Lung cancer risk among workers in the construction industry: results from two case–control studies in Montreal
Notice bibliographique
Résumé
BACKGROUND: Given the large number of workers in the construction industry, it is important to derive accurate and valid estimates of cancer risk, and in particular lung cancer risk. In most previous studies, risks among construction workers were compared with general populations including blue and white collar workers. The main objectives of this study were to assess whether construction workers experience excess lung cancer risk, and whether exposure to selected construction industry exposures carries excess risks. We wished to address these objectives within the sub-population of blue collar workers. METHODS: Two case-control studies were conducted in Montreal. Combined, they included 1593 lung cancer cases and 1427 controls, of whom 1304 cases and 1081 controls had been blue collar workers. Detailed lifetime job histories were obtained and translated by experts into histories of exposure to chemical agents. The two key analyses were to estimate odds ratio (OR) estimates of lung cancer risk: a) for all blue-collar construction workers compared with other blue-collar workers, and b) for construction workers exposed to each of 20 exposure agents found in the construction industry compared with construction workers unexposed to those agents. All analyses were conducted using unconditional logistic regression adjusted for socio-demographic factors and smoking history. RESULTS: The OR for all construction workers combined was 1.11 (95 % CI: 0.90-1.38), based on 381 blue collar construction workers. Analyses of specific exposures were hampered by small numbers and imprecise estimates. While none of 20 occupational agents examined was significantly associated with lung cancer, the following agents manifested non-significantly elevated ORs: asbestos, silica, Portland cement, soil dust, calcium oxide and calcium sulfate. CONCLUSIONS: Compared with other blue collar workers, there was only a slight increased risk of lung cancer for subjects who ever held an occupation in the construction industry. The analyses of agents within the construction industry produced imprecise estimates of risk, but nevertheless pointed to some plausible associations. Excess risks for asbestos and silica were in line with previous knowledge. The possible excess risks with the other inorganic dusts require further corroboration.
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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 | 0,001 | 0,001 |
| 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 ».