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Record W4247169223 · doi:10.1097/jom.0b013e31826647b5

Multiple Myeloma

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

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2012
Typeletter
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMultiple myelomaMedicineIncidence (geometry)Odds ratioInternal medicineConfidence intervalEtiologyEpidemiologyPopulationImmunologyOncologyEnvironmental healthDemography

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0030.001
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0160.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.309
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2012
Admission routes1
Has abstractyes

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