Photo credit: DiasporaEngager (www.DiasporaEngager.com).
Hazel J. Shah, MPH1; Rachel H. Jervis, MPH2; Katie Wymore, MPH3; Tamara Rissman, MPH4; Bethany LaClair, MPH5; Michelle M. Boyle, MPH6; Kirk Smith, DVM, PhD7; Sarah Lathrop, DVM, PhD8; Suzanne McGuire, MPH9; Rosalie Trevejo, DVM, PhD10; Marcy McMillian, MPH11; Stic Harris, DVM12; Joanna Zablotsky Kufel, PhD13; Kennedy Houck, MPH1; Carey E. Lau1; Carey J. Devine, MPH1; Dave Boxrud, MS1; Daniel L. Weller, PhD1 (View author affiliations)
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Summary
What is already known about this topic?
Increased use of culture-independent diagnostic tests (CIDTs) affects observed trends in foodborne infection incidence.
What is added by this report?
During 2023, the incidence of eight domestically acquired infections transmitted commonly through food either increased or remained stable compared with 2016–2018, the baseline used to track progress toward disease reduction goals. Incidence of CIDT-diagnosed infection also increased during 2023.
What are the implications for public health practice?
CIDTs allow for diagnosis of infections that previously would have been undetected; recent increases in incidence appear to be driven by increased CIDT use. Continued surveillance is needed to monitor the impact of changing diagnostic practices on disease trends. Targeted prevention efforts are needed to reduce disease incidence.
Abstract
Reducing foodborne disease incidence is a public health priority. This report summarizes preliminary 2023 Foodborne Diseases Active Surveillance Network (FoodNet) data and highlights efforts to increase the representativeness of FoodNet. During 2023, incidences of domestically acquired campylobacteriosis, Shiga toxin-producing Escherichia coli infection, yersiniosis, vibriosis, and cyclosporiasis increased, whereas those of listeriosis, salmonellosis, and shigellosis remained stable compared with incidences during 2016–2018, the baseline used for tracking progress towards federal disease reduction goals. During 2023, the incidence and percentage of infections diagnosed by culture-independent diagnostic tests (CIDTs) reported to FoodNet continued to increase, and the percentage of cases that yielded an isolate decreased, affecting observed trends in incidence. Because CIDTs allow for diagnosis of infections that previously would have gone undetected, lack of progress toward disease reduction goals might reflect changing diagnostic practices rather than an actual increase in incidence. Continued surveillance is needed to monitor the impact of changing diagnostic practices on disease trends, and targeted prevention efforts are needed to meet disease reduction goals. During 2023, FoodNet expanded its catchment area for the first time since 2004. This expansion improved the representativeness of the FoodNet catchment area, the ability of FoodNet to monitor trends in disease incidence, and the generalizability of FoodNet data.
Introduction
Reducing the incidence of foodborne and enteric diseases is a public health priority. The Healthy People 2030 (HP2030) initiative established disease reduction goals for Campylobacter, Listeria, Salmonella, and Shiga toxin-producing Escherichia coli (STEC) infections (1). To evaluate progress toward HP2030 goals, CDC’s Foodborne Diseases Active Surveillance Network (FoodNet) monitors infections caused by eight pathogens transmitted commonly through food. This report summarizes preliminary 2023 surveillance data and describes changes in incidence compared with average annual incidence during 2016–2018, the reference period used by HP2030 (1).
Methods
Data Source
FoodNet conducts active, population-based surveillance for laboratory-diagnosed Campylobacter, Cyclospora, Listeria, Salmonella, Shigella, STEC, Vibrio, and Yersinia infections and pediatric hemolytic uremic syndrome (HUS) at 10 U.S. sites;* HUS is monitored because it can be a complication of STEC infection. FoodNet’s catchment area expanded during 2023 to include all of Colorado, and now represents 16% of the U.S. population (53.6 million persons); in 2023, the historic catchment area represented 15% of the U.S. population (51.0 million persons). Compared with the historic catchment area, the expansion increased representation for specific populations, including Hispanic or Latino ([Hispanic]; 8% increase), American Indian or Alaska Native (AI/AN; 8% increase), and Native Hawaiian or Pacific Islander (NH/PI; 6% increase) persons (FoodNet collects race and ethnicity as separate variables) as well as persons living in rural counties (10% increase).
Laboratory Testing and Data Collection
Bacterial infections were diagnosed by culture or culture-independent diagnostic tests (CIDTs). Cyclosporiasis was diagnosed by polymerase chain reaction or microscopy. Pediatric HUS surveillance is conducted through a network of nephrologists and infection preventionists and by hospital discharge data review.† This report includes 2022 data on pediatric HUS cases, the most recent year for which data are available. This activity was reviewed by CDC, deemed not research, and conducted in accordance with applicable federal law and CDC policy.§
Statistical Methods
Bayesian negative binomial models were implemented to estimate changes in incidence in the historic catchment area during 2023 compared with average annual incidence during 2016–2018 (overall, and for domestically acquired infections), using R statistical software (version 2.14.0; R Foundation).¶,** Incidence in 2023 was considered substantially different†† from that during 2016–2018 if the 95% credible interval (CrI) for the incidence rate ratio (IRR) did not include 1.0. Cross-tabulations by demographic and other characteristics were also performed.§§
Results
Incidence in 2023 Compared with Average Annual Incidence During 2016–2018
During 2023, FoodNet identified 29,607 infections, 7,234 hospitalizations, and 177 deaths overall (including domestically acquired and travel-associated infections) in the historic catchment area (Table 1) (Table 2), compared with 31,492 infections, 7,588 hospitalizations, and 184 deaths in the expanded catchment area¶¶ (Supplementary Table; https://stacks.cdc.gov/view/cdc/157822). In both the historic and expanded catchment areas, 15% of cases were associated with international travel. Overall, and for domestically acquired infections only, incidence of campylobacteriosis was highest, followed by salmonellosis and STEC infection. In the historic catchment area during 2023, incidences of domestically acquired campylobacteriosis, cyclosporiasis, STEC infection, vibriosis, and yersiniosis increased compared with those during 2016–2018, whereas listeriosis, salmonellosis, and shigellosis incidences remained stable.
Generally, the overall percentage of infections attributable to specific Campylobacter, Shigella, Vibrio, and Yersinia species, Salmonella serotypes, and STEC serogroups was lower in 2023 than in all previous years (Table 3). The overall incidence of infections for which the pathogen was not speciated, serotyped, or serogrouped increased substantially compared with incidence during 2016–2018 (Table 2). During 2023, 78% of all bacterial infections were diagnosed by CIDTs in the historic catchment area, including 46% diagnosed using only CIDTs. The percentage of CIDT-diagnosed infections for which a reflex culture*** was attempted decreased from 71% during 2016–2018 to 68% during 2023. This decrease was largest for Yersinia, Vibrio, and STEC infections. For all illnesses except listeriosis, the percentage of reflex cultures that yielded an isolate (successful [or positive] reflex culture) was lower in 2023 than during previous years (Table 3). This decrease in isolate availability has been associated with a decrease in serotyped, serogrouped, and speciated infections. For example, from 2016–2018 to 2023, the overall incidence of unspeciated infections increased substantially for Campylobacter,††† Shigella,§§§ Yersinia,¶¶¶ and Vibrio****; the percentage of speciated infections declined from 33% to 26% for Campylobacter, from 65% to 41% for Shigella, from 49% to 23% for Yersinia, and from 61% to 34% for Vibrio. Although only culture-independent methods are used to diagnose cyclosporiasis, increases in CIDT-diagnosed cyclosporiasis and cyclosporiasis incidence mirror CIDT-driven increases in bacterial infection incidence.
