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Risk based optimization of the Danish pork Salmonella control program H. Scott Hurd [1], Claes Enøe [2], Lene Sørensen [2], Henrik Wachmann [2], Steven.

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Presentation on theme: "Risk based optimization of the Danish pork Salmonella control program H. Scott Hurd [1], Claes Enøe [2], Lene Sørensen [2], Henrik Wachmann [2], Steven."— Presentation transcript:

1 Risk based optimization of the Danish pork Salmonella control program H. Scott Hurd [1], Claes Enøe [2], Lene Sørensen [2], Henrik Wachmann [2], Steven M Corns [3], Kenneth M. Bryden [3], Helen H. Jensen [4] [1] [2] [3] [4] [1][1] College of Veterinary Medicine, Iowa State University [2][2] Danish Meat Association, Copenhagen, Denmark [3] [3] Mechanical Engineering Department, Iowa State University [4] [4] College of Agriculture, Dept of Economics, Iowa State University

2 Acknowledgments Tine Hald, Matthias Greiner, Rene Bøker, Anne Bisp Lind –Danish Institute for Food and Veterinary Research (DFVF) Funded as a project of the International EpiLab –Guest Scientist at the EpiLab in Denmark, –Employed at the National Animal Disease Center: USDA:ARS, Ames, Iowa, USA

3 Overview Evaluation of the on-farm pork Salmonella control program Has the last 10 years of investment significantly reduced human cases? Simulation/systems model with best data says……

4 U.S M pigs/year 71,000 producers Iowa 144,700 km M pigs/year Denmark 43,000 km 2 23 M pigs/year 13,200 producers Denmark is a small state with a UNIQUE form of pork production and marketing (Coop)

5 Context for this project “We are spending too much for on-farm surveillance” “Carcass decontamination is the cheapest way to control salmonella” “Washing all carcasses, not acceptable. Don’t want to eat feces” “The Level 3 herds are not important” “Pressure on the L3 herds has improved all herds” “Yeah, but has it improved public health?” “The industry just cares about “cheaper” “We are at a standstill in reducing carcass swab prevalence”

6 BACKGROUND The current Salmonella herd classification scheme Serological monitoring of all herds in the “coop” system –Form of a blood test (serum exudate, meat juice) –(up to 60 pigs/herd/year) Assigned to 3 prevalence levels based on last 3 months of serology

7 The current Salmonella herd classification scheme High seroprevalence (~1.5%) assigned –to special hygienic slaughter conditions, –financial penalties, –and hot water decontamination Carcass swab (400cm 2 ) –Pool of 5 samples –cultured for Salmonella –Similar to USDA:FSIS

8 Objectives Estimate the effect of various control interventions –On-farm (pre-harvest) vs. –Abattoir (post-harvest) Overtime –Previous 10 years (retrospective) –Next 10 years (prospective) Final outcome = human cases (unique).

9 Materials and Methods Stochastic systems model –Multiple Excel® spreadsheets Simulate the number of human Salmonella cases as a function of –Herd seroprevalence –Undefined abattoir interventions carcass swab culture status –Human attribution model. Using best AVAILABLE data from Danish surveillance system

10 Systems model of pork attributable human cases (PAHC) of salmonellosis Distribution of herds by seroprevalence category and size Production Module Probability of carcass swab positive given herd seroprevalence category Slaughter module Attributes human cases as a function of positive carcasses Attribution Module Pigs from each seroprevalence category Human cases per year Carcass swab positive pigs

11 Changes in herd seroprevalence due to on-farm control program (actual data)

12 PRODUCTION module converts size distribution to number of pigs, using stochastic version of average herd size

13 Systems model of pork attributable human cases (PAHC) of salmonellosis Distribution of herds by seroprevalence category and size Production Module Probability of carcass swab positive given herd seroprevalence category Slaughter module Attributes human cases as a function of positive carcasses Attribution Module Pigs from each seroprevalence category Human cases per year Carcass swab positive pigs

