Integrating food safety surveillance into early public health detection systems: A framework for preventing foodborne-related cancers
Bibliographic record
Abstract
Foodborne illnesses remain a persistent public health challenge in the United States and globally, with an estimated 600 million cases and over 400,000 deaths worldwide each year. While acute foodborne infections caused by pathogens such as Salmonella, Listeria monocytogenes, and E. coli have received substantial attention, there is increasing concern over the long-term health effects of chronic exposure to foodborne contaminants. Mounting evidence has linked substances such as aflatoxins, nitrites, nitrates, mycotoxins, polycyclic aromatic hydrocarbons, and heavy metals to various forms of cancer, including liver, stomach, colorectal, and esophageal cancers. These carcinogenic agents often enter the food supply chain through poor agricultural practices, inadequate food storage, or insufficient processing standards—especially in underserved communities and resource-limited regions. Current food safety surveillance systems are primarily designed to detect and respond to immediate outbreaks rather than long-term health outcomes. These systems typically operate in silos, disconnected from cancer registries, environmental health monitoring platforms, and early detection programs. This fragmentation creates missed opportunities for early intervention, risk mitigation, and informed policy development. This paper proposes a novel integrated surveillance framework that bridges food safety monitoring with public health data systems, enabling early identification and tracking of foodborne exposure-related cancer risks. Drawing on global best practices in food regulatory models and advances in public health informatics, we present a cross-sectoral approach involving real-time contaminant detection, data integration with electronic medical records (EMRs) and cancer registries, predictive analytics, and targeted screening for high-risk populations. The proposed system supports timely interventions, strengthens regulatory compliance, and contributes to the long-term goal of reducing the cancer burden linked to foodborne exposures. Implementation of this model will require inter-agency collaboration, modern technological infrastructure, clear data-sharing policies, and the development of a skilled workforce. Ultimately, this paper argues for a paradigm shift—one that unites food safety and chronic disease prevention in a coordinated strategy to safeguard population health and advance health equity. Keywords: Food Safety Surveillance, Cancer Prevention, Public Health Integration, Risk Assessment, Regulatory Compliance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".