Salmonella Infections
Of 8,454 total (i.e., both domestically acquired and travel-associated) Salmonella infections during 2023 in the historic catchment area, 83% yielded an isolate; 89% of isolates were fully serotyped. The incidence of nonserotyped infections increased substantially.†††† The incidences of the most frequently reported serotypes, S. Enteritidis and S. Newport, remained stable during 2023 compared with those during 2016–2018, whereas the incidences of the next-most frequently reported serotypes, S. Typhimurium, S. Javiana, and S. I 4,[5],12:i:- decreased substantially.
STEC Infections
Of 3,351 total STEC infections in the historic catchment area during 2023, 57% yielded an isolate; 87% of isolates were fully serogrouped. The incidence of nonserogrouped infections increased substantially in 2023 compared with that during 2016–2018.§§§§ During 2023, STEC O157 incidence decreased compared with incidence during 2016–2018, and non-O157 STEC incidence remained stable.
Hemolytic Uremic Syndrome
During 2022, FoodNet identified 61 cases of postdiarrheal HUS in persons aged <18 years, including 39 among children aged <5 years. The incidence of postdiarrheal HUS among persons aged <18 years (0.6 per 100,000 persons) and those aged <5 years (1.4 per 100,000) remained stable in 2022 compared with that during 2016–2018.¶¶¶¶
Discussion
The current findings and previous FoodNet reports (2,3) suggest a lack of progress toward foodborne disease reduction goals; however, this outcome might reflect changing diagnostic practices such as the increased use of CIDTs rather than an actual increase in disease incidence. Increased use of CIDTs facilitates prompt clinical diagnosis and treatment but also complicates the interpretation of surveillance data and trends because CIDT adoption has varied over time, among clinical labs, and by pathogen. In addition, although CIDTs are generally considered more sensitive than are culture-based methods, some have high false-positive rates for certain pathogens (e.g., Vibrio) (4–6). Previous studies have indicated that increased CIDT use has resulted in the diagnosis of infections that previously would have gone undetected; increased use of CIDTs has been associated with marked increases in reported incidence (4,7).
Increases in CIDT-diagnosed infections are also associated with decreased rates of reflex culture, thereby reducing the number of isolates available for subtyping, whole genome sequencing, and antimicrobial resistance characterization (8). The impact of this reduction differs by species, serotype, and serogroup. Because an isolate is required for speciation, serotyping, and serogrouping, reduced isolate availability might result in underdetection of illnesses attributable to specific Campylobacter, Shigella, Vibrio, and Yersinia species, Salmonella serotypes, and STEC serogroups. The substantial increase in the incidence of infections for which the pathogen was not speciated, serotyped, or serogrouped is likely an artifact of changing diagnostic practices (i.e., increased CIDT use), resulting in a reduced availability of isolates for speciation and typing. Continued reductions in isolate availability might hinder outbreak identification and response (e.g., whole genome sequencing–based cluster identification and source attribution), detection of emerging antimicrobial resistance, and tracking of trends in illnesses attributable to specific species, subtypes, serotypes, and resistant strains. Increasing successful reflex culture rates after a CIDT diagnosis is a public health priority, which requires focused efforts and resources at the federal, state, and local levels.
FoodNet data are used to track trends in enteric illness, monitor progress toward disease reduction goals, and guide food safety policy*****,††††† (1). Because FoodNet is a sentinel surveillance system representing 10 sites, national extrapolation relies on strong assumptions of representativeness. Although the sites included in the FoodNet catchment area were selected nonrandomly, past analyses suggest that FoodNet’s catchment area is broadly representative of the national population (9,10). Previously, the only notable difference between FoodNet’s historic catchment area and the national population identified by these studies was that Hispanic persons were underrepresented in the catchment area relative to national representation (9,10). Investigating enteric disease epidemiology for AI/AN and NH/PI persons using FoodNet data has also been complicated by the small size of these populations in the historic catchment area. By increasing representation for these specific populations in the FoodNet catchment area, FoodNet’s expansion has helped to partially alleviate these limitations and improve the generalizability of FoodNet data. Additional expansion might be needed as national and catchment area demographics change.
Limitations
The findings in this report are subject to at least three limitations. First, underreporting might affect case counts because ill persons must seek care and be tested for their illness to be recorded as a case. Second, although ill persons might meet the FoodNet criteria for hospitalization or death, the underlying reason for hospitalization or death might be unknown. Deaths that occurred >1 week after specimen collection among nonhospitalized persons or after discharge for hospitalized persons might not be recorded. Finally, domestically acquired cases might be overestimated because of the inclusion of persons with unknown travel status.
Implications for Public Health Practice
FoodNet’s surveillance efforts are critical for tracking foodborne and enteric illnesses in the United States. During 2023, FoodNet expanded its catchment area for the first time since 2004, and it now includes all of Colorado. This expansion improved the representativeness of the FoodNet catchment area, and the ability of FoodNet to monitor trends in disease incidence, including the impact of changing diagnostic practices and the generalizability of FoodNet data. Continued surveillance is needed to monitor the impact of changing diagnostic practices on disease trends and evaluate the efficacy of prevention efforts in reducing incidence.
Acknowledgments
Workgroup members, Foodborne Diseases Active Surveillance Network (FoodNet), Emerging Infections Program, CDC.
1Division of Foodborne, Waterborne, and Environmental Diseases, National Center for Emerging and Zoonotic Infectious Diseases, CDC; 2Colorado Department of Public Health and Environment; 3California Emerging Infections Program, Oakland, California; 4Connecticut Emerging Infections Program, Yale School of Public Health, New Haven, Connecticut; 5Georgia Department of Public Health; 6Maryland Department of Health; 7Minnesota Department of Health; 8University of New Mexico, Albuquerque, New Mexico; 9New York State Department of Health; 10Oregon Health Authority; 11Tennessee Department of Health; 12Center for Food Safety and Applied Nutrition, Food and Drug Administration, College Park, Maryland; 13Food Safety and Inspection Service, U.S. Department of Agriculture, Washington, DC.
