title: Application of statistical models for secondary data usage of the US Navy's Occupational Exposure Database (NOED).
abstract: Many organizations around the world have collected data related to individual worker exposures that are used to determine compliance with workplace standards. These data are often warehoused and thereafter rarely used as an information resource. Using appropriate groupings and analysis of OSHA data, Gómez showed that such stored data can provide additional insight on factors affecting occupational exposures. Using data from the Occupational Exposure Database of the United States Navy, the usefulness of statistical models for defining probabilities of exposure above permissible limits for observed work conditions is examined. Analyses have highlighted worker Similar Exposure Groups (SEGs) with potential for overexposure to asbestos and lead. In terms of grouping data, Rappaport et al. defined the Within-Between Lognormal Model, a scale-independent measure for quantifying between-worker variability within a selected worker group: (B)R.95 = exp[3.92s(sB)], representing the ratio of arithmetic mean exposures received by workers in the 97.5th and 2.5th percentiles. To help search for groups, the Proportional Odds Model, a generalization of the logistic model to ordinal data, can predict probabilities for group exposure above the Occupational Exposure Limit (OEL), or the Action Level (AL), which is one-half of the OEL. Worker SEGs have been identified for asbestos workers removing friable asbestos ((B)R.95 = 11.0) and nonfriable asbestos ((B)R.95 = 6.5); metal cleaning workers sandingspecialized equipment ((B)R.95 = 11.3), and workers at target shooting ranges cleaning up lead debris ((B)R.95 = 10). Estimated probabilities for the categories <AL, AL-OEL, and >OEL support current understanding of work processes examined. Differences in probability noted between tasks and levels of ventilation validate this method for evaluating other available workplace exposure determinants, and for predicting probability of membership in categories that may help further define worker exposure groups, and determinants of excessive exposures. Thus, analyses of retrospective exposure data can help identify work site and work practice factors for efficient targeting of remediation resources.
authors.affiliation: US Pacific Fleet, Pearl Harbor, Hawaii, USA.
authors.forename: J A
authors.initials: JA
authors.lastname: Formisano
authors.forename: K
authors.initials: K
authors.lastname: Still
authors.forename: W
authors.initials: W
authors.lastname: Alexander
authors.forename: M
authors.initials: M
authors.lastname: Lippmann
mesh_terms: D016208:Databases, Factual; D006801:Humans; D008889:Military Personnel / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009326:Naval Medicine / Q000706:statistics & numerical data; D016273:Occupational Exposure / Q000706:statistics & numerical data*; D018570:Risk Assessment / Q000706:statistics & numerical data; D014481:United States