Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link

Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released.

Volume 31, Number 4—April 2025
Research

Foodborne Illness Acquired in the United States—Major Pathogens, 2019

On This Page
Methods
Results
Discussion
Suggested Citation
Tables
Table 1
Table 2
Downloads
Appendix 1
Appendix 2
Appendix 3
RIS [TXT - 2 KB]
Article Metrics
Metric Details
Related Articles
Genomic Characterization of Yersinia enterocolitica Isolates, Costa Rica
Chronic Wasting Disease Prions and Venison
Photo Quiz
More articles on Food Safety
Author affiliation: Colorado School of Public Health, Aurora, Colorado, USA (E.J. Scallan Walter); Centers for Disease Control and Prevention, Atlanta, Georgia, USA (Z. Cui, R. Tierney, P.M. Griffin, R.M. Hoekstra, D.C. Payne, E.B. Rose, C. Devine, A.S. Namwase, S.A. Mirza, A.K. Kambhampati, A. Straily, B.B. Bruce); Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA (D.C. Payne); University of Cincinnati College of Medicine, Cincinnati (D.C. Payne)

Suggested citation for this article

Abstract

Estimating the number of illnesses caused by foodborne pathogens is critical for allocating resources and prioritizing interventions. We estimated the number of illnesses, hospitalizations, and deaths in the United States caused by 7 major foodborne pathogens by using surveillance data and other sources, adjusted for underreporting and underdiagnosis. Campylobacter spp., Clostridium perfringens, invasive Listeria monocytogenes, norovirus, nontyphoidal Salmonella serotypes, and Shiga toxin–producing Escherichia coli caused ≈9.9 million (90% credible interval [CrI] 5.9–15.4 million) domestically acquired foodborne illnesses in 2019. Together with Toxoplasma gondii, those pathogens caused 53,300 (90% CrI 35,700–74,500) hospitalizations and 931 (90% CrI 530‒1,460) deaths. Norovirus caused most illnesses (≈5.5 million illnesses, 22,400 hospitalizations), followed by Campylobacter spp. (1.9 million illnesses, 13,000 hospitalizations) and nontyphoidal Salmonella serotypes (1.3 million illnesses, 12,500 hospitalizations). Salmonella infection was the leading cause of death (n = 238). Foodborne illness estimates can inform policy and direct food safety interventions that reduce those illnesses.

Estimates of the number of illnesses caused by pathogens transmitted commonly through food (hereafter foodborne illnesses) provide valuable information for allocating resources and prioritizing interventions. Laboratory-confirmed illnesses are routinely reported to US public health departments, providing data that support trend assessments. However, those reports are limited because not all ill persons seek medical care or receive a diagnosis, and illnesses might not be reported to public health surveillance systems. To overcome reporting challenges, the Centers for Disease Control and Prevention (CDC) periodically estimates the number of foodborne illnesses by adjusting for undercounts in surveillance data (1–3). The estimates can be used to analyze health, economic, and other effects of foodborne illnesses.

Since the last estimates published in 2011 (1), new data and methods have become available, and new regulations and other interventions to prevent foodborne illness have been implemented. Moreover, culture-independent diagnostic tests (CIDTs) have been more widely adopted, increasing the likelihood of identifying pathogens (4).

The Interagency Food Safety Analytics Collaboration considers 4 pathogenic bacteria to be priorities for source attribution and actions by regulatory agencies: Campylobacter spp., Listeria monocytogenes, nontyphoidal Salmonella, and Shiga toxin–producing Escherichia coli (STEC) (5). In this assessment, we also included norovirus, Clostridium perfringens, and Toxoplasma gondii because all 7 pathogens were previously estimated to be the leading causes of domestically acquired foodborne illnesses, hospitalizations, and deaths in the United States (1,6). We provide regulators, policy makers, and others with updated estimates for 7 major foodborne pathogens as of 2019.

Methods

We estimated the number of foodborne illnesses, hospitalizations, and deaths caused by Campylobacter spp., C. perfringens, invasive L. monocytogenes, norovirus, nontyphoidal Salmonella, and STEC and the number of hospitalizations and deaths caused by T. gondii. We produced separate estimates for STEC serogroup O157 and all other non-O157 STEC serogroups, for the top 5 Salmonella enterica serotypes (Enteritidis, I 4,[5],12:i:-, Javiana, Newport, and Typhimurium) and all other nontyphoidal Salmonella serotypes, and for non–pregnancy- and pregnancy-associated listeriosis. We did not estimate the numbers of Toxoplasma-induced illnesses because most infected persons were asymptomatic, and the number of persons experiencing mild illness was unknown.

