Building Equity Improvement into Quality Improvement in the use of New Glucose-lowering Drugs (GLDs) through Individualized Drug Value Assessment in People with Diabetes

通过对糖尿病患者进行个体化药物价值评估，将公平性改进纳入新型降糖药物 (GLD) 使用质量改进中

• 批准号: 10668529
• 负责人: Jingchuan Guo
• 金额: $ 63.15万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-20 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10668529
• 关键词: American; Cardiovascular system; Caring; Clinical; Clinical Research; Comparative Effectiveness Research; Cost Sharing; Data; Diabetes Mellitus; Disparity; Disparity population; Drug Exposure; Drug usage; Economics; Education; Effectiveness; Electronic Health Record; Equity; Event; Glucose; Gold; Guidelines; Health; Health system; Heterogeneity; Income; Individual; Insurance; Intervention; Kidney Diseases; Knowledge; Lead; Legal patent; Link; Louisiana; Machine Learning; Marketing; Measurement; Medicaid; Medical; Medicare; Methods; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Patients; Penetration; Persons; Pharmaceutical Preparations; Policies; Privatization; Proliferating; Public Health; Quality of Care; Recommendation; Records; Research; Safety; Screening for cancer; Structure; Subgroup; System; United States Food and Drug Administration; Work; cardiovascular disorder risk; care seeking; clinical care; comparative effectiveness; comparative safety; computable phenotypes; cost; design; diabetes management; diabetes prevention program; economic evaluation; economic outcome; electronic health record system; experimental study; forest; health disparity; health equity; health plan; high risk; improved; innovation; machine learning algorithm; machine learning method; medication compliance; models and simulation; novel; patient oriented; patient subsets; phenotyping algorithm; programs; screening program; simulation; social; social disparities; social health determinants; study population; treatment effect; uptake

Project Summary Since 2007, more than 40 glucose-lowering drugs (GLDs) have been approved by the US Food and Drug Administration to treat diabetes. These newer GLDs have been proven to have higher cardiorenal benefits than older classes when applied in people at high risk of cardiovascular and kidney disease. However, the introduction of these high-cost GLDs has led to significant quality and equity concerns in diabetes care: socially disadvantaged individuals tend to have limited access to newer GLDs due to barriers related to social attributes (e.g., income, education), resulting in gaps and disparities in achieving optimal health outcomes. There is, therefore, an urgent need to improve the quality of care and equity in using newer GLDs among millions of Americans living with type 2 diabetes (T2D). Previous studies have found that programs that improve the quality of care by promoting treatment in targeted clinically high-benefit user groups lead to equity improvement because high-benefit users from socially disadvantaged subgroups often have larger gaps in care thus benefit more from these programs. However, critical knowledge gaps exist in identifying the clinically high-benefit users of newer GLDs and designing policy- level interventions that can adequately motivate patients’ newer GLDs use while having good long-term health and economic outcomes. Thus, the OBJECTIVE of this proposed project is to identify clinically high-benefit T2D patient subgroups for newer GLDs and generate empirical economic evidence for designing policy-level interventions to improve the quality of care and health equity in T2D care. High-quality comparative effectiveness research (CER) requires the patients to have complete data records which can track event encounters and treatment exposure with high accuracy. These individuals were often referred to as “loyal patients.” In this proposed project, we will develop a computable phenotype (CP) for “loyal patients” using OneFlorida EHRs and cross-network validate the CP using REACHnet EHRs (Aim 1). To identify clinically high-benefit T2D patient subgroups for newer GLDs, we will conduct comparative effectiveness and safety analyses of newer GLDs versus guideline-recommended alternatives across patient subgroups using rigorous causal inference methods and a machine-learning (ML) approach. The high-benefit T2D patient subgroups will be identified using EHRs of “loyal patients” from OneFlorida and cross-validated in REACHnet (Aim 2). At last, we will evaluate the impact of potential policy-level interventions for promoting newer GLDs use in high-benefit users on health, economics, and equity outcomes. Leveraging an advanced ML algorithm developed by PI, we will also identify the ideal cost-sharing structure at a health-plan level to maximize drug adherence while reducing the payers' burden. The proposed research is significant because it will provide solutions for an emergent public health issue in quality of care and health equity in diabetes management. This study is innovative because we will use cutting- edge machine-learning methods, simulation models, instrumental variables, and two of the largest PCORnet EHRs to tackle a challenging and innovative research question.

项目概要 自2007年以来，已有40多种降糖药物（GLD）获得美国食品药品监督管理局的批准 治疗糖尿病的这些新 GLD 已被证明比其他药物具有更高的心肾益处。 较旧的类别适用于心血管和肾脏疾病高风险人群。 这些高成本 GLD 的引入导致了糖尿病护理中的重大质量和公平问题：社会 由于与社会相关的障碍，弱势群体获得新 GLD 的机会往往有限。 属性（例如收入、教育），导致在实现最佳健康结果方面存在差距和差异。 因此，迫切需要提高护理质量和公平性地使用新的 GLD 数百万美国人患有 2 型糖尿病 (T2D)。 先前的研究发现，通过促进有针对性的治疗来提高护理质量的计划 临床上的高收益用户群体会导致公平性的改善，因为高收益用户来自社会 弱势群体往往在护理方面存在较大差距，因此从这些计划中受益更多。 在识别新 GLD 的临床高效益用户和设计政策方面存在关键知识差距 干预措施可以充分激励患者使用新的 GLD，同时保持良好的长期健康 因此，该项目的目标是确定临床高效益。 新 GLD 的 T2D 患者亚组并为设计政策层面生成经验经济证据 提高 T2D 护理质量和健康公平性的干预措施。 高质量的比较有效性研究（CER）需要患者有完整的数据记录 它可以高精度地跟踪这些人的事件遭遇和治疗暴露。 被称为“忠诚的患者”。在这个拟议的项目中，我们将为“忠诚的患者”开发一个可计算的表型（CP）。 “患者”使用 OneFlorida EHR 并使用 REACHnet EHR 跨网络验证 CP（目标 1）。 为新的 GLD 确定临床上高效益的 T2D 患者亚组，我们将进行比较 新 GLD 与指南推荐替代方案在患者中的有效性和安全性分析 使用严格的因果推理方法和机器学习（ML）方法来划分子组。 T2D 患者亚组将使用 OneFlorida 的“忠诚患者”的 EHR 进行识别，并在 REACHnet（目标 2）最后，我们将评估潜在的政策层面干预措施对促进的影响。 较新的 GLD 在健康、经济和公平成果方面用于高效益用户。 由 PI 开发的 ML 算法，我们还将在健康计划层面确定理想的成本分摊结构，以 最大限度地提高药物依从性，同时减轻付款人的负担。 拟议的研究意义重大，因为它将为紧急公共卫生问题提供解决方案 糖尿病管理中的护理质量和健康公平这项研究具有创新性，因为我们将使用切割- 边缘机器学习方法、仿真模型、工具变量和两个最大的 PCORnet 电子病历解决具有挑战性和创新性的研究问题。