dbGaP Study Accession: phs003049

NIH Institute/Center: NIMHD

RADx Data Program: RADx-UP

Release Date: 08/29/2023

DOI: 10.60773/8s4e-d179

Study Description: As of June 30, 2021, 23% of West Virginia's (WV) 55 counties were ranked within the top 20% of most vulnerable counties to COVID-19 in the United States. Central to the state's extreme vulnerability is higher prevalence of medical comorbidities, lower access to care among rural populations, and decreased vaccine uptake compared to urban counterparts. Of considerable concern, testing has decreased statewide to allow for active dispersal of the vaccines. Unfortunately, low testing compounds vulnerability to COVID-19 in medically underserved populations where vaccine uptake is low, as they are extremely susceptible to persistent localized outbreaks of the virus and subsequently higher morbidity and mortality. The RADx-UP Phase Two proposal built upon previously funded RADx-UP Phase One by identifying and targeting vaccine desert communities then tailoring testing event services to the needs of individual communities building upon their perceptions of what is important. Providing a dynamic solution for continued testing is critical. This study defined vaccine deserts using overall vaccination rate and the change in vaccine uptake over a two-week period. Machine learning with time series modeling was used to characterize county level transmissibility, incorporating here for the first-time vaccination rates. Risk estimates at the county level were overlaid with zip codes where vaccine deserts have been identified using bottom decile for overall vaccination rate and change in vaccination over a 14-day period. Once a community was identified, study liaisons connected study staff to advocates to conduct semi-structured interviews to identify partner sites to host testing events and collect data to tailor promotions, food, and media messaging to the specific needs of each community targeted. Testing events involved sample and survey data collection, with promotions and chance giveaways to incentivize communities to participate. The study built upon RADx-UP one activity by focusing heavily on first responders in each community to aid in hosting testing events, and faith based and on profits where applicable. The study involved co-investigators with strong connections to southern WV, an area with limited resources for RADx-UP Phase One. Additionally, this study conducted a pilot study to examine the performance of the ABBOTT ID Now isothermal PCR system in 600 participants. Effect of the intervention was evaluated through monitoring of pre and post testing rate for the county using spatial regression analyses. A unique attribute of the statistical framework that proposed to evaluate the testing strategy was an ability to describe the impact on nearby counties in addition to the targeted community. This project leveraged existing and developed its own unique partnerships with local and state agencies for implementation of a community engaged testing delivery model within vaccine deserts. A critical and novel aspect of this approach is establishment of a grass roots first responders research network which can be leveraged to implement screening programs in isolated medically underserved communities or study first responder health outcomes.

Updated Date: 04/17/2024

Principal Investigator: Hendricks, Brian

Has Data Files: Yes

Study Domain: Artificial Intelligence or Machine Learning; COVID Testing Deserts; Community Outreach Programs; Comorbidities; Social Determinants of Health; Testing Rate or Uptake; Vaccination Rate or Uptake; Virological Testing

Data Collection Method: Molecular Nucleic Acid or PCR Testing Device; Survey

Keywords: COVID Transmission; Comorbid Populations; Non-Individual-Level Data; RNA

Study Design: Longitudinal Cohort

Multi-Center Study: No

Data Types: Questionnaire or Survey

Study Start Date: 01/01/2022

Study End Date: 11/30/2023

Species: Human; Non-Human

Estimated Cohort Size: 300

Study Population Focus: Adults; Children; Lower Socioeconomic Status (SES) Populations; Older Adults or Elderly; Rural Communities; Underserved or Vulnerable Populations

Study Website URL: https://health.wvu.edu/research/grants/identifying-covid-19-vaccine-deserts-using-machine-learning-and-geospatial-analyses-to-target-community-engaged-testing-for-vulnerable-rural-populations-to-prevent-localized-outbreaks/

Publication URL: https://pubmed.ncbi.nlm.nih.gov/36188730/; https://pubmed.ncbi.nlm.nih.gov/36339209/; https://pubmed.ncbi.nlm.nih.gov/36407449/

Acknowledgement Statement: This study was supported through funding, 5U01MD017419-02, for the National Institute on Minority Health and Health Disparities (NIMHD) as part of the RADx-UP program. Approved users should acknowledge the provision of data access by dbGaP for accession phs003049.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.

Funding Opportunity Announcement (FOA) Number: RFA-OD-21-008

NIH Grant or Contract Number(s): 5U01MD017419-02

Consent/Data Use Limitations: General Research Use