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Bridging the evidence-to-practice gap: Evaluating practice facilitation as a strategy to accelerate translation of a systems-level adherence intervention into safety net practices

Bridging the evidence-to-practice gap: Evaluating practice facilitation as a strategy to accelerate translation of a systems-level adherence intervention into safety net practices
弥合证据与实践之间的差距:评估实践促进作为加速将系统级依从性干预转化为安全网实践的策略
批准号:
10323161
负责人:
OLUGBENGA G. OGEDEGBE
金额:
$48.04万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-04-09 至 2023-12-31
关键词:
AddressAdherenceAdministratorAdvisory CommitteesAffectAgeAttitudeAwardAwarenessBehaviorBlack PopulationsBlack raceCOVID-19COVID-19 disparityCOVID-19 morbidityCOVID-19 mortalityCOVID-19 pandemicCOVID-19 testCOVID-19 testingCOVID-19 vaccinationCOVID-19 vaccineCaringCitiesClinicClinicalClinical TrialsCollaborationsCommunicationCommunitiesComplexCoupledDataData CollectionData SourcesDatabasesDeath RateElectronic Health RecordEmploymentEnrollmentEthical IssuesEthicsEthnographyFamilyFamily health statusFederally Qualified Health CenterFocus GroupsFoundationsFundingFutureGuidelinesHealthHealth systemHispanicIndividualInfrastructureInterventionKnowledgeLatinoLatinxLatinx populationLifeLightLow incomeMeasuresMethodologyMethodsMinority GroupsModelingNational Institute on Minority Health and Health DisparitiesNew York CityOrganizational CulturePatient Self-ReportPatientsPhasePhenX ToolkitPopulationPrivacyProviderPublic HealthRADx Underserved PopulationsRecording of previous eventsResearchResearch TechnicsSamplingSavingsServicesSocial WorkSocial supportSocioeconomic StatusSurveysSymptomsSystemTest ResultTestingTimeTranslationsTrustUnderserved PopulationUnited StatesUnited States National Institutes of HealthVaccinationVaccine ResearchVaccinesVirusWait Timebasecare systemscommunity based participatory researchcommunity engaged researchcontextual factorsdata privacydemographicsdesignethnic diversityfollow-uphealth care service utilizationhealth disparityhealth literacyhigh riskhigh risk populationhospitalization ratesimprovedlarge scale datalong-standing disparitiesmemberminority communitiesmultidisciplinarymultilevel analysisoutreachpatient populationpredictive modelingprimary outcomeracial and ethnic disparitiesracial diversityresponsesafety netsocialsociodemographicstesting uptaketoolunderserved communityuptakevulnerable community

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ABSTRACT COVID-19 has shed light on the significant and long-standing disparities in underserved communities. Current data still show hospitalization rates among Black and Latinx individuals in the United States are 4 times greater than that of Whites. The Rapid Acceleration of Diagnostics for Underserved Populations (RADx-UP) initiative supports supplements to individual NIH awards to identify the determinants of COVID-19 testing among underserved populations. For this proposal, we will leverage the infrastructure of a NIMHD-funded project in the Family Health Centers (FHCs) of NYU Langone Health, a network of federally qualified health centers in NYC that serves over 125,000 low-income and racially and ethnically diverse patients. In the current application, we propose a three-phase community-engaged study that will employ a multipronged, sequential mixed methods design (i.e., one methodology builds on the findings of the other) to gain a comprehensive understanding of the multilevel factors that drive uptake of testing (and future vaccination) for COVID-19 of Black and Latinx patients (primary outcome), and participation in follow-up care offered by safety-net health systems. Phase 1 will consist of three steps: In step 1, we will leverage a well-characterized electronic health record database (~75% Black and Latinx) to examine differences in the individual-level factors associated with receiving a positive versus negative PCR test for COVID-19 among 400 Black and Latinx patients who receive care at the FHCs. We will also capture the community- and structural-level determinants of testing in this sample using validated self-report measures (e.g., NIH PhenX Tool Kit). In step 2, we will compare these multilevel factors across three patient groups: Group 1- patients who tested positive and received follow-up care and/or services; Group 2- patients who tested positive but did not receive follow-up care and/or services; and Group 3- patients who were eligible for testing (based on symptoms and probable exposure), but did not get tested. In step 3, we will employ predictive modeling to correctly identify patients at high-risk (group 3). In Phase 2, we will combine data from the previous phase with qualitative data (i.e., ethnographic observations, document analyses, and focus groups with FHC staff, providers, administrators, patients and community members) to capture organizational (e.g., FHC staff/provider attitudes and communications with patients, organizational culture) and ethical issues (e.g., data transparency and privacy) to shed light on important social, cultural, and contextual factors associated with uptake of COVID-19 testing and potential vaccine. Finally, in Phase 3, in collaboration with our Community Oversight Task Force, we will integrate Phase 1 and 2 data to refine, test, and disseminate tailored toolkits and ethical governance guidelines (e.g. clinical trials transparency and data privacy). These toolkits will be designed to increase knowledge and awareness of COVID-19 testing and vaccine research and will be widely disseminated among the FHCs, local community, NYULH, and the RADx-UP Coordination and Data Collection Center.
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