FSIS NARMS Multi-Year Report – 2014-2019 

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Executive Summary

The National Antimicrobial Resistance Monitoring System (NARMS) is a national public health 

surveillance system through which the U.S. Department of Agriculture (USDA) Food Safety and 

Inspection Service’s (FSIS) partners with state and local public health departments, the Centers for 

Disease Control and Prevention (CDC), and the Food and Drug Administration (FDA) to track changes 

in antimicrobial susceptibility of select foodborne bacteria found in ill people (CDC), retail meats (FDA), 

and food animals (FSIS).

This FSIS NARMS Multi-Year Report evaluates trends in Salmonella serotypes and antimicrobial 

resistance (AMR) in certain food animal species and products sampled from 2014 through 2019 as part 

of FSIS NARMS sampling. This is the first multi-year Salmonella trend analysis of NARMS data to 

focus on samples collected by FSIS.

Samples analyzed included cecal (intestinal content) samples from food-producing animals at slaughter 

and product samples tested as part of Pathogen Reduction/Hazard Analysis Critical Control Point 

(PR/HACCP) sampling (henceforth stated as product sampling). Salmonella isolates were analyzed by 

serotyping and antimicrobial susceptibility testing (AST)1 to evaluate differences in sample source and 

slaughter class (chicken, turkey, cattle, and swine). 

This report is intended to highlight certain data trends to identify emerging areas of concern in 

antimicrobial resistance and Salmonella serotypes of public health concern. This report is focused on 

Salmonella findings only.

Key Findings

Salmonella Serotypes

• The overall top three Salmonella serotypes in FSIS NARMS cecal and product sampling were 

Enteritidis, Infantis and Kentucky, with differing distributions by slaughter class and sample 

source.

• The top Salmonella serotypes in the cecal (intestinal) and product2 samples for each slaughter 

class remained the same over the period of the study: Kentucky (chicken), Reading (turkey), 

Montevideo (cattle) and Anatum (swine). 

• Serotype Infantis emerged as one of the top serotypes in chicken in both cecal (21%) and 

product samples (15%). 

Antimicrobial Resistance

• Most of the Salmonella from cecal samples among all slaughter classes were not resistant to 

any of the antimicrobials tested (i.e., they were pan-susceptible) over the 6-year period. 

• The proportion of Salmonella isolates that were pan-susceptible differed among slaughter 

classes and by sample sources (cecal/product samples, respectively): cattle (83%/71%), swine

(65%/64%), chicken (35%/43%) and turkey (34%/32%). 

1 See Glossary

2 Product sample sources can include carcass swabs, rinsates, intact muscle, and comminuted samples. 
FSIS NARMS Multi-Year Report – 2014-2019 

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• Multidrug resistant (MDR) Salmonella gradually increased over time in both cecal and product 

samples, with a significant increase in product samples from 2016 to 2019.

• Serotype Infantis emerged as a major contributor to the increase in MDR Salmonella in chicken. 

Resistance to Critically Important Antimicrobial Drug Classes1 

• Increased resistance to at least one critically important antimicrobial drug class was observed in 

at least one Salmonella serotype isolated from cattle, swine, and chicken.

• Salmonella susceptibility to ciprofloxacin (DSC)1 significantly decreased among isolates from 

cecal and product samples overall.

• Cephalosporin-resistant Salmonella increased over time in product samples from cattle and 

swine.

• Salmonella isolates showed a significant increase in resistance to the critically important 

antimicrobial drugs ciprofloxacin (DSC), ceftriaxone, and trimethoprim-sulfamethoxazole.

Impact and Significance

The continuous tracking of Salmonella serotypes and AMR trends in cecal and product samples serves 

as an early indicator of potential threats to public health. FSIS also uses NARMS data to explore 

opportunities to continually reduce Salmonella contamination in FSIS regulated products and to further

reduce illnesses. The differences observed in Salmonella in cecal and product samples highlight the 

importance of understanding Salmonella from farm to slaughter. 

Collaboration and Communication 

FSIS continues to engage with NARMS partners, stakeholders, academia, and industry to bridge AMR 

information gaps. FSIS NARMS collaborations help identify opportunities for combating the emergence 

and spread of AMR pathogens during pre- and post-slaughter. Since December 2020, FSIS has made 

its AMR data publicly available in its quarterly data tables to promote communication and collaboration 

with its NARMS partners and stakeholders.

**************************************

Distribution of Salmonella in FSIS-collected samples

From 2014 through 2019, a total of 32,798 cecal samples yielded 7,908 Salmonella isolates from 

chicken (1,487), turkey (291), cattle (beef and dairy) (2,435) and swine 

(market swine and sows) (3,695) (Table 1). Recovery of Salmonella

isolates (% positive) from swine ceca was consistently higher than 

other slaughter categories (49% in 2019). A rising trend in Salmonella 

recovery was observed in chicken ceca from 2016 (23.6%) to 2019

(49%) (Figure 1). 

During the same time frame, a total of 226,741 meat and poultry 

samples yielded 15,780 Salmonella isolates from chicken (10,269), 

turkey (1,328), beef (1,669) and pork (2,514) (Table 2). 

Annual NARMS datasets are 

available on the FDA’s NARMS 

website as interactive 

dashboards that allow users to 

explore the data in different 

ways. The data are also 

available as a raw dataset. Users 

of these public datasets who 

create their own estimates 

should exercise caution when 

analyzing and interpreting these 

data in this manner.
FSIS NARMS Multi-Year Report – 2014-2019 

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Salmonella recovery was positively impacted in 2016 when FSIS switched its sampling media from 

buffered peptone water (BPW) to neutralizing BPW (nBPW) for chicken carcass and parts sampling 

(Williams, 2018). This contributed to increased recovery of Salmonella in chicken carcass samples 

(Table 2). The change to nBPW did not impact cecal sample testing.

Table 1: Number of Cecal Samples Screened and Salmonella Isolates Tested for Antimicrobial 

Resistance, 2014 - 2019a

 

 a Source: FSIS Laboratory Information Management System (LIMS)

Table 2: Number of Product Samples Screened and Salmonella Isolates Tested for Antimicrobial Resistance, 

2014 - 2019a,b 

a Source: FSIS Laboratory Information Management System (LIMS)

b The number of product samples and isolates only include those that are part of the NARMS datasets except for exploratory pork product sample 

isolates

*NT – not tested

Year Chickens

(n)

Turkeys

(n)

Cattle

(n)

Swine

(n)

2014 Samples 575 264 2858 1295

Isolates 101 44 318 606

2015 Samples 553 266 2733 1179

Isolates 130 25 359 494

2016 Samples 569 266 2824 1299

Isolates 134 49 367 610

2017 Samples 835 307 3330 1337

Isolates 314 42 419 687

2018 Samples 869 437 3577 1398

Isolates 399 66 469 693

2019 Samples 811 428 3554 1234

Isolates 407 64 500 605

Year Chicken

(n)

Turkey

(n)

Beef

(n)

Pork

(n)

2014 Samples 11906 3501 16710 NT*

Isolates 936 299 344 NT*

2015 Samples 12019 2565 14825 1544

Isolates 1491 185 291 200

2016 Samples 16968 2737 15257 3296

Isolates 1859 135 286 598

2017 Samples 18392 3016 14776 3931

Isolates 2098 149 235 647

2018 Samples 18909 3399 14617 4085

Isolates 1956 264 298 541

2019 Samples 22377 3292 15062 3557

Isolates 1929 296 215 528
FSIS NARMS Multi-Year Report – 2014-2019 

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Figure 1: Salmonella in Cecal Samples by Animal Slaughter Class, 2014 - 2019 (percent positive)a 

 a Source: FSIS Laboratory Information Management System (LIMS)

Salmonella Serotypes 

During the 6-year sampling period, the leading Salmonella serotypes isolated 

by slaughter class were the same in both cecal and product samples: 

Kentucky in chicken, Reading in turkey, Montevideo in cattle and Anatum in 

swine (Appendix A). However, the distribution of the top serotypes varied 

when slaughter class data were aggregated and analyzed by sample source. 

