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Statistical Analysis of Occupational Exposure Data



Details

  • Personal Author:
    Jones RM
  • Description:
    This K-01 award (5K01OH010537) enhanced the skills and research capacity of the Investigator in the area of statistical analysis of occupational exposure data through training and mentored research activities. The first research activity in this award characterized variability and determinants of lead exposure during surface preparation activities. The exposure data used in this activity were cross-classified, which means that worker's exposures were measured while the worker participated in one or more groups. Cross-classified designs are an alternative to more traditional hierarchical designs in which one worker is in only one group, and may be particularly advantageous to determine task-based exposures from longer-term exposure measurements (during which a worker performed multiple tasks). Within-worker variability (day-to-day) was the primary driver of variability in workers' personal exposures to lead, and lead concentrations measured at fixed locations within containment were not associated with workers' personal exposures. These findings suggest a worker's own activities and the emission that these activities generate are likely the drivers of exposure levels. We also found the mean exposure measured inside air-supplied blasting hoods of workers was 2.4-fold lower, on average, than the mean exposure measured outside workers' half-mask respirators, which is low relative to the Assigned Protection Factor for these hoods (>=25). While an imperfect measure of respirator performance, this finding suggests respirator effectiveness may not always equal the expected performance. The second research activity in this award focused on Bayesian methods for the analysis of occupational exposure data. Current methods are limited in scope, focusing on the 95th percentile of the exposure profile to evaluate compliance, or extremely complicated. We described and demonstrated three accessible methods for Bayesian analysis that use conjugate prior distributions: With conjugate priors, the posterior distributions for the mean and variance of the logarithm of the exposure profile are analytical expressions that can be readily sampled from in most statistical software packages and in Microsoft Excel. Sample codes are available to the public. As a result, the methods are readily accessible to industrial hygiene researchers and professionals. The research activities of this award have involved innovative applications of statistical methods for the analysis of occupational exposure data, and contribute to toolbox for occupational exposure analysis. [Description provided by NIOSH]
  • Subjects:
  • Keywords:
  • Series:
    Grant Final Reports
  • Publisher:
    National Institute for Occupational Safety and Health
  • Document Type:
    Text
  • Funding:
    Grant
  • Genre:
    Final Grant Report
  • Place as Subject:
  • CIO:
    National Institute for Occupational Safety and Health (NIOSH)
  • Division:
    OEP - Office of Extramural Programs
  • Topic:
    Workplace Safety & Health
  • Location:
    North America
  • Pages in Document:
    1-9
  • NIOSHTIC Number:
    nn:20053035
  • NTIS Accession Number:
    PB2019-100118
  • Citation:
    Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, K01-OH-010537, 2017 Jun; :1-9
  • Contact Point Address:
    Rachael M. Jones, School of Public Health, University of Illinois at Chicago, 2121 W Taylor St., Chicago, IL 60612
  • Email:
    rjones25@uic.edu
  • Federal Fiscal Year:
    2017
  • Performing Organization:
    University of Illinois at Chicago
  • Peer Reviewed:
    False
  • Start Date:
    20140401
  • Source Full Name:
    National Institute for Occupational Safety and Health
  • End Date:
    20170331
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:ec815d439b193c0248a2bc852d73245f805d781d75602a0159f67b09c1b771229e95dea5c4f18eb20a02f09f1afb0159dd25f50dbc0ca255fd50f65a6eb51264
  • Download URL:
  • File Type:
    Filetype[PDF - 175.07 KB ]
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