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.

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".

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.

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

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.

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 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".

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

Analysis of ‘NNDSS - TABLE 1EE. Salmonella Paratyphi infection to Salmonellosis (excluding Salmonella Typhi infection and Salmonella Paratyphi infection)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d43f682d-30dc-425c-baa2-8f54150bbcaa on 27 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

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.

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.

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.

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

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.

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.

"4 million Canadians get sick each year from contaminated food. Over 11,500 hospitalizations and 240 deaths occur each year due to food-related illnesses. Numbers includes both estimates for 30 foodborne pathogens and unknown causes of acute gastrointestinal illness. "

Invasive non-typhoidal Salmonella infections are responsible for >75 000 deaths/year and >500 000 cases/year globally. Seventy-five percent of these cases occur in Sub-Saharan Africa, an increasing number of which are from multi-drug resistant strains. Interactions between bacteria and platelets can lead to thrombus formation, which can be beneficial for control of infection (immunothrombosis), or harmful through uncontrolled inflammation and organ damage (thromboinflammation). It is unknown whether Salmonella Typhimurium can activate human platelets. To assess this, light transmission aggregometry was used to measure platelet activation by two different Salmonella Typhimurium strains in 26 healthy donors in platelet-rich plasma and washed platelets. In platelet-rich plasma, but not in washed platelets, Salmonella Typhimurium activated platelets in a donor- and strain-dependent manner mediated through the low affinity immune receptor FcγRIIA and the feedback agonists, ADP and thromboxane A2. Plasma swap studies between strong and weak responders demonstrated a plasma component was responsible for the variation between donors. Depletion of anti-Salmonella antibodies from plasma abolished Salmonella-induced platelet aggregation responses, and addition of polyclonal anti-Salmonella antibody allowed aggregation in washed platelets. Correlating levels of anti-Salmonella total IgG or the IgG1, IgG2, IgG3 and IgG4 subclasses to platelet responses revealed total IgG levels, rather than levels of individual subclasses, positively correlated with maximum platelet aggregation results, and negatively with lag times. Overall, we show that anti-Salmonella IgG antibodies are responsible for donor variation in platelet aggregation responses to Salmonella and mediate this activity through FcγRIIA. Salmonella is widely known as a bacterial pathogen that causes diarrhea, with recovery occurring after about a week. However, there exists another, less well known and more severe form of Salmonella, called invasive non-typhoidal Salmonella. This type of Salmonella infection gets across the gut barrier, enters the bloodstream and spreads through the body. There are over half a million of these kinds of invasive Salmonella infections globally every year, leading to over 75 000 deaths. The majority of these are in sub-Saharan Africa but also are significant causes of harm in the elderly or those with compromised immune systems. When the bacteria are in the bloodstream, they can come into contact with various different types of cells, including platelets, the cells that cause blood to clot. Sometimes the presence of bacteria in the blood can cause platelets to clump together, leading to blood clots, which can block the blood vessels and cause organ damage, and death. The ability of Salmonella bacteria to cause clots has not been investigated in depth. Salmonella is widely known as a bacterial pathogen that causes diarrhea, with recovery occurring after about a week. However, there exists another, less well known and more severe form of Salmonella, called invasive non-typhoidal Salmonella. This type of Salmonella infection gets across the gut barrier, enters the bloodstream and spreads through the body. There are over half a million of these kinds of invasive Salmonella infections globally every year, leading to over 75 000 deaths. The majority of these are in sub-Saharan Africa but also are significant causes of harm in the elderly or those with compromised immune systems. When the bacteria are in the bloodstream, they can come into contact with various different types of cells, including platelets, the cells that cause blood to clot. Sometimes the presence of bacteria in the blood can cause platelets to clump together, leading to blood clots, which can block the blood vessels and cause organ damage, and death. The ability of Salmonella bacteria to cause clots has not been investigated in depth. What is new?We show that the bacteria that cause invasive Salmonella infections are able to trigger platelets to clump together and form a blood clot. However, the response of platelets in blood plasma from donors varies widely - some people’s platelets do not clump together much, whilst others form large clots. We show that the amount people’s platelets clot is linked to the level of antibodies that bind to Salmonella. We show that the bacteria that cause invasive Salmonella infections are able to trigger platelets to clump together and form a blood clot. However, the response of platelets in blood plasma from donors varies widely - some people’s platelets do not clump together much, whilst others form large clots. We show that the amount people’s platelets clot is linked to the level of antibodies that bind to Salmonella. What is the impact?There are limited treatment options for invasive non-typhoidal Salmonella infections due to increasing antimicrobial resistance. Therefore, understanding the mechanisms behind how Salmonella interacts with blood cells such as platelets could help direct further research to identify new treatment options for these infections. There are limited treatment options for invasive non-typhoidal Salmonella infections due to increasing antimicrobial resistance. Therefore, understanding the mechanisms behind how Salmonella interacts with blood cells such as platelets could help direct further research to identify new treatment options for these infections.

Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets now being generated from routine pathogen surveillance for epidemiological purposes. Here, we measure the burden of atypical mutations in protein coding genes across independently evolved Salmonella enterica lineages, and use these as input to train a random forest classifier to identify strains associated with extraintestinal disease. Members of the species fall along a continuum, from pathovars which cause gastrointestinal infection and low mortality, associated with a broad host-range, to those that cause invasive infection and high mortality, associated with a narrowed host range. Our random forest classifier learned to perfectly discriminate long-established gastrointestinal and invasive serovars of Salmonella. Additionally, it was able to discriminate recently emerged Salmonella Enteritidis and Typhimurium lineages associated with invasive disease in immunocompromised populations in sub-Saharan Africa, and within-host adaptation to invasive infection. We dissect the architecture of the model to identify the genes that were most informative of phenotype, revealing a common theme of degradation of metabolic pathways in extraintestinal lineages. This approach accurately identifies patterns of gene degradation and diversifying selection specific to invasive serovars that have been captured by more labour-intensive investigations, but can be readily scaled to larger analyses.

Pathogen Food Safety Testing Market Outlook

The global pathogen food safety testing market size was valued at approximately USD 10 billion in 2023 and is projected to reach around USD 18.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This growth is driven by rising food safety concerns and stringent regulations across the globe. The increasing incidence of foodborne illnesses, alongside heightened consumer awareness regarding food safety, are key factors propelling market expansion.

One of the primary growth factors for the pathogen food safety testing market is the rising prevalence of foodborne diseases. According to the World Health Organization (WHO), approximately 600 million people fall ill after consuming contaminated food every year, leading to 420,000 deaths. This alarming statistic highlights the critical need for reliable pathogen testing methods to ensure food safety. Additionally, the globalization of the food supply chain has added complexity to food safety management, necessitating comprehensive testing solutions to detect pathogens at various stages of the supply chain.

Another significant growth driver is the stringent food safety regulations imposed by governments and regulatory bodies worldwide. Regulatory frameworks like the Food Safety Modernization Act (FSMA) in the United States and the European Union's General Food Law Regulation mandate rigorous testing of food products to prevent contamination and ensure public health. These regulations compel food manufacturers and service providers to adopt advanced pathogen testing technologies to comply with legal requirements, thereby fueling market growth.

Technological advancements in pathogen detection methods are also contributing to market growth. Traditional testing methods, while effective, often require longer turnaround times and may lack sensitivity. In contrast, rapid detection technologies such as Polymerase Chain Reaction (PCR), Immunoassays, and biosensors offer quicker and more accurate results, making them increasingly popular. The adoption of these advanced technologies not only enhances the efficiency of food safety testing but also boosts the overall market growth.

Meat Food Safety Testing plays a critical role in ensuring the safety of meat and poultry products, which are often susceptible to contamination by pathogens such as Salmonella and E. coli. The complexity of meat processing, along with the potential for cross-contamination, necessitates rigorous testing protocols to protect consumer health. Advanced testing methods, including rapid detection technologies, are increasingly being adopted to enhance the monitoring and control of pathogens in meat products. These technologies not only improve the efficiency of testing processes but also provide more accurate results, thereby reducing the risk of foodborne illnesses and ensuring the safety of the food supply chain.

Regionally, North America holds a significant share of the pathogen food safety testing market, owing to the presence of a robust regulatory framework and a high level of consumer awareness regarding food safety. Europe follows closely, with stringent food safety regulations and a well-established food industry driving market demand. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by increasing urbanization, rising disposable incomes, and growing concerns about foodborne illnesses. Latin America and the Middle East & Africa are also anticipated to experience substantial growth, albeit at a slower rate, due to improving food safety standards and regulatory initiatives.

Pathogen Type Analysis

Within the pathogen food safety testing market, segmentation by pathogen type reveals that Salmonella is one of the most significant contributors. The prevalence of Salmonella in various food products, particularly poultry and eggs, makes it a critical focus area for testing. The Centers for Disease Control and Prevention (CDC) estimates that Salmonella causes approximately 1.35 million infections, 26,500 hospitalizations, and 420 deaths annually in the United States alone. As a result, there is a substantial demand for effective testing methods to detect and mitigate Salmonella contamination in the food supply chain.

Escherichia coli (E. coli) is another major pathogen type driving the food safety testing market. E. coli outbreaks, often linked to c