A review of lead exposure source attributional studies
Bibliographic record
Abstract
Despite the global phase-out of leaded gasoline, lead poisoning is estimated to cause 5.5 million premature deaths and the loss of 765 million IQ points annually. However, the contributions of different lead exposure sources to blood lead levels (BLLs) are poorly understood. We conducted a systematic literature review using the Scopus database, examining 39 studies that attribute BLLs to specific sources of lead exposure, published since the year 2000 and with sample sizes >100. The 39 studies were from 26 countries; 22 were from low- and middle-income countries, with an average sample size of 1003 participants. Twenty-three of the studies reported absolute BLL impacts (μg/L) from lead exposure sources, other studies reported odds ratios for elevated BLLs (>50 or > 100 μg/L). Averaged across the studies, the BLL impacts were 42.3 μg/L from living near industrial lead pollution hotspots, 31.4 μg/L from occupational and take-home exposure, 28.0 μg/L from deteriorated paint, 19.8 μg/L from traditional medicines and cosmetics, 19.3 μg/L from foodware (glazed ceramics and melamine plates), 17.3 μg/L from smoking, 15.4 μg/L from foods, and 12.9 μg/L from geophagy. Only one of the reviewed studies assessed the BLL impact of metal cookware, and did not find a significant relationship with BLLs. However, the statistical power of the attributional studies to detect relationships with BLLs was often limited. Future studies should investigate the ingestion routes from industrial pollution, the contamination of foods and spices, BLL impacts of lead-contaminated metal cookware, and traditional medicines administered to young children and infants.
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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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".