to Seek Diagnostic Testing for SARS-CoV-2 With Home, Drive-through, and Clinic-Based Specimen Collection Locations

1%) 59977 (24.5%) 29718 (20.6%) --24412 (27.8%) 65283 (21.7%) White

Positive Cases, n (%) 73562 (7.9%) 47896 (7.9%) 25666 (7.8%) 15916 (17.7%) 29841 (10.9%)

Time Period, n (%)

5%) 474167 (78.2%) 250706 (76.4%) 65283 (72.8%) 215104 (78.8%) --Footnote: *Overall Missing values: Age Category: 2,859

A c c e p t e d M a n u s c r i p t 15 A c c e p t e d M a n u s c r i p t 16 A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t 18 Table 1 

A c c e p t e d M a n u s c r i p t