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197 The benefits of integrating compensation data with survey data: the Prospective Outcomes of Injury Study experience
  1. A M ’t Mannetje1,
  2. De Roos2,
  3. Boffetta3,
  4. Cocco4,
  5. Benke5,
  6. Blair6,
  7. Brennan7,
  8. Chiu8,
  9. Clavel9,
  10. De Sanjose10,
  11. Hartge11,
  12. Holly12,
  13. Roman13,
  14. Seniori Costantini14,
  15. Spinelli15,
  16. Zheng16,
  17. Kricker17
  1. 1Massey University, Wellington, New Zealand
  2. 2Fred Hutchinson Cancer Research Center, Seattle, United States of America
  3. 3IPRI, Lyon, France
  4. 4University of Cagliari, Cagliari, Italy
  5. 5University of Melbourne, Melbourne, Australia
  6. 6National Cancer Institute, Washington, United States of America
  7. 7IARC, Lyon, France
  8. 8University of Chicago, Chicago, United States of America
  9. 9Inserm, Villejuif, France
  10. 10Catalan Institute of Oncology, Barcelona, Spain
  11. 11NCI, Washington, United States of America
  12. 12University of California, San Francisco, United States of America
  13. 13University of York, York, United Kingdom
  14. 14Center for Study and Prevention of Cancer, Florence, Italy
  15. 15BC Cancer Research Center, Vancouver, Canada
  16. 16Yale school of Public Health, New Haven, United States of America
  17. 17University of Sydney, Sydney, Australia


Objectives A range of occupations have been associated inconsistently with an elevated NHL risk. In this large, pooled study, we investigate the relationship between occupation and NHL and NHL subtypes.

Methods This pooled study of 10 NHL case-control studies participating in the InterLymph consortium, included 10,046 cases uniformly classified by subtype and 12,025 controls. Occupational histories were classified according to the ISCO 1968 classification, and occupations previously associated with increases in hematologic cancer risk were grouped into 26 a priori high risk occupational groups. Odds ratios, adjusting for centre, age and sex were determined for the a priori groups as well as all ISCO occupational codes including a minimum of 10 cases. Analyses were repeated by sex and for the subtypes diffuse large B-cell lymphoma (DLBCL; n = 3,061), follicular lymphoma (FL; n = 2,140), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL; n = 1,014) and T-cell lymphoma (n = 632).

Results DLBCL risk was elevated for textile workers (OR: 1.19; 95%CI: 1.01–1.41); field crop and vegetable farm workers (1.50; 1.15–1.97); charworkers, cleaners and related workers (1.27; 1.03–1.58) and hairdressers (1.47; 1.08–2.00). FL risk was elevated for unspecified labourers (1.28; 1.06–1.55) and spray painters (2.67; 1.36–5.25). CLL/SLL risk was elevated for women’s hairdressers (2.69; 1.43–5.05); general farm workers (1.44; 1.13–1.84); pre-primary education teachers (2.00; 1.04–3.87) and printing pressmen (6.52; 2.79–15.2). T-cell lymphoma risk was elevated for textile workers (1.60; 1.18–2.17); wood workers (1.54; 1.04–2.27) and painters (1.80; 1.14–2.84). ORs differed significantly among subtypes for hairdressers, textile workers and printing pressmen.

Conclusions This pooled analysis supports a role for farming, textile, and hairdressing related exposures in the development of NHL. Occupations with potential exposure to solvents, metals, wood dust, infectious agents and mineral dust were also positively associated with NHL. For all four studied NHL subtypes occupational risk factors play a role, with notable differences in risk occupations across subtypes.

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