Abstract IA22: Impact of workplace exposures on cancer disparities
Notice bibliographique
Résumé
Abstract Workplace exposures can have a big impact on cancer disparities. Perhaps the earliest lens through which disparities were examined was through the UK “Registrar-General’s Social Classes,” which were introduced in 1913. These six classes were based on occupation ranging from “professional occupations” to “unskilled occupations” and were predictive of mortality. One can think of these occupational groups representing both education and income, but they also represent power, control, social prestige/status, and mobility, as well as differential exposure to a wide range of workplace carcinogens. While nonoccupational behavioral risk factors could potentially confound the relationship between social class based on occupation and cancer, adjustment for these factors often does not remove associations. For example, in IARC’s Synergy project, a pooled analysis of lung cancer case-control studies, strong associations between Treiman’s Standard International Occupational Prestige Scale and International Socio-Economic Index of Occupational Status remained after adjustment for smoking. In studies of occupational cancer, our focus has most often been on “skilled manual” and “semi-skilled” occupations, because it has been easier to document exposure and their employment by either large companies or unionization facilitates recruitment. Although potentially carcinogenic exposures can occur among a wide range of occupations, the risk is not spread evenly. For example, exposure to common workplace lung carcinogens such as asbestos, diesel exhaust, crystalline silica, nickel, and chromium remains prevalent among workers in construction, transportation, mining, and certain manufacturing and service industries, and very rare among managerial, professional, and sales workers. Although the first occupational chemical carcinogen identified was soot, to which chimney sweeps were exposed as child workers, few studies have focused on the lowest, most vulnerable social class because of difficulties recruiting and other practical challenges. For example, studies of pesticides and cancer have most often recruited farmers and pesticide applicators, and not the more vulnerable farm workers, particularly migrant workers. Other examples of vulnerable groups with potentially carcinogenic exposures are electronic waste, nail salon, and cleaning workers. Further studies of cancer among these vulnerable groups are needed. Citation Format: Paul A. Demers. Impact of workplace exposures on cancer disparities [abstract]. In: Proceedings of the AACR Special Conference on Environmental Carcinogenesis: Potential Pathway to Cancer Prevention; 2019 Jun 22-24; Charlotte, NC. Philadelphia (PA): AACR; Can Prev Res 2020;13(7 Suppl): Abstract nr IA22.
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 | 0,000 |
| 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) | 0,021 | 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 ».