Residential Air Pollution and Lung Cancer
Notice bibliographique
Résumé
The authors respond: Thank you for the opportunity1 to further discuss the potential in our study for residual confounding by smoking and misclassification of exposure.2 Although these concerns1 have been raised regarding many epidemiologic studies of air pollution, it is unlikely that they are responsible for the positive associations we reported. Numerous studies3 have found that self-reported measures produce valid estimates of smoking behavior. We found negative correlations between all smoking variables (ie, smoking pack-years, years since cessation, and residential and occupational second-hand smoke exposure) and air pollution exposures, which limits any positive bias in our results. While the potential for response and recall bias exists in all case-control studies, this population-based study has a relatively high response rate for cases (62%) and controls (67%). Furthermore, we found no difference between cases and controls in the completeness of the self-reported residential histories that we used to assign air pollution exposures. Our long-term exposure assessment approach represents clear improvements over past studies. Specifically, we included complete residential histories over a 20-year period and applied multiple spatiotemporal models of PM2.5, NO2, and O3. While some degree of exposure misclassification is present, this error is likely nondifferential and thus would produce bias toward (rather than away from) the null. As reported,2 the increased lung cancer odds ratio for NO2 exposures derived from fixed-site monitors likely represents contributions from PM2.5 due to the high correlation of these two pollutants. Furthermore, all other sensitivity analyses using various spatiotemporal models revealed consistent associations. Thus, our study offers a useful contribution to the epidemiologic evidence regarding air pollution exposure and lung cancer incidence. While we made no claims of a causal association in our article, we concur with recent commentaries4 and systematic reviews and meta-analyses5,6 that the current weight of evidence supports an association of PM2.5 and NO2 exposures with lung cancer incidence. Whether these associations are causal is the focus of the upcoming International Agency for Research on Cancer monograph evaluating the carcinogenicity of ambient air pollution.7 We do note, however, that the Environmental Protection Agency’s Integrated Science Assessment8 mentioned by Drs. Sax and Goodman concluded that the evidence is “suggestive of a causal relationship between long-term exposures to PM2.5 and cancer.” We also highlight that NO2 itself is not likely to be responsible for the increase in lung cancer risk but rather is a marker for other traffic-related carcinogens. Perry Hystad School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada, [email protected] Paul A. Demers Occupational Cancer Research Centre, Cancer Care Ontario, Ontario, Canada Kenneth C. Johnson Department of Epidemiology and Community Health, University of Ottawa, Ottawa, Ontario, Canada Richard M. Carpiano Department of Sociology, University of British Columbia, Vancouver, BC, Canada Michael Brauer School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».