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Outbreak characteristics associated with identification of contributing factors to foodborne illness outbreaks.

Laura G BrownE R HooverC A SelmanE W ColemanH Schurz Rogers
Published in: Epidemiology and infection (2017)
Information on the factors that cause or amplify foodborne illness outbreaks (contributing factors), such as ill workers or cross-contamination of food by workers, is critical to outbreak prevention. However, only about half of foodborne illness outbreaks reported to the United States' Centers for Disease Control and Prevention (CDC) have an identified contributing factor, and data on outbreak characteristics that promote contributing factor identification are limited. To address these gaps, we analyzed data from 297 single-setting outbreaks reported to CDC's new outbreak surveillance system, which collects data from the environmental health component of outbreak investigations (often called environmental assessments), to identify outbreak characteristics associated with contributing factor identification. These analyses showed that outbreak contributing factors were more often identified when an outbreak etiologic agent had been identified, when the outbreak establishment prepared all meals on location and served more than 150 meals a day, when investigators contacted the establishment to schedule the environmental assessment within a day of the establishment being linked with an outbreak, and when multiple establishment visits were made to complete the environmental assessment. These findings suggest that contributing factor identification is influenced by multiple outbreak characteristics, and that timely and comprehensive environmental assessments are important to contributing factor identification. They also highlight the need for strong environmental health and food safety programs that have the capacity to complete such environmental assessments during outbreak investigations.
Keyphrases
  • human health
  • public health
  • healthcare
  • mental health
  • electronic health record
  • machine learning
  • life cycle
  • big data
  • bioinformatics analysis
  • infectious diseases
  • data analysis