tetano
Editor, Senior Moderator
JMIR Form Res
. 2025 Feb 11:9:e62802.
doi: 10.2196/62802. Increasing COVID-19 Testing and Vaccination Uptake in the Take Care Texas Community-Based Randomized Trial: Adaptive Geospatial Analysis
Kehe Zhang[SUP] 1 2 [/SUP], Jocelyn V Hunyadi[SUP] 1 2 [/SUP], Marcia C de Oliveira Otto[SUP] 3 [/SUP], Miryoung Lee[SUP] 4 [/SUP], Zitong Zhang[SUP] 1 [/SUP], Ryan Ramphul[SUP] 3 [/SUP], Jose-Miguel Yamal[SUP] 1 [/SUP], Ashraf Yaseen[SUP] 1 [/SUP], Alanna C Morrison[SUP] 3 [/SUP], Shreela Sharma[SUP] 3 5 [/SUP], Mohammad Hossein Rahbar[SUP] 3 6 7 [/SUP], Xu Zhang[SUP] 7 [/SUP], Stephen Linder[SUP] 8 [/SUP], Dritana Marko[SUP] 9 [/SUP], Rachel White Roy[SUP] 10 [/SUP], Deborah Banerjee[SUP] 11 [/SUP], Esmeralda Guajardo[SUP] 12 [/SUP], Michelle Crum[SUP] 13 [/SUP], Belinda Reininger[SUP] 4 [/SUP], Maria E Fernandez[SUP] 14 [/SUP], Cici Bauer[SUP] 1 2 [/SUP]
Affiliations
Background: Geospatial data science can be a powerful tool to aid the design, reach, efficiency, and impact of community-based intervention trials. The project titled Take Care Texas aims to develop and test an adaptive, multilevel, community-based intervention to increase COVID-19 testing and vaccination uptake among vulnerable populations in 3 Texas regions: Harris County, Cameron County, and Northeast Texas.
Objective: We aimed to develop a novel procedure for adaptive selections of census block groups (CBGs) to include in the community-based randomized trial for the Take Care Texas project.
Methods: CBG selection was conducted across 3 Texas regions over a 17-month period (May 2021 to October 2022). We developed persistent and recent COVID-19 burden metrics, using real-time SARS-CoV-2 monitoring data to capture dynamic infection patterns. To identify vulnerable populations, we also developed a CBG-level community disparity index, using 12 contextual social determinants of health (SDOH) measures from US census data. In each adaptive round, we determined the priority CBGs based on their COVID-19 burden and disparity index, ensuring geographic separation to minimize intervention "spillover." Community input and feedback from local partners and health workers further refined the selection. The selected CBGs were then randomized into 2 intervention arms-multilevel intervention and just-in-time adaptive intervention-and 1 control arm, using covariate adaptive randomization, at a 1:1:1 ratio. We developed interactive data dashboards, which included maps displaying the locations of selected CBGs and community-level information, to inform the selection process and guide intervention delivery. Selection and randomization occurred across 10 adaptive rounds.
Results: A total of 120 CBGs were selected and followed the stepped planning and interventions, with 60 in Harris County, 30 in Cameron County, and 30 in Northeast Texas counties. COVID-19 burden presented substantial temporal changes and local variations across CBGs. COVID-19 burden and community disparity exhibited some common geographical patterns but also displayed distinct variations, particularly at different time points throughout this study. This underscores the importance of incorporating both real-time monitoring data and contextual SDOH in the selection process.
Conclusions: The novel procedure integrated real-time monitoring data and geospatial data science to enhance the design and adaptive delivery of a community-based randomized trial. Adaptive selection effectively prioritized the most in-need communities and allowed for a rigorous evaluation of community-based interventions in a multilevel trial. This methodology has broad applicability and can be adapted to other public health intervention and prevention programs, providing a powerful tool for improving population health and addressing health disparities.
Keywords: COVID-19 testing; COVID-19 vaccination; community-based interventions; data dashboard; geospatial analysis; public health; social determinants of health; study design.
