methicillin-resistant staphylococcus aureus in children
TRANSCRIPT
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Methicillin-Resistant Staphylococcus aureus in Children Living with Industrial Hog Operation Workers
Bayesian Network Risk Models
Jacqueline MacDonald Gibson, Associate ProfessorDepartment of Environmental Sciences and Engineering
Gillings School of Global Public HealthUniversity of North Carolina at Chapel Hill
November 1, 2018
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Outline
• Introduction Antibiotic use in livestock Prior evidence of risk to humans
• MethodsData set from eastern North Carolina—high-intensity hog farmingBayesian network learning algorithmsModel evaluation and selection
• Results Insights on risk factors Bayesian network model accuracy
• Conclusions: interventions to decrease risk
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Modern Livestock Production: Breeding Ground for Antibiotic Resistance?
71% of domestic antibiotic use is for livestock production —mostly subtherapeuticSOURCES: Wendy Nicole, Env. Health Perspectives, 2015; FDA, 2015
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Use of Medically Important Antibiotics for Livestock Is Increasing
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2.E+06
4.E+06
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2008 2009 2010 2011 2012 2013 2014 2015 2016
Kg
Use
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YearSOURCE: FDA, 2015, Antimicrobials Sold or Distributed for Use in Food-Producing Animals
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Staphylococcus aureus Bacteria Can Transmit from Hogs to Humans
• S. aureus can acquire antibiotic resistance in hogs
• Resistant strains can pass to humans
• Example: methicillin-resistant S. aureus (MRSA)
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First Documented Case of MRSA Transmission from Hogs in 2004
• 6-month-old Dutch infant
• Parents also colonized
• Remained colonized for months despite aggressive treatment
• Source traced to hogs on the family’s farm
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Mounting Evidence of Hog-to-Human Transmission Since 2004
SOURCE: George, Stewart, and MacDonald Gibson, in review, Environmental Health Perspectives.
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Percentage of Human Subjects Colonized with Hog-Associated MRSA
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Prevalence of Human S. aureus Infections Resistant to Methicillin is High
Prevalence of invasive S. aureus infections resistant to methicillin
How much does animal use of antibiotics contribute?
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Objective
• Characterize risk factors for MRSA transmission to hog workers’ children
• Build a predictive model to assess potential interventions
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Methods
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Data from Hog Workers in Eastern North Carolina (NC)
• 198 children living with hog workers
• MRSA colonization tested via nasal swabs
• Questionnaires administered to adult household members
Eastern NC has among highest industrial hog operation densities in world.
SOURCE: Wing et al., 2000.
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Questionnaire Included 98 Potential Explanatory VariablesParent MRSA
Recent hospitalization
Daycare
Age, gender, race
Education
Race
Handwashing
Household pet
Jobs
Workplace hygiene . . .
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Previously Published Analysis Found Only Four of 98 Items Influenced Risk
“The other IHO work activities and factors evaluated . . . were null or non-interpretable due to insufficient numbers.”
Factor Prevalence RatioTake work clothing home
12
Use disinfectant 6Work with nursery pigs
2.2
Handle dead pigs 3.2
Risk Factors Identified by Log-Binomial Regression
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We Tested 17 BayesiaLab Algorithms to Build Predictive Model, Gain Insights
Model Taboo Equivalence Class
Max Weight Tree
Augmented Naïve Bayes
Add Prior Evidence
1 1 2 3*2 1 2 3* 43 1 2*4 1 2* 35 1 2*6 1 2* 3. . .13 1 2…17 1*
*With variable selection
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Accuracy Tested in Cross-Validation
• Five-fold cross validation
• Repeated 20 times- Different random train/test split each time
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Area Under Receiver-Operating Characteristic (ROC) Curve Used as Metric
Diagnostic accuracy = area under curve (1=perfect)
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Trade-off between specificity and sensitivity
Area Under Receiver-Operating Characteristic (ROC) Curve Used as Metric
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Results
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14% of Workers’ Children Colonized
By comparison, 6% of children in community referent group were colonized.
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17 Algorithms Yielded Four Model Structures
1. Taboo EQ Augmented naïve Bayes, variable selection
3. Taboo remove if mutual information p>0.2 Augmented naïve Bayes
2. Net 1 + Prior evidence
3. Net 3 + Prior evidence
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Occam’s Razor
1. Taboo EQ Augmented naïve Bayes, variable selection
Entia non suntmultiplicanda praeternecessitatem.[
More things should not be used than are necessary.
- William of Occam, 14th
Century
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The Most Parsimonious Model Had High Accuracy
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Accuracy Was Well Preserved Under Cross-Validation
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Model Shows Workplace Strategies for Decreasing Risks
Bringing home work clothing or mask
Leaving work clothing and mask at hog operation
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Model Also Reveals Surprising Health Insurance Association
Risk increases with company insurance
Risk decreases with public insurance
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Web-Based, User-Friendly Version for Communicating Results
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Limitations
Small sample sizeCross-sectional studyBut . . .
- Strong performance for sample size
No MRSA MRSAPredict No MRSA
83% 7%
Predict MRSA
13% 93%
High sensitivity with reasonable false-positive rate
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Future: Use Simulator to Promote Interventions
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Summary
• Bayesian network identified factors associated with MRSA transmission risk- Bringing PPE home
- Company insurance
- Contact with hog manure
• Web-based platform could help communicate effects of interventions
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Acknowledgements
• Funding sources- Thrasher Research Fund award 10287
- National Science Foundation grant 1316318
• Collaborators- Jill R. Stewart, UNC
- Sarah M. Hatcher, UNC
- Sarah M. Rhodes, UNC
- Devon Hall, Rural Empowerment for Community Health
- Christopher D. Heaney, Johns Hopkins
- Households, other researchers assisting with study design and data analysis