Exposure Assessments

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Exposure Assessments

• Paul Wambach, CIH
• DOE, Worker Safety Health Policy (HS-11)
• Phone [phone]
• Email Paul.Wambach_at_hq.doe.gov

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Exposure Assessment and Management
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Proving Exposures are Unacceptable Regulatory
Action Level

• NIOSH publication 77-173 Occupational Exposure
  Sampling Strategy Manual (Yellow Book)
• An OSHA Inspector assumes the workplace is safe
  until proven unsafe
• Very few industrial operations have day-to-day
  environmental GSDs less than about 1.2
• . . . If one measured daily exposure average is
  at one half the standard then . . . at least 5
  of unmeasured true daily averages exceed the
  standard.
• One measurement can prove that working conditions
  are unsafe.

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Proving Exposures are Acceptable

• NIOSH 77-173 In statistical terms, the employer
  should try to attain 95 confidence that no more
  than 5 of employee days are over the standard.
• Tuggle -1982 Assessment of Occupational Exposure
  Using One-Sided Tolerance Limits
• Hewett and Ganser 1997 Simple Procedures for
  Calculating Confidence Intervals Around the
  Sample Mean and Exceedance Fraction . . .

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Similarly Exposed Group (SEG)

• AIHA 2006 A group of workers having the same
  general exposure profile for an agent because of
  the similarity and frequency of the task(s) they
  perform, the similarity of the materials and
  processes with which they work, and the
  similarity of the way they perform the task(s).
  
• Rappaport 1991 Ratio of the 97.5tile mean
  to the 2.5tile mean 4 or equivalently the
  between worker geometric standard deviation (GSD)
  is 1.4

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Theory

• Prove SEG Exposures are Unacceptable
• Null hypothesis 95 of exposures lt OEL
• Accept alternative if 95-95 UTL gt OEL
• Rule-of-thumb, any value gt ½ OEL
• Prove SEG Exposures are Acceptable
• Null hypothesis 95 of exposure gt OEL
• Accept alternative if 95-95 UTL lt OEL
• Rule-of-thumb, 10 values lt 1/10 OEL
• Uncertain if neither of the above are true

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Problems

• SEGs are the exception rather than the rule
• Tasks, locations, materials, and tools vary,
  especially for service and construction workers
• For mature programs, recognized exposures are
  being controlled and the usual determinants of
  exposures are not good predictors job, task and
  location.
• Materials with no OELs
• If a high percentage of exposure monitoring
  results are non-detects many samples are needed

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Line Fit Plot for 533 Samples for 23 Beryllium
Machinist During an ORR
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Needed Solutions

• Methods for heterogeneous groups.
• Groups based on the organization structure used
  to administer health protection programs
• Must be large enough for non-parametric
  statistics (greater than 60 samples per year)
• Methods for establishing provisional OELs
• EPA IRIS, AIHA WEEL, etc standard methods for
  converting animal data to limits
• Conservative default for unstudied materials
• Data Quality Objectives
• Sufficient number of samples to answer the
  question
• Laboratory reporting limits arent arbitrarily
  set too high.

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Exposure SurveillanceSecondary Use of
Information

• Public health surveillance
• Health information collected from patient
  encounters analyzed to detect unusual events or
  pattern of events
• LaMontagne 2002
• Occupational exposure surveillance is the
  ongoing collection, management, and aggregate
  analysis of exposure data, and in turn the
  application of findings to guide workplace
  prevention and control efforts.
• Are health risk management programs preventing
  exposures above OELs?

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Examples Jones and Nicas 2005 Validity of COSHH
Essentials

• Under-control With recommended controls, the
  upper limit of the control band was exceeded in
• 78 (139/179) of vapor degreasing and
• 48 (76/159) of bag filling operations
• Over-control Without recommended controls,
  concentrations were within the control band
• 61 (102/167) at vapor degreasing and
• 8 (3/26) at bag filling operations

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Example Marchant et al 2009 Database for
Synthetic Vitreous Fibers
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Example DOE Beryllium-Associated Worker
Registry (BAWR)
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BAWR Percent Exceeding 0.2 mg/m3 by Work
Activity Category
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Conclusions

• Proving compliance is more difficult than proving
  non-compliance.
• Data quality objectives are different.
• Establish the exposure profile.
• Exposure surveillance provides a conceptual
  framework for progress.
• Computerized information management makes routine
  analyses of aggregated data feasible.