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)

基本信息

批准号：
10732506
负责人：
Olivia J Lindly
金额：
$ 74.03万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10732506
关键词：
3 year old Address Affect Age Alaska Native American Indians Autism diagnostic Automobile Driving Behavior Therapy Big Data Black race Caring Cessation of life Child Child health equity Childhood Chronic Communities Complex Consensus Data Data Aggregation Data Analyses Data Science Data Set Development Diagnosis Disparity Disparity in diagnosis Early Intervention Education Equity Ethnic Origin Family Family member Funding General Population Guidelines Health Health Disparities Research Health Policy Health Services Health Services Accessibility Health system Healthcare Home Information Systems Intervention Knowledge Longevity Machine Learning Measures Medicaid Medicine Methods National Institute of Mental Health Neighborhoods Nursery Schools Pharmaceutical Preparations Policies Policy Maker Prevalence Race Recommendation Research Research Personnel Schools Services Special Education Strategic Planning System Techniques United States United States National Institutes of Health Work access disparities adolescent with autism spectrum disorder adult with autism spectrum disorder autism spectrum disorder autistic children base behavioral health child services children of color community based service contextual factors data harmonization data quality disparity reduction ethnic disparity evidence base health care disparity health equity health inequalities improved improved outcome indexing individuals with autism spectrum disorder innovation interest intervention program lower income families programs psychopharmacologic racial determinant racial disparity screening secondary analysis social structural health determinants treatment disparity waiver

项目摘要

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.

项目摘要自闭症谱系障碍（ASD）在美国影响多达四分之一的儿童中的1个，其患病率已有在过去的10年中增加。尽管早期获得自闭症护理可以改善结果，但有色人种来自低收入家庭的孩子以后获得护理，并有未满足的护理需求。虽然有一个广泛的了解社区，社区和州一级的上下文因素会影响自闭症健康护理差异，这些因素在很大程度上没有探索。在这项拟议的研究中，我们将汇编最多有史以来全面，最大的自闭症服务使用数据集，将16个州的医疗补助索赔与来自儿童机会指数以及州教育和医疗保健政策数据的社区级数据。我们将使用此功能强大的新数据集来发现儿童自闭症服务的可修改决定因素使用差异。分析将集中于诊断时代以及用药和行为治疗的使用。然后我们将通过参与社区和研究的共识小组来利用发现将研究带入行动专家根据研究发现建议以数据为驱动的，可行的干预措施。我们还将使用我们的研究通过赞助不足的学者，扩大自闭症差异研究领域的多样性在医学和/或具有健康差异研究兴趣的人中，使用由此产生的数据集来进行其他数据分析。在该项目结束时，我们希望有证据表明的发现关于哪些孩子访问哪些服务，哪些背景因素会影响服务访问以及哪些可以部署干预措施来解决自闭症健康不平等。