Application of Statistical Cross-Extrapolation Techniques to Derive Surrogate Acute Exposure Guideline Levels (AEGLs) Public
Chu, MyDzung Thi (2012)
Abstract
Abstract
Application of Statistical Cross-Extrapolation Techniques to
Derive Surrogate Acute Exposure Guideline Levels
(AEGLs)
AEGLs are comprehensively peer-reviewed health guidance values
(HGVs) for assessing the risk of acute once-in-a-lifetime or rare
exposures to hazardous inhalation chemicals. For each inhalation
compound, up to fifteen AEGL values may be developed that address
three health effects severity thresholds (AEGL-1:
discomfort/reversible, AEGL-2: disabling/irreversible, AEGL-3: life
threatening) at five exposure durations (1/6, 1/2, 1, 4, and 8
hours). Currently, only 74 compounds have Finalized AEGLs, while
187 are Interim and 12 are Proposed. Among these, 42% have
unassigned AEGLs due to insufficient data or biological
implausibility of estimates. Also as of November 2011, the AEGL
Program no longer reviews new compounds. Therefore, a need for a
rapid and cost-effective substitute for AEGL development is
imminent. The aim of the present work was to develop an efficient
method for the derivation of provisional AEGLs for inhalable
hazardous compounds with unassigned AEGLs. Such method is plausible
due to uniformity of procedures by which the AEGLs have been
developed, and due to similarities in the physical-chemical
characteristics of inhalable compounds.Qualitative and quantitative
data for AEGLs were derived from the US Environmental Protection
Agency's published technical support documents. Pearson correlation
and Deming linear regression (DLR) analyses of the AEGL database
were employed to develop a total of 105 unique univariate
cross-extrapolation models for duration-and-threshold-specific
AEGLs. 95% confidence and prediction intervals (CIs and PIs) of
each model were constructed using bootstrap resampling. The most
predictive DLR models were applied to compounds with unassigned
AEGLs. Obtained estimates were externally validated using other
available health guidance data, including occupational exposure
limits (OELs). Model performance was also internally validated by
comparing estimated and actual AEGLs for compounds with the full
set of data. All Pearson correlation coefficients ( r) were
greater than 0.88. Higher coefficients generally corresponded to
cross-extrapolation models with narrower 95% PIs. The narrowest
PIs, i.e. the most confident cross-extrapolation, were observed for
pairs of AEGLs that were most similar in exposure duration and
severity of health effects. Conversely, the widest PIs were
obtained for functionally most distant AEGL pairs; however, even
the worst estimates were within two orders of magnitude of the
actual values. Comparison of estimated AEGLs to occupational HGVs
suggested that numerically STELs and TWAs were more correlated with
AEGL-1 and -2s at 4 h and 8 h. External validation of
cross-extrapolated numbers against these occupational HGVs for a
test set of 14 chemicals showed statistical identity at the 95%
level for 8 of the 14 compounds. Our findings suggest that the DLR
models are statistically valid and predictive of unassigned AEGL
values for compounds in the database. Model performance is
dependent on the severity threshold and exposure duration of the
cross-extrapolated quantities. External validation using
occupational HGVs shows that our cross-extrapolation estimates are
sound. Yet, the uncovered relationships are not fully vetted. In
the future,structure-activity, time-scaling, and the biological
plausibility of AEGL predictions will be investigated.
Application of Statistical Cross-Extrapolation Techniques to
Derive Surrogate Acute
Exposure Guideline Levels (AEGLs)
B.A.
Smith College
2009
Thesis Committee Chair:
P. Barry Ryan, PhD
Eugene Demchuk, PhD
A thesis submitted to the Faculty of the Rollins School of Public
Health of Emory University
in partial fulfillment of the requirements for the degree of Master
of Science in Public Health in Environmental Health-Epidemiology
2012
Table of Contents
TABLE OF CONTENTS
I. INTRODUCTION
i. Significance/Rationale
ii. Specific Aims
iii. Background and Literature review
iv. AEGL application in public health
II. METHODS
i. Hypothesis
ii. Methods of data collection
iii. Methods of analysis and rationale
III. RESULTS
i. Descriptive
statistics of AEGL database
ii. Model building
iii. Model analysis
iv Model selection
v. Model application
vi. Cross-validation with existing HGVs
IV. DISCUSSION
V. CONCLUSIONS
VI. RECOMMENDATIONS / FUTURE RSEARCH
VII. REFERENCES
i. Software
ii. Literature
VIII. TABLES / FIGURES
IX. APPENDIX
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