When serotype data for the slaughter classes were combined, the overall top 

three Salmonella serotypes were Kentucky (16.7%), Enteritidis (10.6%) and

Infantis (10.3%) (Table 3). These three serotypes accounted for over 60% of 

all Salmonella in chicken cecal and product samples (Appendix A, Table 

A1). Among the other slaughter classes, these serotypes comprised less than 

20% in cecal and product samples (Appendix A, Tables A2-A4). Serotype 

changes observed over time in turkey, cattle and swine did not show the

emergence of any distinct or new serotypes, or any statistically significant 

changes.

Table 3: Salmonella Serotype Distribution in Cecal and Product Samples, Combined Slaughter Classes, 

2014 - 2019a 

FSIS NARMS Samples

Serotype Rank

Overall 

(N = 23,693) Rank

Cecal 

(N = 7,908) Rank

Product 

 (N = 15,785)

Kentucky 1 3,958 (16.7%) 5 527 (6.7%) 1 3431 (21.7%)

Enteritidis 2 2,502 (10.6%) 8 253 (3.0%) 2 2249 (14.2%)

Infantis 3 2,447 (10.3%) 2 590 (7.5%) 3 1857 (11.8%)

Typhimurium 4 1,514 (6.4%) 8 390 (4.9%) 4 1124 (7.1%)

Anatum 5 1,442 (6.1%) 1 911 (11.5%) 7 531 (3.4%)

Schwarzengrund 6 1,075 (4.5%) 12 190 (2.4%) 5 885 (5.6%)

Montevideo 7 1,066 (4.5%) 3 587 (7.5%) 8 479 (3.0%)

I 4,[5],12:i:- 8 707 (3.0%) 7 269 (3.4%) 9 438 (2.8%)

Cerro 9 665 (2.8%) 4 536 (6.8%) 20 129 (0.8%)

Derby 10 662 (2.8%) 6 428 (5.4%) 13 234 (1.5%)

0%

20%

40%

60%

2014 2015 2016 2017 2018 2019

Percent Positive

Cattle Chickens Swine Turkeys

In this report, statistically 

significant findings are referred 

to as “significant” while other 

important findings that are not 

statistically significant are 

referred to as “notable.” 

Unless otherwise stated, 

percent changes 

(increase/decrease) stated 

indicate a net difference in 

data points from 2014 to 2019. 
FSIS NARMS Multi-Year Report – 2014-2019 

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Others below top 10 7,655 (32.%) 3,227 (41.0%) 4428 (28.1%) a Source: FSIS Laboratory Information Management System (LIMS)

Chicken

Infantis was one of the top serotypes in both cecal (ranked second) and product samples (ranked third)

over the 6-year period (cumulative data) (Figure 2 and Appendix A, Table A1). Infantis significantly

increased 25.2% in cecal and 23.6% in product samples from 2016 – 2019, making Infantis a codominant serotype with Kentucky in both sample sources in 2019 (Appendix B, Table B1).

Typhimurium (ranked fourth) decreased 13.9% in cecal and 3.0% in product samples. Kentucky

(ranked first) decreased 10.4% in product samples. Enteritidis increased 3.4% in cecal (ranked second) 

and 7.8% in product samples (ranked third).

Figure 2: Top 5 Salmonella serotypes for Chicken Cecal and Product Sampling, 2014 - 2019a

 a Source: FSIS Laboratory Information Management System (LIMS)

Turkey

Reading was the top-ranked serotype in both cecal and product samples over the 6-year period 

(cumulative data). Reading increased 11% and constituted one-third of all serotypes recovered from 

both turkey cecal and product samples. (Figure 3 and Appendix A, Table A2). Interestingly, the 

increasing trends in Reading (turkey) and Infantis (chicken) began during the same period (2016–

2017). 
FSIS NARMS Multi-Year Report – 2014-2019 

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Figure 3: Top 5 Salmonella serotypes for Turkey Cecal and Product Sampling, 2014 - 2019a

 a Source: FSIS Laboratory Information Management System (LIMS)

Cattle

Montevideo was the top-ranked serotype in both cattle cecal and beef product samples over the 6-year 

period (cumulative data). Serotype differences were seen among Salmonella from cattle cecal and 

product samples and between beef and dairy cattle cecal samples. (Figure 4 and Appendix A, Tables

A3a and A3b). Dublin ranked 30th or lower in both beef and dairy cattle cecal samples but ranked 

second in beef product samples (8%)3. Muenchen ranked tenth in cattle cecal samples but ranked

fourth in beef cow, steer, and heifer cecal and product samples. Newport ranked fifth in cattle cecal and 

seventh in product samples. Muenchen and Newport varied in their top ten rankings in cecal samples

among the slaughter subclasses (beef and dairy cow, steer and heifer). Muenchen did not rank among 

the top 10 serotypes in dairy cow cecal samples2. Newport did not rank among the top 10 serotypes in 

steer and heifer cecal samples.

3 Appendix B tables include rankings for only for the top 10 Salmonella serotypes for each slaughter class and sample 

source.
FSIS NARMS Multi-Year Report – 2014-2019 

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Figure 4: Top 5 Salmonella serotypes for Cattle Cecal and Beef Product Sampling, 2014 - 2019a

 a Source: FSIS Laboratory Information Management System (LIMS)

Swine

Anatum was the top-ranked serotype in both swine cecal and pork product samples over the 6-year 

period (cumulative data) (Figure 5 and Appendix A, Tables A4a and A4b). Anatum was among the 

dominant serotypes in sow and market swine cecal samples (ranked first and second, respectively). In 

sow cecal samples, Anatum was almost two-fold higher (23%) compared to the next ranked serotype, 

Johannesburg (12%). I 4,[5],12:i:- increased 8% in market swine cecal and 4% in pork product 

samples but decreased 2% in sow cecal samples.

Figure 5: Top 5 Salmonella serotypes for Swine Cecal and Pork Product Sampling, 2014 - 2019a

 a Source: FSIS Laboratory Information Management System (LIMS)
FSIS NARMS Multi-Year Report – 2014-2019 

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2019 Top 10 Serotypes of Public Health Significance

The distribution of CDC’s 2019 top 10 Salmonella serotypes of public health significance (Tack, et al, 

2020) was compared to the 2019 data for combined FSIS cecal and product samples (Table 4) and by 

individual slaughter classes (chicken, turkey, cattle and swine) (Appendix B, Tables B1-B4). It showed 

that the CDC top serotypes of public health significance differed from the top serotypes recovered from 

FSIS cecal and product samples. It should be noted that these observations are not informed by 

attribution; the Salmonella serotypes reported through CDC reflect human infections resulting from 

exposures reported through the Foodborne Diseases Active Surveillance Network (FoodNet). These 

exposure include foods that are not under FSIS jurisdiction.

Table 4: Top 10 Salmonella serotypes for Combined FSIS NARMS Product and Cecal Sampling (2019)a and the 

Centers for Disease Control and Prevention (CDC) Clinical Isolates (2019)b

FSIS NARMS Sampling 

(chicken, turkey, cattle and swine combined) CDC1,2

Serotype Rank Product 

(N = 2968) Rank Cecal (N = 1,576) Rank

Infantis 1 627 (21%) 1 157 (10%) 6 

Kentucky 2 555 (19%) 2 139 (9%) * 

Enteritidis 3 423 (14%) 6 72 (5%) 1 

Schwarzengrund 4 165 (6%) 11 54 (3%) * 

Typhimurium 5 140 (5%) 8 60 (4%) 3 

Anatum 6 110 (4%) 3 137 (9%) * 

Reading 7 110 (4%) 16 30 (2%) * 

I 4,[5],12:i:- 8 83 (3%) 9 56 (4%) 5 

Montevideo 9 58 (2%) 4 117 (7%) 10

Johannesburg 10 56 (2%) 7 62 (4%) * 

Others below top 10 641 (22%) 692 (44%) N/A

a Source: FSIS Laboratory Information Management System (LIMS)

b

Tack, et al, 2020

1 2019 CDC Salmonella serotypes of public health significance – clinical samples (incidence rate greater than or equal to 0.20 per 100,000). 2 The 2019 CDC ten most common fully serotyped Salmonella isolates included #1 – Enteriditis, #2 – Newport, #3 – Typhimirium, #4 – 

Javiana, #5 – I 4,[5],12:i-, #6 – Infantis, #7 – Saintpaul, #8 – Braenderup, #9 – Muenchen and #10 – Montevideo.

