General Interest

Response to Questions Posed by the Food Safety and Inspection Service:

Enhancing Salmonella Control in Poultry Products

The National Advisory Committee on Microbiological Criteria in Foods (NACMCF) 1

ARTICLE INFO

Keywords:

Microbiological criteria

Poultry

Quantification

Salmonella

Serotypes

ABSTRACT

Executive summary

Chicken and turkey are major sources of animal protein in the United States. Per capita chicken consumption increased from 77.4 lb. in

2000 to a projected 99.6 lb. in 2023, with processed and cut‐up

chicken parts comprising the majority of purchases, whereas beef

has declined from 67.5 lb. to an estimated 55.8 lb. during the same

period (National Chicken Council, 2022). Of the estimated over 1 million cases of foodborne salmonellosis acquired annually in the U.S.

(Scallan, Hoekstra, et al., 2011), over 24% are attributed to consumption of chicken and turkey products (Fig. 1) (Interagency Food Safety

Analytics Collaboration (IFSAC), 2022; Painter et al., 2013). Although

Salmonella is killed by adequate cooking, undercooking (e.g., in raw

breaded or stuffed chicken products), and cross‐contamination with

other ready‐to‐eat foods in the refrigerator or during preparation are

contributing factors to transmission of this pathogen. Therefore, FSIS

instituted Salmonella performance standards for raw poultry carcasses,

chicken parts, and raw‐comminuted poultry products. Since their

implementation, data revealed a steady decline in Salmonella prevalence. For example, the prevalence on chicken parts (legs, breast,

and wings) decreased 75% over an 8‐year period from 2012 to 2020

(Williams et al., 2022). Furthermore, Salmonella Typhimurium and

Heidelberg infections associated with poultry have declined in the past

two decades, likely due to the use of group B Salmonella poultry vaccines on broiler farms and other interventions. Notwithstanding this

reduction of Salmonella prevalence in poultry products, there has not

been a concomitant reduction in overall Salmonella illnesses (Fig. 2)

(Centers for Disease Control and Prevention (CDC), 2021) nor in the

proportion attributed to poultry from outbreak data (Fig. 1)

(Interagency Food Safety Analytics Collaboration (IFSAC), 2022).

Meanwhile, the proportion of illnesses associated with serotypes

Enteritidis and Infantis have increased (Williams et al., 2022).

NACMCF reviewed the scientific evidence on Salmonella control in

the U.S. and abroad, foodborne illness surveillance data, quantitative

microbial risk assessments, and microbiological testing of indicator

organisms vs. Salmonella on poultry throughout the farm‐to‐fork continuum. Based on this information, this document seeks to provide

guidance to FSIS and the poultry industry on what types of microbiological criteria might be used to identify and incentivize effective

intervention strategies pre‐ and postharvest to reduce Salmonella in

poultry products and thereby prevent human Salmonella infections

associated with these products.

The infectious dose of Salmonella varies widely between serotypes

(Teunis et al., 2010). Recent data suggest that most poultry‐

associated outbreaks in the U.S. involve Enteritidis, Typhimurium,

I:4,5,12:i:‐, Infantis and Heidelberg, and even fewer serogroups:

groups O:4 (formerly group B), O:7 (group C1), and O:9 (group D1).

In live poultry, serovars Infantis and Typhimurium are predominant

in the Atlantic region, whereas a higher proportion of serovar Schwarzengrund are found in the Southeast, with the exception of the

state of Georgia, which has a higher proportion of Kentucky. Vaccination against specific serotypes, such as S. Typhimurium, are common

among U.S. broiler breeders, a strategy which has reduced the incidence of contamination of that serotype (Mountainspring &

Burleson, 2018). However, vaccine development takes years and lags

behind the shifting of predominant serotypes found in flocks. Other

U.S. preharvest management practices include competitive exclusion,

controlling the quality of feed, biosecurity, moisture control in poultry

https://doi.org/10.1016/j.jfp.2023.100168

Received 15 June 2023; Accepted 22 September 2023

Available online 7 November 2023

0362-028X/Published by Elsevier Inc. on behalf of International Association for Food Protection.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

1 2021-2023 NACMCF, Report Adopted 15 November 2022, Revised Considering Public Comments 13 March 2023, Washington, DC

Journal of Food Protection 87 (2024) 100168

Contents lists available at ScienceDirect

Journal of Food Protection

journal homepage: www.elsevier.com/locate/jfp
houses, and clean transport coops. Microbial testing (qualitative testing) for total Salmonella at breeder or broiler farms can be focused

on environmental samples (boot or drag swabs) and cecal testing

(Bourassa, 2016); however, sufficiently sensitive tests and specific serotype testing are needed to determine if any changes are required to

control Salmonella serovars that are most often associated with human

illness. One suggestion is that, based on results from microbial testing

at farms, highly contaminated birds can be targeted for logistic slaughter (i.e., scheduling their slaughter after less contaminated flocks) or

other interventions. However, the value of this approach for scheduling purposes has not yet been realized (Nauta et al., 2009;

Rasschaert et al., 2008).

As noted above, the relative number of infections in the U.S. caused

by Typhimurium and Heidelberg has declined during the past 20 years

(Centers for Disease Control and Prevention (CDC), 2021). This

decrease corresponds to the use of a commercial poultry vaccine

against S. Typhimurium, which may also deliver cross‐protection

against Heidelberg (Muniz et al., 2017), targeted environmental testing to develop strategic management programs, as well as implementing other intervention strategies. While progress has been made

against these two serotypes, overall cases of salmonellosis attributed

to poultry remain unchanged with Enteritidis, Typhimurium, Infantis,

and I:4,5,12:i:‐ accounting for 83% of the chicken‐associated illnesses.

These data suggest that alternate methods for control of Salmonella or

detection that do not rely solely on serotype may be needed. While

vaccines are effective serotype‐specific intervention strategies, other

methods need development to reduce carriage of Salmonella in the

bird. Furthermore, attribution data does not specifically identify

whether sources of Salmonella were whole carcasses, parts, comminuted product, or breaded raw poultry products. More granular data

will help determine if all poultry products pose the same risk and allow

a targeted management program.

Indicator organisms such as Enterobacteriaceae (Eb) or aerobic

plate counts (APCs) have been used by the industry as gauges of process control and to measure microbial reduction on carcasses from

slaughter to postchill (Williams et al., 2015). However, some studies

have shown that populations of these indicators are not directly

correlated to populations of Salmonella (Bueno López et al., 2022; De

Villena et al., 2022; Sanchez‐Plata, 2022). Because of the conflicting

and apparent weak correlation between indicators and either the presence or level of Salmonella postcarcass wash (Williams et al., 2015;

Williams et al., 2017), one approach is to base microbiological criteria

on Salmonella enumeration. Microbial risk assessments suggest that

diverting ground turkey product which tests above a set threshold of

Salmonella colony forming units (CFU) per gram, compared to current

protocols (i.e. not diverting), is expected to remove product from the

market that has higher chances of causing infection and may therefore

reduce illness (Lambertini et al., 2021). Such a threshold would need

to be clearly linked to health‐based targets. This concept is currently

used by the industry whereby poultry used in breaded and stuffed

raw chicken product are enumerated for Salmonella by quantitative

PCR (qPCR), not by targeting specific serotypes (USDA Food Safety

and Inspection Service (FSIS), 2022i). qPCR presents logistical advantages over MPN (most probable number) methods due to its relatively

rapid time to detection and, is therefore, more actionable (such as

diverting product to high‐pressure pasteurization or cooking) during

a production day. However, qPCR methods must have high sensitivity

to consistently detect Salmonella levels at the ranges most often seen in

poultry products.

In October 2022, the USDA‐FSIS announced that they will be

proposing a revised regulatory framework to reduce Salmonella infections associated with poultry products (USDA Food Safety and

Inspection Service (FSIS), 2022g). The key components include requiring that incoming flocks be tested for Salmonella before entering an

establishment, that establishments enhance process control monitoring, and that the agency implements an enforceable final product

standard.

Current performance standards include all Salmonella serotypes,

rather than quantification of specific highly pathogenic serotypes. An

approach targeting highly pathogenic serotypes could trigger additional mitigating actions and could be more effective in diverting products that have higher infectious potential. The public health benefits of

this approach should be evaluated using the quantitative risk assessment approach that incorporates multiple data sources, including data

Figure 1. Estimated percentage of foodborne Salmonella illnesses (with 90% credibility intervals) for 2020, in descending order, attributed to each of 17 food

categories, based on multiyear outbreak data,a United States. (Interagency Food Safety Analytics Collaboration (IFSAC), 2022). a

Based on a model using outbreak

data that gives equal weight to each of the most recent five years of data (2016-2020) and exponentially less weight to each earlier year (1998-2015). Over 75% of

illnesses were attributed to seven food categories: Chicken, Fruits, Pork, Seeded Vegetables (such as tomatoes), Other Produce (such as fungi, herbs, nuts, and root

vegetables), Beef, and Turkey. The credibility intervals for each of the seven food categories that account for 77.5% of all illnesses overlap with some of the others.

B. Liu Journal of Food Protection 87 (2024) 100168

2
collected by industry, if available. Independently from the type of MC

(microbiological criteria), ensuring that mitigating actions triggered

by a MC are implemented is key to its effectiveness.

The Committee’s responses to the charge questions are based on the

information available at the time of writing. The Committee identified

a multitude of data gaps that could affect findings and recommendations to FSIS, including the need for completion of the two quantitative

risk assessments for chicken and turkey that are currently in progress

(USDA Food Safety and Inspection Service (FSIS), 2022c). These risk

assessments will evaluate the public health impact (change in illnesses,

hospitalizations, and deaths) achieved by eliminating a proportion of

poultry or poultry products that are contaminated with specific levels

of Salmonella and/or specific Salmonella subtypes. The risk assessments will also explore the public health impacts of various monitoring and enforcing process control methods, from the rehang to

postchill stages. Finally, the risk assessments will address the public

health impact of implementing combinations of the aforementioned

risk management strategies.

Recommendations:

1. Collect appropriate data to refine food attribution models and

determine which form(s) of raw poultry exposure (e.g., consuming

processed, parts, whole carcasses, handling live poultry, exposure

to poultry manure, etc.) and food handler practices contribute most

to salmonellosis associated with chicken and turkey.

2. Expand systematic sampling for Salmonella levels, prevalence, and

serotypes on poultry preharvest (hatcheries, feed, poultry houses)

and FSIS postharvest sampling (slaughter through processing). Prioritize product lines that historically are more frequently contaminated, which are not further processed using a validated lethality

step and have been linked to illness such as comminuted poultry

products, chicken parts/pieces, and breaded stuffed raw chicken

products, to identify Salmonella levels and the evolution of predominant serotypes.

3. Incentivize industry to deposit data (anonymized, nonpunitive) on

levels of indicator organisms and Salmonella prevalence, concentration, and serotypes found at various stages of processing (preharvest through final product) along with practices that may

mitigate contamination.

a) Analyze data to identify alternate process control indicators

and to use in risk assessments to update performance

standards.

b) Analyze data to determine how non‐Salmonella quality indicator sampling (perhaps other than APC) could be established

that would provide guidance to companies on how to identify

houses that have a higher probability of Salmonella contamination and how to modify mitigation strategies accordingly.

4. Frequently (e.g., every 2–3 years) compare the serotypes that are

isolated from patients with those isolated from poultry products

to determine if intervention strategies used by the industry are

effective against all Salmonella equally or are selecting for specific

serotypes.

5. Develop and validate quantitative testing methods to determine if

and how testing and processing adaptations can reduce the likelihood that carcasses and parts with higher levels of Salmonella (or

serotypes) that are most capable of causing illness are released into

commerce.

6. Complete risk assessments for chicken and turkey to assess public

health impacts of different risk‐based Salmonella control strategies,

including qualitative and quantitative performance standards, possibly complemented by serotype identification.

7. Upon completion of the risk assessments, consider developing

changes to performance standards based on the findings of the risk

assessments.

8. Incentivize the industry to develop, validate, and universally implement robust Salmonella mitigation programs and qualitative Salmonella testing at the breeder, hatchery, grow out, and transport

levels. Target conditions in houses, transport crates, and holding

areas that harbor and transmit Salmonella by universal implementation of known and validated mitigation strategies.

9. Due to extensive data gaps identified by the Committee, the agency

should reevaluate this document within 3–5 years after appropriate

data have been collected and risk assessments are complete.

Charge from USDA-FSIS to NACMCF

According to the Centers for Disease Control and Prevention (CDC),

Salmonella is responsible for approximately 1.35 million cases of foodborne illness each year in the United States. The U.S. Department of

Agriculture (USDA) Food Safety and Inspection Service (FSIS) has

established qualitative performance standards to limit the occurrence

of Salmonella in poultry products (i.e., carcasses, parts, and comminuted products). The goal of these performance standards is to allow

FSIS to verify each regulated establishment’s control for this pathogen

in raw products throughout the slaughter process, and, by meeting

these standards, achieve national food safety goals. Over the past

25 years, there have been significant reductions in the proportion of

poultry contaminated with Salmonella, but no meaningful reduction

in human Salmonella infections attributed to poultry products

(Williams et al., 2020). Therefore, FSIS is seeking guidance on how

to improve the current system for reducing Salmonella in poultry to

better protect public health.

In 2019, the National Advisory Committee on Microbiological Criteria for Food (NACMCF) recommended that FSIS move toward a risk‐

based disposition of finished raw poultry product, informed by Salmonella levels and serotype (National Advisory Committee on

Microbiological Criteria for Foods (NACMCF), 2019). Since then, several studies suggest that setting microbiological criteria (e.g., performance standards) to limit the amount of Salmonella in products

and/or to address serotypes more frequently associated with foodborne illness would better protect public health than the current

approach. In addition, several studies have demonstrated the feasibility of developing quantitative microbiological criteria based on a

change in the concentration of indicator organisms correlated to Salmonella occurrence. These findings, along with new technologies

and advances in rapid quantification of pathogens in products, present

opportunities for FSIS to enhance the microbiological criteria it establishes to measure industry control of Salmonella in poultry products.

FSIS seeks input from the NACMCF on the best options for using quantification and/or particular pathogen characteristics, along with a relevant pathogen indicator, to enhance its microbiological criteria and

reduce Salmonella illnesses attributed to poultry products consumed

in the U.S.

Background

Salmonella bacteria are a leading cause of foodborne illness.

According to CDC estimates, Salmonella is responsible for approximately 1.35 million illnesses, 26,500 hospitalizations, and 420 deaths

every year in the United States. Using weighted outbreak data from

1998 to 2018, the Interagency Food Safety Analytics Collaboration,

a joint effort between CDC, the U.S. Food and Drug Administration,

and USDA‐FSIS, estimates that over 20% of foodborne salmonellosis

are attributed to poultry products (Interagency Food Safety Analytics

Collaboration (IFSAC), 2022).

FSIS established limits, referred to as performance standards, on

the occurrence of Salmonella in poultry products as part of the Pathogen Reduction, Hazard Analysis and Critical Control Point

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(PR/HACCP) Systems Final Rule. These standards were designed to

improve food safety, reduce the risk of foodborne illness, and enable

FSIS to verify process control. Process control is a defined procedure

or set of procedures designed by an establishment to provide control

of those operating conditions that are necessary for the production

of safe, wholesome food. The procedures typically include some means

of observing or measuring system performance, analyzing the results

generated in order to define a set of control criteria, and taking action

when necessary to ensure that the system continues to perform within

the control criteria. FSIS has since updated those original performance

standards. FSIS relies on qualitative (presence/absence) pathogen sampling data to apply performance standards for Salmonella in poultry

products. These standards are based on quantitative microbiological

risk assessments (Ebel et al., 2012; Williams et al., 2011) and are

designed to achieve the national food safety goals. The Healthy People

2020 food safety goal was set at a 25% reduction in foodborne Salmonella illnesses, to achieve fewer than 11.5 Salmonella infections

per 100,000 population per year. In 2020, the case rate for Salmonella

infection was 13.3 per 100,000 population (Ray et al., 2021). FSIS has

proposed performance standards for beef products and intends to propose performance standards for pork products.

Since the implementation of Salmonella performance standards for

poultry, FSIS has measured a substantively lower occurrence of this

pathogen in raw poultry products, but the incidence of human illness

associated with the consumption of poultry products has not decreased

(Williams et al., 2022). FSIS is interested in developing microbiological criteria (i.e., an alternative type of performance standard(s)) that

will result in a substantively reduced level of human illness from Salmonella in poultry. FSIS is considering microbiological criteria such as

the levels of Salmonella in products, and the presence of Salmonella serotypes more frequently associated with human illness, rather than the

presence/absence of any Salmonella. Criteria could be set at various

points in the food safety system to better assess industry control over

Salmonella in these products, for example, prior to establishment interventions and after establishment interventions, to evaluate the effectiveness of an establishment’s food safety system in mitigating

Salmonella in products during the slaughter process.

Several recent studies showed a correlation between indicator

organisms and Salmonella in poultry. Specifically, a correlation was

shown between a change in the quantity of an indicator (i.e., Aerobic

Plate Count, APC) from the carcass to finished product and the occurrence of Salmonella in beef, pork, and poultry (Williams et al., 2015;

Williams et al., 2017). However, the university research on biomapping and the poultry industry’s internal testing has evaluated the use

of indicator bacteria and found that the populations of indicators offer

little to no predictive value for the presence of non‐Typhoidal Salmonella. Regardless, based on FSIS findings, FSIS is considering developing a quantitative “log‐reduction” microbiological criterion (e.g.,

performance standard) to measure the effectiveness of an establishment’s food safety system in controlling Salmonella in products. FSIS

believes that this type of performance standard would allow for continued monitoring of industry performance in achieving the Healthy People 2030 national food safety goals, thereby improving public health

outcomes, while providing better insights into pathogen control

throughout the food safety system. As part of this consideration, FSIS

would also like to know how the Agency could address Salmonella serotypes more frequently associated with human illness, strain characteristics (e.g., virulence factors), and/or the quantity of Salmonella in

a subset of products tested, prior to and after interventions, when evaluating the industry’s control of Salmonella.

FSIS is considering the following microbiological criteria to assess

industry control:

• The presence of any Salmonella or only specific serotypes more

likely to cause illness, at preharvest (e.g., as measured at the receiving step in the slaughter process)

• The amount of Salmonella and/or presence of serotypes more likely

to cause illness, throughout the slaughter process; and

• A relevant indicator for Salmonella, throughout the slaughter

process

Charge questions

FSIS is seeking guidance on the overarching risk management

question: What types of microbiological criteria (e.g., Salmonella

performance standards) might FSIS use to encourage reductions

in Salmonella in poultry products so that they are more effective

in preventing human Salmonella infections associated with these

products?

Specific risk management questions posed to NACMCF are as

follows:

1. Can we assess the public health impact (e.g., reduction in salmonellosis) of controlling specific Salmonella serotypes and/or amount

(levels) in poultry products?What types of approaches could be used?

2. What types of microbiological criteria could be established to

encourage control of Salmonella at preharvest (i.e., in live birds

on‐farm)?

Should FSIS consider qualitative microbiological criteria for control

of the presence of Salmonella in a flock when they are presented for

slaughter?

a) How could FSIS use these criteria to address Salmonella serotypes most frequently associated with human illness?

b) What industry data would provide evidence of control?

3. What types of microbiological criteria could be established for

poultry carcasses, parts, and comminuted products prior to applying interventions and after interventions, considering current

technology?

a) Could the quantity of Salmonella or quantity of microbiological

indicator organisms (e.g., APC) be used? What are the key

parameters that need to be considered? What data analysis techniques could be used? How would these criteria be linked to

human illness?

b) How could serotypes frequently associated with human illness

be considered in the development of microbiological criteria?

4. How might foodborne illness surveillance data on human Salmonella illnesses, data from foodborne outbreaks associated with

Salmonella in poultry, and data on Salmonella serotypes in poultry

products be used to identify the Salmonella serotypes of greatest

public health concern associated with specific poultry products?

a) Should only the most current data (e.g., 5 years) of foodborne

illness surveillance, outbreak, and/or pathogen testing data be

used?

b) Going forward, what methodology and criteria would focus on

those Salmonella serotypes most frequently associated with

human illness and attributable to poultry products?

c) How frequently should the priority Salmonella serotypes associated with poultry be revised considering changes in their occurrence while still ensuring continuity in industry and regulatory

testing?

5. There is a documented correlation between a reduction in the

quantity of APC between carcasses and finished products and the

occurrence of Salmonella in finished products for beef, pork, and

poultry. How might this information be used to set microbiological

criteria to assess process (pathogen) control in poultry?

6. What rapid methods and technologies are available for the quantification of Salmonella? How should FSIS make the best use of

these methods?

7. Are there particular approaches that would result in selective identification of the serotypes of public health concern? For example,

are there approaches to mitigate a potential strain selection bias

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introduced by the laboratory method? If needed, what type of

research could be conducted to ensure performance characteristics

of current laboratory methods (e.g., enrichment, incubation, prescreening) do not result in a biased serotype detection?

8. How should pathogen characteristics derived from whole genome

sequencing (e.g., serotype, virulence, antimicrobial resistance) be

considered in the development of microbiological criteria?

9. What research is needed to support FSIS’ new Salmonella strategy

in terms of setting microbiological criteria?

Committee responses

Approach by the Committee: The Committee organized the charge

questions into three groups: (1) The impact of Salmonella and poultry

on public health (Q1 and 4); (2) The role of microbial testing at the

preharvest and postharvest levels for process control (Q2, 3, and 5);

and (3) Methodologies for detection and enumeration of Salmonella

(Q6, 7, and 8). The working groups considered Q3‐a and Q5 on the

use of aerobic plate counts as indicators for Salmonella contamination

similar and, thus, are merged for response. Data gaps for each individual charge question are summarized in response to Q9. The charge did

not specifically request an evaluation of the efficacy of various intervention strategies on the farm or postharvest, but the Committee

briefly discussed practices that may affect the presence, levels, or serotypes of Salmonella throughout the chain.

Q1. Assessing public health impact

Can we assess the public health impact (e.g., reduction in

salmonellosis) of controlling specific Salmonella serotypes and/or

amount (levels) in poultry products? What types of approaches could

be used?

Summary of Question 1 Response Two perspectives are

deemed necessary to answer this question: (A) how to predict

the public health impact of hypothetical changes in Salmonella

control strategies in poultry products prior to their implementation, and (B) how to assess the effectiveness of the standards in

reducing salmonellosis once they are implemented.

Part A: Quantitative microbial risk assessment (QMRA) methods can be used to predict salmonellosis reductions resulting from

changes in microbial criteria in poultry products. Incorporating

and comparing prevalence, level, and subtype in QMRAs might

require the incorporation of emerging Omics methodologies while

acknowledging data quality/availability issues.

Part B: Several types of public health surveillance data are

available with which to assess the efficacy of standards in reducing salmonellosis. Allowing for some year‐to‐year variability,

case‐based serotype‐ and genotype‐based surveillance may reflect

important changes within 2–5 years. The goal of reducing poultry‐

associated salmonellosis by 25% is an important step towards

meeting the HHS Healthy People 2030 goal of reducing domestically acquired salmonellosis by 25% by 2030 (U.S. Department of

Health and Human Services, 2022).

The objective of microbiological criteria (MC) (i.e., performance

standards) is to establish control limits for the contamination of pathogens in foods or food production environments along the supply chain

based on a specified measurement method and sampling plan, and ultimately reduce foodborne illnesses. The Salmonella Poultry Performance Standards currently employed by USDA FSIS for different

poultry products follow a prevalence‐based approach, regulating a

fraction of maximum allowable positives over the large number of Salmonella‐positive samples collected and analyzed in a specified time

window (USDA Food Safety and Inspection Service (FSIS), 2019a).

As an alternative to the current method, controlling specific Salmonella

serotypes and/or amount (levels) in poultry and in poultry products

may potentially further reduce exposure to Salmonella through poultry

product consumption and therefore mitigate the public health risks. To

effectively guide the changes in MC, the associated public health

impact needs to be comprehensively evaluated before their implementation, which can be achieved using quantitative microbiological risk

assessment (QMRA) approaches.

QMRA approaches have been applied to assess the potential impact

of MC in other commodities and hazards in support of risk management decisions, in the U.S. and other countries, for example for Campylobacter in poultry (European Food Safety Authority Panel on

Biological Hazards, 2011; Nauta et al., 2012; Swart et al., 2013) and

Salmonella in pork and comminuted beef (USDA Food Safety and

Inspection Service (FSIS), 2019b, 2022f). While this existing body of

work does not fully account for the current U.S. situation and the range

of possible MC to consider, it provides relevant approaches, tools, and

application examples for how risk‐based models can be used to support

the establishment of level/serotype‐oriented MC for poultry products.

(European Food Safety Authority Panel on Biological Hazards, 2011;

Nauta et al., 2012; Swart et al., 2013) and Salmonella in pork and comminuted beef (USDA Food Safety and Inspection Service (FSIS), 2019b,

2022f).

Evaluating the public health impact of a MC that controls for prevalence and amount of Salmonella on poultry products is feasible using

traditional QMRA approaches. QMRAs use a “bottom‐up” mechanistic

approach to modeling the change in amount of Salmonella on poultry

products through the production and processing stages relevant to

the considered MC, possibly including primary production, slaughter,

processing, distribution, through consumer handling and cooking until

consumption. Variability and uncertainty in the parameters considered

at this stage can be represented. As a result, QMRA models allow estimating the probability distribution of exposure dose (number of Salmonella cells ingested) as well as the associated distribution of risk

(e.g.., illnesses, hospitalizations, deaths in a population) using a

dose–response relationship, if a suitable one is available.

