In 2019, there were 17.78 new cases of salmonellosis per 100,000 population. This statistic shows the number of new cases of salmonellosis per 100,000 population in the United States from 1960 to 2019.

This statistic represents the rate of Salmonella in the United States, as of 2018, by state. As of that year, South Dakota had the second highest rate of Salmonella in the United States with almost 35 new cases per every 100,000 population.

Salmonella in the United States

Within the U.S., the rate of salmonella was the second highest in South Dakota, totaling about 35.4 new cases per 100,000 population, as of 2018, only topped by Mississippi with 39.8 new cases per 100,000 population. In total, there were 16.7 cases of salmonellosis per every 100,000 population in the United States. Between 1991 and 2016, there were 3,796 illnesses due to salmonella linked to live poultry in the country as well as 6 deaths.

Salmonellosis, an infection caused by Salmonella, usually lasts between 4 to 7 days and generally patients recover without any treatment. Salmonella can be transferred from animal products to humans so the best preventative measures are to cook food such as poultry, ground beef, and eggs thoroughly. Cross-contamination of these food items should also be avoided. In very young and elderly patients, the bacteria may enter the bloodstream and require antibiotherapy to cure the patient. Salmonella can also lead to other illnesses such as typhoid fever, paratyphoid fever, and food poisoning. About 67.9 million U.S. dollars were donated for research and development on the Salmonella infection globally in 2015.

Average Salmonella incidence rates (per 100, 000) for the years 2011 to 2013 and expected Salmonella incidence rate for 2028.

For the year 2021, around 19 percent of foodborne Salmonella cases in the United States were attributed to chicken while 11 percent were caused by seeded vegetables. This statistic shows the percentage of foodborne Salmonella illnesses in the United States that were attributed to specific food categories for the year 2021.

NNDSS - TABLE 1EE. Salmonella Paratyphi infection to Salmonellosis (excluding Salmonella Typhi infection and Salmonella Paratyphi infection) – 2022. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents. Notes: • These are weekly cases of selected infectious national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables available at https://www.cdc.gov/nndss/data-statistics/index.html. Cases reported by state health departments to CDC for weekly publication are subject to ongoing revision of information and delayed reporting. Therefore, numbers listed in later weeks may reflect changes made to these counts as additional information becomes available. Case counts in the tables are presented as published each week. See also Guide to Interpreting Provisional and Finalized NNDSS Data at https://www.cdc.gov/nndss/docs/Readers-Guide-WONDER-Tables-20210421-508.pdf. • Notices, errata, and other notes are available in the Notice To Data Users page at https://wonder.cdc.gov/nndss/NTR.html. • The list of national notifiable infectious diseases and conditions and their national surveillance case definitions are available at https://ndc.services.cdc.gov/. This list incorporates the Council of State and Territorial Epidemiologists (CSTE) position statements approved by CSTE for national surveillance. Footnotes: *Case counts for reporting years 2021 and 2022 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://www.cdc.gov/nndss/docs/Readers-Guide-WONDER-Tables-20210421-508.pdf. †Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data). U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data. -: No reported cases — The reporting jurisdiction did not submit any cases to CDC. N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction. NN: Not nationally notifiable — This condition was not designated as being nationally notifiable. NP: Nationally notifiable but not published. NC: Not calculated — There is insufficient data available to support the calculation of this statistic. Cum: Cumulative year-to-date counts. Max: Maximum — Maximum case count during the previous 52 weeks.

Non-typhoidal Salmonella enterica infections cause a high disease burden in the United States with an estimated 1.2 million illnesses annually. The state of Florida consistently has a relatively high incidence compared to other states in the United States. Nevertheless, studies regarding the epidemiology of nontyphoidal salmonellosis and its spatial and temporal patterns in Florida were rarely reported. We examined the spatial and temporal patterns of 62,947 salmonellosis cases reported to FL Health Charts between 2009 and 2018. Dominant serotypes circulating in Florida were also explored using whole genome sequencing (WGS) based serotype-prediction for 2,507 Salmonella isolates sequenced by the Florida Department of Health during 2017 and 2018. The representativeness of laboratory-sequenced isolates for reported cases was determined by regression modeling. The annual incidence rate of salmonellosis decreased from 36.0 per 100,000 population in 2009 to 27.8 per 100,000 in 2016, and gradually increased in 2017 and 2018. Increased use of culture-independent testing did not fully explain this increase. The highest incidence rate was observed in children, contributing 40.9% of total reported cases during this period. A seasonal pattern was observed with the incidence peaking in September and October, later than the national average pattern. Over these 10 years, the Northeast and Northwest regions of the state had higher reported incidence rates, while reported rates in the Southeast and South were gradually increasing over time. Serotypes were predicted based on WGS data in the EnteroBase platform. The top-five most prevalent serotypes in Florida during 2017–2018 were Enteritidis, Newport, Javiana, Sandiego and Braenderup. The highest percentage of isolates was from children under 5 years of age (41.4%), and stool (84.7%) was the major source of samples. A zero-inflated negative binomial regression model showed that the reported case number was a strong predictor for the number of lab-sequenced isolates in individual counties, and the geospatial distribution of sequenced isolates was not biased by other factors such as age group. The spatial and temporal patterns identified in this study along with the prevalence of different serotypes will be helpful for the development of efficient prevention and control strategies for salmonellosis in Florida.