Illnesses

To estimate illnesses, we obtained counts of laboratory-confirmed illnesses and adjusted for undercounts caused by laboratory-confirmed illnesses not being reported to public health departments (underreporting) or caused by a lack of diagnosis because the ill person did not seek medical care or submit a specimen or because the laboratory did not test for or identify the causative agent (underdiagnosis). We made adjustments by using a series of pathogen-specific multipliers estimated by using specific methods and data sources (Appendix 1; Appendix 2).

To determine the number of illnesses caused by Campylobacter spp., nontyphoidal Salmonella, and STEC, we used 2017–2019 data from the Foodborne Diseases Active Surveillance Network (FoodNet, https://www.cdc.gov/foodnet). FoodNet conducts active, population-based surveillance of laboratory-diagnosed infections at 10 sites (≈15% of the US population). We estimated the number of laboratory-confirmed illnesses nationwide by applying the incidence obtained from FoodNet according to year and site to US population estimates. We randomly assigned Salmonella isolates with unidentified serotypes to 1 of 6 serotype groups according to weights equal to the proportions of isolates known to be in that group. Because of marked differences in clinical illness caused by O157 and non-O157 STEC serogroups, we imputed isolates with missing serogroup designations to O157 or non-O157 by using a supervised random forest model, which included patient demographics, symptoms, illness severity, travel information, and outbreak association (Appendix 1).

We used 2016–2019 data from the CDC’s nationwide Listeria Initiative surveillance system to determine the number of illnesses caused by invasive L. monocytogenes, defined as isolation of Listeria from a specimen collected from a normally sterile site (blood or cerebrospinal fluid) or product of conception (for pregnancy-associated illnesses: placenta, amniotic fluid, umbilical cord, or chorion). L. monocytogenes can also cause gastroenteritis and fever; however, fecal specimens are almost never tested for Listeria bacteria except occasionally during outbreaks. In surveillance systems, mother–infant pairs are counted as a single case; however, we counted both mother and infant as separate cases of illness if L. monocytogenes was isolated from that person’s specimen or if the mother or infant of a person with an invasive case had symptoms (e.g., fever, vomiting, or diarrhea). We proportionally distributed infants with unknown outcomes according to infants with known outcomes.

We used 10 years (2010–2019) of data from the Foodborne Disease Outbreak Surveillance System (FDOSS) to estimate the number of illnesses caused by C. perfringens because of high year-to-year variability in reported outbreaks. We derived incidence of norovirus illnesses by combining estimates from 2 studies of norovirus medical encounters; 1 study used active surveillance from Kaiser Permanente Northwest (2014–2016), and the other used administrative data from MarketScan Commercial and Medicare Supplemental databases (2001–2015) (7,8).

Because FoodNet conducts active surveillance, we assumed all laboratory-confirmed illnesses were reported. The CDC’s Listeria Initiative is a passive surveillance system, but we assumed all diagnosed cases were reported because of the severity of invasive listeriosis, an assumption supported by similar reported rates in FoodNet and non-FoodNet sites (9,10). For C. perfringens, we estimated the number of laboratory-confirmed illnesses by using an outbreak underreporting multiplier, which was the inverse of the proportion of laboratory-confirmed illnesses for pathogens ascertained through FoodNet also reported to FDOSS as outbreak-associated (Appendix 1). We did not adjust norovirus estimates for underreporting because of the source data.

To adjust for underdiagnosis resulting from variations in medical care seeking and specimen submission for Campylobacter spp., nontyphoidal Salmonella, and STEC infections, we created multipliers for each laboratory-confirmed case in surveillance according to characteristics (age group, sex, race, ethnicity, and presence of fever or bloody diarrhea) of persons with acute diarrheal illness who reported seeking medical care and submitting a fecal sample in the 2018–2019 FoodNet Population Survey (11). Acute diarrheal illness was defined as diarrhea (>3 loose stools in 24 hours) lasting >1 day or causing restricted daily activities, excluding persons who indicated their illness was caused by a long-lasting or chronic illness or condition (e.g., colitis, irritable bowel syndrome). For norovirus, we adjusted for underdiagnosis by estimating the percentage of FoodNet Population Survey respondents who had acute gastroenteritis (diarrhea with >3 loose stools in a 24-hour period beginning in the previous month) lasting <3 days who had sought medical care, similar to a previous study (12). We did not adjust for fecal sample submission because norovirus inputs were from persons seeking medical care who had submitted a fecal sample (7,8). We assumed persons with invasive L. monocytogenes infections had high rates of medical care seeking (90%) and specimen submission (100%).

