Association between prenatal exposure to PM2.5 and the increased risk of specified infant mortality in South Korea
Bibliographic record
Abstract
Findings from previous studies on the association between exposure to fine particulate matter (PM2.5) and the risk of infant mortality were inconsistent. Thus, two main objectives of our study were to examine the association between exposure to PM2.5 and specified infant mortality and to identify critical trimesters. We retrospectively created a birth cohort of singleton full-term infants born in South Korea between 2010 and 2015 using national birth and infant mortality data. The specified causes of infant mortality were circulatory and respiratory diseases, perinatal conditions, congenital anomalies, and sudden infant death syndrome. We performed 1:10 propensity score matching for various exposure windows: each trimester, prenatal, and postnatal (up to age 1). Conditional logistic regression was applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs), while accounting for gestational age, birth weight, maternal education level, season of birth, and regions (metropolitan areas/provinces). We also conducted sex-stratified analyses and used different matching ratios for sensitivity analyses. A total of 2,501,836 births and 761 deaths (0.03%) were identified in the birth cohort. We found an increased risk of infant mortality per 10 µg/m3 increase in PM2.5 exposure during the prenatal period (OR: 1.29, 95% CI: 1.07–1.55). Exposure in the 1st and 2nd trimesters was linked to an elevated risk (OR: 1.19, 95% CI: 1.02–1.37; OR: 1.21, 95% CI: 1.04–1.40). However, no association was shown in the third trimester. PM2.5 exposure in the 1st and 2nd trimesters was associated with elevated male infant mortality, but did not reach statistical significance in female infants. The use of different matching ratios did not significantly affect the results. The study findings suggest that exposure to PM2.5 could affect infant mortality differently by the timing of exposure and sex, which suggests a relation to fetal development. However, further investigations are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".