Integrative Strategy for Finding Co-Location Patterns between Adverse Birth Outcomes and Industrial Air Pollution
Bibliographic record
Abstract
The relationship of many hazardous pollutants present in ambient air with adverse birth outcomes (ABO) is still unknown, particularly from those released by industry. The knowledge gap is even greater when considering the impact of mixtures of hazardous pollutants on pregnancy outcomes. We aimed to collaboratively overcome methodological limitations to identify potential hazardous mixtures of industrial air pollutants spatially related to the occurrence of ABO in Alberta.We applied integrative data analyses on large existing databases (2006-2012) from (i) the National Pollutant Release Inventory on chemicals released into air by industry; (ii) the Alberta Perinatal Health Program on births, known maternal risk factors for ABO, and the maternal postal code at birth; (iii) an area-level socioeconomic status index (SES). We developed a novel spatial data mining (DM) algorithm to identify significant spatial colocation rules of combinations of emitted chemicals and ABO (i.e., preterm birth [PTB], small for gestational age [SGA], and low birth weight at term [LBWT]). We used a Geographic Information System (GIS) to assign exposures to residences within 10 km of emission sites. We used epidemiological analysis to calculate the corresponding odds ratios of exposures to chemical mixtures and the occurrence of ABO, adjusted by maternal covariates and SES.DM distinguished five rules combining three chemicals - gases, particulate matter and organics - associated with ABO. GIS assigned exposure and epidemiological analysis identified all rules with increased odds for induced PTB, four for SGA, and only two for LBWT.The combined interdisciplinary use of data mining, GIS, and epidemiology strategies enabled us to generate new hypothesis by identifying the risks associated with mixtures of hazardous chemicals and ABO - findings that proved elusive when applying methodologies individually. These discoveries enhance our understanding of the role of chemical mixtures in ABO.
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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 | 0.001 |
| 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".