Follow-Up of Elevated Blood Lead Levels and Sources in a Cohort of Children in Benin
Bibliographic record
Abstract
Lead exposure is associated with poor cognitive development in children. Very few studies in sub-Saharan Africa (SSA) have studied blood lead levels (BLLs) and non-gasoline sources of exposure in children. Data from a birth cohort in Benin (2011-2013) suggested that 58% of 1-year-old children had BLLs > 50 ug/L. We aimed to investigate the prevalence of elevated BLLs (>50 µg/L and >100 µg /L) among 425 of these children at 6 years of age in 2016-2018 and to compare BLLs between age 1 and 6 years, and study sources of lead at age 6 years. BLLs were analysed by inductively coupled plasma mass spectrometry. Multiple linear regression and quantile regressions were used to study potential sources of lead. The prevalence of BLLs > 50 µg/L in children was 59.5% (Geometric Mean (GM) 56.4 µg/L, 95% CI: 54.1-58.7) at 6 years of age compared to 54.8% (GM 56.5 µg/L, 95% CI: 53.4-59.6) at 1 year of age. The prevalence of children with BLLs > 100 µg/L decreased from 14.4% at 1 year of age to 8.2% at 6 years of age. After adjustment for all other covariates, consumption of peanuts more than once per month was significantly associated with a 22.0% (95% CI: 4.6, 42.5) increment in BLLs at age 6 years compared with no consumption. Consumption of bushmeat killed by lead bullets at age 6 years was associated with an increase in the higher percentiles of BLLs (P75) compared with the absence of this source. Other potential sources of lead associated with BLLs with marginal significance were consumption of rice, paternal occupational exposure, and the presence of activity with the potential use of lead. This prospective cohort confirms the persistently high prevalence of elevated BLLs in children residing in a rural region in the south of Benin, as well as the presence of multiple and continuous sources of lead. These results highlight the need for prevention programs to reduce and eliminate lead exposure in children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".