# A Scoping Review of Microsimulation Models on Obesity-Related Policy Evaluation

## TL;DR

This review examines how microsimulation models are used to evaluate obesity-related policies, highlighting their strengths and areas for improvement in assessing long-term health and economic impacts.

## Contribution

The study provides a comprehensive overview of microsimulation applications in obesity policy evaluation, identifying gaps in equity analysis and methodological transparency.

## Key findings

- Most studies used dynamic, stochastic, individual-level microsimulation models with diverse behavioral assumptions.
- Only one study used a formal quantitative equity metric despite most stratifying outcomes by socioeconomic or demographic groups.
- The review highlights the need for standardized equity assessment and broader application beyond high-income settings.

## Abstract

Background/Objectives: Obesity is a major global public health and economic challenge. Governments worldwide have implemented nutrition-focused policies such as sugar-sweetened beverage taxes, front-of-pack labeling, food assistance reforms, and school nutrition standards to improve diet quality and reduce obesity. Because large-scale randomized controlled trials are often infeasible and conventional epidemiologic methods overlook population heterogeneity and behavioral feedback, microsimulation modeling has become a key tool for evaluating long-term and distributional policy impacts. This scoping review examined the application of microsimulation to obesity-related nutrition policies, focusing on model structure, behavioral parameterization, and integration of economic and equity analyses. Methods: Following PRISMA guidelines (PROSPERO CRD42024599769), five databases were searched for peer-reviewed studies. Data were extracted on policy mechanisms, model design, parameterization, and equity analysis. Study quality was assessed using a customized 21-item checklist adapted from CHEERS and NIH tools. Results: Twenty-nine studies met the inclusion criteria, with most policy settings based in the United States. Most employed dynamic, stochastic, individual-level microsimulation models with diverse behavioral assumptions, obesity equations, and calibration approaches. While most studies stratified outcomes by socioeconomic or demographic group, only one used a formal quantitative equity metric. Conclusions: Microsimulation modeling provides valuable evidence on the long-term health, economic, and distributional impacts of nutrition policies. Future work should strengthen methodological transparency, standardize equity assessment, and expand application beyond high-income settings to improve the comparability, credibility, and policy relevance of simulation-based nutrition policy research.