* Serotypes not reported as one of the top 10 Salmonella isolates in CDC clinical samples. 

Antimicrobial Susceptibility and MDR 

AMR Overall 

To determine overall AMR, Salmonella results in all slaughter classes were combined for 2014-2019. 

Pansusceptibility (no antimicrobial resistance) in Salmonella isolates recovered from food animals 

during 2014-2019 was 64% in cecal and 45% in product samples. Resistance to less than 3 classes 

decreased 2% in cecal (2.2%) and product (2.1%). MDR increased by 4% overall in both cecal (4.1%) 

and product (8.3%) samples. MDR in product showed a consistent increase from 2016 to 2019. Trends 
FSIS NARMS Multi-Year Report – 2014-2019 

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in extreme drug resistance (XDR) remained very low across all slaughter classes and sample sources

(Figure 6 and Appendix C, Table C1 and C2). 

Figure 6: Trends in Antimicrobial Susceptibility, Cecal and Product Samples, Combined Slaughter Classes, 

2014 - 2019a,b

 a Source: FSIS Laboratory Information Management System (LIMS)

b Includes chicken, turkey, cattle, and swine (cecal samples) and chicken, turkey, beef, and pork (product samples)

Figure 7: Distribution of Antimicrobial Resistant Salmonella in Cecal Samples by Animal Slaughter Class, 2014 - 2019a 

 a Source: FSIS Laboratory Information Management System (LIMS)
FSIS NARMS Multi-Year Report – 2014-2019 

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Figure 8: Distribution of Antimicrobial Resistant Salmonella in Product Samples by Animal Slaughter Class, 

2014 - 2019a 

 a Source: FSIS Laboratory Information Management System (LIMS)

A significant increase in the numbers of ciprofloxacin-nonsusceptible Salmonella (also referred to as 

decreased susceptibility to ciprofloxacin or DSC Salmonella)1 was observed in cecal (8.5%) and 

product (20.9%) samples overall (Figure 9). An overall increasing trend was observed in product 

samples in both cattle (4.9%) and swine (4.9%)(Figures 12 and 13). Resistance to meropenem 

(carbapenem drug class)4 was not seen in Salmonella from cecal or product samples in all slaughter 

classes over the 6-year period. 

Figure 9: Trends in Salmonella Resistance to Selected Critically Important Antimicrobial Drugs, 

Cecal and Product Samples, Combined Slaughter Classes, 2014 - 2019a

 a Source: FSIS Laboratory Information Management System (LIMS)

 

4 Draft Guidance for Industry (GFI) #152, Food and Drug Administration, 2022. 
FSIS NARMS Multi-Year Report – 2014-2019 

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Chicken

Pansusceptibility decreased 17% in cecal and 16% product samples (Figure 7 and 8). MDR increased 

17.7% in cecal samples and 20.3% in product samples (Figures 10). In product samples, this trend 

was significant. These changes were primarily driven by the emergence of serotype Infantis. 

Resistance to less than 3 classes increased 17% in cecal and 21% in product. 

Resistance to the critically important antimicrobials increased in cecal and product samples 

(respectively) for ciprofloxacin5 (31.7%/30%), ceftriaxone (3.8%/6.2%), and trimethoprimsulfamethoxazole (8.4%/7.1%) (Figure 10 and Appendix E). The increase in ciprofloxacinnonsusceptibility (DSC) Salmonella was significant in both cecal and product samples. This is 

noteworthy since the DSC phenomenon, relatively uncommon before 2014, was seen in almost 30% of 

all chicken cecal and product samples in 2019. 

Figure 10: Trends in Salmonella Resistance to Selected Critically Important Antimicrobial Drugs, 

Chicken Cecal and Product Samples, 2014 - 2019a

 a Source: FSIS Laboratory Information Management System (LIMS)

Turkey

Pansusceptibility increased 15% in cecal and 17% in product samples (Figure 7 and 8 and Appendix 

C, Table C2). MDR decreased 13% in cecal samples and 23% in product samples The change in cecal 

samples was significant. Resistance to less than 3 classes increased 1% in cecal and 8% in product

samples.

Resistance to the critically important antimicrobial drug ceftriaxone decreased 8% in cecal and 9% in

product samples (Figure 11 and Appendix E). The small number of resistant isolates recovered from 

turkey samples limited the trend analysis. 

5 Expressed as DSC (decreased susceptibility to ciprofloxacin)
FSIS NARMS Multi-Year Report – 2014-2019 

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Figure 11: Trends in Salmonella Resistance to Selected Critically Important Antimicrobial Drugs, 

Turkey Cecal and Product Samples, 2014 - 2019a

 a Source: FSIS Laboratory Information Management System (LIMS)

Cattle/Beef

Pansusceptibility increased 7% in cecal and 4% in product samples (Figures 7 and 8 and Appendix 

C, Table C2). MDR decreased 3% in both cattle cecal and product samples. In comparison, MDR 

fluctuated between 5% and 9% in beef cattle cecal samples and significantly decreased from 10% to 

4% in dairy cattle cecal samples. In product samples, MDR fluctuated over time (between 12% and 

18%) but showed an overall net reduction. Resistance to less than 3 classes decreased 4% in both 

cecal and product samples.

Figure 12: Trends in Salmonella Resistance to Selected Critically Important Antimicrobial Drugs, 

Cattle Cecal and Beef Product Samples, 2014 - 2019a

 a Source: FSIS Laboratory Information Management System (LIMS)

Resistance to critically important antimicrobial drugs showed marked differences between cattle cecal

and product samples over the 6-year period: resistance to ceftriaxone decreased 1.2% in cecal 

samples and increased 4.8% in product samples. From 2018-2019, increased resistance to ceftriaxone

(3.3%), trimethoprim-sulfamethoxazole (1.6%) and ciprofloxacin5 (3.8%) was seen in product samples 

(Figure 12 and Appendix E). Azithromycin resistance was observed in a small number of beef cattle 

cecal samples from 2017–2019.
FSIS NARMS Multi-Year Report – 2014-2019 

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Swine/Pork

Pansusceptibility increased 6% in cecal and 20% in product samples (Figures 7 and 8 and Appendix 

C, Table C2). MDR significantly increased 3% in swine cecal samples and increased 9% in pork 

product samples. In comparison, MDR trends in cecal samples were relatively low in sows (range of 3-

10%) and relatively high in market swine (range of 17-24%). Resistance to less than 3 classes 

decreased 9% in cecal and 29% in product samples. 

Figure 13: Trends in Salmonella Resistance to Selected Critically Important Antimicrobial Drugs, 

Swine Cecal and Pork Product Samples, 2014 - 2019a,b

 a Source: FSIS Laboratory Information Management System (LIMS)

b Pork product exploratory sampling began in 2015

Resistance to the critically important antimicrobial drugs increased in pork product samples for 

ceftriaxone (2.7%), ciprofloxacin5 (4.9%), trimethoprim-sulfamethoxazole (1.3%), and azithromycin

(0.9%) (Figure 13 and Appendix E). In cecal samples, resistance to critically important antimicrobial

drugs increased for ciprofloxacin5 (2.2%), azithromycin (0.6%) and trimethoprim-sulfamethoxazole 

(0.9%).