References
- Office of Disease Prevention and Health Promotion. Healthy people 2030: foodborne illness. Washington, DC: U.S. Department of Health and Human Services; 2023. https://health.gov/healthypeople/objectives-and-data/browse-objectives/foodborne-illness
- Delahoy MJ, Shah HJ, Weller DL, et al. Preliminary incidence and trends of infections caused by pathogens transmitted commonly through food—Foodborne Diseases Active Surveillance Network, 10 U.S. sites, 2022. MMWR Morb Mortal Wkly Rep 2023;72:701–6. https://doi.org/10.15585/mmwr.mm7226a1 PMID:37384552
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- Langley G, Besser J, Iwamoto M, et al. Effect of culture-independent diagnostic tests on future Emerging Infections Program surveillance. Emerg Infect Dis 2015;21:1582–8. https://doi.org/10.3201/eid2109.150570 PMID:26291736
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- Healy JM, Ray L, Tack DM, et al. Modelling counterfactual incidence during the transition towards culture-independent diagnostic testing. Int J Epidemiol 2023;53:dyad133. https://doi.org/10.1093/ije/dyad133 PMID:37820050
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- Henao O. PopSurvey_SupportingStatement_B_11.28.2018. In: Office of Management and Budget: FoodNet Population Survey. Washington, DC: The White House, Office of Management and Budget; 2018. https://omb.report/icr/201812-0920-001/doc/87992501
- Angulo F, Scallan E. Activities, achievements, and lessons learned during the first 10 years of the Foodborne Diseases Active Surveillance Network: 1996–2005. Clin Infect Dis 2007;44:718–25. https://doi.org/10.1086/511648 PMID:17278067
| Characteristic | Historic catchment, no. (%) | Expanded catchment, no. (%) | % Increase¶ | |||
|---|---|---|---|---|---|---|
| Cases | Catchment | Cases | Catchment | Cases | Catchment | |
| Total | 29,607 | 51,000,988 | 31,492 | 53,588,559 | 6.4 | 5.1 |
| Age group, yrs | ||||||
| ≤4 | 3,534 (11.9) | 2,776,555 (5.4) | 3,733 (11.9) | 2,914,934 (5.4) | 5.6 | 5.0 |
| 5–17 | 2,611 (8.8) | 8,123,969 (15.9) | 2,780 (8.8) | 8,533,731 (15.9) | 6.5 | 5.0 |
| 18–59 | 14,806 (50.0) | 28,111,004 (55.1) | 15,738 (50.0) | 29,538,011 (55.1) | 6.3 | 5.1 |
| ≥60 | 8,656 (29.2) | 11,989,460 (23.5) | 9,241 (29.3) | 12,601,883 (23.5) | 6.8 | 5.1 |
| Not reported | 0 (—)** | —** | 0 (—)** | —** | —** | —** |
| Sex | ||||||
| Female | 14,577 (49.2) | 25,810,919 (50.6) | 15,565 (49.4) | 27,076,136 (50.5) | 6.8 | 4.9 |
| Male | 14,966 (50.6) | 25,190,069 (49.4) | 15,863 (50.4) | 26,512,423 (49.5) | 6.0 | 5.2 |
| Not reported | 64 (0.2) | —** | 64 (0.2) | —** | —** | —** |
| Ethnicity†† | ||||||
| Hispanic or Latino | 4,041 (13.6) | 6,786,543 (13.3) | 4,428 (14.1) | 7,348,445 (13.7) | 9.6 | 8.3 |
| Not Hispanic or Latino | 21,575 (72.9) | 44,214,445 (86.7) | 23,011 (73.1) | 46,240,114 (86.3) | 6.7 | 4.6 |
| Not reported | 3,991 (13.5) | —** | 4,053 (12.9) | —** | 1.6 | —** |
| Race | ||||||
| AI/AN | 254 (0.9) | 676,635 (1.3) | 263 (0.8) | 728,099 (1.4) | 3.5 | 7.6 |
| Asian | 1,562 (5.3) | 3,263,553 (6.4) | 1,574 (5.0) | 3,317,521 (6.2) | 0.8 | 1.7 |
| Black or African American | 3,430 (11.6) | 8,743,160 (17.1) | 3,471 (11.0) | 8,825,882 (16.5) | 1.2 | 0.9 |
| NH/PI | 54 (0.2) | 100,062 (0.2) | 60 (0.2) | 106,048 (0.2) | 11.1 | 6.0 |
| White | 19,599 (66.2) | 36,674,840 (71.9) | 21,147 (67.2) | 38,980,732 (72.7) | 7.9 | 6.3 |
| Other | 1,895 (6.4) | —** | 2,024 (6.4) | —** | 6.8 | —** |
| Multiple races | 413 (1.4) | 1,542,738 (3.0) | 452 (1.4) | 1,630,277 (3.0) | 9.4 | 5.7 |
| Not reported | 2,400 (8.1) | —** | 2,501 (7.9) | —** | 4.2 | —** |
| Urbanicity§§ | ||||||
| Urban | 24,565 (83.0) | 43,609,552 (85.5) | 25,740 (81.7) | 45,481,094 (84.9) | 4.8 | 4.3 |
| Rural | 5,041 (17.0) | 7,391,436 (14.5) | 5,751 (18.3) | 8,107,465 (15.1) | 14.1 | 9.7 |
Abbreviations: AI/AN = American Indian or Alaska Native; FoodNet = Foodborne Diseases Active Surveillance Network; NH/PI = Native Hawaiian or Pacific Islander; RUCC = rural-urban continuum code.
* Case data for 2023 are preliminary.
† When FoodNet was founded in 1996, the catchment included Minnesota and Oregon and counties in California (two), Connecticut (two), and Georgia (eight). The catchment expanded consistently during 1996–2004 and remained stable during 2004–2022. The historic catchment includes sites under surveillance since 2004, including Connecticut, Georgia, Maryland, Minnesota, New Mexico, Oregon, and Tennessee, and counties in California (three), Colorado (seven), and New York (34).
§ In 2023, the remaining 57 Colorado counties were enrolled in the FoodNet catchment. The expanded catchment includes those sites that were part of the historic catchment and the remaining 57 Colorado counties. Because yersiniosis is not a notifiable disease in Colorado, FoodNet only collected data on yersiniosis cases in the seven Colorado counties included in the historic catchment. Therefore, yersiniosis data for the historic and expanded catchments will be the same.
¶ Percent increase among persons with the given demographic characteristic in the expanded catchment compared with the historic catchment using the equation (No. in expanded – No. in historic) / (No. in historic) × 100.
** Dashes indicate that the given data point was unknown, not reported, or otherwise missing from the FoodNet data. The U.S. Census Bureau data used to describe catchment characteristics do not include a comparable “not reported” category for any of the characteristics of interest.
†† FoodNet’s data collection mechanism includes separate questions about ethnic and racial identity. As a result, persons could identify as any combination of ethnicity and race.
