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Balancing food safety and sustainability: trade-off risk assessments and predictive modeling
Summary
Researchers reviewed how holistic trade-off risk assessments that balance food safety against broader sustainability and public health goals can replace zero-risk frameworks, highlighting Monte Carlo simulations, AI-based predictive tools, and One Health approaches as key methods for defining proportionate food safety targets.
The importance of food safety to public health is reflected in its inclusion in the United Nations Sustainable Development Goals (SDGs)—SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-being), and SDG 12 (Responsible Consumption and Production)—and the World Health Organization’s food safety strategy. Its inclusion across multiple areas underscores how food safety is not an isolated objective but is closely tied to broader public health and sustainability goals. While the public often expects food to be “absolutely” safe, experts recognize that all foods carry a residual risk of causing foodborne illness and that zero risk is neither achievable nor desirable. Advances in diagnostics and surveillance systems (e.g., increases in test sensitivity and specificity) have increased the frequency of hazard detection in foods, including detection of hazards at levels that may pose minimal public health risks. However, efforts to manage these negligible risks can divert attention from more significant threats and may introduce unintended consequences that outweigh the intended benefits. To address this, holistic approaches and trade-off risk assessments are needed, accounting for the interrelationship between the health of humans, animals, and the environment (i.e., One Health) and evaluating both the costs and benefits of food safety measures, including direct expenses, externalities, social or legal constraints, and consumer preferences. Key tools enabling these risk assessments include Monte Carlo simulations and other modeling tools that are also being adopted for food safety applications, such as geographic information system models, agent-based models, and artificial intelligence (AI)-based predictive tools. These efforts can help define quantitative food safety goals that ensure appropriate, but not absolute, safety, so long as implemented controls are validated and verified. Technological advances, such as AI-enabled risk negotiation, offer new opportunities to integrate trade-offs in risk analysis and support more balanced, effective food safety strategies.
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