SUBNORMAL BIRTHWEIGHT AND INDUSTRIAL AIR POLLUTANTS – A COMPARISON OF SPATIAL-TEMPORAL HOT SPOT PATTERNS
Bibliographic record
Abstract
Abstract BACKGROUND Disorders related to short gestation and low birth weight are the 2nd cause of infant death in Canada and have been increasing, especially in Alberta. Individual maternal risks are important but environmental exposures during pregnancy may restrict fetal growth. This contributes to small for gestational age (SGA: < tenth percentile weight for pregnancy duration) and low birth weight at term (LBWT: <2500 grams at ≥37 weeks-gestation). OBJECTIVES We examined the spatial-temporal patterns of SGA and LBWT with patterns of pollutants around conception, middle trimester, and birth. DESIGN/METHODS We aggregated postal code locations of mothers’ residences from the 2006–2012 birth registry in to space-time bins to analyze emerging hot spots. We applied the space-time pattern analysis on 70 industrial chemical emissions from the National Pollutant Release Inventory (NPRI) in estimated three month intervals. Then we statistically associated the classified patterns of SGA/LBWT with the pollutant patterns using the kappa statistic to determine how much the hot spot categories agree. The difference between kappa values indicated which trimester would be more important for which chemical. RESULTS There was an increasing trend for SGA (consecutive hot spots) and for LBWT (sporadic hot spots) in major urban centers. There was an increasing trend for 15 chemicals (varying hot spots). 28 chemical patterns had a kappa index greater than 0.2 with SGA or LBWT patterns. Although there is poor agreement between the space-time patterns, the maximum kappa values occurred mostly with LBWT and around birth. CONCLUSION Spatial-temporal patterns of chemicals identified in published literature (e.g. particulate matter and gases) agreed more with timing around conception; however, there were additional pollutants identified during the birth trimester. Our research is moving us toward a better understanding of the spatial-temporal link between environment and early health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".