§§ Urbanicity was determined using RUCC (https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/) for the county of residence. During 2020, U.S. Census Bureau data were generated for Connecticut planning regions instead of Connecticut counties; as a result, the 2023 RUCC estimates were calculated for Connecticut planning regions but not Connecticut counties. Because FoodNet collected county of residence (as opposed to planning region of residence) for Connecticut cases, the catchment population and all cases from all sites except Connecticut were stratified into urban and rural using 2023 RUCC data. Connecticut cases were classified as urban or rural using 2013 RUCC data; 2013 is the most recent year for which RUCC data are available for Connecticut counties.
| Pathogen | Infections, no.¶ | No. (%) | Crude average incidence,¶¶ 2016–2018 | 2023 incidence | IRR (95% CrI)††† | HP2030 incidence target§§§ | |||
|---|---|---|---|---|---|---|---|---|---|
| Hospitalizations** | Deaths†† | Outbreak-associated§§ | Crude*** | Estimated (95% CrI)††† | |||||
| All cases (including international travel–associated)¶¶¶ | |||||||||
| Bacteria | |||||||||
| Campylobacter | 11,926 | 2,482 (20.8) | 49 (0.4) | 30 (0.3) | 18.2 | 23.4 | 21.52 (20.34–22.75) | 1.19 (1.11–1.26) | NA |
| Salmonella**** | 8,454 | 2,456 (29.1) | 55 (0.7) | 644 (7.6) | 16.9 | 16.6 | 15.79 (14.89–16.75) | 0.97 (0.91–1.04) | NA |
| S. Enteritidis | 1,597 | 460 (28.8) | 12 (0.8) | 178 (11.1) | 2.6 | 3.1 | 2.72 (2.47–3.00) | 1.04 (0.93–1.16) | NA |
| S. Newport | 566 | 179 (31.6) | 0 (—)†††† | 23 (4.1) | 1.6 | 1.1 | 1.26 (1.02–1.64) | 0.80 (0.62–1.07) | NA |
| S. Typhimurium | 541 | 154 (28.5) | 1 (0.2) | 77 (14.2) | 1.4 | 1.1 | 1.21 (1.09–1.35) | 0.84 (0.75–0.94) | NA |
| S. Javiana | 324 | 114 (35.2) | 4 (1.2) | 10 (3.1) | 1.2 | 0.6 | 0.73 (0.58–0.97) | 0.59 (0.44–0.81) | NA |
| S. I 4,[5],12:i:- | 279 | 79 (28.3) | 3 (1.1) | 36 (12.9) | 0.9 | 0.5 | 0.56 (0.47–0.68) | 0.65 (0.54–0.81) | NA |
| Other serotypes | 2,850 | 920 (32.3) | 22 (0.8) | 277 (9.7) | 5.9 | 5.5 | 5.43 (5.10–5.79) | 0.92 (0.85–0.99) | NA |
| Not serotyped | 2,297 | 550 (23.9) | 13 (0.6) | 43 (1.9) | 2.6 | 4.6 | 4.56 (4.97–5.29) | 1.74 (1.44–2.08) | NA |
| STEC§§§§ | 3,351 | 685 (20.4) | 14 (0.4) | 108 (3.2) | 4.7 | 6.6 | 6.29 (5.62–7.07) | 1.33 (1.16–1.52) | NA |
| non-O157¶¶¶¶ | 1,112 | 183 (16.5) | 3 (0.3) | 42 (3.8) | 2.1 | 2.2 | 2.11 (1.83–2.46) | 1.03 (0.87–1.23) | NA |
| O157 | 298 | 114 (38.3) | 3 (1.0) | 51 (17.1) | 0.8 | 0.6 | 0.61 (0.53–0.69) | 0.72 (0.62–0.82) | NA |
| Not serogrouped | 1,941 | 388 (20.0) | 8 (0.4) | 15 (0.8) | 2.1 | 3.8 | 4.03 (3.11–5.60) | 1.93 (1.42–2.83) | NA |
| Shigella | 3,186 | 969 (30.4) | 9 (0.3) | 124 (3.9) | 4.8 | 6.2 | 5.44 (4.49–6.69) | 1.13 (0.91–1.40) | NA |
| Yersinia | 1,437 | 325 (22.6) | 9 (0.6) | 0 (—)†††† | 0.8 | 2.8 | 2.59 (2.28–2.97) | 3.43 (2.94–4.06) | NA |
| Vibrio | 567 | 118 (20.8) | 5 (0.9) | 10 (1.8) | 0.7 | 1.1 | 1.15 (1.01–1.31) | 1.69 (1.47–1.94) | NA |
| Listeria | 163 | 159 (97.5) | 36 (22.1) | 5 (3.1) | 0.3 | 0.3 | 0.30 (0.26–0.34) | 1.13 (0.98–1.31) | NA |
| Parasite | |||||||||
| Cyclospora | 523 | 40 (7.6) | 0 (—)†††† | 40 (7.6) | 0.3 | 1.0 | 1.56 (1.00–2.97) | 4.75 (2.76–9.50) | NA |
| Total | 29,607 | 7,234 (24.4) | 177 (0.6) | 961(3.2) | —†††† | —†††† | —†††† | —†††† | —†††† |
| Domestically acquired cases only***** | |||||||||
| Bacteria | |||||||||
| Campylobacter | 10,516 | 2,368 (22.5) | 48 (0.5) | 30 (0.3) | 15.8 | 20.6 | 19.32 (18.30–20.41) | 1.22 (1.15–1.30) | 10.9 |
| Salmonella | 7,237 | 2,202 (30.4) | 53 (0.7) | 621 (8.6) | 14.8 | 14.2 | 13.92 (13.10–14.81) | 0.94 (0.87–1.01) | 11.5 |
| S. Enteritidis | 1,238 | 398 (32.1) | 10 (0.8) | 167 (13.5) | 2.1 | 2.4 | 2.13 (1.92–2.38) | 1.01 (0.89–1.14) | NA |
| S. Newport | 523 | 170 (32.5) | 0 (—)†††† | 23 (4.4) | 1.5 | 1.0 | 1.17 (0.94–1.55) | 0.77 (0.58–1.06) | NA |
| S. Typhimurium | 505 | 150 (29.7) | 1 (0.2) | 76 (15.0) | 1.4 | 1.0 | 1.15 (1.03–1.29) | 0.84 (0.74–0.95) | NA |
| S. Javiana | 303 | 108 (35.6) | 4 (1.3) | 4 (1.3) | 1.2 | 0.6 | 0.70 (0.55–0.95) | 0.58 (0.43–0.81) | NA |
| S. I 4,[5],12:i:- | 240 | 72 (30.0) | 3 (1.3) | 36 (15.0) | 0.8 | 0.5 | 0.51 (0.42–0.62) | 0.64 (0.52–0.80) | NA |
| Other serotypes | 2,436 | 789 (32.4) | 22 (0.9) | 273 (11.2) | 4.8 | 4.7 | 4.80 (4.49–5.15) | 0.90 (0.83–0.98) | NA |
| Not serotyped | 1,992 | 515 (25.9) | 13 (0.7) | 42 (2.1) | 2.4 | 4.0 | 4.04 (3.51–4.71) | 1.67 (1.38–2.01) | NA |
| STEC | 2,703 | 617 (22.8) | 14 (0.5) | 103 (3.8) | 4.1 | 5.3 | 5.15 (4.63–5.75) | 1.25 (1.10–1.42) | 3.7 |
| non-O157††††† | 882 | 160 (18.1) | 3 (0.3) | 39 (4.4) | 1.7 | 1.7 | 1.70 (1.47–1.98) | 1.00 (0.85–1.19) | NA |
| O157 | 276 | 111 (40.2) | 3 (1.1) | 50 (18.1) | 0.8 | 0.5 | 0.57 (0.50–0.65) | 0.71 (0.61–0.81) | NA |
| Not serogrouped | 1,545 | 346 (22.4) | 8 (0.5) | 14 (0.9) | 1.8 | 3.0 | 3.19 (2.52–4.26) | 1.79 (1.36–2.48) | NA |
| Shigella | 2,370 | 886 (37.4) | 8 (0.3) | 123 (5.2) | 4.1 | 4.6 | 4.21 (3.42–5.26) | 1.02 (0.81–1.29) | NA |
| Yersinia | 1,376 | 319 (23.2) | 9 (0.7) | 0 (—)†††† | 0.7 | 2.7 | 2.49 (2.18–2.85) | 3.47 (2.97–4.08) | NA |
| Vibrio | 501 | 108 (21.6) | 5 (1.0) | 10 (2.0) | 0.6 | 1.0 | 1.02 (0.90–1.16) | 1.64 (1.43–1.90) | NA |
| Listeria§§§§§ | 160 | 156 (97.5) | 36 (22.5) | 5 (3.1) | 0.26 | 0.31 | 0.29 (0.26–0.34) | 1.13 (0.98–1.32) | 0.22 |
| Parasite | |||||||||
| Cyclospora | 367 | 34 (9.3) | 0 (—)†††† | 39 (10.6) | 0.3 | 0.7 | 1.25 (0.73–2.87) | 5.06 (2.65–12.43) | NA |
| Total | 25,230 | 6,690 (26.5) | 173 (0.7) | 931 (3.7) | —†††† | —†††† | —†††† | —†††† | —†††† |
