World Health Organization estimates of the global and regional disease burden of four foodborne chemical toxins, 2010: a data synthesis
Bibliographic record
Abstract
Background Chemical exposures have been associated with a variety of health effects; however, little is known about the global disease burden from foodborne chemicals. Food can be a major pathway for the general population's exposure to chemicals, and for some chemicals, it accounts for almost 100% of exposure. Methods and Findings Groups of foodborne chemicals, both natural and anthropogenic, were evaluated for their ability to contribute to the burden of disease. The results of the analyses on four chemicals are presented here - cyanide in cassava, peanut allergen, aflatoxin, and dioxin. Systematic reviews of the literature were conducted to develop age- and sex-specific disease incidence and mortality estimates due to these chemicals. From these estimates, the numbers of cases, deaths and disability adjusted life years (DALYs) were calculated. For these four chemicals combined, the total number of illnesses, deaths, and DALYs in 2010 is estimated to be 339,000 (95% uncertainty interval [UI]: 186,000-1,239,000); 20,000 (95% UI: 8,000-52,000); and 1,012,000 (95% UI: 562,000-2,822,000), respectively. Both cyanide in cassava and aflatoxin are associated with diseases with high case-fatality ratios. Virtually all human exposure to these four chemicals is through the food supply. Conclusion Chemicals in the food supply, as evidenced by the results for only four chemicals, can have a significant impact on the global burden of disease. The case-fatality rates for these four chemicals range from low (e.g., peanut allergen) to extremely high (aflatoxin and liver cancer). The effects associated with these four chemicals are neurologic (cyanide in cassava), cancer (aflatoxin), allergic response (peanut allergen), endocrine (dioxin), and reproductive (dioxin).
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.024 | 0.031 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".