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Comparison of two expert-based assessments of diesel exhaust exposure in a case–control study: programmable decision rules versus expert review of individual jobs
  1. Anjoeka Pronk1,2,
  2. Patricia A Stewart1,3,
  3. Joseph B Coble1,4,
  4. Hormuzd A Katki5,
  5. David C Wheeler1,6,
  6. Joanne S Colt1,
  7. Dalsu Baris1,
  8. Molly Schwenn7,
  9. Margaret R Karagas8,
  10. Alison Johnson9,
  11. Richard Waddell8,
  12. Castine Verrill7,
  13. Sai Cherala10,
  14. Debra T Silverman1,
  15. Melissa C Friesen1
  1. 1Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
  2. 2TNO Quality and Safety, Zeist, The Netherlands
  3. 3Stewart Exposure Assessments, LLC, Arlington, Virginia, USA
  4. 4Annapolis, Maryland, USA
  5. 5Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, DHHS, Bethesda, Maryland, USA
  6. 6Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA
  7. 7Maine Cancer Registry, Augusta, Maine, USA
  8. 8Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire, USA
  9. 9Vermont Cancer Registry, Burlington, Vermont, USA
  10. 10New Hampshire Department of Health and Human Services, Concord, New Hampshire, USA
  1. Correspondence to Dr Melissa C Friesen, Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd, Room 8106, MSC 7240, Bethesda, MD 20892-7240, USA; friesenmc{at}mail.nih.gov

Abstract

Objectives Professional judgment is necessary to assess occupational exposure in population-based case–control studies; however, the assessments lack transparency and are time-consuming to perform. To improve transparency and efficiency, we systematically applied decision rules to questionnaire responses to assess diesel exhaust exposure in the population-based case–control New England Bladder Cancer Study.

Methods 2631 participants reported 14 983 jobs; 2749 jobs were administered questionnaires (‘modules’) with diesel-relevant questions. We applied decision rules to assign exposure metrics based either on the occupational history (OH) responses (OH estimates) or on the module responses (module estimates); we then combined the separate OH and module estimates (OH/module estimates). Each job was also reviewed individually to assign exposure (one-by-one review estimates). We evaluated the agreement between the OH, OH/module and one-by-one review estimates.

Results The proportion of exposed jobs was 20–25% for all jobs, depending on approach, and 54–60% for jobs with diesel-relevant modules. The OH/module and one-by-one review estimates had moderately high agreement for all jobs (κw=0.68–0.81) and for jobs with diesel-relevant modules (κw=0.62–0.78) for the probability, intensity and frequency metrics. For exposed subjects, the Spearman correlation statistic was 0.72 between the cumulative OH/module and one-by-one review estimates.

Conclusions The agreement seen here may represent an upper level of agreement because the algorithm and one-by-one review estimates were not fully independent. This study shows that applying decision-based rules can reproduce a one-by-one review, increase transparency and efficiency, and provide a mechanism to replicate exposure decisions in other studies.

  • Methodology, speciality
  • Exposure assessment
  • Methodology, speciality
  • Retrospective exposure assessment
  • Materials, exposures and occupational groups
  • Diesel fumes

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Footnotes

  • Competing interest None.

  • Funding The research was funded by the Intramural Research Programme of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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