Barriers to stool specimen collection during foodborne and enteric illness outbreak investigations in Arizona and Colorado

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.

A Prediction Tool to Identify the Causative Agent of Enteric Disease Outbreaks Using Outbreak Surveillance Data

Information on the causative agent in an enteric disease outbreak can be used to generate hypotheses about the route of transmission and possible vehicles, to guide environmental assessments, and to target outbreak control measures. However, only about 40% of outbreaks reported in the United States include a confirmed etiology. The goal of this project was to identify clinical and demographic characteristics that can be used to predict the causative agent in an enteric disease outbreak and to use these data to develop an online tool for investigators to use during an outbreak when hypothesizing about the causative agent. Using data on enteric disease outbreaks from all transmission routes (animal contact, environmental contamination, foodborne, person-to-person, waterborne, unknown) reported to the U.S. Centers for Disease Control and Prevention, we developed random forest models to predict the etiology of an outbreak based on aggregated clinical and demographic characteristics at both the etiology category (i.e., bacteria, parasites, toxins, viruses) and individual etiology (Clostridium perfringens, Campylobacter, Cryptosporidium, norovirus, Salmonella, Shiga toxin-producing Escherichia coli, and Shigella) levels. The etiology category model had a kappa of 0.85 and an accuracy of 0.92, whereas the etiology-specific model had a kappa of 0.75 and an accuracy of 0.86. The highest sensitivities in the etiology category model were for bacteria and viruses; all categories had high specificities (>0.90). For the etiology-specific model, norovirus and Salmonella had the highest sensitivity and all etiologies had high specificities. When laboratory confirmation is unavailable, information on the clinical signs and symptoms reported by people associated with the outbreak, with other characteristics including case demographics and illness severity, can be used to predict the etiology or etiology category. An online publicly available tool was developed to assist investigators in their enteric disease outbreak investigations.