Development and Validation of a Discrete Event Simulation Model to Evaluate the Cardiovascular Impact of Population Policies for Obesity.

INTRODUCTION

Our aim was to describe the development and validation of an obesity model representing the cardiovascular risks associated with different body mass index (BMI) categories, through simulation, designed to evaluate the epidemiological and economic impact of population policies for obesity.

METHODS

A discrete event simulation model was built in R considering the risk of cardiovascular events (heart failure, stroke, coronary heart disease, and diabetes) associated with BMI categories in the Spanish population. The main parameters included in the model were estimated from Spanish hospital discharge records and the Spanish Health Survey and allowed both first-order and second-order (probabilistic sensitivity analysis) uncertainty to be programmed into the model. The simulation yielded the incidence and prevalence of cardiovascular events as validation outputs. To illustrate the capacity of the model, we estimated the reduction in cardiovascular events and cost-utility (incremental cost/incremental quality-adjusted life-years [QALYs]) of a hypothetical intervention that fully eliminated the cardiovascular risks associated with obesity and overweight.

RESULTS

The Validation Status of Health-Economic decision models (AdViSHE) tool was applied. Internal validation plots showed adequate goodness of fit for the Spanish population. External validation was achieved by comparing the simulated and real incidence by age group for stroke, acute myocardial infarction, and heart failure. The intervention reduced the population hazard ratios of stroke, acute myocardial infarction, and heart failure to 0.81, 0.74, and 0.78, respectively, and added 0.74 QALYs to the whole population.

CONCLUSIONS

This obesity simulation model evidenced good properties for estimating the long-term epidemiological and economic impact of policies to tackle obesity in Spain. The conceptual model could be implemented for other counties using country-specific input data.