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Addressing Structural Disparities in Autism Spectrum Disorder through Analysis of Secondary Data (ASD3)

Addressing Structural Disparities in Autism Spectrum Disorder through Analysis of Secondary Data (ASD3)
通过二手数据分析解决自闭症谱系障碍的结构性差异 (ASD3)
批准号:
10732506
负责人:
Olivia J Lindly
金额:
$74.03万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-01 至 2027-06-30
关键词:
3 year oldAddressAffectAgeAlaska NativeAmerican IndiansAutism diagnosticAutomobile DrivingBehavior TherapyBig DataBlack raceCaringCessation of lifeChildChild health equityChildhoodChronicCommunitiesComplexConsensusDataData AggregationData AnalysesData ScienceData SetDevelopmentDiagnosisDisparityDisparity in diagnosisEarly InterventionEducationEquityEthnic OriginFamilyFamily memberFundingGeneral PopulationGuidelinesHealthHealth Disparities ResearchHealth PolicyHealth ServicesHealth Services AccessibilityHealth systemHealthcareHomeInformation SystemsInterventionKnowledgeLongevityMachine LearningMeasuresMedicaidMedicineMethodsNational Institute of Mental HealthNeighborhoodsNursery SchoolsPharmaceutical PreparationsPoliciesPolicy MakerPrevalenceRaceRecommendationResearchResearch PersonnelSchoolsServicesSpecial EducationStrategic PlanningSystemTechniquesUnited StatesUnited States National Institutes of HealthWorkaccess disparitiesadolescent with autism spectrum disorderadult with autism spectrum disorderautism spectrum disorderautistic childrenbasebehavioral healthchild serviceschildren of colorcommunity based servicecontextual factorsdata harmonizationdata qualitydisparity reductionethnic disparityevidence basehealth care disparityhealth equityhealth inequalitiesimprovedimproved outcomeindexingindividuals with autism spectrum disorderinnovationinterestintervention programlower income familiesprogramspsychopharmacologicracial determinantracial disparityscreeningsecondary analysissocialstructural health determinantstreatment disparitywaiver

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PROJECT SUMMARY Autism Spectrum Disorder (ASD) affects up to 1 in 44 children in the United States, and its prevalence has increased over the past 10 years. Though early access to autism care improves outcomes, children of color and children from low-income families access care later and have unmet care needs. Though there is a broad understanding that contextual factors on the neighborhood, community, and state levels impact autism health care disparities, these factors are largely unexplored. In this proposed research, we will compile the most comprehensive and largest autism service use dataset ever, combining Medicaid claims from 16 states with community-level data from the Child Opportunity Index, as well as state education and health care policy data. We will use this powerful new data set to uncover modifiable determinants of child autism services use disparities. Analyses will focus on age of diagnosis as well as medication and behavioral therapy use. Then we will use findings to bring research to action, by engaging a consensus panel of community and research experts to suggest data-driven, feasible interventions based on study findings. We will also use our research to expand the diversity of the autism disparities research field, by sponsoring scholars who are under- represented in medicine and/or who have health disparities research interests, to use the resulting dataset to pursue additional data analyses. At the end of this project, we expect to have evidence-informed findings regarding which children access which services, which contextual factors affect services access, and which interventions can be deployed to address autism health inequities.
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