Role of State Medicaid Policies in Improving Access to Dental Care among Low-Income Adults

项目摘要 本研究的目的是探讨如何获得牙科保健低收入成年人的影响， 关于资格、牙科保险、费用分摊和牙医报销的州医疗补助政策。口服 健康是整体健康、经济生产力和福祉的组成部分。然而，获得牙科护理的机会继续增加， 对低收入成年人来说，这是一个主要的公共卫生问题，导致未经治疗的疾病，疼痛，系统性不良， 健康和巨大经济负担。近年来，根据《平价医疗法案》， (ACA)例如医疗补助扩大和家属护理覆盖范围可能会严重影响牙科服务的获得 照顾贫困的非老年人。此外，在牙科方面存在广泛的跨州差异 为医疗补助成年人提供的福利。这些变化提供了自然实验来检查 关于获得牙科护理的医疗补助政策。此外，在目前的卫生改革讨论中， 医疗补助计划预计将获得资源分配的自主权，但可能会收到明显减少 联邦资金，使得医疗补助政策替代方案的相对有效性需要强有力的证据 提供给最高法院。这项申请的首要目标是推进研究事业的 候选人（Singhal博士）通过保护她的时间进行培训和研究这些关键问题的口头 卫生政策。这一目标将通过以下补充学科的额外培训来实现： 计量经济学和机器学习，以及它们在估计医疗补助覆盖范围和 在获得牙科护理方面的慷慨支付。该项目汇集了互补的二级数据 来自多个来源，包括：大型国家调查数据（国家健康访谈调查），行政 登记和索赔数据（Medicaid Analytic eXtract）以及链接的调查和索赔数据。利用这些 数据和培训资源，该项目将：1）制定医疗补助牙科覆盖率的综合指数， 支付慷慨; 2）估计每个政策选择和综合慷慨指数的影响， 获得牙科保健的各种措施;和3）预测政策选择的相对有效性， 他们的组合对获得牙科保健的影响。我们将使用计量经济学方法，包括工具 变量、差异中的差异和回归不连续性模型，以制定慷慨指数， 估计医疗补助慷慨的影响。为了开发和验证预测模型，我们将使用 使用关联的调查和索赔数据的监督机器学习方法（例如决策树学习）。的 研究结果将为未来的战略提供必要的证据，以确保有效利用卫生保健资源 减少口腔健康差距。该项目的完成还将使Singhal博士能够建立一个 研究计划作为一个独立的研究调查员与她的多学科指导的支持 并为波士顿大学的团队和充满活力的研究社区提供建议。

PROJECT SUMMARY The purpose of this study is to examine how access to dental care among low-income adults is impacted by state Medicaid policies regarding eligibility, dental coverage, cost-sharing and dentist reimbursement. Oral health is integral to overall health, economic productivity and well-being. Yet access to dental care continues to be a major public health problem for low-income adults, leading to untreated disease, pain, poor systemic health and substantial economic burden. In recent years, several policy changes under the Affordable Care Act (ACA) such as Medicaid expansion and dependent care coverage could significantly impact access to dental care among the poor non-elderly adults. In addition, there is extensive cross-state variation in terms of dental benefits covered for Medicaid adults. These variations provide natural experiments to examine the effect of Medicaid policies on access to dental care. Moreover, under the current health reform discussions, state Medicaid programs are anticipated to gain autonomy in resource allocation, yet may receive significantly less federal funding, making the need for robust evidence on relative effectiveness of Medicaid policy alternatives available to the states paramount. The overarching goal of this application is to advance the research career of the candidate (Dr. Singhal) by protecting her time for training and research on these critical issues in oral health policy. This goal will be accomplished through additional training in complementary disciplines of econometrics and machine learning, and their application to estimate the impacts of Medicaid coverage and payment generosities on access to dental care. The project brings together complementary secondary data from multiple sources including: large national survey data (National Health Interview Survey), administrative enrollment and claims data (Medicaid Analytic eXtract) and linked survey and claims data. Leveraging these data and training resources, this project will: 1) develop synthetic indices of Medicaid dental coverage and payment generosities; 2) estimate the effect of each policy alternative and combined generosity indices on various measures of access to dental care; and 3) predict the relative effectiveness of policy alternatives and their combinations on access to dental care. We will use econometric methods including instrumental variables, difference-in-difference and regression discontinuity models to develop the generosity indices and to estimate the impact of Medicaid generosity. To develop and validate the predictive model, we will use supervised machine learning approaches (e.g. decision tree learning) using linked survey and claims data. The results will provide evidence needed to inform future strategies to ensure efficient use of health care resources to reduce oral health disparities. The completion of this project will also enable Dr. Singhal to establish a research program as an independent research investigator with support from her multi-disciplinary mentoring and advising teams and the vibrant research community at Boston University.