Abbreviations: CIDT = culture-independent diagnostic test; CrI = credible interval; FoodNet = Foodborne Diseases Active Surveillance Network; HP2030 = Healthy People 2030; IRR = incidence rate ratio; NA = not applicable; STEC = Shiga toxin-producing Escherichia coli.
* When FoodNet was founded in 1996, the catchment included Minnesota and Oregon and counties in California (two) Connecticut (two), and Georgia (eight). The catchment expanded consistently during 1996–2004; it remained stable during 2004–2022. The historic catchment includes sites under surveillance since 2004, including Connecticut, Georgia, Maryland, Minnesota, New Mexico, Oregon, and Tennessee, and counties in California (three), Colorado (seven), and New York (34). To facilitate comparability with past FoodNet reports, data for the historic catchment are presented in-text.
† Case data for 2023 are preliminary.
§ HP2030 is a 10-year plan for addressing critical public health priorities and challenges. U.S. Department of Health and Human Services releases priority objectives as part of HP2030, including incidence targets for Campylobacter, Salmonella, STEC, and Listeria infections, to be met by 2030. https://health.gov/healthypeople/objectives-and-data/browse-objectives/foodborne-illness
¶ Bacterial infections were diagnosed using culture or culture-independent diagnostic tests. Cyclospora infections were diagnosed using microscopy or polymerase chain reaction.
** Admission to an inpatient unit or an observation stay of >24 hours ≤7 days before or after specimen collection or determined to be related to the infection if beyond this time frame. The average percentage of infections resulting in hospitalizations during 2016–2018, by pathogen, were Campylobacter (20%), Salmonella (27%), STEC (22%), Shigella (24%), Yersinia (26%), Vibrio (30%), Listeria (96%), Cyclospora (6%), and overall (24%). Infections with unknown hospitalization status (6% of infections during 2023 and 4% during 2016–2018) were included in the denominator only.
†† Attributed to infection when death occurred during hospitalization or ≤7 days after specimen collection from nonhospitalized patients. The average percentage of infections resulting in death during 2016–2018 were, by pathogen, Campylobacter (0.4%), Salmonella (0.4%), STEC (0.4%), Shigella (0.1%), Yersinia (1.2%), Vibrio (2.1%), Listeria (18.6%), Cyclospora (0.2%), and overall (0.5%). Infections with unknown death status (8% of infections during 2023 and 3% during 2016–2018) were included in the denominator only.
§§ Generally defined as two or more cases of similar illness associated with a common exposure; some sites also stipulate illnesses be from one or more households. The average percentage of outbreak-associated infections during 2016–2018 were, by pathogen, Campylobacter (<1%), Salmonella (7%), STEC (4%), Shigella (5%), Yersinia (<1%), Vibrio (4%), Listeria (5%), Cyclospora (24%), and overall (4%).
¶¶ Cases per 100,000 persons.
*** Crude incidence is unadjusted and is calculated as cases of infection per 100,000 persons.
††† A Bayesian, negative binomial model with penalized thin plate splines adjusting for state-specific trends was used to quantify adjusted incidence in 2023 and the IRR in 2023 compared with average incidence during 2016–2018. Incidence during 2023 was described as increased or decreased compared with 2016–2018 if the 95% CrI for the IRR did not include 1. A 95% CrI is analogous to a 95% CI in frequentist statistics and can be interpreted similarly, meaning a 95% probability of the true IRR for incidence in 2023 compared with average annual incidence during 2016–2018 is within the 95% CrI. https://www.medrxiv.org/content/10.1101/2022.09.14.22279742v1.full.pdf
§§§ HP2030 incidence targets are based on domestically acquired infection incidence only. If the ill person did not report international travel or had unknown travel history, the illness was considered domestically acquired. A history of international travel was defined as travel ≤30 days before listeriosis and Salmonella Typhi and S. Paratyphi infection onset, ≤14 days before cyclosporiasis onset, and ≤7 days before onset for other infections. According to the Bayesian splines model, of the four illnesses with an HP2030 goal (Campylobacter, Salmonella, STEC, and Listeria infection), no evidence of a decrease was observed; instead, incidence of Campylobacter and STEC infection appears to have increased.
¶¶¶ Includes both international travel–associated infections and domestically acquired infections.
**** Infections that were not serotyped include all cases that were diagnosed by CIDT only, CIDT-diagnosed cases that failed to yield an isolate during reflex culture, and cases that yielded an isolate (both culture and CIDT-diagnosed) where the isolate was partially serotyped or not serotyped.
†††† Dashes indicate that the given data point was unknown, not reported, otherwise missing from the FoodNet data, or was not quantified.
§§§§ Incidences for STEC O157 and overall STEC non-O157 represent only a proportion of the total STEC incidence because 1,425 (43%) infections yielded an isolate, and only 1,410 (42%) were fully serogrouped and classified as STEC O157 or STEC non-O157 during 2023. Thus, IRRs for STEC O157 and STEC non-O157 partially reflect the increasing proportion of STEC infections with unknown serogroup. Infections that were not serogrouped include all cases that were diagnosed by CIDT only, CIDT-diagnosed cases that failed to yield an isolate during reflex culture, and cases that yielded an isolate in which the isolate was partially serogrouped or not serogrouped.
