Risk Assessment of the Impact of Lethality Standards on Salmonellosis from Ready-to-eat Meat and Poultry Products

1. Risk Assessment of the Impact of Lethality Standards on Salmonellosis from Ready-to-eat Meat and Poultry Products Greg Paoli Decisionalysis Risk Consultants, Inc. March 24, 2005

2. Outline Scope of Risk Assessment Overview of Risk Estimation Process Review of Key Assumptions Review of Risk Estimates Uncertainty and Limitations Summary

3. Risk Management Question: This presentation deals with the question below: The proposed RTE rule has a minimum lethality performance standard of a 6.5-log reduction of Salmonella spp. in meat for all categories (cooked, fermented, salt-cured and dried). What would be the public health impact of alternative lethality standards of 5.0 and 6.5/7.0-log reductions of Salmonella?

4. Scope of the Risk Assessment Estimation of the number of cases of salmonellosis resulting from salmonellae in contaminated raw materials that survive lethality treatments applied to ready-to-eat (RTE) meat and poultry products (MPPs) Addresses 16 RTE meat and poultry product categories

5. Scope of Risk Assessment Does not include: Illnesses caused by other pathogens Examples: E. coli O157:H7, Campylobacter jejuni, Listeria monocytogenes This is primarily a technical limitation Does not include: post-lethality product contamination after the lethality step, by food preparers or at any other stage acute process failures … since the risk from these events is not sensitive to the level of the lethality standard

7. Scope Issues Validation data are not available FSIS sampling program could provide an upper-bound on estimates of contamination levels Product categories are not exhaustive But, they are representative of a diverse set of important, high-volume product categories They span key lethality processes: cooked, fermented, dried and salt-cured

9. Overview of Risk Estimation Process Stage 1 Develop representative product categories Assign raw material streams to product categories Estimate the expected number of organisms in raw materials, for a given mass of product

10. Overview of Risk Estimation Process Stage 2 Apply lethality treatment at a prescribed level Adjust lethality treatment based on compliance Apply thermal process safety factors, if any ? This provides an estimate of the number of surviving organisms in a given mass of product

11. Overview of Risk Estimation Process Stage 3 Estimate the growth of the organism population during storage at retail and in home, if any Apply heat treatment by preparer, if any ?This provides a distribution of the number of consumed organisms in servings

12. Overview of Risk Estimation Process Stage 4 Apply a dose-response relationship to convert the distribution of ingested doses to the probability of illness ? This provides an estimate of the expected number of cases of salmonellosis for a given mass of product

13. Overview of Risk Estimation Process Stage 5 Apply the amount of consumption of each product category, in a year ? This provides an estimate of the expected number of cases of salmonellosis in a year for each product category, and in total

14. Model Implementation Implemented using modeling software Analytica version 3.0 Model ‘Player’ available (free): Users can browse the model Contains visual layout and documentation Users can run the model Has user interface, allowing certain assumptions to be changed by the user

15. Model Screenshots

16. Model Screenshots

18. Review of Key Assumptions Types of assumptions Designation of product categories Data selection and treatment Estimation methods and simplifications Reasoned assumptions in the presence of data and theory gaps

19. Product Categories Designed to be compatible with: Risk management requirements Data sources (e.g., production, consumption) Distinctions important to risk estimation Manageable number of categories Coverage of major products in all four lethality categories Cooked, fermented, dried and salt-cured

21. Raw Material Pathogen Burden Used FSIS Microbiological Baseline Surveys Estimate the expected number of salmonellae in a given mass of raw materials (per gram, per million kilograms) Separate estimates made for: beef, pork, chicken, turkey Both ground and intact for each

23. Lethality Treatments Expressed in units of ‘log reductions’ This is the base-10 logarithm of the reduction factor A 5-log reduction means the population will be reduced by, on average, 5 factors of 10, or 100,000 Equivalently, we could say that each organism has a 1 in 100,000 chance of survival of the process If a million organisms (6-logs) were subjected to a “5-log process” we would expect, on average, 10 survivors (1-log).

24. Lethality Scenarios Three alternate policy scenarios were requested: “All 5-log” All products require, at least, a 5-log reduction “All 6.5/7.0-log” All products (except if they contain poultry) require 6.5-log if they contain poultry they require a 7.0 log reduction. “Split” (Default) All cooked products require 6.5/7.0-log reductions; all other products require a 5.0-log reduction. One exception is fully-cooked beef patties which would require 5.0-log When not otherwise indicated, estimates refer to the “Split” scenario.

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26. Lethality Compliance Factors Based on expert elicitation study (RTI, 2004) “What proportion of the producers of product Y achieve an X-log reduction.” Full compliance results in some additional lethality Assumes some overshoot of target Assumes all cooked products in full compliance with 6.5/7.0 Deviations from full compliance result in reduced lethality Largest adjustment for fermented products in the 6.5/7.0 log scenario.