QMRAs have been conducted to align MC (i.e., an allowable level)

for Salmonella prevalence and amount, with some consideration of serotypes of public health concern, in poultry products (postchill) to

achieve a desired reduction in foodborne illnesses (e.g., percentage

reduction in attributable illnesses) (Ebel and Williams, 2015; Ebel

et al., 2012; Lambertini et al., 2019; Lambertini et al., 2021; Oscar,

2021a; Williams et al., 2015). None of the existing QMRAs that evaluate Salmonella MC for poultry products consider the public health

impact of controlling all three factors discussed here (prevalence,

level, and subtype). We discuss elements that should be incorporated

into QMRAs to assess all three criteria. Accounting for differences in

infectiousness and severity of Salmonella subtypes might require the

incorporation of emerging omics methodologies while also acknowledging data quality and quantity issues. This will allow for subtype‐

level differentiation of infectivity (probability of infection, given exposure to the subtype) and virulence (probability of severe illness, given

infection from subtype).

Measuring the operational success of MC requires incorporating

different sources of existing public health surveillance available for

salmonellosis and adjusting for seasonal and cyclical fluctuations to

ensure that the efficacy measurement is robust and unbiased. Although

roughly 91% of reported salmonellosis is sporadic (cannot be attributed to a recognized outbreak), successful control of Salmonella in poultry would be expected to reduce both outbreak‐associated cases and

sporadic cases (Ray et al., 2021). Current attribution of salmonellosis

to different sources is primarily based on outbreak investigations.

Emerging source attribution technologies based on Whole Genome

Sequencing (WGS) might allow for better estimation of success of

the MC in poultry as it would also allow for attribution of sporadic

salmonellosis cases.

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Q1.A Using quantitative microbial risk assessment (QMRA)

methods to predict the public health impact of changes in Microbiological Criteria (MC) in poultry products.

The public health impact of controlling specific Salmonella serotypes and/or amount (levels) in poultry and poultry products can be

estimated using QMRA. QMRA approaches have been applied to assess

the potential impact of MC in other commodities and hazards in support of risk management decisions, in the U.S. and other countries,

and can provide useful templates for additional assessments. Examples

of recent QMRA studies relevant to the charge question are reviewed

and discussed in this section.

MC can be classified into two main categories: (1) MC defined as a

threshold (prevalence and/or concentration) of a target organism in

the finished product (or other specified production stage), also called

food safety criteria, and (2) as a threshold in a target variable associated with processing conditions conducive to the control of the target

organism, also called process hygiene criteria. While QMRA can be

used to assess the impact of both, most assessments have focused on

food safety criteria.

MC can have an impact on public health only if they are associated

with risk control actions that keep or bring a process back into control/compliance, and if such actions are enforced. Models used to estimate the impact of a MC must include these risk control actions. For

example, in the case of a MC on finished product, a batch of product

not meeting the MC may undergo additional validated processing that

reduce contamination by a certain number of log10 CFU/g such Log

reduction will be accounted for in the model (note: “log” is used in

the remainder of the document to mean log10). Risk control actions

involving the processing environment can also be modeled, although

they are generally more complex to represent in detail. For example,

an establishment not meeting a MC may undergo in‐depth sanitization,

validated within the establishment to bring the MC variable below the

MC threshold, or to impart a known reduction. While most commonly

product‐level MC are paired with product‐level risk control measures,

and establishment‐level MC are paired with establishment‐level risk

control measures, MC and measures can also be associated differently.

For example, an establishment whose product does not meet a MC may

undergo in‐depth sanitization, possibly in addition to product

treatment.

QMRAs have been conducted to support the development or assessment of MC for Salmonella prevalence and/or levels, with some consideration of serotypes of public health concern, in poultry products

(postchill), including to align a MC with a desired target reduction

in attributable illness (Ebel and Williams, 2015; Ebel et al., 2012;

Lambertini et al., 2019, 2021; Oscar, 2021a; Williams et al., 2015,

2020) (Table 1).

MC and associated risk management measures can be set for different and multiple stages of the production and processing chain. Risk‐

based models can be developed to assess the impacts of such MC, by

including the production steps downstream of the MC. Hence, to

model MC upstream of the finished product, a higher number of variables, as well as their uncertainty and assumptions, would need to be

included. Most published studies on MC in postharvest poultry processing have considered MC on the finished product.

QMRAs often treat all Salmonella subtypes or serovars equally, for

lack of serovar‐specific data, in particular dose–response relationships.

That is, these models implicitly assume that all serovars have the same

probability of infection following an exposure (infectivity) and same

probability of a severe illness resulting from an infection (virulence).

Infectivity is usually modeled with a dose–response function or curve

(Haas et al., 2014), whereas virulence is often modeled as proportions

of severe illnesses based on different disease severity outcomes, such

as extraintestinal infections, hospitalizations, and/or deaths.

Ebel et al. (Ebel and Williams, 2015; Ebel et al., 2012) provide an

approach for establishing a prevalence‐reducing MC Salmonella in

poultry products. This is based on a finding that at lower Salmonella

prevalence, there is a linear dose–response relationship between

prevalence in product and salmonellosis in humans. The advantage

Table 1

Risk assessments of the impact of microbiological criteria (MC) for Salmonella and poultry

Main topic Country Reference

Assess effectiveness of qualitative testing results sufficient in predicting a reduction in illnesses in a

microbiological food safety risk assessment.

U.S. (Ebel and Williams, 2015)

Risk-based modelling assessment of the effectiveness of revised performance standards for Salmonella

contamination of comminuted poultry.

U.S. (Ebel and Williams, 2020)

Simplified framework for predicting changes in public health from performance standards applied in slaughter

establishments.

U.S. (Ebel et al., 2012)

Risk-based modelling assessment in support of MC for Salmonella in poultry. E.U. (European Food Safety Authority (EFSA),

2011)

Link between Salmonella MC at different stages of the poultry chain. E.U. (European Food Safety Authority Panel on

Biological Hazards, 2010)

Risk-based modelling assessment of public health effects of raw chicken parts and comminuted chicken and turkey

performance standards. (Salmonella and Campylobacter).

U.S. (USDA Food Safety and Inspection Service

(FSIS), 2015b)

Risk-based modelling assessment of Salmonella and Campylobacter performance guidance for young chicken and

turkeys.

U.S. (USDA Food Safety and Inspection Service

(FSIS), 2011)

Risk-based modelling assessment comparing different prevalence- and concentration-based MC for Salmonella in

chicken parts (finished product).

U.S. (Lambertini et al., 2021)

Risk-based modelling assessment of a specific quantitative MC in comminuted turkey (finished product). U.S. (Lambertini et al., 2021)

Factors affecting MC for Salmonella in raw poultry U.S. (Mead et al., 2010)

Assessing Salmonella prevalence as indicator of poultry food safety. U.S. (Oscar, 2021a)

Risk-based assessment of the effectiveness of performance standards based on APC for Salmonella contamination

of chicken parts.

U.S. (Williams et al., 2022)

Additional supporting work on MCs in poultry or Salmonella (examples)

Includes risk-based modelling assessment of performance targets for Campylobacter in broiler meat; includes

impact of different control options.

E.U. (European Food Safety Authority Panel on

Biological Hazards, 2011)

Risk assessment on performance standards for Salmonella in pork. U.S. (USDA Food Safety and Inspection Service

(FSIS), 2022f)

Public health effects of performance standards for ground beef and beef

manufacturing trimmings (Salmonella).

U.S. (USDA Food Safety and Inspection Service

(FSIS), 2019b)

Risk-based Campylobacter MC in poultry. E.U. (Nauta et al., 2012)

Comparison of two MC approaches: microbiological limit, and relative risk limit (Campylobacter in broilers). general (Nauta et al., 2015)

Risk-based Campylobacter MC in poultry. NL (Swart et al., 2013)

Assessing impacts of a MC based on indicator organisms, for Salmonella (and E. coli O157:H7) in beef carcasses. U.S. (Williams et al., 2017)

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of this QMRA approach is that it simplifies the modeling by reducing

the number of parameters and eliminates the uncertainty common to

exposure pathways associated with consumer handling and preparation, cross‐contamination, and Salmonella dose–response. The limitation of this approach is that it assumes the amount of Salmonella on

contaminated products is independent of its presence, and it treats

all serovars as equally infective and virulent.

Lambertini et al. (Lambertini et al., 2019, 2021) provided a modeling framework to evaluate the potential relative reduction in public

health impact associated with prevalence‐based and concentration‐

based MC for Salmonella in poultry products (comminuted turkey

and chicken parts), tested at the end of processing. These QMRAs

use a “bottom‐up” approach to modeling the change in the amount

of Salmonella on poultry products from the end of the production process, where sampling is assumed to occur, through consumer handling

and consumption to estimate exposure. Corrective actions considered

include simplified lot‐level Salmonella reduction measures and

establishment‐wide hygiene improvements. The comparison of

prevalence‐ and concentration‐based MC, as well as a combination of

the two, suggests that both approaches can lead to a significant illness

burden reduction, when 100% sensitive testing is frequent and when

MC are enforced, (i.e., an effective corrective action is implemented

upon noncompliance) (Lambertini et al., 2019). These studies identified significant gaps in data on prevalence and concentration levels

representative of the range of U.S. processing establishments, concentration variability within and between product lots, conditions during

transport and retail, and consumer handling and preparation practices,

including transfer coefficients to model cross‐contamination in kitchens. Due to the scope of the studies, these two models did not assess

the impact of different infectivity or virulence associated with different serovars, assuming that control measures applied at processing,

the case of noncompliance, would have a similar impact on all

serovars.

In another study, researchers proposed a QMRA model for Salmonella control in ground turkey where both the amount of Salmonella and hypothetical differences in serovar infectivity were

incorporated. For this model, the authors grouped serovars in high

and low infectivity groups and used dose–response models from

experimental and outbreak data as a proxy for the infectivity in the

high and low groups. The model further assumed that all lots with

high contamination (>1MPN/g) would be detected and removed

using a test with 100% sensitivity and predicted up to 50 million

annual salmonellosis cases from ground turkey in the US. Recently,

significant issues were reported with this approach (Zagmutt &

Pouillot, 2022) that resulted in the retraction of the Sampedro

et al. article (Sampedro et al., 2019).

Oscar (Oscar, 2019; Oscar, 2021a; Oscar, 2021b) also used a comparable framework to that of Sampedro et al. to contrast enumeration

vs. prevalence standards in ground turkey. The approach relied on a

series of untested modeling assumptions such as adjusting the dose‐

responses by serovar that resulted in broad infective doses (ID50)

ranging from 1 to 10 log CFU/g.

None of the existing QMRAs that evaluate Salmonella MC for poultry products fully consider the public health impact of controlling all

three dimensions considered in this question (prevalence, level, and

subtype) while also considering realistic scenarios of frequency and

sensitivity of product testing and its resulting impact on the contamination of the final product. For example, one scenario that could be

investigated is testing finished product batches for both the presence

and concentration of the pathogen and contrast it to the less frequent

sampling currently done by FSIS. Evaluating the public health impact

of a MC that controls for prevalence and amount of Salmonella on poultry products is feasible using QMRA approaches. However, evaluating

the public health impact of controlling certain Salmonella subtypes

(i.e., serotypes and genetic factors related to virulence) would require

their identification, information on their presence in poultry products,

and the ability to predict differences in the probability of illness from

foodborne exposure to these Salmonella subtypes.

Parameters needed to model MC

Possible inputs and parameters that can be used to model a MC are

shown in Table 2 (considering a specific processing stage where a MC

is set, i.e., not including all stages needed to assess exposure to consumers). The inclusion of specific parameters depends on the MC

options evaluated. It is important to highlight that the number of samples collected in relation to product flow rate plays a key role in the

value of information obtained by a testing program and, depending

on the context, could make a larger difference than other parameters.

This answer does not include a comprehensive review of available

data in relation to QMRA model needs. However, this committee

agrees on the importance of adequate data to support risk estimates

and to estimate the impact of a MC with sufficient precision. For

instance, current data on Salmonella prevalence or concentration collected by USDA‐FSIS as part of routine monitoring or targeted data collection efforts only partially support QMRA efforts. Presence/absence

data are collected infrequently, in particular for smaller establishments. Until recently, concentration data were not routinely collected,

the last data collection effort on chicken products, representing only a

portion of establishments, was carried out in 2012 (USDA Food Safety

and Inspection Service (FSIS), 2012). However, FSIS has launched a 4‐

month program for collecting paired chicken samples collected at

rehang and postchill (USDA Food Safety and Inspection Service

(FSIS), 2022h). These samples are tested for Salmonella presence,

levels (CFU/g), and serotypes. FSIS plans to continue testing chicken

carcasses at postchill for Salmonella levels, which would make this testing routine. The industry has also been sampling product and processing environments. Combining industry and government data could

improve the accuracy of Salmonella contamination models.

Including indicator organisms in models

Models to estimate the impact of MC can also include indicator

organisms different from the target pathogen, complementing information on the occurrence of the target pathogen, or possibly instead

of it. For the inclusion of indicators to be appropriate, a reliable quantitative relationship needs to be available that ties the presence and/or

level of the indicator organism to the presence and/or level of the target pathogen. This relationship can be present in the processing environment and/or in the product, depending on the type of MC, and may

be general or specific to each establishment. Question 5 further discusses possible indicators relevant to the control of Salmonella in poultry in the U.S. At the time of writing, such indicator/Salmonella

relationships are not established, although preliminary data exist.

When validated data are available, the status of an establishment,

based on the selected indicator, can be used in models to estimate

the likelihood that the target pathogen is present at an unacceptable

level at a certain processing stage, or the likelihood that a risk‐

relevant event may occur (e.g., cross‐contamination). Examples of

models and standards, including indicator organisms, exist in other

domains. For example, drinking water standards are often based on

indicators of fecal contamination, since that is a primary contamination route and the presence of fecal indicators at unacceptable levels

indicates a lack of process control (National Research Council, 2004).

Identifying and incorporating serovar virulence into QMRAs

Very recent developments in WGS and bioinformatics have resulted

in several potential approaches to use genomics to identify genetic

markers of Salmonella of public health concern. This information can

be used to create groups of isolates or serovars, which can then be used

in QMRAs.

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Karanth et al. (Karanth et al., 2022) provided an exploratory

approach to use genomics and machine learning to identify severe Salmonella serovars. The authors tested the association between WGS data

and severity of Salmonella‐related health outcomes in the host, indicative of the virulence potential of different strains of Salmonella.

Sequence reads were collected for 150 Salmonella isolates from

humans, poultry, and swine sources, categorized as gastrointestinal

vs extraintestinal and the pangenome (i.e., all genes from all isolates)

used in various statistical models to predict disease presentation. The

best model had a predictive accuracy of 76%. As the methodology used

the pangenome, their methodology was computationally intensive,

which might explain why only 150 isolates were used. The need for

intense computation limits its scalability at the present time. Also, disease presentation information was not present for nonhuman isolates

(e.g., a swine isolate could not have a human disease presentation

unless it was related to a traced foodborne outbreak), so it had to be

imputed.

Chen et al., (Chen et al., 2022) used Salmonella serovar Saintpaul

(S. Saintpaul) as a model to identify potentially hypervirulent strains.

Single nucleotide polymorphism (SNP) clusters (i.e., groupings of isolates with a low number of point mutations) were queried on NCBI for

SNP clusters with either a high proportion of human clinical isolates

(HA) or a low proportion of human clinical isolates (NHA). A total

of 211 S. Saintpaul isolates were combined with 313 S. enterica isolates

from other serovars to produce a phylogeny of S. Saintpaul illustrating

the serovar is polyphyletic. Nine SNP clusters from group I (single

monophyletic clade) were chosen for further analysis with 5 HA and

4 NHA isolates. Ten isolates were sampled from each SNP cluster to

generate an analysis set of 90 contiguous (contig) assemblies. Differential carriage of plasmid, prophage, and individual gene carriage

(pangenome‐wide associate study) were assessed between the HA

and NHA clusters. Seven isolates (4 HA, 3 NHA) were tested for cell

invasion and intracellular survival (both in vitro) to quantify phenotypic differences between the SNP clusters. As S. Saintpaul is polyphyletic (i.e., same taxonomic grouping, different evolutionary

paths), this serovar may not be the best model to test for differences

between human and nonhuman isolates.

Recently, Fenske et al., (Fenske et al., 2022) reported a method to

group isolates, based on genetic virulence markers and validated the

groups against epidemiological data using beef isolates and outbreaks

as a model. For this, contig assemblies (de novo genome assemblies

from short reads) of Salmonella isolates from beef, cattle, and humans

were collected from FSIS and FDA and surveillance programs

(n = 12,337). Putative virulence factors were predicted, representing

a virulence gene catalog for each respective isolate. The pairwise similarity between each virulence gene catalog (measure of genomic relatedness between isolates in regions which may impact phenotypic

virulence) was estimated using an unsupervised random forest and

used in a hierarchical clustering algorithm to group isolates by similarity. Serovar designation was provided posthoc and was not used to generate genomic clusters to control for the impact of polyphyletic

serovars. The five groups were externally validated against epidemiological foodborne illness data (overall, and beef attributed) to explore

if the genetic‐based groups correlated with observed virulence at the

serovar level. The isolates in the “high virulence” cluster were most

often implicated in foodborne outbreaks of any cause and in beef‐

associated outbreaks but were less common in beef than all the other

four groups combined. They also resulted in a 1.5 times higher incidence of disease and higher hospitalization rates than all other groups

combined.

Table 2

Key variables to model microbiological criteria (MC) using quantitative microbial risk assessment (QMRA) approaches

Parameter/Variable Description

Production/processing stage Stage where samples are collected (may be multiple)

Proportion of establishments Proportion or number of establishments on which the MC assessment is based. May be

100% or a subset, by type of establishment.

Number of lots/units produced per time unit (e.g., per week) Production may differ by establishment category.

Lot size Weight of product in each lot (lbs./lot)

Prevalence of Salmonella, by serovars Number of positives out of total samples tested. If MC selectively targets serovars of

concern, results should be provided by serovar.

Concentration distribution, within lots Parameters needed to describe the distribution of concentration within each lot (CFU/g)

Concentration variability across lots If applicable; represents variability in concentration across lots. Modeling approach may

vary.

Concentration variability across establishments If applicable; represents variability in contamination for different establishment groups.

Number of lots/units sampled per time unit Number or proportion of lots sampled per time unit

Lot sampling algorithm Testing protocol within MC determines which lots are sampled, e.g., systematic,

randomized, tier randomized.

Pooled sampling approach (if applicable) Which samples are pooled, how many and weight of each.

Number of samples per lot MC parameter. Number of individually analyzed samples (not pooled).

Microbiological criteria (MC) concentration threshold MC parameter. Multiple thresholds can be established. In the presence/ absence of MC, m

is the detection limit of the assay

Number of samples allowed to be above the MC threshold, among the n samples. If applicable. MC parameter.

Assay sensitivity Ability of the assay to detect the target, if the target is present. Tied to the rate of false

negatives.

Assay specificity Ability of the assay to exclusively detect the target instead of other organisms. Associated

with the rate of false positives.

Assay limit of detection (LOD) Number of cells in a sample (or per g or mL) that would yield the assay to reliably detect

the sample as “positive.” See glossary.

Assay limit of quantification (LOQ) Number of cells in a sample (or per g or mL) above which the assay can reliably and

accurately quantify concentration. See glossary.

Batch-level risk management action in case MC is not met (food safety criteria), and

associated reduction in target organism in product

E.g., a lot not meeting a MC may undergo additional processing that results in a known

log CFU/g reduction. Different tiers of action can be established if there are multiple tiers

of MC compliance.

Establishment-level risk management action in case MC is not met (process hygiene

criteria), and associated reduction in target organism in product (directly or

indirectly)

Quantitative impact of establishment-level corrective action. (e.g., establishment whose

product or environmental/ process control samples have not met the MC undergoes indepth cleaning known to bring the process variable within control, or to or target to a

specified level, or yield a specified reduction).

Level of implementation of risk management actions Proportion of establishments or lots where corrective action is implemented; may be by

establishment category.

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The studies above illustrate the potential of genomic methodologies in the application for QMRAs in poultry where differential MC

for serovars are possible, with the Karanth et al. (Karanth et al.,

2022) method using the most genes, and the Fenske et al. (Fenske

et al., 2022) method being the most scalable as it used only the subset

of genes in a virulence catalog and removed all repeated, core Salmonella genes.

Using potential genetic serovar or isolate groups to adjust dose–response

(DR) models

Only a few DR models exist for specific Salmonella serovars. These

models have been derived based on animal studies, volunteer feeding

trials, and outbreak investigations, so their application to entire populations has resulted in large disconnects between model‐predicted illnesses and observed foodborne illnesses recorded in national

surveillance systems such as FoodNet (see Question 4). For this reason, QMRAs often use adjustment factors to calibrate the model predictions so that they match surveillance data.

A QMRA that is designed to assess MC based on specific target serovars and corresponding levels should thus be able to incorporate

dose–response models that apply to the serovars of interest and be able

to predict annual salmonellosis cases that are in agreement with

surveillance data. Teunis et al. (Teunis et al., 2010) provide a mathematical framework to adjust different dose–response curves by serovars based on outbreak data, where the mean CFU/g of Salmonella in

the food vehicle, number of exposed individuals, and numbers ill

(and/or infected) was available. Although the Teunis framework was

developed for individual serovars, this methodology could be

expanded to accommodate genomics‐based serovar groupings such

as those described by Fenske et al. (Fenske et al., 2022). A modification of the Teunis framework, allowing for serovar groupings, would

also have to provide a way to map the exposure from poultry to the

resulting dose–response, which could be done using calibration methods. Note that these approaches have the advantage of incorporating

specific genetic virulence characteristics (Fenske et al., 2022). Such

methods can be paired with surveillance‐based methods for source

attribution‐ such as those covered in Question 4‐ to provide necessary

adjustments to the dose–response curves.

Incorporating quantitative molecular methods into QMRAs

A variety of emerging molecular assays to detect or quantify Salmonella are reviewed in Question 6. The sensitivity and specificity

parameters, and hence the limit of detection (LOD) and limit of quantification (LOQ) of these assays, are key to understand the efficacy of

MC standards in reducing foodborne illnesses and should be incorporated in QMRA models. It is crucial that a quantitative assay is sufficiently precise in the range of the MC threshold.

In addition, assay sensitivity impacts sampling plans. Models need

to represent the interplay between LOD and LOQ of quantitative

assays, the MC threshold, and the number of samples that need to be

collected to accurately assess the distribution of Salmonella concentration in a lot or other unit. For instance, quantitative methods such as

qPCR are generally less sensitive to detect the presence of Salmonella

than qualitative PCR, i.e., they have a higher LOD. A lower sensitivity

would result in the need to collect more samples to determine with

high confidence if a quantitative threshold has been exceeded. In addition, the efficiency of the sampling plan can be affected by the variability of the pathogen concentration within and between lots (Mussida

et al., 2013). Thus, depending on the MC threshold concentration versus the assay’s LOQ quantitative MC may require a higher number of

samples per product unit to correctly classify the unit (or a derivative

metric of multiple units, such as an establishment). This would be

more so for a theoretical standard based on the level of specific serovars, since each serovar would be present in a sample in cell numbers

equal to or lower than the cell number of all Salmonella spp. serovars

summed together. Any model used to determine the impact of an MC

and associated sampling plans should account for realistic sensitivity

values, and the realistic number of samples that can be feasibly

collected.

There is evidence that a large proportion of Salmonella‐positive

poultry product samples harbor low concentrations. For example, in

2012, Nationwide Microbiological Data Collection Program 26% of

chicken part samples were positive at screening; these screening‐

positive samples were analyzed via MPN and 31% of them were found

to be below the LOD of the MPN quantitative assay (0.030 MPN/mL in

rinsate, corresponding to 0.0066 MPN/g of product); 79% were below

0.30 MPN/mL. Similar results were also observed in previous years

(2007–2008) for carcass rinse postchill (USDA Food Safety and

Inspection Service (FSIS), 2009a). Numerical examples of the impact

of these results are provided under the “Assay Parameters” heading,

in the answer to Question 3. The practical implication is that if the

LOD of an assay were set to detect high levels in chicken products

(e.g., 10 CFU/g), its sensitivity would by definition be reduced. As a

consequence, a higher number of samples would be required to accurately estimate the proportion of samples contaminated at the lower

levels observed in these studies. If only a subset of serovars of concern

are included, the number of samples to be tested would have to be

increased further.

In conclusion, both in risk models and cost‐benefit models, it is

important to include sensitivity and specificity parameters of the sampling assay, as well as the sample volume or weight needed to detect

positives with a high level of confidence in an individual sample.

For currently available assays to support quantitative MC, they have

to perform well at all concentrations (low and high) commonly found

in poultry meat, as currently established using MPN assays.

Q1.B Assessing the efficacy of microbiological criteria (MC;

e.g., standards) in reducing salmonellosis once they are

implemented

Q1.B (1). What is the operational definition of success for a

MC? Can we devise an operational definition of success of one

or more microbiological standards, in terms of reduced health

burden? If so, what should or could be measured?

The Healthy People 2030 Food Safety goals agreed upon by FSIS,

FDA, and CDC include a specific target of reducing domestically‐

acquired human salmonellosis by 25% by the year 2030, as measured

through FoodNet, and using a baseline period of cases reported in

2016–2018 (U.S. Department of Health and Human Services). If

between 25 and 50% of domestically acquired salmonellosis is from

poultry (see Q4), then this target is congruent with the FSIS goal under

evaluation, of reducing poultry‐associated salmonellosis by 25%.

Prevention that targets other sources of salmonellosis will also be

important to ensure that we reach the overall Healthy People goal.

The goal for FSIS pertains to domestically acquired salmonellosis, as

the actions taken in the US to control salmonellosis may have little

impact on exposures occurring in other countries. According to FoodNet, approximately 10% of salmonellosis in 2017–2019 was associated

with foreign travel (Tack et al., 2020).

Q1.B (2). What existing public health surveillance data are

available for salmonellosis?