Mean annual Salmonella cases and incidence (per 100, 000) for all age groups (1999–2013).

Annual number of Salmonella cases and percent of cases by age for the years 2011 to 2013 (average) and 2028 (expected).

NNDSS - TABLE 1EE. Salmonella Paratyphi infection to Salmonellosis (excluding Salmonella Typhi infection and Salmonella Paratyphi infection) – 2020. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

Note: This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

Footnotes: U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data. -: No reported cases — The reporting jurisdiction did not submit any cases to CDC. N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction. NN: Not nationally notifiable — This condition was not designated as being nationally notifiable. NP: Nationally notifiable but not published. NC: Not calculated — There is insufficient data available to support the calculation of this statistic. Cum: Cumulative year-to-date counts. Max: Maximum — Maximum case count during the previous 52 weeks. * Case counts for reporting years 2019 and 2020 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf. †Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data). § In previous years, cases were reported as Salmonellosis. Beginning in January 2019, cases began to be reported as Salmonella Paratyphi infection. ¶ In previous years, cases were reported as typhoid fever. Beginning in January 2019, cases began to be reported as Salmonella Typhi infection. ** In previous years, cases were reported as Salmonellosis (excluding paratyphoid fever and typhoid fever). Beginning in January 2019, cases began to be reported as Salmonellosis (excluding Salmonella Typhi infection and Salmonella Paratyphi infection).

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

BackgroundBloodstream infections (BSI) caused by Salmonella Typhi and invasive non-Typhoidal Salmonella (iNTS) frequently affect children living in rural sub-Saharan Africa but data about incidence and serotype distribution are rare.ObjectiveThe present study assessed the population-based incidence of Salmonella BSI and severe malaria in a Health and Demographic Surveillance System in a rural area with seasonal malaria transmission in Nanoro, Burkina Faso.MethodsChildren between 2 months—15 years old with severe febrile illness were enrolled during a one-year surveillance period (May 2013—May 2014). Thick blood films and blood cultures were sampled and processed upon admission. Population-based incidences were corrected for non-referral, health seeking behavior, non-inclusion and blood culture sensitivity. Adjusted incidence rates were expressed per 100,000 person-years of observations (PYO).ResultsAmong children < 5 years old, incidence rates for iNTS, Salmonella Typhi and severe malaria per 100,000 PYO were 4,138 (95% Confidence Interval (CI): 3,740–4,572), 224 (95% CI: 138–340) and 2,866 (95% CI: 2,538–3,233) respectively. Among those aged 5–15 years, corresponding incidence rates were 25 (95% CI: 8–60), 273 (95% CI: 203–355) and 135 (95% CI: 87–195) respectively. Most iNTS occurred during the peak of the rainy season and in parallel with the increase of Plasmodium falciparum malaria; for Salmonella Typhi no clear seasonal pattern was observed. Salmonella Typhi and iNTS accounted for 13.3% and 55.8% of all 118 BSI episodes; 71.6% of iNTS (48/67) isolates were Salmonella enterica serovar Typhimurium and 25.4% (17/67) Salmonella enterica serovar Enteritidis; there was no apparent geographical clustering.ConclusionThe present findings from rural West-Africa confirm high incidences of Salmonella Typhi and iNTS, the latter with a seasonal and Plasmodium falciparum-related pattern. It urges prioritization of the development and implementation of Salmonella Typhi as well as iNTS vaccines in this setting.

Over 10,300 cases of salmonella infections were reported in Poland in 2023, the highest number in the observed period. However, preliminary data for 2024 indicates a decrease to more than 9,200 cases.

In 2023, almost 78 thousand cases of salmonellosis were recorded across the EEA. The highest number of cases of salmonellosis were recorded in Spain, with approximately 12 thousand cases, followed by France and Germany. This statistic displays the number of cases of salmonellosis reported in Europe in 2023, by country.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Population and number (incidence per 100,000 population) of invasive non-typhoidal Salmonella (iNTS) cases per year, Gauteng Province, South Africa, 2003–2013.

This trend chart show the salmonella incidence rate per 100,000 in New York County (Manhattan) beginning in 2003. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York. The CHIRS trend data table presents data for close to 300 health indicators and are provided for all 62 counties, for New York State, for New York City, and Rest of State. . For more information: check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset.

