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Epi-Ready

Epi-Ready. Surveillance and Detection – Implications for Response. Module 4. Module Objectives. By the end of this module, participants will be able to describe the various methods used to detect foodborne illness and determine if a response action is necessary.

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Epi-Ready

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  1. Epi-Ready Surveillance and Detection – Implications for Response Module 4

  2. Module Objectives • By the end of this module, participants will be able to describe the various methods used to detect foodborne illness and determine if a response action is necessary. • Identify the various surveillance systems available to detect foodborne illness. • Compare and contrast pathogen-specific surveillance and complaint-based surveillance to detect foodborne illness. • Identify initial outbreak response activities.

  3. Surveillance • Public health surveillance is a system that acquires, organizes, and interprets data for action to prevent and control disease the population.

  4. Characteristics of Effective Surveillance

  5. Methods of Surveillance

  6. Types of Surveillance

  7. Burden of Illness Pyramid Reported to Health Department/CDC 2% 100% Adapted from a CDC Source

  8. Syndromic Surveillance • Surveillance of health-related data prior to diagnosis • Clinical or non-clinical • Used continuously or for special events • National Syndromic Surveillance Program

  9. Sentinel Surveillance FoodNet Sites

  10. Laboratory Surveillance Systems • Pathogen-specific in nature • High quality data • very sensitive and has a positive predictive value • Not always procedurally simple or timely

  11. Timeframe of Laboratory Analysis - Salmonella 5 to 28 days to identify an illness as part of a cluster through PulseNet Adapted from a CDC source

  12. Laboratory Surveillance Systems

  13. PulseNet • Main laboratory cluster detection method • Considered molecular epidemiology • DNA Analysis • Important system to detect multistate outbreaks

  14. CaliciNet • Norovirus surveillance network • Started in 2009 • Outbreak surveillance of norovirus at genetic level • Monitors circulating norovirus strains • 50% more norovirus illness in years when there is a new strain of the virus circulating Source: CDC

  15. CryptoNet • Established in 2010 • One species now is 30, many with multiple subtypes • Culture-independent identification Oocysts of Cryptosporidium Source: CDC

  16. NARMS • Established in 1996, nationwide in 2003 • Collaboration of state and local public health with: • CDC (humans) • FDA (retail meats) • USDA (food animals) • Covers 18 antimicrobial agents in: • Salmonella • Shigella • E. coli • Campylobacter • Certain Vibrio species

  17. Epidemiologic Surveillance Systems

  18. National Notifiable Disease Surveillance System • Important component of all public health system • Reporting of infectious, noninfectious and outbreak conditions • Major foodborne illness pathogens are listed as well foodborne disease outbreaks • States may add conditions • States require timely reporting • Provider- Local/State PH • Local PH – State PH Source: CDC

  19. Foodborne Illness Complaint Surveillance • Based on complaints of illness • Coordination and follow-up enhances the system • Need for uniform practice of investigation across jurisdictions

  20. Electronic and Social Media

  21. The Interview Process Primary tool to gather information from complainants and case-patients regarding possible exposure.

  22. Activity This table group exercise will demonstrate how to conduct an interview using a food history form. Choose a person to be interviewed and an interviewer. The remaining members of the table group will be evaluators. This activity should take 15 minutes. Instructors will be available to assist as necessary.

  23. Thoughts on the Exercise

  24. Activity Work in table groups to determine how you would approach this list of complaints received at the Health Department. Use the space in the participant’s manual to record your thoughts. 15 minute activity. 10 minutes working in groups and 5 minutes for class discussion.

  25. Moving to Response–Cluster Detection

  26. Laboratory-Identified Clusters • Pathogen identified by DNA analysis indicate clusters of disease • Long lag periods from exposure to identification as part of a cluster may be unavoidable • Recall becomes a challenge • Provides specificity to the epidemiologic investigator • CIDT identified illness, by itself, will not identify a cluster

  27. Complaint-based Cluster Identification • Good record keeping and prompt response necessary • Good for the identification of: • Illness with short incubation periods • Illness isolated to a single jurisdiction – usually a single event • Recall bias – last meal bias • Focus on food histories and geographic information • Provider notification without laboratory identification handled as a complaint

  28. Summary

  29. Coming Up Next

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