Statistical Models of Exposure Determinants

Details

• Personal Author:
  Armstrong TW ; Virji, M. Abbas
• Description:
  In this chapter, we have attempted to describe statistical models of exposure determinants and demonstrate their utility in understanding factors affecting exposure. These models generally require large quantities of exposure measurements along with contextual information on characteristics of the work environment that arc collected during monitoring. Additionally, some of the exposure determinants models utilizing full-shift exposure measurements capture only a modest fraction of exposure variability and identify few specific activities or control measures. Perhaps developing these models using exposure determinants from a conceptual model based on the first principle of mass conservation may improve the fraction of exposure variation explained by the models. Additionally, consideration of time-varying exposure determinants with real-time exposure measurement can identify specific activities and control measures and can have a significant impact on intervention strategies. Improvements in exposure assessment methodologies require both exposure monitoring and modeling, both statistical and deterministic. This chapter provides only a brief overview of techniques available for using exposure data to generate models to estimate historical exposures, to identify exposure factors contributing to increased or decreased exposures, and to develop a targeted control strategy. Consultation with statistical texts and software packages are recommended prior to proceeding. However, with this overview information and additional reading of studies in journals, the reader is encouraged to evaluate exposure databases to determine if exposure determinants can be evaluated. While this approach requires significantly more data than may exist in small worksites, multi-site industries or multi-industries with similar processes may aggregate exposure data and develop models to determine key workplace environmental factors that affect exposures. While a mixture of factors and branches of logic may be used in different situations, a conceptual framework can help to organize the many considerations. Efforts by regulators, researchers, and industry groups are encouraged to pursue data aggregation, with clearly and uniformly defined datasets including information on workplaces, materials, work methods, and environmental data that may contribute to task- and industry-specific workplace exposure determinant models. Using data to identify key contributors to exposure is critical to understanding and controlling exposure risk factors, and to providing evidence-based methods for prioritizing intervention strategies. [Description provided by NIOSH]
• Subjects:
  Cardiovascular System Coal Coal Mining Emergency Responders Fatigue Lung Manufacturing And Industrial Facilities Mining Occupational Health Respiratory Function Tests Respiratory System Safety

  More +
• Keywords:
  Computer-models Exposure-assessment Mathematical-models Models Statistical-analysis
• ISBN:
  9781935082101
• Publisher:
  American Industrial Hygiene Association
• Document Type:
  Text
• Genre:
  Book
• Place as Subject:
  OSHA Region 3 ; Virginia ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DRDS - Division of Respiratory Disease Studies
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  71-80
• NIOSHTIC Number:
  nn:20036359
• Citation:
  Mathematical models for estimating occupational exposure to chemicals, 2nd edition. Keil CB, Simmons CE, Anthony TR, eds. Fairfax, VA: American Industrial Hygiene Association, 2009 Jan; :71-80
• Editor(s):
  Keil CB; Simmons CE; Anthony TR
• Federal Fiscal Year:
  2009
• Peer Reviewed:
  False
• Source Full Name:
  Mathematical models for estimating occupational exposure to chemicals, 2nd edition
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:6455623cb5a772558c531a0cbe97cf18a69491817bba18fb0984fb4f06bcff75e81fb850a591a286b8593fadba1308c40ba47e73ee9cfdc8eac68e20689662aa
• Download URL:
  https://stacks.cdc.gov/view/cdc/187825/cdc_187825_DS1.pdf
• File Type:
  [PDF - 7.13 MB ]