Non-typhoidal Salmonella infections represent a substantial burden of illness in humans, and the increasing prevalence of antimicrobial resistance among these infections is a growing concern. Using a combination of Salmonella isolate short-read whole-genome sequence data from select human cases, raccoons, livestock and environmental sources, and an epidemiological framework, our objective was to determine if there was evidence for potential transmission of Salmonella and associated antimicrobial resistance determinants between these different sources in the Grand River watershed in Ontario, Canada. Logistic regression models were used to assess the potential associations between source type and the presence of select resistance genes and plasmid incompatibility types. A total of 608 isolates were obtained from the following sources: humans (n = 58), raccoons (n = 92), livestock (n = 329), and environmental samples (n = 129). Resistance genes of public health importance, including blaCMY−2, were identified in humans, livestock, and environmental sources, but not in raccoons. Most resistance genes analyzed were significantly more likely to be identified in livestock and/or human isolates than in raccoon isolates. Based on a 3,002-loci core genome multi-locus sequence typing (cgMLST) scheme, human Salmonella isolates were often more similar to isolates from livestock and environmental sources, than with those from raccoons. Rare instances of serovars S. Heidelberg and S. Enteritidis in raccoons likely represent incidental infections and highlight possible acquisition and dissemination of predominantly poultry-associated Salmonella by raccoons within these ecosystems. Raccoon-predominant serovars were either not identified among human isolates (S. Agona, S. Thompson) or differed by more than 350 cgMLST loci (S. Newport). Collectively, our findings suggest that the rural population of raccoons on swine farms in the Grand River watershed are unlikely to be major contributors to antimicrobial resistant human Salmonella cases in this region.

This datasets summarizes and lists all the recalls of meat and poultry products produced by FSIS federally inspected establishments for the calendar year. Recalls are characterized by date, recall class, product, reason and pounds recalled. More detailed information can be found in each recall announcement posted on the FSIS website.

Non-typhoidal Salmonella (NTS) are important enteric pathogens causing over 1 million foodborne illnesses in the U.S. annually. The widespread emergence of antibiotic resistance in NTS isolates has limited the availability of antibiotics that can be used for therapy. Since Michigan is not part of the FoodNet surveillance system, few studies have quantified antibiotic resistance frequencies and identified risk factors for NTS infections in the state. We obtained 198 clinical NTS isolates via active surveillance at four Michigan hospitals from 2011 to 2014 for classification of serovars and susceptibility to 24 antibiotics using broth microdilution. The 198 isolates belonged to 35 different serovars with Enteritidis (36.9%) predominating followed by Typhimurium (19.5%) and Newport (9.7%), though the proportion of each varied by year, residence, and season. The number of Enteritidis and Typhimurium cases was higher in the summer, while Enteritidis cases were significantly more common among urban vs. rural residents. A total of 30 (15.2%) NTS isolates were resistant to ≥1 antibiotic and 15 (7.5%) were resistant to ≥3 antimicrobial classes; a significantly greater proportion of Typhimurium isolates were resistant compared to Enteritidis isolates and an increasing trend in the frequency of tetracycline resistance and multidrug resistance was observed over the 4-year period. Resistant infections were associated with longer hospital stays as the mean stay was 5.9 days for patients with resistant isolates relative to 4.0 days for patients infected with susceptible isolates. Multinomial logistic regression indicated that infection with serovars other than Enteritidis [Odds ratio (OR): 3.8, 95% confidence interval (CI): 1.23–11.82] as well as infection during the fall (OR: 3.0; 95% CI: 1.22–7.60) were independently associated with resistance. Together, these findings demonstrate the importance of surveillance, monitoring resistance frequencies, and identifying risk factors that can aid in the development of new prevention strategies.

This dataset shows the total number of Salmonella isolations and incidents in rabbits on GB Premises from 2010-2014. The data is grouped by Salmonella subspecies, the numbers of isolations and incidents are given, and the whole is grouped by year. An 'isolation' is defined as the first report of a salmonella isolate (a cultured instance of Salmonella from a sample) from a known group of animals on a single occasion. An 'incident' is the confirmation of the same Salmonella type on one or more occasion within a set time period (usually thirty days), and within the same group of animals or same location. The laboratory facilities are UKAS accredited to BS EN ISO 17025:2000 (Lab Nos. 0941, 1769 and 2112) for an extensive range of tests supported by proficiency testing accredited to ISO/IEC Guide 43-1 1997 (Lab No. 0004). APHA is certificated to BS EN ISO 9001:2000 for ‘the provision of a range of specialist veterinary scientific services to the Government and other interested parties worldwide’ (Certificate Nos. LRQ 4000436, 4001071, 0962413 and 4001392). Additionally, APHA holds Good Laboratory Practice and Good Manufacturing Practice approval and complies with the Joint Code of Practice for Research projects and Good Clinical Veterinary Practice quality standards. APHA Weybridge is accredited to BS EN ISO 14001:2004 for environmental management system.