Analysis to Inform Decisions: Evaluating BSE Joshua Cohen and George Gray Harvard Center for Risk Analysis Harvard School of Public Health

Contributors Harvard Center for Risk Analysis Harvard Center for Risk Analysis Joshua T. Cohen Joshua T. Cohen Keith Duggar Keith Duggar George M. Gray George M. Gray Silvia Kreindel Silvia Kreindel Center for Computational Epidemiology, College of Veterinary Medicine, Tuskegee University Center for Computational Epidemiology, College of Veterinary Medicine, Tuskegee University Hatim Gubara Hatim Gubara Tsegaye HabteMariam Tsegaye HabteMariam David Oryang David Oryang Berhanu Tameru Berhanu Tameru Harvard Center for Risk Analysis Harvard Center for Risk Analysis Joshua T. Cohen Joshua T. Cohen Keith Duggar Keith Duggar George M. Gray George M. Gray Silvia Kreindel Silvia Kreindel Center for Computational Epidemiology, College of Veterinary Medicine, Tuskegee University Center for Computational Epidemiology, College of Veterinary Medicine, Tuskegee University Hatim Gubara Hatim Gubara Tsegaye HabteMariam Tsegaye HabteMariam David Oryang David Oryang Berhanu Tameru Berhanu Tameru

What USDA Asked Us to Do Identify and characterize possible sources for BSE (or a TSE disease with similar clinical and pathologic signs as BSE - will refer to as BSE for brevity) infectivity in U.S. cattle Identify and characterize possible sources for BSE (or a TSE disease with similar clinical and pathologic signs as BSE - will refer to as BSE for brevity) infectivity in U.S. cattle Identify and characterize pathways for cattle-derived BSE infectivity in the U.S. cattle herd or human food supply Identify and characterize pathways for cattle-derived BSE infectivity in the U.S. cattle herd or human food supply Evaluate implications over time of possible introduction of BSE into US system Evaluate implications over time of possible introduction of BSE into US system

Why We Chose a Simulation Approach No historical data - build understanding up from biology, agriculture, etc. No historical data - build understanding up from biology, agriculture, etc. Time matters - e.g., incubation period of BSE Time matters - e.g., incubation period of BSE Allow quantitative comparison of importance of different pathways of spread and different risk management Allow quantitative comparison of importance of different pathways of spread and different risk management Can help focus collection of information Can help focus collection of information

Learning from UK Experience We assume the prevailing hypothesis of UK BSE spread is correct:

Model Overview Cattle Population Number Infected Number Clinical Slaughter Rendering and Feed Production Infectivity Sources Human Food Disposal Death and Disposal Other Uses and Elimination from System Other Protein Sources Feed Administered to Cattle Death / Rendering

Cattle Dynamics

Key Assumptions - Susceptibility

Infectivity Level in Bovine vs. Time Since Infection

Distribution of Infectivity Relative Infectivity of Specific Tissues Specified from an Infected Bovine (Based on [SSC, 1999a]) a

Slaughter Process Sick Animal Characteristics Antemortem Inspection Disposition of Brain Stunning Exsanguination Tissues to rendering SplittingPostmortem Inspection Tissues for Possible Human Consumption AMR/ Spinal Cord/DRG Processing Out

Rendering and Feed Production Tissues to rendering Prohibited MBM production Prohibited feed production Feeding of cattle on farm Non-prohibited MBM production Non-Prohibited feed production Blood

Analyses Base Case Base Case Assume BSE not currently present in U.S. Assume BSE not currently present in U.S. Introduce 10 BSE infected animals (also simulated importation of 1 to 500 BSE infected cows) Introduce 10 BSE infected animals (also simulated importation of 1 to 500 BSE infected cows) Follow for 20 years Follow for 20 years Example Risk management Options Example Risk management Options Ban on rendering cattle that die on farm Ban on rendering cattle that die on farm UK-style “Specified Risk Material” ban UK-style “Specified Risk Material” ban Test with introduction of 10 infected animals and follow for 20 years Test with introduction of 10 infected animals and follow for 20 years Others Others Potential for pre-1989 imports from England to introduce BSE to U.S. Potential for pre-1989 imports from England to introduce BSE to U.S. Switzerland Switzerland Spontaneous Spontaneous Scrapie as source Scrapie as source

