Recent Use of Novel Data Streams During Foodborne Illness Cluster Investigations by the United States Food and Drug Administration: Qualitative Review.
IF 3.5 2区 医学Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Michael C Bazaco, Christina K Carstens, Tiffany Greenlee, Tyann Blessington, Evelyn Pereira, Sharon Seelman, Stranjae Ivory, Temesgen Jemaneh, Margaret Kirchner, Alvin Crosby, Stelios Viazis, Sheila van Twuyver, Michael Gwathmey, Tanya Malais, Oliver Ou, Stephanie Kenez, Nichole Nolan, Andrew Karasick, Cecile Punzalan, Colin Schwensohn, Laura Gieraltowski, Cary Chen Parker, Erin Jenkins, Stic Harris
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引用次数: 0
Abstract
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.
期刊介绍:
JMIR Public Health & Surveillance (JPHS) is a renowned scholarly journal indexed on PubMed. It follows a rigorous peer-review process and covers a wide range of disciplines. The journal distinguishes itself by its unique focus on the intersection of technology and innovation in the field of public health. JPHS delves into diverse topics such as public health informatics, surveillance systems, rapid reports, participatory epidemiology, infodemiology, infoveillance, digital disease detection, digital epidemiology, electronic public health interventions, mass media and social media campaigns, health communication, and emerging population health analysis systems and tools.