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

Abstract

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.

Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models

“…In the final simulation model, as the competitive risk of death from other causes would reduce the number of strokes especially after 85 y of age, the Gompertz distribution was selected. 10…”

“…In our case, we start from the number of cases of the first stroke in men that occurred in 2013 in Spain obtained from the hospital minimum data set. 10 From the number of cases by age and the Spanish population of men by age obtained from the Spanish Statistical Office (Instituto Nacional de Estadı´stica), 11 we calculated for first stroke in men the instantaneous rate (hazard) for each year of age, the cumulative hazard function, and the empirical survival function as follows.…”

“…To randomly assign the time to stroke by age and sex, we need to define age- and sex-specific survival functions to be applied to individuals. 10 In addition, these functions should be able to incorporate parameter (second-order) uncertainty needed for probabilistic sensitivity analysis.…”

Excess healthcare costs of mental disorders in children, adolescents and young adults in the Basque population registry adjusted for socioeconomic status and sex

“…Nonetheless, the cost of mental disorders is a poorly understood driver of decision-making about which interventions should be implemented in mental health [ 5 , 6 ]. More economic evaluations in the field of mental illness have started to be conducted [ 7 – 10 ], but their limited use in decision-making contrasts with the importance placed on this type of research in the incorporation of preventive treatments and interventions in cancer and cardiovascular diseases [ 11 – 13 ]. Moreover, experts highlight that the cost implications are not adequately measured and large evidence gaps still exist regarding the economic case for mental health care [ 14 ], including inequalities by gender and socioeconomic status (SES) [ 6 ].…”