Evaluation of a Land-Use Regression Model to Assess Exposure to Air Pollution During Pregnancy: Use of GPS Tracking and Personal Monitoring
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».