Antimicrobial Resistance and Whole Genome Sequencing (WGS)

WGS provides a deeper insight into the genetic information necessary to characterize Salmonella

isolates and identify AMR mechanisms and emerging resistance patterns. Between 2014 and 2016, 

NARMS added genotypic methods to its AMR testing. Unlike earlier phenotypic antimicrobial tests (e.g., 

the minimum inhibitory concentration (MIC) test), WGS provides information on both resistance and the 

underlying genes, location of the genes and potential genetic insertions, deletions, shifts and drifts. A 

shift was observed in genes responsible for extended spectrum beta lactamase (responsible for 

resistance to third-generation cephalosporins) and ciprofloxacin resistance/decreased susceptibility. 

FSIS NARMS Salmonella isolates that were phenotypically resistant to critically important 

antimicrobials and contained specific genetic determinants for antimicrobial resistance (ARGD) are

further described below.

Cephalosporin-resistance

Ceftriaxone is a critically important, third-generation cephalosporin antimicrobial drug. Different types of 

cephalosporin-resistance genes were detected in ceftriaxone resistant Salmonella isolates (Figure 14).

Among those with genetic determinants (n=1,638), two types of cephalosporin-resistance genes ––

blaCMY-2 and blaCTX-M-65 ––showed opposite trends from 2015 to 2019. The blaCTX-M-65 gene, which was 
FSIS NARMS Multi-Year Report – 2014-2019 

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nonexistent among FSIS Salmonella isolates before 2015, largely replaced the once predominant 

blaCMY-2. While the blaCMY-2 gene was seen in several serotypes (including Typhimurium, Kentucky and 

Newport) isolated from various animal commodities, the majority of blaCTX-M-65 gene occurrence was in 

Infantis isolated from chicken (Appendix F, Table F1). 

Figure 14: Distribution of Ceftriaxone-resistance Genes blaCMY-2 and blaCTX-M-65 in Salmonella Serotypes from 

Cecal and Product Samples in Combined Slaughter Classes, 2014 - 2019a 

 a Source: FSIS Laboratory Information Management System (LIMS)

Azithromycin-resistance

The number of phenotypically azithromycin-resistant Salmonella (n=51) in FSIS NARMS sources have 

remained low. While most of the phenotypic azithromycin-resistance in Salmonella was due to the 

mph(A) genes, other azithromycin-resistance genes including mph(E), erm(42) and msr(E) were 

observed. All the azithromycin-resistance genes were located on mobile genetic elements/structures 

called plasmids, which are known to spread horizontally among bacteria. 

Quinolone-resistance

Quinolone resistance in Salmonella can be due to mutational gyrase (gyrA) genes or acquired

(plasmid-mediated) qnr genes. Of the DSC Salmonella isolates reported (n=1,646), the majority were

from chicken sources. More than 90% of DSC in Salmonella was attributable to mutations in gyrA and 

remained relatively stable over time (93–100%) (Appendix F, Figure F1). The plasmid-mediated 

quinolone resistance (PMQR) was mostly associated with presence of variants of quinolone- resistance 

(qnr) genes. In contrast to mutational gyrA in chicken Salmonella, the plasmid-mediated qnr genes 

were predominantly seen in swine (market swine and sows) Salmonella and fluctuated over time 

(market swine: 37–75%; sows: 6–33%)(Appendix F, Figure F2). Additionally, a few Salmonella also 

showed both mutational gyrA and acquired, plasmid-mediated qnr genes. Unlike the mutational 

resistance, resistance located on plasmids is generally known to spread horizontally among bacteria.

Impact and Significance 

FSIS NARMS sampling provides reliable data for the surveillance of AMR Salmonella. It is essential to 

have an in-depth understanding of AMR Salmonella trends along the farm-to-slaughter continuum, to 

develop effective strategies to reduce AMR Salmonella in food. Some of these strategies and 

considerations are described in FSIS’ proposed framework for reducing Salmonella in poultry6 as well 

6 Reducing Salmonella in Poultry, USDA Food Safety and Inspection Service, 2022. 
FSIS NARMS Multi-Year Report – 2014-2019 

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as FDA’s Draft Guidance for Industry (GFI) #152, and GFI #2137. Although the leading Salmonella

serotypes in cecal and product samples over the 6-year period were the same within slaughter classes, 

the trends in AMR Salmonella were not. Differences and similarities observed in the AMR and 

Salmonella serotypes between cecal and food product samples highlight the importance of these 

discrete sampling points.

From 2016–2019, the dominant serotypes in chicken cecal and product samples were Infantis, 

Kentucky, Enteritidis and Typhimurium. MDR Infantis was first detected in chicken in 2015 and showed 

a marked increase beginning in 2016. Despite mixed serotype trends over time between chicken cecal 

and product samples, the sustained and pronounced increase in Infantis from 2016 to 2019 suggests 

that genetic changes in Infantis continue to provide this serotype a competitive advantage in both cecal 

and product environments. A recent NARMS publication showed that close to 93% of Infantis from 

chicken contained the pESI plasmid (McMillan et al., 2020). This plasmid is known for its transmissibility 

and multidrug resistance; it contains several genes that improve survival and adaptability in product or 

cecal environments. The emergence of Infantis underscores the rapid manner in which the profile of

Salmonella can shift over time given certain environmental and host factors.

The rationale for the NARMS program is to examine if the antimicrobials used in food production could

lead to increased resistance to antimicrobials in pathogenic and indicator bacteria. Antibiotic use on 

farms and AMR bacteria found in food do not have a one-to-one relationship. While some believe 

antibiotic use in agriculture is one of the primary drivers for AMR emergence, the fact is we have an 

incomplete understanding of the factors that contribute to AMR in various settings8. It is noteworthy that 

observations of bacterial resistance to certain antimicrobial drugs do not automatically indicate 

antimicrobial drug use in animals (Baker-Austin, 2006). This may be due to a process called coselection, which can lead to the development of non-specific antimicrobial resistance. Several nonspecific triggers can facilitate the development and/or spread of co-resistance to antimicrobial drugs. 

Some of these triggers include acquired AMR/MDR through mobile genetic elements on plasmids, use 

of antimicrobials outside of food-producing animal systems, and environmental factors (e.g., heavy 

metals and AMR association) (Baker-Austin, 2006; Yu, 2017). The resulting AMR bacteria can spread 

geographically to regions where certain antimicrobial drugs may not be in use.

Examining the connections between AMR and the related genes or genetic sequences helps the 

NARMS partners understand the types of AMR genes and resistance mechanisms, their location

(whether chromosomal or plasmid), their potential for spread, pertinent evolutionary and geographic

relationships, potential factors for genetic changes and trends over time. FSIS routinely engages with 

NARMS partners and the food production industry to bridge information gaps between the NARMS 

findings and pre-slaughter changes. This report shows how the analysis of both cecal and product 

samples provides a more complete picture of changes in Salmonella AMR and continually serves as an 

early indicator of emerging AMR Salmonella threats to public health. AMR monitoring at a genomic 

level has helped FSIS, public health partners and stakeholders understand the specific genomic events 

or changes in Salmonella. This information is crucial in our efforts to develop appropriate solutions to 

prevent and contain AMR.

*************************

7 Draft Guidance for Industry (GFI) #213, Food and Drug Administration, 2022.

8 What is the relationship between antibiotic use in agriculture and AMR bacteria? USDA Antimicrobial Resistance Overview 

(AMR) www.usda.gov/topics/animals/one-health/antimicrobial-resistance-overview-amr. 
FSIS NARMS Multi-Year Report – 2014-2019 

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About NARMS

NARMS is a national public health surveillance system that monitors enteric bacteria and select animal 

pathogens to determine if they are resistant to antimicrobial agents used in human and veterinary 

medicine. NARMS is a collaboration of agencies within the U.S. Department of Health and Human 

Services (Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) 

and USDA (FSIS, Animal and Plant Health Inspection Service (APHIS), and Agricultural Research 

Service). 

The NARMS program tracks trends in antimicrobial resistance over 

time, identifies new types and patterns of resistance, and helps 

measure the impact of interventions designed to limit the spread of 

resistance. NARMS data are used by FDA in the regulatory review of 

new animal antimicrobial drugs and in the development of policies on 

judicious antimicrobial use in animals. To minimize potential 

consumer exposure to pathogens and antimicrobial resistance 

thereof, the CDC and FSIS use NARMS information on a case-bycase basis in foodborne illness and outbreak investigations. 

