Computational model for policy simulation and prediction of the regulatory impact of front-of-package food labels

Non-communicable diseases (NCDs), a significant cause of global mortality, necessitate targeted policy measures to reduce exposure to risk factors, particularly nutrition-related ones. Front-of-pack food labeling aims to shape consumer behavior and decrease exposure to nutritional risk factors. There are various forms of this policy, including traffic light (TL), nutrient profile model (NPM), and reference value (RV) labels. Mathematical models incorporating experimental and dose–response data support determining the most effective form. When selecting a modeling method, it is essential to consider the uncertainty involved in estimating risk factors. This study aims to create a computational model to predict the health and economic impacts of different policy options using an improved version of the Scarborough algorithm, incorporating a validation process to address potential uncertainties. The model shows that NPM was the most effective, reducing the estimated annual mortality rate from 514,247 cases to 447,394 cases (13% reduction). The following labels were TL (11% reduction) and RV (6% reduction). Economically, the RV label affects consumer spending the least, predicting an 8% increase in monthly spending per individual from the baseline. When assessed from both health and economic perspectives, the RV label has emerged as the most advantageous front-of-pack food labeling policy. The proposed model can aid policymakers in evaluating policy alternatives and thus selecting the most advantageous policies based on health and economic outcomes while ensuring the model's accuracy and applicability for the community.