Several types of public health surveillance data are available to help

track salmonellosis (see Table 3). Basic case surveillance in the US is

based on reporting to CDC of cases of salmonellosis through state epidemiology departments, and the serotypes and strains isolated from

patients’specimens through public health laboratories. Routine serotyping in thoselaboratories began in 1963 with reporting to CDC (Brachman

and JB, 1965). Among the approximately 2,600 serotypes described

(Centers for Disease Control and Prevention (CDC), 2011), 883 were

reported to the National SalmonellaSurveillance System to have been isolated from humans during 2006 through 2016 (Centers for Disease

Control and Prevention (CDC), 2018). The top 20 serotypes accounted

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for 80.7% of isolates with a reported serotype. The same five serotypes

have persisted as the most common causes of human illness for the last

decade (Enteritidis, Newport, Typhimurium, Javiana, I:4,5,12:i:‐)

(Collins, 2022). In 1996, state public health laboratories began conducting molecular subtyping, using pulsed‐field gel electrophoresis (PFGE)

patterns to identify clusters of Salmonella with similar patterns and submitting the subtyping information to CDC through PulseNet, the national

network for molecular subtyping which, this became routine for Salmonella isolates in all states by the early 2000s (Gerner‐Smidt et al.,

2006; Swaminathan et al., 2006). Routine subtyping has greatly

improved detection and investigation of dispersed outbreaks, and source

attribution. In mid‐2019, PulseNet switched to genome sequencing for

subtyping Salmonella (multilocus core genome sequence typing,

cgMLST),which offers substantially greater precision inmeasuring relatedness than did PFGE. Whole‐genome sequencing (WGS) data allow the

systematic prediction of serotype and antibiotic resistance profile from

sequence data (McDermott et al., 2016; Zhang et al., 2019). PulseNet,

which includes all state and some large city health department public

health laboratories, and both FSIS and FDA as members, previously used

PFGE and now used WGS to characterize regulatory isolates as well. The

sequences of approximately 40,000 Salmonella isolates from humans are

reported to PulseNet each year (Bacteria, Enterics, Amoeba, and Mycotics (BEAM) Dashboard)) (Centers for Disease Control and Prevention

(CDC), 2022a).Table 4.

FoodNet. More specialized surveillance includes FoodNet, CDC’s

sentinel site surveillance system, supported by FSIS and FDA, which

collects standardized information on all laboratory‐diagnosed infections with Salmonella and seven other pathogens in a panel of 10 states

or multicounty areas, encompassing 15% of the US population (Henao

et al., 2010). Human salmonellosis incidence of laboratory‐diagnosed

cases reported to FoodNet has not decreased since the 1990 s

(Fig. 2). Approximately 8,500 Salmonella infections are reported to

FoodNet sites each year, and are summarized on the public‐facing

website FoodNet Fast (Centers for Disease Control and Prevention

(CDC), 2021). Data on long‐term health sequelae are not collected.

Travel history data are collected; if 10% of those infections are

travel‐associated, FoodNet gathers reports of approximately 7,200

domestically‐acquired cases per year. Standardized case exposure data

have been collected in FoodNet since 2014 that are now being linked

to PulseNet genomic data for future analysis.

NARMS. The National Antimicrobial Resistance Monitoring System

(NARMS) is another tri‐agency surveillance collaboration. Since 1997,

as part of NARMS, CDC has determined antibiotic resistance profiles of

Salmonella by phenotypic methods, based on 1 in 20 isolates from Salmonella from human infections, referred by state public health departments (Centers for Disease Control and Prevention (CDC), 2020,

2022d). As part of NARMS, these human samples are tested in parallel

with isolates collected from various meats at slaughter by FSIS (USDA

Food Safety and Inspection Service (FSIS), 2022e), and from retail

food samples of meat and poultry collected by FDA (U.S. Food and

Drug Administration (FDA), 2022a, 2022b). CDC PulseNet now provides resistance predictions obtained by WGS to NARMS.

Data on Salmonella serotypes in poultry products. FSIS participates in PulseNet and NARMS, submitting characterization of Salmonella isolates obtained at slaughter from cecal samples (samples

from individual animals before extensive processing), as well as from

carcass rinsates, cultures of poultry parts and comminuted poultry. The

FDA tests samples of chicken breasts obtained at retail as part of

NARMS, and also sequences the Salmonella they isolate, entering the

results into the NCBI database, and PulseNet. Noteworthy for its

absence from NARMS is the systematic sampling and characterization

of Salmonella from poultry at earlier stages of production, such broiler

breeder flocks, or grandparent flocks, and from feeds or feedstuffs.

Reports of foodborne outbreaks associated with Salmonella in

poultry. Possible outbreaks of cases that appear to be related can be

detected because they occur in a local group with an obvious place

or other exposure in common, or because a dispersed group or cluster

of cases is identified that have the same molecular or genomic subtype.

Epidemiological investigations of these possible outbreaks may identify a common source, confirming them as outbreaks. Investigated outTable 3

U.S. Sources of Salmonella surveillance information

Case-based surveillance

National Salmonella Surveillance System Case-based reporting from state and local health department epidemiology offices to CDC. This includes serotype for the great

majority. Summarized in 2016 46,623 cases were reported (of which 39,980 were serotyped) (Centers for Disease Control and

Prevention (CDC), 2018).

PulseNet Individual clinical isolates of Salmonella are sequenced in local and state public health laboratories and reported to CDC’s PulseNet

database. They are summarized in BEAM Dashboard, starting with data from 2017. Between 2017 and 2019 a mean of 41,278

Salmonella isolates were reported to PulseNet (Centers for Disease Control and Prevention (CDC), 2022a). CDC and state health

departments test human isolates, FSIS and FDA test nonhuman strains.

National Notifiable Disease Surveillance

System (NNDSS)

This is information reported to CDC about diagnosed salmonellosis cases and other notifiable diseases that does not include

serotype. It is summarized annually on the NNDSS website (Centers for Disease Control and Prevention (CDC), 2022e)

Outbreak-based surveillance

Foodborne Disease Outbreak Surveillance

System (FDOSS)

Foodborne Disease Outbreak Surveillance System (FDOSS)(Centers for Disease Control and Prevention (CDC), 2022b), as reported

to the National Outbreak Reporting System (NORS)(Centers for Disease Control and Prevention (CDC), 2022f) by local and state

health departments and investigating offices at CDC. Summarized on NORS Dashboard website (Centers for Disease Control and

Prevention (CDC), 2022f). Between 2016 and 2019, an annual mean of 143 foodborne salmonellosis outbreaks were reported.

Specialized surveillance

FoodNet Part of the CDC’s Emerging Infections Network, and supported by FSIS and FDA, 10 state and large county group sites report all

diagnosed infections of seven types of bacteria, in addition to Cyclospora, including salmonellosis, since 1996. Since 2014, a

subset of patients has been interviewed about a standardized set of exposures (“Case Exposure Ascertainment”) and these data are

linked with corresponding PulseNet, NARMS, and outbreak data. FoodNet data are summarized in annual and periodic

publications and at FoodNet Fast (Centers for Disease Control and Prevention (CDC), 2021). Between 2016 and 2019, a mean of

8472 salmonellosis cases were reported to FoodNet, of which 10% were travel-associated, 7% were outbreak associated, and 10%

were diagnosed by culture-independent methods alone (Centers for Disease Control and Prevention (CDC), 2021).

National Antimicrobial Resistance Monitoring

System (NARMS)

A tri-agency collaboration since 1997. CDC measures the antimicrobial susceptibility of a sample of 1 out of 20 Salmonella isolates

from humans, referred by state public health labs. FDA tests retail samples of meat and poultry for Salmonella and other

organisms, and FSIS tests samples gathered during inspection (poultry cecal samples since 2013). Human NARMS is summarized

on the NARMS NOW website (Centers for Disease Control and Prevention (CDC), 2020). Between 2016 and 2019, NARMS CDC

tested an annual mean of 2543 Salmonella strains per year. Results for testing by FDA and FSIS are also available on respective

websites (U.S. Food and Drug Administration (FDA), 2022; USDA Food Safety and Inspection Service (FSIS), 2022e).

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breaks are reported by public health departments and the CDC to

CDC’s Foodborne Disease Outbreak Surveillance System (FDOSS)

using the National Outbreak Reporting System (NORS) platform

(Wikswo et al., 2022). FDOSS collects details on implicated food types,

characteristics of illnesses, and location of exposure (Centers for

Disease Control and Prevention (CDC), 2022b). To aid in source attribution, CDC applies the categories of implicated foods developed by

the Interagency Food Safety Analytics Collaboration (IFSAC)

(composed of experts from the FDA, CDC, and USDA‐FSIS) to the free

text fields reported through NORS (Richardson et al., 2017). Approximately 150 foodborne salmonellosis outbreaks were reported to

FDOSS in NORS annually between 2013 and 2019 (NORS Dashboard)

(Centers for Disease Control and Prevention (CDC), 2022f). This system distinguishes outbreaks by route of exposure, allowing to differentiate foodborne (and waterborne) outbreak‐associated cases from

those resulting from animal contact. It also includes outbreak‐

associated cases resulting from environmental, person‐to‐person, and

indeterminate/unknown exposures.

Q1.B (3). What can be done to improve the power and discrimination of current foodborne illness datasets?

Successful control of a major source of salmonellosis would be

expected to reduce both outbreak‐associated cases and sporadic cases.

The impact on human domestic salmonellosis can be tracked using

case‐surveillance data. Most reported cases are not part of identified

outbreaks: Food Net has reported that in 2017–2019, 91% of reported

salmonellosis was not part of recognized outbreaks (Ray et al., 2021).

There are challenges to surveillance data collection and opportunities for improvement. The utility of case surveillance for source attribution and trend tracking is increased by FoodNet interviews,

particularly by the more detailed Case Exposure Ascertainment

interviews, which began to be collected in 2014, when they can be

linked to NGS (next generation sequencing) of isolates. Streamlined

data gathering and reporting for FoodNet may make those data available and analyzed more quickly.

The COVID19 pandemic of 2020–2022 affected Salmonella surveillance, as it did many other public health efforts. Reported cases of

salmonellosis were 22% lower in 2020 than in the preceding 3 years

(Ray et al., 2021). Contributing factors likely included the steep

decline in international travel, changes in where and what people

ate, and changes in health care‐seeking behavior as some avoided

direct contact with the medical facilities. These effects may not be permanent after the pandemic is resolved, and it may be prudent to

exclude data from the years 2020 and 2021 in future assessments of

longer‐term trends.

Surveillance based on serotyping and genetic subtyping currently

depends on having the actual isolate (i.e., bacterial cells cultured from

the original sample) in hand, so that serotype and antimicrobial resistance can be predicted from sequence data. The growing use of

culture‐independent diagnostic tests (CIDTs) in clinical laboratories

means that doctor and patient may more quickly learn the cause of

the illness, though without antibiotic resistance determination. However, unless an isolate can be obtained, public health will not have a

sequence for tracking, outbreak detection, or source attribution. The

current solution is to encourage following up a CIDT positive test with

a “reflex” culture of the specimen that was positive in the CID test to

retrieve an isolate, which can then be characterized and sequenced

(Association of Public Health Laboratories (APHL), 2022). A longer‐

term solution is to develop and deploy more advanced metagenomic

methods to retrieve the Salmonella genome from the specimen that is

positive by CIDT (Association of Public Health Laboratories (APHL),

Table 4

Salmonella poultry performance standards (USDA Food Safety and Inspection Service (FSIS), 2019a)

Product Performance Standarda Maximum Acceptable Percent Positive Minimum Number of Samples to Assess Process Controlb

Broiler Carcasses 5 of 51 9.8% 11

Turkey Carcasses 4 of 56 7.1% 14

Comminuted Chicken 13 of 52 25.0% 10

Comminuted Turkey 7 of 52 13.5% 10

Chicken Parts 8 of 52 15.4% 10

a The performance standard is represented as a fraction of maximum allowable positives over the target number of samples collected and analyzed in a 52-week

window.

b FSIS must analyze at least this number of samples in a single 52-week window in order to categorize an establishment for the standard listed.

Figure 2. Trends in salmonellosis incidence in the U.S., as detected by culture-independent diagnostic test (CIDT) only vs. CIDT plus culture confirmation.

(Centers for Disease Control and Prevention (CDC), 2021).

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2022). A pilot test of one such approach began in two states in 2021

(personal communication, Heather Carleton, CDC). Recently, a high‐

throughput quantitative PCR assay was validated that is able to identify 40 Salmonella serotypes and distinguish different genomic lineages and polyphyletic profiles (Cadel‐Six et al., 2022). Once a

metagenomic replacement for culture can be developed and implemented across the 50 states, it will bridge current PulseNet surveillance to a culture‐independent future for public health. Until this as

achieved, state health departments will need to obtain isolates from

reflex cultures and to sequence those isolates.

Q1.B (4). What analyses of datasets can help inform progress

towards the FSIS goal?

Annual summaries of the frequency of Salmonella infections by serotype have been part of national surveillance reports for many years.

Reducing the lag in publishing them would make them more immediately useful, for example by predicting them from PulseNet and providing them as provisional data through the BEAM Dashboard

(Centers for Disease Control and Prevention (CDC), 2022a). Annual

FoodNet surveillance summaries are produced the following year,

and the data in FoodNet Fast are also updated at the same time. These

case surveillance data are robust enough to permit annual assessments

of progress in reducing the frequency of the most common serotypes in

human infections, using established statistical approaches to detect

significant differences from previous years. Based on these data, attribution models (see next section and Question 4) can estimate the proportion of cases attributable to poultry or subcategories thereof.

Serotype data on nonhuman Salmonella isolates collected from

poultry sources as part of NARMS and in the regulatory sampling of

USDA could be summarized each year and compared with trends in

human infections. A time trend model that smoothed surveillance data

across multiple years, adjusting for seasonal and cyclical differences,

could be helpful so that the estimation of success is not biased by

the fluctuations in epidemiological data in any given year. A possible

approach has been proposed by Williams et al. (Williams et al., 2021),

who combined FoodNet and FSIS sampling data and penalized cubic B‐

spline regression methods (Powell, 2016; Powell et al., 2018) to compare trends in Campylobacter on chicken meat and campylobacteriosis

cases in humans. USDA’s Office of Risk Assessment and Cost Benefit

Analysis has provided a preliminary analysis of data collected from

1996‐2021 (M. Powell, USDA, personal communication, Docket

FSIS‐2022–0031) using similar methodology to compare trends in Salmonella serovar presence in chicken meat against trends in serovars

causing Salmonella infections in humans. The preliminary analysis suggests that cases of salmonellosis due to serotypes associated with

chicken have gradually decreased similar to the decrease in Salmonella

in chicken products. Nonchicken serotypes have increased during the

same period. FDOSS outbreak data may also be summarized at regular

intervals, likely annually, once the pressure of the COVID pandemic on

state and local public health departments allows them to report outbreak investigations more quickly to NORS.

Other regular analyses that would be helpful would include: 1) Outbreak data such as the annual number of outbreaks and outbreak‐

associated illnesses associated with chicken, with turkey, and with all

poultry combined; 2) More detailed data routinely provided on the type

of chicken product that was the source, (e.g., parts, whole, comminuted, frozen breaded, frozen breaded stuffed, etc.); 3) Annual ranking,

by frequency, of serotypes by assessment of the burden of outbreak illnesses transmitted by poultry, and whether that burden is decreasing,

stable or increasing; 4) Poultry sampling data, such as the annual ranking of serotypes obtained from FSIS poultry sampling overall, from raw

poultry products destined for restaurants or institutions separately from

those destined for grocery stores; and 5) Annual ranking of serotypes

obtained from NARMS FDA retail chicken samples.

In addition, CDC could produce an updated estimate of the overall

health burden of salmonellosis and other foodborne infections at more

frequent intervals (last estimated in 2011) (Scallan, Griffin et al., 2011;

Scallan, Hoekstra et al., 2011). This would depend on conducting more

frequent, ideally annual FoodNet population surveys to assess current

care‐seeking and laboratory specimen submission behaviors.

Q1.B (5). Available source attribution methodology and how it

can be used to sufficiently discriminate poultry‐caused illnesses

from both outbreak and sporadic salmonellosis cases.

This question is answered in Q4.

Q1.B (6). Are year‐to‐year changes, trends, and lags captured

in the existing foodborne datasets and source attribution?

Year‐to‐year changes are captured in case‐based surveillance with

large numbers reported each year in each of the major systems, allowing for robust comparisons. As noted above, some smoothing across

several years may be useful to limit the impact of single large events.

PulseNet data in particular are analyzed continuously to look for clusters of related cases and may be used in the future to produce regular

updates of sequenced‐based source attribution estimates. (See answer

to Q4 for more discussion on this topic.)

The annual burden of microbiological foodborne illness, published

by the CDC in 2011, which updated the previous 1999 estimates

(Mead et al., 1999), still serves as the best available general estimates

of the health burden of foodborne diseases in the United States, by

pathogen. The reported incidence of infections with four priority bacterial pathogens (E. coli O157, Listeria monocytogenes, Salmonella, and

Campylobacter) has changed little in the last decade (Centers for

Disease Control and Prevention (CDC), 2021), and thus the health burden may also be presumed to have changed little. Should the incidence

of these and other infections decrease substantially, the burden estimates will become increasingly dated. Although the process of revising

these burden estimates and providing uncertainty ranges has begun, it

can take several years to complete (personal communication, B. Bruce,

CDC). Therefore, some lag in identifying current disease incidence is

likely to persist and needs to be considered by FSIS in evaluating the

success of implementing new MC.

Foodborne illness surveillance and monitoring systems only detect a

fraction of all cases that occur, and most reported cases do not have a

known source. Also, infrequent large events can have a marked effect

on the case count in a given year. This adds statistical “noise” to the

numerical signal needed to detect temporal changes in foodborne

salmonellosis trends and its main food sources. Using FoodNet as an

example, Ebel et al. (Ebel et al., 2017) estimated a 4% chance of detecting a true 10% change in salmonellosis 1 year after a risk reduction

change has taken effect. In a different scenario, a sustained reduction

of 30% in salmonellosis for 4 years still resulted in only approximately

50% chance of detecting such change. In a separate study, a similar calculation was used to show that a 25% reduction in salmonellosis cases

from comminuted chicken would not be detected by FoodNet alone, as

this annual reduction would be smaller than the expected year‐to‐year

variability of the surveillance system (Ebel and Williams, 2020; Ebel

et al., 2017). In the future, serotype prediction based on PulseNet

sequencing, and swifter analysis and presentation of preliminary

National PulseNet surveillance data (e.g., on the BEAM dashboard)

may increase the sensitivity of national public health surveillance to

detect changes of this magnitude in a shorter time frame.

Because of the statistical power limitations of individual Salmonella

surveillance and monitoring systems, for assessments of changes (or stability) in the incidence of salmonellosis, this committee advises the use of

data from more than one system. For example, although FoodNet utilizes

high‐quality data, it comes from only 15% of the population, while other

Salmonella surveillance systems are nationwide in scope. We also advise

the use of genomic data and models for both incidence and source attribution, as described in answers to questions 1 and 4.

Because the data in FDOSS/NORS are more limited, they need to be

combined across several years to define and analyze trends for specific

food categories. Currently, the IFSAC outbreak‐based source attribution model estimates (described in Question 4) are updated annually,

although the data are smoothed over several years.

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Q1.B (7). What new approaches are being developed and could

provide more accurate/precise burden data in the future?

The microbiological foodborne illness burden estimates, developed

by the CDC, depend on accounting for the sequential steps in the diagnosis and reporting of specific infections. These include assessment of

the frequency of acute gastrointestinal illness in the population, of

health care‐seeking behavior, of the frequency with which diagnostic

tests are ordered and performed, and of the frequency of reporting.

Future estimates could be improved by reassessing these probabilities

as practices change, evaluating the effects of variation in clinical severity on the likelihood of hospitalization, and could also incorporate estimates of the burden of long‐term health sequelae.

Q4: Identifying serotypes of greatest public health impact

A) How might foodborne illness surveillance data on human Salmonella illnesses, data from foodborne outbreaks associated

with Salmonella in poultry, and data on Salmonella serotypes

in poultry products be used to identify the Salmonella serotypes of greatest public health concern associated with specific

poultry products?

B) Should only the most current data (e.g., 5 years) of foodborne

illness surveillance outbreak and or pathogen/testing be used?

C) Going forward, what methodology and criteria would focus on

those Salmonella serotypes most frequently associated with

human illness and attributable to poultry products?

D) How frequently should the priority Salmonella serotypes associated with poultry be revised considering changes in their

occurrence while still ensuring continuity in industry and regulatory testing?

Summary of Question 4 Response

Several approaches have been used to attribute human

salmonellosis to specific foods and sources. These include case‐

control studies, analysis of reported foodborne outbreaks, and

most recently, source attribution based on whole genome

sequence genotyping. Attribution based on outbreak data and on

genotype, both, give the greatest weight to data from the most

recent years. Like attribution based on reported outbreaks,

genotype‐based attribution indicates that poultry is the leading

source of human salmonellosis. It also indicates that a small number of serotypes account for most poultry‐associated salmonellosis

in recent years, led by Enteritidis, Typhimurium, I:4,5,12:i:‐,

Infantis, and Heidelberg, and even fewer serogroups: groups O:4

(formerly group B), O:7 (group C1), and O:9 (group D1). The

effectiveness of a prevention strategy that includes serogroup or

serotype targets should be evaluated annually with case‐based

surveillance; changes in the attribution model would likely take

several years to observe. The most current data should be used

for all analyses, but the number of years to be included depends

on the rate at which new information is added. Combining data

from several recent years in a trend model can smooth out the

effects of single annual fluctuations, such as large outbreaks. It

is anticipated that targeted intervention strategies in the industry

will affect predominant serotypes found to be associated with

human illness. Subsequently, as other serotypes become predominant in illness, this in turn will affect industry responses.

Before answering the charge question on the predominant serotypes associated with poultry‐related salmonellosis, we considered

several of the serotype‐focused interventions used by the industry

for live birds. These control programs (described below) have resulted

in various levels of success, but ultimately affect and help explain

changes in predominant serotypes responsible for foodborne illness

attributed to poultry products (also see response to Question 2).

Effect of serotype‐targeted interventions to reduce Salmonella in the

live bird populations

• In the 1990 s, the United Kingdom experienced a rapid increase

in Salmonella Enteritidis (SE) infections related to both eggs and

poultry meat, much of it caused by SE phage type 4; at the peak,

SE comprised 70% of salmonellosis (Lane et al., 2014; O'Brien,

2013). With a voluntary industry effort based on vaccination

and other flock‐based measures, first targeting layer flocks,

then including broiler breeders, the number of SE phage type

4 infections reported in the UK dropped dramatically, from

∼18,000 in 1997 to 459 in 2010, and the overall number of Salmonella infections reported annually fell from ∼33,000 in 1997

to ∼9,000 in 2010 (European Food Safety Authority Panel on

Biological Hazards, 2019; Lane et al., 2014; O'Brien, 2013)

(Fig. 3). No other serotype emerged to “replace” SE.

· The United States has experienced a profound decline in the

incidence of infections caused by Typhimurium and Heidelberg over the last 20 years (Centers for Disease Control and

Prevention (CDC), 2021) (see Fig. 4). The reasons for these

declines have not been well documented, but they were corresponding to when a commercial poultry vaccine against Typhimurium became available. Highly publicized recalls in 2013 of

retail chicken from a West Coast producer whose products

were implicated in an outbreak of Heidelberg infections may

have accelerated wider vaccine use (Gieraltowski et al.,

2016). Typhimurium vaccines in poultry appear to decrease

colonization and infection not only of Typhimurium but of

other serotypes that share the same O group antigens, such

as Heidelberg, as both share antigenic O formula: II:4,5,12:i:‐

(Crouch et al., 2020; Muniz et al., 2017). Some producers

may have also implemented targeted environmental testing

with a focus on breeder flocks and increased other prevention

measures as well. With a mix of strategies, by 2019, Typhimurium had dropped from its long‐standing position as the most

common serotype isolated from humans as late as 2007, to

third most common. The incidence of Typhimurium infections

decreased by 70%, from 3.7 per 100,000 in 1999 to 1.3 in

2019 (Centers for Disease Control and Prevention (CDC),

2021). Heidelberg had dropped from the 4th most common

serotype isolated from humans in the 1990 s to the 24th most

common in 2019. The incidence decreased by 92%, from 1.07

per 100,000 in 1999 to 0.08 in 2019 (Centers for Disease

Control and Prevention (CDC), 2021).

· In 2003, the European Union (EU) issued regulations for breeder flocks requiring stringent control measures for specific serotypes that were, based on comparing serotypes found in food

animal reservoirs with those most commonly causing human

infections. They chose Enteritidis, Typhimurium (including

the monophasic variant I:4,5,12:i:‐), Hadar, Virchow, and

Infantis as targets for prevention (European Food Safety

Authority Panel on Biological Hazards, 2019). This was based

on their frequency in human infections, their transmissibility

through food, evidence of recent increase or spread, and

increases in resistance to treatment. In 2019, based on changes

in prevalence (World Health Organization (WHO), 2022b), the

suggested group of serotypes to target was updated to Enteritidis, Typhimurium (including the monophasic variant), Infantis, and another to be determined – perhaps Heidelberg or

Kentucky (European Food Safety Authority Panel on

Biological Hazards, 2019). Updates to the list may vary by

country (Leati et al., 2021). Serotyping is carried out via culture methods (White‐Kauffmann‐Le Minor scheme) (European

Commission, 2005).

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Q4.A(1) and (2); and Q4.C. How might foodborne illness

surveillance data on human Salmonella illnesses, data from foodborne outbreaks associated with Salmonella in poultry, and data

on Salmonella serotypes in poultry products be used to identify

the Salmonella serotypes of greatest public health concern associated with specific poultry products? What methodology and criteria would focus on those Salmonella serotypes most frequently

associated with human illness and attributable to poultry

products?

Question 4.A(1): Surveillance data on human illnesses caused by

Salmonella: Several surveillance and monitoring systems provide data

on individual Salmonella infections, on outbreak events, on antimicrobial resistance in human and nonhuman isolates, and on frequency in

meat and poultry (see response to Q1 Part 2b and Table 3).