The FSIS NARMS sampling program provides data on selected 

enteric bacteria and their AMR profiles from slaughter classes. The 

FSIS NARMS program can detect AMR trends and novel findings of 

public health concern in real-time. Consequently, FSIS can use NARMS findings9 as early warning 

indicators to inform the NARMS partners, industry and other stakeholders.

Under FSIS’ verification sampling (PR/HACCP sampling), FSIS routinely collects samples from meat 

and poultry products at regulated facilities across the United States. Prior to 2013, these meat and 

poultry samples were tested to study antimicrobial resistance using the phenotypic (or observable 

characteristics) antimicrobial susceptibility testing (AST) approach. After that, genomic sequencing was 

implemented to identify and understand the genes and mechanisms involved in AMR. AST is still used 

in cecal samples to monitor for novel phenotypic changes.

In March 2013, FDA and FSIS added cecal (intestinal) sampling, thereby providing insight into AMR of 

the microbial population in the animal coming to slaughter. The food animal species in this program 

included young chickens, young turkeys, swine (market swine and sow) and cattle (beef cow and dairy 

cow). Over time, additional animal classes have been added (e.g., bob veal calves, Siluriformes fish). 

More information on the transition and subsequent developments in FSIS’ NARMS program is available 

on FSIS’ NARMS webpage. 

Data and Analytical Considerations

The FSIS NARMS samples10 are collected from two distinct sources: cecal samples and product 

samples. There are differences between the cecal sampling program and product sampling program, 

including the sampling design, sampling sources, laboratory methodologies and analytic approaches. 

Product samples are collected post-interventions/processing and the number of samples collected are 

based on the planned number of samples to analyze as published in FSIS’ Annual Sampling Plan.

Cecal sampling is conducted using a statistical design based on establishment production volume and 

predicted positive rates to reach a target number of bacterial isolates for antimicrobial susceptibility 

9 See NARMS Interim Data Updates 10 See FSIS Annual Sampling Plan

FSIS Quarterly Sampling Reports 

on Antimicrobial Resistance can 

be found on FSIS’ Science and 

Data webpage, under Data Sets 

and Visualizations > Microbiology.

Data sets for FSIS NARMS cecal 

and product sampling programs 

can be found on the FDA’s 

Integrated Reports/Summaries

webpage. 
FSIS NARMS Multi-Year Report – 2014-2019 

17

testing. These samples provide industry-wide data on antimicrobial susceptibility among four targeted 

bacteria (Salmonella, Campylobacter, generic Escherichia coli and Enterococcus) at a point in the 

slaughter process prior to the application of antimicrobial interventions. 

For the sampling period 2014-2019, no separate swine/pork product sampling program existed. 

Therefore, data from the Raw Pork Products Exploratory Sampling Program (conducted 2015-2018) 

was used to compare trends under the product sampling program. 

In FSIS NARMS data sets there are differences in sample collection points (cecal vs. finished product) 

and the number of samples collected from the various sampling projects. These differences necessitate 

caution when interpreting these data. 

Sampling Program Design

The laboratory methods used to retrieve and isolate Salmonella from samples are described in the 

FSIS Microbiology Laboratory Guidebook. The laboratory methods used for antimicrobial susceptibility 

testing are described in the FDA NARMS Manual of Laboratory Methods. As a part of bacterial 

characterization, FSIS routinely conducts WGS on Salmonella isolates from cecal and product 

(PR/HACCP) sampling programs. Sampling information and isolate characterization results have been 

linked in this report to facilitate further analysis. Additional information on design of the sampling 

program and analytical methods used can be found on the FSIS NARMS webpage. 

It is important to note that FSIS regulations require the entire digestive track, including the cecum, be 

removed during slaughter. FSIS verification activities include sampling and testing for microbial 

pathogens and indicator organisms. FSIS conducts these activities to monitor the effectiveness of 

procedures to prevent contamination of carcasses with digestive tract contents, including pathogenic 

bacteria, and thus prevent such contamination from entering the food supply. 

 

GLOSSARY 

Antimicrobial resistance: when bacteria develop the ability to defeat the drugs designed to kill them. 

Antimicrobial resistance patterns:

 Pan-susceptible: bacterial isolates that are susceptible to all antimicrobial drugs that are 

represented by the nine antimicrobial drug classes included in the NARMS testing panel. 

 Resistant: bacterial isolates resistant to one or two drug classes. 

 Multidrug-resistant (MDR): bacterial isolates resistant to three to seven drug classes. 

 Extreme drug resistant (XDR): MDR bacterial isolates that are resistant to eight or all nine drug 

classes. 

Antimicrobial drugs: drugs that treat infections by killing or slowing the growth of bacteria or other 

microbial organisms that cause infections, including antibiotics.

Antimicrobial susceptibility testing (AST): Laboratory testing performed on bacteria to find out if 

they are susceptible or resistant to one or more antimicrobial drugs. The antimicrobial drugs selected 

for testing are based on their importance in human and veterinary medicine. AST is used to guide

options for antimicrobial drug selection and potential for effective patient treatment. 

Decreased Susceptibility to Ciprofloxacin (DSC) Salmonella: Salmonella with MIC ≥ 0.12 ug/ml. 

Because even small increases in quinolone MICs can negatively impact the response to treatment, the 

DSC classification which includes Salmonella with lower-level resistance helps to decrease the 
FSIS NARMS Multi-Year Report – 2014-2019 

18

likelihood of inadvertent ciprofloxacin treatment failures (see the NARMS Interpretive Criteria for 

Susceptibility Testing of Salmonella and E. coli - Breakpoints, Table 1 footnote, accessible at 

www.fda.gov/media/108180/download). 

Isolate: pure sample of bacteria.

Medically important antimicrobial drugs (antibiotics): drugs that are commonly needed to treat 

infections in people. 

Pathogen: an organism, such as a bacterium, that can cause infections. 

Plasmid: a small, often circular DNA molecule found in bacteria and other cells. Plasmids are separate 

from bacterial chromosomes and replicate independently of it. They generally carry only a small 

number of genes, notably some associated with antibiotic resistance. Plasmids are mobile genetic 

elements that may be passed between different bacterial cells.

REFERENCES 

Baker-Austin, C., Wright, M., Stepanauskas, R., McArthur, J. (2006). Co-selection of antibiotic and 

metal resistance. Trends in Microbiology 14(4), 176-182. doi.org/10.1016/j.tim.2006.02.006. 

Foley, S.L., Nayak, R., Hanning, I. B., Johnson, T.J., Han, J., Ricke, S.C. (2011). Population Dynamics 

of Salmonella enterica Serotypes in Commercial Egg and Poultry Production. Applied and

Environmental Microbiology, 77(13), 4273-4279. doi:doi:10.1128/AEM.00598-11. 

Gilbert, R.O. (1987). Statistical methods for environmental pollution monitoring. New York: Van 

Nostrand Reinhold Co.

Karp, B.E., Tate, H., Plumbee, J.R., Dessai, U., Whichard, J.M., et al. (2017). National Antimicrobial 

Resistance Monitoring System: Two Decades of Advancing Public Health Through Integrated 

Surveillance of Antimicrobial Resistance. Foodborne Pathogens and Disease 14(10), 545-557. 

doi.org/10.1089/fpd.2017.2283. 

Kendall, M.G. (1970). Rank correlation methods. London: Griffin.

Mann, H.B. (1945). Nonparametric Tests Against Trend. Econometrica, 13(3), 245-259. 

doi:10.2307/1907187. 

McMillan, E., Wasilenko, J., Tagg, K., Chen, J., Simmons, M., Gupta, S., Frye, J. (2020). Carriage and 

Gene Content Variability of the pESI-Like Plasmid Associated with Salmonella Infantis Recently 

Established in United States Poultry Production. Genes, 11, 1516. doi:10.3390/genes11121516. 

