title: Using hierarchical cluster models to systematically identify groups of jobs with similar occupational questionnaire response patterns to assist rule-based expert exposure assessment in population-based studies.
abstract: OBJECTIVE
  Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure.
  
  
  METHODS
  Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m(-3) respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters' homogeneity (defined as >75% with the same estimate) was examined compared to a dichotomized probability estimate (<5 versus ≥5%; <50 versus ≥50%). Second, for the ordinal probability metric and continuous intensity and frequency metrics, we calculated the intraclass correlation coefficients (ICCs) between each job's estimate and the mean estimate for all jobs within the cluster.
  
  
  RESULTS
  Within-cluster homogeneity increased when more clusters were used. For example, ≥80% of the clusters were homogeneous when 500 clusters were used. Similarly, ICCs were generally above 0.7 when ≥200 clusters were used, indicating minimal within-cluster variability. The most within-cluster variability was observed for the frequency metric (ICCs from 0.4 to 0.8). We estimated that using an expert to assign exposure at the cluster-level assignment and then to review each job in non-homogeneous clusters would require ~2000 decisions per expert, in contrast to evaluating 4255 unique questionnaire patterns or 14983 individual jobs.
  
  
  CONCLUSIONS
  This proof-of-concept shows that using cluster models as a data reduction step to identify jobs with similar response patterns prior to obtaining expert ratings has the potential to aid rule-based assessment by systematically reducing the number of exposure decisions needed. While promising, additional research is needed to quantify the actual reduction in exposure decisions and the resulting homogeneity of exposure estimates within clusters for an exposure assessment effort that obtains cluster-level expert assessments as part of the assessment process.
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA friesenmc@mail.nih.gov.
authors.forename: Melissa C
authors.initials: MC
authors.lastname: Friesen
authors.affiliation: 2.Biostatistics, Group Health Research Institute, Seattle, WA 98101-1448, USA.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Shortreed
authors.affiliation: 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA 3.Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA.
authors.forename: David C
authors.initials: DC
authors.lastname: Wheeler
authors.affiliation: 4.Department of Environmental and Occupational Health, Drexel University, Philadelphia, PA 19104, USA.
authors.forename: Igor
authors.initials: I
authors.lastname: Burstyn
authors.affiliation: 5.Utrecht University, Utrecht, The Netherlands.
authors.forename: Roel
authors.initials: R
authors.lastname: Vermeulen
authors.affiliation: 6.TNO, Utrecht, 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 20892, 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 20892, USA.
authors.forename: Dalsu
authors.initials: D
authors.lastname: Baris
authors.affiliation: 7.Geisel School of Medicine at Dartmouth, Hanover, NH 03756, USA.
authors.forename: Margaret R
authors.initials: MR
authors.lastname: Karagas
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 20892, USA.
authors.forename: Debra T
authors.initials: DT
authors.lastname: Silverman
authors.affiliation: 11.Biostatistics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Yu
mesh_terms: D000395:Air Pollutants, Occupational / Q000032:analysis; D016022:Case-Control Studies; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D016273:Occupational Exposure / Q000032:analysis*; D009790:Occupations; D052638:Particulate Matter; D011159:Population Surveillance; D012107:Research Design; D011795:Surveys and Questionnaires; D013997:Time Factors; D001335:Vehicle Emissions / Q000032:analysis
chemical_list: D000395:Air Pollutants, Occupational; D052638:Particulate Matter; D001335:Vehicle Emissions
keywords: case–control studies; diesel exhaust; hierarchical clusters; occupational exposures