¶¶¶¶ The most frequently detected non-O157 serogroups were O103 (188) and O26 (173). The incidence of STEC O103 infection remained stable in 2023 compared with the 2016–2018 baseline (IRR = 0.89; 95% CrI = 0.70–1.14), and the incidence of STEC O26 infection decreased substantially (IRR = 0.75; 95% CrI = 0.63–0.90) during 2023.
***** Includes only domestically acquired infections (those for which the patient had no history of international travel or unknown travel history).
††††† The incidence of domestically acquired STEC O103 infection remained stable in 2023 compared with the 2016–2018 baseline (IRR = 0.86; 95% CrI = 0.67–1.11), and the incidence of domestically acquired STEC O26 infection decreased substantially (IRR = 0.77; 95% CrI = 0.66–0.93) during 2023.
§§§§§ For ease of comparison with the HP2030 goal, the reported incidence of domestically acquired Listeria infections during 2023 is shown to the second decimal place.
| Pathogen | Total, no. (% of total)†† | Diagnosis method, no. (% of species/serotype/serogroup total**) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CIDT | Culture-based methods only (Cx+) | |||||||||||
| Any CIDT§§ | Positive reflex culture (CIDT+/Cx+) | |||||||||||
| 2010–2015 | 2016–2018 | 2023 | 2010–2015 | 2016–2018 | 2023 | 2010–2015 | 2016–2018 | 2023 | 2010–2015 | 2016–2018 | 2023 | |
| Campylobacter¶¶ | ||||||||||||
| All*** | 44,698 | 27,977 | 11,926 | 6,517 (14.6) | 14,867 (53.1) | 9,704 (81.4) | 1,157 (2.6) | 4,912 (17.6) | 2,867 (24.0) | 38,181 (85.4) | 13,110 (46.9) | 2,222 (18.6) |
| C. jejuni | 14,675 (32.8) | 8,024 (28.7) | 2,672 (22.4) | —††† | —††† | —††† | 908 (6.2) | 3,397 (42.3) | 2,155 (80.7) | 13,767 (93.8) | 4,627 (57.7) | 517 (19.3) |
| C. coli | 1,399 (3.1) | 841 (3.0) | 316 (2.6) | —††† | —††† | —††† | 69 (4.9) | 365 (43.4) | 247 (78.2) | 1,330 (95.1) | 476 (56.6) | 69 (21.8) |
| C. upsaliensis | 332 (0.7) | 188 (0.7) | 79 (0.7) | —††† | —††† | —††† | 10 (3.0) | 115 (61.2) | 74 (93.7) | 332 (100.0) | 73 (38.8) | 5 (6.3) |
| C. lari | 101 (0.2) | 65 (0.2) | 13 (0.1) | —††† | —††† | —††† | 1 (1.0) | 14 (21.5) | 7 (53.8) | 100 (99.0) | 51 (78.5) | 6 (46.2) |
| C. fetus | 36 (0.1) | 47 (0.2) | 9 (0.1) | —††† | —††† | —††† | 0 (—)††† | 1 (2.1) | 1 (11.1) | 36 (100.0) | 46 (97.9) | 8 (88.9) |
| Other species | 49 (0.1) | 77 (0.3) | 38 (0.3) | —††† | —††† | —††† | 6 (12.2) | 28 (36.4) | 11 (28.9) | 43 (87.8) | 49 (63.6) | 27 (71.1) |
| Not speciated | 28,106 (62.9) | 18,735 (67.0) | 8,799 (73.8) | —††† | —††† | —††† | 163 (0.6) | 992 (5.3) | 372 (4.2) | 22,583 (80.4) | 7,788 (41.6) | 1,590 18.1) |
| Salmonella§§§ | ||||||||||||
| All¶¶¶ | 47,131 | 25,291 | 8,454 | 1,516 (3.2) | 7,516 (29.7) | 5,022 (59.4) | 778 (1.7) | 5,177 (20.5) | 3,496 (41.4) | 45,615 (96.8) | 17,775 (70.3) | 3,432 (40.6) |
| S. Enteritidis | 8,396 (17.8) | 4,034 (16.0) | 1,597 (18.9) | —††† | —††† | —††† | 103 (1.2) | 988 (24.5) | 901 (56.4) | 8,293 (98.8) | 3,046 (75.5) | 696 (43.6) |
| S. Newport | 5,258 (11.2) | 2,407 (9.5) | 566 (6.7) | —††† | —††† | —††† | 84 (1.6) | 528 (21.9) | 306 (54.1) | 5,174 (98.4) | 1,879 (78.1) | 260 (45.9) |
| S. Typhimurium | 5,445 (11.6) | 2,235 (8.8) | 541 (6.4) | —††† | —††† | —††† | 92 (1.7) | 615 (27.5) | 324 (59.9) | 5,353 (98.3) | 1,620 (72.5) | 217 (40.1) |
| S. Javiana | 4,261 (9.0) | 1,880 (7.4) | 324 (3.8) | —††† | —††† | —††† | 50 (1.2) | 358 (19.0) | 153 (47.2) | 4,211 (98.8) | 1,522 (81.0) | 171 (52.8) |
| S. I 4,[5],12:i:- | 2,227 (4.7) | 1,329 (5.3) | 279 (3.3) | —††† | —††† | —††† | 51 (2.3) | 398 (29.9) | 179 (64.2) | 2,176 (97.7) | 931 (70.1) | 100 (35.8) |
| Other serotypes | 18,300 (38.8) | 9,520 (37.6) | 2,878 (34.0) | —††† | —††† | —††† | 309 (1.7) | 2,110 (22.2) | 1,414 (49.1) | 17,991 (98.3) | 7,410 (77.8) | 1,464 (50.9) |
| Not serotyped | 3,244 (6.9) | 3,886 (15.4) | 2,269 (26.8) | —††† | —††† | —††† | 89 (2.7) | 180 (4.6) | 219 (9.7) | 2,417 (74.5) | 1,367 (35.2) | 524 (23.1) |
| STEC**** | ||||||||||||
| All†††† | 7,824 | 7,953 | 3,351 | 5,821 (74.4) | 7,919 (99.6) | 3,348 (99.9) | 4,577 (58.5) | 4,493 (56.5) | 1,422 (42.4) | 2,003 (25.6) | 34 (0.4) | 3 (0.1) |
| O157 | 2,905 (37.1) | 1,380 (17.4) | 298 (8.9) | —††† | —††† | —††† | 1,932 (66.5) | 1,357 (98.3) | 295 (99.0) | 973 (33.5) | 23 (1.7) | 3 (1.0) |
