Predicting and Reducing Future Health Disparities for U.S. Adults with Diabetes

项目总结 尽管公共卫生更加重视减少糖尿病及其并发症，但糖尿病及其并发症方面的差异仍然存在 他们。与患有糖尿病的白人相比，黑人中风、截肢和 终末期肾病(ESRD)；拉美裔和亚洲人患眼睛问题的风险高20%至50%， 患ESRD的风险至少高出80%。已经开发了许多干预措施来减少糖尿病的健康 差距；然而，尽管有累积的证据，采取干预措施消除差距 在医疗机构和支付者中进展缓慢。制定和采用以下政策的关键障碍 消除糖尿病健康差距是缺乏关于这些影响的经济和临床影响的长期数据 干预措施。仿真模型是复杂关系的数学表示 预测因素(例如，血红蛋白A1c)和结果(例如，终末期肾病)在特定人群中。糖尿病模拟 十多年来，人们一直使用模型来描述临床指南、临床指南 干预措施，以及新的药物和设备。然而，它们很少被用来研究不同的影响 人口之间的差距，即健康差距。如果没有这样的模型，就很难比较 不同的临床和公共卫生干预措施，以减少健康差距，政策制定者难以 决定将资源分配到哪里。之所以不存在这样的模式，是因为目前的大多数 糖尿病模拟模型依赖于来自UKPDS和其他来源的数据，这些数据没有很大的 拉美裔和/或亚洲患者群体。发展一个更有用的多民族的主要挑战 糖尿病模拟模型需要具有跨医疗保健利用的所有领域的全面数据， 包括临床测量的风险因素(例如，血压和实验室结果)和种族/民族数据。 我们将使用来自Kaiser Permanente North California的数据，这是一个多种族、社会经济多样化的 糖尿病患者(n~192,000)(16%拉丁裔，11%非裔美国人，7%南亚裔，4%中国人；13% 英语困难)开发患者风险因素和患者之间关系的数学模型 使用Kaiser数据的结果，然后将国家数据和公布的数据输入模型，以便 预测减少糖尿病健康差距的努力的长期影响。我们的具体目标是1) 为白人、非裔美国人、拉丁裔、南亚人和 2)预测过去危险因素控制的变化对未来糖尿病健康的影响 差异；以及3)确定糖尿病健康差异干预的成本效益。

PROJECT SUMMARY Disparities in diabetes and its complications have persisted, despite increased public health focus on reducing them. Compared to whites living with diabetes, blacks have a two-fold increased risk of stroke, amputation, and end-stage renal disease (ESRD); Hispanics and Asians have a 20 to 50% higher risk for eye problems, and at least an 80% higher risk of ESRD. Many interventions have been developed to reduce diabetes health disparities; however, despite accumulated evidence, adoption of interventions to eliminate disparities has been slow across healthcare organizations and payers. A critical barrier to developing and adopting policies to eliminate diabetes health disparities is the lack of long-term data on the economic and clinical impact of these interventions. Simulation models are mathematical representations of the complex relationships between predictors (e.g., hemoglobin A1c) and outcomes (e.g., ESRD) among specific populations. Diabetes simulation models have been used for over a decade to describe the cost-effectiveness of clinical guidelines, clinical interventions, and new drugs and devices. However, they have been rarely used to study the disparate impact between populations, i.e. health disparities. Without such a model, it is difficult to compare the relative value of different clinical and public health interventions to reduce health disparities and difficult for policymakers to decide where to allocate resources. The reason that such a model does not exist is that the majority of current diabetes simulation models rely on data from the UKPDS and other sources, which did not have large populations of Hispanic and/or Asian patients. The major challenge to developing a more useful multi-ethnic diabetes simulation model is the need to have comprehensive data across all domains of healthcare utilization, including clinically-measured risk factors (e.g., blood pressure and laboratory results) and race/ethnicity data. We will use data from Kaiser Permanente Northern California, a multi-ethnic, socioeconomically diverse population with diabetes (n~192,000) (16% Latino, 11% African American, 7% South Asian, 4% Chinese; 13% difficulty with English) to develop a mathematical model of the relationships between patient risk factors and outcomes using Kaiser data, and then to input national data and published data into the model in order to forecast the long-term implications of efforts to reduce diabetes health disparities. Our specific aims are to 1) develop a simulation model of diabetes outcomes for white, African American, Latino, South Asian, and Chinese populations; 2) forecast the impact of past changes in risk factor control on future diabetes health disparities; and 3) determine the cost-effectiveness of diabetes health disparities interventions.