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

University Of Michigan At Ann Arbor, Ann Arbor MI

Investigators

Linked publications, trials & patents

Abstract

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.