Abstract No:
1700
Abstract Type:
Student Poster
Authors:
A Stern1, K Garrett1, M Johnson1
Institutions:
1CUNY School of Public Health, New York, NY
Presenter:
Aaron Stern, CSP, CHMM
CUNY School of Public Health
Faculty Advisor(s):
Kim Garrett, PhD
CUNY School of Public Health
Mike Johnson, CIH, PE, MS
CUNY School of Public Health
Description:
This project reanalyzed personal breathing zone (PBZ) data from 10 NIOSH Health Hazard Evaluation (HHE) reports using Bayesian Decision Analysis (BDA) with the Structured Deterministic Model 2.0 as an informed prior. Posterior results were compared with original HHE conclusions to evaluate whether statistical analysis would have altered exposure interpretations or control recommendations.
Situation/Problem:
Per OSHA, compliance is achieved when exposures are within the permissible exposure limit (PEL) on the day sampled.1 Certain substance specific standards follow the NIOSH 1977 Occupational Exposure Sampling Strategy Manual (OESSM) which applies the action level and iterative sampling depending on the result. The OESSM strategy is not as effective as originally designed for modern estimates of typical occupational exposure variability (GSD ~2.5).2,3 Downsides to this approach include not accounting for uncertainty and not discussing exposure in terms of risk of exceeding an OEL on unmeasured days.3
In 2024, NIOSH proposed a strategy of modeling combined with BDA to determine acceptable exposures.4 This retrospective analysis compared HHE conclusions obtained via the original compliance assessment strategy (small datasets to assess PEL compliance) against the proposed stats analysis method using BDA.
Methods:
This analysis evaluated HHE report conclusions to compare if they would have been different using BDA.
Inclusion criteria:
• ≥3 PBZ samples
• Minimum info to obtain an informed prior per SEG – ventilation
• Characteristics to define the SEG
Analysis:
1. Inputs to SDM 2.0: ventilation, OEL
2. Output from SDM 2.0: ECC estimate per SEG
3. Inputs to IHDA-AIHA: ECC estimate (informed prior); certainty level; PBZ data
4. Outputs from IHDA-AIHA: probabilistic stats, corresponding risk category5
The analysis cross-examined the stats results against HHE conclusions by:
1) The category 4 posterior to assess whether the exceedance risk was >30%, an unacceptable risk categorization
2) The geometric standard deviation (GSD) for SEG identification
3) Controls recommendations
Results / Conclusions:
Ten HHE reports were selected and 10 analytes and 21 SEGS were analyzed in this study. Bayesian-derived ECCs were aligned with the original HHE conclusions including control recommendations.
If NIOSH adopted the proposed strategy in HHEs, then they would need to incorporate a more robust approach to characterize SEGs. In this analysis, some SEGs were excluded due to inadequate characterization (ventilation, tasks, frequency, duration).
Although BDA did not change exposure decisions for the evaluated SEGs, stats analysis provides benefits that direct OEL comparisons do not. One SEG had a GSD >3.0 which should have triggered a re-evaluation of those workers as a SEG. BDA outputs encourage discussion of risk as opposed to a simple comparison of results being higher or lower on the days sampled.4 This can encourage additional controls when overexposures are not captured in the data.
Given the limitations of small samples sizes, lacking SEG characterization, and direct comparison to OELs, adopting the proposed NIOSH strategy in HHE reports can help improve exposure judgements for NIOSH consultants and practitioners that review HHEs for technical guidance.
Core Competencies:
Exposure Assessment
Secondary Core Competencies:
Biostatistics and Epidemiology
Risk Assessment
Choose at least one (1), and up to five, (5) keywords from the following list. These selections will optimize your presentation's search results for attendees.
Aerosol and airborne particulate monitoring
Exposure Assessment
Gas and vapor detection
Regulatory Compliance
Risk assessment and management
Based on the information that will be presented during your proposed session, please indicate the targeted audience practice level: (select one)
Professional: Professional is a job title given to persons who have obtained a baccalaureate or graduate degree in IH/OH, public health, safety, environmental sciences, biology, chemistry, physics, or engineering or who have a degree in another area that meets the standards set forth in the next section, Knowledge and Skill Sets of IH/OH Practice Levels, and has had 4 or more years of practice. One significant way of demonstrating professional competence is to achieve certification by a 3rd party whose certification scheme is recognized by the International Occupational Hygiene Association (IOHA) such as the Board of Global EHS Credentialing (BGC).
Was this session organized by an AIHA Technical Committee, Special Interest Group, Working Group, Advisory Group or other AIHA project Team?
No
Are worker exposure data and/or results of worker exposure data analysis presented?
Yes
If yes, i.e., If worker exposure data and/or results of worker exposure data analysis are to be presented please describe the statistical methods and tools (e.g. IHSTAT, Expostats, IHSTAT_Bayes, IHDA-AIHA, or other statistical tool, please specify) used for analysis of the data.
IHDA-AIHA provide Bayesian decision analysis methods for this work.
How will this help advance the science of IH/OH?
This work demonstrates the feasibility of retrospectively applying Bayesian Decision Analysis to legacy exposure datasets. The findings support probabilistic interpretation as a complementary method for documenting uncertainty, strengthening exposure decision transparency, and modernizing exposure assessment practice consistent with AIHA guidance.
Have you presented this information before?
No
I have read and agree to these guidelines.
Yes