| Non-O157 | 3,571 (45.6) | 3,120 (39.2) | 1,112 (33.2) | —††† | —††† | —††† | 2,605 (72.9) | 3,109 (99.6) | 1,112 (100.0) | 966 (27.1) | 11 (0.4) | 0 (—)††† |
| Not serogrouped | 1,348 (17.2) | 3,453 (43.4) | 1,941 (57.9) | —††† | —††† | —††† | 40 (3.0) | 27 (0.8) | 15 (0.8) | 64 (4.8) | 0 (—)††† | 0 (—)††† |
| Shigella§§§§ | ||||||||||||
| All¶¶¶¶ | 14,098 | 7,533 | 3,186 | 1,379 (9.8) | 3,697 (49.1) | 2,679 (84.1) | 493 (3.5) | 1,490 (19.8) | 1,010 (31.7) | 12,719 (90.2) | 3,836 (50.9) | 507 (15.9) |
| S. sonnei | 10,093 (71.6) | 3,411 (45.3) | 364 (11.4) | —††† | —††† | —††† | 436 (4.3) | 887 (26.0) | 256 (70.3) | 9,657 (95.7) | 2,524 (74.0) | 108 (29.7) |
| S. flexneri | 2,142 (15.2) | 1,487 (19.7) | 926 (29.1) | —††† | —††† | —††† | 41 (1.9) | 525 (35.3) | 644 (69.5) | 2,101 (98.1) | 962 (64.7) | 282 (30.5) |
| S. boydii | 76 (0.5) | 33 (0.4) | 14 (0.4) | —††† | —††† | —††† | 2 (2.6) | 10 (30.3) | 8 (57.1) | 74 (97.4) | 23 (69.7) | 6 (42.9) |
| S. dysenteriae | 24 (0.2) | 5 (0.1) | 3 (0.1) | —††† | —††† | —††† | 1 (4.2) | 1 (20.0) | 3 (100.0) | 23 (95.8) | 4 (80.0) | 0 (—)††† |
| Not speciated | 1,326 (9.4) | 2,597 (34.5) | 1,879 (59.0) | —††† | —††† | —††† | 6 (0.5) | 67 (2.6) | 99 (5.3) | 434 (32.7) | 323 (12.4) | 111 (5.9) |
| Yersinia***** | ||||||||||||
| All††††† | 951 | 1,293 | 1,437 | 26 (2.7) | 898 (69.5) | 1,325 (92.2) | 4 (0.4) | 297 (23.0) | 247 (17.2) | 925 (97.3) | 395 (30.5) | 112 (7.8) |
| Y. enterocolitica | 771 (81.1) | 554 (42.9) | 294 (20.5) | —††† | —††† | —††† | 4 (0.5) | 268 (48.4) | 213 (72.4) | 767 (99.5) | 286 (51.6) | 81 (27.6) |
| Y. frederiksenii | 39 (4.1) | 30 (2.3) | 15 (1.0) | —††† | —††† | —††† | 0 (—)††† | 7 (23.3) | 10 (66.7) | 39 (100.0) | 23 (77) | 5 (33.3) |
| Y. intermedia | 17 (1.8) | 33 (2.6) | 8 (0.6) | —††† | —††† | —††† | 0 (—)††† | 11 (33.3) | 4 (50.0) | 17 (100.0) | 22 (66.7) | 4 (50.0) |
| Y. kristensenii | 8 (0.8) | 10 (0.8) | 5 (0.4) | —††† | —††† | —††† | 0 (—)††† | 1 (10.0) | 2 (40.0) | 8 (100.0) | 9 (90) | 3 (60.0) |
| Y. pseudotuberculosis | 10 (1.1) | 6 (0.5) | 2 (0.1) | —††† | —††† | —††† | 0 (—)††† | 1 (16.7) | 0 (—)††† | 10 (100.0) | 5 (83.3) | 2 (100.0) |
| Other species | 41 (4.3 | 11 (0.9) | 7 (0.5) | —††† | —††† | —††† | 0 (—)††† | 3 (27.3) | 4 (57.1) | 41 (100,0) | 8 (72.7) | 3 (42.9) |
| Not speciated | 65 (6.8) | 649 (50.2) | 1,106 (77.0) | —††† | —††† | —††† | 0 (—)††† | 6 (0.9) | 14 (1.3) | 43 (66.2) | 42 (6.5) | 14 (1.3) |
| Vibrio§§§§§ | ||||||||||||
| All¶¶¶¶¶ | 1,234 | 1,174 | 567 | 26 (2.1) | 524 (44.6) | 433 (76.4) | 7 (0.6) | 164 (14.0) | 77 (13.6) | 1,208 (97.9) | 650 (55.4) | 134 (23.6) |
| V. parahaemolyticus | 692 (56.1) | 403 (34.3) | 105 (18.5) | —††† | —††† | —††† | 4 (0.6) | 112 (27.8) | 52 (49.5) | 688 (99.4) | 290 (72.0) | 53 (50.5) |
| V. alginolyticus | 151 (12.2) | 109 (9.3) | 29 (5.1) | —††† | —††† | —††† | 0 (—)††† | 1 (0.9) | 0 (—)††† | 151 (100.0) | 108 (99.1) | 29 (100.0) |
| V. vulnificus | 120 (9.7) | 72 (6.1) | 13 (2.3) | —††† | —††† | —††† | 0 (—)††† | 1 (1.4) | 0 (—)††† | 120 (100.0) | 71 (98.6) | 13 (100.0) |
| V. cholerae | 68 (5.5) | 73 (6.2) | 20 (3.5) | —††† | —††† | —††† | 1 (1.5) | 21 (28.8) | 13 (65.0) | 67 (98.5) | 52 (71.2) | 7 (35.0) |
| V. fluvialis | 73 (5.9) | 55 (4.7) | 25 (4.4) | —††† | —††† | —††† | 1 (1.4) | 8 (14.5) | 8 (32.0) | 72 (98.6) | 47 (85.5) | 17 (68.0) |
| Other species | 84 (6.8) | 25 (2.1) | 5 (0.9) | —††† | —††† | —††† | 1 (1.2) | 5 (20) | 1 (20.0) | 83 (98.8) | 20 (80.0) | 4 (80.0) |
| Not speciated | 692 (56.1) | 437 (37.2) | 370 (65.3) | —††† | —††† | —††† | 0 (—)††† | 16 (3.7) | 3 (0.8) | 27 (58.7) | 62 (14.2) | 11 (3.0) |
| Listeria monocytogenes****** | 738 | 420 | 163 | 4 (0.5) | 17 (4.0) | 44 (27.0) | 4 (0.5) | 15 (3.6) | 42 (25.8) | 734 (99.5) | 403 (96.0) | 119 (73.0) |
| Total | 116,674 | 71,641 | 29,084 | 15,289 (13.1) | 35,438 (49.5) | 22,555 (77.6) | 7,020 (6.0) | 16,548 (23.1) | 9,161 (31.5) | 101,385 (86.9) | 36,203 (50.5) | 6,529 (22.4) |
Abbreviations: CIDT = culture-independent diagnostic test; CIDT+ = positive CIDT result; Cx = culture-based method; Cx+ = positive culture result; Cx– = negative culture result; FoodNet = Foodborne Diseases Active Surveillance Network; HP2030 = Healthy People 2030; STEC = Shiga toxin-producing Escherichia coli.