We determined the percentage of laboratories that routinely tested onsite or offsite for specific pathogens by using data from FoodNet laboratory surveys (13). We estimated test sensitivity for Campylobacter spp., nontyphoidal Salmonella, and STEC separately for illnesses diagnosed by using cultures and CIDTs. Most CIDTs are PCR-based and are highly sensitive (14); therefore, we assumed 100% CIDT sensitivity and estimated culture sensitivity by using data from FoodNet, which indicated the number of CIDT-positive tests that were also positive by reflex culture (cultures performed in response to a positive CIDT test). We used this number as the lower parameter of the program evaluation and review technique (PERT) distribution (Appendix 2). We did not adjust for specificity because we made estimates on the basis of persons who sought medical care; we considered the identified pathogen to be the most likely cause of illness. We assumed that most persons with listeriosis who submitted a specimen for testing would be tested for L. monocytogenes; we determined test sensitivity according to the sensitivity of the blood culture (15). For C. perfringens, we used the overall Salmonella underdiagnosis multiplier to adjust for underdiagnosis.

Hospitalizations and Deaths

For Campylobacter spp., nontyphoidal Salmonella, and STEC infections, we estimated the numbers of hospitalizations and deaths by using FoodNet surveillance data (2017–2019); we determined the percentage of illnesses causing hospitalization or death according to year and site and applied those percentages to the estimated number of laboratory-confirmed illnesses. We used the same approach for L. monocytogenes by using the Listeria Initiative (2016–2019) and C. perfringens by using FDOSS (2010–2019) data. We also estimated the number of pregnancy-associated infections causing fetal death (abortion, miscarriage, or stillbirth). We estimated rates of hospitalizations and deaths caused by T. gondii infections by using the Healthcare Cost and Utilization Project National Inpatient Sample (2016–2019) and those caused by norovirus by using both the National Inpatient Sample (hospitalizations) and the National Center for Health Statistics multiple-cause-of-mortality dataset (deaths) (8); we applied those data to the average US population during 2017–2019.

We adjusted all hospitalizations and deaths for underdiagnosis, except for those caused by norovirus. We adjusted Campylobacter spp., nontyphoidal Salmonella, and STEC underdiagnoses according to published studies reporting the percentage of hospitalized patients with nonspecific gastroenteritis diagnostic codes who submitted a fecal sample for bacterial culture, along with the frequency of laboratory testing and test sensitivity for that pathogen (16,17). We applied the Salmonella underdiagnosis multiplier for hospitalizations and deaths to estimate hospitalizations and deaths caused by C. perfringens and T. gondii infections. Because of the high hospitalization rate, we assumed the underdiagnosis multiplier for invasive L. monocytogenes hospitalizations and deaths was the same as that for illnesses.

Domestically Acquired Foodborne Illnesses

For Campylobacter spp., L. monocytogenes, nontyphoidal Salmonella, and STEC infections, we used surveillance data from FoodNet and the Listeria Initiative to estimate the percentage of illnesses acquired while persons had traveled outside the United States. We considered the remaining percentages of illnesses to be domestically acquired. Because of the rapid onset and short duration of illness, we assumed that ≈100% of C. perfringens and norovirus illnesses were domestically acquired. We also assumed toxoplasmosis was mostly domestically acquired.

We estimated numbers of domestically acquired foodborne illnesses caused by Campylobacter spp., nontyphoidal Salmonella (including specific serotypes), STEC, T. gondii, or norovirus according to a structured expert judgment study (18). We assumed illnesses caused by C. perfringens to be 100% foodborne because illness counts were from foodborne outbreaks. We assumed Listeria-induced illnesses to be ≈100% foodborne.

Uncertainty Analysis

We used empirical data, when available, to define entire distributions or parameters of distributions (Appendix 1, 2). When data were sparse, we made reasoned judgments according to context, plausibility, and previously published estimates. The parametric distribution used for most multipliers was a 4-parameter β (modified PERT) distribution. The first 3 parameters were low, modal, and high; the fourth parameter was related to the variability of the distribution. We fixed the last parameter at 4, which yields a simple PERT distribution. However, when describing the outbreak reporting multiplier, we used a value of 64, which produced a PERT distribution mean equal to the target mean of our multiplier distribution (Appendix 1). When combining distributions from 2 sources, we used random sampling with weights equal to the number of events from each source.

Uncertainty in the estimates is the cumulative effect of uncertainty of each of the model inputs. We iteratively generated sets of independent pathogen-specific adjustment factors and used those multipliers to estimate illnesses, hospitalizations, and deaths. Using 10,000 iterations, we obtained empirical distributions of counts corresponding to Bayesian posterior distributions and used those posterior distributions to generate a point estimate (posterior mean) and 90% credible intervals (CrIs). Because incidence of illnesses differed by location (e.g., FoodNet sites) and over time, we included those variables in the models, which led to larger CrIs than if we had assumed inputs represented independent random samples of a fixed US population. We used R version 4.1.1 (The R Project for Statistical Computing, https://www.r-project.org) for analyses. The code is available from the authors upon request.