Question 4.A(2): How can [these data] be used to identify the Salmonella serotypes of greatest public health concern associated with

specific poultry products? and Question 4.C: What methodology

and criteria would focus on those Salmonella serotypes most frequently associated with human illness and attributable to poultry

products.

The relevant serotypes of greatest public health concern are those

that are common causes of reported human illness, are present in poultry, and are transmitted through foods. CDC surveillance provides data

on the frequency of diagnosed illness caused by each serotype. Human

illness reporting tends to be skewed towards more severe illnesses as

milder cases are less likely to be diagnosed or reported, so models

accounting for this are used to estimate the true number of illnesses

in burden studies (Scallan, Hoekstra et al., 2011; Scallan et al.,

2021). If the use of culture‐independent diagnostic tests increases

without an increase in associated reflex culturing, a greater proportion

of reported infections will lack serotype data (Ray et al., 2021). FSIS

data from slaughterhouses and retail surveys can confirm the presence

and frequency of serotypes in raw poultry.

One way to quantify the importance of poultry as a source of

human infection has been through case‐control studies of sporadic

infections. For example, a FoodNet study conducted in 1996–1997

identified eating chicken, and specifically chicken prepared outside

the home, as a specific risk factor for serotype Enteritidis (SE) infections (Kimura et al., 2004). This study attributed 27% of SE infections

to that source; before this study, SE infections had been attributed

Figure 3. Trends in the reporting of incidents of Salmonella enterica in chickens in Great Britain versus laboratory reporting of human S. enterica serovar

Enteritidis infection, England and Wales, 1985–2011, S. enterica serovar Enteritidis phage type 4 (SE4). Figure shows the most recent trends from the UK, showing

a systematic decline in S. Enteritidis postintervention (Lane et al., 2014).

Figure 4. Trends in salmonellosis incidence associated with S. Typhimurium and S. Heidelberg in the U.S. Salmonella infections by year: 1966-2019. Incidence

per 100,000 population – FoodNet sites; cultured confirmed (Centers for Disease Control and Prevention (CDC) (2022c)).

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mostly to eggs. However, a parallel study of Typhimurium infections

did not identify a specific source, although it did clarify the importance of preceding antibiotic exposure in increasing host susceptibility

(Glynn et al., 2004). Such broad studies of sporadic cases can provide

important insights and help to determine the overall burden of illness.

Case‐control studies attribute disease to the point of consumption, in

contrast to some other methods of attribution which can include attribution to reservoir as well, thereby capturing cross‐contamination of

foods by an upstream source. Moreover, conducting case‐control studies requires considerable time to design, execute, and analyze, is

expensive, and has often yielded sparse new information (Fullerton

et al., 2012). Thus, FoodNet has conducted few case‐control studies

in recent years. However, improved methods and data could make

case‐control analyses an important component in the assessment of

major sources in the future. Nearest neighbors matched analysis is a

statistical method that matches a number of each case’s characteristics

with those of controls. Method provides a stronger comparison group

for each variable under analysis, with calculation of odds ratios and

population‐attributable risks (Cui et al., 2022), and is being explored

as a less resource‐intensive approach to case‐control studies. These

methods may be applied to cases from FoodNet’s case exposure ascertainment data, using the recent FoodNet Population Survey, conducted

during 2018–2019 as a source for control data. The future possibility

of an ongoing FoodNet Population Survey to provide timely data from

control persons about exposures paired with matching questions posed

to cases could provide more timely estimates of the major sources and

their changes over time.

Outbreak investigations of foodborne salmonellosis can provide

direct evidence that foodborne transmission of a particular serotype

occurs. Outbreak data in FDOSS can provide information by year on

food categories that have been reported as sources of particular serotypes. Detailed analysis of FDOSS data also can indicate changes over

time in the serotypes and broad categories of poultry products linked

to outbreaks, (e.g., parts, whole chickens, and frozen meals). These

data can help assess the importance of various serotypes attributed

to particular foods. For example, in the mid‐1980 s, CDC epidemiologists noted that although few SE outbreaks were attributed directly to

shell eggs, most of the foods implicated contained eggs; they proposed

that eggs were the source of the global increase in SE infections (St.

Louis et al., 1988).

For the past decade, IFSAC has annually updated the results of an

attribution model that estimates the percentage of all salmonellosis

that can be attributed to 17 food source categories (Interagency

Food Safety Analytics Collaboration (IFSAC), 2022). The model uses

outbreak data reported to FDOSS; it gives less weight to outbreaks that

occurred more than 5 years earlier (back to 1998) and accounts for

other factors, such as whether the outbreak was very large or occurred

in multiple states (Batz et al., 2021; Richardson et al., 2017). The estimates for poultry were 10% for chicken and 7% for turkey in 2012

(17% collectively), and 17.3% for chicken and 5.9% for turkey

(23.2% collectively) as of 2020 (Interagency Food Safety Analytics

Collaboration (IFSAC), 2015, 2022). These estimates are based on

vehicles identified in foodborne outbreaks, and do not capture the

original source when Salmonella in raw products such as poultry

cross‐contaminates other foods, which are then the source of an outbreak. They currently provide the best ongoing estimates of the

sources of domestically acquired salmonellosis in the U.S. These IFSAC

estimates do not include serotype because data on individual serotypes

are sparse; however, a pilot analysis of data on SE, the most common

cause of salmonellosis, has been attempted (CDC unpublished data).

Source attribution can also be predicted by comparing isolates from

humans with isolates from a variety of animal and other sources. In

Denmark, this was done using multilocus variable number tandem

repeat typing (de Knegt et al., 2016). In the United States, a current

IFSAC effort is using a genome sequence‐based approach for source

attribution. This approach may address some limitations of outbreak

vehicle‐based source attribution, as it can directly assess sporadic

cases, and is likely to reflect an original source or reservoir before

cross‐contamination, if those sources are represented in the nonhuman

isolates. Led by CDC’s Analytics team, this IFSAC study has compared

isolates from humans with those from a variety of foods and animals

(Rose et al., 2022). The model was initially trained using the nonhuman isolates to identify the alleles that best differentiate these strains

by animal and food source. Then human isolates were evaluated in the

trained model. The initial assessment used nonhuman isolates collected over the past two decades, and human isolates collected

2014–––2017 in FoodNet sites after excluding those known to be associated with foreign travel. The model predicts likely food category

sources for these infections. Overall, 26% of infections were predicted

to have a poultry source (Fig. 1). Restricting analysis to the subset of

isolates with > 50% probability of attribution to a single food source,

46% of such infections were predicted to be due to chicken and 3% to

turkey. As the IFSAC study using WGS aims at identifying original

source or reservoir (e.g., hatchery or grower level), the immediate

source of exposure (e.g., consuming poultry meat or handling live

bird) is not included in the estimates. For example, the total attribution

of cases to poultry does not differentiate between sporadic salmonellosis cases resulting from chicken consumption versus cases from handling live chickens (Centers for Disease Control and Prevention (CDC),

2022g). Future sporadic case‐control studies could address this gap.

The model was also able to predict food sources for the most common serotypes: Chicken was the predicted source for 86% of SE infections, 66% of Typhimurium, 55% of Infantis, 53% of Heidelberg, 29%

of I:4,5,12:i:‐, and <1% of Newport and Javiana infections. Turkey

was the predicted source for 9% of Heidelberg infections and ≤1%

of infections caused by the other serotypes listed above for chicken.

These percentages can be applied to estimates of the incidence or number of illnesses caused by each serotype to obtain an estimate of

chicken‐associated illnesses caused by each serotype or a ranking of

the most important serotypes. Doing this results in an estimate that

the serotypes that cause the most chicken‐associated illnesses are, in

order, SE, Typhimurium, Infantis, and I:4,5,12:i:‐; together these four

account for an estimated 70.4% of chicken‐ and turkey‐associated illnesses. Although a large percentage of Heidelberg infections are attributed to chicken, that serotype has now become uncommon, so it

results in relatively few attributed illnesses. Retaining Heidelberg as

a serotype of concern may be justified by concern that it could increase

if targeted control measures already in place are lifted.

Some of the poultry‐associated serotypes identified by the IFSAC

WGS source attribution model share serogroup‐specific O‐antigens,

relevant to vaccine choice: Typhimurium, I:4,5,12:i:‐ and Heidelberg

are part of serogroup 4, (previously known as Group B), SE is part of

serogroup 9 (Group D), and Infantis is part of serogroup 7 (Group

C1) (Centers for Disease Control and Prevention (CDC), 2011). In

the future, the serotypes chosen as targets for control could also

include other poultry‐associated serotypes with important antimicrobial resistance, or likelihood of hospitalization as additional criteria.

One can also compare the frequency of serotypes in different food

animal reservoirs with those that cause human infections. An

advanced version of this method has been used in Europe to develop

a Salmonella source attribution model across 24 countries (de Knegt

et al., 2015). Because animals can be reservoirs of nontyphoidal Salmonella, one would expect a change in the presence of a serotype in

poultry to precede a change in the same direction in illnesses caused

by that serotype in humans. Moreover, one would expect the changes

in serotype frequency in poultry and food to be detected from the variety of sampling programs done pre‐ and postharvest, such as FSIS’

cecal samples for testing in the NARMS program, and carcass samples

for regulatory purposes. Measures of illness in humans may lag behind

animal and food sampling because of the time it takes for a new strain

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to spread though many flocks, the time it takes for animals to be raised

and slaughtered, the resulting food distributed and because most

chicken is cooked sufficiently to kill Salmonella. Therefore, assessing

the frequency of serotypes in live poultry and poultry at slaughter

would help to monitor those serotypes most likely to cause illness

now or in the near future. Similarly, routine sampling of poultry feed

and feed ingredients could be relevant because this is a documented

way that serotypes enter the food supply, where even low levels of

contamination could be important because pathogens can multiply

in moist environments on farms and because animals consume large

volumes of feed. The routine practice of choosing one isolate to represent the Salmonella population present in a sample may mean that

important serotypes present at lower frequency may be missed

(Siceloff et al., 2022). In the future, it may be helpful to adopt metagenomic methods able to detect and characterize multiple serotypes that

may be present in a sample.

A complementary approach to the source attribution methods

described here also involves identifying serovars of concern using

genomics. These emerging methods are reviewed on the answer to

question #1. By directly targeting profiles of virulence genes or the

pangenome, these methods can provide an early warning of possible

serovars or indeed isolate‐level pathogen subpopulations that might

be responsible for an increased risk of salmonellosis burden. Since

these methods can be regularly updated using all relevant Salmonella

isolates submitted to NCBI, they could be combined with other foodborne surveillance and monitoring systems to provide a more rapid

response to evolving changes in Salmonella infectivity and virulence.

This would address some of the statistical power and resulting time

lag limitations described earlier for the Salmonella surveillance and

monitoring systems.

Q4.B Should only the most current data (e.g., 5 years) of foodborne illness surveillance outbreak and or pathogen/testing be

used?

The most current data should be used for all analyses, but the number of years to be included depends on the rate at which new information is added regarding the spectrum of serotypes responsible for

illness changes. Combining data from several recent years in a trend

model can smooth out the effects of single annual fluctuations, such

as large outbreaks. When data from the most recent 3–5 years do

not provide sufficient sensitivity to detect change, data from the most

recent years could be combined with discounted data from preceding

years; such analyses can still be updated each year with new data, as is

done in the IFSAC annual source attribution report. PulseNet collects

data from approximately 40,000 isolates each year; FoodNet has data

on about 7,200 domestically–acquired illnesses each year, as the

surveillance area covers 15% of the U.S. population, while FDOSS collects about 16 poultry–associated outbreaks each year. For poultry

samples, many isolates are obtained from carcasses, fewer from ceca

and other sources, resulting in thousands of sequenced isolates submitted to FDA’s GenomeTrakr. Data from food samples are more limited;

they include isolates from the NARMS retail food program and isolates

submitted to NCBI by state agriculture departments.

There is also an opportunity to study the results from 2020 and

2021, when the COVID19 pandemic disrupted many aspects of the

food supply. Whether there were serotype‐ or food source‐specific

effects remains to be examined in detail. If there were, then future estimations may need to consider excluding those years from more general models of attribution.

Q4.C How frequently should the priority Salmonella serotypes

associated with poultry be revised considering changes in their

occurrence while still ensuring continuity in industry and regulatory testing?

Ideally, a serotype, serogroup, or subtype‐based control strategy

would be reevaluated at annual intervals for evidence of the effectiveness of control methods, and to provide a timely alert for the need for

possible changes in control measures based on a decline, or lack of

decline, in illnesses, or emergence of other serotypes. It would seem

prudent to retain targets even if those serotypes become rare, as halting the control measures that led to their decrease may provide an

opportunity for a rebound.

The frequency of reported infections with targeted serotypes or

subtypes can be reviewed annually using case surveillance data.

Outbreak‐based attribution can also be analyzed annually, but the

sparsity of data will limit the sensitivity for detecting change quickly,

particularly at the serotype level. In the last decade, approximately 16

chicken‐ or turkey‐ associated outbreaks of salmonellosis were

reported each year. To detect meaningful change at the level of a common serotype for one food category, at least 5 years or ∼80 such outbreaks may be needed to provide reasonable sensitivity to detect a

change. Successful prevention, that reduces the number of outbreaks,

could stretch this time span further.

Ideally, source attribution analyses based on the IFSAC WGS model

would be repeated annually so progress can be measured by the estimated overall number of illnesses that can be attributed to chicken

as well as the number caused by targeted serotypes, and other serotypes emerging as sources. This method depends on two analytic datasets, one of nonhuman isolate sequences, and the other of human

isolate sequences. The dataset of nonhuman isolate sequences may

increase at a rate that would allow the training data analysis to be

repeated at regular intervals, (e.g., 1–3 years), and data representing

older sources are minimized or excluded. More strain sequences could

be added from other sources, (e.g., the APHIS NAHMS studies or state

agricultural databases). Sequences of Salmonella strains isolated from

humans are now being added to the PulseNet database at the rate of

∼40,000 each year, of which ∼7,200 are in FoodNet sites. Once appropriate sequencing workflows and automation can be established, it will

likely be possible to repeat the WGS‐based IFSAC source attribution

predictions at annual intervals.

Subtype‐specific prevention strategies will likely need to be modified as the incidence of infection with specific serotype or subtype targets change. It would be appropriate to include a regular assessment of

the need for modification of targets, ideally annually, as part of a regulation, as well as guidance on likely methods to be used and a mechanism to evaluate the role of improved methods and data sources in

making assessments. For example, reevaluating the prevention strategy could be indicated if: (1) no change was observed in the incidence

of infections caused by a serotype (as tracked in FoodNet surveillance)

at the end of at least two years of full implementation (or other time

window indicated by a power analysis, accounting for the expected

rate of reduction) compared with a chosen baseline, such as the

prepandemic one (2016–2018) used to assess progress towards the

Healthy People 2030 Goal, and (2) the WGS‐based IFSAC model and

possibly other analyses indicate no significant change in the percentage of infections caused by that serotype attributed to chicken. A statistically significant or major (e.g., ≥15% increase or decrease) change

in illnesses attributed to chicken overall or in a targeted serotype, serogroup, or subtype, should merit reassessment of the prevention strategy. A method should be clearly defined for the target metrics (e.g.,

confidence metric, p value, % change) required changes in guidance

or policy based on the results of these assessments. Because of data

limitations, reassessment may also be deemed appropriate to protect

public health, even if the changes do not reach formal statistical significance, for example, due to applicable lessons learned from other contexts or advances in research.

Q2: Microbial criteria at preharvest level

What types of microbiological criteria could be established to

encourage control of Salmonella at preharvest (i.e., in live birds

on‐farm)? Should FSIS consider qualitative microbiological criteria

for control of the presence of Salmonella in a flock when they are

presented for slaughter? How could FSIS use these criteria to address

Salmonella serotypes most frequently associated with human illness?

What industry data would provide evidence of control?

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Q2.A Should FSIS consider qualitative microbiological criteria

for control of the presence of Salmonella in a flock when they are

presented for slaughter?

To determine qualitative MC that are meaningful to assess the presence of Salmonella in flocks presented at slaughter, one should consider the existing data regarding testing to verify Salmonella control

on the farm. The Joint FAO/WHO Expert Meeting on Microbial Risk

Assessment (JEMRA) recently reviewed data on control measures for

Salmonella in the broiler production chain (World Health

Organization (WHO), 2022a). The panel concluded that no single control measure is sufficient to control contamination prevalence or level

in poultry meat. Rather, utilizing multiple strategies, such as those outlined below as best practices for the poultry industry, is most effective

to ultimately reduce the carriage of Salmonella through to the food supply. Testing for Salmonella in breeder flocks and eggs intended for

broilers is recommended to exclude eggs from infected flocks being

transported for hatching.

Current US practices include testing for the presence of Salmonella

in hatcheries, as well as at breeder and broiler houses, to inform voluntary management actions by the company (see Fig. 5 for description

of US commercial broiler production and (The Poultry Site, 2011;

USDA Animal and Plant Health Inspection Service (APHIS), 2017)).

The overall compliance with the USDA FSIS performance standard

during processing by the poultry industry is high and further reduction

in prevalence may need an incentive for the industry to increase the

testing of live birds before processing. A proposed requirement to test

flocks before entering the processing plant by FSIS USDA would

increase available information to base risk management decisions

on, but it is unclear what the impact would be, or what incentives

the industry would respond to.

Testing the flocks for the presence/absence of Salmonella prior to

scheduling for slaughter is a tool with limited documented impact

(Nauta et al., 2009; Rasschaert et al., 2008). Testing for specific

serotyping could help target vaccine development and focus their

use where needed but focusing solely on the currently highly prevalent

serotypes may miss shifts in the strains that cause illness. In addition,

the fact that serovars detected at processing or in the product might

show different patterns from those most commonly observed on farms

complicates the use of serovar‐specific on‐farm testing information to

inform risk control in the product. Thus, the industry should consider

Summary of Question 2 Response FSIS does not have jurisdiction to mandate testing on the farm level. However, because

of microbial criteria set for postharvest poultry, testing of incoming flocks and their environment may assist in processors in

achieving target reduction of Salmonella prevalence and levels

coming into their facilities. Before deciding whether qualitative

vs. quantitative data is best for determining control of Salmonella

in a flock, substantially more data (including industry and university research data) should be analyzed to ascertain, the correlation

between Salmonella prevalence, levels, and serovars, either in

flocks presented at slaughter or on farm shortly before presenting

for slaughter. Power of detection and enumeration needs to be sufficient to provide evidence on both process control and long‐term

association of contamination trends in flocks with farm features

and farm management measures (including any potential associations with indicator organisms), (i.e., the real‐world cost‐

effectiveness of on‐farm control measures).

In considering possible MC or testing programs to encourage

Salmonella control preharvest, the following conclusions emerged:

• U.S. poultry producers voluntarily implement several Salmonella control practices on farms and use qualitative testing

to monitor the effectiveness of management in breeder flocks,

hatchery, grow out, and transport. This approach has resulted

in the lower prevalence on chilled carcasses, but not resulted in

fewer illnesses associated with poultry.

• Multiple routes of contamination have been observed, and no

single control measure has been found to be effective in controlling Salmonella on farms. Hence, farms should be incentivized to apply a rigorous multiple‐barriers approach that

combines a suite of best practices, including testing at multiple

points (such as feed, litter, etc.) to inform farm management

operations.

• Qualitative Salmonella testing of breeder flocks is recommended to avoid using contaminated eggs for hatching.

• Vaccines are likely the only serovar‐specific control strategy at

preharvest but will not eliminate all Salmonella from flocks.

Other farm‐based measures (see Management Practices for

Controlling Salmonella at preharvest in this document), while

not directly serovar‐specific, will similarly reduce but may

not eliminate all Salmonella.

• Feed treatments (e.g., heat), preventing recontamination, and

qualitative testing for the presence of Salmonella in feed are

recommended to control this important entry route.

• Sampling protocols and assays exist to test on‐farm environmental samples (e.g., litter, feed, water, dust) for both detection (qualitative assays) and enumeration (quantitative

assays). While quantitative data theoretically could support a

more accurate assessment of risk and mitigating actions, little

published research exists to assess the additional value of enumerating Salmonella in environmental samples compared to

presence/absence, or what levels of Salmonella should trigger

corrective actions. In designing sampling protocols, frequency,

and number of samples need to account for currently achievable sensitivity.

• Testing the farm environment (litter, feed, water) for Salmonella 1–2 weeks prior to slaughter could help identify highly

contaminated flocks and inform immediate management

actions (e.g., processing schedules, transport, remediation of

houses, or other mitigation strategies on the flock).

• Testing of Salmonella in birds at receiving for slaughter could

also inform future control actions and process control to be

implemented on farms or at processing. These tests could be

complemented by testing for a subset of high‐concern serovars.

However, complex serovar occurrence patterns at different

stages are likely to complicate interpretation and actionability.

• More research is needed to quantify the impact of preharvest

control measures and combinations thereof on patterns of Salmonella levels, which would also improve the ability of risk

models to estimate impacts and, hence, the cost‐effectiveness

of Salmonella control actions.

• Assays to detect specific serovars of concern exist but are currently cumbersome. New molecular methods to rapidly identify (or quantify) multiple serovars are promising but require

additional validation.

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utilizing testing approaches that test overall Salmonella as a means to

measure the effectiveness of different interventions at preharvest,

but in combination with serovar‐specific monitoring to determine if

the interventions select for or exclude virulent strains.

What have we been testing, and what could be tested?

In the U.S., there are currently no required industry‐wide routine

on‐farm quality indicator sampling plans or sampling techniques.

Hatchery sampling most often relies on testing settle plates (open

plates for a given period of time for air monitoring, such as Salmonella

and dust) within hatcheries and/or testing of paper tray liners, placed

under newly hatched chicks, for the presence of Salmonella. For primary breeders, breeders, or broilers, Salmonella testing is most often

done with boot socks (socks worn over shoes as the technician walks

around the broiler house) or drag swabs (filter swabs moistened and

dragged around the house) (Bourassa, 2016; Buhr et al., 2007). Some

facilities have implemented environmental sampling programs where

rodents, flies, beetles, and soil around houses are sampled for Salmonella. The results of these tests may be used to inform (voluntary)

management actions by individual establishments. A range of Salmonella control actions that have been or could be implemented in

the U.S. context are summarized in the section “Management Practices

for Controlling Salmonella at preharvest” (Question 2). This is in contrast to the approach used in Sweden, where a flock is destroyed if Salmonella is detected, and all litter is removed and composted for at least

6 months to prevent contamination of the surrounding environments

(Appendix B, Case Study). This approach is equivalent to requiring

negative tests for admittance of a flock to a slaughterhouse.

Overall, sampling and laboratory testing methods exist that are

technically feasible to use on farms and which could be used to assess

the contamination status of either flock or farm (Garcia et al., 2011;

New Zealand Food Safety, 2022; U.S. Food and Drug Administration

(FDA), 2008; USDA Animal Plant Health Inspection Service (APHIS),

2019). The most meaningful matrices that can be practically sampled

on farms include litter (fecal matter from a flock mixed with litter

material), dust deposited on surfaces, feed, and water. Feed and water

can be sampled directly. Environmental dust deposited on surfaces can

be collected using swabs. Environmental sampling protocol guidelines

exist for layers, although not for broiler chickens (U.S. Food and Drug

Administration (FDA), 2008). All these matrices can be analyzed using

either qualitative or quantitative assays. Cloacal swabs, blood, or

organ samples from individual birds can be collected if health issues

are suspected with the flock, but are impractical for routine testing,

can pose animal welfare issues, and in the case of cloacal swabs, are

less sensitive than other approaches (Garcia et al., 2011).

The boot sock method is a simple and relatively inexpensive

method to collect litter samples. Samples can be analyzed using

culture‐based or molecular assays in either qualitative or quantitative

fashion. While this sampling approach has sensitivity limitations, in a

recent study using IMS‐PCR and artificially inoculated litter, the boot

sock method was found to have a Salmonella LOD of 10, 1, 0.1 CFU/g

using enrichment steps of 0, 4–6, and 8 h respectively, with good

agreement between IMS‐PCR results and culture methods (Hyeon

et al., 2019). ISODS (Intermittently stepped‐on drag swabs) were also

found to have similar detection power as boot socks (as % detected),

and higher than drag swabs or direct sampling of litter or feces

(Buhr et al., 2007).

In terms of laboratory assays, qualitative (presence/absence) assays

include culture‐based enrichments, qualitative PCR, and antibody tests

(see response to Question 6). Quantitative culture‐independent molecular assays that can be used to test on‐farm matrices (e.g., litter, dust,

feed, water, etc.) are reviewed in Question 6 (Appendix B Table B2 for

approved quantitative methods, and Table B3 for select examples

methods under development but which have not yet been validated

as time of writing of this document). Quantitative culture‐based assays

are primarily MPN approaches (USDA Food Safety and Inspection

Service (FSIS), 2014b). Qualitative (presence/absence) assays include

culture‐based enrichments, qualitative PCR, and antibody tests. Many

of the qualitative or semiquantitative (i.e., above/below a specified

concentration threshold) assays are approved by NPIP, AFNOR, or

AOAC (USDA Food Safety and Inspection Service (FSIS), 2022b).

Q2.B How could FSIS use microbiological criteria to

address Salmonella serotypes most frequently associated with

human illness?

There are multiple entry points for Salmonella during production

that can lead to contaminated carcasses at slaughter (Fig. 5), and it

can be important to distinguish the serovars present between different

Figure 5. Commercial broiler production in the USA. Assumption 1M birds/week complex (many complexes are 2.5 – 4 x larger).

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stages to identify entry routes and enact appropriate controls where

they can be most effective. Serovar occurrence patterns vary by region.

In an analysis of data from 2016 – 2020, Siceloff (Siceloff et al., 2022)

found regional differences including higher proportions of serovars

Infantis and Typhimurium in the Atlantic region (states including

CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VT, VA, and WV) and higher

proportion of serovar Schwarzengrund in the Southeast (states including AL, GA, FL, KY, MS, NC, SC, and TN), with the exception of the

state of Georgia, which has a higher proportion of Kentucky. In addition, serovar occurrence patterns vary through the production and processing chain (Fig. 6). For example, for broilers produced in Georgia,

serovar Kentucky was the most common serovar in breeders (68%)

during production but not at processing (Siceloff et al., 2022).

