@Article{info:doi/10.2196/62802,
author="Zhang, Kehe
and Hunyadi, Jocelyn V
and de Oliveira Otto, Marcia C
and Lee, Miryoung
and Zhang, Zitong
and Ramphul, Ryan
and Yamal, Jose-Miguel
and Yaseen, Ashraf
and Morrison, Alanna C
and Sharma, Shreela
and Rahbar, Mohammad Hossein
and Zhang, Xu
and Linder, Stephen
and Marko, Dritana
and Roy, Rachel White
and Banerjee, Deborah
and Guajardo, Esmeralda
and Crum, Michelle
and Reininger, Belinda
and Fernandez, Maria E
and Bauer, Cici",
title="Increasing COVID-19 Testing and Vaccination Uptake in the Take Care Texas Community-Based Randomized Trial: Adaptive Geospatial Analysis",
journal="JMIR Form Res",
year="2025",
month="Feb",
day="11",
volume="9",
pages="e62802",
keywords="COVID-19 testing; COVID-19 vaccination; study design; community-based interventions; geospatial analysis; public health; social determinants of health; data dashboard",
abstract="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. ",
issn="2561-326X",
doi="10.2196/62802",
url="https://formative.jmir.org/2025/1/e62802",
url="https://doi.org/10.2196/62802"
}