An Overview of Foodborne Sample-Initiated Retrospective Outbreak Investigations and Interagency Collaboration in the United States

Genomic Surveillance of Foodborne Pathogens: Advances and Obstacles

CONTEXT

The genomic surveillance of foodborne pathogens in the United States has grown exponentially in the past decade, grounded in a powerful combination of novel sequencing technologies, bioinformatic approaches, data-sharing networks, and metadata harmonization efforts. This practice report examines recent advances in genomic epidemiology as applied to food safety programs and delineates State, Tribal, Local, and Territorial infrastructure necessary for continued life-saving improvements in public health.

PROGRAM

National databases of foodborne pathogen genomes, along with data sharing and evaluation networks such as GenomeTrakr and PulseNet, have transformed how connections are made among isolates and how root causes of outbreaks are determined, allowing much more timely interventions to protect public health. Freely available bioinformatics tools such as GalaxyTrakr and the National Center for Biotechnology Information Pathogen Detection database have allowed laboratories with limited local computing resources to participate in surveillance efforts and contribute to traceback investigations.

IMPLEMENTATION

In this report, we describe advances in genomic epidemiology that have occurred over the past decade and examine obstacles to fully implementing this technology within State, Tribal, Local, and Territorial public health systems.

EVALUATION

Despite a clear return on investment from governmental expenditures on genomic surveillance of foodborne pathogens, we identify significant obstacles to further sustained progress. These obstacles include workforce gaps, ineffective data sharing, and lack of constitutive and sustained funding.

DISCUSSION

Many public health laboratories face major obstacles to widespread and routine adoption of genomic surveillance technologies. While whole genome sequencing has become an integral part of routine public health microbiology, the seamless integration of these protocols into the existing practices, laboratory workflows, and information systems remains challenging. Centralized efforts to address these issues include (1) support through the Food and Drug Administration Laboratory Flexible Funding Model, (2) training and proficiency assessments, (3) open-source, standardized protocols for collecting high-quality genomic data, and (4) open access informatics software.

Significance of Whole-Genome Sequencing for the Traceability of Foodborne Pathogens: During the Processing of Meat and Dairy Products

The complexity of tracing foodborne pathogens in the food chain has increased significantly due to the long and complicated chain, the involvement of numerous links, and the presence of various types of pathogens at different stages and environments. Traditional typing techniques are not sufficient to meet the requirements of tracing pathogens in the food chain. Whole-Genome Sequencing (WGS) has gradually become an important technological tool for characterizing and tracing pathogens in the food chain due to comprehensive information, speed, and superior discriminatory power. This paper provides an overview of the advantages of WGS and its application in foodborne pathogen traceability. This paper focused on foodborne pathogen contamination pathways during the processing of animal foods in commercial restaurant kitchens and the potential contamination of milk, milk powder, and other dairy products by pathogens during processing in the dairy industry chain and environments. Improper handling practices during meat processing (i.e., using cloths, washing hands without soap, and cleaning boards with knives) were a critical point of foodborne pathogen cross-contamination in commercial kitchen premises. However, in dairy products, contamination of pathogens in raw milk was the main cause of foodborne disease outbreaks. Therefore, preventing the contamination of pathogens in food should not only be focused on hygiene measures during processing and in environments but also on the quality and hygiene of raw materials to prevent the spread of foodborne pathogens throughout the entire production chain. Further, Whole-Metagenome Sequencing and DNA sequence markers are considered to be the future direction of WGS. The purpose of this work is to promote the wider application of WGS during the processing of meat and dairy products and provide theoretical support for the rapid investigation and accurate traceability of foodborne pathogen outbreaks in food.

Recent Use of Novel Data Streams During Foodborne Illness Cluster Investigations by the United States Food and Drug Administration: Qualitative Review

Unlabelled

Foodborne illness is a continuous public health risk. The recognition of signals indicating a cluster of foodborne illness is key to the detection, mitigation, and prevention of foodborne adverse event incidents and outbreaks. With increased internet availability and access, novel data streams (NDSs) for foodborne illness reports initiated by users outside of the traditional public health framework have emerged. These include, but are not limited to, social media websites, web-based product reviews posted to retailer websites, and private companies that host public-generated notices of foodborne illnesses. Information gathered by these platforms can help identify early signals of foodborne illness clusters or help inform ongoing public health investigations. Here we present an overview of NDSs and 3 investigations of foodborne illness incidents by the US Food and Drug Administration that included the use of NDSs at various stages. Each example demonstrates how these data were collected, integrated into traditional data sources, and used to inform the investigation. NDSs present a unique opportunity for public health agencies to identify clusters that may not have been identified otherwise, due to new or unique etiologies, as shown in the 3 examples. Clusters may also be identified earlier than they would have been through traditional sources. NDSs can further provide investigators supplemental information that may help confirm or rule out a source of illness. However, data collected from NDSs are often incomplete and lack critical details for investigators, such as product information (eg, lot numbers), clinical or medical details (eg, laboratory results of affected individuals), and contact information for report follow-up. In the future, public health agencies may wish to standardize an approach to maximize the potential of NDSs to catalyze and supplement adverse event investigations. Additionally, the collection of essential data elements by NDS platforms and data-sharing processes with public health agencies may aid in the investigation of foodborne illness clusters and inform subsequent public health and regulatory actions.