14 Slaughter module “Creates” carcass swab positive pigs As a function of average annual seroprevalence Combines data from all Danish abattoirs in the program Regression parameter –adjusted to match historical carcass swab results with the distribution of average annual seroprevalence for that year

15 Note how coefficient improves over time due to changes in abattoir processes

16 Systems model of pork attributable human cases (PAHC) of salmonellosis Distribution of herds by seroprevalence category and size Production Module Probability of carcass swab positive given herd seroprevalence category Slaughter module Attributes human cases as a function of positive carcasses Attribution Module Pigs from each seroprevalence category Human cases per year Carcass swab positive pigs

17 Attribution module Uses attribution data –Methods shown in Hald et al Risk Analysis Connects positive carcasses –(servings) produced that year with number of pork attributed human Salmonella cases Attribution factor –pert distribution, Same factor is used in retrospective (historical) and prospective (future) simulations

18 Simulation 5,000 iterations Outputs of interests –pork attributable salmonellosis cases Retrospective (historical) simulations –( ) –Uses available historical trend data on On-farm seroprevalence or Abattoir regression parameter Prospective (future) simulations –( )

19 Systems model of pork attributable human cases (PAHC) of salmonellosis Distribution of herds by seroprevalence category and size Production Module Probability of carcass swab positive given herd seroprevalence category Slaughter module Attributes human cases as a function of positive carcasses Attribution Module Pigs from each seroprevalence category Human cases per year Carcass swab positive pigs

20 Retrospective simulations (3 scenarios) Both combined (Historical) Abattoir parameter improvements only Post-harvest Farm at 1995 seroprevalence distribution Farm at 2003 seroprevalence distribution Changes in herd seroprevalence over time Pre-harvest Abattoir 1995 regression parameter Abattoir 2003 regression parameter

21 Historical: Combined effect of on-farm (pre-harvest) changes and abattoir (post-harvest) Significant (P <0.05) changes in risk occurred

22 Changes in herd seroprevalence over time: Abattoir parameter at 1995 level = Effect of on-farm (pre-harvest) program only 19% decrease, 69 cases (P>0.05)

23 Abattoir (post-harvest) improvements only: If no on-farm control program? (i.e. seroprevalence stayed at 1995 level) A difference of 81 cases (P>0.05). Due to abattoir improvements only

24 Where do we go from here? Prospective simulations ( ) Compare on-farm to abattoir –Improvements on-farm at same rate as the past –Improvements in abattoir at same rate as the past Carcass decontamination in slaughter –Reduced Pr (CS+) by 90%

25 Improve abattoir only, 40 cases Improve farm only = no change Human cases based on holding on-farm seroprevalence levels at 2003 levels and improving abattoir methods (Farm2003ImpAb); reverting on-farm to 1995 values while keeping abattoir values constant (RevF); and continuing to improve on-farm while keeping abattoir values constant (ImpF).

26 Reduce all herds to <25% ave. annual seroprevalence Decrease cases by ~10 to 190/yr

27 Cases while steadily improving abattoir methods for all on-farm values set at 25% or less (AllFarm25); reverting on-farm to 1995 values while keeping abattoir values constant (RevF); and continuing to improve on-farm while keeping abattoir values constant (ImpF). Still end up with 110 cases/yr

28 What else can be done? Carcass decontamination

29 Summary of results - Retrospective On-farm program reduced risk in early years (‘95-’98) Improvements since ‘98 due to unspecified abattoir improvements On-farm program ALONE would have only reduced average annual cases by ~69

30 Summary of results - Prospective Massive on-farm improvements needed to further reduce risk (new technology?) Don’t go back (on-farm seroprevalence) Steady improvement in general slaughter hygiene will make minimal reduction in risk Some form of carcass decontamination has the most significant impact on risk

31 Interesting points to ponder Denmark is a health policy “lab” Lessons on the economics of food safety Use of “attribution” model Shows what happens when “connect all the dots” Systems models provide a tool for policy and research decision making

32 Future work: Add economics to a U.S model

33 Contaminated servings or potential infectious servings


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