Results

Of 37.6 million estimated episodes of illness caused by Campylobacter spp., C. perfringens, invasive L. monocytogenes, norovirus, nontyphoidal Salmonella, and STEC infections, 9.9 million (90% CrI 5.9–15.4 million) illnesses were estimated to be acquired domestically and were foodborne (Table 1). Together with T. gondii, domestically acquired foodborne illnesses caused by those pathogens resulted in an estimated 53,300 (90% CrI 35,800–74,600) hospitalizations and 931 (90% CrI 530–1,460) deaths (Table 2).

Foodborne Illnesses

Of the pathogens included in this assessment, norovirus was estimated to cause most (5.5 million/year) domestically acquired foodborne illnesses, followed by Campylobacter spp. (1.9 million/year), nontyphoidal Salmonella (1.3 million/year), C. perfringens (889,000/year), STEC (357,000/year), and invasive L. monocytogenes (1,250/year). Of the illnesses caused by Salmonella, 297,000 (23%) were estimated to be caused by serotype Enteritidis, followed by serotypes Newport (179,000 [14%]), Typhimurium (135,000 [11%]), I 4,[5],12:i:- (93,900 [7%]), and Javiana (91,800 [7%]). More STEC illnesses were caused by all non-O157 serogroups (271,000 [76%]) than by STEC O157 (86,200 [24%]). An estimated 1,050 non–pregnancy-associated and 198 (106 mothers, 92 infants) pregnancy-associated illnesses were caused by L. monocytogenes. For pathogens with available surveillance data, underdiagnosis multipliers ranged from 1.7 for invasive L. monocytogenes to ≈39 for nontyphoidal Salmonella and STEC O157, 42.7 for non-O157 STEC, and 52.8 for Campylobacter spp. (Table 1).

Hospitalizations

Norovirus was the leading cause of hospitalizations resulting from domestically acquired foodborne illnesses (22,400/year), followed by Campylobacter spp. (13,000/year), nontyphoidal Salmonella (12,500/year), STEC (3,150/year), invasive L. monocytogenes (1,070/year), T. gondii (848/year), and C. perfringens (338/year). The order of Salmonella serotypes causing most hospitalizations was the same as that for illnesses. Among domestically acquired foodborne illnesses, STEC O157 caused more (1,730 [55%]) hospitalizations than all non-O157 STEC serogroups (1,410 [45%]), although the overall number of hospitalizations caused by non-O157 STEC was slightly higher (Table 2). Invasive L. monocytogenes caused 920 non–pregnancy-associated and 148 (74 mothers, 74 infants) pregnancy-associated hospitalizations.

Deaths

Domestically acquired, foodborne nontyphoidal Salmonella caused ≈238 deaths each year, followed by Campylobacter spp. (197/year), norovirus (174/year), invasive L. monocytogenes (172/year), STEC (66/year), T. gondii (44/year), and C. perfringens (41/year). Among the annual deaths caused by nontyphoidal Salmonella, 63 (26%) were caused by Salmonella serotype Enteritidis, followed by serotypes Typhimurium (40 [17%]), I 4,[5],12:i- (26 [11%]), Newport (17 [7%]), and Javiana (5 [2%]). We estimated 166 nonpregnancy-associated deaths and 6 pregnancy-associated deaths (all among infants) caused by L. monocytogenes (Table 2). In addition, we estimated 37 (90% CrI 27‒45) fetal deaths.

Discussion

We estimated that foods contaminated with Campylobacter spp., C. perfringens, invasive L. monocytogenes, nontyphoidal Salmonella, STEC, and norovirus caused ≈9.9 million domestically acquired foodborne illnesses in the United States each year circa 2019. Domestically acquired foodborne illness caused by those 6 pathogen groups plus T. gondii resulted in ≈53,300 hospitalizations and 931 deaths. Similar to estimates from 2006, norovirus was the leading cause of domestically acquired foodborne illness (1). Norovirus, Campylobacter spp., and nontyphoidal Salmonella caused most hospitalizations; nontyphoidal Salmonella, Campylobacter spp., norovirus, and invasive L. monocytogenes caused most deaths. Pathogen rankings were similar to previous estimates except that Campylobacter spp. ranked higher for all outcomes (higher than Salmonella for illnesses and higher than norovirus for deaths); norovirus ranked higher than Campylobacter spp. and nontyphoidal Salmonella for hospitalizations, and T. gondii ranked lowest for both hospitalizations and deaths (1). Global estimates have also indicated norovirus as the most frequent cause of foodborne illness (19,20), and similar studies in other countries have reported Campylobacter spp. and nontyphoidal Salmonella as leading causes of bacterial foodborne illness (Campylobacter spp. caused more illnesses) (21–26). Nontyphoidal Salmonella, Campylobacter spp., and invasive L. monocytogenes have generally been the leading causes of death worldwide, the rank order varying by country. The estimated rank of foodborne norovirus deaths also varies.

