Prenatal pesticide exposure and respiratory health outcomes in the first year of life: Results from the infants’ Environmental Health (ISA) study
Bibliographic record
Abstract
BACKGROUND: Growing evidence suggests that pesticide exposure may influence respiratory health, but data in young children are very limited. We examined the association of prenatal pesticide exposure with lower respiratory tract infections (LRTIs) and wheeze at one year of age in children from the Infants' Environmental Health (ISA) study in Costa Rica. METHODS: We measured seven pesticide metabolites, including ethylenethiourea (ETU, metabolite of mancozeb), in maternal urine samples collected repeatedly during pregnancy. For each woman, we averaged pesticide concentrations during each half of pregnancy (≤20 and >20 weeks of gestation) and across repeated samples collected over the course of pregnancy. We collected information about LRTIs (n = 355) and wheezing (n = 272) during the first year of life from mothers when their children were 11-19 months old. We fit multivariable logistic regression models using high (quartile 4) vs. low (quartiles 1-3) urinary pesticide concentrations as exposures and adjusted models for maternal age, education, parity, gestational age at birth, and child sex. RESULTS: Ten percent of the children had at least one LRTI and 39% had at least one episode of wheezing during their first year of life. Median (25-75th percentile) specific gravity-corrected urinary ETU concentrations during the first half, second half, and over the course of pregnancy were 3.4 (2.1-5.0), 3.3 (2.2-4.7), and 3.4 (2.4-5.0) ng/mL, respectively. We observed that high urinary ETU concentrations during the first half of pregnancy were associated with increased odds of LRTI (OR = 2.45; 95% CI: 0.96, 6.26), whereas high urinary ETU concentrations during the second half of pregnancy were associated with decreased odds of wheezing (OR = 0.50; 95% CI: 0.26, 0.96). We found that the association between high urinary ETU concentrations during the first half of pregnancy and LRTIs persisted among mother-child pairs with either high or low ETU concentrations during the second half. In contrast, the association of high urinary ETU concentrations during the second half of pregnancy with wheezing was attenuated when we simultaneously adjusted for urinary ETU concentrations during the first half. We observed null associations between other pesticide metabolites measured during pregnancy and respiratory outcomes. CONCLUSIONS: Our data indicate that exposure to mancozeb/ETU during the first half of pregnancy may be associated with respiratory outcomes in the first year of life.
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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 | 0.001 | 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 | 0.000 |
| 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".