A Binational Sample-Initiated Retrospective Outbreak Investigation of Listeria monocytogenes Infections in the United States and Canada Linked to Enoki Mushrooms Imported from China 2022-2023

In 2022, the U.S. Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), U.S. state and local partners, the Public Health Agency of Canada (PHAC), and the Canadian Food Inspection Agency (CFIA) conducted a binational sample-initiated retrospective outbreak investigation (SIROI) of Listeria monocytogenes illnesses linked to enoki mushrooms. The FDA and CDC investigated the first known L. monocytogenes outbreak linked to enoki mushrooms from 2016 to 2020, making the 2022 outbreak the second time this pathogen-commodity pair was investigated by FDA and CDC. The 2022 outbreak included six ill people, all of whom were hospitalized. Epidemiologic, laboratory, and traceback evidence led to multiple public health actions, including voluntary recalls by firms, public communications about the outbreak, and FDA's country-wide Import Alert for enoki mushrooms from China. This SIROI illustrates the importance of surveillance sampling, national and international coordination of efforts, and rapid information sharing to identify and stop foodborne outbreaks on a global scale. To reduce the risk of listeriosis illnesses linked to contaminated enoki mushrooms, public health and regulatory agencies in the United States and Canada remain committed to conducting comprehensive surveillance for Listeria in foods and in people, efficiently investigating identified outbreaks, and implementing control measures to potentially minimize the impact of future outbreaks.

An Investigation of Salmonella Senftenberg Illnesses in the United States Linked to Peanut Butter-2022

In 2022, the U.S. Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), and state partners conducted a sample-initiated investigation of a multistate outbreak of Salmonella Senftenberg illnesses linked to peanut butter. Twenty-one illnesses and four hospitalizations were reported in 17 states, with a significant epidemiological signal for peanut butter from Firm A. Whole genome sequence (WGS) data from a Salmonella-positive environmental swab sample collected at Firm A in 2010 yielded the outbreak strain that was a match to the WGS data from the 2022 clinical isolates. Lot code information collected from patients indicated Firm A's facility in Kentucky as a common manufacturing source, and FDA and state partners initiated an inspection. In 2021, Firm A installed two new roasters with at least one of the cooling air supply vents leaking, allowing unfiltered air and rainwater to enter the cooling section after the roasting process. Investigators noted the limitations of Firm A's finished product testing program to identify contamination. Investigative partners from five states collected and analyzed 14 product samples, and FDA collected 205 environmental swabs, and all were negative. Although the exact source and route of the contamination were not determined, epidemiological and traceback evidence confirmed peanut butter consumed by patients was produced by Firm A. Firm A voluntarily recalled all implicated products and provided a plan for corrective actions and restart to FDA. This was the first major domestic investigation of a multistate-foodborne illness outbreak linked to peanut butter since 2012. This investigation demonstrates the importance of caution with reliance on finished product testing, taking appropriate corrective actions when detection occurs, and potential benefits for industry to incorporate WGS as a tool in their environmental monitoring program.

An Outbreak Investigation of Salmonella Weltevreden Illnesses in the United States Linked to Frozen Precooked Shrimp Imported from India - 2021

In 2021, the U.S. Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and state partners investigated a multistate sample-initiated retrospective outbreak investigation (SIROI) consisting of a cluster of nine Salmonella Weltevreden illnesses associated with frozen, precooked shrimp imported from India. Import surveillance testing identified Salmonella Weltevreden recovered from a cooked shrimp sample from Supplier B. In total, nine patients with clinical isolates highly related via whole genome sequencing were reported in four states with illness onset dates between February 26 and July 17, 2021. Epidemiologic data were gathered by state partners for seven patients, who all reported exposure to shrimp. Five patients reported consuming shrimp cocktail from the same retailer. A traceback investigation for five of the six patients converged on Supplier B. This evidence demonstrated that the outbreak of Salmonella Weltevreden illnesses was caused by the consumption of cooked, ready-to-eat shrimp manufactured by Supplier B. At the time of the investigation, outbreak and recall information was shared with Indian competent authorities. In March 2022, a follow-up inspection of Supplier B's facility in India was conducted, and insanitary conditions and practices were observed. This outbreak investigation highlighted the importance of multidisciplinary national and international public health partnerships. The lessons learned from this investigation should continue to inform investigational activities and food safety guidance for the industry.

