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

J Med Econ. 2018 Sep;21(9):936-943. doi: 10.1080/13696998.2018.1496922. Epub 2018 Jul 17.


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: C15; C53; Obesity management; cost of illness; population health policy; prevention research; simulation; sub-populations.

MeSH terms

  • Absenteeism
  • Adult
  • Age Factors
  • Body Mass Index
  • Computer Simulation
  • Cost-Benefit Analysis
  • Diabetes Mellitus / economics
  • Diabetes Mellitus / epidemiology
  • Female
  • Health Resources / economics
  • Health Resources / statistics & numerical data
  • Health Services / economics
  • Health Services / statistics & numerical data
  • Humans
  • Male
  • Markov Chains
  • Middle Aged
  • Models, Econometric
  • Obesity / epidemiology
  • Obesity / therapy*
  • Obesity Management / economics*
  • Obesity Management / methods*
  • Policy*
  • Severity of Illness Index
  • Sex Factors
  • Socioeconomic Factors