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

SRA 2013 Annual Meeting Abstracts
T2-E.4 Levy, JI*; Fabian, MP; Peters, JL; Korrick, SA; Boston
University School of Public Health; Channing Division of
Network Medicine, Brigham and Women's Hospital;
jonlevy@bu.edu
Geographic and demographic patterns of health risks
associated with chemical and non-chemical stressor
exposures in a low-income community
Evaluating environmental health risks in communities requires
models characterizing geographic and demographic patterns of
exposure to multiple stressors, with dose-response models that
account for the effects of joint exposures and can be applied to
all community residents. In this study, we used simulation
methods and structural equation models (SEMs) to construct
integrated models of exposure and health risk for a low-income
community (New Bedford, Massachusetts) facing multiple
chemical and non-chemical stressors. We focused on ADHD-like
behavior and blood pressure, two outcomes of interest in New
Bedford. We first constructed simulated microdata with
multivariate characteristics for all New Bedford residents,
applying probabilistic reweighting using simulated annealing to
data from the American Community Survey. We then developed
exposure models using exposure and demographic data from
the Behavioral Risk Factor Surveillance System, the National
Health and Nutrition Examination Survey, and a long-standing
birth cohort in New Bedford, and applied these models to the
microdata to predict exposures to multiple stressors associated
with health outcomes of interest. Finally, we developed SEMs of
the simultaneous relationships among exposure predictors
represented in the microdata, chemical and non-chemical
stressor exposures, and health outcomes. SEMs for ADHD-like
behavior and blood pressure indicated that multiple
demographic variables (e.g., income, country of origin,
education) predicted both exposures and outcomes, and that
multiple chemical and non-chemical stressors were directly
associated with outcomes. Linkage of our SEMs with our
exposure models yielded spatially and demographically refined
characterization of high-risk subpopulations. This combination
of statistical and simulation methods provides a foundation for
community-based cumulative risk assessment studies that
leverage epidemiological findings.
T1-J.3 Lew, N*; Nardinelli, C; Schick, A; Ashley, E; U.S. Food
and Drug Administration; Office of Management and Budget;
nellie.lew@fda.hhs.gov
A Retrospective Cost-Benefit Analysis of the Bar Code
Rule
With an objective to reduce the number of medication
administration errors that occur in hospitals and other
healthcare settings each year, FDA published a final regulation
in 2004 that requires pharmaceutical manufacturers to place
linear bar codes on certain human drug and biological
products. At a minimum, the linear barcode must contain the
drug’s National Drug Code (NDC) number, which represents
the product’s identifying information. The intent was that bar
codes would be part of a system where healthcare professionals
would use bar code scanning equipment and software to
electronically verify against a patient’s medication regimen that
the correct medication is being given to the patient before it is
administered. By requiring commercial drug product packages
and unit-dose blister packages to carry bar code labels, it was
anticipated that the rule would stimulate widespread adoption
of bar code medication administration (BCMA) technology
among hospitals and other facilities, thereby generating public
health benefits in the form of averted medication errors. We
use the 2004 prospective regulatory impact analysis as the
basis to reassess the costs and benefits of the bar code rule.
Employing the most recent data available on actual adoption
rates of bar code medication administration technology since
2004 and other key determinants of the costs and benefits, we
examine the impacts of the bar code rule since its
implementation and identify changes in technology that have
occurred. In this retrospective study, we also use alternative
models of health information technology diffusion to improve
estimates of counterfactual scenarios against which we
compare the effects of the bar code rule.
M2-B.2 Lewis, RJ*; Money, C; Boogaard, PJ; ExxonMobil
Biomedical Sciences, Inc.; r.jeffrey.lewis@exxonmobil.com
Judging the Quality of Evidence for REACH
An approach has been developed for how the quality of human
data can be systematically assessed and categorized. The
approach mirrors that applied for animal data quality
considerations (Klimisch scores), in order that human data can
be addressed in a complementary manner to help facilitate
transparent (and repeatable) weight of evidence comparisons.
The definition of quality proposed accounts for both absolute
quality and interpretability, thereby enabling consistent and
trasparent incorporation of data into human health risk
assessments. Using examples, the presentation will illustrate
how the scheme can be applied for weight of evidence
comparisons, building from a systematic assessment of
available data quality and adequacy. The utility of the scheme
for describing data reliability, especially when contributing
entries to the IUCLID database, will also be shown.
T3-D.1 Li, X*; Lovell, RA; Proescholdt, TA; Benz, SA;
McChesney, DG; Division of Animal Feeds, Office of
Surveillance and Compliance, Center for Veterinary Medicine,
Food and Drug Administration; xin.li@fda.hhs.gov
Surveillance of Salmonella Prevalence in Pet Food, Pet
Treats and Pet Nutritional Supplements by the United
States Food and Drug Administration in 2002 – 2012
The Center for Veterinary Medicine, FDA, conducted a
surveillance study to monitor the trend of Salmonella
contamination in pet food, pet treats and pet nutritional
supplements. The samples were randomly collected from pet
food, pet treats and pet nutritional supplements in interstate
commerce and at United States ports of entry for surveillance
purposes. These samples were tested for the presence of
Salmonella. There were 2,026 samples collected in 2002-2012,
and 100 were positive for Salmonella (4.94%). This total
included 524 pet food samples, of which 28 were positive for
Salmonella (5.34%); 1,328 pet treat samples, with 61 were
positive for Salmonella (4.59%); and 174 pet nutritional
supplement samples, with 11 were positive for Salmonella
(6.32%). A significant overall reduction in Salmonella
prevalence (p ≤ 0.05) in all three types of samples was
observed in the samples collected in 2012 (2.21%) compared to
the samples collected in 2002 (12.40%). This study does not
include the results of samples collected from follow up to
consumer complaints and violations or Salmonella outbreak
investigations associated with pet food, pet treats and pet
nutritional supplements, which average approximately 30
additional positive Salmonella cases for the past three years
(2010-2012). The findings of the FDA Salmonella surveillance
study provide Salmonella prevalence information that can be
used to address Salmonella contamination problems. The
findings can also be used to educate pet owners when handling
pet food, pet treats and pet nutritional supplements at home to
prevent salmonellosis.
December 8-11, 2013 - Baltimore, MD