Skip Navigation

Publication Detail

Title: Geographic Associations Between Social Factors and SARS-CoV-2 Testing Early in the COVID-19 Pandemic, February-June 2020, Massachusetts.

Authors: Troppy, Scott; Wilt, Grete E; Whiteman, Ari; Hallisey, Elaine; Crockett, Molly; Sharpe, J Danielle; Haney, Gillian; Cranston, Kevin; Klevens, R Monina

Published In Public Health Rep, (2021 Nov-Dec)

Abstract: OBJECTIVES: Widespread SARS-CoV-2 testing is critical to identify infected people and implement public health action to interrupt transmission. With SARS-CoV-2 testing supplies and laboratory capacity now widely available in the United States, understanding the spatial heterogeneity of associations between social determinants and the use of SARS-CoV-2 testing is essential to improve testing availability in populations disproportionately affected by SARS-CoV-2. METHODS: We assessed positive and negative results of SARS-CoV-2 molecular tests conducted from February 1 through June 17, 2020, from the Massachusetts Virtual Epidemiologic Network, an integrated web-based surveillance and case management system in Massachusetts. Using geographically weighted regression and Moran's I spatial autocorrelation tests, we quantified the associations between SARS-CoV-2 testing rates and 11 metrics of the Social Vulnerability Index in all 351 towns in Massachusetts. RESULTS: Median SARS-CoV-2 testing rates decreased with increasing percentages of residents with limited English proficiency (median relative risk [interquartile range] = 0.96 [0.95-0.99]), residents aged ≥65 (0.97 [0.87-0.98]), residents without health insurance (0.96 [0.95-1.04], and people residing in crowded housing conditions (0.89 [0.80-0.94]). These associations differed spatially across Massachusetts, and localized models improved the explainable variation in SARS-CoV-2 testing rates by 8% to 12%. CONCLUSION: Indicators of social vulnerability are associated with variations in SARS-CoV-2 testing rates. Accounting for the spatial heterogeneity in these associations may improve the ability to explain and address the SARS-CoV-2 pandemic at substate levels.

PubMed ID: 34388054 Exiting the NIEHS site

MeSH Terms: Age Factors; COVID-19 Testing; COVID-19/diagnosis*; COVID-19/epidemiology*; Housing; Humans; Language; Massachusetts/epidemiology; Pandemics; Public Health; SARS-CoV-2; Socioeconomic Factors; Spatial Analysis; Vulnerable Populations/statistics & numerical data*

Back
to Top