# Presentation: 500761

## 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

## Outline

- Scope of Risk Assessment
- Overview of Risk Estimation Process
- Review of Key Assumptions
- Review of Risk Estimates
- Uncertainty and Limitations
- Summary

## 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?

## 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

## 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

## Slide 6

## 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

## {

- 6

- 7

- 8

- 9

- 10

- Raw Material Burden

- Log10 CFU per Million Kg

- -8

- Lethality Standard

- Log10 Change

- -7

- -6

- -5

- -4

- Compliance Factor

- Log10 Change

- Large-4

- Log10 Change

- Small0

- Moderate-2

- Storage, Growth and Reheating Factors
- Dose-Response Function

- Relative Log10 Risk per Contaminated Serving

- Low-Survival Foods

- No-Growth

- Low-Growth (Refrig.)

- Growth in Refrigerated  Storage

- Always Thoroughly

- Log10 Change

- Rarely

- Always

- Usually

- -13

- -8

- +2

- 7

- Product Risk

- Crude Product Risk (Log10 Illnesses per Million Kg)

- -3

- Ideal

- Poor

- Very Good

- Good

- Thermal Process Safety Factor

- Fair

- Very Poor

**Schematic of Estimation Process**

## 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

## 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

## 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

## 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

## 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

## 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

## Model Screenshots

## Model Screenshots

## {

- 6

- 7

- 8

- 9

- 10

- Raw Material Burden

- Log10 CFU per Million Kg

- -8

- Lethality Standard

- Log10 Change

- -7

- -6

- -5

- -4

- Compliance Factor

- Log10 Change

- Large-4

- Log10 Change

- Small0

- Moderate-2

- Storage, Growth and Reheating Factors
- Dose-Response Function

- Relative Log10 Risk per Contaminated Serving

- Low-Survival Foods

- No-Growth

- Low-Growth (Refrig.)

- Growth in Refrigerated  Storage

- Always Thoroughly

- Log10 Change

- Rarely

- Always

- Usually

- -13

- -8

- +2

- 7

- Product Risk

- Crude Product Risk (Log10 Illnesses per Million Kg)

- -3

- Ideal

- Poor

- Very Good

- Good

- Thermal Process Safety Factor

- Fair

- Very Poor

**Schematic of Estimation Process**

## 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

## 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

## Slide 20

| PRODUCT CATEGORY | PRODUCT CODE |
| --- | --- |
| Roast Beef, Corned Beef | RBCB |
| Fully Cooked Beef Patties | FCBP |
| Cooked Pork (Cooked Ham, Pork BBQ) | CPCH |
| Cooked Turkey (non-Deli) | CTND |
| Cooked Chicken (Nuggets, Tenders, non-Deli) | CCND |
| Cooked Poultry Deli Meat | CPD |
| Cooked Chicken Patties | CCP |
| Beef / Pork Frankfurters | BPF |
| Beef / Pork Bologna | BPB |
| Poultry Frankfurters | PF |
| Summer Sausage, Thuringer, Cooked Pepperoni | SSCP |
| Salami, Uncooked Pepperoni, Chorizo, Soudjuk | SUP |
| Meat Sticks | MS |
| Beef Jerky | BJ |
| Uncooked Country Ham | UCH |
| Prosciutto, cappicola, pancetta, basturma | PCPB |

## 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

## Slide 22

| CODE | RAW MATERIAL COMPOSITION | PATHOGEN BURDEN |  |
| --- | --- | --- | --- |
|  |  | Log CFU/g | Log CFU/Mkg |
| RBCB | Intact Beef | -2.7 | 6.3 |
| FCBP | Ground Beef | -0.3 | 8.7 |
| CPCH | Intact Pork | -2.7 | 6.3 |
| CTND | Ground Turkey | 0 * | 9.0 |
| CCND | Ground Chicken | 1 | 10.0 |
| CPD | 50% Ground Chicken, 50% Ground Turkey | 0.7 | 9.7 |
| CCP | Ground Chicken | 1 | 10.0 |
| BPF | 50% Ground Beef, 50% Ground Pork | -0.6 | 8.4 |
| BPB | 50% Ground Beef, 50% Ground Pork | -0.6 | 8.4 |
| PF | 50% Ground Chicken, 50% Ground Turkey | 0.7 | 9.7 |
| SSCP | 50% Ground Beef, 50% Ground Pork | -0.6 | 8.4 |
| SUP | 50% Ground Beef, 50% Ground Pork | -0.6 | 8.4 |
| MS | Ground Beef | -0.3 | 8.7 |
| BJ | Ground Beef | -0.3 | 8.7 |
| UCH | Intact Pork | -2.7 | 6.3 |
| PCPB | Intact Pork | -2.7 | 6.3 |

## 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).

## 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.

## Slide 25

| PRODUCT CATEGORY | CODE | SCENARIO |  |  |
| --- | --- | --- | --- | --- |
|  |  | All 5 | Split | All 6.5/7 |
| Roast Beef, Corned Beef | RBCB | 5.0 | 6.5 | 6.5 |
| Fully Cooked Beef Patties | FCBP | 5.0 | 5.0 | 6.5 |
| Cooked Pork (Cooked Ham, Pork BBQ) | CPCH | 5.0 | 6.5 | 6.5 |
| Cooked Turkey (non-Deli) | CTND | 5.0 | 7.0 | 7.0 |
| Cooked Chicken (Nuggets, Tenders, non-Deli) | CCND | 5.0 | 7.0 | 7.0 |
| Cooked Poultry Deli Meat | CPD | 5.0 | 7.0 | 7.0 |
| Cooked Chicken Patties | CCP | 5.0 | 7.0 | 7.0 |
| Beef / Pork Frankfurters | BPF | 5.0 | 6.5 | 6.5 |
| Beef / Pork Bologna | BPB | 5.0 | 6.5 | 6.5 |
| Poultry Frankfurters | PF | 5.0 | 7.0 | 7.0 |
| Summer Sausage, Thuringer, Cooked Pepperoni | SSCP | 5.0 | 5.0 | 6.5 |
| Salami, Uncooked Pepperoni, Chorizo, Soudjuk | SUP | 5.0 | 5.0 | 6.5 |
| Meat Sticks | MS | 5.0 | 5.0 | 6.5 |
| Beef Jerky | BJ | 5.0 | 5.0 | 6.5 |
| Uncooked Country Ham | UCH | 5.0 | 5.0 | 6.5 |
| Prosciutto, cappicola, pancetta, basturma | PCPB | 5.0 | 5.0 | 6.5 |

## 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.

## 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.

## 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

## 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

## 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.

## 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

## 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

## 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

## 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.

## 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

## 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

## Dose-Response Modeling

## 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

## 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

## Product Risk (Log Scale, Equal Mass Basis)

## Annual Product Risk (Log scale, Production Weighted)

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

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

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

## 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 ***

## 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.

## 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.

## Thank you for your attention