Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

SRA 2013 Annual Meeting Abstracts
M3-B.3 Ross, MA*; Owens, BO; Vandenberg, JM; U.S.
Environmental Protection Agency; ross.mary@epa.gov
The EPA Causality Framework for Assessment of Air
Pollution-Related Health Effects
The periodic review of U.S. National Ambient Air Quality
Standards (NAAQS) for each of the six criteria air pollutants --
ozone, particulate matter, carbon monoxide, nitrogen oxides,
sulfur oxides and lead -- starts with the synthesis and evaluation
of the most policy-relevant science in Integrated Science
Assessments (ISAs). EPA has developed an approach for formal
characterization of the strength of the scientific evidence and
drawing conclusions on causality for exposure-effect
relationships. The framework establishes uniform language
concerning causality and brings greater consistency and
specificity to the ISAs. EPA drew on relevant approaches for
similar scientific decision-making processes by EPA and other
organizations. In Findings from multiple lines of evidence --
controlled human exposure, epidemiologic and toxicological
studies -- are evaluated and integrated to draw conclusions on
health effects with regard to factors such as consistency,
coherence and biological plausibility. The relative importance
of different types of evidence varies by pollutant or assessment,
as does the availability of different types of evidence for
causality determination. The use of the framework is
demonstrated with several examples of determinations for
various health outcomes and pollutants, particularly drawing
from the recently-completed ISA for Lead (Pb). Disclaimer: The
views expressed are those of the authors and do not necessarily
reflect the views or policies of the US EPA.
M2-H.3 Rothschild, C; McLay, LA*; Guikema, SD; University of
Wisconsin-Madison; lmclay@wisc.edu
Adversarial risk analysis with incomplete information: A
level-k approach
This paper proposes, develops, and illustrates the application of
level-k game theory to adversarial risk analysis. Level¬-k
reasoning, which assumes that players play strategically but
have bounded rationality, is useful for operationalizing a
Bayesian approach to adversarial risk analysis. It can be
applied in a broad class of settings, including settings with
asynchronous play and partial but incomplete revelation of
early moves. Its computational and elicitation requirements are
modest. We illustrate the approach with an application to a
simple bioterrorism Defend-Attack model in which the
defender’s countermeasures are revealed with a probability
less than one to the attacker before he decides on how or
whether to attack.
T2-H.3 Rouse, J.F.; Arete Associates, Joint Staff;
jnbeth@yahoo.com
The Chairman of the Joint Chiefs of Staff: Risk
Assessment System
The Chairman of the Joint Chiefs of Staff (CJCS) serves as the
principal military advisor to the President, the Secretary of
Defense, and the National Security Council. He is also required
to annually submit to Congress his assessment of the strategic
and military risks in executing the missions called for in the
National Military Strategy. At the intersection of these two
responsibilities lies the Chairman's Risk Assessment System;
which incorporates the major tenets of the International Risk
Governance Council’s 2003 White Paper- Risk Governance-an
Integrative Approach. This framework has now been employed
by three different Chairmen, with each focusing efforts on
different components – highlighting the highly individual
interaction of the senior leader with his supporting risk system.
This paper describes the current system and how it has adapted
to meet the needs of different Chairman and support strategic
decision-making - with specific examples of national decisions
that were impacted. Lessons learned have direct applicability to
other government agencies and efforts to deal with highly
qualitative risks arising from the strategic environment.
T4-C.2 Sacks, JD*; Vinikoor-Imler, LC; Ross, M; U.S.
Environmental Protection Agency; sacks.jason@epa.gov
Identifying populations at-risk of air pollution-induced
health effects through the use of a novel classification
scheme
Under the U.S. Clean Air Act, the National Ambient Air Quality
Standards (NAAQS) are established that provide an adequate
margin of safety requisite to protect the health of portions of
the population at increased risk of an air pollution-induced
health effect. It is, therefore, imperative that the available
scientific evidence that examines factors that may result in
certain populations being at increased risk is fully
characterized to inform decisions made in risk and exposure
assessments as part of the NAAQS review process.
Investigators have often classified these “at-risk” populations as
being either susceptible or vulnerable to air pollution. However,
this dichotomous approach ignores the complexities involved in
identifying these populations. It has been well characterized
that risk to air pollution-induced health effects can be
influenced by: (1) intrinsic factors, (2) extrinsic factors, (3)
increased dose, and/or (4) increased exposure. The U.S. EPA in
the development of Integrated Science Assessments
characterizes populations that may be at increased risk of an
air pollutant-induced health effect to support policy decisions
made during the course of the NAAQS review process. The first
step of this characterization consists of integrating evidence
across scientific disciplines, i.e., epidemiologic, controlled
human exposure, toxicological, and exposure sciences studies.
To facilitate the identification of factors that contribute to
increased risk we then developed an approach to evaluate the
strength of evidence and determine the level of confidence that
a specific factor affects risk of an air pollutant-induced health
effect. The classification for a specific factor is based on
whether: consistent effects are observed within a discipline;
there is evidence for coherence of effects across disciplines;
and there is evidence for biological plausibility. We will
demonstrate the application of this framework using examples
from recent NAAQS reviews.
December 8-11, 2013 - Baltimore, MD