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: 10502997
• 负责人: Jingchuan Guo
• 金额: $ 66.39万
• 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/10502997
• 关键词: American; Cardiovascular system; Caring; Clinical; Clinical Research; Comparative Effectiveness Research; Cost Sharing; Data; Diabetes Mellitus; Drug Design; Drug Exposure; Drug usage; Economics; Education; Effectiveness; Electronic Health Record; Event; Glucose; Gold; Guidelines; Health; Health system; Heterogeneity; Income; Individual; Insurance; Intervention; Kidney Diseases; Knowledge; Lead; Legal patent; Link; Louisiana; Machine Learning; Measurement; Medical; Medicare; Medicare/Medicaid; Methods; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Patients; Penetration; Persons; Pharmaceutical Preparations; Policies; Privatization; Public Health; Quality of Care; 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; experimental study; forest; health disparity; health economics; 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; simulation; social; social disadvantage; 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.

项目摘要 自2007年以来，美国食品和药物已批准了40多种降糖药物（GLD） 治疗糖尿病。这些新的GLD已被证明具有比 较老的班级将在患有心血管和肾脏疾病的高风险的人中。但是， 这些高成本GLD的引入导致糖尿病护理中的质量和股权关注：社会上 由于与社会有关的障碍，处境不利 属性（例如收入，教育），导致实现最佳健康成果的差距和差异。 因此，迫切需要提高使用新的GLD的护理和公平质量 数以百万计的美国人患有2型糖尿病（T2D）。 先前的研究发现，通过促进针对性的治疗来改善护理质量的计划 临床上高效益的用户群体可改善股权，因为来自社会上的高效益用户 不利的亚组通常在护理上的差距较大，因此从这些计划中受益更多。然而， 确定新GLD的临床高效率用户并设计政策 - 可以充分激发患者新的GLD使用的同时，具有良好的长期健康状况的水平干预措施 和经济成果。这是这个拟议项目的目的是确定临床上的高效益 T2D患者子组针对新的GLD，并为设计政策级别的经验经济证据 提高T2D护理中护理和健康公平质量的干预措施。 高质量的比较有效性研究（CER）要求患者具有完整的数据记录 可以以高精度跟踪事件遇到和治疗暴露。这些人经常 称为“忠实的患者”。在这个拟议的项目中，我们将开发一个可计算的表型（CP） 使用Oneflorida EHR和跨网络的患者使用Reachnet EHR验证CP（AIM 1）。 确定临床上较高的T2D患者亚组的新GLD，我们将进行比较 对新的GLDS与指南的有效性和安全分析，以培养患者的替代方案 使用严格的因果推理方法和机器学习方法（ML）方法的亚组。高收益 T2D患者亚组将使用Oneflorida的“忠实患者”的EHR确定，并在 REACHNET（AIM 2）。最后，我们将评估潜在政策级干预措施促进的影响 较新的GLD用于健康，经济学和公平成果中的高效率用户。利用高级 由PI开发的ML算法，我们还将确定健康计划水平的理想成本分担结构 在减少付款人的伯恩（Burnen）的同时，最大化毒品依从性。 拟议的研究很重要，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平等。这项研究具有创新性，因为我们将使用剪裁 - 边缘机器学习方法，仿真模型，仪器变量和两个最大的PCORNET EHR解决一个挑战和创新的研究问题。