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Enregistrement W4247169223 · doi:10.1097/jom.0b013e31826647b5

Multiple Myeloma

2012· letter· en· W4247169223 sur OpenAlexaboutno aff
Judith M. Graber, Leslie Stayner, Michael D. Attfield

Notice bibliographique

RevueJournal of Occupational and Environmental Medicine · 2012
Typeletter
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaMedicineIncidence (geometry)Odds ratioInternal medicineConfidence intervalEtiologyEpidemiologyPopulationImmunologyOncologyEnvironmental healthDemography

Résumé

récupéré en direct d'OpenAlex

To the Editor: We read with much interest the article by Ghosh et al.1 titled “Multiple Myeloma and Occupational Exposures: A Population-Based Case–Control Study,” which appeared in the June 2011 issue of this journal. The authors reported their findings from a case–control study of multiple myeloma among 342 Canadian men, which included a positive and significant association between occupational exposure to coal dust and an increased incidence of multiple myeloma (odds ratio [OR], 1.6; 95% confidence interval [CI], 1.2–2.3; n = 62 cases). Multiple myeloma is a B-cell malignancy characterized by a monoclonal proliferation of plasma cells in the bone marrow. It makes up about 1% of cancer diagnosed in the United States.2 Over the last three decades, more than 60 studies have investigated the etiology of multiple myeloma,3; however, the etiology remains largely unknown.4 Epidemiological studies have found mixed results regarding associations between multiple myeloma incidence and mortality with specific occupations, most consistently with farming3,5 and pesticide exposure3 and less consistently with exposure to various industrial chemicals and petrochemicals,6–8 as well as diesel exhaust.9 Other factors explored in the literature include a family history of other cancers,1,10 and exposure to the herpes zoster virus.10,11 Little has been reported regarding a possible association between occupational coal-dust exposure and/or work as a coal miner and multiple myeloma incidence or mortality. Sonoda et al.12 reported nonsignificant associations between multiple myeloma incidence and occupation as a miner (OR, 1.8; 95% CI, 0.2–21.6; n = 5). A similar finding was reported by Nanni et al.11 On the basis of only 2 cases, they observed that occupation as a miner was associated with a statistically nonsignificant elevated risk of mortality from multiple myeloma (OR, 1.8; 95% CI, 0.3–10.1). Also, an excess of multiple myeloma was reported from a coal-mining community in Texas (SIR, 1.9; 95% CI, 1.0–3.1; n = 14 cases).13 A finding specifically among coal miners was reported by Demers et al.14 They observed that working in a coal mine for 10 years or more was associated with an elevated risk of multiple myeloma (RR, 2.9; 95% CI, 0.8–9.9; n = 6 cases). In a recent mortality analysis after 37 years of follow-up of a cohort of 8829 U.S. underground coal miners, we observed a statistically significant excess and a positive exposure–response association with coal-mine dust and multiple myeloma as the underlying cause of death (eighth or ninth International Classification of Diseases [ICD] revision code 203 or ICD-10 revision codes C88.7, C88.9, and C90). Nevertheless, the significant findings were among only African American and not white coal miners. The standardized mortality ratio we observed for multiple myeloma among African American miners was 2.8 (95% CI, 1.1–6.4; n = 6 cases), whereas that among white miners was 0.9 (95% CI, 0.6–1.4; n = 22 cases). In a Cox proportional hazards model, the relationship between multiple myeloma and coal-mine dust exposure was modified by race (P value for interaction, 0.008) such that the association for African American miners was elevated and statistically significant (hazard ratio for cohort's mean exposure of 64.6 mg/m3-years, 10.51; 95% CI, 2.93–37.65), whereas that for white miners was not significant (hazard ratio for cohort's mean exposure of 64.6 mg/m3-years, 1.6; 95% CI, 0.6–4.0). Our analysis controlled for age, smoking status (current, former, and ever), pack-years at study enrollment (1968 to 1971), and birth year. Region (east-west) and body mass index were excluded from the model because their inclusion did not improve the model fit. We found no evidence of a significant association between multiple myeloma and cumulative silica exposure. Of note, we did not observe significant interactions by race with any other cancer outcome. In the United States, the age-adjusted incidence and mortality of multiple myeloma are about two-fold higher in African American than in white men.15 In our study, race was classified by self-report. Self-identified race may reflect a complex mix of social and genetic factors. In an occupation in which the majority of the workforce is white, this may manifest itself in differential exposure to hazardous occupational exposures. We saw clear evidence of differential exposure by race in our cohort, which was over 95% white. The average cumulative exposure was higher among African American miners compared with white miners for both coal-mine dust (50.1 vs 40.1 mg/m3-years; P value for t test < 0.0001) and respirable silica (3.7 vs 3.2 mg/m3-years; P < 0.0001). Nevertheless, the finding for multiple myeloma among African American miners in our study may also reflect the influence of genetic factors and/or nonoccupationally related harmful environmental factors, including peridomestic environmental exposure to carcinogens, stressful home or work environments, poor nutrition, and/or exposure to infectious agents. As a result of the lack of knowledge of the etiology of multiple myeloma, no useful public health interventions have yet been identified, which might reduce the incidence of this disease.3 Further investigation into the potential role of coal dust and other occupational exposures in the causation of multiple myeloma seems warranted. Future studies on this issue should include a careful investigation of the role of racial disparities in both exposure and outcomes. Judith M. Graber, PhD Leslie T. Stayner, PhD Michael D. Attfield, PhD University of Illinois at Chicago Chicago, Ill

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,054

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,016
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,001
Communication savante0,0030,004
Science ouverte0,0030,001
Intégrité de la recherche0,0120,016
Charge utile insuffisante (le modèle a refusé de juger)0,0160,013

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.

Tête enseignante Opus0,045
Tête enseignante GPT0,309
Écart entre enseignants0,264 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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