An Overview of the Recent Use of Novel Data Streams During Federal Foodborne Illness Cluster Investigations in the United States (Preprint)

UNSTRUCTURED Foodborne illness is a continuous public health risk. Therefore, 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 (NDS) 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, online 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 and/or help inform ongoing public health investigations. Here we present three investigations of foodborne illness incidents that included the use of NDS at various stages. Each example demonstrates how these data were collected, integrated into traditional data sources, and used to inform the investigation. NDS present a unique opportunity for public health agencies to identify clusters that may not have been identified otherwise, in these examples due to new or unique etiologies. Clusters may also be identified earlier than they would have through traditional sources. NDS can further provide to investigators during ongoing investigations supplemental information that may help confirm or rule out a source of illness. However, data collected from NDS are often incomplete and lack critical details for investigators, such as product information (e.g., lot numbers), clinical/medical details (e.g., 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 NDS to catalyze and/or supplement adverse event investigations. Additionally, the collection of essential data elements by NDS platforms may aid in the investigation of foodborne illness clusters and inform subsequent public health and regulatory actions.