After Food and Drug Administration–approved culture-independent syndromic panels were introduced in 2013, most clinical laboratories adopted CIDTs to diagnose enteric pathogens—a dramatic shift from culture-based methods (27). The shift toward rapid multipathogen diagnostic assays correlates with increased detection of infections that might have otherwise gone undetected and unreported in US surveillance systems (13). This shift, along with advances in analytical methods, might have contributed to higher numbers of estimated domestically acquired foodborne illnesses in this study compared with estimates published in 2011 (1): Campylobacter spp., 845,000 (90% CrI 337,000–1,610,000) in 2011 compared with 1,870,000 (90% CrI 696,000‒3,760,000) in this report; nontyphoidal Salmonella, 1,030,000 (90% CrI 645,000–1,680,000) compared with 1,280,000 (90% CrI 866,000‒1,760,000); STEC O157, 63,200 (90% CrI 17,600–150,000) compared with 86,200 (90% CrI 31,400‒201,000); and non-O157 STEC serogroups, 113,000 (90% CrI 11,500–287,000) compared with 271,000 (90% CrI 97,200‒546,000). Diagnosing infections caused by non-O157 STEC serogroups was especially challenging before the introduction of CIDTs. Although serogroup O157 causes most STEC-induced hospitalizations and deaths, non-O157 serogroups cause 3-fold more illnesses than O157 and cause a substantial number of hospitalizations and deaths.

Advances in our approach to adjusting for underdiagnosis also contributed to higher numbers of estimated illnesses caused by Campylobacter spp., nontyphoidal Salmonella, and STEC. Previously, we used bloody diarrhea as a marker of severity and adjusted for underdiagnosis caused by medical care seeking and fecal sample submission behaviors separately for persons with bloody and nonbloody diarrhea (1). Although bloody diarrhea is associated with medical care seeking, other drivers include illness duration, recent foreign travel, age, and socioeconomic status (28–30). Therefore, we included additional factors in the current estimates. When compared with previous estimates (1), the magnitude of underdiagnosis multipliers increased in this report for Campylobacter spp. (30.3 vs. 52.8), nontyphoidal Salmonella (29.3 vs. ≈39), and STEC O157 (26.1 vs. 39), indicating that adjusting for bloody diarrhea alone might have previously underestimated diagnosis rates.

Estimates of the number of domestically acquired foodborne illnesses attributable to norovirus were similar to those previously described (1) (5,460,000 [90% CrI 3,230,000–8,310,000] in 2011 vs. 5,540,000 [90% CrI 2,230,000‒10,300,000] in this report) despite improved data sources and changes in methods. The number of illnesses caused by C. perfringens (966,000 [90% CrI 192,000–2,480,000] vs. 889,000 [90% CrI: 19,300‒3,090,000]) and invasive L. monocytogenes (1,590 [90% CrI: 557–3,160] vs. 1,250 [90% CI: 1,080‒1,460) were lower in this report compared with those described previously (1). Differences in the estimated number of C. perfringens illnesses were mostly caused by a lower number of outbreak-associated cases reported during the study period, whereas the lower number of estimated invasive L. monocytogenes illnesses was mostly caused by using a lower underdiagnosis multiplier (driven by higher estimates of specimen submission frequency). Compared with previous estimates (1), the lower numbers for hospitalizations (4,430 [90% CrI 2,630–6,670] vs. 848 [90% CrI 173‒1,700]) and deaths (327 [90% CrI 200–482] vs. 44 [90% CrI 9‒90]) caused by T. gondii in the current estimates mirror trends in hospital discharge data and serosurveys (31–33) and likely reflect the increased availability of antiretroviral drugs for HIV, which, along with other immunocompromising conditions, is a major risk factor for severe illness from T. gondii reactivation (34).

Estimates of the percentage of illness transmitted by food and the percentage acquired while traveling abroad can profoundly affect the results and have important limitations. Many foodborne pathogens can be transmitted by other modes; however, data used to estimate foodborne illness percentages are often lacking. We used data from a structured expert judgment (18). Although subject to limitations, including expert bias, this approach offered consistency among all pathogens and ensured that transmission by all possible modes added up to 100%. For most foodborne pathogens, percentages were lower in this report than previously estimated (1). For norovirus, the uncertainty interval was large, reflecting a lack of consensus among experts. We used surveillance data to estimate the percentage of travel-related illnesses for most pathogens; however, the most recent FoodNet Population Survey found international travel was associated with medical care seeking (30). Therefore, our use of surveillance data might overestimate travel-related illnesses. For hospitalizations and deaths from toxoplasmosis, we assumed that most cases were domestically acquired, but that possibility might be unlikely because birth outside the United States is a risk factor for T. gondii infections (31).

