Adjustment for Confounding in Occupational Cancer Epidemiology

  • J. J. Spinelli
  • P. R. Band
  • R. P. Gallagher
Part of the Recent Results in Cancer Research book series (RECENTCANCER, volume 120)


The historical cohort mortality study, the most common method for determining occupational cancer risk factors, is usually conducted without adjusting for important confounding variables, such as smoking. This is because obtaining data on confounders is often costly and difficult. To what extent this lack of information is likely to cause serious bias in the estimation of occupation-disease relationships is the object of this paper.


Risk Ratio Heavy Smoker Standardize Mortality Ratio Standard Industrial Classification Unadjusted Odds Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Asp S (1984) Confounding by variable smoking habits in different occupational groups. Scand J Work Environ Health 10: 325–326PubMedCrossRefGoogle Scholar
  2. Axelson O (1978) Aspects on confounding in occupational health epidemiology. Scand J Work Environ Health 4: 85–89Google Scholar
  3. Blair A, Hoar S, Walrath J (1985) Comparison of crude and smoking-adjusted standardized mortality ratios. J Occup Med 27: 881–884PubMedGoogle Scholar
  4. Breslow NE, Day NE (1980) Statistical methods in cancer research, vol 1. The analysis of case-control studies. International Agency for Research on Cancer, Lyon (IARC sci publ no 32 )Google Scholar
  5. Chan-Yeung M, Wong R, MacLean L, Tan F, Schulzer M, Enarson D, Martin A, Dennis R, Grzybowski S (1983) Epidemiologic health study of workers in an aluminum smelter in British Columbia: effects on the respiratory system. Am Rev Respir Dis 127: 465–469PubMedGoogle Scholar
  6. Gail MH, Wacholder S, Lubin JH (1988) Indirect corrections for confounding under multiplicative and additive risk models. Am J Ind Med 13: 119–130PubMedCrossRefGoogle Scholar
  7. Jassa D (1983) Smoking behaviour of Canadians. Supply and Services Canada, OttawaGoogle Scholar
  8. Miettinen OS (1972) Components of the crude risk ratio. Am J Epidemiol 96: 168–172PubMedGoogle Scholar
  9. Siemiatycki J, Wacholder S, Dewar R, Cardis E, Greenwood C, Richardson L (1988) Degree of confounding bias related to smoking, ethnic group and socioeconomic status in estimates of the associations between occupation and cancer. J Occup Med 30: 617–625PubMedCrossRefGoogle Scholar
  10. Statistics Canada (1981a) Standard occupational classification 1980. Statistics Canada, Ottawa (catalogue 12–565; ISBN 0–660–10672–8)Google Scholar
  11. Statistics Canada (1981b) Standard industrial classification 1980. Statistics Canada, Ottawa (catalogue 12–501; ISBN 0–660–10672–8)Google Scholar
  12. Storer BE, Wacholder S, Breslow NE (1983) Maximum likelihood fitting of general risk models to stratified data. Appl Stat 32: 177–181CrossRefGoogle Scholar
  13. US Surgeon General (1985) The health consequences of smoking, cancer and chronic lung disease in the workplace. Department of Health and Human Services, Rockville Md.Google Scholar

Copyright information

© Springer-Verlag Berlin·Heidelberg 1990

Authors and Affiliations

  • J. J. Spinelli
    • 1
  • P. R. Band
    • 1
  • R. P. Gallagher
    • 1
  1. 1.Division of Epidemiology, Biometry and Occupational OncologyCancer Control Agency of British ColumbiaVancouverCanada

Personalised recommendations