CRISPR‐SeroSeq performed on these breeder samples showed that

32% of samples contained multiple serovars, with up to 11 serovars

found in a single flock (Siceloff et al., 2022). Hence, potential MC

set at different stages of the production chain should consider which

serotypes are most abundant at that stage, and whether that stage is

a critical control point for a serotype.

Potential microbiological criteria at preharvest

Potential MC applied at preharvest discussed in this section

include:

• Salmonella testing of the farm environment (e.g., litter, feces, water,

dust (Arnold et al., 2009; Bourassa, 2016; Buhr et al., 2007;

Mueller‐Doblies et al., 2009)) to assess contamination of a specific

flock and/or the farm; tests can be qualitative (or as additional risk

assessment data and assays become available, quantitative), complemented by serovar identification to assess the presence of

high‐concern serovars; this test, if sufficiently sensitive, could

inform both processing actions for the flock as well as farm management actions.

• Indicator testing of the farm environment, if an association with

Salmonella presence or levels is demonstrated, with similar purpose

as above.

• Salmonella testing of a flock presented at slaughter, to verify on‐

farm process control and provide feedback regarding farm management actions (custom processing of a flock tested at receiving

would be unfeasible under common operations).

• Salmonella testing of breeder flocks (qualitative, possibly quantitative), to control vertical transmission to broiler flocks.

MC at any stage rely on the availability of a sampling and testing

protocol with adequate sensitivity and specificity compared to thresholds of public health importance. In addition, the achievable sensitivity of a testing protocol needs to be matched by adequate sampling

frequency or sample numbers to achieve a reasonable detection power,

so that results can meaningfully inform management actions associated with the MC, as discussed in Question 1.

While there are currently no routine sampling programs being utilized industry‐wide, sampling methods and laboratory assays (qualitative and quantitative) that could support preharvest testing or MC

exist. For instance, hypothetically, a validated method (qualitative,

semiquantitative, or quantitative, see Question 6) might be used to

test flocks one to two weeks before processing, to inform risk management at processing (as well as during transport, and on farm (e.g., to

prepare for the next flock). For example, additional risk‐reduction

measures could be implemented on higher‐risk flocks, or flocks from

high‐risk farms, when received for processing (see Question 3)

(USDA Food Safety and Inspection Service (FSIS), 2021a). For this purpose, testing for generic Salmonella is expected to provide the most

cost‐effective and actionable information, although complementary

identification of serotypes of concern could allow for a more accurate

estimate of risk from a flock and, hence, of the degree of Salmonella

reduction necessary at processing, if processing conditions allow for

such flexibility. Further analyses would be needed to define what

Figure 6. Salmonella survival through broiler production (Shariat, 2022).

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“high‐risk flock” (or “high‐risk farm”) means operationally in relation

to farm process control and risk management actions, and in relation

to (increased) risk mitigation measures needed during processing.

On‐farm flock testing results could also potentially be used to

reduce the risk of cross‐contamination across flocks during processing,

e.g., by scheduling more heavily contaminated flocks at the end of a

processing day or potentially on days with other contaminated flocks.

However, it should be noted that many factors need to be considered if

scheduled days for processing flocks are changed. Some of these factors include, (1) trucks may need to be filled from multiple houses,

(2) it could be costly for trucks to visit multiple houses or farms in a

single day, and (3) growers are paid based on weight from a

house/farm. Furthermore, studies suggest that changes in processing

schedules to account for higher or lower flock contamination (so‐

called “logistical slaughtering”) result in a weak correlation with

pathogen reduction in finished meat samples (Nauta et al., 2009;

Rasschaert et al., 2008). One hypothesis for the lack of correlation is

possible cross‐contamination with inadequately sanitized transport

crates or slaughter equipment.

The FSIS does not have jurisdiction at the farm level and cannot

require testing. However, per FSIS 2022 proposed framework to

reduce Salmonella, processing facilities may be required to test incoming flocks for Salmonella at receiving (USDA Food Safety and

Inspection Service (FSIS), 2022g). Operationally, testing would most

feasibly be completed on the farm prior to transport, and data shared

with the processing facility. While testing on farm would be informative, coordination across agencies (FSIS and APHIS) would be needed

to implement an MC of regulatory relevance.

Salmonella spp. and/or serotype data collected at flock receiving

(and potentially at other early processing stages) may be used to provide feedback and create an incentive for targeted control mechanisms

applied on farm during live production. For example, as an illustrative

scenario where a flock is tested for Salmonella levels at receiving, the

producers whose flocks are, over a set time window, performing worse

than the industry average or worse than a specified threshold for the

prevalence or levels of Salmonella (or select serovars) could be subjected to additional preventive or remedial actions, or be subjected

to financial disincentives from the processor. Independently from

potential regulatory actions, the information would provide feedback

to producer farms about their process control or the need for increased

control actions. For this purpose, Salmonella testing on a flock at or

shortly after receiving, possibly complemented by serotyping if not

already carried out in previous preharvest stages, could provide valuable information to focus on‐farm measures.

Conversely, testing at flock receiving is unlikely to have a sufficiently fast time‐to‐results to inform processing actions on that flock;

testing on farm may provide more timely and actionable information

on the contamination status of a flock, albeit possibly less accurate.

We recommend comparing the value of information vs. cost of testing

flocks on farm vs. at presenting for slaughter, using statistical modeling approaches.

More research is needed to determine if qualitative and/or quantitative testing on farm and resulting changes in processing (see also

Question 3) would significantly affect Salmonella prevalence and

levels on carcasses and in finished products. In addition, research is

needed to determine if testing for nonSalmonella indicator(s) could

provide guidance to companies on which houses have a higher probability of Salmonella contamination or level of contamination. At this

time, there is no clear correlation between non‐Salmonella populations

(i.e., indicator microorganisms) that can be used to predict which

flocks are likely to be highly contaminated; testing specifically for Salmonella appears to be the best strategy (see also Question 5).

Industry data could be leveraged to better understand how a MC at

flock receiving, as well as testing on farms, could be used to inform

management actions on farms, and whether enumeration of APC (or

other indicator organisms) could predict the likelihood of Salmonella

occurrence or its levels.

Testing breeders, feed, and the farm environment could provide

information on specific Salmonella entry or spread routes known to

be associated with broiler farm contamination. For example, feed testing implemented in Sweden, and subsequent exclusion of positive lots,

has contributed to the reduction of Salmonella (National Veterinary

Institute (SVA), 2020). Breeder flocks have been found to often be

the likely source of Salmonella in broilers (European Food Safety

Authority Panel on Biological Hazards, 2019). For the purpose of controlling contamination from these routes, qualitative or quantitative

testing for generic Salmonella –carried out before feed or eggs/chicks

reach the next stage‐ is likely to provide sufficient information to

inform process control on farm. Some serovars can be closely associated with some routes, (e.g., S. Enteritidis was back‐traced from broilers to breeders more often than other serovars, in some studies) (Byrd

et al., 1998; Kim et al., 2007). In these cases, testing and controlling

for relevant serovars of concern in a stage/route (upstream of the

grow‐out farm) could be more cost‐effective than testing for generic

Salmonella. However, in general, a serovar does not consistently map

to one source only, and hence serovar testing on farm is unlikely to

be cost‐effective for on‐farm process control.

Management practices for controlling Salmonella at preharvest

Any MC, as well as other approaches to risk management, need to

be associated with effective risk control measures. MC can verify that

control measures are effective and can trigger additional actions in

case the MC is not met. In this section, we provide a brief summary

of available Salmonella control actions that could be used in conjunction with a MC, or on their own, while, in the next section, we discuss

serovar‐specific MC and control strategies.

Several strategies to control Salmonella at multiple preharvest

stages exist and have been implemented. Currently, in the U.S., these

measures are voluntary; many are recommended as best practices

(Buhr, 2022; The Poultry Site, 2011; USDA Animal and Plant Health

Inspection Service (APHIS), 2017; USDA Food Safety and Inspection

Service (FSIS), 2021a; World Health Organization (WHO), 2022a).

Evidence of effectiveness varies by measure, and it is recognized that

no single measure will control Salmonella. While high animal welfare

practices (e.g., housing, transport conditions, etc.) have been reported

by Italian researchers to be associated with lower carriage of Salmonella in skin and cecal content samples, levels of Campylobacter were

higher in that group (Iannetti et al., 2020). Therefore, practices should

be evaluated comprehensively across hazards as to not trade one contamination issue for another. A nonexhaustive list of preventive control measures at different preharvest stages and potential testing

points include:

Breeders:

• Cull visibly soiled eggs that may be contaminated with Salmonella‐

containing feces (Fulnechek, 2022)

• Salmonella‐free chicks

• Competitive exclusion treatments, prebiotics, probiotics

• Vaccination program (including serotypes from Groups B, C, and D)

(Fulnechek, 2022)

• Biosecurity (Cunningham and Fairchild, 2009)

• Rodent and insect control program

• Footbaths / movement of workers

• Testing for Salmonella in chicks could be used to verify process

control

• Qualitative or quantitative testing in breeder flocks can be used to

prevent eggs from contaminated flocks reaching the hatchery

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Feed:

• Testing for Salmonella in feed could verify that feed does not introduce Salmonella on farms

• Control quality of ingredients

• Allow sufficient time in conditioner to give time/temperature/-

moisture for Salmonella inactivation

• Prevent postpelleting (processing) recontamination. Pay particular

attention to cooling area to avoid conditions favorable to Salmonella growth

• Clean and sanitize feeders between flocks

Hatchery:

• Cleaning/sanitation programs

• Control air movement in hatchery

• Chemical disinfection program in hatch cabinets during the hatch

period

• Do not reuse tray liners

• Competitive exclusion, probiotics

• Testing for Salmonella in chicks could verify process control and

inform Salmonella control actions at the hatchery and/or on a chick

flock (e.g., not shipping contaminated chick flocks to grow‐out

farms)

Grow‐out (broilers):

• Only Salmonella‐free chicks

• Competitive exclusion treatments, prebiotics, probiotics

• Implement biosecurity and hygiene plans

• Litter treatments prior to flock placement and reapplication as

needed

• Moisture control (no leaking nipple drinkers)

• Proper working ventilation system (reduce stress on birds – litter

amendments if necessary)

• Rodent and insect control program

• Limit movement of workers / visitors

• Testing of flock or flock environment for Salmonella before transport to slaughter could determine the contamination level of a

specific flock and inform risk mitigation actions at slaughter and

processing

Transport:

• Proper feed and water withdrawal time

• Clean and disinfect transport coops to reduce cross‐contamination

from previously transported flocks that were heavily contaminated

to uncolonized flocks

• To the extent possible, limit time in transport cages

• Testing a flock at receiving or at slaughter, summarized by farm

over a time window, could provide feedback to producer farms

and inform farm management actions. However, test results on

individual flocks received are unlikely to be useful to inform management actions for that flock at processing (unless, in the future, a

real‐time test is developed and used)

Response when Salmonella is detected in the preharvest environment (The Poultry Site, 2011)

• Determine the origin of the contamination

• Dispose of poultry litter/feces and other potentially contaminated

farm waste in a safe manner

• Enhance cleaning and disinfection of buildings, surfaces, and

equipment

• Allow adequate time between flocks if previous flocks were Salmonella‐positive

• Before restocking the facility, a bacteriological examination should

be carried out

As summarized above, a range of Salmonella control strategies are

or can be implemented at preharvest. A Joint FAO/WHO Expert Meeting on Microbiological Risk Assessment (JEMRA) has reviewed evidence on the effectiveness of Salmonella control measures in poultry,

both on farms and during processing, although findings of the review

are not yet available at the time of finalizing this report (World Health

Organization (WHO), 2022a). Sweden instituted a rigorous Salmonella

control program to reduce the prevalence of Salmonella on birds grown

and processed in Sweden (European Food Safety Authority (EFSA),

2012) (see Appendix B Case Study for details and comparison with

U.S. practices). Some practices used to reduce Salmonella on U.S.

farms, such as biosecurity and control of feed to exclude Salmonella,

are also incorporated in the Sweden program. The Sweden model further relies on microbial testing for the presence of Salmonella in cecal

droppings or cecum. If any serotype of Salmonella is isolated, the flock

is destroyed (Wierup et al., 1995). Because the method of control

depends on the eradication of Salmonella‐positive breeders or broilers,

replication of this system in U.S. operations has been, thus far, considered controversial and potentially too costly to implement, while some

advocates suggest that a rigorous cost/benefit analysis be conducted.

While the economic burden associated with poultry‐related salmonellosis has been estimated (Scharff, 2020), a cost assessment of implementing an eradication model, compared to other approaches, has

not been carried out for the U.S.

Serotype-Specific control strategy and MC

Vaccines can target a specific serotype or serogroup and are likely

the only serotype‐specific intervention strategies. Almost all broiler

breeders in the U.S. are vaccinated against some serovars of Salmonella

(Mountainspring and Burleson, 2018). Vaccines can be live deletion

mutant strains that are derived from a specific serovar of Salmonella,

(i.e., S. Typhimurium) or autogenous killed‐cell vaccines against a

cocktail of strains found in a local area.

Commercially available vaccines have been developed for Salmonella in the poultry industry. For food safety purposes, they are

meant to reduce infective pressure in a flock. Vaccines are commonly

administered in‐ovo, stimulating the innate and adaptive immune

responses for zoonotic poultry diseases and may be administered posthatch as a finely‐aerosolized spray or eye drops, administered the day

of placement in broiler houses. Salmonella vaccinations of broilers are

generally considered impractical as the bird’s immune system is not

fully competent until the birds are a week or older and handling of

birds in the broiler house is problematic. Although autogenous vaccines may require less time for development, they require multiple injections, are labor‐intensive to administer, and may be impractical on the

large production scale production found in the U.S. Furthermore, they

must be remade with newly isolated Salmonella from the flock

regularly.

Vaccines have likely contributed to a significant reduction in S.

Enteritidis in eggs and in S. Typhimurium for the broiler industry; they

may have contributed to the reduction in S. Typhimurium compared to

other serotypes (Dorea et al., 2010; Hurst et al., 2023; Young et al.,

2007). The vaccine industry is currently developing new vaccines

against additional serotypes of Salmonella. Safety and efficacy requirements of commercial vaccine development take three or more years to

develop. Therefore, changes in target serovars – if implemented‐

should take into account vaccine development timelines.

Serovar‐specific control strategies, such as vaccines, could be

paired with generic Salmonella MC to monitor the cumulative effectiveness of a suite of on‐farm control measures. While serovar‐

specific MC should be considered, serovar shifts over time, as well as

inconsistent occurrence patterns between preharvest and processing

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dance, going from breeders to carcasses and products (Siceloff et al.,

2022), may complicate the interpretation of test results and risk control decisions). A recent study by the European Food Safety Authority

(EFSA) pointed out the expected higher effectiveness of an all‐Salmonella target for breeding hens, compared to a target including five

high‐concern serovars (European Food Safety Authority Panel on

Biological Hazards, 2019). On the other hand, an additional serovar‐

specific MC at preharvest could allow focusing resources on strains

of higher concern (i.e., be more risk‐based) using more targeted measures (primarily vaccines, targeting known sources of specific serovars). Combinations of all‐Salmonella and serovar‐specific MC, at

preharvest or throughout the preharvest‐processing chain in

multiple‐barrier fashion, could offer cost‐effective trade‐offs. However,

since vaccines alone do not eliminate a Salmonella strain (Buhr, 2022;

Young et al., 2007), and a suite of control measures are needed, quantitative Salmonella testing is likely to offer the most actionable

information.

Several examples of serovar‐specific control efforts implemented at

a national level, including MC implemented as part of these efforts, are

summarized in Question 1 (part 2), showing various degrees of

success.

Q2.C What industry data would provide evidence of control?

Testing for Salmonella in birds at receiving for slaughter could

inform process control to be implemented on farms or at processing. These tests could be complemented by testing for a subset of

high‐concern serovars. However, complex serovar occurrence patterns at different stages are likely to complicate interpretation and

actionability. More research is needed to quantify the impact of

preharvest control measures and combinations thereof on patterns

of Salmonella levels, which would also improve the ability of risk

models to estimate impacts and hence the cost‐effectiveness of

Salmonella control actions.

The Working Group for Questions 3 and 5 felt that Questions 3 and

5 were related, and in fact Question 5 may be a subpart of Question 3.

The consensus was to develop a knowledge base of the information currently available and use that to further develop an answer to these

questions.

Q3: Microbial criteria on carcasses, parts, comminuted poultry

What types of microbiological criteria could be established for

poultry carcasses, parts, and comminuted products prior to

applying interventions and after interventions, considering current technology?

A. Could the quantity of Salmonella or quantity of microbiological indicator organisms (e.g., APC) be used? What are

the key parameters that need to be considered? What data

analysis techniques could be used? How would these criteria be linked to human illness?

B. How could serotypes frequently associated with human illness be considered in the development of microbiological

criteria?

Summary of Question 3 Response While indicator organisms

may be useful for process control to verify that an intervention

step has been applied (such as an antimicrobial carcass rinse), several unpublished studies (university and industry personal communications) suggest a very weak correlation between the

concentration of indicator organisms (e.g., APC and Enterobacteriaceae) and presence or level of Salmonella postchill. Rather, enumeration of Salmonella (not serotype‐specific) with a set limit of

quantification (limit to be determined pending further research)

could be used to identify highly contaminated lots to be diverted

for further processing. Removing materials that are highly contaminated is expected to reduce consumer exposure. This may

be even more important for comminuted, breaded, stuffed chicken

products, injected poultry, parts and pieces, and similar products

with a high degree of handling that can disseminate Salmonella

through commingling of contaminated poultry meat. The lack of

serovar‐specific enumeration techniques limits the options to

quantitation of generic Salmonella to be used in combination with

serotyping isolates. Tracking changes in predominant serotypes

from incoming birds to finished product, trends over time (e.g.,

3–5 years), and comparison with clinical isolates associated with

poultry will help direct improvement in preharvest control, understand harborage points in the birds and environment, and develop

processing intervention strategies.

Q3.A(1) Use of quantification of Salmonella vs. indicator

organisms as microbial criteria in poultry before and after applying interventions.

Salmonella prevalence (qualitative) testing does not differentiate

between carcasses with low levels of Salmonella vs. those harboring

high levels that are more likely to cause illness (McEntire et al.,

2014; Sanchez‐Plata, 2022; Siemens, 2022). Risk assessments suggest

that while the prevalence of Salmonella, which is currently used in

the FSIS performance standards for poultry, affects the risk of

salmonellosis, neither prevalence alone nor total bacteria populations

(e.g., APC; discussed in detail in Question 5 response) are correlated

with the risk of salmonellosis (Lambertini et al., 2019; Oscar,

2021a). Rather, prevalence could be used in conjunction with Salmonella enumeration or semiquantitative testing. In the context of current strategies to control Salmonella during processing, quantification

of Salmonella as a performance standard may have a role in identifying

product batches harboring higher pathogen levels, which could be

diverted for alternate processing. Further refinement of standards

may be possible as additional data and interventions become available.

It is recognized that several postproduction factors could lead to an

increase in Salmonella levels in the product before consumption;

hence, low but nonzero levels at product packaging could still pose a

nonnegligible risk.

Currently, the FSIS Pathogen Reduction Performance Standards do

not require that poultry processors conduct pathogen testing. Rather,

monitoring compliance with the Pathogen Reduction regulatory

requirements is conducted by FSIS. Statistically demonstrable performance that is worse than a set standard (or, in the future, not meeting

a selected Key Performance Indicator‐ KPI (USDA Food Safety and

Inspection Service (FSIS), 2022d)) could trigger a Comprehensive

Food Safety Assessment by FSIS (or other measures to be established).

Establishments that fail to maintain the required documentation,

including documented efforts to improve performance, would be subjected to the Rules of Practice at 9 CFR, Section 500 (Code of Federal

Regulations (CFR), 2022), including, as appropriate, the Suspension of

Inspection. There is some evidence that public posting of establishments’ performance may have contributed to prevalence reduction

across the industry (Ollinger and Bovay, 2020).

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Potential MC approaches applicable to postharvest poultry processing

In poultry processing establishments, data on Salmonella occurrence and/or levels could be collected at the following multiple in‐

process steps (Fig. 5): stunned/slaughtered/“feather on” carcasses,

postpicked carcasses, carcasses at rehang, postevisceration/prechill

carcasses (both on‐line processed carcasses and off‐line reprocessed

carcasses and parts), postchill carcasses and parts (off‐line processed

parts), and postprocessing (immediately prior to packaging) finished

products (e.g., cut portions, deboned portions, giblets, and comminuted product). Such monitoring data could be used in several ways

to inform process control and risk management actions and could be

used for performance standards in lieu of the current Salmonella prevalence model.

When choosing MC, the feasibility and usefulness of serovar‐

specific approaches should be considered. For example, the lack of

enumeration techniques for specific serovars limits the option of

serovar‐specific MC to qualitative ones. Several options for MC during

processing could be considered, including:

• Prevalence‐based (qualitative) MC on the pathogen in the finished

product

• Concentration‐based (quantitative or semiquantitative) MC on the

pathogen in the finished product

• Concentration‐based (quantitative) MC on the pathogen during

processing (e.g., at one stage, or reduction between two or more

stages)

• Concentration‐based (quantitative) MC on an indicator organism

during processing (e.g., at one stage, or reduction between two or

more stages)

• A combination, (e.g., a quantitative indicator MC at processing and

a quantitative or qualitative pathogen MC in the finished product)

• A combination, (e.g., of a qualitative MC on selected serovars and a

quantitative MC on all Salmonella).

To implement the MC approaches under different scenarios as

aforementioned, regular samplings at different stages of the poultry

supply chain are required. Although no sampling plan can warrant

the absolute absence of Salmonella, sampling on a regular basis at targeted points, following well‐designed protocols, can be used to measure the deviation of contamination from baseline, determine the

needs of corrective plans, and verify the effectiveness of implemented

safety management practices and corrective actions. Simulation results

conducted by EFSA showed that an increased number of samples

tested for Salmonella is associated with a reduced possibility of produced batches being rejected (European Food Safety Authority Panel

on Biological Hazards, 2010).

If a population‐based Salmonella MC is established, once the MC

threshold population is defined, it is necessary to identify with sufficient accuracy, whether a sample is above or below the threshold

(i.e., a semiquantitative approach) in order to prove that the MC has

been met. This approach is already used by some companies to assess

their processes (Siemens, 2022). At the same time, the full quantification information provides additional information that can be used for

trend and root cause analyses in support of process control protocols.

The tradeoffs of test sensitivity and quantitative target are discussed in

Question 1.

For the purposes of evaluating the microbiological reductions

achieved during processing, as well as plant hygiene and process control (including biomapping), metrics based on indicator microorganisms such as Enterobacteriaceae (EB) or Aerobic Plate Count (APC)

could be tested (Bueno López et al., 2022; De Villena et al., 2022;

Sanchez‐Plata, 2022). The data deriving from such testing are relatively inexpensive and, given that there is no need for a pathogenic

positive control culture, the testing may be conducted using simple,

standard microbiological methods at onsite laboratories – avoiding

the need for expensive and time‐consuming sample shipment to centralized biosafety level‐2 microbiological testing laboratories. However, at this time, data on the association between metrics based on

levels of indicator organisms and presence or levels of Salmonella in

poultry during processing are still scarce and inconclusive (see Question 5).

MC combining Salmonella thresholds (or thresholds in selected serotypes) and indicator microorganism thresholds are possible and could

provide complementary information on both process control and public health risk. For example, more frequent indicator testing combined

with relatively less frequent Salmonella testing might provide cost‐

effective information to assess whether additional risk control measures are necessary, in a timely fashion. Although more evidence is

needed to estimate the effectiveness of such approaches, QMRAs based

on existing evidence to derive different scenarios of efficacy could

inform the public health impact of such approaches and inform more

targeted data collection efforts.

Examples of Canadian and EU Microbial Criteria for Salmonella

in finished poultry products, and Salmonella control programs:

• In Canada, a surge in Salmonella Enteritidis (SE) infections

occurred in 2016–2018, and many outbreaks were linked to

raw breaded processed poultry products, including chicken

nuggets, chicken strips, and chicken tenders (personal communication, Kate Thomas, Canadian Food Inspection Agency;

(Hurst et al., 2023)). The outbreaks prompted regulatory

changes requiring that such products either be cooked before

being sold or tested and shown to be Salmonella‐free; the rules

were finalized in early 2019 (Canadian Food Inspection

Agency (CFIA), 2019). SE cases reported in Canada fell by

more than 50%, from ∼13 per 100,000 in 2017 to 5.8 per

100,000 in 2019 (Canadian Food Inspection Agency (CFIA),

2019).

• The EU adopted a similar approach for finished poultry products to what was adopted for flocks. The EU regulation (European Commission, 2003) was set in 2003 to take effect in

December 2011 targeting the absence of Salmonella spp. in carcasses and fresh poultry meat (broilers, laying hens, turkeys).

Since by December 2011 most poultry flocks would have been

out of compliance from a target <=1% flock‐level prevalence,

the EU created a new regulation (European Commission,

2011), also starting in December 2011, where only S. Typhimurium and S. Enteritidis would have needed to be absent

from fresh poultry meat and carcasses, based on an EFSA

report citing those two serovars as causing approximately

80% of all salmonellosis in the EU (European Food Safety

Authority (EFSA), 2010). Serotyping is carried out via culture

methods (White‐Kauffmann‐Le Minor scheme) (European

Commission, 2005). In contrast, poultry meat preparations

intended to be eaten cooked (e.g., salted raw poultry meat)

were regulated under a different law (European Commission,

2005) requiring the absence of all Salmonella in 25 grams

and remained unmodified. Despite these measures, no consistent decline has been observed in human salmonellosis in the

EU as a whole (Fig. 7).