Model is Probabilistic Initialize Model Run Simulation Record Results Run 3 Run 2 Run 1 … Run 1000 Number of Infected Cattle over 20 Years

Results: Base Case Few new cases of BSE Few new cases of BSE mean = 3 and 95th percentile = 11 mean = 3 and 95th percentile = 11 Primarily through feed ban leaks Primarily through feed ban leaks 40% of animals predicted to die on farm introduce 96% of infectivity to system 40% of animals predicted to die on farm introduce 96% of infectivity to system BSE gone within 20 years of introduction BSE gone within 20 years of introduction

Base Case Results (continued) Little infectivity for potential human exposure (mean 35 cattle oral ID50s, 95th 170) Little infectivity for potential human exposure (mean 35 cattle oral ID50s, 95th 170) Brain26% Brain26% Beef on bone11% Beef on bone11% AMR meat56% AMR meat56% Spinal cord5% Spinal cord5% Conservative assumptions (e.g., no change if case detected) Conservative assumptions (e.g., no change if case detected)

Base Case – Summary

Base Case - Summary

Number of Cattle Infected: Probability of Prevalence Value Exceeding Zero

Base Case - Summary Number of Cattle Infected: Range of Prevalence Values

Base Case – Summary Number of Cattle Clinical: Probability of Prevalence Exceeding Zero

Base Case – Changes Over Time Number of Cattle Clinical: Range of Prevalence Values

Model Predictions for More Substantial Imports of Infected Cattle Number of BSE-Infected Cattle Imported Additional Infected Cattle

Model Predictions for More Substantial Imports of Infected Cattle Number of BSE-Infected Cattle Imported Number of ID 50 s Available for Potential Human Consumption

Key Sources of Uncertainty Influencing the Predicted Number of Infected Cattle

Key Sources of Uncertainty Influencing Predicted Human Exposure (ID 50 s Available for Human Consumption)

Key Management Points Spread in cattle herd Spread in cattle herd Mostly due to leaks in FDA feed ban and some maternal transmission Mostly due to leaks in FDA feed ban and some maternal transmission Animals that die on farm with provide greatest infectivity to animal feed system Animals that die on farm with provide greatest infectivity to animal feed system Potential human exposure Potential human exposure Handling of brain and spinal cord in processing very important Handling of brain and spinal cord in processing very important Primary routes of exposure are cattle brain, spinal cord, beef on bone and AMR meat Primary routes of exposure are cattle brain, spinal cord, beef on bone and AMR meat

Imports from England Before 1989 Evaluated potential for 173 (of 334) English imports not known to have been destroyed to introduce infectivity to U.S. cattle and implications Evaluated potential for 173 (of 334) English imports not known to have been destroyed to introduce infectivity to U.S. cattle and implications Used information on birth year, export year, animal type and sex, last sighting and more to estimate likelihood and potential magnitude of introductions of BSE infectivity to U.S. cattle feed Used information on birth year, export year, animal type and sex, last sighting and more to estimate likelihood and potential magnitude of introductions of BSE infectivity to U.S. cattle feed Used model to look at new BSE cases if introduction of different sizes did occur Used model to look at new BSE cases if introduction of different sizes did occur

Cumulative Distribution for the U.S. Cattle Exposure to Cattle Oral ID50s from Animals Imported from the UK During the 1980s

Cumulative Distribution for the Number of BSE-Clinical Cattle in the Year 2000 for Different Levels of Infectivity Introduced via Import of UK Cattle During the 1980s

Strengths of Analytic Approach Identify key assumptions and data Identify key assumptions and data Understand relative importance of different paths Understand relative importance of different paths Compare relative effectiveness of different risk management measures Compare relative effectiveness of different risk management measures Facilitates value of information (VOI) analysis to identify critical research areas Facilitates value of information (VOI) analysis to identify critical research areas

Weaknesses of Analytic Approach Overconfidence in results? Overconfidence in results? Dependent on underlying structure and assumptions Dependent on underlying structure and assumptions Difficulty in calibration/validation Difficulty in calibration/validation What is the alternative? What is the alternative?