. 2025 Feb 11:9:e62802.
doi: 10.2196/62802. Increasing COVID-19 Testing and Vaccination Uptake in the Take Care Texas Community-Based Randomized Trial: Adaptive Geospatial Analysis
Kehe Zhang[SUP] 1 2 [/SUP], Jocelyn V Hunyadi[SUP] 1 2 [/SUP], Marcia C de Oliveira Otto[SUP] 3 [/SUP], Miryoung Lee[SUP] 4 [/SUP], Zitong Zhang[SUP] 1 [/SUP], Ryan Ramphul[SUP] 3 [/SUP], Jose-Miguel Yamal[SUP] 1 [/SUP], Ashraf Yaseen[SUP] 1 [/SUP], Alanna C Morrison[SUP] 3 [/SUP], Shreela Sharma[SUP] 3 5 [/SUP], Mohammad Hossein Rahbar[SUP] 3 6 7 [/SUP], Xu Zhang[SUP] 7 [/SUP], Stephen Linder[SUP] 8 [/SUP], Dritana Marko[SUP] 9 [/SUP], Rachel White Roy[SUP] 10 [/SUP], Deborah Banerjee[SUP] 11 [/SUP], Esmeralda Guajardo[SUP] 12 [/SUP], Michelle Crum[SUP] 13 [/SUP], Belinda Reininger[SUP] 4 [/SUP], Maria E Fernandez[SUP] 14 [/SUP], Cici Bauer[SUP] 1 2 [/SUP]
Affiliations
- PMID: 39935005
- DOI: 10.2196/62802
Background: Geospatial data science can be a powerful tool to aid the design, reach, efficiency, and impact of community-based intervention trials. The project titled Take Care Texas aims to develop and test an adaptive, multilevel, community-based intervention to increase COVID-19 testing and vaccination uptake among vulnerable populations in 3 Texas regions: Harris County, Cameron County, and Northeast Texas.
Objective: We aimed to develop a novel procedure for adaptive selections of census block groups (CBGs) to include in the community-based randomized trial for the Take Care Texas project.
Methods: CBG selection was conducted across 3 Texas regions over a 17-month period (May 2021 to October 2022). We developed persistent and recent COVID-19 burden metrics, using real-time SARS-CoV-2 monitoring data to capture dynamic infection patterns. To identify vulnerable populations, we also developed a CBG-level community disparity index, using 12 contextual social determinants of health (SDOH) measures from US census data. In each adaptive round, we determined the priority CBGs based on their COVID-19 burden and disparity index, ensuring geographic separation to minimize intervention "spillover." Community input and feedback from local partners and health workers further refined the selection. The selected CBGs were then randomized into 2 intervention arms-multilevel intervention and just-in-time adaptive intervention-and 1 control arm, using covariate adaptive randomization, at a 1:1:1 ratio. We developed interactive data dashboards, which included maps displaying the locations of selected CBGs and community-level information, to inform the selection process and guide intervention delivery. Selection and randomization occurred across 10 adaptive rounds.
Results: A total of 120 CBGs were selected and followed the stepped planning and interventions, with 60 in Harris County, 30 in Cameron County, and 30 in Northeast Texas counties. COVID-19 burden presented substantial temporal changes and local variations across CBGs. COVID-19 burden and community disparity exhibited some common geographical patterns but also displayed distinct variations, particularly at different time points throughout this study. This underscores the importance of incorporating both real-time monitoring data and contextual SDOH in the selection process.
Conclusions: The novel procedure integrated real-time monitoring data and geospatial data science to enhance the design and adaptive delivery of a community-based randomized trial. Adaptive selection effectively prioritized the most in-need communities and allowed for a rigorous evaluation of community-based interventions in a multilevel trial. This methodology has broad applicability and can be adapted to other public health intervention and prevention programs, providing a powerful tool for improving population health and addressing health disparities.
Keywords: COVID-19 testing; COVID-19 vaccination; community-based interventions; data dashboard; geospatial analysis; public health; social determinants of health; study design.