Aflatoxin contamination of maize flour in Kenya: Results from multi-city, multi-round surveillance
Bibliographic record
Abstract
Foodborne illness is a major source of the global burden of disease, but public monitoring of hazards in food systems is overwhelmingly focused on the formal sector in high income countries. We contribute to the development of an evidence base on food safety risk in low-income and informal settings by monitoring aflatoxin prevalence in maize flour in Kenya. Aflatoxin is a contaminant which causes liver cancer and has been linked to childhood stunting. We carry out systematic monitoring of formally and informally processed maize flour from a range of retail vendors across ten urban sites in Kenya and analyze aflatoxin levels in commercial samples. Samples were obtained every two months from February-December 2021 and 1255 samples in total were analyzed. Almost all samples (97%) showed detectable levels of aflatoxin, with 16% of tested samples exceeding the national regulatory limit of 10 ppb. Mean contamination levels are significantly higher (p < 0.001) in informal market samples (9.9 ppb) than in packaged flour in the formal sector (4.9 ppb). We find important seasonal variation in aflatoxin levels, which are highest in our June sample and lowest in December, which we attribute to variation in sourcing of maize grain. Our results demonstrate the need for policy interventions to reduce aflatoxin exposure in Kenya and demonstrate the utility of coordinated monitoring efforts to track levels of food safety risk in low-income settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".