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

项目摘要 自2007年以来，已有40多种降糖药物（GLD）获得美国食品和药物管理局（FDA）的批准。 给药以治疗糖尿病。这些较新的GLD已被证明具有更高的心肾益处， 老年类应用于高风险的心血管和肾脏疾病的人。但 这些高成本GLD的引入导致了对糖尿病护理质量和公平性的重大担忧： 由于与社会相关的障碍，弱势群体获得较新的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成果方面存在差距和差异。 因此，迫切需要提高护理质量，并在使用较新的一般最低限度标准方面做到公平， 2型糖尿病（T2 D）的发病率有多少？ 以前的研究发现，通过促进有针对性的治疗来提高护理质量的项目， 临床高受益用户组导致公平性改善，因为来自社会的高受益用户 处境不利的亚群体往往在护理方面存在较大差距，因此从这些方案中受益更多。然而，在这方面， 在确定新GLD的临床高获益用户和制定政策方面存在重大知识差距， 水平干预，可以充分激励患者使用新的GLD，同时保持良好的长期健康 和经济成果。因此，本拟议项目的目的是确定临床高获益 新GLD的T2 D患者亚组，并为设计政策水平生成经验经济证据 干预措施，以提高护理质量和T2 D护理的健康公平性。 高质量的比较有效性研究（CER）要求患者有完整的数据记录 其可以高精度地跟踪事件遭遇和治疗暴露。这些人往往 被称为“忠诚的病人”在这个项目中，我们将开发一个可计算的表型（CP）的“忠诚 使用OneFlorida EHR和跨网络的患者使用REACHnet EHR验证CP（目标1）。到 确定新GLD的临床高获益T2 D患者亚组，我们将进行比较 患者中较新GLD与指南推荐替代方案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）方法的子组。高效益 T2 D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干预措施对促进 较新的GLD用于健康、经济和公平结果方面的高收益用户。利用先进的 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， 最大限度地提高药物依从性，同时减轻支付者的负担。 这项拟议中的研究意义重大，因为它将为一个紧急的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性。这项研究是创新的，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 EHR解决了一个具有挑战性和创新性的研究问题。

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.