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A SARS-COV-2 prevalence study in fifteen diverse communities in the United States
Zangeneh, S. Z., Skalland, T., Yuhas, K., Emel, L., Tapsoba, J. D. D., Reed, D., Amos, C. I., Donnell, D., Moore, A., Justman, J. E., & and the CoVPN 5002 Study Team (2024). Adaptive time location sampling for COMPASS: A SARS-COV-2 prevalence study in fifteen diverse communities in the United States. Epidemiology, 35(3), 389-397. https://doi.org/10.1097/EDE.0000000000001705
BACKGROUND: COVID-19 has placed a disproportionate burden on underserved racial and ethnic groups, community members working in essential industries, those living in areas of high population density, and those reliant on in-person services such as transportation. The goal of this study was to estimate the cross-sectional prevalence of SARS-CoV-2 (active SARS-CoV-2 or prior SARS-CoV-2 infection) in children and adults attending public venues in 15 sociodemographically diverse communities in the United States and to develop a statistical design that could be rigorously implemented amidst unpredictable stay-at-home COVID-19 guidelines.
METHODS: We used time-location sampling with complex sampling involving stratification, clustering of units, and unequal probabilities of selection to recruit individuals from selected communities. We safely conducted informed consent, specimen collection, and face-to-face interviews outside of public venues immediately following recruitment.
RESULTS: We developed an innovative sampling design that adapted to constraints such as closure of venues, changing infection hotspots, and uncertain policies. We updated both the sampling frame and the selection probabilities over time using information acquired from prior weeks. We created site-specific survey weights that adjusted sampling probabilities for nonresponse and calibrated to county-level margins on age and sex at birth.
CONCLUSIONS: Although the study itself was specific to COVID-19, the strategies presented in this article could serve as a case study that can be adapted for performing population-level inferences in similar settings and could help inform rapid and effective responses to future global public health challenges.
