title: Trends in OSHA Compliance Monitoring Data 1979-2011: Statistical Modeling of Ancillary Information across 77 Chemicals.
abstract: OBJECTIVE
  The Integrated Management Information System (IMIS) is the largest multi-industry source of exposure measurements available in North America. However, many have suspected that the criteria through which worksites are selected for inspection are related to exposure levels. We investigated associations between exposure levels and ancillary variables in IMIS in order to understand the predictors of high exposure within an enforcement context.
  
  
  METHODS
  We analyzed the association between nine variables (reason for inspection, establishment size, total amount of penalty, Occupational Safety and Health Administration (OSHA) plan, OSHA region, union status, inspection scope, year, and industry) and exposure levels in IMIS using multimodel inference for 77 agents. For each agent, we used two different types of models: (i) logistic models were used for the odds ratio (OR) of exposure being above the threshold limit value (TLV) and (ii) linear models were used for exposure concentrations restricted to detected results to estimate percent increase in exposure level, i.e. relative index of exposure (RIE). Meta-analytic methods were used to combine results for each variable across agents.
  
  
  RESULTS
  A total of 511,047 exposure measurements were modeled for logistic models and 299,791 for linear models. Higher exposures were measured during follow-up inspections than planned inspections [meta-OR = 1.61, 95% confidence interval (CI): 1.44-1.81; meta-RIE = 1.06, 95% CI: 1.03-1.09]. Lower exposures were observed for measurements collected under state OSHA plans compared to measurements collected under federal OSHA (meta-OR = 0.82, 95% CI: 0.73-0.92; meta-RIE = 0.86, 95% CI: 0.81-0.91). A 'high' total historical amount of penalty relative to none was associated with higher exposures (meta-OR = 1.54, 95% CI: 1.40-1.71; meta-RIE = 1.18, 95% CI: 1.13-1.23).
  
  
  CONCLUSIONS
  The relationships observed between exposure levels and ancillary variables across a vast majority of agents suggest that certain elements of OSHA's process of selecting worksites for inspection influence the exposure levels that OSHA inspectors encounter. Nonetheless, given the paucity of other sources of exposure data and the lack of a more demonstrably representative data source, our study considers the use of IMIS data for the estimation of exposures in the broader universe of worksites in the USA.
authors.affiliation: 1.Chemical and Biological Hazards Prevention, Institut de recherche Robert-Sauvé en santé et en sécurité du travail, Montréal, Québec H3A 3C2, Canada; 2.Department of Occupational and Environmental Health, Université de Montréal, Montréal, Québec, Canada; philippe.sarazin@irsst.qc.ca.
authors.forename: Philippe
authors.initials: P
authors.lastname: Sarazin
authors.affiliation: 3.Environmental and Occupational Health, Drexel University, Philadelphia, PA 19102, USA;
authors.forename: Igor
authors.initials: I
authors.lastname: Burstyn
authors.affiliation: 4.College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA;
authors.forename: Laurel
authors.initials: L
authors.lastname: Kincl
authors.affiliation: 2.Department of Occupational and Environmental Health, Université de Montréal, Montréal, Québec, Canada; 5.Risks, Prevention, and Health Promotion, University of Montreal Hospital Research Centre, Montréal, Québec H3A 3C2, Canada.
authors.forename: Jérôme
authors.initials: J
authors.lastname: Lavoué
mesh_terms: D003627:Data Interpretation, Statistical; D015386:Hazardous Substances / Q000032:analysis*; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008331:Management Information Systems / Q000592:standards*; D016273:Occupational Exposure / Q000032:analysis* / Q000706:statistics & numerical data; D014481:United States; D014489:United States Occupational Safety and Health Administration / Q000592:standards* / Q000706:statistics & numerical data
chemical_list: D015386:Hazardous Substances
keywords: IMIS; mixed-effect models; multimodel inference; occupational exposure; statistical exposure model