Reducing oral health disparities in children using predictive analytics and mathematical modeling

使用预测分析和数学模型减少儿童口腔健康差异

• 批准号: 10345140
• 负责人: Sung Eun Choi
• 金额: $ 12.68万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-10 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10345140
• 关键词: Address; Adolescent; Affect; Age; Alabama; Area; Arkansas; Behavior; Black American; Black race; Caring; Characteristics; Child; Child Health; Childhood; Chronic Disease; Colorado; Communities; Complement; Connecticut; Cost Analysis; Cost of Illness; County; Data; Data Set; Decision Making; Dental; Dental Care; Dental Epidemiology; Dental Hygiene; Dental Insurance; Dental caries; Dentists; Development Plans; Disease Outcome; Disease Progression; Electronic Health Record; Ethnic Origin; Gender; Goals; Health; Health Benefit; Health Disparities Research; Health Insurance; Health Policy; Health behavior; Healthcare; Heterogeneity; Hispanic; Hispanic Americans; Improve Access; Incidence; Individual; Interdisciplinary Study; Intervention; Learning; Machine Learning; Measures; Medicaid; Mentorship; Minority Groups; Modeling; Mouth Diseases; North Carolina; Oklahoma; Oral health; Outcome; Policies; Predictive Analytics; Process Measure; Provider; Quality Indicator; Quality of Care; Quality of life; Race; Recording of previous events; Research; Research Training; Resource Allocation; Risk; Risk Factors; Schools; Science; Statistical Models; Survival Analysis; System; Techniques; Testing; Training; Training Programs; Underserved Population; Vulnerable Populations; Work; advanced analytics; analytical method; base; beneficiary; career; career development; contextual factors; cost; cost effective; cost effectiveness; cost outcomes; dietary; disparity reduction; evidence base; experience; health disparity; high risk; improved; mathematical model; models and simulation; modifiable risk; multiple data sources; neglect; novel strategies; programs; public health insurance; public health intervention; racial and ethnic; racial and ethnic disparities; response; risk prediction model; skills; stem; theories; treatment effect; trend

Project Summary Tooth decay is the most common chronic disease among U.S. children.2,3 Despite efforts to increase utilization among minority populations by improving coverage for dental insurance via Medicaid and Children’s Health Insurance Program (CHIP), large oral health disparities remain with Black and Hispanic children having the poorest oral health of any racial groups in the US.5-8 In addition to lack of access to recommended care, individual health behaviors (e.g., poor dietary and oral hygiene) and community- and provider-related structural factors also contribute to the high risk of severe dental caries in minority populations.6,9 State Medicaid and CHIP dental programs are encouraged to consider strategies to reduce oral health disparities in their delivery system improvement efforts,6 however, they are challenged with improving quality and reducing quality disparities among high-need beneficiaries in a cost-effective way.6 Steps toward successful program improvements include the ability to validly measure the value of care (defined by both health outcomes and costs) delivered to their beneficiaries, as well as incorporating racial/ethnic disparities in the assessment. There is a critical gap in our understanding of the influence of race/ethnicity and its interaction with multilevel risk factors on disparities in quality and oral health outcomes. The scientific objective of this research plan is to study multilevel determinants of oral health and disparities in quality of dental care and assess the value of improving care and eliminating racial/ethnic disparities in quality. In Aim 1, we will develop a risk prediction model of severe dental caries by applying machine-learning based survival analysis4,10 on electronic health record (EHR) data to understand the influence of race/ethnicity on progression of severe caries and explore heterogenous treatment effects of dental care. In Aim 2, we will analyze individual-level claims in Medicaid Analytic eXtract (MAX) data combined with multiple data sources to comprehensively measure racial/ethnic disparities in overall quality of dental care, using evidence-based quality indicators, and identify modifiable structural risk factors creating disparities. In Aim 3, incorporating results from Aims 1 and 2, a microsimulation model of severe caries, integrating individual-level data with data on key contextual factors, will be developed and used to assess the cost-effectiveness and value of improvements in care stemming from dental quality measures and eliminating racial/ethnic disparities in quality. Findings from this study will support decision-making by policymakers and stakeholders, and will form the basis of an R01 application to study novel strategies that target underserved and vulnerable populations. This research plan is complemented by a career development plan that builds on the applicant’s background in health policy and decision science. Specifically, this career development plan outlines new training in three areas: (1) oral health epidemiology, (2) health disparities research, and (3) advanced analytics methods. The combined research and training plan will prepare the applicant for a successful independent research career identifying, evaluating, and implementing multilevel interventions to reduce racial/ethnic disparities in oral health.

项目摘要 龋齿是美国儿童中最常见的慢性病。2，3尽管努力提高利用率 通过医疗补助和儿童健康改善牙科保险覆盖面，在少数族裔人群中 保险计划(CHIP)，巨大的口腔健康差距仍然存在，黑人和西班牙裔儿童患有 口腔健康是美国所有种族中最差的。5-8除了无法获得推荐的护理外，个人 健康行为(例如，不良的饮食和口腔卫生)以及与社区和提供者有关的结构性因素 也增加了少数族裔人口患严重龋齿的高风险。6、9州医疗补助和CHIP牙科 鼓励项目考虑减少其输送系统中口腔健康差距的策略 改进努力，6然而，它们面临着提高质量和减少以下方面的质量差距的挑战 以具有成本效益的方式满足高需求的受益者。6成功改进方案的步骤包括 能够有效地衡量提供给他们的医疗保健的价值(由健康结果和成本定义) 在评估中纳入了种族/族裔差异。我们有一个严重的差距 了解种族/民族及其与多水平危险因素的交互作用对性别差异的影响 质量和口腔健康结果。这项研究计划的科学目标是研究多水平决定因素。 了解口腔健康和牙科护理质量的差异，并评估改善护理和消除 种族/民族在质量上的差异。在目标1中，我们将开发一个严重龋齿的风险预测模型，通过 在电子健康记录(EHR)数据上应用基于机器学习的生存分析4，10以理解 人种/民族对重度龋病进展的影响及探讨牙科异质性治疗效果 关心。在目标2中，我们将分析医疗补助分析摘要(MAX)数据中的个人级别索赔，并结合 多个数据来源，综合衡量种族/民族在牙科护理总体质量方面的差异，使用 以证据为基础的质量指标，并找出造成差异的可改变的结构性风险因素。在《目标3》中， 结合AIMS 1和AIMS 2的结果，建立了一个严重龋病的微观模拟模型，整合了个体水平 将开发包含关键背景因素数据的数据，并用于评估成本效益和价值 由牙科质量措施和消除种族/民族差异所带来的保健改善 质量。这项研究的结果将支持政策制定者和利益相关者的决策，并将形成 R01应用程序的基础，用于研究针对服务不足和弱势群体的新战略。 这项研究计划与职业发展计划相辅相成，该计划建立在申请人的 卫生政策和决策科学。具体地说，这份职业发展计划概述了三年内的新培训 领域：(1)口腔健康流行病学，(2)健康差距研究，(3)先进分析方法。这个 综合研究和培训计划将使申请者为成功的独立研究生涯做好准备 确定、评估和实施多层次干预措施，以减少口腔健康方面的种族/民族差异。