Cumulative childhood lead exposure estimation and school-age IQ in a prospective birth cohort
Bibliographic record
Abstract
BACKGROUND: Lead is a well-known neurotoxicant with no identified safe level. Prior studies found that childhood lead exposure is associated with decreased intelligence quotient (IQ) scores. However, most studies rely on a limited number of blood lead measurements. In this prospective pregnancy and birth cohort, we estimated cumulative childhood lead exposure using repeated blood lead concentrations and regression calibration, allowing for more accurate assessment of lead burden over time and its association with IQ. METHODS: This prospective study included 262 mother-child dyads from Greater Cincinnati enrolled in the Health Outcomes and Measures of the Environment (HOME) Study from 2003 to 2006. We obtained serial blood lead measurements and estimated cumulative childhood lead exposure using a regression calibration method. Outcome was assessed via Wechsler-based IQ testing at ages 5-12 years. We examined the association between estimated cumulative childhood lead exposure and child IQ using linear regression models. RESULTS: Our cohort had low levels of estimated lifetime average lead exposure (geometric mean: 1.21 μg/dL). Overall, estimated lead exposure decreased from age 12 months to time of IQ test. Cumulative childhood lead exposure estimate was associated with decreased IQ at ages 5-12 years in unadjusted analyses, but not after adjusting for maternal IQ, household income, reported prenatal vitamin use, Home Observation for Measurement of the Environment score, and maternal serum cotinine. Sensitivity analyses additionally adjusting for prenatal total folate did not markedly change our results. We assessed early-life, school-age, or concurrent blood lead exposure estimate in place of cumulative childhood lead exposure estimate and observed a similar pattern of results. CONCLUSIONS: We used a regression calibration method to leverage robust, repeated lead exposure data in our prospective pregnancy and birth cohort. In this cohort with low levels of lead exposure, cumulative childhood lead exposure estimate was negatively associated with school-age IQ in unadjusted analyses but not adjusted analyses. We considered sociodemographic and maternal factors previously associated with cognitive development. Our results suggest these factors may confound the association between low-level child lead exposure and child IQ.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".