A randomized controlled trial of a housing intervention to reduce endocrine disrupting chemical exposures in children
Bibliographic record
Abstract
Few studies have considered household interventions for reducing endocrine disrupting chemical (EDC) exposures. We conducted a secondary analysis of a randomized controlled trial, originally designed to reduce lead exposure, to evaluate if the intervention lowered EDC exposures in young children. Study participants were children from the Cincinnati, Ohio area (n = 250, HOME Study). Prenatally, families received a housing intervention that included paint stabilization and dust mitigation, or as a control, injury prevention measures. At 24-months, we measured organophosphate esters (OPEs) and phthalates or their metabolites in dust and urine. We measured perfluoroalkyl substances (PFAS) in dust and serum at 24- and 36-months, respectively. We assessed associations between dust and biomarker EDCs using Spearman correlations, characterized EDC mixtures via principal components analysis, and investigated treatment effects using linear regression. To mitigate selection bias, we fit statistical models using inverse probability of retention weights. Correlations between dust EDCs and analogous biomarkers were weak-to-moderate (ρ's ≤ 0.3). The intervention was associated with 23 % (95 % CI: -38, -3) lower urinary DEHP metabolites and, in a per-protocol analysis, 34 % lower (95 % CI: -55, -2) urinary MBZP. Additionally, among Black or African American children, the intervention was associated with lower serum concentrations of several PFAS (e.g., -42 %; 95 % CI: -63, -8 for PFNA). Household interventions that include paint stabilization and dust mitigation may reduce childhood exposures to some phthalates and PFAS in Blacks/African Americans. These findings highlight the need for larger studies with tailored and sustained housing interventions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".