A language-matching model to improve equity and efficiency of COVID-19 contact tracing.

Autor:	Lu L; Regulation, Evaluation, and Governance Laboratory, Stanford University, Stanford, CA 94305., Anderson B; Regulation, Evaluation, and Governance Laboratory, Stanford University, Stanford, CA 94305., Ha R; Regulation, Evaluation, and Governance Laboratory, Stanford University, Stanford, CA 94305., D'Agostino A; County of Santa Clara Public Health Department, San Jose, CA 95126., Rudman SL; County of Santa Clara Public Health Department, San Jose, CA 95126., Ouyang D; Regulation, Evaluation, and Governance Laboratory, Stanford University, Stanford, CA 94305., Ho DE; Regulation, Evaluation, and Governance Laboratory, Stanford University, Stanford, CA 94305; deho@stanford.edu.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2021 Oct 26; Vol. 118 (43).
DOI:	10.1073/pnas.2109443118
Abstrakt:	Contact tracing is a pillar of COVID-19 response, but language access and equity have posed major obstacles. COVID-19 has disproportionately affected minority communities with many non-English-speaking members. Language discordance can increase processing times and hamper the trust building necessary for effective contact tracing. We demonstrate how matching predicted patient language with contact tracer language can enhance contact tracing. First, we show how to use machine learning to combine information from sparse laboratory reports with richer census data to predict the language of an incoming case. Second, we embed this method in the highly demanding environment of actual contact tracing with high volumes of cases in Santa Clara County, CA. Third, we evaluate this language-matching intervention in a randomized controlled trial. We show that this low-touch intervention results in 1) significant time savings, shortening the time from opening of cases to completion of the initial interview by nearly 14 h and increasing same-day completion by 12%, and 2) improved engagement, reducing the refusal to interview by 4%. These findings have important implications for reducing social disparities in COVID-19; improving equity in healthcare access; and, more broadly, leveling language differences in public services. Competing Interests: The authors declare no competing interest. (Copyright © 2021 the Author(s). Published by PNAS.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu