CONTRASTING EVIDENCE WHEN USING HOSPITAL OR POPULATION CONTROLS: THE EXAMPLE OF THE ASSOCIATION BETWEEN EXPOSURE TO GASOLINE AND DIESEL EXHAUST, AND LUNG CANCER
Notice bibliographique
Résumé
ISEE-407 Introduction: Much of the evidence for an association between exposure to gasoline and diesel exhausts and lung cancer comes from occupational case-control studies. The choice of controls is critical in providing valid estimates. There is no such thing, however, as a perfect control group. Although hospital controls may be less subject to reporting bias and show higher response rates than population controls, diseases in the control pool may themselves be associated with the exposures of interest, leading to biased estimates. Do the two types of control groups tend to give similar answers? Methods: In the 1980s, we conducted a large population-based case-control study to assess the role of occupational exposures and circumstances on cancer incidence in Montreal. Incident cases from all area hospitals were ascertained. We focus here on the results from an in-depth analysis of our data on gasoline and diesel exhausts and lung cancer. For this analysis, we used 857 lung cancer cases and two distinct control series: one consisted of 533 controls from the general population, and the other, comprising 1349 patients with cancers at sites other than the lung. All subjects were interviewed to obtain a detailed job history and relevant data on potential confounders. A team of chemists and hygienists translated each job into a list of potential exposures. Analyses were carried out for exposure to gasoline and diesel exhausts, as well as for occupations presumed to have entailed exposure to those agents. Results: The odds ratios (OR) for lung cancer associated with nonsubstantial and substantial exposure to gasoline exhaust were 0.9 and 0.9, respectively, using either population or cancer controls. However, for diesel engine emissions, the two control groups yielded somewhat different estimates. Using population controls, the OR was 1.1 [95% confidence intervals (95% CI): 0.7-1.7] for nonsubstantial exposure, and 1.6 (95% CI: 0.9-2.8) for substantial exposure. Using cancer controls, the corresponding values were 1.0 (95% CI: 0.7-1.4) and 1.0 (95% CI: 0.7-1.5). Discussion: Few studies offer the opportunity to contrast results obtained with several control groups. Although they may be difficult to reconcile, our results underline the challenge in obtaining both valid occupational exposure information and representative control subjects.
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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 | 0,002 |
| 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 ».