Identifying COVID-19 vaccine deserts using Machine Learning and Geospatial Analyses to target Community -engaged testing for vulnerable rural populations to prevent localized outbreaks
Identifying COVID-19 vaccine deserts using Machine Learning and Geospatial Analyses to target Community -engaged testing for vulnerable rural populations to prevent localized outbreaks
批准号:
10544766
负责人:
Brian Hendricks
金额:
$113.89万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-01-01 至 2023-11-30
关键词:
AddressAdvocateAppalachian RegionAreaAutomobile DrivingAwarenessBayesian ModelingCOVID-19COVID-19 morbidityCOVID-19 outbreakCOVID-19 testCOVID-19 testingCOVID-19 vaccineColorCommon Data ElementCommunitiesCommunity HealthComputer softwareCountyDataData CollectionDiseaseDisease OutbreaksEarly identificationElectronicsEmergency medical serviceEventFaithFire - disastersFoodFundingFutureGeographic LocationsGeographyGoalsHealthHealth Services AccessibilityHigh PrevalenceIncentivesIndividualInfrastructureInterventionInterviewKnowledgeMachine LearningMapsMarketingMedicalMethodologyModelingMonitorMorbidity - disease rateMotivationOutcomeParticipantPerceptionPerformancePersonsPhasePilot ProjectsPoaceaePredispositionPrimary Care PhysicianProceduresRADx Underserved PopulationsRegression AnalysisReportingResearchResearch PersonnelResourcesRisk EstimateRural CommunityRural PopulationSARS-CoV-2 positiveSamplingSeriesService delivery modelServicesSiteStructureSurveysSystemTestingTimeUnited StatesVaccinationVaccinesVariantVirusVulnerable PopulationsWest VirginiaWorkasymptomatic COVID-19barrier to testingcommunity buildingcommunity engagementcommunity partnershipcomorbiditydemographicseconometricsexperiencefirst responderhealth literacyhigh riskinnovationintervention effectmachine learning frameworkmedically underservedmedically underserved populationmortalitynasal swabnovelpandemic diseasepreventrecruitreproductiveruralityscreeningscreening programsecondary endpointsocioeconomic disadvantagetrendunderserved communityvaccine acceptancevaccine accessvulnerable community
中文摘要
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英文摘要
PROJECT ABSTRACT
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. Our RADx-UP Phase Two proposal
builds 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. We define vaccine deserts
using overall vaccination rate and the change in vaccine uptake over a two-week period. Machine learning with
time series modeling is used to characterize county level transmissibility, incorporating here for the first-time
vaccination rates. Risk estimates at the county level are 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 is identified study liaisons will connect 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 will involve sample and survey
data collection, with promotions and chance giveaways to incentivize communities to participate. We build
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. We involve co-investigators with strong connections to
southern WV, an area with limited resources for RADx-UP Phase One. Additionally, we conduct a pilot study to
examine the performance of the ABBOTT ID Now isothermal PCR system in 600 participants. Effect of the
intervention is evaluated through monitoring of pre and post testing rate for the county using spatial regression
analyses. A unique attribute of the statistical framework we propose to evaluate our testing strategy is an ability
to describe the impact on nearby counties in addition to the targeted community. This project will leverage
existing and develop 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 our 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.
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Identifying COVID-19 vaccine deserts using Machine Learning and Geospatial Analyses to target Community -engaged testing for vulnerable rural populations to prevent localized outbreaks
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批准号:10446844
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项目类别:
-
资助金额:$101.53万
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财政年份:2022
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负责人:Brian Hendricks
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依托单位:
海外基金