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

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. Ther...

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Bibliographic Details
Published in:Discover Food Vol. 4; no. 1; pp. 110 - 22
Main Authors: Alhumaidan, Omar A., Alkhunein, Sarah M., Alakeel, Shihana A., Fallata, Ghadir A., Alsukait, Reem F., Binsheehah, Rahaf H., Alnassar, Razan F., Alqaseer, Hanan M.
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01-12-2024
Springer Nature B.V
Springer
Subjects:
Agriculture
Algorithms
Annual mortality rate
Cardiovascular disease
Chemistry
Chemistry and Materials Science
Confounding (Statistics)
Consumer behavior
Consumer spending
Descriptive labeling
Economic impact
Food
Food labeling
Food Microbiology
Food Science
Front-of-package food labels
Mathematical models
Mortality
Nutrients
Nutrition
Nutrition research
Policy simulation
Public health
Random variables
Risk factors
Simulation
Supply chains
Consumer spending
Policy simulation
Front-of-package food labels
Annual mortality rate
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Summary: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.
ISSN:2731-4286
2731-4286
DOI:10.1007/s44187-024-00192-7