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

SRA 2013 Annual Meeting Abstracts
M3-D.4 Shoemaker, K*; Siegrist, J; Ferson, S; Stony Brook
University, Applied Biomathematics;
kevintshoemaker@gmail.com
Mixing good data with bad
Data sets have different qualities. Some data are collected with
careful attention to proper protocols and careful measurement
using highly precise instruments. In contrast, some data are
hastily collected by sloppy or unmotivated people with bad
instruments or shoddy protocols under uncontrolled conditions.
Statistical methods make it possible to formally combine these
two kinds of data in a single analysis. But is it always a good
idea to do so? Interval statistics is one convenient method that
accounts for the different qualities of data in an analysis. High
quality data have tighter intervals and poor quality data have
wider intervals, and the two can be legitimately pooled using
interval statistics, but it appears that it is not always advisable
for an analyst to combine good data with bad. We describe
examples showing that, under some circumstances, including
more data without regard for its quality unnecessarily increases
the amount of uncertainty in the final output of an analysis.
Ordinarily, statistical judgment would frown on throwing away
any data, but as demonstrated by these examples, it seems
clearly advantageous sometimes to ignore this judgment. More
data does not always lead to more statistical power, and
increasing the precision of measurements sometimes provides a
decidedly more efficient return on research effort. This result is
highly intuitive even though these examples imply a notion of
negative information, which traditional Bayesian analyses do
not allow.
T2-E.3 Shortridge, JE*; Guikema, SD; The Johns Hopkins
University ; jshortr1@jhu.edu
Measuring health impacts from breaks in water
distribution systems using internet search data
The aging condition of drinking water distribution
infrastructure has been identified as a factor in waterborne
disease outbreaks and a priority area for research. Pipe breaks
pose a particular risk, as they result in low or negative pressure
which could result in contamination of drinking water from
adjacent soils. However, measuring this phenomenon is
challenging because the most likely health impact is mild
gastrointestinal (GI) illness, which is unlikely to go reported to
doctors or hospitals although it can result in significant social
costs. Here we present a novel method that uses data mining
techniques to assess the correlation between pipe breaks and
internet search volume related to symptoms of GI illness in two
major U.S. cities. Weekly search volume for the term “diarrhea”
was regressed against the number of pipe breaks in each city,
and additional covariates were used to control for seasonal
patterns, search volume persistence, and other sources of GI
illness. The fit and predictive accuracy of multiple regression
and data mining techniques were compared, and a Random
Forest models resulted in significantly lower predictive errors
in both cities. Pipe breaks were found to be an important and
positively correlated predictor of internet search volume in both
cities, as were seasonal fluctuations and search volume
persistence. This correlation indicates that breaks in the water
distribution system could present health risks that are unlikely
to be considered when estimating the benefits associated with
infrastructure repair and investment.
P.144 Smith, MN; Port, JA; Cullen, AC; Wallace, JC; Faustman,
EM*; University of Washington; faustman@u.washington.edu
A tool to facilitate the incorporation of metagenomic data
into environmental microbial decision-making and risk
analysis
Advances in microbial genomics have opened up new
opportunities for translation to public health risk research.
Traditionally, methods for studying changes in the population
dynamics of microbial communities have required cell culture
and have focused on single organisms. Some microbes cannot
be cultured with current methods or are poorly characterized,
leading to incomplete assessment of microbial environmental
health. The development of metagenomics, in which DNA is
extracted directly from environmental samples and sequenced,
now allows for the characterization of the taxonomic and
functional potential of microbial communities, provides an
expanded tool for microbial environmental health monitoring.
However, new bioinformatics and analytical challenges have
arisen in interpretation and translation of metagenomic data to
decision-making and risk management. We provide a tool for
the translation of metagenomic data to environmental health
monitoring information relevant to public health
decision-making and risk assessments. This framework allows
functional data from Clusters of Orthologous Groups of proteins
(COGs) to be interpreted within the context of public health.
Using metagenomic data from 107 published biomes, we
performed a functional analysis to identify COGs that are
reflective of potential human impacts. Biomes with higher
known potential human impact, such as the WWTP had a
greater percentage of public health relevant COG relative
abundance. Overall, we demonstrate that this is a valuable tool
for distinguishing between environments with differing levels of
human impact in the public health context. This project is
supported by the NOAA-funded Pacific Northwest Consortium
for Pre- and Post-doctoral Traineeships in Oceans and Human
Health and the UW Pacific Northwest Center for Human Health
and Ocean Studies (NIEHS: P50 ESO12762 and NSF:
OCE-0434087), NOAA (UCAR S08-67883) and the Center for
Ecogenetics and Environmental Health (5 P30 ES007033).
P.74 Smith, D.W.; Conestoga-Rovers & Associates;
dwsmith@craworld.com
Atrazine effects on amphibians: Is it safe to go back into
the water?
In the mid-2000s, scientific debate about atrazine’s potential to
cause endocrine disruption of amphibians played out in the
bright lights and high stakes of EPA re-registration. As part of
that process, EPA had its Science Advisory Board evaluate the
available science on endocrine disruption. Depending on which
conventional wisdom on the internet one reads, the SAB review
either found serious errors in the science showing significant
negative effects on amphibian development at low atrazine
concentrations. Other sources claim the complete opposite,
e.g., that the SAB “found each and every one of the studies
[showing no significant effects] to be fundamentally or
methodologically flawed, some containing defects as egregious
as allowing control and test subjects to intermix.” Since
industry funded much of the latter research, this debate
devolved into the all too familiar acrimonious discussions about
conflict of interests, and even contained a published paper
purported showing a relationship between funding source and
results. From afar – I luckily had no involvement at all in any of
this -- the debate seems to exemplify some issues bedeviling
science in general and risk assessment in particular.
Specifically, are scientists in general and the SAB in this
specific case so poor at communicating to the public.
Secondarily, what exactly are “conflicts of interests", who has
the, and what effects do they likely have on science. Thirdly,
what are professional ethics in the midst of debate about
potentially important environmental risks. Thus, my talk will
review this incident, give my best science view of what exactly
the SAB said, and review current discussions of scientific
ethics.
December 8-11, 2013 - Baltimore, MD