O'Bryan, C.A., Ricke, S.C., Marcy, J.A. (2022). Public health impact of Salmonella spp. on raw poultry: 

Current concepts and future prospects in the United States. Food Control, 132, 108539. 

doi:https://doi.org/10.1016/j.foodcont.2021.108539. 

Rabsch, W., Hargis, B.M., Tsolis, R.M., Kingsley, R.A., Hinz, K.H., Tschäpe, H., et al. Competitive 

exclusion of Salmonella enteritidis by Salmonella gallinarum in poultry. Emerging Infect 

Dis (2000) 6(5):443–8. doi:10.3201/eid0605.000501. 
FSIS NARMS Multi-Year Report – 2014-2019 

19

Sen, P.K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the 

American Statistical Association, 63(324), 1379-1389. doi:10.1080/01621459.1968.10480934. 

Tableau (version. 9.1). (2016). Journal of the Medical Library Association: JMLA, 104(2), 182-183. 

doi:10.3163/1536-5050.104.2.022. 

Tack, D.M., et al. (2020). Preliminary Incidence and Trends of Infections with Pathogens Transmitted 

Commonly Through Food - Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2016-

2019. MMWR. Morbidity and Mortality Weekly Report, 69(17), 509-514. doi: 

http://dx.doi.org/10.15585/mmwr.mm6917a1. 

U.S. Department of Agriculture, Food Safety and Inspection Service. Reducing Salmonella in Poultry. 

Accessed December 20, 2022. (available at www.fsis.usda.gov/inspection/inspectionprograms/inspection-poultry-products/reducing-salmonella-poultry) 

U.S. Department of Health and Human Services, Food and Drug Administration. (2022). Draft 

Guidance for Industry: Evaluating the Safety of Antimicrobial New Animal Drugs with Regards to Their 

Microbiological Effects on Bacteria of Human Health Concern (GFI #152) (available at

ww.fda.gov/regulatory-information/search-fda-guidance-documents/cvm-gfi-152-evaluating-safetyantimicrobial-new-animal-drugs-regard-their-microbiological-effects). 

U.S. Department of Health and Human Services, Food and Drug Administration. (2013). Guidance for 

Industry New Animal Drugs and New Animal Drug Combination Products Administered in or on 

Medicated Feed or Drinking Water of Food Producing Animals: Recommendations for Drug Sponsors 

for Voluntarily Aligning Product Use Conditions with GFI #209 (GFI #213) (available at

www.fda.gov/regulatory-information/search-fda-guidance-documents/cvm-gfi-213-new-animal-drugsand-new-animal-drug-combination-products-administered-or-medicated-feed). 

U.S. Department of Health and Human Services, Food and Drug Administration. The National 

Antimicrobial Resistance Monitoring System: NARMS Integrated Reports. Accessed March 25, 2022. 

(available at www.fda.gov/animal-veterinary/national-antimicrobial-resistance-monitoringsystem/integrated-reportssummaries). 

Williams, M.S., Ebel, E.D., Hretz, S.A., Golden, N.J. (2018). Adoption of Neutralizing Buffered Peptone 

Water Coincides with Changes in Apparent Prevalence of Salmonella and Campylobacter of Broiler 

Rinse Samples. Journal of Food Protection, 81(11), 1851-1863. doi:10.4315/0362-028x.Jfp-18-124. 

Williams, M.S., Ebel, E.D., Saini, G., Nyrabagizi, E. (2020). Changes in Salmonella Contamination in 

Meat and Poultry Since the Introduction of the Pathogen Reduction and Hazard Analysis and Critical 

Control Point Rule. Journal of Food Protection, 83(10), 1707-1717. doi:10.4315/jfp-20-126. 

Yu, Z., Gunn, L., Wall, P., Fanning, S.(2017). Antimicrobial resistance and its association with tolerance 

to heavy metals in agriculture production. Food Microbiology 64, 23-32. 

doi.org/10.1016/j.fm.2016.12.009. 
FSIS NARMS Multi-Year Report – 2014-2019 

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APPENDIX A – Top Ten Salmonella Serotype Rankings by Slaughter Class and Sample Source, 

2014 – 2019

Table A1. Salmonella Serotype Distribution in Chicken Cecal and Product Samples, 2014 - 2019

Table A2. Salmonella Serotype Distribution in Turkey Cecal and Product Samples, 2014 - 2019

 
FSIS NARMS Multi-Year Report – 2014-2019 

21

Table A3a. Salmonella Serotype Distribution in Cattle Cecala and Product Samples, 2014 - 2019

 a Cattle cecal sampling includes beef cow, dairy cow, steers and heifers 

Table A3b. Salmonella Serotype Distribution in Beef and Dairy Cow Cecal Samples, 2014 - 2019
FSIS NARMS Multi-Year Report – 2014-2019 

22

Table A3c. Salmonella Serotype Distribution in Steer and Heifer Cecal Samples, 2014 – 2019

Table A4a. Salmonella Serotype Distribution in Swine Cecala and Product Samples, 2014 - 2019

 a Swine cecal sampling includes market swine and sows 
FSIS NARMS Multi-Year Report – 2014-2019 

23

Table A4b. Salmonella Serotype Distribution in Sow and Market Hog Cecal Samples, 2014 - 2019
FSIS NARMS Multi-Year Report – 2014-2019 

24

APPENDIX B – Top 10 Salmonella serotypes for FSIS NARMS Cecal and Product Sampling by Slaughter 

Class (2019)a and the Centers for Disease Control and Prevention (CDC) Clinical Isolates (2019)a,b

Table B1: Chicken

2019 FSIS NARMS Sampling - Chicken CDC1,2

Serotype Rank Product 

(N = 1929) Rank Cecal (N = 407) Rank

Infantis 1 544 (28.2%) 1 121 (29.7%) 6 

Kentucky 2 543 (28.1%) 2 119 (29.2%) * 

Enteritidis 3 419 (21.7%) 3 70 (17.2%) 1 

Schwarzengrund 4 138 (7.2%) 4 33 (8.1%) * 

Typhimurium 5 106 (5.5%) 5 26 (6.4%) 3 

I 4,[5],12:i:- 6 25 (1.3%) 12 2 (0.5%) 5 

Braenderup 7 24 (1.2%) 6 6 (1.5%) 8 

Heidelberg 8 24 (1.2%) 7 5 (1.2%) * 

Thompson 9 22 (1.1%) NA NA * 

Rough_O:r:1,5 10 16 (0.8%) 14 2 (0.5%) * 

Table B2: Turkey 

2019 FSIS NARMS Sampling - Turkey CDC1,2

Serotype Rank Product 

(N = 296) Rank Cecal (N = 64) Rank

Reading 1 94 (31.8%) 1 20 (31.3%) * 

Uganda 2 23 (7.8%) 3 6 (9.4%) * 

Agona 3 19 (6.4%) 4 4 (6.3%) * 

Typhimurium 4 18 (6.1%) 20 1 (1.6%) 3 

Schwarzengrund 5 17 (5.7%) 6 4 (6.3%) * 

Muenchen 6 16 (5.4%) 8 2 (3.1%) 9 

Anatum 7 15 (5.1%) 12 1 (1.6%) * 

Infantis 8 15 (5.1%) 15 1 (1.6%) 6 

Hadar 9 13 (4.4%) 2 8 (12.5%) * 

Senftenberg 10 12 (4.1%) 10 2 (3.1%) * 
FSIS NARMS Multi-Year Report – 2014-2019 

25

Table B3: Cattle 

2019 FSIS NARMS Sampling - Cattle CDC1,2

Serotype Rank Product 

(N = 215) Rank

Beef Cattle

Cecal 

(N = 275)

Rank

Dairy Cattle 

Cecal 

(N = 275)

