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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

基本信息

批准号：
10548840
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
项目类别：
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/10548840
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 Disparity Electronic Health Record Equity 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 Policy Maker Policy Making Predictive Analytics Process Measure Provider Quality Indicator Quality of Care Quality of life Race Recommendation Recording of previous events Research Resource Allocation Risk Risk Factors Schools Science Statistical Models Survival Analysis System Techniques Testing Training Training Programs Underserved Population Vulnerable Populations Work access disparities advanced analytics analytical method beneficiary career career development contextual factors cost cost effective cost effectiveness cost outcomes dietary disparity reduction ethnic disparity evidence base experience health assessment 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 disparity racial population response risk prediction model skills stem structural determinants systemic barrier theories treatment effect trend

项目摘要

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.

项目概要蛀牙是美国儿童中最常见的慢性疾病。2,3 尽管努力提高利用率通过医疗补助和儿童健康提高牙科保险覆盖范围，覆盖少数族裔人口保险计划 (CHIP)，黑人和西班牙裔儿童的口腔健康状况仍然存在巨大差异美国所有种族群体中口腔健康状况最差的。5-8 除了无法获得推荐的护理之外，个人健康行为（例如不良饮食和口腔卫生）以及社区和提供者相关的结构因素也导致少数群体患严重龋齿的高风险。6,9 国家医疗补助和 CHIP 牙科鼓励各计划考虑减少其交付系统中口腔健康差异的策略改进工作，6 然而，他们面临着提高质量和减少质量差异的挑战。以具有成本效益的方式帮助高需求受益人。6 成功改进计划的步骤包括：有效衡量向其提供的护理价值（由健康结果和成本定义）的能力受益人，以及在评估中纳入种族/民族差异。我们的能力存在重大差距了解种族/民族的影响及其与多层次风险因素的相互作用对差异的影响质量和口腔健康结果。该研究计划的科学目标是研究多层次决定因素口腔健康和牙科护理质量的差异，并评估改善护理和消除牙科护理的价值种族/民族的质量差异。在目标 1 中，我们将通过以下方法开发严重龋齿的风险预测模型：对电子健康记录 (EHR) 数据应用基于机器学习的生存分析4,10，以了解种族/民族对严重龋齿进展的影响并探讨牙科的异质性治疗效果关心。在目标 2 中，我们将结合 Medicaid Analytic eXtract (MAX) 数据分析个人层面的索赔多个数据源综合衡量牙科护理整体质量的种族/民族差异，使用基于证据的质量指标，并确定造成差异的可改变的结构性风险因素。在目标 3 中，结合了目标 1 和 2 的结果（严重龋齿的微观模拟模型），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牙科质量措施和消除种族/民族差异带来的护理改善质量。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策略。该研究计划由以申请人的背景为基础的职业发展计划作为补充。卫生政策和决策科学。具体来说，该职业发展计划概述了三个方面的新培训领域：(1) 口腔健康流行病学，(2) 健康差异研究，以及 (3) 高级分析方法。这研究和培训相结合的计划将为申请人的成功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。