Evaluation of a Land-Use Regression Model to Assess Exposure to Air Pollution During Pregnancy: Use of GPS Tracking and Personal Monitoring
Bibliographic record
Abstract
SM5-PD-02 Introduction: The Border Air Quality Study (BAQS) includes a cohort study of the relationship between exposure to traffic-based air pollutants during pregnancy and adverse birth outcomes. This cohort study (BAQS) estimates exposures using a land-use regression model and geo-coded home locations. To evaluate this approach, we compared measured and modeled exposures for a sample of pregnant women. Methods: We measured and modeled 48-hour exposures (NO, NO2, NOx, and black carbon) for 31 pregnant, nonsmoking women. Measured personal exposures (Ogawa passive samplers and Personal Exposure Monitors [PEMs]) were compared with 2 modeled exposure estimates both based on locations (home and work) and a land-use regression model of ambient pollution concentrations. The first estimate simply assumes subjects stayed at home all day; the second accounts for time at home and at work using time-varying location data from a data-logging GPS receiver. In addition, since 6-digit postal codes, but not actual address locations, were available for the BAQS cohort study, we also modeled exposures based on postal codes for each of the subjects’ home and work locations. Results: Modeled outdoor exposures at home address locations were highly correlated with those based on postal code centroids for all pollutants (Spearman's rho, 0.95–0.99). Therefore, we used postal code estimates for all subsequent analyses. For NO2, geometric mean measured exposures for each of 4 groups defined by quartiles of modeled home exposure were: 10, 19, 20, and 22 ppb (significant differences between the first quartile and second, third, or fourth: Kruskal-Wallis P value <0.05). Similar group differences were seen for NOx and NO (K-W, P < 0.05) but not for black carbon. For NO2, there was a modest correlation (Spearman's rho = 0.43) between measured and modeled home exposures. Incorporating modeled work exposures slightly improved this correlation (rho = 0.45). No such associations were observed for other pollutants (range rho, 0.03–0.08). Discussion: Modeling personal exposure using home postal codes was comparable to using exact home addresses. This is important for large cohort studies, since privacy regulations typically restrict geocoding to the postal code level. For the pregnant women monitored in this study, modeled exposure quartiles were moderately predictive of high or low measured exposures for NO, NO2, and NOx. These results indicate that exposure classification of this population-based on postal code geo-coding and land-use regression models is appropriate for NO, NO2, and NOx but not black carbon.
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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 | 0.002 | 0.002 |
| 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".