The first limitation of our study is that the underlying data came from a variety of sources, which had variable quality and representativeness. Only data on foodborne outbreak-related illnesses were available for C. perfringens; it is unknown if those data are representative of all illnesses. For Campylobacter spp., nontyphoidal Salmonella, and STEC, we used data from FoodNet, an active surveillance system representing a 15% convenience sample of the US population. However, the distribution of Salmonella serotypes is not geographically homogeneous. Estimates of norovirus and T. gondii relied on administrative data sources that are not nationally representative. Second, ≈10% of data inputs were from nonempirical data. When data were sparse, we made reasoned judgments according to context, plausibility, and previously published estimates (e.g., applying the multiplier for Salmonella to C. perfringens illnesses). Third, illnesses caused by some pathogens might be underrepresented (e.g., T. gondii or febrile gastroenteritis caused by L. monocytogenes). We estimated numbers of fetal deaths caused by L. monocytogenes; however, early spontaneous abortion or miscarriage might be underdiagnosed. Moreover, many illnesses caused by foodborne pathogens can result in sequelae. Fourth, the source data has many unquantified uncertainties. The FoodNet catchment area is a critical contributor of unquantified uncertainty because of geographic variation in incidence rates for all pathogens. The FoodNet Population Survey is also a source of uncertainty; the survey responses were likely subject to recall and other biases. Our estimates for medical care seeking and sample submission make assumptions about the representativeness of survey participants relative to the target populations. Furthermore, the definition of acute diarrheal illness among survey participants could be made more or less restrictive, leading to smaller or larger numbers of reported acute diarrheal episodes used to estimate medical care seeking and sample submissions, adding additional uncertainties.

In conclusion, we estimated the number of domestically acquired foodborne illnesses, hospitalizations, and deaths caused by 7 major pathogens in the United States as of 2019. Our estimates can be used as a platform to attribute illnesses to specific food categories and also to estimate societal and economic costs of foodborne illness and the numbers of persons who develop disease sequelae. When considered with other information, such as major food sources of illnesses, population groups affected, and costs of making foods safer, the estimates can be used to prioritize food safety interventions.

Dr. Scallan Walter is a professor in the Department of Epidemiology at the Colorado School of Public Health. Her research interests focus on estimating the burden of foodborne disease and attributing foodborne illnesses to specific sources.

Top

Acknowledgments

We thank Sindoos Awel, Ellie Click, Kennedy Houck, Carey E. Lau, Susan Montgomery, and Hazel J. Shah for providing data and expert advice for estimates described in this article.

This study did not meet the definition of human subjects research (as defined in the US Code of Federal Regulations, Title 45 Part 46) and was not subject to review by an institutional review board.

