A Simple Probabilistic Modelling Tool to Estimate Children's Blood Lead Levels Resulting from High Variations of Daily Exposure through Drinking Water in Schools and Daycares
Bibliographic record
Abstract
BACKGROUND Spatiotemporal variations of lead (Pb) concentrations in drinking water ([Pb]DW) from schools and daycares can exceed an order of magnitude, with rare transient peaks possibly surpassing 1 mg/L. Available kinetic models that predict blood Pb levels (BLL) in exposed children hardly allow to account for variations of such magnitude and frequency. Therefore the aim of this study was to develop a simple tool that simulates the daily evolution of BLL in children exposed to ([Pb]DW) at school or daycare.METHODS Basic toxicokinetic equations were assembled to simulate BLL in a typical infant, toddler and child, respectively aged 0.5, 2 and 6 years. Modelling tool validation was done by comparing its predictions of BLL at steady-state with those obtained with the widely accepted Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK). BLL were simulated for each typical individual assuming daily exposure to [Pb]DW over an academic year. Monte Carlo simulations were run to account for uncertainty and variability in [Pb]DW and model parameters.RESULTS The modelling tool predict steady-state BLL that fits (r2 = 0.99) IEUBK predictions for [Pb]DW in the range of 10 – 925 µg/L. For a median [Pb]DW of 14 µg/L (90th percentile = 168 µg/L), average annual BLL (median, 97.5th percentile) vary between 2.5 and 5.4 µg/dL in infant and 1.9 and 3.9 µg/dL in child. Correspondingly, maximum annual BLL are 3.4 and 7.8 µg/dL, and 2.7 and 5.7 µg/dL. The infant and child present BLL > 5 µg/dL for up to respectively 191 and 24 days. Toddler’s and infant’s results are similar.CONCLUSIONS Exposure to [Pb]DW in schools and daycares may lead to increased BLL in children. Along with average level, the spatiotemporal nature of the exposure pattern is in itself an important determinant of BLL. Thus, better characterization, in schools and daycares, of [Pb]DW and children’s drinking water consumption habits are required to evaluate their resulting risk of increased BLL.
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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 | 0.000 | 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 | 0.001 |
| 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".