The Influences of Sociodemographic Characteristics and Changes in Blood Lead on the Concentration-Response Relationship between Blood Lead Level and Children's Intelligence Quotient
Bibliographic record
Abstract
Blood lead levels (BLLs) have decreased over the last several decades but lead exposure remains a risk and the relationship between socioeconomic status, lead, and neurodevelopment is not well-understood. Differences in the distributions of sociodemographic characteristics between children with higher and lower BLLs may account for the nonlinear concentration-response (C-R) relationship observed between BLL and cognitive effects in multiple studies. Specifically, adjustment for sociodemographic characteristics may be an over-adjustment leading to an underestimate of the association at the upper end of the distribution. We analyzed data from the US cohorts examined in the pooled analysis of Lanphear et al. 2005. Like the original analysis, we analyzed relationships of BLL and sociodemographic factors with child IQ using fixed effects multivariable generalized linear regression, and stratified the dataset into children with peak BLLs < 7.5 and ≥ 7.5 µg/dL. Unlike the original analysis we considered the interaction between concurrent BLL and sociodemographic factors and estimated the cumulative impact of BLL and sociodemographic characteristics across the distribution of IQ using quantile regression. The correlation of concurrent BLL with sociodemographic characteristics was generally stronger in the high peak blood lead group, potentially reducing our ability to distinguish the independent effect of blood lead from the effect sociodemographic factors at the upper end of the distribution. The cumulative effect of BLL and sociodemographic factors was largest at the upper end of the IQ distribution in the low peak BLL group suggesting the importance of considering baseline IQ. Overall, this analysis suggests that distribution of sociodemographic factors across the range of BLLs may explain, in part, the attenuation of the C-R relationship at higher BLLs. Disclaimer: Views expressed in abstract are those of authors and do not represent views/policies of the US EPA.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".