Top

References

  1. Scallan  E, Hoekstra  RM, Angulo  FJ, Tauxe  RV, Widdowson  MA, Roy  SL, et al. Foodborne illness acquired in the United States—major pathogens. Emerg Infect Dis. 2011;17:7–15. DOIPubMedGoogle Scholar
  2. Scallan  E, Griffin  PM, Angulo  FJ, Tauxe  RV, Hoekstra  RM. Foodborne illness acquired in the United States—unspecified agents. Emerg Infect Dis. 2011;17:16–22. DOIPubMedGoogle Scholar
  3. Mead  PS, Slutsker  L, Dietz  V, McCaig  LF, Bresee  JS, Shapiro  C, et al. Food-related illness and death in the United States. Emerg Infect Dis. 1999;5:607–25. DOIPubMedGoogle Scholar
  4. Collins  JP, Shah  HJ, Weller  DL, Ray  LC, Smith  K, McGuire  S, et al. Preliminary incidence and trends of infections caused by pathogens transmitted commonly through food—Foodborne Diseases Active Surveillance Network, 10 U.S. sites, 2016–2021. MMWR Morb Mortal Wkly Rep. 2022;71:1260–4. DOIPubMedGoogle Scholar
  5. US Centers for Disease Control and Prevention. Interagency Food Safety Analytics Collaboration (IFSAC) priorities for 2024–2028. 2023 [cited 2023 Dec 5]. https://www.cdc.gov/ifsac/media/pdfs/IFSACPriorities2024-2028.pdf
  6. Scallan  E, Hoekstra  RM, Mahon  BE, Jones  TF, Griffin  PM. An assessment of the human health impact of seven leading foodborne pathogens in the United States using disability adjusted life years. Epidemiol Infect. 2015;143:2795–804. DOIPubMedGoogle Scholar
  7. Burke  RM, Mattison  CP, Marsh  Z, Shioda  K, Donald  J, Salas  SB, et al. Norovirus and other viral causes of medically attended acute gastroenteritis across the age spectrum: results from the medically attended acute gastroenteritis study in the United States. Clin Infect Dis. 2021;73:e913–20. DOIPubMedGoogle Scholar
  8. Burke  RM, Mattison  CP, Pindyck  T, Dahl  RM, Rudd  J, Bi  D, et al. Burden of norovirus in the United States, as estimated based on administrative data: updates for medically attended illness and mortality, 2001–2015. Clin Infect Dis. 2021;73:e1–8. DOIPubMedGoogle Scholar
  9. Delahoy  MJ, Shah  HJ, Weller  DL, Ray  LC, Smith  K, McGuire  S, 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. DOIPubMedGoogle Scholar
  10. Centers for Disease Control and Prevention. Listeria annual summary, 2019 [cited 2023 Dec 5] https://www.cdc.gov/listeria/php/surveillance/annual-summary-2019.html
  11. Devine  CJ, Molinari  NA, Shah  HJ, Blackstock  AJ, Geissler  A, Marder  EP, et al. The 2018-2019 FoodNet Population Survey: a tool to estimate risks and behaviors associated with enteric infections. Am J Epidemiol. 2025;194:5–11. DOIPubMedGoogle Scholar
  12. Hall  AJ, Rosenthal  M, Gregoricus  N, Greene  SA, Ferguson  J, Henao  OL, et al. Incidence of acute gastroenteritis and role of norovirus, Georgia, USA, 2004-2005. Emerg Infect Dis. 2011;17:1381–8. DOIPubMedGoogle Scholar
  13. Ray  LC, Griffin  PM, Wymore  K, Wilson  E, Hurd  S, LaClair  B, et al. Changing diagnostic testing practices for foodborne pathogens, Foodborne Diseases Active Surveillance Network, 2012–2019. Open Forum Infect Dis. 2022;9:ofac344. DOIPubMedGoogle Scholar
  14. Buss  SN, Leber  A, Chapin  K, Fey  PD, Bankowski  MJ, Jones  MK, et al. Multicenter evaluation of the BioFire FilmArray gastrointestinal panel for etiologic diagnosis of infectious gastroenteritis. J Clin Microbiol. 2015;53:915–25. DOIPubMedGoogle Scholar
  15. Mylonakis  E, Hohmann  EL, Calderwood  SB. Central nervous system infection with Listeria monocytogenes. 33 years’ experience at a general hospital and review of 776 episodes from the literature. Medicine (Baltimore). 1998;77:313–36. DOIPubMedGoogle Scholar
  16. Scallan  E, Griffin  PM, McLean  HQ, Mahon  BE. Hospitalisations due to bacterial gastroenteritis: A comparison of surveillance and hospital discharge data. Epidemiol Infect. 2018;146:954–60. DOIPubMedGoogle Scholar
  17. Scallan Walter  EJ, McLean  HQ, Griffin  PM. Hospital discharge data underascertain enteric bacterial infections among children. Foodborne Pathog Dis. 2020;17:530–2. DOIPubMedGoogle Scholar
  18. Beshearse  E, Bruce  BB, Nane  GF, Cooke  RM, Aspinall  W, Hald  T, et al. Attribution of illnesses transmitted by food and water to comprehensive transmission pathways using structured expert judgment, United States. Emerg Infect Dis. 2021;27:182–95. DOIPubMedGoogle Scholar
  19. Havelaar  AH, Kirk  MD, Torgerson  PR, Gibb  HJ, Hald  T, Lake  RJ, et al.; World Health Organization Foodborne Disease Burden Epidemiology Reference Group. World Health Organization global estimates and regional comparisons of the burden of foodborne disease in 2010. PLoS Med. 2015;12:e1001923. DOIPubMedGoogle Scholar
  20. Kirk  MD, Pires  SM, Black  RE, Caipo  M, Crump  JA, Devleesschauwer  B, et al. World Health Organization estimates of the global and regional disease burden of 22 foodborne bacterial, protozoal, and viral diseases, 2010: a data synthesis. PLoS Med. 2015;12:e1001921. DOIPubMedGoogle Scholar
