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

项目总结 这项研究的目的是考察低收入成年人获得牙科保健的机会如何受到以下因素的影响 关于资格、牙科覆盖范围、费用分担和牙医报销的州医疗补助政策。口头的 健康是整体健康、经济生产力和福祉不可或缺的一部分。然而，获得牙科保健的机会继续增加 对低收入成年人来说是一个主要的公共卫生问题，导致得不到治疗的疾病、疼痛、系统性差 健康和巨大的经济负担。近年来，根据《平价医疗法案》的几项政策变化 (ACA)，如扩大医疗补助和家属护理覆盖范围，可能会对获得牙科服务产生重大影响 在贫困的非老年成年人中提供护理。此外，在牙科方面也存在广泛的跨州差异。 为医疗补助成年人提供的福利。这些变异提供了自然的实验来检验 关于获得牙科护理的医疗补助政策。此外，在目前的医改讨论中，国家 预计医疗补助计划将在资源分配方面获得自主权，但获得的资金可能会明显减少 联邦资金，使得需要强有力的证据来证明医疗补助政策替代方案的相对有效性 对各州来说是最重要的。这个应用程序的总体目标是推进 候选人(Singhal博士)通过保护她的口头培训和研究这些关键问题的时间 健康政策。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面和 在获得牙科护理方面支付慷慨的费用。该项目汇集了补充的次要数据 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(医疗补助分析摘录)以及相关联的调查和索赔数据。利用这些优势 数据和培训资源，该项目将：1)开发医疗补助牙科覆盖面的综合指数和 支付慷慨；2)估计每种政策选择和综合慷慨指数对 获得牙科保健的各种措施；以及3)预测政策备选办法的相对有效性和 他们在获得牙科保健方面的组合。我们将使用计量经济学方法，包括工具 变量、差值和回归不连续模型，以开发慷慨指数和 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有监督的机器学习方法(例如决策树学习)。这个 结果将提供必要的证据，为确保有效利用卫生保健资源的未来战略提供参考 以减少口腔健康差距。该项目的完成还将使辛格尔博士能够建立一个 作为一名独立的研究调查员，在她的多学科指导下进行研究计划 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.