Population Health Impact of a Self-Insured Employer's Policy Change to Cover Weight Reduction and Diabetes Prevention Interventions for Employees, Dependents, and Retirees with Prediabetes

摘要： 2015年9月，密歇根大学(U Of M)改变了其医疗福利，以涵盖干预措施 为大约20,000名超重或肥胖者免费减肥和预防糖尿病 在其约85,000名员工、家属和退休人员中，有糖尿病前期的员工、家属和退休人员 退休人员。我们将首先通过比较来评估这一大规模政策变化对人口健康的影响 用于识别非糖尿病员工、家属和退休人员的四种策略的收益率 糖尿病前期。这些策略包括使用声明、糖化血红蛋白和体重指数水平，这些水平可用密歇根大学的自我- 资助健康保险数据库以识别糖尿病前期患者；应用经过验证的筛查算法 到同一数据库，以确定≥40岁的其他非糖尿病患者为 糖尿病前期糖化血红蛋白检测；通过要求个人使用疾病预防控制中心糖尿病前期自我筛查 筛查试验(如阳性则进行HbA1c试验)；以及进行糖尿病前期筛查和HbA1c 作为工作场所健康计划的一部分进行测试。我们还将评估定制的增量收益 反馈，让初级保健医生参与病例发现，以及对成员参与的财务激励 在放映中。其次，我们将描述干预的吸收，并达到和评估个人对in-in的偏好。 以个人和互联网为基础的生活方式干预和二甲双胍治疗。我们将尝试解释 基于健康信念模型的干预参与，并进一步描述和比较摄取， 依从性、保持性和结果(体重、BMI、糖化血红蛋白和生活质量)的变化 干预。我们还将评估干预措施对人口健康的影响。最后，我们将对 相对于一年和两年不干预，干预措施的有效性、成本和成本效用 使用经过验证的计算机模拟模型对5年和10年的时间范围进行了过度模拟。严谨的 对肥胖政策的这一大规模变化的评估将涉及研究小组之间的合作， 患者、提供者、提供者团体、干预提供者、密歇根大学及其健康保险公司。评选结果 评估将促进科学知识，并将对福利设计、卫生政策、 和人口健康。

ABSTRACT: In September 2015, the University of Michigan (U of M) changed its healthcare benefits to cover interventions for weight reduction and diabetes prevention at no out-of-pocket cost for the ~20,000 overweight or obese employees, dependents, and retirees with prediabetes among its ~85,000 employees, dependents, and retirees. We will evaluate the impact of this large scale policy change on population health by first comparing the yield of four strategies being used to identify nondiabetic employees, dependents, and retirees with prediabetes. These strategies include using claims, HbA1c, and BMI levels available in the U of M's self- funded health insurance database to identify prediabetic individuals; applying a validated screening algorithm to the same database to identify additional nondiabetic individuals ≥40 years of age at highest risk for prediabetes for HbA1c testing; by asking individuals to self-screen for prediabetes using the CDC Prediabetes Screening Test (and to have HbA1c testing if positive); and by performing prediabetes screening and HbA1c testing as part of a worksite wellness program. We will also evaluate the incremental benefits of tailored feedback, engaging primary care physicians in case finding, and financial incentives for member participation in screening. Second, we will describe intervention uptake and reach and assess individual preferences for in- person and internet-based lifestyle interventions and for metformin therapy. We will attempt to explain intervention participation based on the Health Belief Model, and further describe and compare the uptake, adherence, retention, and change in outcomes (weight, BMI, HbA1c, and quality-of-life) associated with each intervention. We will also assess the impact of the interventions on population health. Finally, we will model the effectiveness, costs, and cost-utility of the interventions relative to no intervention over one and two years and over simulated 5- and 10-year time horizons using a validated computer simulation model. The rigorous evaluation of this large scale change in obesity policy will involve collaboration among the research group, patients, providers, provider groups, intervention providers, the U of M, and its health insurer. The results of the evaluation will advance scientific knowledge and will have major implications for benefit design, health policy, and population health.