* Bacterial infections could be diagnosed by culture-based methods only (Cx+), by CIDTs only (CIDT+), or by CIDTs with a reflex culture. When diagnosed by CIDTs with a reflex culture, the culture could either yield an isolate (CIDT+/Cx+) or fail to yield an isolate (CIDT+/Cx–). Because speciation and subtyping are only possible when an isolate is available, species and subtype data are only reported in the table for infections diagnosed by culture and by CIDTs with a reflex culture that yielded an isolate (CIDT+/Cx+). To facilitate readability, data for cases diagnosed by CIDTs only (i.e., where no reflex culture was performed) is not shown; these data can be calculated by subtracting the values in the footnotes for negative reflex culture (CIDT+/Cx–) and the Positive Reflex Culture (CIDT+/Cx+) column from the Any CIDT column.
† The historic catchment area includes those sites under surveillance since 2004, including Connecticut, Georgia, Maryland, Minnesota, New Mexico, Oregon, Tennessee, and selected counties in California (three), Colorado (seven), and New York (34).
§ Periods were selected to facilitate comparison of 2023 data with 1) 2016–2018, the reference period used by HP2030, and 2) 2010–2015, when CIDTs were not widely available or used. Comparing 2023 data with these two periods shows increased CIDT availability and adoption have affected the reported frequency of different bacterial pathogens in the FoodNet catchment area.
¶ Case data for 2023 are preliminary.
** Percentage of cases linked to a specific species or subtype that were diagnosed by the given method. For example, in 2023, 81% of C. jejuni cases were diagnosed by CIDTs followed by reflex culture, and 19% were diagnosed using culture-based methods only.
†† Total number of isolates reported during that time. To obtain the average annual number reported, divide this total by the number of years. For example, 22% of Campylobacter isolates in 2023 were identified as C. jejuni (2,672), whereas 29% were identified as C. jejuni during 2016–2018 (8,024, or an average of 2,675 per year).
§§ Includes cases diagnosed by CIDTs only (CIDT+) or by CIDTs with either a positive (CIDT+/Cx+) or negative (CIDT+/Cx–) reflex culture.
¶¶ During 2010–2023, the other Campylobacter species reported, in order of frequency, were C. ureolyticus, C. concisus, C. hyointestinalis, C. curvus, C. gracilis, C. rectus, C. sputorum, C. helveticus, C. lanienae, C. mucosalis, C. showae, C. hominis, C. paraureolyticus, and C. pelordis.
*** During 2010–2015, 5% (2,404) of reported Campylobacter infections were diagnosed by CIDT and had a negative reflex culture (CIDT+/Cx–). During 2016–2018, 14% (3,947) of reported Campylobacter infections were diagnosed by CIDT+/Cx–. During 2023, 21% (2,484) of reported Campylobacter infections were diagnosed by CIDT+/Cx–.
††† Dashes indicate that the given data point could not be quantified because of the absence of isolates for cases diagnosed by CIDT in which reflex culture was not performed or reflex culture was negative. Illnesses diagnosed by CIDT in which reflex culture was not performed or reflex culture was negative lack an isolate. As a result, these infections cannot be attributed to a specific species, serotype or serogroup.
§§§ During 2010–2023, other commonly identified Salmonella serotypes (at least 1,000 cases) were, in order of frequency: Infantis, Saintpaul, Muenchen, Montevideo, Braenderup, Oranienburg, Thompson, Heidelburg, Bareilly, Mississippi, and I 13,23:b:-.
¶¶¶ During 2010–2015, 0.3% (155) of reported Salmonella infections were diagnosed by CIDT+/Cx–. During 2016–2018, 3% (730) of reported Salmonella infections were diagnosed by CIDT+/Cx–. During 2023, 8% (707) of reported Salmonella infections were diagnosed by CIDT+/Cx–.
**** During 2010–2023, the most common non-O157 STEC subtypes reported (more than 500 isolates), in order of frequency, were O26, O103, O111, and O121.
†††† During 2010–2015, 11% (860) of reported STEC infections were diagnosed by CIDT+/Cx–. During 2016–2018, 31% (2,442) of reported STEC infections were diagnosed by CIDT+/Cx–. During 2023, 33% (1,094) of reported STEC infections were diagnosed by CIDT+/Cx–.
§§§§ The increase in reflex cultures that fail to yield an isolate might be partially attributable to the fact that existing CIDT panels cannot distinguish between Shigella and enteroinvasive E. coli.
¶¶¶¶ During 2010–2015, 1% (204) of reported Shigella infections were diagnosed by CIDT+/Cx–. During 2016–2018, 14% (1,073) of reported Shigella infections were diagnosed by CIDT+/Cx–. During 2023, 36% (1,151) of reported Shigella infections were diagnosed by CIDT+/Cx–.
***** During 2010–2023, the other Yersinia species reported, in order of frequency, were Y. ruckeri, Y. massiliensis, Y. aldovae, Y. mollaretii, Y. aleksiciae, Y. rohdei, and Y. bercovieri.
††††† During 2010–2015, 0.5% (5) of reported Yersinia infections were diagnosed by CIDT+/Cx–. During 2016–2018, 25% (322) of reported Yersinia infections were diagnosed by CIDT+/Cx–. During 2023, 31% (443) of reported Yersinia infections were diagnosed by CIDT+/Cx–.
§§§§§ During 2010–2023, the other Vibrio species reported, in order of frequency, were V. mimicus, V. hollisae, V. furnissii, V. damsela, V. metschnikovii, V. cincinnatiensis, V. harveyi, V. navarrensis, V. fischeri, and V. metoecus.
¶¶¶¶¶ During 2010–2015, 1% (12) of reported Vibrio infections were diagnosed by CIDT+/Cx–. During 2016–2018, 23% (272) of reported Vibrio infections were diagnosed by CIDT+/Cx–. During 2023, 37% (211) of reported Vibrio infections were diagnosed by CIDT+/Cx–.
****** During 2010–2015, no Listeria monocytogenes infections were diagnosed by CIDT+/Cx–. During 2016–2018, 0.5% (2) of reported L. monocytogenes infections were diagnosed by CIDT+/Cx–. During 2023, 0.6% (1) of reported L. monocytogenes infections were diagnosed by CIDT+/Cx–.
Suggested citation for this article: Shah HJ, Jervis RH, Wymore K, et al. Reported Incidence of Infections Caused by Pathogens Transmitted Commonly Through Food: Impact of Increased Use of Culture-Independent Diagnostic Tests — Foodborne Diseases Active Surveillance Network, 1996–2023. MMWR Morb Mortal Wkly Rep 2024;73:584–593. DOI: http://dx.doi.org/10.15585/mmwr.mm7326a1.
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