Prenatal Exposure to Heavy Metals and Adverse Birth Outcomes: Evidence From an E‐Waste Area in China
Bibliographic record
Abstract
Abstract Electronic waste that has not been properly treated can lead to environmental contamination including of heavy metals, which can pose risks to human health. Infants, a sensitive group, are highly susceptible to heavy metals exposure. The aim of this study was to investigate the association between prenatal heavy metal exposure and infant birth outcomes in an e‐waste recycling area in China. We analyzed cadmium (Cd), chromium (Cr), manganese (Mn), lead (Pb), copper (Cu), and arsenic (As) concentrations in 102 human milk samples collected 4 weeks after delivery. The results showed that 34.3% of participants for Cr, which exceeds the World Health Organization (WHO) guidelines, as well as the mean exposure of Cr exceeded the WHO guidelines. We collected data on the birth weight (BW) and length of infants and analyzed the association between metal concentration in human milk and birth outcomes using multivariable linear regression. We observed a significant negative association between the Cd concentration in maternal milk and BW in female infants ( β = −162.72, 95% CI = −303.16, −22.25). In contrast, heavy metals did not associate with birth outcomes in male infants. In this study, we found that 34.3% of participants in an e‐waste recycling area had a Cr concentration that exceeded WHO guidelines, and there was a significant negative association between prenatal exposure to the Cd and infant BW in females. These results suggest that prenatal exposure to heavy metals in e‐waste recycling areas may lead to adverse birth outcomes, especially for female 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".