Reaching Latinx Communities with Algorithmic Optimization for SARS-CoV-2 Testing Locations

被引:9
作者
Searcy, Jacob A. [1 ]
Cioffi, Camille C. [2 ]
Tavalire, Hannah F. [2 ]
Budd, Elizabeth L. [2 ,3 ]
Cresko, William A. [1 ,4 ]
DeGarmo, David S. [2 ,3 ]
Leve, Leslie D. [2 ,3 ]
机构
[1] Univ Oregon, Presidential Initiat Data Sci, 203 Pacific Hall, Eugene, OR 97403 USA
[2] Univ Oregon, Prevent Sci Inst, Eugene, OR 97403 USA
[3] Univ Oregon, Dept Counseling Psychol & Human Serv, Eugene, OR 97403 USA
[4] Univ Oregon, Inst Ecol & Evolut, Eugene, OR 97403 USA
基金
美国国家卫生研究院;
关键词
Facilities location problem; Latino/a/x population; COVID-19; testing; Community-informed research;
D O I
10.1007/s11121-022-01478-x
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
收藏
页码:1249 / 1260
页数:12
相关论文
共 34 条
[1]   A survey of healthcare facility location [J].
Ahmadi-Javid, Amir ;
Seyedi, Pardis ;
Syam, Siddhartha S. .
COMPUTERS & OPERATIONS RESEARCH, 2017, 79 :223-263
[2]  
[Anonymous], New York Times
[3]  
[Anonymous], AM COMMUNITY SURVEY
[4]  
[Anonymous], 2017, PLANET DUMP
[5]  
[Anonymous], COVID-19 Race and Ethnicity Data
[6]   OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks [J].
Boeing, Geoff .
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2017, 65 :126-139
[7]  
Bureau U. C, TIGER LIN SHAP
[8]   Estimating the Characteristics of Unauthorized Immigrants Using US Census Data: Combined Sample Multiple Imputation [J].
Capps, Randy ;
Bachmeier, James D. ;
Van Hook, Jennifer .
ANNALS OF THE AMERICAN ACADEMY OF POLITICAL AND SOCIAL SCIENCE, 2018, 677 (01) :165-179
[9]  
Centers for Disease Control and Prevention, COVID-19 vaccinations in the United States, County
[10]   Racial and ethnic inequities in the early distribution of US COVID-19 testing sites and mortality [J].
Dalva-Baird, Nathan P. ;
Alobuia, Wilson M. ;
Bendavid, Eran ;
Bhattacharya, Jay .
EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, 2021, 51 (11)