One possible approach relevant to MC – as well as to other risk management frameworks‐ involves process control (Halim Lim et al., 2017;

Lim et al., 2014; Lim et al., 2015), where the standard to be met is

phrased as the consistent maintenance of a microbial (or other) parameter at (or below, if appropriate) a specified level over time, within a

specified tolerance. A detailed description of statistical process control

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in the context of food processing, including poultry, can be found in a

previous NACMCF report (National Advisory Committee on

Microbiological Criteria for Foods (NACMCF), 2018). Data from each

monitoring location would be statistically analyzed to assess process

trends over time and space, (e.g., utilizing Statistical Process Control

Charts (e.g., Xi & MR charts)). Other relevant measures may be recorded,

such as source of the flock being slaughtered, season of the year, line

speed, temperature of scalding, and any relevant metrics of interventions being employed. Statically significant trends departing from SPC

parameters (i.e., “out of control” signals) could trigger a corrective

action plan to restore the target performance level or to capture the

improved performance towards such target. Corrective actions could

be informed by root cause analyses or longer‐term trend analyses, which

could identify factors associated with deviations from target metrics,

possibly including occurrence/levels of the pathogen to control. Deviation from process control could also trigger risk control actions in the

case that product had been processed while not in‐control, for example,

possibly including in the future treatments of proven effectiveness. Documented Salmonella abatement technologies applicable to raw poultry

products exist, such as high‐pressure processing or of irradiation

(Silva et al., 2018) or peracetic acid (Cano et al., 2021).

A process being in statistical control is not equivalent to the process

meeting microbiological specifications (National Advisory Committee

on Microbiological Criteria for Foods (NACMCF), 2018). For process

control metrics to be part of a MC, a quantitative relationship to Salmonella presence or concentration would be needed. That is, this

approach would require defining an acceptable risk‐based target level

of the adopted metric. A process control metric could be assessed at a

specific processing stage (similarly to a MC), but could also be evaluated over longer time spans, hence, capturing longer‐term processes

including the impact of corrective actions that take longer times to

be implemented or take effect. Quantitative microbiological analyses

would provide substantially improved data to assess the status of process control. Documenting corrective actions would be needed to both

demonstrate that such action was taken, and to build further evidence

of its effectiveness in real settings. A process control approach involves

extensive data collection and analysis, and, hence, could be resource‐

intensive. However, advances in management and rapid analysis of

large datasets could increase feasibility. Issues of equity across large

and small establishments should be considered.

Q3.A(2) What are the key parameters that need to be considered in developing MC?

Parameters of the sampling and testing protocol can impact the

value of the resulting information, for instance, the point of sampling,

the frequency of sampling in relation to product flow rate, sample volume or weight, assay sensitivity and specificity, and how test results

are analyzed (e.g., summarized over which time frame or moving window, or by‐product flow rate). Key parameters may vary depending on

the scope of a MC, (e.g., characterizing the performance of an establishment (“process hygiene criteria”) vs. assessing a batch of product

(“food safety criteria”)). A summary of MC parameters impacting risk,

and hence also QMRA estimates of MC impacts, are outlined in Question 1. Selected aspects are illustrated below.

Assay parameters. If considering molecular quantification methods for Salmonella, instead of culture‐based methods such as MPN,

the assays should be able to provide consistent, sensitive quantification at low bacterial loads (e.g., under 1 CFU/g, depending on which

MC threshold would result in the target illness reduction) in order to

capture the majority of the bacterial concentrations observed postchill.

For example, in the 2007–2008 poultry baseline study (USDA Food

Safety and Inspection Service (FSIS), 2009a), FSIS collected 3,275

samples postchill, and 267 (8.15%) were positive. From those positive

samples, the most common level was 0.036 CFU/g, while only 18 samples (6.7%) exhibited concentrations above 1 CFU/g. Extrapolating

those figures to the current Salmonella prevalence in poultry of roughly

4.5% and assuming that bacterial concentrations have remained the

same (it is likely lower, since prevalence has declined since), if

10,000 poultry samples were taken annually, over 93% of the 450

expected positive samples would have concentrations under 1 CFU/

g. In contrast, an average of 2–3 samples a month would have loads

above that threshold. This stresses the importance of quantification

methods reliable at low concentrations, in particular, in the range of

considered MC thresholds. The different performances of quantitative

tests are further discussed in Question 6.

The point above is also true for serotype‐specific MC, which would

also require high specificity to the considered serotype. In addition,

Figure 7. Evaluation of the incidence of Salmonella infection in poultry populations and the number of reported human cases of salmonellosis (European Food

Safety Authority Panel on Biological Hazards, 2019) Figure shows recent trends in EU member states, highlighting no clear decline in salmonellosis

postintervention. The intervention implemented in 2011 consists of MC prescribing the absence of S. Typhimurium or Enteritidis in 25 g fresh poultry meat, and

for all Salmonella in poultry meat preparations (intended to be eaten cooked).

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biases in the detection and quantification of different serotypes may be

introduced by different assays, as discussed in Question 7.

Metrics demonstrating effective process control. Key parameters include the absolute quantitative microbiological performance

or other Key Performance Indicator (KPI) demonstrating sequential

microbiological reduction (as measured by the indicator microorganisms, as the expected low quantitative levels of Salmonella spp. results

may not result in a statistically meaningful reduction between sequential processing steps) through each step of the process. Process control

could be demonstrated by statistical stability demonstrating consistent

microbiological reductions through the process and/or indications of

statistical trends that are evaluated via root cause analyses to drive

continuous process improvement. A quantitative threshold in Salmonella levels at a specific stage (e.g., finished product) would also

be needed to assess whether the process consistently results in sufficient bacterial reduction to protect public health, based on risk targets

(while still in the context of continuous improvement).

Q3.A(3). How would these criteria be linked to human illness?

Any MC and associated risk control measures should be risk‐based,

that is linked to human illness burden targets, and/or associated Salmonella levels in finished products. As discussed in Question 1, if

MC result in a sustained reduction of human salmonellosis, the combination of measures implemented to control Salmonella would be

reflected in epidemiological data. However, given the year‐to‐year

variability in salmonellosis reporting, a consistent reduction in

salmonellosis might still take several years to be evident in the epidemiological data. QMRA models can be used to estimate the impact

of individual measures and help determine the extent of testing and

corrective actions necessary to reduce the burden by a target amount.

For example, QMRA models indicate that reduced concentrations of

Salmonella on consumer‐ready poultry products are likely to yield a

reduction in human cases of salmonellosis (see Question 1).

Through process control and associated continuous improvement

methodologies, the poultry industry could be required to demonstrate

processes are consistently capable of meeting the quantitative microbiological performance standards/MC combined with efforts to continuously improve process performance and capability.

Qualitative vs. quantitative MC. Qualitative MC are based on

presence/absence (detection/nondetection) of the pathogen in a sample and are thus represented in terms of the proportion of positives out

of the total samples (i.e., prevalence). Quantitative MC, instead, are

based on the concentration of the pathogen found in a sample. But

both prevalence and concentration metrics stem from the same underlying distribution of concentration, i.e., cell numbers per unit weight

in the product. Hence, prevalence and concentration MC are not completely separated approaches. As described in Question 1 and under

Assay parameters in this chapter, Salmonella concentrations in U.S.

poultry are low, with only 6.7% of samples above 1 CFU/g postchill

in the 2007–2008 poultry baseline study performed by FSIS (USDA

Food Safety and Inspection Service (FSIS), 2009a). So, in order to

implement a quantitative MC, assays should be able to characterize

all concentrations found in poultry products, and at the very least at

and above a candidate MC threshold of public health significance.

Complexity is added by variability across lots, flocks, establishments,

regions, etc., which is not well characterized. Some authors have proposed that a linear relationship between prevalence and illness incidence exists for Salmonella at low concentrations (Ebel and Williams,

2015; Ebel et al., 2012), which would make it sufficient to use prevalence in low concentration settings. However, the current qualitative

performance standards and subsequent prevalence reduction in poultry meat and products have not resulted in a corresponding decrease

in human salmonellosis from poultry in the U.S. Current evidence supports that a quantitative MC on finished product, if set at an appropriately protective threshold and using a rapid test, could identify

product batches that require additional processing before being sent

to retail (in addition to informing process control). More evidence

would be needed to support a quantitative MC upstream during processing, (e.g., carcasses at rehang, which would allow for more time

to adapt processing operations based on test results). Evidence relevant to MC options, including levels of indicator organisms, is discussed in Question 5.

It is possible that a quantitative MC for selected or all serovars, in

combination with a qualitative MC targeting select serovars of high

public health concern, might result in a more marked reduction in

human salmonellosis, if a stricter MC were to be applied for serovars

of high concern compared to current qualitative performance standards. The efficacy of such MC options and specific MC thresholds

should be evaluated using QMRAs, as further described in Question

1. Relevant target metrics would be the concentration of generic Salmonella in a sample of set weight, and the frequency of detection of

high‐concern serovars. Even more important than the distinction

between qualitative and quantitative targets is the implementation

of risk control measures associated with a MC, either voluntarily or

via enforcement. Question 1 provides a brief review of risk‐based studies that illustrate how both prevalence‐based and concentration‐based

MC, associated with effective control actions, can potentially reduce

illness burden, if control actions are implemented.

Q3.B. How could serotypes frequently associated with human

illness be considered in the development of microbiological

criteria?

Considerations outlined above, including both MC on finished

products as well as MC approaches at establishment level that adopt

a process control approach, are also applicable to serovar‐specific MC.

Adopting a process control approach, the poultry processing industry could potentially be required to assess the serotype of each Salmonella spp. sample positive result against an FSIS‐maintained list of

serotypes of public health concern and maintain failure rate statistics

or other KPIs from tracking trends in the frequency of detection of

those serotypes. To provide a hypothetical example, a process control

metric/KPI could be required to track and trend the frequency of isolation of serotypes of public health concern, reported per standard production unit of measure (e.g., per 1,000 birds slaughtered). Such KPI

could be calculated annually by FSIS from accumulated

establishment‐specific data for each establishment size (as defined in

the PR‐HACCP regulations). Frequency of sampling is a key parameter

to obtain adequate detection power and actionable information and

should be included in assessments of MC impact (see Q1). Continuing

with this example, all statistically significant trends above a threshold,

signaling recurring loss of process control, could trigger a Comprehensive Food Safety Assessment by FSIS, or other measures to be established. Establishments that fail to maintain the required

documentation, demonstrating efforts to improve performance and

reduce the frequency of the target Salmonella serotypes, could be subjected to the Rules of Practice at 9 CFR, Section 500, including, as

appropriate, the Suspension of Inspection. Trends signaling loss of process control could also be evaluated (voluntarily or not) using root

cause analysis tools to determine the cause, to facilitate efforts to

ensure the reduced frequency of detection is sustainably maintained

in the future, hence driving continuous process improvement efficiently. In assessing the potential impact of different MC, the effectiveness of establishment‐level corrective actions (e.g., a FSIS Food Safety

Assessment) should be compared to the effectiveness of risk control

measures demonstrated to reduce Salmonella (or the target serovars)

on the product, as well as combinations of establishment‐level and product batch‐level approaches.

The answer to Question 4 provides a reasoned review of the factors and available data and approaches to consider in vetting Salmonella serovars of priority public health interest linked to poultry

consumption, for the potential design of a serovar‐specific MC.

Q5: Use of indicator organisms to evaluate efficacy of microbial

reduction steps

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There is a documented correlation between a reduction in the

quantity of APC between carcasses and finished products and the

occurrence of Salmonella in finished products for beef, pork, and

poultry. How might this information be used to set microbiological criteria to assess process (pathogen) control in poultry?

The objective of microbiological performance standards or MC is to

control the contamination of pathogens in foods along the supply

chain, and ultimately mitigate associated public health risks. From this

perspective, a pathogen‐based performance standard provides the

most relevant information on which standard to base risk management

actions. However, its implementation in practice can be laborious,

highly resource‐demanding, and sometimes problematic; for instance,

considering the very large sample size needed for an acceptable statistical power to correctly classify a food production establishment into

either high or low contamination groups, in particular, when the rate

of occurrence of contaminated food units is low (Williams et al., 2015).

Hence, it is worthwhile to explore methods to supplement the

pathogen‐based system for the development and implementation of

microbiological performance standard by regulatory agencies and

the food industry in a more feasible and effective manner.

Employing indicator organisms to reflect the microbiological status

in water and foods can be traced back to more than one century ago.

Since then, their use has become widespread in microbiological testing

programs employed by regulatory agencies and the industry. Albeit,

the most common use is to evaluate the microbiological quality of food

and predict the product shelf life, indicator organisms are frequently

used as safety indicators to determine the presence of foodborne

pathogens, identify the insufficiency in meeting GMP requirements,

and assess the integrity of process control. The long‐established usage

of indicators suggests the potential of an indicator‐based performance

standard in foodborne pathogen control, representing an analytical

substitute for the detection of the target pathogen. Indicator organisms

do not directly represent the specific target pathogen but are used to

suggest the possible presence of a specific organism or source of contamination, or the occurrence of poor hygiene conditions.

While there are no identified organisms that directly reflect the

presence or absence of Salmonella in poultry (USDA Food Safety and

Inspection Service (FSIS), 2021a), several indicators have been used

for foods in specific applications for ongoing verification of process

control, including aerobic plate count (APC), coliform group, generic

E. coli, and Enterobacteriaceae. These commonly tested nonpathogenic

bacteria can be considered as the candidates to evaluate their suitability in the use of indicator‐based performance standard for Salmonella

control, for several reasons. These indicators naturally share ecological

association with Salmonella, such as their common enteric origin and

similarity in taxonomic classification. USDA FSIS uses E. coli as the

major determinant for the development of process control verification

criteria in meat and poultry (9 CFR 310.25). Nationwide, FSIS conducted the baseline sampling of the four indicator bacteria through

Young Chicken Survey (USDA Food Safety and Inspection Service

(FSIS), 2009a) and Young Turkey Survey (USDA Food Safety and

Inspection Service (FSIS), 2009b) to inform the establishment of statistical process control limits and procedures. In addition, a substantial

amount of data is generated by poultry industry and/or academic

researchers. Federally inspected establishments may collect samples

on a routine basis at more processing steps and a higher frequency

in comparison to the regulatory sampling programs (De Villena

et al., 2022). With the increasing availability of extensive nationally

representative data, there is an opportunity to combine and analyze

these multisource datasets to address the important question of

whether an alternative performance standard built upon indicator

organisms can be effective in process control improvement and food

safety protection against Salmonella.

When considering candidate indicator organisms, several attributes

should be considered. Based on the International Commission on

Microbiological Specifications for Foods (ICMSF), the following attributes are suggested for the selection of a safety indicator suitable for

the use in an indicator‐based performance standard (International

Commission on Microbiological Specifications for Foods (ICMSF),

2018; Tortorello, 2019):

• Characteristics of the indicator can be stably identified over time

and through food production and processing steps.

• The presence or level of the indicator organism indicates (e.g.,

semiquantitatively or quantitatively) the degree of the potential

for contamination, or lack of process control. Testing results provide actionable feedback for process control.

• The detection and/or quantification of the indicator should be easy,

rapid, inexpensive, sensitive, reliable, and safe for an in‐

establishment use.

• The responses of the indicator to the intrinsic properties of foods

and extrinsic conditions of the food production and processing

environment should behave in a similar but slightly more conservative manner in comparison to the target pathogen (e.g., inactivation, growth, and survival kinetics). For example, growth of the

indicator should be slightly faster, while survival of the indicator

should be greater in response to the food safety hurdles.

• Quantitative/qualitative correlation between the level of the indicator and the level of target pathogen can be established.

The usefulness of an indicator depends on the purpose of its testing.

The reliability of an indicator to predict the presence or amount of a

target pathogen, needed in a risk‐based MC, is related to the strength

of the quantitative correlation between the presence or amount of the

two. In many cases, there may not be a strong statistical correlation

between the presence or amount of an indicator and the presence or

amount of a specific target organism. However, this does not rule

out the usefulness of the indicator for the industry as a method of monitoring process control, where indicator trends and patterns can highlight deviations from normal or target conditions and inform

adjustments, (e.g., in terms of plant hygiene, achieved microbial

reduction, or presence of a deviation to investigate) (Erkman and

Bozoglu, 2016). Trends in the levels of the indicator organism may

be associated with other process variables, which may ultimately contribute to the presence of the target pathogen. This is an area where

biomapping research and industry data on Salmonella levels could provide valuable insights.

Mesophilic aerobic populations may be useful to indicate the

degree of process control, with the understanding that there are many

variables, which may affect the outcome. Mesophilic aerobic bacterial

populations, commonly referred to as Aerobic Plate Count (APC), are

simply a reflection of the bacteria present in a sample which will grow

under the prescribed conditions of plating media, incubation temperature and incubation time. As such, the initial composition of the microbiota in the sample has a significant effect on the outcome of the

analyses and the APC in no way reflects the “total” microbial population in the sample.

There is limited and inconclusive evidence on the correlation

between indicator organisms and Salmonella at specific stages during

poultry processing. For instance, one microbiological survey of generic

E. coli, Salmonella, and Campylobacter in 20 poultry processing plants

suggested a correlation between lower levels of E. coli and that of

the two pathogens (Altekruse et al., 2009). While one set of FSIS data

lacked a meaningful correlation between generic E. coli (GEC) and Salmonella (Williams and Ebel, 2014). In addition, unpublished data provided by the poultry industry and university researchers suggests that

indicator bacteria have very limited predictive value for Salmonella

prevalence [unpublished industry data, personal communications, A.

Siemens, Cargill; R. Kalinowski, Tyson; E. Moorman, Butterball, and

ref. (Sanchez‐Plata, 2022).].

Approaches to Salmonella control based on the concentration of

indicator organisms have been investigated or applied for other prodB. Liu Journal of Food Protection 87 (2024) 100168

26
ucts. For example, the Agricultural Marketing Service (AMS) analyzes

beef samples as part of procurement requirements for federal nutrition

assistance programs (Vial et al., 2019, 2020). This program tests samples at two points during processing (boneless beef trims, and the finished product, i.e., ground beef) for Aerobic Plate Count (APC), Total

Coliforms, and Generic E. coli (GEC), as well as for the presence of Salmonella and STEC and/or E. coli O157:H7. Any lot or sublot exceeding

critical limits (i.e., MC), including specified indicator concentrations

and presence of Salmonella in 325 g of product, are identified and

diverted from federal nutrition assistance programs (Vial et al.,

2019, 2020). AMS beef data from 2015 to 2018 suggest trends could

be investigated in the context of other MC that combine target pathogen(s) and indicators. Overall, exceeding of indicator critical limits

was not significantly correlated with the presence of either Salmonella

or pathogenic E. coli (Vial et al., 2019, 2020). Based on preliminary

findings, the correlation between indicator concentration and Salmonella prevalence also appeared weak [personal communication, D.

Doersher, USDA‐Agriculture Marketing Service and (Vial et al.,

2020)]. However, some trends emerged. For example, the correlation

between any of the three indicators was found to be higher in

pathogen‐positive samples, and indicator levels were higher on some

days of the week.

The predictive value of indicator patterns and time trends in providing process control and pathogen occurrence information should

also be considered, in addition to indicator‐Salmonella association at

a specific point. A promising example is APC reduction between two

process stages. Currently, many poultry establishments are sampling

poultry carcasses at the rehang step and after chilling using a whole

carcass rinse (USDA Food Safety and Inspection Service (FSIS),

1996). Based on retrospective analyses of regulatory data, one study

found a weak but nonnegligible correlation between Salmonella prevalence and the log10 difference in APC levels from rehang to postchill

carcass samples (correlation coefficient of −0.4), i.e. the higher the

reduction in APC, the lower the prevalence of Salmonella (Williams

et al., 2015). The “true” correlation of this relationship may vary from

establishment to establishment, but the change in population may be

useful as a means of monitoring overall process control and application

of antimicrobial treatments. If these findings were confirmed by additional data, ideally including Salmonella levels, a MC based on a minimum APC reduction between two processing stages, quantifiable via

existing assays, might be a viable MC candidate metric. An assessment

of the potential performance of a similar MC was carried out for U.S.

beef carcasses (Williams et al., 2017). Conversely, one study compared

two groups of establishments implementing two different intervention

approaches. Both groups had significant reduction in APC and EB

levels throughout processing. However, differences in indicator reduction between the two groups did not correspond to different Salmonella

levels in the product, suggesting a lack of or weak correlation that warrants further analysis (De Villena et al., 2022).

Another possible approach to indicator‐based metrics is provided

by the 2014 Modernization of Poultry Slaughter Inspection Rule

(USDA Food Safety and Inspection Service (FSIS), 2014a). Rule provisions include voluntary testing for indicators at prechill (rehang) and

postchill and encourage the use of the reduction in indicator levels

between these two stages as a metric supporting process control, in

addition to biomapping (USDA Food Safety and Inspection Service

(FSIS), 2015a). Analysis of industry data on indicators collected as part

of this program, combined with regulatory and voluntary testing for

Salmonella, could contribute insights into the effectiveness of

indicator‐based metrics in predicting potential pathogen contamination, and in supporting process control. Moreover, indicator‐based

metrics should be considered together with nonmicrobiological process metrics or online inspection endpoints, such as the presence of visible fecal matter on a carcass, or off‐line inspection procedures, such as

scheduled‐and‐performed procedures and observed and recorded noncompliances (USDA Food Safety and Inspection Service (FSIS), 2015a,

2017). If this data is used to enhance process control, then the results

could be beneficial in lowering the prevalence of nontyphoidal Salmonella in raw poultry.

Evidence is insufficient on the association of indicators with specific Salmonella serotypes. Data from the poultry industry has shown a

quantifiable reduction in Salmonella concentration during processing,

from hot rehang to the subsequent processing stages to the finished

product (chicken parts), consistent with the decline in Salmonella

prevalence along the process (Beers, 2022; Krombeen, 2022). These

data illustrate that, while quantification at the low concentrations

observed in finished product can be problematic, quantifications at

earlier processing stages for the purposes of a process control target/

MC is feasible, as an alternative to process control metrics based on

indicator organisms.

In conclusion, within the limitations and evidence gaps described

above, APC of other indicators measured at a specific processing stage

are unlikely to have a satisfactory predictive value for the presence,

level, or virulence of nontyphoidal Salmonella. That is, Salmonella

may be present in samples which contain low, intermediate, and high

populations of indicator bacteria. However, a change in APC from an

early sampling point on the slaughter line to a final sampling point

on the processing line, as well as absolute levels at the final point,

may provide useful information about the effectiveness of the process

in maintaining hygienic conditions. As such, APC trends may be useful

to enhance process control protocols, which in turn could be beneficial

for controlling Salmonella. Further analyses would be necessary to

determine whether it could be beneficial to more explicitly include

indicator trends as additional MC metrics. In the future, once Salmonella prevalence and/or levels are low enough that testing assays

may not be able to reliably detect its presence, (i.e., the proportion

of nondetects increases), the value of information from indicator

organisms to indicate departures from process control may increase.

Additional considerations on how indicator organisms can be

included in MC are provided in Question 3. Additional consideration

on how indicator organisms can be included in QMRA models to estimate the impact of MC is provided in Question 1.

Q6: Methods for quantification of Salmonella

What rapid methods and technologies are available for the

quantification of Salmonella? How should FSIS make the best

use of these methods?

Quantitative MC may be used in the processing plant under FSIS

jurisdiction as either a product‐release criteria or as a method to measure effectiveness of interventions. Rapid molecular quantitative methods might also be used by the farm to establish levels of Salmonella

present, both as a potential hazard entering the plant and potentially

also to evaluate interventions on the farm. There are two commercially

available, AOAC‐validated methods for the rapid molecular quantification of Salmonella at the time of publication, in addition to traditional

cultural approaches such as MPN or plate counts on selective or chromogenic agars. One method includes an enrichment step, while the

other does not (Appendix B Table B2). Enrichment means that the sample is kept for a defined time at conditions that favor Salmonella

growth, if present, to increase the sensitivity of the assay. In currently

available assays, a nonselective followed by a selective enrichment

allows detection and quantification of Salmonella at concentrations

as low as 1 CFU/g or mL. However, significant biases due to variability

in growth rate and competitive background microbiota are associated

with enrichment (see Question 7). Not including the enrichment step

eliminates most of these biases, but only achieves a higher limit of

quantification of about 10 CFU/g or mL.

Q6.A Background: Traditional culture methods for

enumeration

Cultural‐based methods have historically been used to determine

the concentration of microorganisms in a sample (i.e., for bacteria,

cells per unit of volume or weight). The population is enumerated or

estimated by either using agar plate count or Most Probable Number

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(MPN) methods. MPN is utilized when organisms are at lower levels

(i.e., <10 CFU/g or mL) or when an enrichment is needed to address

resuscitation, possibly from bacterial injury. In the food processing

chain, Salmonella is subjected to various unfavorable environmental

conditions (i.e., heat, sanitizers, storage conditions, etc.) that, if not

resulting in an inactivation, can lead to the formation of injured,

stressed, or viable but not culturable cells, which can negatively

impact enumeration (Du et al., 2007; Petran et al., 2015; Salive

et al., 2020).

MPN analysis requires multiple test portions to be analyzed from

the same sample, requiring significant resources of time and supplies

(media and laboratory disposables) and associated costs. Time‐to‐

result is dependent upon the detection system used for determining

the presence of Salmonella. After incubation, a reference cultural

method or a rapid detection system can be used to determine positive

growth. The USDA Microbiology Laboratory Guidebook presents a

dilution scheme for MPNs in Appendix 2.05 (USDA Food Safety and

Inspection Service (FSIS), 2014b), with analyses following the method

in Chapter 4.12 for the detection of Salmonella (USDA Food Safety and

Inspection Service (FSIS), 2021b). This method describes both the cultural approach and molecular detection system (USDA Food Safety and

Inspection Service (FSIS), 2022a). Using a rapid detection system, following the enrichment of multiple samples for MPNs, shortens the

time‐to‐result (USDA Food Safety and Inspection Service (FSIS),

2014b, 2021b).

