Society For Risk Analysis Annual Meeting 2017

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

W3-H
Understanding Antimicrobial Resistance as a Global Concern

Room: Salon J   1:30 pm–3:00 pm

Chair(s): Abani Pradhan, Jade Mitchell   akp@umd.edu

Sponsored by Microbial Risk Analysis Specialty Group



W3-H.1  1:30 pm  A Theoretical Approach to Network Modeling of Antibiotic Resistance. Keisler M, Foran C, Keisler J*, Linkov I; University of Massachusetts Amherst    mkeisler@umass.edu

Abstract: Multiple resistance or “multi-resistance” is when two or more anti-microbial measures would fail to kill a microorganism or colony. The rapid development of multi-resistance is a risk to all of society, because if antibiotic treatments of last resort become ineffective, patients will die from otherwise routine infections. Multi-resistance develops when a person infected with a microbe resistant to one one or more anti-microbial develops further resistance to other anti-microbials. This can happen either as a new resistance developing to the anti-microbial with which the patient is treated, or through infection with a second resistant microbial strain followed by horizontal gene transfer between microbes in the patient’s body. We model the development of multi-resistance with a two-scale network model. Specifically, at any time, an individual is served by one hospital. From one long period to the next, individuals may become well, stay well, become ill or stay ill, and may stay in the same community or move to a new community. Within one long period, the sick individuals within a community may be cured of a microbial infection, may spontaneously evolve resistance to an antibiotic, may acquire a new microbe with resistance to an antibiotic, or may convey resistance present in one microbial infection to another microbial infection through horizontal gene transfer. Representing this network process using Markov chains, we can explore what the degree to various factors affect the risk of multi-resistance development. We can also explore how decisions about screening, hygiene, antibiotic choice, adherence management and other protocols may help.

W3-H.2  1:50 pm  Antibiotic-Resistant Staphylococcus Aureus Transmission from Hog Farms to Humans: Bayesian Network Risk Assessment Models. MacDonald-Gibson J*, George A; University of North Carolina at Chapel Hill   jackie.macdonald@unc.edu

Abstract: Since the first documented transmission of methicillin-resistant Staphylococcus aureus (MRSA) from livestock to humans was documented in the Netherlands in 2004, concern about the contribution of antibiotic use in livestock to the development of resistant human pathogens has increased. This study quantifies the contribution of antibiotic use in industrial hog farms in North Carolina—the second-largest hog producer among U.S. states—to carriage of MRSA and multi-drug-resistant Staphylococcus auerus (MDRSA) among humans. We will compare two different Bayesian belief network models for estimating the risk of MRSA and MDRSA transmission from industrial hog farms to humans. The first model is machine-learned from a study of 400 adult-child pairs in areas of intensive hog farming in North Carolina, with half of the pairs representing households of hog farm workers and the others representing control households. Human samples were taken from each pair, and questionnaires concerning household behaviors and occupational activities were administered. A Bayesian network model predicting MRSA and MDRSA carriage in children as a function of parent’s occupation (hog farm worker or other), hog farm operational characteristics, individual behavioral variables, and demographic characteristics was learned from the data set. The second model was built from an expert-informed influence diagram parameterized with conditional probability tables derived from a systematic review of previous relevant studies. This presentation will report on hog farm operational practices and community factors contributing to MRSA and MDRSA risk and will compare and contrast the predictions of the two models.

W3-H.3  2:10 pm  Comparative exposure assessment of ESBL-producing Escherichia coli through meat consumption. Evers EG*, Pielaat A, Smid JH, van Duijkeren E, Vennemann FBC, Wijnands LM, Chardon JE; RIVM The Netherlands   eric.evers@rivm.nl

Abstract: The presence of extended-spectrum β-lactamase (ESBL) and plasmidic AmpC (pAmpC) producing Escherichia coli (EEC) in food animals, especially broilers, has become a major public health concern. The aim of the present study was to quantify the EEC exposure of humans in The Netherlands through the consumption of meat from different food animals. Calculations were done with a simplified Quantitative Microbiological Risk Assessment (QMRA) model. The model took the effect of pre-retail processing, storage at the consumers home and preparation in the kitchen (cross-contamination and heating) on EEC numbers on/in the raw meat products into account. The contribution of beef products (78%) to the total EEC exposure of the Dutch population through the consumption of meat was much higher than for chicken (18%), pork (4.5%), veal (0.1%) and lamb (0%). After slaughter, chicken meat accounted for 97% of total EEC load on meat, but chicken meat experienced a relatively large effect of heating during food preparation. Exposure via consumption of filet americain (a minced beef product consumed raw) was predicted to be highest (61% of total EEC exposure), followed by chicken fillet (13%). It was estimated that only 18% of EEC exposure occurred via cross-contamination during preparation in the kitchen, which was the only route by which EEC survived for surface-contaminated products. Sensitivity analysis showed that model output is not sensitive for most parameters. However, EEC concentration on meat other than chicken meat was an important data gap. In conclusion, the model assessed that consumption of beef products led to a higher exposure to EEC than chicken products, although the prevalence of EEC on raw chicken meat was much higher than on beef. The (relative) risk of this exposure for public health is yet unknown given the lack of a modelling framework and of exposure studies for other potential transmission routes.

W3-H.4  2:30 pm  Toward preventing a doomsday pandemic. Macal CM*, MacDonell MM, Mishra SK, Trail JB, Chang YS, Cooke RM; Authors 1-5: Argonne National Laboratory; Author 6; Resources for the Future   macdonell@anl.gov

Abstract: Naturally occurring pandemics pose a growing concern for global security due to a combination of factors. These factors include increasing densities of human and animal populations; expanding human mobility with increasing trade, transport, and migration; changing reservoirs and vector patterns related to climate change; and continued emergence of new pathogens and antimicrobial resistance. At the same time, advances in synthetic biology ranging from gene editing to designer microbes have broadened this concern to engineered bio-threats, whether inadvertently or intentionally released. The impact of pandemics on health, welfare, and social and economic stability is substantial, with recent estimates of economic losses alone exceeding $6 trillion over the next century. Evolving approaches and tools can be applied to help predict pandemics and highlight key intervention needs. Agent-based modeling (ABM) and high-performance computing are being harnessed to simulate infectious disease transmission in synthetic populations, as illustrated by a disease scenario (as well as a zombie apocalypse) for a U.S. city with nearly three million people, involving several trillion individual contacts over the representative activities, locations, and time frame evaluated. The paper will illustrate an integrated assessment framework for evaluating potential pandemics and guiding intervention plans, using an approach that (1) incorporates ABM and cumulative risk concepts; (2) explores what combination of conditions might lead to a serious pandemic that could cause millions of fatalities; and (3) links outputs to an economic assessment of intervention costs and benefits, with an emphasis on health care infrastructure. The ultimate aim is to strengthen risk analysis tools to help understand the potential for pandemics and guide the development of priority interventions, toward ultimately avoiding a doomsday scenario.



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