Food safety policy enforcement and associated actions reduce lead chromate adulteration in turmeric across Bangladesh
Bibliographic record
Abstract
Turmeric adulterated with lead chromate pigment has been previously identified as a primary source of lead exposure in Bangladesh. This study assesses the impact of a multi-faceted intervention between 2017 and 2021 to reduce lead-tainted turmeric in Bangladesh. The intervention involved: i) disseminating findings from scientific studies via news media that identified turmeric as a source of lead poisoning, ii) educating consumers and businesspeople about the risks of lead chromate in turmeric via public notices and face-to-face meetings, and iii) collaborating with the Bangladesh Food Safety Authority to utilize a rapid lead detection technology to enforce policy disallowing turmeric adulteration. Before and after the intervention, evidence of lead chromate turmeric adulteration was assessed at the nation's largest turmeric wholesale market and at turmeric polishing mills across the country. Blood lead levels of workers at two mills were also assessed. Forty-seven interviews were conducted with consumers, businesspeople, and government officials to assess changes in supply, demand, and regulatory capacity. The proportion of market turmeric samples containing detectable lead decreased from 47% pre-intervention in 2019 to 0% in 2021 (n = 631, p < 0.0001). The proportion of mills with direct evidence of lead chromate adulteration (pigment on-site) decreased from 30% pre-intervention in 2017 to 0% in 2021 (n = 33, p < 0.0001). Blood lead levels dropped a median of 30% (IQR: 21-43%), while the 90th percentile dropped 49% from 18.2 μg/dL to 9.2 μg/dL 16 months after the intervention (n = 15, p = 0.033). Media attention, credible information, rapid lead detection tools and swift government action to enforce penalties all contributed to the intervention's success. Subsequent efforts should evaluate if this is an example of an effective intervention that can be replicated to reduce lead chromate adulteration of spices globally.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".