Plating for enumeration requires selective media, including chromogenic media, to allow for the identification of typical Salmonella isolates in the presence of high levels of background microbiota as what

would be found in raw poultry. While plating for enumeration is relatively easy from a laboratory operations perspective, in comparison

with MPNs, there are challenges to this method due to the inability

to recover injured cells or detect fastidious strains or serotypes that

may be inhibited by selective additives. As a result, plating methods

can underestimate levels of viable Salmonella that pose a risk to public

health (Du et al., 2007; Li et al., 2014; Oliver, 2010; Reissbrodt et al.,

2000; Salive et al., 2020). These limitations can be mitigated by using

fluorescent‐based methods (Cho and Kim, 1999; Reissbrodt et al.,

1996) and non–culture‐based enumeration techniques (Ahmad et al.,

2017; Ou et al., 2021).

Q6.B. Available rapid methods and technologies

In recent years, methods have been developed using quantitative

PCR (qPCR) for enumerating Salmonella in poultry. The cycle threshold

(CT) value, which is inversely related to the amount of Salmonella in

the sample, is used to quantify the number of colony‐forming units

(CFUs) (AOAC International, 2021). Many of these methods require

that the sample is enriched in specified media for a prescribed time

and temperature (See Appendix B Tables B2 and B3). One method that

has been developed without enrichment uses centrifugation of the

sample to concentrate cells (bioMerieux Gene‐Up Quant Salmonella).

Two of these methods, Hygiena’s SalQuantTM and bioMérieux’s GENE‐

UP® QUANT Salmonella, have been validated by AOAC Performance

Tested Method certification for enumeration of Salmonella in 2021

and 2022, respectively as presented in Appendix B Table B6.

Other enumeration assays that have been developed or are

expected to be commercialized with varying levels of evaluations from

internal data or collaborations with service laboratories or poultry producers are presented in Appendix B Table B7. These methods include

dd‐Check Salmonella, iQ‐Check Salmonella, SalLimits, Molecular Detection Assay 2‐ Salmonella Quantification, and SureCount Salmonella

Species. Enumeration of Salmonella has been studied by using digital

droplet PCR (ddPCR) that uses partitioning live encapsulated bacterial

cells (Bio‐Rad). This approach can be achieved without enrichment.

Other methods are being developed including Loop‐mediated isothermal amplification (LAMP, see Appendix B Table B3). ThermoFisher

developed a multiplex PCR method for enumeration of S.

Typhimurium and S. Enteritidis that is in the process of being validated at the time of this writing.

Q6.C. How can USDA FSIS use these methods

Appendix B Table B6 shows two currently validated enumeration

methods commercially available and their specifications (as of the

time of adoption of this document). These two methods, Hygiena’s SalQuantTM and bioMérieux’s GENE‐UP Quant Salmonella, have AOAC

Performance Tested Method (PTM) approval being evaluated for

robustness and tested in an independent laboratory according to

guidelines for selective matrices (AOAC International, 2019). The

methods were compared to the USDA FSIS MLG MPN method as presented in Chapter 4.12 (USDA Food Safety and Inspection Service

(FSIS), 2022a) and according to Appendix 2.05 (USDA Food Safety

and Inspection Service (FSIS), 2014b). Since these are enumeration

methods, the quantitative validation guidelines were used for the comparison (AOAC International, 2019). The limits of quantification, as

shown in Appendix B Table B2, present both the claim based on the

target levels as defined in the guideline and the actual limits from

the validation study. It should be noted that, as part of AOAC’s PTM,

concentrations as low as 37 CFU/mL in poultry carcass rinse were

tested (AOAC International, 2021, 2022a). Given that most concentrations observed in poultry products are lower than this limit, additional

validation may be warranted before new assays are adopted for poultry testing.

USDA FSIS can consider the validation status of each method, the

impact of bias during the test methods, the workflow when selecting

Salmonella enumeration methods, and the ability of the methods to

detect the pathogen concentrations commonly observed in poultry.

An enumeration assay should also be considered in the broader context

of the sampling protocol, including number and frequency of samples

collected in relation to product flow rate. Assay parameters relevant to

MC are also discussed in Question 1 (Table 2).

As indicated in the responses above, quantitative methods could be

used to establish levels of Salmonella leaving the farm, both as a potential hazard entering the plant and also as a method to evaluate interventions on the farm. However, insufficient data is currently

available to define actionable limits. Quantitative methods could also

be used in the processing plant as either a product‐release criteria,

(USDA Food Safety and Inspection Service (FSIS), 2022i) or as a

method to measure the effectiveness of interventions.

Q7: Methods for selective identification of serotypes/biases

Are there particular approaches that would result in selective

identification of the serotypes of public health concern? Is there

strain selection bias introduced by laboratory methods? And if

so, what strategies can be used to mitigate this bias?

a. For example, are there approaches to mitigate a potential

strain selection bias introduced by the laboratory method?

b. If needed, what type of research could be conducted to

ensure performance characteristics of current laboratory

methods (e.g., enrichment, incubation, prescreening) do

not result in a biased serotype detection?

Summary of Question 7 Response

Laboratory methods used for detection, enumeration, and isolation

can be biased toward specific Salmonella strains depending on media

type, temperature, and incubation time. While the intent of preenrichment is to support growth of all Salmonella equally, differences in

growth characteristics of Salmonella and background microbiota may

result in a greater proportion of strains that are more robust. Further

bias is introduced if an insufficient number of colonies are chosen

for identification, which results in nondetected serotypes within a

mixed culture. There is no single procedure to mitigate bias. A single

media type or no enrichment mitigates bias by allowing a wider variety of Salmonella to be recovered that may be affected by selective

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components. On the other hand, multiple selective enrichments or

selective plating media often lead to the recovery of a narrower variety

of Salmonella strains and serotypes. At the time of this publication,

detection and quantification methods are under development that,

when successful, will offer the possibility of simultaneous detection

and quantification of multiple serotypes in the same sample.

Culture‐based quantitative methods, as well as enrichments carried

out prior to molecular assays, provide an estimate of the number of

organisms in a food, relying on the medium employed and the time

and temperature of incubation (Petran et al., 2015). This is also the

case for qualitative methods for determining presence or absence. Both

testing approaches depend upon the conditions for growth and

whether they are optimal for the target organism or group of organisms. Failure of the target organism or group of organisms to be

detected (or enumerated) can be attributed to intrinsic or extrinsic factors (e.g., time, temperature, media) influencing growth. For the purposes of this response, the focus will remain on Salmonella and strain

selection bias.

It is well‐known that sampling, method choice, and enrichment

practices have inherent biases due to a variety of reasons. These biases

impact the detection of Salmonella and the selective identification of

serotypes. Some examples include:

• Whole carcass rinse preenrichment released more Salmonella cells

than standard rinse aliquot preenrichment. However, false negatives were observed at times and the approach failed to detect all

serotypes present; nonetheless, it was found to be the best method

to determine true prevalence by Cox et al. (2019). Preenrichment

bias impacts all serovars, although it is possible that different serovars are impacted to different degrees.

• Nonselective media recipe may affect the detection of Salmonella.

Williams et al. (Williams et al., 2022) reported that the proportion of chicken carcass samples that tested positive for Salmonella

increased from approximately 0.02 to almost 0.06 when the rinse

changed from BPW to BPW with a neutralizing agent. This was

not the case for detection of Salmonella in chicken parts though.

Media formulation impacts all serovars. Although it is possible

that different serovars are impacted to different degrees depending on their individual growth characteristics, such as generation

time under otherwise ideal conditions, utilization of nutrients,

and ability to equally compete with other microbes in the

enrichment.

• Selective media may affect the growth of Salmonella. It was

reported that S. Enteritidis is selected in Tetrathionate (TT) broth

and S. Schwarzengrund is selected in Rappaport‐Vassiliadis (RV)

broth (Cox et al., 2019). The shift in serotypes throughout the

enrichment time limits the ability to identify Salmonella by culture

methods. The use of multiple media recipes (i.e., selective, nonselective broths and agar plates) mitigates the bias by accounting

for variation in sensitivity to selective ingredients and atypical biochemical profiles that affect growth on the media.

• Approaches for confirming presumptive Salmonella may only

require sampling 1 typical colony from a selective plate, to be confirmed (USDA Food Safety and Inspection Service (FSIS), 2022a).

Colonies from other selective plates may not be selected if the initial colony confirms as Salmonella. Even when a method requires up

to 12 colonies to be confirmed across three selective plates representing two selective enrichments (USDA Food Safety and

Inspection Service (FSIS), 2022a), routine laboratory practice

may only identify 1–2 colonies. It only requires one isolate to confirm and report a sample as positive for Salmonella saving on labor

and supplies (Cox et al., 2019; O'Bryan et al., 2022).

• The shift in serotypes throughout the enrichment time limits the

ability to identify Salmonella serovars by culture‐based approaches.

This shift over time may be due to different growth kinetics of different serovars in selective media, or possibly due to competitive

inhibition. Cox et al. (2019) using clustered regularly interspaced

short palindromic repeats (CRISPR) analyses to differentiate individual serovars demonstrated that the Salmonella serovar profile

shifted when original isolates from poultry carcasses were compared with the serovar profile after enrichment.

• This last example of CRISPR demonstrates one approach to mitigating bias, although this approach is not readily available for routine

testing in laboratories.

• There are many methods currently and historically available that

can differentiate serotypes that are commonly associated with disease. Example platforms include PCR‐based approaches (e.g., TaqMan, MLST), WGS, and classic serotyping. However, serotypes

are typically identified after selective enrichment for Salmonella,

which introduces selection bias, as discussed above (USDA Food

Safety and Inspection Service (FSIS), 2022a).

• Bacterial cells, including Salmonella, could enter a distinct state

called the viable but nonculturable (VBNC) state, which is potentially able to be resuscitated. This could impact the accurate detection for Salmonella in the VBNC state, hindering enumeration and

underestimation of the viable pathogen (Reissbrodt et al., 2000)

A current study characterizing Salmonella is being conducted at

USDA Agriculture Research Service (ARS) that will investigate a novel

molecular assay for the detection of select Salmonella serovars in raw

meat enrichments that would identify Highly Pathogenic Salmonella

(HPS) preventing the bias of serotyping from an isolate [(Harhay,

2020) and https://www.ars.usda.gov/research/project/?accnNo=

437924)]. The work has potential to be applied to poultry samples

for culture isolation using Salmonella‐specific immunomagnetic separation (IMS) to determine prevalence, direct plating enumeration of

Salmonella present within an enrichment to examine the limit of detection, the HPS molecular detection assay, and the Neogen (legacy 3 M)

Salmonella Molecular detection assay for comparison to the FSIS

approved molecular detection method. This study will also characterize all Salmonella isolated for determination of serotype and antibiotic

resistance (https://www.ars.usda.gov/research/project/?accnNo=

437924), which is an example of how both identification and detection can be achieved.

Methods for qualitative detection of multiple targets are commercially available (Appendix B Table B3), but at the time of adoption

of this report not all have completed AOAC approval process and

refinement of applications. FSIS list five AOAC‐validated rapid tests

for Salmonella serotypes or serogroup (iQ‐Check S. Enteritidis, GENE‐

UP S. Enteritidis & S. Typhimurium (SEST), RapidCheck SELECT Salmonella Enteritidis Test System & RapidChek CONFIRM Salmonella

Enteritidis Immunomagnetic Separation (IMS) Kit, Thermo Scientific

RapidFinder Salmonella species, Typhimurium and Enteritidis Multiplex PCR Kit, Reveal 2.0 Group D1 Salmonella) (USDA Food Safety

and Inspection Service (FSIS), 2022b). These methods have been validated for raw poultry product, and environmental samples (e.g., raw

chicken breast, chicken nuggets, boot and drag swabs, stainless steel,

shell eggs, chicken carcass rinsates, poultry feed, etc.) and could be

employed as a screen to determine which samples do not contain the

target serotype (USDA Food Safety and Inspection Service (FSIS),

2022b).

As serotypes of concern change over time, multiplex PCR assays can

target specific serotypes and others are expected to be developed. For

example, ANSES (the French Agency for Food, Environmental and

Occupational Health & Safety) has recently developed and validated

a high‐throughput qPCR assay able to identify 40 Salmonella serotypes,

and distinguish different genomic lineages and polyphyletic profiles

(Cadel‐Six et al., 2022). This PCR platform can be used either on complex matrices (e.g., foods, carcasses washes, etc.) for rapid screening,

or with pure isolates as a confirmation step (personal communication,

P. Fach, ANSES). However, at the time of this writing, it is not yet commercially available.

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After detection and isolation of Salmonella, commercial Salmonella

serotype identification methods can be utilized. Two include:

• The Neogen NeoSeek Salmonella serotyping is a technology that utilizes a targeted amplicon sequencing approach to identify the serotype(s) of Salmonella present in a sample. Samples may be

submitted as isolates or enrichment broths. If a broth is submitted,

up to three Salmonella serotypes can be identified if present. Briefly,

sample processing involves extraction and purification of DNA,

amplification of targets using multiplex PCR, and sequencing on

an Illumina MiSeq. Serotypes are called using proprietary software

to analyze sequencing results, including MLST (Multilocus

sequence typing) targets. This service is not AOAC validated.

(https://www.neogen.com/categories/bacterial-sequencing/neoseek-salmonella-serotyping/?recommendationId=

2128188658911; Accessed October 29, 2022).

• Check & Trace Salmonella (CTS) is a rapid genetic test based on a

microarray platform to identify Salmonella serotypes (Diep et al.,

2019). Each position on the microarray represents a specific DNA

marker associated with a unique Salmonella target sequence. Spots

only become visible if the DNA markers exactly match the corresponding DNA sequences of the Salmonella isolate. The combination of present and absent spots yields a pattern. The database

includes top serotypes from outbreaks, although is limited and is

periodically updated. This method is AOAC Performance Tested

Method (PTM) validated (AOAC International, 2022b)

Research is needed to address the items listed above to mitigate

bias in Salmonella detection and specifically changes in the serotypes

of concern due to virulence. A list of select research needs is reported

in Question 9.

Q8: Role of whole genome sequencing in developing microbial

criteria

How should pathogen characteristics derived from whole genome

sequencing (e.g., serotype, virulence, antimicrobial resistance) be

considered in the development of microbiological criteria?

There is insufficient information on the presence and absence of

genes correlated with pathogenicity and virulence to make recommendations, at the time of writing this document.

Q8.A Recent WGS‐based advancements in Salmonella

characterization

Numerous studies support that Whole Genome Sequencing (WGS)

is a critical element in establishing microbiological criteria for Salmonella. WGS can define serotype, predict antimicrobial resistance

profiles, and offer considerable insight into virulence capacity of an

isolate as thoroughly reviewed in Cheng et al., 2019 (Cheng et al.,

2019). The greatest value of WGS is at the convergence of serotype,

epidemiological, and phenotypic data to differentiate Salmonella with

high public health relevance from Salmonella of limited public health

relevance. Some serotypes in the U.S. (e.g., S. Cerro and S. Kentucky)

(Centers for Disease Control and Prevention (CDC), 2018) are much

less likely to cause severe, invasive human disease than others (e.g.,

S. Choleraesuis, S. Dublin (Jones et al., 2008)). However, S. Kentucky

has also been identified as a highly invasive strain, notably in Africa

(Igomu, 2020).

WGS can be used to differentiate hypo‐ and hypervirulent serovars

and clades by leveraging data beyond serotyping. The presence or

absence of virulence genes can and has been defined by WGS. Thus,

WGS can contribute to identifying and weighting relevant strain characteristics, including virulence, which inform best targets for control

resulting in the greatest public health benefit. WGS studies found that

S. Cerro isolates have a premature stop‐codon in sopA (Kovac et al.,

2017; Rodriguez‐Rivera et al., 2014), which contributes to host cell

entry (Raffatellu et al., 2005). S. Kentucky is the most commonly isolated serovar from broiler chickens in the United States (USDA Food

Safety and Inspection Service (FSIS), 2014c), yet constitutes 0.1% of

reported human salmonellosis cases (Centers for Disease Control and

Prevention (CDC), 2018). These S. Kentucky isolates lack certain virulence genes (e.g., grvA, sseI, sopE, and sodCI (Beutlich et al., 2011;

Cheng et al., 2019) and other metabolism‐associated genes (Tasmin

et al., 2017) that may reduce its ability to cause severe disease.

Recent phylogenetic analyses have determined that commonly isolated serovars (viz., Newport, Montevideo, Kentucky, Paratyphi B,

Derby, Nchanga, Cerro, Bareilly, Stanleyville, Dusseldorf, Livingstone,

etc.) are polyphyletic (Cao et al., 2013; den Bakker et al., 2011;

Sévellec et al., 2018; Timme et al., 2013; Worley et al., 2018;

Yoshida et al., 2016). This underscores that serotype alone cannot

define the relative virulence potential of a strain. There are also important clinical implications of polyphyletic Salmonella serovars whereas

clades of the same serovar differ in the virulence factors that they

encode (Miller and Wiedmann, 2016). One example of this is Salmonella Kentucky where two distinct clades have been defined by

WGS. (Chen et al., 2020).

Q8.B. Current limits to WG sequencing to characterize pathogen virulence

There are several limitations to WGS to characterize pathogen virulence. First, there is a need to expand the public databases (e.g.,

NCBI) of sequenced isolates associated with illness to determine if

eliminating a given serotype by intervention strategies implemented

by the poultry industry will simply give rise to another serotype.

Research on polyphyletic serovars will offer more insight.

Numerous virulence factors have been identified that contribute to

Salmonella pathogenicity (Daigle, 2008; Sabbagh et al., 2010). The

interactions of these factors and the resulting strain virulence and

pathogenicity have not been completely elucidated, but single genes

and pathogenicity islands have been identified as key virulence traits.

However, there is currently no agreed‐upon definition of virulence

genes presence/absence profile that can reliably predict the severity

of disease.

Host‐interactions (e.g., environmental conditions) further complicate the ability to predict virulence. For example, in a study examining

the pathogenicity islands (SPI) from Salmonella isolated from different

environments that included warm‐blooded mammals (i.e., porcine,

bovine, equine, and avian), environment, and human clinical isolates,

it was observed that SPI‐1, SPI‐3, and SPI‐5 had genetic variation

across the 13 Salmonella serovars tested, while SPI‐2 and SPI‐4 were

well conserved across the same serovars examined.

There is emerging potential for WGS data to be incorporated into

quantitative microbial risk assessments (QMRA), but it is currently

limited by a lack of standardization in assembly, processing, and integration of WGS and metadata (Chen et al., 2020). The application of

WGS to further inform QMRAs and selectively assess serovars of concern is further discussed in Question 1.

Nevertheless, WGS subtyping adds an additional level of discriminatory power that can be used to aid in epidemiologic investigation

traceback studies. With continued WGS, a better understanding of

important virulence traits and their association with foodborne illness

outbreaks may be possible.

Q9: Research needs

What research is needed to support FSIS’ new Salmonella strategy

in terms of setting microbiological criteria?

Questions 1 and 4 data gaps

• Update estimates of the health burden of salmonellosis and other

foodborne diseases at regular intervals

• Analysis of CEA FoodNet data linked with PulseNet to provide the

next generation of attribution.

• With appropriate anonymization, add sequences of Salmonella isolates from live animal sampling from poultry labs, USDA Animal

Plant Health Inspection Service (APHIS) field studies, and the

National Veterinary Services Laboratories (NVSL) to national databases, as well as from sampling of feed and feedstuffs.

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30
• Conduct a risk assessment that determines the public health

impacts of different risk‐based Salmonella control strategies to

enhance our ability to select and implement risk‐based Salmonella

control strategies. This will require substantially more data to

inform which strategy will have the largest public health impact.

For example, it remains unknown whether a reduction in Salmonella levels in food or a more targeted reduction in specific serotypes or subtypes would result in fewer cases of salmonellosis.

• Complete the Salmonella quantitative risk assessments for chicken

and poultry to answer risk management questions. https://www.

fsis.usda.gov/inspection/inspection-programs/inspection-poultryproducts/reducing-Salmonella-poultry/Salmonella-1

• Assess the role of antimicrobial use for Salmonella control at preharvest (and its change as a result of implementing a MC and associated Salmonella control measures) in the burden of poultry‐

attributable human salmonellosis, and, more widely, to the spread

of antimicrobial‐resistant Salmonella and genes.

• Assess health impacts associated with long‐term health sequelae of

Salmonella infection, and account for such burden and costs in a

cost‐benefit analysis.

• Collect quantitative information needed for QMRAs, including Salmonella prevalence, levels, and subtypes of concern on poultry

within a product lot, between product lots, product form (ground

vs. parts, etc.), and between slaughter and processing

establishments.

• Refine attribution models that differentiate illness associated with

different products, specifically with comminuted, parts, or whole

carcasses to focus resources for intervention on highest risk poultry

products.

• Identification of those Salmonella serotypes or subtypes that pose

the greatest concern (e.g., most virulence) and their presence in

specific poultry products.

• Dose‐response data and fitted relationships for Salmonella serotypes

of concern.

• Evaluate the frequency of consumer handling and type of preparation practices for specific types of poultry products that contribute

to the potential for cross‐contamination with other foods or

undercooking.

• Conduct studies on transfer coefficients for transfer of the amount

of Salmonella to other foods and food contact surfaces during consumer preparation of poultry products as part of meals.

• Study human behavior as a route of transmission during normal

consumer preparation of poultry products.

• Develop a public awareness campaign to educate consumers on

proper handling, preparation, cooking, and storage practices for

carcasses, parts, and ground poultry products.

• Increase the number of Salmonella isolates sequenced from produce

and other nonmeat/poultry sources to strengthen the IFSAC Source

attribution model.

• Expand NARMS or other sampling programs to include animal

feeds and feed ingredients, as well as poultry‐based pet foods.

• Examine the results from 2020 and 2021, when the COVID19 pandemic disrupted many aspects of the food supply. If there were food

source‐specific effects, then future estimations may need to consider excluding those years from more general models of

attribution.

• When metagenomic models are available, assess the presence of

even low numbers of relevant serotypes in samples from farm or

slaughterhouses.

Question 2 data gaps

• Continue research to determine if non‐Salmonella quality indicator

organism sampling could be implemented, that would provide

guidance to companies on which houses have a higher probability

of Salmonella contamination.

• Develop a cost‐benefit assessment for the U.S., accounting for both

intervention implementation costs and cost of illness, for various

degrees of standard stringency including qualitative nondetection

of Salmonella in flocks.

• Identify industry practices that alter the presence and variability of

Salmonella serovars between lots of product.

• Eliminate conditions in house that encourage Salmonella.

• Develop cultures for competitive exclusion that are capable of controlling more than a single serotype.

Question 3 data gaps

• Conduct research to determine if qualitative and/or quantitative

testing and processing scheduling would significantly affect the

number of Salmonella‐positive processed carcasses.

• Increase the FSIS sampling frequency and sequencing of Salmonella

isolates from different types of poultry, including comminuted

poultry products, mechanically separated meat, breaded raw

stuffed chicken, and other highly processed products.

• Expand training and knowledge for plant personnel in statistical

process control and Continuous Improvement methodologies.

• Continue research to determine if qualitative and/or quantitative

testing and resulting changes in processing scheduling would significantly affect the number of positive processed Salmonella

carcasses.

• Develop predictive microbiology models (i.e., growth, survival, and

thermal inactivation) and/or biomapping studies, characterizing

the responses of Salmonella prevalence, concentration, and subtypes to operation/handling conditions during processing and postprocessing steps.

• Collect evidence on public health impacts of preharvest vs postharvest MC (or an integrated combination). (Q2 and Q3)

• Collect evidence on incentives and BCA on preharvest vs. postharvest MC (or an integrated combination). (Q2 and Q3)

Question 5 data gaps:

• Provide incentive for industry to deposit data (anonymized, nonpunitive) on the presence or concentration of indicator organisms

(such as APC or others), and the presence/prevalence or concentration of Salmonella (possibly by serotype), in the finished product

and/or at critical processing stages in the establishment. Use

pooled data to develop a comprehensive analysis of quantitative

relationship between indicators and Salmonella.

• Develop trend analyses and root cause analyses quantitatively linking indicator levels (or other process control metrics, such as variability, trends, or changes between processing stages) to the

presence or concentration of Salmonella in the finished product

and/or at critical processing stages in the establishment.

Question 6 data gaps

• Develop detection and enumeration methods that are based on virulence rather than serotypes that are changing over time.

• Develop enumeration methods that are more laboratory friendly,

considering operational workflow.

• Develop a probabilistic evaluation of the accuracy of qPCR methods

at the concentrations found in poultry and poultry products.

• Estimates of sensitivity and specificity of quantitative Salmonella

assays.

Question 7 data gaps

• Develop and evaluate methods that reduce or eliminate the preenrichment step of methods. Investigate the power of whole genome

sequencing to accomplishment this.

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31
• Evaluate media recipes, specifically with relevant serotypes from

poultry and competition, within the matrices of concerns.

• Determine the significance of atypical phenotypical and biochemical strains of Salmonella that routine laboratories may be down‐

selecting during analyses.

• Evaluate enrichment conditions impacting the growth of serovars

of concern (e.g., time, temperature, media formulation, detection).

• Develop metrics to evaluate and mitigate bias in detection, isolation, and identification methods.

Declaration of competing interest

The authors declare that they have no known competing financial

interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledments

NACMCF 2021‐2023 Subcommittee members: Dr. Joseph (Stan)

Bailey (bioMérieux), Dr. James Dickson (Iowa State University), Dr.

Kathleen Glass (University of Wisconsin‐Madison), Ms. Janell Kause

(U.S. Department of Agriculture Food Safety and Inspection Service),

Dr. Elisabetta Lambertini (Global Alliance for Improved Nutrition),

Dr. Wendy McMahon (Silliker, Inc.), Dr. Haley F. Oliver (Purdue

University), Dr. Omar Oyarzabal (EAS Consulting Group, LLC), Dr.

