Where can obesity management policy make the largest impact? Evaluating sub-populations through a microsimulation approach

Abstract

Background: There is a critical need to focus limited resources on sub-groups of patients with obesity where we expect the largest return on investment. This paper identifies patient sub-groups where an investment may result in larger positive economic and health outcomes.

Methods: The baseline population with obesity was derived from a public survey database and divided into sub-populations defined by demographics and disease status. In 2016, a validated model was used to simulate the incidence of diabetes, absenteeism, and direct medical cost in five care settings. Research findings were derived from the difference in population outcomes with and without weight loss over 15 years. Modeled weight loss scenarios included initial 5% or 12% reduction in body mass index followed by a gradual weight regain. Additional simulations were conducted to show alternative outcomes from different time courses and maintenance scenarios.

Results: Univariate analyses showed that age 45–64, pre-diabetes, female, or obesity class III are independently predictive of larger savings. After considering the correlation between these factors, multivariate analyses projected young females with obesity class I as the optimal sub-group to control obesity-related medical expenditures. In contrast, the population aged 20–35 with obesity class III will yield the best health outcomes. Also, the sub-group aged 45–54 with obesity class I will produce the biggest productivity improvement. Each additional year of weight loss maintained showed increased financial benefits.

Conclusions: This paper studied the heterogeneity between many sub-populations affected by obesity and recommended different priorities for decision-makers in economic, productivity, and health realms.

Keywords:

• Obesity management
• sub-populations
• simulation
• cost of illness
• prevention research
• population health policy

JEL classification codes:

• C15
• C53

Transparency

Declaration of funding

Funding for this study came from Novo Nordisk, Inc.

Declaration of financial/other relationships

WS, FC, TMD, and TKK provide paid consulting services to the study sponsor for this and other research. TZ is a paid employee of the study sponsor. LP received personal fees for scientific advising and/or speaking from the study sponsor. JME peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgment

The authors wish to thank Joanna Huang for initiating the study and her contributions at the early phase.