Variability and Predictors of Urinary Concentrations of Phthalate Metabolites during Early Childhood
Bibliographic record
Abstract
The variability and predictors of urinary concentrations of phthalate metabolites in preschool-aged children have not been thoroughly examined. Additionally, the impact of temporal changes in the use and restriction of phthalates in children's products has not been assessed. Our objective was to identify demographic, behavioral, and temporal predictors of urinary phthalate metabolite concentrations in young children. Between 2004 and 2011, we collected up to five urine samples from each of 296 children participating in a prospective birth cohort during annual study visits at ages 1-5 years. We used linear mixed models to calculate intraclass correlation coefficients (ICCs), a measure of within-individual reproducibility, and identify demographic predictors of urinary phthalate metabolites. We used multivariable linear regression to examine cross-sectional relationships between food packaging or personal care product use and phthalate metabolites measured at age 5 years. Across annual measurements, monoethyl phthalate exhibited the least variation (ICC = 0.38), while di-2-ethylhexyl phthalate (ΣDEHP) metabolites exhibited the most variation (ICC = 0.09). Concentrations changed with age, suggesting age-related changes in phthalate exposure and perhaps metabolism. Our findings suggest that fast food consumption may be a source of butylbenzyl phthalate and di-isononyl phthalate (DiNP) exposure, and some personal care products may be sources of diethyl phthalate exposure. Concentrations of ΣDEHP metabolites decreased over the study period; however, concentrations of DiNP metabolites increased. This finding suggests that manufacturer practices and regulations, like the Consumer Product Safety Improvement Act of 2008, may decrease DEHP exposure, but additional work characterizing the nature and toxicity of replacements is critically needed.
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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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".