Adaptive Time Location Sampling for COMPASS, A SARS-COV-2 Prevalence Study in Fifteen Diverse Communities in The United States.

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 socio-demographically 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 paper 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.

Informations de copyright

Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: None to report