title: Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study.
abstract: OBJECTIVE
  To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study.
  
  
  METHODS
  First, previously extracted CT decision rules were used to obtain initial ordinal (0-3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rule's agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κ w) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions.
  
  
  RESULTS
  Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κ w: 0.81-0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κ w: 0.42-0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κ w: 0.09-0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was <0.8, when the CT ordinal ratings were low to moderate, or when a module with diesel questions was available.
  
  
  CONCLUSIONS
  Our reliability assessment provided important insight into where to prioritize additional expert review; as a result, only 14% of the jobs underwent expert review, substantially reducing the exposure assessment burden. Overall, we found that we could efficiently, reproducibly, and reliably apply CT decision rules from one study to assess exposure in another study.
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 208952, USA; friesenmc@mail.nih.gov.
authors.forename: Melissa C
authors.initials: MC
authors.lastname: Friesen
authors.affiliation: 2.Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA;
authors.forename: David C
authors.initials: DC
authors.lastname: Wheeler
authors.affiliation: 3.Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands;
authors.forename: Roel
authors.initials: R
authors.lastname: Vermeulen
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 208952, USA;
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: Locke
authors.affiliation: 4.Westat, Rockville, MD 20850, USA;
authors.forename: Dennis D
authors.initials: DD
authors.lastname: Zaebst
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 208952, USA;
authors.forename: Stella
authors.initials: S
authors.lastname: Koutros
authors.affiliation: 5.TNO, 3700 Zeist, The Netherlands;
authors.forename: Anjoeka
authors.initials: A
authors.lastname: Pronk
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 208952, USA;
authors.forename: Joanne S
authors.initials: JS
authors.lastname: Colt
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 208952, USA;
authors.forename: Dalsu
authors.initials: D
authors.lastname: Baris
authors.affiliation: 6.Geisel School of Medicine at Dartmouth, Hanover, NH 03756, USA;
authors.forename: Margaret R
authors.initials: MR
authors.lastname: Karagas
authors.affiliation: 7.Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Center (CNIO), 28029 Madrid, Spain;
authors.forename: Nuria
authors.initials: N
authors.lastname: Malats
authors.affiliation: 8.Maine Cancer Registry, Augusta, ME 04333-0011, USA;
authors.forename: Molly
authors.initials: M
authors.lastname: Schwenn
authors.affiliation: 9.Vermont Cancer Registry, Burlington, VT 05402-0070, USA;
authors.forename: Alison
authors.initials: A
authors.lastname: Johnson
authors.affiliation: 10.New Hampshire Department of Health and Human Services, Division of Public Health Services, Bureau of Public Health Statistics and Informatics, Concord, NH 03301, USA;
authors.forename: Karla R
authors.initials: KR
authors.lastname: Armenti
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 208952, USA;
authors.forename: Nathanial
authors.initials: N
authors.lastname: Rothman
authors.affiliation: 11.Stewart Exposure Assessments LLC, Arlington, VA 22207, USA;
authors.forename: Patricia A
authors.initials: PA
authors.lastname: Stewart
authors.affiliation: 12.Centre for Research in Environmental Epidemiology (CREAL), 08003 Barcelona, Spain; 13.CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Barcelona, Spain; 14.IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain;
authors.forename: Manolis
authors.initials: M
authors.lastname: Kogevinas
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 208952, USA;
authors.forename: Debra T
authors.initials: DT
authors.lastname: Silverman
mesh_terms: D000395:Air Pollutants, Occupational / Q000032:analysis*; D016022:Case-Control Studies; D003661:Decision Support Techniques; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008962:Models, Theoretical; D016273:Occupational Exposure / Q000032:analysis*; D015203:Reproducibility of Results; D013030:Spain; D001335:Vehicle Emissions / Q000032:analysis*
chemical_list: D000395:Air Pollutants, Occupational; D001335:Vehicle Emissions
keywords: case-control; diesel exhaust; exposure assessment methodology; occupational exposure; statistical learning