Occupation and Risk of Non-Hodgkin Lymphoma and Its Subtypes: A Pooled Analysis from the InterLymph Consortium
Bibliographic record
Abstract
BACKGROUND: Various occupations have been associated with an elevated risk of non-Hodgkin lymphoma (NHL), but results have been inconsistent across studies. OBJECTIVES: We investigated occupational risk of NHL and of four common NHL subtypes with particular focus on occupations of a priori interest. METHODS: We conducted a pooled analysis of 10,046 cases and 12,025 controls from 10 NHL studies participating in the InterLymph Consortium. We harmonized the occupational coding using the 1968 International Standard Classification of Occupations (ISCO-1968) and grouped occupations previously associated with NHL into 25 a priori groups. Odds ratios (ORs) adjusted for center, age, and sex were determined for NHL overall and for the following four subtypes: diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), and peripheral T-cell lymphoma (PTCL). RESULTS: We confirmed previously reported positive associations between NHL and farming occupations [field crop/vegetable farm workers OR = 1.26; 95% confidence interval (CI): 1.05, 1.51; general farm workers OR = 1.19; 95% CI: 1.03, 1.37]; we also confirmed associations of NHL with specific occupations such as women's hairdressers (OR = 1.34; 95% CI: 1.02, 1.74), charworkers/cleaners (OR = 1.17; 95% CI: 1.01, 1.36), spray-painters (OR = 2.07; 95% CI: 1.30, 3.29), electrical wiremen (OR = 1.24; 95% CI: 1.00, 1.54), and carpenters (OR = 1.42; 95% CI: 1.04, 1.93). We observed subtype-specific associations for DLBCL and CLL/SLL in women's hairdressers and for DLBCL and PTCL in textile workers. CONCLUSIONS: Our pooled analysis of 10 international studies adds to evidence suggesting that farming, hairdressing, and textile industry-related exposures may contribute to NHL risk. Associations with women's hairdresser and textile occupations may be specific for certain NHL subtypes. CITATION: 't Mannetje A, De Roos AJ, Boffetta P, Vermeulen R, Benke G, Fritschi L, Brennan P, Foretova L, Maynadié M, Becker N, Nieters A, Staines A, Campagna M, Chiu B, Clavel J, de Sanjose S, Hartge P, Holly EA, Bracci P, Linet MS, Monnereau A, Orsi L, Purdue MP, Rothman N, Lan Q, Kane E, Seniori Costantini A, Miligi L, Spinelli JJ, Zheng T, Cocco P, Kricker A. 2016. Occupation and risk of non-Hodgkin lymphoma and its subtypes: a pooled analysis from the InterLymph Consortium. Environ Health Perspect 124:396-405; http://dx.doi.org/10.1289/ehp.1409294.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".