27. Thermal Process Safety Factors For a variety of reasons, processors may apply a process that yields a much smaller average probability of survival than is implied by the strict interpretation of being in compliance with the required lethality. For example, in complying with a 7-log reduction requirement, a process may actually achieve a mean probability of survival equivalent to an 11-log reduction.

28. Thermal Process Safety Factors Selected reasons for safety factors: Product geometry and heat transfer to interior Consumer preference for texture, ‘done-ness’ Design and validation of processes with strains that are much more resistant than ‘average’ (e.g. Salmonella enterica serovar Senftenberg) Motivated to achieve significant reductions of other pathogens, for example, E. coli O157:H7 Where contamination is limited to the surface combined with intense heating to warm the inside

29. Thermal Process Safety Factors Estimation Challenge: We know they exist … and that they can have a large impact on the risk estimation process (for certain products). They may be simulated or known for certain products and processes, but we need to know the net impact across the whole industry that is producing a product. The industry-wide TPSF is strongly influenced by the proportion of processors having lower safety factors. Most data geared toward assuring compliance; they are not readily applied for estimation of product risk

30. Thermal Process Safety Factors Requires reasoned assumptions Implemented as having three possibilities: Small ? 0-log increment Medium ? 2-log increment Large ? 4-log increment Each product category is assigned one of these three factors Model allows adjustment and removal for sensitivity analysis.

31. Single Organism Assumption Survival of organisms (at the level of serving-sized pieces of meat) is modeled as a rare event. The implication is that, in each contaminated serving, we assume that there is only surviving organism (prior to considering growth). A further implication is that we can simplify the representation of the pathogen burden to the total number of organisms without concern for variations in density. Implications for Validation Rare events, recognizable outbreaks not expected from this contamination pathway

32. Storage and Growth Four scenarios are assigned to products: No-Growth, regardless of temperature Low-Survival, a further 1-log reduction during storage Normal Growth, Refrigerated Storage minimum temperature is applied Low Growth, Refrigerated Storage half the growth rate of Normal Growth with minimum temperature applied

33. Storage and Growth Detailed growth modeling for diverse products, even within one product category would be very time and resource intensive. Data and models to accommodate this variety in distinct products are relatively limited Growth and Low-Growth categories are modeled using a ‘square-root’ model, relating growth rate to temperature

34. Storage and Growth For products allowing growth … Growth is modeled in two stages: retail and consumer storage A variety of time and temperature distributions are provided for in the model to explore sensitivity, but only defaults are carried through to final calculations.

35. Reheating Three levels of lethality due to reheating are considered: None ? 0-log (no change in population) Minimal ? 2-log additional reduction Thorough ? 4-log additional reduction Products are assigned to reheating pattern categories which specify the proportions of consumers applying each of the three levels above. Never, Rarely, Usually, Always, Always Thoroughly

36. Dose-Response Model A Beta-Poisson model is applied based on outbreak data as developed in WHO/FAO Expert Consultations. This converts the dose of organisms into a probability of illness. Note: No minimum infective dose WHO/FAO Hazard Characterization Guidelines Probability of illness from a single salmonellae P(ill|1 cfu) ~ 0.0025

37. Dose-Response Modeling

38. Consumption Volumes For most product categories, based on Economic Census (Dept. of Commerce) For a few product categories, based on a database product (EDEA, Environ Corp.) containing CSFII data (ARS, USDA). CSFII: Continuing Survey of Food Intake by Individuals Highly variable uncertainties Quite high for smaller volume products

39. Risk Estimates Main estimates (salmonellosis): Annual Cases per Million Kg of Product Class Total Cases due to Product Class Total Cases due to All Product Classes Options for estimates Exclude Thermal Process Safety Factor Exclude Reheating Exclude Compliance Adjustment Assumptions for Behavior under Relaxed Lethality Standard Baseline: industry relaxes lethality to lowered standard where applicable

40. Product Risk (Log Scale, Equal Mass Basis)

41. Annual Product Risk (Log scale, Production Weighted)

42. Scenario: “All 5-log” Estimate: 66,000 cases per year

43. Scenario: “Split” Estimate: 1,900 cases per year

44. Scenario: “All 6.5/7.0-log” Estimate: 1,100 cases per year

45. Uncertainty and Limitations Much of the uncertainty is not readily quantifiable. Raw material pathogen burden ** Uncertainty in compliance * Thermal process safety factors **** Storage and Growth ** Reheating Impact * Production Volumes ** Categorizations ** Dose-Response ***

46. Uncertainty and Limitations Risk estimates presented should be considered to fall within a broad range of uncertainty. They may be several factors of 10 smaller or larger. Given this uncertainty, the relative ranking (or attribution of total risk) should be considered correspondingly uncertain.

47. Summary The risk assessment provides policymakers with estimates of the impact of alternate lethality standards (5-log, 6.5-log, 7-log reductions) on the expected number of cases of salmonellosis, for 16 product categories. Model software is designed to allow for exploration of the impact of alternate assumptions at numerous stages in the estimation process. The risk assessment model and the report will be revised in response to public comment.

48. Thank you for your attention