title: Comparison of two expert-based assessments of diesel exhaust exposure in a case-control study: programmable decision rules versus expert review of individual jobs.
abstract: OBJECTIVE
  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 (κ(w)=0.68-0.81) and for jobs with diesel-relevant modules (κ(w)=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.
authors: 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.
authors: Anjoeka
authors: A
authors: Pronk
authors: Patricia A
authors: PA
authors: Stewart
authors: Joseph B
authors: JB
authors: Coble
authors: Hormuzd A
authors: HA
authors: Katki
authors: David C
authors: DC
authors: Wheeler
authors: Joanne S
authors: JS
authors: Colt
authors: Dalsu
authors: D
authors: Baris
authors: Molly
authors: M
authors: Schwenn
authors: Margaret R
authors: MR
authors: Karagas
authors: Alison
authors: A
authors: Johnson
authors: Richard
authors: R
authors: Waddell
authors: Castine
authors: C
authors: Verrill
authors: Sai
authors: S
authors: Cherala
authors: Debra T
authors: DT
authors: Silverman
authors: Melissa C
authors: MC
authors: Friesen
mesh_terms: D000395:Air Pollutants, Occupational / Q000032:analysis* / Q000633:toxicity; D000465:Algorithms; D016022:Case-Control Studies; D003661:Decision Support Techniques; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D005104:Expert Testimony; D006801:Humans; D008962:Models, Theoretical; D016273:Occupational Exposure / Q000009:adverse effects / Q000032:analysis* / Q000706:statistics & numerical data; D012189:Retrospective Studies; D011795:Surveys and Questionnaires; D001749:Urinary Bladder Neoplasms / Q000139:chemically induced; D001335:Vehicle Emissions / Q000032:analysis* / Q000633:toxicity
chemical_list: D000395:Air Pollutants, Occupational; D001335:Vehicle Emissions