  21. Van Cauteren  D, Le Strat  Y, Sommen  C, Bruyand  M, Tourdjman  M, Da Silva  NJ, et al. Estimated annual numbers of foodborne pathogen–associated illnesses, hospitalizations, and deaths, France, 2008–2013. Emerg Infect Dis. 2017;23:1486–92. DOIPubMedGoogle Scholar
  22. Monteiro Pires  S, Jakobsen  LS, Ellis-Iversen  J, Pessoa  J, Ethelberg  S. Burden of disease estimates of seven pathogens commonly transmitted through foods in Denmark, 2017. Foodborne Pathog Dis. 2020;17:322–39. DOIPubMedGoogle Scholar
  23. Glass  K, McLure  A, Bourke  S, Cribb  DM, Kirk  MD, March  J, et al. The cost of foodborne illness and its sequelae in Australia circa 2019. Foodborne Pathog Dis. 2023;20:419–26. DOIPubMedGoogle Scholar
  24. Haagsma  JA, Geenen  PL, Ethelberg  S, Fetsch  A, Hansdotter  F, Jansen  A, et al.; Med-Vet-Net Working Group. Community incidence of pathogen-specific gastroenteritis: reconstructing the surveillance pyramid for seven pathogens in seven European Union member states. Epidemiol Infect. 2013;141:1625–39. DOIPubMedGoogle Scholar
  25. Thomas  MK, Murray  R, Flockhart  L, Pintar  K, Pollari  F, Fazil  A, et al. Estimates of the burden of foodborne illness in Canada for 30 specified pathogens and unspecified agents, circa 2006. Foodborne Pathog Dis. 2013;10:639–48. DOIPubMedGoogle Scholar
  26. Sundström  K. Cost of illness for five major foodborne illnesses and sequelae in Sweden. Appl Health Econ Health Policy. 2018;16:243–57. DOIPubMedGoogle Scholar
  27. Binnicker  MJ. Multiplex molecular panels for diagnosis of gastrointestinal infection: performance, result interpretation, and cost-effectiveness. J Clin Microbiol. 2015;53:3723–8. DOIPubMedGoogle Scholar
  28. Scallan  E, Jones  TF, Cronquist  A, Thomas  S, Frenzen  P, Hoefer  D, et al.; FoodNet Working Group. Factors associated with seeking medical care and submitting a stool sample in estimating the burden of foodborne illness. Foodborne Pathog Dis. 2006;3:432–8. DOIPubMedGoogle Scholar
  29. Tam  CC, Rodrigues  LC, O’Brien  SJ. The study of infectious intestinal disease in England: what risk factors for presentation to general practice tell us about potential for selection bias in case-control studies of reported cases of diarrhoea. Int J Epidemiol. 2003;32:99–105. DOIPubMedGoogle Scholar
  30. Scallan Walter  EJ, Devine  C, Payne  DC, Hoekstra  RM, Griffin  PM, Bruce  BB. Factors associated with medical care-seeking and stool sample submission for diarrheal illness, FoodNet, United States, 2018–2019. Foodborne Pathog Dis. 2024;fpd.2024.0114; Epub ahead of print. DOIPubMedGoogle Scholar
  31. Liu  EW, Elder  ES, Rivera  HN, Kruszon-Moran  D, Handali  S, Jones  JL. Concurrent seroprevalence of antibodies to Toxoplasma gondii and Toxocara species in the United States, 2011–2014. Clin Infect Dis. 2019;68:712–3. DOIPubMedGoogle Scholar
  32. Jones  JL, Kruszon-Moran  D, Elder  S, Rivera  HN, Press  C, Montoya  JG, et al. Toxoplasma gondii Infection in the United States, 2011-2014. Am J Trop Med Hyg. 2018;98:551–7. DOIPubMedGoogle Scholar
  33. Jones  JL, Roberts  JM. Toxoplasmosis hospitalizations in the United States, 2008, and trends, 1993-2008. Clin Infect Dis. 2012;54:e58–61. DOIPubMedGoogle Scholar
  34. Wang  ZD, Wang  SC, Liu  HH, Ma  HY, Li  ZY, Wei  F, et al. Prevalence and burden of Toxoplasma gondii infection in HIV-infected people: a systematic review and meta-analysis. Lancet HIV. 2017;4:e177–88. DOIPubMedGoogle Scholar

Top

Figures
Tables

Top

Suggested citation for this article: Scallan Walter EJ, Cui Z, Tierney R, Griffin PM, Hoekstra RM, Payne DC, et al. Foodborne illness acquired in the United States—major pathogens, 2019. Emerg Infect Dis. 2025 Apr [date cited]. https://doi.org/10.3201/eid3104.240913

DOI: 10.3201/eid3104.240913

Original Publication Date: March 19, 2025

Table of Contents – Volume 31, Number 4—April 2025

EID Search Options
presentation_01 Advanced Article Search – Search articles by author and/or keyword.
presentation_01 Articles by Country Search – Search articles by the topic country.
presentation_01 Article Type Search – Search articles by article type and issue.

Top

Comments

Please use the form below to submit correspondence to the authors or contact them at the following address:

Elaine Scallan Walter, Department of Epidemiology, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Fitzsimons Building, Mail Stop B119, 13001 E 17th Pl, Aurora, CO 80045, USA

Send To

10000 character(s) remaining.

Top

Page created: February 04, 2025
Page updated: March 19, 2025
Page reviewed: March 19, 2025
The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
file_external