RIPS (rapid intuitive pathogen surveillance): a tool for surveillance of genome sequence data from foodborne bacterial pathogens

Monitoring data submitted to the National Center for Biotechnology Information's Pathogen Detection whole-genome sequence database, which includes the foodborne bacterial pathogens Listeria monocytogenes, Salmonella enterica, and Escherichia coli, has proven effective for detecting emerging outbreaks. As part of the submission process, new sequence data are typed using a whole-genome multi-locus sequence typing scheme and clustered with sequences already in the database. Publicly available text files contain the results of these analyses. However, contextualizing and interpreting this information is complex. We present the Rapid Intuitive Pathogen Surveillance (RIPS) tool, which shows the results of the NCBI Rapid Reports, along with appropriate metadata, in a graphical, interactive dashboard. RIPS makes the information in the Rapid Reports useful for real-time surveillance of genome sequence databases.

Foodborne Disease Outbreaks Caused by Biotoxins in Yantai City: A 10-Year Spatiotemporal Monitoring Study

Unsafe food causes 600 million cases of foodborne diseases and 420,000 deaths every year. Meanwhile, biological toxins such as poisonous mushrooms, saponins, and aflatoxin can cause significant damage to humans. Therefore, it is particularly important to study foodborne disease outbreaks caused by biotoxins (FDOB). We collected FDOB in Yantai City from 2013 to 2022 and further established a corresponding database. Statistical analysis was carried out according to time, place, pathogen, and contamination of pathogenic factors. There were 128 FDOB, resulting in 417 patients and 6 deaths. The third quarter was a high season for foodborne disease outbreaks, the number of events, patients and deaths accounted for 65.63% (84/128), 55.88% (233/417), and 100% (6/6) of the total number, respectively. The highest number of outbreaks per 10,000 persons was Qixia (0.41), followed by Zhifu (0.36) and Laiyang (0.33). The top three causes of outbreaks were poisonous mushroom toxin, saponins and hemagglutinin, and Lagenaria siceraria (Molina) Standl. Sixty-five (50.78%) outbreaks were attributed to poisonous mushroom toxin, 18 (14.06%) outbreaks to saponin and hemagglutinin, and 12 (9.38%) outbreaks to L. siceraria (Molina) Standl. The largest number of outbreaks, patients and deaths all occurred in families, accounting for 82.81% (106/128) outbreaks, 66.19% (276/417) patients, and 100% (6/6) deaths, respectively. Followed by catering service establishments, accounting for 14.84% (19/128), 30.22% (126/417), and 0% (0/6), respectively. The main poisoning link of outbreaks was ingestion and misuse, accounting for 72.66% (93/128), followed by improper processing, accounting for 20.31% (26/128). It is necessary to carry out targeted family publicity and education, strengthen the integration of medical and prevention, explore innovative monitoring and early warning mechanisms for foodborne diseases, and reduce the occurrence of underreporting of foodborne disease outbreaks.

Comprehensive Review on the Biocontrol of Listeria monocytogenes in Food Products

Listeria monocytogenes is a foodborne pathogen that causes listeriosis, a group of human illnesses that appear more frequently in countries with better-developed food supply systems. This review discusses the efficacy of actual biocontrol methods combined with the main types of food involved in illnesses. Comments on bacteriophages, lactic acid bacteria, bacteriocins, essential oils, and endolysins and derivatives, as main biological antilisterial agents, are made bearing in mind that, using them, food processors can intervene to protect consumers. Both commercially available antilisterial products and solutions presented in scientific papers for mitigating the risk of contamination are emphasized. Potential combinations between different types of antilisterial agents are highlighted for their synergic effects (bacteriocins and essential oils, phages and bacteriocins, lactic acid bacteria with natural or synthetic preservatives, etc.). The possibility to use various antilisterial biological agents in active packaging is also presented to reveal the diversity of means that food processors may adopt to assure the safety of their products. Integrating biocontrol solutions into food processing practices can proactively prevent outbreaks and reduce the occurrences of L. monocytogenes-related illnesses.

Predicting Food Sources of Listeria monocytogenes Based on Genomic Profiling Using Random Forest Model

Listeria monocytogenes can cause severe foodborne illness, including miscarriage during pregnancy or death in newborn infants. When outbreaks of L. monocytogenes illness occur, it may be possible to determine the food source of the outbreak. However, most reported L. monocytogenes illnesses do not occur as part of a recognized outbreak and most of the time the food source of sporadic L. monocytogenes illness in people cannot be determined. In the United States, L. monocytogenes isolates from patients, foods, and environments are routinely sequenced and analyzed by whole genome multilocus sequence typing (wgMLST) for outbreak detection by PulseNet, the national molecular surveillance system for foodborne illnesses. We investigated whether machine learning approaches applied to wgMLST allele call data could assist in attribution analysis of food source of L. monocytogenes isolates. We compiled isolates with a known source from five food categories (dairy, fruit, meat, seafood, and vegetable) using the metadata of L. monocytogenes isolates in PulseNet, deduplicated closely genetically related isolates, and developed random forest models to predict the food sources of isolates. Prediction accuracy of the final model varied across the food categories; it was highest for meat (65%), followed by fruit (45%), vegetable (45%), dairy (44%), and seafood (37%); overall accuracy was 49%, compared with the naive prediction accuracy of 28%. Our results show that random forest can be used to capture genetically complex features of high-resolution wgMLST for attribution of isolates to their sources.