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Reaching Latinx Communities with Algorithmic Optimization for SARS-CoV-2 Testing Locations.
Searcy, Jacob A; Cioffi, Camille C; Tavalire, Hannah F; Budd, Elizabeth L; Cresko, William A; DeGarmo, David S; Leve, Leslie D.
Afiliação
  • Searcy JA; Presidential Initiative in Data Science, University of Oregon, 203 Pacific Hall, Eugene, OR, 97403, USA. jsearcy@uoregon.edu.
  • Cioffi CC; Prevention Science Institute, University of Oregon, Eugene, OR, USA.
  • Tavalire HF; Prevention Science Institute, University of Oregon, Eugene, OR, USA.
  • Budd EL; Prevention Science Institute, University of Oregon, Eugene, OR, USA.
  • Cresko WA; Department of Counseling Psychology and Human Services, University of Oregon, Eugene, OR, USA.
  • DeGarmo DS; Presidential Initiative in Data Science, University of Oregon, 203 Pacific Hall, Eugene, OR, 97403, USA.
  • Leve LD; Institute of Ecology and Evolution, University of Oregon, Eugene, OR, USA.
Prev Sci ; 24(6): 1249-1260, 2023 08.
Article em En | MEDLINE | ID: mdl-36622480
ABSTRACT
The COVID-19 pandemic has disproportionately affected communities of color, including Latinx communities. Oregon Saludable Juntos Podemos (OSJP) is a randomized clinical trial aimed at reducing this disparity by both increasing access to testing for SARS-CoV-2, the virus that causes COVID-19, for Oregon Latinx community members and studying the effectiveness of health and behavioral health interventions on turnout and health outcomes. OSJP established SARS-CoV-2 testing events at sites across Oregon. A critical early question was how to locate these sites to best serve Latinx community members. To propose sites in each participating county, we implemented an algorithmic approach solving a facilities location problem. This algorithm was based on minimizing driving time from Latinx population centers to SARS-CoV-2 testing locations. OSJP staff presented these proposed testing locations to community partners as a starting place for identifying final testing sites. Due to differences in geography, population distributions, and potential site accessibility, the study sites exhibited variation in how well the algorithmic optimization objectives could be satisfied. From this variation, we inferred the effects of the drive time optimization metric on the likelihood of Latinx community members utilizing SARS-CoV-2 testing services. After controlling for potential confounders, we found that minimizing the drive time optimization metric was strongly correlated with increased turnout among Latinx community members. This paper presents the algorithm and data sources used for site proposals and discusses challenges and opportunities for community-based health promotion research when translating algorithm proposals into action across a range of health outcomes.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: SARS-CoV-2 / COVID-19 Tipo de estudo: Clinical_trials Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: SARS-CoV-2 / COVID-19 Tipo de estudo: Clinical_trials Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article