Disparities in SARS-CoV-2 Testing in Massachusetts During the COVID-19 Pandemic

Early deficiencies in testing capacity have contributed to poor control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),1 particularly among minority (ie, Black and Latino/Latina) and socioeconomically vulnerable communities.2,3 Allocating testing resources to locations of greatest need is important to mitigate subsequent waves of coronavirus disease 2019 (COVID-19).4 In the context of improved SARS-CoV-2 testing infrastructure, we examine the alignment of testing to epidemic intensity in Massachusetts.


Introduction
Early deficiencies in testing capacity have contributed to poor control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 1 particularly among minority (ie, Black and Latino/Latina) and socioeconomically vulnerable communities. 2,3 Allocating testing resources to locations of greatest need is important to mitigate subsequent waves of coronavirus disease 2019 . 4 In the context of improved SARS-CoV-2 testing infrastructure, we examine the alignment of testing to epidemic intensity in Massachusetts. We defined testing intensity as the number of SARS-CoV-2 tests performed weekly per 100 000 population and epidemic intensity as weekly test positivity. We considered optimal alignment of testing resources to be matching community ranks of testing and positivity. In communities with a testing gap (ie, the testing rank was lower than the positivity rank) in a given week, the gap was calculated as additional testing required to achieve matching ranks. For example, the testing gap for a community with the third highest positivity is the difference between its testing rate and that of the community with the third highest testing intensity.

Methods
Responses from the American Community Survey (2014-2018) 5 were aggregated to characterize communities. Negative binomial models using robust sandwich estimators to account for repeated measures at the community level were fit to assess associations of the magnitude of the weekly testing gap with time (linearly by week), selected Centers for Disease Control and Prevention Social Vulnerability Index 6 domains (eg, Socioeconomic Status and Minority Status/Language), and large university student population (>10% of residents). Owing to collinearity, the model of Boston neighborhoods only assessed associations with time and socioeconomic vulnerability. Two-sided Wald tests were used to assess significance at a threshold of P < .05. Data analysis was performed using R statistical software version 3.6.1 (R Project for Statistical Computing).

Results
During the observation period, 4  tests per 100 000) varied considerably between communities, with observed increased testing in Community socioeconomic vulnerability (A and B) was estimated by the percentile from the Socioeconomic Status domain of the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). Testing intensity (C and D) included total tests (including repeat tests in same individual) for Massachusetts but tested individuals (not including repeat testing) for Boston neighborhoods. The weekly testing gap (E and F) was calculated as the mean gap during the observation period. Blue squares indicate communities with large university student populations (>10% of residents). Data were broken into 3 categories for illustrative purposes, but statistical models considered the gap as continuous and socioeconomic vulnerability as quartiles of the US population. less socioeconomically vulnerable localities, vacation regions, and areas near universities.
Considerable overlap was observed between communities with the highest socioeconomic vulnerability and those with the largest testing gaps (Figure 1). In a multivariable model of statewide testing, the relative testing gap increased by 9.0% per week (adjusted rate ratio [aRR], 1.09; 95% CI, 1.08-1.10; P < .001) (Figure 2). Increasing levels of socioeconomic vulnerability were associated with increased testing gaps (aRR, 1.35 per quartile; 95% CI, 1.23-1.49; P < .001). Communities with the highest quartile of minority status or language vulnerability had larger testing gaps after accounting for socioeconomic status, but the difference was not significant (aRR, 1.46; 95% CI, 0.96-2.23; P = .08). The presence of a large university student population was associated with decreased testing gaps (aRR, 0.21; 95% CI, 0.12-0.38; P < .001).

Discussion
These analyses indicate that, despite programs to promote equity and enhance epidemic control in socioeconomically vulnerable communities, testing resources across Massachusetts have been