Rank

Montevideo 1 45 (20.9%) 2 71 (25.8%) 2 71 (25.8%) 10

Dublin 2 24 (11.2%) NA NA NA NA * 

Anatum 3 14 (6.5%) 7 11 (4.0%) 7 11 (4.0%) * 

Muenchen 4 12 (5.6%) 8 9 (3.3%) 8 9 (3.3%) 9 

Newport 5 12 (5.6%) 6 12 (4.4%) 6 12 (4.4%) 2 

Cerro 6 10 (4.7%) 1 80 (29.1%) 1 80 (29.1%) * 

Kentucky 7 9 (4.2%) 4 12 (4.4%) 4 12 (4.4%) * 

Muenster 8 9 (4.2%) 5 12 (4.4%) 5 12 (4.4%) * 

Give 9 8 (3.7%) 14 3 (1.1%) 14 3 (1.1%) * 

Typhimurium 10 8 (3.7%) 9 7 (2.5%) 9 7 (2.5%) 3 

Table B4: Swine

2019 FSIS NARMS Sampling - Swine CDC1,2

Serotype Rank Product

(N= 528) Rank

Market Swine 

Cecal 

(N = 433)

Rank

Sows 

Cecal 

(N = 172)

Rank

Anatum 1 79 (15%) 3 39 (9.0%) 1 41 (23.8%) * 

Infantis 2 61 (12%) 7 22 (5.1%) 11 5 (2.9%) 6 

I4,[5],12:i:- 3 52 (9.9%) 1 47 (10.9%) 13 3 (1.7%) 5 

Johannesburg 4 40 (7.6%) 4 36 (8.3%) 2 24 (4.0%) * 

Derby 5 36 (6.8%) 2 42 (9.7%) 4 12 (7.0%) * 

Adelaide 6 32 (6.1%) 5 26 (6.0%) 5 10 (5.8%) * 

London 7 26 (4.9%) 6 25 (5.8%) 3 13 (7.6%) * 

Ohio 8 18 (3.4%) 10 13 (3.0%) 24 1 (0.6%) * 

Uganda 9 15 (2.8%) 16 9 (2.1%) 6 9 (5.2%) * 

Agona 10 13 (2.5%) 12 12 (2.8%) 9 5 (2.9%) * 

a Data sources: FSIS Laboratory Information Management System (LIMS) and CDC FoodNet (https://wwwn.cdc.gov/foodnetfast/) b

Tack, et al, 2020

1 2019 CDC Salmonella serotypes of public health significance – clinical samples (incidence rate greater than or equal to 0.20 per 100,000). 2 The 2019 CDC ten most common fully serotyped Salmonella isolates included #1 – Enteriditis, #2 – Newport, #3 – Typhimirium, #4 – Javiana, 

#5 – I 4,[5],12:i-, #6 – Infantis, #7 – Saintpaul, #8 – Braenderup, #9 – Muenchen and #10 – Montevideo.

* Serotypes not reported as one of the top 10 Salmonella isolates in CDC clinical samples. 

NA Serotypes not reported in FSIS cecal samples
FSIS NARMS Multi-Year Report – 2014-2019 

26

APPENDIX C – Salmonella Antimicrobial Susceptibility Trends and Distribution of the Topranked FSIS Salmonella Serotype by Major Animal Commodities, Sample Sources and Year,

2014 – 2019

Table C1: Salmonella Antimicrobial Susceptibility in Cecal and Product Samples, 

Combined Slaughter Classes, 2014 - 2019a,b,c

a Source: FSIS Laboratory Information Management System (LIMS)

b Includes chicken, turkey, cattle, and swine (cecal samples) and chicken, turkey, beef, and pork (product samples)

c XDR data not shown in the table; comprised less than 1% of the total

Table C2: Salmonella Antimicrobial Susceptibility in Cecal and Product Samples by Slaughter 

Class, 2014 – 2019 Aggregate Dataa,b

a Source: FSIS Laboratory Information Management System (LIMS) b XDR data not shown in the table; comprised less than 1% of the total

Cecal Sampling Product Sampling

Resistance pattern 2014 2015 2016 2017 2018 2019 Total 2014 2015 2016 2017 2018 2019 Total

Pan-susceptible (0) 66%

(708)

65%

(652)

67%

(773)

66%

(964)

58%

(937)

64%

(1,007)

64%

(5,041)

50%

(792)

50%

(1,087)

50%

(1,444)

50%

(1,577)

46%

(1,411)

44%

(1,309)

48%

(7,620)

Resistant (1 or 2) 21%

(224)

24%

(238)

20%

(232)

20%

(288)

23%

(371)

19%

(294)

21%

(1,647)

34%

(529)

36%

(778)

37%

(1,065)

34%

(1,058)

35%

(1,067)

31%

(934)

34%

(5,431)

MDR (3 or more) 13%

(143)

12%

(118)

13%

(155)

14%

(210)

20%

(319)

17%

(275)

15%

(1,220)

16%

(258)

14%

(302)

13%

(369)

16%

(494)

19%

(581)

25%

(730)

17%

(2,734)

Total (N) 1,075 1,008 1,160 1,462 1,627 1,576 7,908 1,579 2,167 2,878 3,129 3,059 2,973 15,785

Cecal Sampling Product Sampling

Resistance 

pattern

Chicken Turkey Beef 

Cattle

Dairy 

Cattle

Market 

Swine Sows Total Chicken Turkey Beef Pork Total

Pan-susceptible 

(0)

35%

(523)

32%

(93)

80%

(804)

85%

(1,223)

58%

(1,310)

75%

(1,088)

64%

(5,041)

43%

(4,373)

34%

(452)

71%

(1,182)

64%

(1,613)

46%

(7,620)

Resistant (1 - 2) 39%

(573)

35%

(101)

12%

(124)

7.8%

(112)

21%

(474)

18%

(263)

21%

(1,647)

40%

(4,085)

35%

(467)

12%

(195)

27%

(684)

34%

(5,431)

MDR (3 or more) 26%

(391)

33%

(97)

7.4%

(74)

6.8%

(98)

21%

(462)

6.8%

(98)

15%

(1,220)

18%

(1,814)

31%

(409)

18%

(294)

8.6%

(217)

17%

(2,734)

Total (N) 1,487 291 1,002 1,433 2,246 1,449 7,908 10,272 1,328 1,671 2,514 15,785
FSIS NARMS Multi-Year Report – 2014-2019 

27

 Figure C1: Kentucky 

Serotype Animal Host 2014 2015 2016 2017 2018 2019

Kentucky Chicken 205 483 735 922 993 927 

Turkey 5 3 1 1 4 2 

Cattle/Beef 22 24 27 17 38 32 

Swine/Pork 2 11 3 5 1 3 

Serotype Sample Source 2014 2015 2016 2017 2018 2019

Kentucky Cecal 41 41 50 105 151 138 

PR/HACCP 193 480 716 840 885 826 
FSIS NARMS Multi-Year Report – 2014-2019 

28

 Figure C2: Reading

Serotype Animal Host 2014 2015 2016 2017 2018 2019

Reading Chicken 1 2

Turkey 13 34 41 43 159 132

Cattle/Beef 5 10 3 5 8 5

Swine/Pork 8 12 9 15 9 26

Serotype Sample Source 2014 2015 2016 2017 2018 2019

Reading Cecal 14 16 16 17 19 30

PR/HACCP 12 40 38 46 157 135

 
FSIS NARMS Multi-Year Report – 2014-2019 

29

 Figure C3: Montevideo 

Serotype Animal Host 2014 2015 2016 2017 2018 2019

Montevideo Chicken 1 17 16 14 31 10 

Turkey 2 3 3 7 7 7

Cattle/Beef 153 154 142 173 202 178

Swine/Pork 7 5 14 12 16 10

Serotype Sample Source 2014 2015 2016 2017 2018 2019

Montevideo Cecal 60 81 87 121 123 119

PR/HACCP 103 98 88 85 133 86

 
FSIS NARMS Multi-Year Report – 2014-2019 

30

Figure C4: Anatum 

Serotype Animal Host 2014 2015 2016 2017 2018 2019

Anatum Chicken 9 7 6 10 5

Turkey 4 3 8 7 8 20

Cattle/Beef 64 59 66 77 63 82

Swine/Pork* 98 125 252 224 144 188

Serotype Sample Source 2014 2015 2016 2017 2018 2019

Anatum Cecal 142 145 181 175 133 137

PR/HACCP* 24 51 152 139 92 158

 *Exploratory pork sampling started in May 2015
FSIS NARMS Multi-Year Report – 2014-2019 

