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

自我保险雇主改变政策以涵盖患有糖尿病前期的雇员、家属和退休人员的减重和糖尿病预防干预措施对人口健康的影响

• 批准号: 9154983
• 负责人: WILLIAM H HERMAN
• 金额: $ 46.32万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-07 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9154983
• 关键词: Address; Adherence; Adult; Affect; Age-Years; Algorithms; American; Behavior; Body Weight decreased; Cardiovascular Diseases; Centers for Disease Control and Prevention (U.S.); Clinical; Collaborations; Computer Simulation; Cost utility; Databases; Diabetes prevention; Diagnosis; Diet; Employee; Ensure; Evaluation; Feedback; Fostering; Funding; Glycosylated hemoglobin A; Health; Health Insurance; Health Policy; Healthcare; Individual; Insurance Carriers; Internet; Intervention; Knowledge; Life Style; Metformin; Michigan; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Overweight; Participant; Patients; Persons; Physical activity; Policies; Population Heterogeneity; Prediabetes syndrome; Preventive Intervention; Primary Care Physician; Provider; Quality of life; Research; Research Project Grants; Risk; Risk Factors; Scientific Advances and Accomplishments; Testing; Time; Universities; Weight; Wellness Program; Workplace; base; case finding; comparative effectiveness; cost; cost effectiveness; design; diabetes risk; evidence base; financial incentive; group intervention; health belief; health related quality of life; high risk; improved; intervention program; lifestyle intervention; member; non-diabetic; population based; population health; preference; prevent; programs; retiree; screening; uptake

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.

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