Economic modeling for improved prediction of saving estimates in healthcare costs from consumption of healthy foods: the Mediterranean-style diet case study

Autor: Peter B. Jones, Mohammad M. H. Abdullah, Jason P. H. Jones, Dallas Wood
Rok vydání: 2019
Předmět:
Zdroj: Food & Nutrition Research, Vol 63, Iss 0, Pp 1-10 (2019)
Food & Nutrition Research
ISSN: 1654-661X
DOI: 10.29219/fnr.v63.3418
Popis: Background By design, existing scenario-based nutrition economics studies on the financial benefits of healthy dietary behaviors generally report uncertainty in inputs and wide ranges of outcome estimates. Objectives This modeling exercise aimed to establish precision in prediction of the potential healthcare cost savings that would follow a reduction in the incidence of cardiovascular disease (CVD) consistent with an increase in adherence to a Mediterranean-style diet (MedDiet). Design Using a Monte Carlo simulation model on a cost-of-illness analysis assessing MedDiet adherence, CVD incidence reduction, and healthcare cost savings in the United States and Canada, short- and long-term cost savings that are likely to accrue to the American and Canadian healthcare systems were estimated using 20 and 80% increases in MedDiet adherence scenarios. Results Increasing percentage of population adhering to a MedDiet by 20% beyond the current adherence level produced annual savings in CVD-related costs of US$8.2 billion (95% confidence interval [CI], $7.5-$8.8 billion) in the United States and Can$0.32 billion (95% CI, $0.29-$0.34 billion) in Canada. An 80% increase in adherence resulted in savings equal to US$31 billion (95% CI, $28.6-$33.3 billion) and Can$1.2 billion (95% CI, $1.11-$1.30 billion) in each respective country. Conclusion Computational techniques with stochastic parameter inputs, such as the Monte Carlo simulation, could be an effective way of incorporating variability of modeling parameters in nutrition economics studies for improved precision in estimating the monetary value of healthy eating habits.
Databáze: OpenAIRE