31

APPENDIX D – Distribution of the Top Three Ranked 2019 CDC Salmonella Serotype and MDR 

Trends by Major Animal Commodities, Sample Source and Year, 2014 - 2019

Figure D1: Enteriditis

Serotype Animal Host 2014 2015 2016 2017 2018 2019

Enteritidis Chicken 52 278 475 597 730 633

Turkey 1 1 1 1 1 5

Cattle/Beef 1 1 4 2 4 2

Swine/Pork 1 3 4 4 1 5

Serotype Sample Source 2014 2015 2016 2017 2018 2019

Enteritidis Cecal 19 21 33 55 52 72

PR/HACCP 36 262 451 549 684 573
FSIS NARMS Multi-Year Report – 2014-2019 

32

 Figure D2: Newport

Serotype Animal Host 2014 2015 2016 2017 2018 2019

Newport Chicken 2 8 4 2

Turkey 1 1 4

Cattle/Beef 38 29 22 29 45 36

Swine/Pork 6 9 11 2 1 6

Serotype Sample Source 2014 2015 2016 2017 2018 2019

Newport Cecal 22 17 20 17 16 25

PR/HACCP 22 22 15 22 35 23
FSIS NARMS Multi-Year Report – 2014-2019 

33

Figure D3: Typhimurium

Serotype Animal Host 2014 2015 2016 2017 2018 2019

Typhimurium Chicken 48 186 258 221 257 189

Turkey 3 4 9 4 12 22

Cattle/Beef 34 46 43 35 29 32

Swine/Pork 19 25 35 39 37 35

Serotype Sample Source 2014 2015 2016 2017 2018 2019

Typhimurium Cecal 62 57 57 75 80 61

PR/HACCP 42 204 288 224 255 217
FSIS NARMS Multi-Year Report – 2014-2019 

34

APPENDIX E – Critically Important Antimicrobial Drugs

Table E1. Salmonella Resistance to Selected Critically Important Antimicrobial Drugsa,b by 

Animal Host, Sample Source and Year (2014 - 2019) 

Animal 

Host

Year Isolates 

(n)

Cecal 

(%)

Isolates 

(n)

Product 

(%)

Azi Axo Cip Mer Cot Azi Axo Cip Mer Cot

Chicken 2014 101 1.0 8.7 0.0 0.0 1.9 936 0.0 6.6 0.2 0.0 0.4

2015 130 0.8 10.0 3.8 0.0 7.7 1491 0.3 6.5 0.4 0.0 0.8

2016 134 0.0 10.4 3.7 0.0 2.2 1859 0.2 7.9 3.7 0.0 3.4

2017 314 0.0 11.1 17.5 0.0 8.0 2098 0.0 9.3 13.6 0.0 6.1

2018 399 0.0 15.3 26.3 0.0 10.0 1956 0.0 9.7 19.7 0.0 6.4

2019 407 0.0 12.5 31.7 0.0 10.3 1929 0.0 12.8 30.2 0.0 7.5

Turkey 2014 44 2.2 11.1 0.0 0.0 6.7 299 0.3 13.4 0.7 0.0 0.7

2015 25 0.0 8.0 0.0 0.0 0.0 185 0.5 15.7 1.1 0.0 3.2

2016 49 0.0 16.3 2.0 0.0 2.0 135 0.0 7.4 0.0 0.0 0.0

2017 42 0.0 11.9 2.4 0.0 0.0 149 0.0 6.7 6.7 0.0 4.7

2018 66 0.0 3.0 1.5 0.0 3.0 264 0.4 5.3 6.4 0.0 3.4

2019 64 0.0 3.1 1.6 0.0 3.1 296 0.3 4.4 4.7 0.0 1.7

Cattle/Beef 2014 318 0.3 4.0 0.6 0.0 1.2 344 0.0 7.6 2.9 0.0 1.7

2015 359 0.0 5.3 0.3 0.0 0.6 291 0.0 15.8 4.8 0.0 2.1

2016 367 0.0 5.2 0.8 0.0 0.8 286 0.3 10.8 4.9 0.0 2.4

2017 419 0.2 3.8 0.5 0.0 0.0 235 0.0 8.5 3.4 0.0 1.3

2018 469 0.6 4.3 1.9 0.0 0.9 298 0.0 9.1 4.0 0.0 0.7

2019 500 0.4 2.8 1.2 0.0 0.6 215 0.0 12.4 7.8 0.0 2.3

Swine/Pork 2014 606 0.3 2.6 3.6 0.0 1.2 * * * * * *

2015 494 0.0 1.6 1.9 0.0 0.4 200 0.0 0.5 1.5 0.0 1.0

2016 610 0.3 3.2 2.9 0.0 1.3 598 0.3 3.5 3.2 0.0 2.0

2017 687 0.5 2.9 3.7 0.0 1.7 647 0.8 3.2 4.5 0.0 2.0

2018 693 0.3 3.8 5.9 0.0 2.0 541 0.2 4.3 5.7 0.0 1.8

2019 605 1.0 2.6 5.8 0.0 2.0 528 0.9 3.2 6.4 0.0 2.3

Overall 2014 1069 0.5 4.0 2.2 0.0 1.5 1579 0.1 8.1 0.9 0.0 0.8

2015 1008 0.1 4.4 1.6 0.0 1.3 2167 0.2 8.0 1.2 0.0 1.2

2016 1160 0.2 5.1 2.2 0.0 0.9 2878 0.2 7.3 3.5 0.0 2.9

2017 1462 0.2 5.1 5.4 0.0 2.4 3129 0.2 7.9 10.6 0.0 4.8

2018 1627 0.4 6.6 9.6 0.0 3.7 3059 0.1 8.3 14.6 0.0 4.8

2019 1576 0.5 5.1 10.7 0.0 3.7 2968 0.2 10.3 21.8 0.0 5.6
FSIS NARMS Multi-Year Report – 2014-2019 

35

a Critically important antimicrobials include azithromycin (Azi), ceftriaxone (Axo),ciprofloxacin (Cip), meropenem (Mer) and 

trimethoprim-sulfamethoxazole (Cot). b Draft Guidance for Industry (GFI) #152, Food and Drug Administration, 2022. * Data not available.

APPENDIX F – Whole Genome Sequencing

Table F1. Distribution of the genetic determinants blaCMY-2 and blaCTX-M-65 by animal host, sample 

source and Salmonella serotypes (2014 - 2019) 

Animal 

Host

blaCMY-2 blaCTX-M-65

Typhimurium

(n)

Kentucky

(n)

Newport

(n)

Dublin

(n)

Agona

(n)

Heidelberg

(n)

Others

(n)

Total

(n)

Infantis

(n)

Others

(n)

Total

(n)

Beef 

CattleC 8 11 4 6 29

CattleP 10 1 23 29 3 7 73 3 3 

ChickensC,P 78 84 1 13 6 182 614 15 629

Dairy 

CattleC 18 34 1 1 6 60 1 1 

Market 

SwineC 1 2 1 16 2 37 59 2 2 

SowsC 2 6 14 22

TurkeysC,P 2 5 8 22 37 23 23

Grand Total

(n, %)

119

25.8%

87

18.8%

70

15.2%

34

7.3%

28

6.1%

26

5.6%

98

21.2%

462

100%

643

97.7%

15

2.3%

658

100% C Cecal sampling, P Product sampling

Figure F1: Distribution of DSC Salmonella isolates with mutational resistance (gyrA) by animal 

host, 2014 - 2019
FSIS NARMS Multi-Year Report – 2014-2019 

36

Figure F2: Distribution of DSC Salmonella isolates with acquired resistance by animal host, 

2014 - 2019