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

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

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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.

项目摘要蛀牙是美国儿童中最常见的慢性疾病。2,3尽管努力增加利用率通过医疗补助和儿童健康改善牙科保险的承保范围，在少数群体中保险计划（CHIP），大口腔健康差异仍然存在，黑人和西班牙裔儿童美国任何种族群体的口腔健康状况最差。5-8除了无法获得推荐护理，个人健康行为（例如，饮食和口腔卫生差）以及社区以及与提供者相关的结构因素也有助于少数族裔的高风险。6,9州医疗补助和芯片牙齿鼓励计划考虑减少其交付系统中口腔健康分布的策略改进工作，6然而，他们面临着提高质量和降低质量差异的挑战以具有成本效益的方式进行高层受益人。6成功的计划改进的步骤包括能够有效衡量提供给他们的护理价值（由健康成果和成本定义）受益人以及评估中的种族/种族差异。我们有关键的差距了解种族/种族的影响及其与多层次风险因素对分布的相互作用质量和口腔健康结果。该研究计划的科学目标是研究多级决定者口腔健康和牙科护理质量差异和评估改善护理和消除的价值种族/种族质量差异。在AIM 1中，我们将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了解种族/民族对严重龋齿进展的影响并探索牙齿的异源治疗效果关心。在AIM 2中，我们将分析医疗补助分析提取物（MAX）数据中的个人级别主张多种数据源可全面衡量牙科护理总体质量的种族/种族分布，使用基于证据的质量指标，并确定可修改的结构性风险因素造成差异。在AIM 3中，合并AIM 1和2的结果，这是严重携带的微仿真模型，整合了个体级别与关键上下文因素有关的数据，将开发并用于评估成本效益和价值牙科质量措施的改善，并消除种族/种族差异质量。这项研究的结果将支持决策者和利益相关者的决策，并将形成 R01应用于研究针对服务不足和脆弱人群的新型策略的基础。该研究计划由一项职业发展计划完成，该计划以申请人的背景为基础卫生政策和决策科学。具体而言，该职业发展计划概述了三个新培训区域：（1）口腔健康流行病学，（2）健康差异研究和（3）高级分析方法。联合研究和培训计划将使申请人为成功的独立研究职业做好准备识别，评估和实施多层次干预措施，以减少口腔健康中的种族/种族差异。