Tanya Roberts (Center for Foodborne Illness Research and Prevention), Dr. Scott Stillwell (Stillwell Consultative Services, LLC), Dr.

Robert Tauxe (Centers for Disease Control and Prevention), Dr. Valentina Trinetta (Kansas State University), Dr. Bing Wang (University of

Nebraska‐Lincoln), Dr. Randy W. Worobo (Cornell University), Dr.

Teshome Yehualaeshet (Tuskegee University), and Dr. Francisco Zagmutt (EpiX Analytics, LLC).

The NACMCF acknowledges the following individuals for serving

as subject matter experts to this subcommittee and providing invaluable scientific information: Marc Allard (Food and Drug Administration Center for Food Safety & Applied Nutrition), Rebecca Bell (Food

and Drug Administration Center for Food Safety & Applied Nutrition),

Beau Bruce (Centers for Disease Control and Prevention), Eric Ebel (U.

S. Department of Agriculture Food Safety and Inspection Service), Patti

Fields (Centers for Disease Control and Prevention), Fenske Gavin

(Epix Analytics), Neal Golden (U.S. Department of Agriculture Food

Safety and Inspection Service), Patricia Griffin (Centers for Disease

Control and Prevention), Dayna Harhay (U.S. Department of Agriculture Agricultural Research Service), Maria Hoffman (Food and Drug

Administration Center for Food Safety & Applied Nutrition), Art Liang

(Centers for Disease Control and Prevention), Scott Malcolm (U.S.

Department of Agriculture Food Safety and Inspection Service), Nikki

Shariat (University of Georgia), and Mike Williams (U.S. Department

of Agriculture Food Safety and Inspection Service).

NACMCF acknowledges the contribution of Dr. Susan R. Hammons

(U.S. Department of Agriculture Food Safety and Inspection Service)

for critical review and Shantel Williams and Kristal Southern for formatting this report for publication. Mention of trade names or commercial products in this publication is solely for the purpose of

providing specific information and does not imply recommendation

or endorsement by the U.S. Department of Agriculture and other participating agencies.

Appendix A. Glossary

Term Definition

Colony‐forming Units

(CFUs)

CFUs is an estimation of the number of

culturable microbial cells in a sample.

CFUs are typically expressed as a rate

per unit of volume or mass such as

CFU/g or CFU/mL.

Fit‐for‐purpose Demonstration (validation) that a

specific method delivers expected

results in a specific matrix or

conditions, in relation to the purpose

of the information/results.

Infectivity The ability of an organism to cause

infection. In risk assessments, this is

incorporated as the probability of

human infection following oral

exposure to any amount of Salmonella.

This probability can vary depending

on pathogen factors such as the

serovar or subtype, and host

susceptibility.

Limit of Detection (LoD) LoD is the lowest concentration of

microbial cells (CFU per g or per mL)

that can be reliably detected using a

standard test.

Limit of Quantification

(LoQ)

LoQ is the lowest concentration of

microbial cells (CFU per g or per mL)

that can be quantified based on

predefined goals for of confidence in

the estimation. LoQ is typically higher

than the LoD as estimating a

numerical value requires more

information than requiring a positive/

negative result.

Key Performance

Indicators (KPI)

Critical (key) indicators of progress

toward an intended result or goal. Per

FSIS: “The KPI will measure the

percent reduction in raw poultry

samples contaminated with Salmonella

serotypes commonly associated with

human illness as a percentage of all

samples analyzed for all types of

Salmonella contamination. The

samples considered in the KPI are

those collected from products subject

to a performance standard (i.e., from

chicken parts, chicken and turkey

carcasses, and comminuted chicken

and turkey).”(USDA Food Safety and

Inspection Service (FSIS), 2022d)

Mean Time Between

Failure (MTBF)

Mean period of time between two

“failures”, (i.e., the inverse of the

frequency of failure, where failure is

defined as an episode of departure

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32
Glossary (continued)

Term Definition

from a set target or standard (which

may or may not have regulatory

relevance)).

Microbiological criteria

(MC)

Also called performance standards,

MC defines the acceptability of a

product based on presence (absence)

or number of microorganisms per unit

of the product. It can also be based on

a biomarker of the microorganism,

such as toxins, metabolites, or genetic

components. The product unit can be

defined as a lot, volume, mass, or area.

Omics Characterization and quantification of

pools of biological molecules that

translate into the structure, function,

and dynamics of an organism or

organisms. Includes disciplines such

as genomics, proteomics,

metabolomics, metagenomics,

phenomics and transcriptomics.

Pathogenicity The ability of an organism to cause

disease. In risk assessments, this is

usually modeled as the probability of

clinical disease given infection. This

probability can vary depending on

pathogen factors such as the serovar

or subtype, and host susceptibility.

Performance standards The establishment of system

standards, targets, and goals to

improve public health practices.

Performance standards set the results

to be achieved but not the specific

means used to achieve those results.

Performance Test

Methods (PTM)

An AOAC program that provides a

third‐party review and certification

for proprietary test method

performance.

Poultry Domestic food producing birds,

specifically chickens (Gallus gallus

domesticus) and turkey (Meleagris

gallopavo domesticus).

Quantitative microbial

risk assessment

(QMRA)

Quantitative microbial risk assessment

is a mathematical modeling approach

used to estimate the risk of infection

and/or illness when a population is

exposed to microorganisms from a

variety of sources, in this case in

foods. QMRA estimates can be used to

predict the potential reduction

(increase) in foodborne illnesses

resulting from the implementation of

strategies to mitigate foodborne

pathogens in foods.

Serotype See serovar definition.

Serovar The term serovar is used to distinguish

groups within a Salmonella species

that share distinctive surface

structures, namely the O surface

antigen and the H antigen that is part

of the flagella (7). Consequently,

serovars represent phenotypical

Glossary (continued)

Term Definition

differences between individual

bacteria belonging to a Salmonella

species, and do not necessarily

represent evolutionary differences as

elucidated in the Salmonella genome.

Note that in this report, the term

serovar and serotype are used

interchangeably.

Subtype Salmonella subtype is a term used to

distinguish differences within a

serovar (serotype), such as defined

using whole genome sequencing

(WGS), pulsed‐field gel

electrophoresis (PFGE) or multi‐locus

sequence typing (MLST). Subtyping

provides a more detailed

characterization of heterogeneity

between Salmonella bacteria than

serovar groupings as it is based on

genetic differences.

Test sensitivity The probability that a test performed

on a contaminated sample will yield a

positive result. For qualitative

(positive/negative) test results. This

probability is affected by the limit of

detection of the test; whereas, for

quantitative test results, the

probability is affected by the limit of

quantification.

Viable but not culturable

bacteria (VNCB)

Bacteria that are in the state of

exceptionally low metabolic activity

and do not divide but are alive and

have the ability to become culturable

once resuscitated.

Virulence The ability of an organism to cause

severe illness. In risk assessments, this

is usually modeled as the probability

of severe illness given infection.

Virulence in bacteria is mediated by

genes often called “virulence factors.”

Both pathogen and host factors

contribute to whether disease occurs

and to disease severity.

Appendix B. Supporting resources for questions 1–7

(See Figs. B1–B7)

Appendix B Q2‐Case Study: Sweden’s Salmonella Control Program and comparison with U.S. production practices

The Swedish Board of Agriculture (SBA) began a Salmonella control

program for poultry in 1970 whereby broiler producers participating

in the program were required to meet SBA’s requirements and agree

to governmental monitoring of all aspects of broiler production. All

flocks that tested positive for Salmonella are destroyed and houses

are remediated. For those broiler producers participating in SBA’s initial program, the government paid the costs related to destroying the

infected flock(s). The concept of control in the Sweden program is that

if broilers are never exposed to Salmonella, then they cannot become

colonized and subsequently they will not be contaminated after processing. Therefore, no chemical treatments are used in the processing

B. Liu Journal of Food Protection 87 (2024) 100168

33
plant due to excluding Salmonella positive birds from entering the

plant.

In 1984, Sweden made Salmonella testing compulsory 10–14 days

before slaughter, using a boot‐sock method. The government also

stopped payments to broiler producers for infected flocks, with private

insurers paying 90% of the losses if the producer was a participant in

Sweden’s SBA Salmonella control program. As a result of the on‐farm

Salmonella control program, the European Food Safety Authority

reported in 2010 that less than 0.1% of meat samples collected at

the slaughterhouse or cutting plant were positive for Salmonella

(European Food Safety Authority (EFSA), 2012, National Veterinary

Institute (SVA), 2021). The latest report also shows very low prevalence (0.27% in chicken meat at production stage; 0% in turkey meat

at production stage) (National Veterinary Institute (SVA), 2020). It

should be noted, in contrast with other programs, that vaccines are

not used in Sweden. Beyond controls used at the farm level, Sweden

does not allow immersion cooling in order to reduce the potential

for cross contamination at the processing level.

Aside from the mandatory aspect of testing and destroying positive

flocks, other aspects of Sweden’s control program for Salmonella free

broiler production are used in the U.S. to reduce Salmonella in poultry,

poultry feed and the poultry environment, but on a voluntary basis.

The programs do not specifically address Campylobacter, which

remains a predominant foodborne bacterial pathogen in the EU and

the U.S.

The Sweden case study is also useful to illustrate not only options

and potential impact of MC and multiple‐barrier Salmonella control

measures, but also adopting a food system approach, the crucial role

of a strong enabling environment (e.g., insurance, government program, etc.) to support a feasible and sustainable implementation.

Although the sizes of individual houses are similar between the U.S.

and Sweden, the number of farms and birds are significantly greater

in the U.S. (Table B1). This results in unique challenges in applying

the Swedish model to U.S. operations in terms of sustainability during

any implementation step. However, advocates of the program suggest

that the U.S. also has a strong enabling environment that could be

leveraged.

Sweden’s Five Principles of Salmonella‐Free Broiler Production

(Lindblad, 2007)

1) Start with Salmonella‐free day‐old chickens

2) Rear chicks in a Salmonella‐free environment

3) Provide feed and water that is free from Salmonella

4) Regularly monitor and test for Salmonella in the whole production chain

5) Take immediate action whenever Salmonella is detected

To implement these principles, Sweden adopted the following practices and regulations (Roberts and Lindblad, 2018):

• All imported birds are quarantined until it is determined that they

are Salmonella free.

• All feed is tested for Salmonella and has strict regulations regarding

fertilizing, harvesting, transportation and storage.

• All breeder and broiler houses must be free of rodents and wild

birds, and the houses must have an “ante room” for personal

hygiene control ‐‐ some ante rooms include showers.

• Most of Sweden broiler houses have cement floors, not dirt floors,

to improve cleaning and sanitation.

• When a flock is moved out of a house, all the litter must be removed

within 24 hours. The broiler house must then be cleaned and sanitized and must sit idle for a minimum of 2 weeks.

• If Salmonella is found, the flock is destroyed, and all the litter is

removed and composted for at least 6 months to prevent contamination of the surrounding environment(s).

• Twice a year, a state veterinarian visits each facility and takes environmental samples to test for Salmonella. Broiler houses and feed

mills must be Salmonella free to operate.

• Sweden also requires “neck skin” samples to be collected twice a

day every day (after carcass air chilling) in broiler slaughterhouses.

• When Salmonella is detected, a veterinarian is appointed by the SBA

to investigate.

• Whenever Salmonella is verified, regardless of prevalence or serotype, the flock is destroyed.

Benefits/Cost Analysis (BCA). In Sweden, a BCA compared Sweden’s Salmonella regulations to the less‐rigorous controls in Denmark

and the Netherlands. Sundstrom et al. (Sundstrom et al., 2014) found

the expected increase in human salmonellosis cases and the associated

increase in reactive arthritis and irritable bowel syndrome (IBS) to be

more costly than any reduction in Salmonella‐control costs in those

countries. Evaluating the costs and benefits of Salmonella‐free broilers

in the United States involves tallying up the externalities of broiler production that is currently borne by the U.S. public. Externalities are

defined as costs such as the acute and long‐term health costs, productivity losses, and pain and suffering due to illness from eating or working with Salmonella‐contaminated foods. Scharff (Scharff, 2020)

estimates that salmonellosis costs for the U.S. are $5–16 billion annually, with poultry estimated to be responsible for 23% of salmonellosis

cases (Painter et al., 2013).

In addition to medical costs and loss of life, Salmonella in poultry

contributes to environmental losses and potential of spread of antibiotic resistant Salmonella strains. For example, the nutrient‐rich runoff

from broiler facilities has caused algae blooms and “dead zones” in

U.S. bays and oceans – the Chesapeake Bay contamination is extremely

notable in causing losses to both the fishing and crabbing industries

(US Environmental Protection Agency (EPA), 2016). Another concern

is the spread of multi‐drug resistant bacteria. The impact of adding

antibiotics to animal feed can be significant. For example,

Armbruster and Roberts, (2018) suggest that 60–80% of tetracycline

used in food animals is excreted in feces or urine, ending in animal

waste, which, in turn, can be applied as a farm fertilizer and lead to

the development of more antibiotic resistant bacterial strains

(Armbruster and Roberts, 2018).

(See Table B1, Table B2, Table B3).

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Table B1

Comparison of broiler production in Sweden and the United States

Feature Sweden (Lindblad, 2007) United States (MacDonald, 2014) CommentsGenetics Ross and Cobb 99% (fast)

4.3 lbs. in 33 daysFeed Conversion = 1.67 grow outs/year

Fast growing birds6.13 lbs. in 49.5 daysFeed Conversion = 1.95.5 grow outs/yr

Sweden harvests birds ea

Grow out house size Aver. Size 15,780 sq. ft.

Aver. 4.4 houses/farm100 broiler growers

Aver. size 18,618 sq. ft.Aver. 4 houses/farm233,770 poultry farms including broilers, eggs, andturkeys (National Agriculture Statistics Service, 2015)

U.S. slaughters 162 millioERS estimates 33 billion pin 2016 (Haley, 2017)

Housing and

cleanout between flocks

Enclosed with sanitary perimeters, as hygiene barrier, with change offootwear and coveralls100% total cleanout and sanitation between flocks

Enclosed, fan ventilation77% not cleaned out and sanitized between flocks,56% no change of clothing, 17 days between flocks

U.S. has no Salmonella co

regulations between flock

Litter disposal All litter removed between flocks and sold as fertilizer to farmers. But

if test Salmonella positive, on-farm composting of litter.

33% of litter sold for use by other farms in 2011 U.S. has no Salmonella re

U.S. contamination of soi(US Environmental Prote

Feed Farm-grown wheat. Purchased feed must be Salmonella-negative

Fishmeal is not allowed in feed.

Provided by contractor-mostly corn and soybeans.No regulations prohibiting Salmonella in feed.

U.S. has no regulations oSweden allows no fishme

Antibiotic use in feed Not allowed. Antibiotics important in human health being phased

out of animal feed by FDA.

U.S. antibiotics used on-fpathogens.

Vaccination Not used Vaccinations against select serogroups used by more

than 90% of broiler houses (Mountainspring andBurleson, 2018)

Vaccine development lagfound in U.S. broiler pop

On-farm Salmonella tests Negative Salmonella test in grandparent breeding stock for broilers,

hatchery, breeders, feed. Flock tested before slaughter: must beSalmonella-negative to get into slaughterhouse

No on-farm regulations for Salmonella control. U.S. has no on-farm regu

tests required but conducOn-farm control highest lCharitable Trusts and the 

Slaughterhouse Salmonella

tests

Neck skin tests daily for Salmonella, gas kill, blast air chill (noimmersion chill), no chlorine or other chemicals allowed. If test isSalmonella-positive, flock is depopulated.

Performance standard allows 10% Salmonella rate inwhole carcass rinse test (USDA Food Safety andInspection Service (FSIS), 2019a).

U.S. does not test daily; alSafety and Inspection SerChlorine, water absorptioflavor & tenderness (Smahttps://www.smartchicke

35
Table B2

Summary of Approved Methods for Molecular Quantitation of Salmonella in Poultry Products (see individual suppler websites for updates)

bioMérieux Hygiena

Website for updated information https://www.biomerieux-industry.com/en-us/products/

gene-up-quant-Salmonella

https://www.hygiena.com/food-safety-solutions/pathogen-detection/bax-

Product Name GENE-UP® QUANT Salmonella SalQuantTMTechnology/ platform qPCRNonenrichment concentration qPCR

Enrichment

Approvals AOAC Research Institute (RI) Performance Tested Method

(PTM) #061801GENE-UP QUANT Salmonella is an application of theEnviroPro Sal (PTM #061801) (AOAC International,2022a)

AOAC RI PTM #081201, (AOAC International, 2021)

Official Methods of Analysis submitted in October 2022 (AOAC Internatio

Applicable matrices and test portion sizes AOAC (AOAC International, 2022a)

Chicken carcass rinsates (40 mL) (USDA Food Safety andInspection Service (FSIS), 2021b)Raw ground turkey (100 g)Microtally (40 mL taken from 200 mL enrichment volume)Internal studies: boot swabs, poultry organs, intestinal tract(personal communication, bioMérieux)

Validated matrix extensions at AOAC in 2021 – comminuted chicken (322021)Validated matrix extensions at AOAC in 2022 – poultry rinsates (30 mL) Internal studies: boot swabs, dust swabs, feet swabs, cloacal swabs, poultrswabs, cecal tonsils, crop, lungs, liver, spleen, low-level rinsates, chicken raw chicken products

Time to result (TTR) TTR = <4h TTR = 7.5 – 9.5 hLOQ Limit of Quantification Claim (actual

validation study)

Nonenrichment: 10 CFU/g or mL (AOAC PTM study 99 CFU/g ormL).

1 CFU/mL 6h rinsate enrichment (AOAC PTM study results present 1 CFU/g 8h comminuted ground enrichment (AOAC PTM study res

Range of quantification 10 – 10,000,000 CFU/g or mL. 1–10,000 CFU/g or mL.Accuracy Within 0.5 log CFU when compared to MPN result Within 0.5 log CFU when compared to MPN result.References User guide available from vendor upon request

AOAC RI # 061,801 (AOAC International, 2022a).Poster Presentation (Mills et al., 2022).

User guide https://cdn.brandfolder.io/KA71VJV5/at/gxkwprpcrs9nAOAC RI #081201 (AOAC International, 2021)

Quantification Guidebook

Poster Presentation (Stephens et al., 2021; Weller et al., 2021)

Advantages Nonenrichment reduces bias by eliminating culture-based

enrichment, concentrating cells initially and provides faster timeto result.<4h same shift procedureUniversal workflow and data analysis algorithm

Proven compatibility with primary production samples (on farm,boot, internal work).

Universal workflow is utilized with RT Salmonella Assay that was AOin 2012 (Wallace et al. (2014)). https://cdn.brandfolder.io/KA71VJassay-salmonella-rt.pdf

Prevalence sample and quantitative sample come from same samplshorter enrichment time and keeps incubating for the prevalence saLarge dynamic range (1–10,000 CFU/g or mL) or 5 log range.Regulatory sample size validated.

Limitations Procedure is not the same as the standard qualitative method for

presence/absence.Sample prep method requires centrifugation.Data to show the capability distinguishing live versus dead cellsto be provided.

Individual curve per matrix requires validation when adding a new Culture based bias from the impact of natural microbiota and deter

36
Table B3

Examples1 of Methods in Development for Molecular Quantification of Salmonella (check individual supplier websites for updates on validation and approvals)

Bio-Rad Bio-Rad Hygiena Neogen (Legacy 3 M) Thermo Fisher

Website for updated

information

https://www.bio-rad.

com/en-us/category/

digital-pcr-foodapplications?ID=

PU4PWZRT8IG9

https://www.bio-rad.com/

en-us/product/iq-checkSalmonella-ii-pcr-detectionkit?ID=d23decd6-2349-

4e87-9087-3fe30ec6d3be

https://www.

hygiena.com/foodsafety-solutions/

pathogen-detection/

bax-system-pcr-assaysfor-salmonella/

https://www.3 m.com/

3M/en_US/p/d/

v000228752/ for

molecular detection

qualitative assay

https://www.

thermofisher.com/

order/catalog/product/

PT0100A

Product Name dd-Check Salmonellaa iQ-Check Salmonella SalLimitsa 3 MTM Molecular Detection

Assay 2- Salmonella

Quantificationa

SureCount Salmonella

Species, Typhimurium and

Enteritidis Multiplex PCR

Assaya

Technology/ platform ddPCR qPCR RT PCR threshold

testing (positive or

negative at 10 cells)

Loop-mediated isothermal

amplification (LAMP) and

bioluminescence, along

with cloud-based software

qPCR

Approvals Internal studies

supporting the use of

the method

Internal studies supporting

the use of the method

AOAC 081,904 (AOAC

International, 2022c)

Internal studies

supporting the use of

the method

In development for

quantification using the

qualitative molecular

detection assay kit platform

Internal studies supporting

and submission for approval

at AOAC in process as of

October 2022

Applicable matrices Internal work by

collaborating with

outside service

laboratory and poultry

producers:

Poultry rinsates

(30 mL)

Ground poultry

Microtally

Internal work by

collaborating with outside

service laboratory and

poultry producers:):

Ground turkey (325 g)

Poultry rinsate (30 mL)

Internal studies by

collaborating with

outside service

laboratory and poultry

producers:

Comminuted chicken

(325 g)

Turkey (325 g)

Poultry rinsates

(30 mL)

Internal data:

poultry rinse (30 mL)

raw poultry (325 g)

primary production (i.e.,

boot swabs/socks, others in

development)

5 matrices in the current study

for quantitation (data

submitted to AOAC in October

2022) (Hughes et al., 2022;

Leak et al., 2022)

fresh raw ground turkey

(325 g)

chicken piece rinse (30 mL)

30 mL poultry carcass rinse

Time to result (TTR) TTR = 5.5–6 h TTR = 4.5–5 h TTR = 4–8 h at LOD10 Total = 7.5–8 h nBPW

rinses at LOQ 1 CFU/mL

TTR = 8 h

LOQ Poultry Rinse 1 CFU/

mL

Table showing in Bio-Rad's

technical note (need

reference) based on strain/

serotype and matrix

10 CFU/g

LOD10 based on

threshold

4 h rinsate enrichment

6 h comminuted

ground enrichment

a

Validated at LOD1 for

6 h and 8 h,

respectively

LOQ may vary by matrix.

LOQ for poultry rinse:

1–10 CFU per original

30 mL rinse sample

The LOQs for finished goods:

1 CFU per 30 mL rinse from

chicken parts or carcasses;

1 CFU per 30 mL

homogenized ground meat

sample, including turkey,

pork, and beef.

Environmental 30 CFU per

30 mL

Range of quantification 1 – 1,000,000 CFU/mL 1 CFU/mL or 10 CFU/mL

limit

Threshold above

10 CFU/g (mL),

qualitative result

<10 = negative at

this LOD

>10 = positive

1–2000 CFU / 30 mL rinse Finished products: 1 to

10,000 CFU per sample

Accuracy Twofold difference

(personal

communication,

BioRad)

Technical note Not applicable due to

threshold reporting

Within 0.5 log, CFU (est.)

compared to MPN result

Within 1 log, 99.8%

agreement

Within 0.5 log 80%

agreement

References https://www.bio-rad.

com/en-us/category/

digital-pcr-foodapplications?ID=

PU4PWZRT8IG9

User guide upon request

Technical note and internal

research upon request

User guide

Quantification

Guidebook

Peer-reviewed

publications available

from Hygiena

Available at upon

commercialization

Instruction-For-Use

AOAC-PTM study

(Stephenson et al., 2022)

Advantages Nonenrichment

reduces bias by

eliminating culturebased enrichment and

partitioning of intact

bacterium

cells initially and

provides faster time to

result

Endpoint PCR when the

droplet lights up

(positive)

No enrichment

(addressing bias and

different strains)

Shortened time to

result in 6 h

Savings in time and

labor for creating std

Universal workflow with

same Salmonella assay

Extensive research with a

variety of strains addressing

growth rate bias

Sample sizes up to 375 g,

including regulatory sizes

Shortened time to results in

4.5 – 5 h, total

Same assay as qualitative

Universal workflow

with RT Salmonella

Assay

Shorter enrichment

time compared to

SalQuant (faster)

Regulatory sample size

validated

Universal workflow with

same Salmonella assay

Technology utilized is

recognized and utilized by

USDA in the MLG 4.12

Shorter enrichment time

compared to qualitative

assay

Can maintain enrichments

for prevalence

Calculations are

automatically provided

with the MDS Software

(planned version 2.8.0.0 or

later)

Simultaneous detection,

quantitation, and

differentiation of the three

target analytes, (i.e.,

Salmonella spp., S. Enteritidis,

S. Typhimurium)

Universal workflow with

same Salmonella assay with

nonproprietary media that

runs with the Applied

Biosystems QuantStudio 5,

maximizing multiplexing

Calculations are all

automatically managed with

the RapidFinder Analysis

Software (V2.0 or later)

5 log range of quantitation

LOQ at 1 CFU for finished

product samples

(continued on next page)

B. Liu Journal of Food Protection 87 (2024) 100168

37
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Table B3 (continued)

Bio-Rad Bio-Rad Hygiena Neogen (Legacy 3 M) Thermo Fisher

curve

Sample sizes up to

375 g

Validated with

nonproprietary media for all

matrices

Limitations Working on

improvement of the

throughput allowing

more samples to be

analyzed

Hands-on technician

time not optimized and

making some

improvements with the

filter and centrifuge

steps

Additional

instrumentation

needed as compared to

qPCR

Individual curve per matrix

requiring validation when

adding a new matrix

Impact of microbiota due to

enrichment to be

considered

Look at SalQuant list

and use related

Implementation process to

verify method and

application as expected

(i.e., impact of background

microbiota, degree of

injury)

Offered in Q3 (to be

determined)

Eight-hour enrichment puts

time constraints on

completing test in a single

work shift

Defining finished product vs.

environment samples in the

process

QS5 new

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may not include methods developed after November 2022, nor should inclusion on this table be considered as an endorsement by NACMCF or FSIS, without

validation and AOAC approval.

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