Foodborne Illness and Outbreak Surveillance in the USA. Alison Samuel, Naghmeh Parto, Emily Peterson

Transcription

1 Foodborne Illness and Outbreak Surveillance in the USA Alison Samuel, Naghmeh Parto, Emily Peterson 1

2 Context Where is the information coming from: Attended the CDC/ Emory University; Environmental Microbiology: Control of Foodborne and Waterborne Disease course in January 2012 to learn about foodborne and waterborne illness/ outbreak and surveillance in USA. The course covered a lot of information; we are focusing on the surveillance systems for foodborne illnesses and outbreaks. 2

3 CDC, Foodborne Illness Surveillance Program Surveillance data come from many systems. Composed of many interrelated surveillance systems. Each system has a different purpose. Additional resources/ networking/ surveillance programs are implemented to fill in the gaps where the lack of regulatory requirement for reporting exists. PulseNet NNDSS-LEDS NARMS FoodNet FDOSS CaliciNet Listeria Initiative NVEAIS 3

4 Major Foodborne Illness Surveillance Systems National Notifiable Disease Surveillance System (NNDSS), 1878: Health care and laboratory professionals are required by state law to report cases of certain diseases to health departments, who then report to CDC. Laboratory-based Enteric Disease Surveillance (LEDS), 1963: State labs send serotype data electronically to CDC used to track trends and detect outbreaks, in synergy with PulseNet. 4

5 Major Foodborne Illness Surveillance Systems cont d National Antimicrobial Resistance Monitoring System (NARMS), 1996: CDC/FDA/USDA Monitor resistance among bacteria isolated from humans, retail meat, animal carcasses 5

6 Major Foodborne Illness Surveillance Systems cont d Listeria Initiative, 2004: Interview all cases with a standard food questionnaire All L. monocytogenes isolates are forwarded to state public health laboratories for subtyping When PulseNet detects a cluster, CDC compares food exposures among cluster cases to non-cluster cases to identify suspect foods Allows for rapid case-control/ case-case analyses. 6

7 Major Foodborne Illness Surveillance Systems cont d. Foodborne Disease Outbreak Surveillance System (FDOSS), 1973: Report outbreaks each year through the National Outbreak Reporting System (NORS). Data used to determine pathogen-food combinations to target for prevention Captures outbreak data on agents, foods, and settings responsible for illness. CaliciNet, 2009: National Electronic Norovirus Outbreak Network; Comparing Norovirus DNA sequences, Determine outbreaks Identify new strains. 7

8 Major Foodborne Illness Surveillance Systems cont d National Voluntary Environmental Assessment Information System (NVEAIS), 2012: developed from the EHS-Net Foodborne Outbreak Study from Tracks environmental factors that contribute to foodborne outbreaks Intended to fill the gap contributing factors and environmental precursors of foodborne illness outbreak prevention PulseNet; 1996: Connects cases of illness nationwide using standardized molecular subtyping (PFGE patterns) in central database Early detection of common source outbreaks Detection of/links outbreaks that are widespread/national/international 8

9 Major Foodborne Illness Surveillance Systems cont d FoodNet, States (CDC/USDA/FDA) Regular contact between state and laboratory's Active surveillance of laboratory cases for 9 infections and HUS Surveys of lab, physicians, general population Epi studies (case-control, cohort and other ) Allows for: Monitoring of trends Attribution of burden of illness to specific foods and settings Calculation of burden of foodborne illness 9

10 Major Foodborne Illness Surveillance Systems cont d FoodCORE, states (Core group in state that works on foodborne illness issue) detect, investigate, respond to, and control outbreaks of foodborne diseases Interview all cases of Salmonella, Shiga toxin-producing E. coli, and Listeria for detailed exposure information (Will also investigate viral and parasitic foodborne diseases) Have graduate students collect info in timely manner Allows for: Faster hypothesis generation and more accurate source attribution Removal of burden of interviewing away from county health unit 10

11 Example of CDC Systems Working Together Salmonella Outbreak (New York 2011) 11

12 CDC s Vision for Surveillance: Timely, reliable data on incidence, trends, and implicated foods is available to all. Data from every pathogen-confirmed illness and every outbreak is captured. Molecular tests are developed that provide information for public health as well as patient care. IT system links data from many surveillance systems. Food industry becomes a real partner in public health. What is the vision for Ontario/ Canada? What should that include? 12

13 USA Availability of Systems National Notifiable Disease Surveillance System (NNDSS) Laboratory-based Enteric Disease Surveillance (LEDS) National Antimicrobial Resistance Monitoring System (NARMS) Listeria Initiative Foodborne Disease Outbreak Surveillance System (FDOSS) National Voluntary Environmental Assessment Information System (NVEAIS) CaliciNet CDC Pulse Net Canada/Ontario National Notifiable Disease List.... Ontario under HPPA National Enteric Surveillance Program (NESP).... Ontario -TPHL reporting Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) No equivalent.... Different Canadian list of initiatives (since 2008) No equivalent.... Some info in Integrated Public Health Information System (iphis) PulseNet Canada FoodNet No equivalent.... Some similarities with C- EnterNet FoodCORE 13

14 Enteric Outbreaks, Minnesota, and Centralized Interviewing Dr. Dean Middleton Senior Public Health Consultant Enteric, Zoonotic and Vectorborne Diseases Unit May 8, 2012

15 Minnesota 15

16 Minnesota Population 5 million MPH functions as one health unit for the state Reportable Diseases reported to MPH from the state Team Diarrhea interviews every enteric RD except Campylobacter Ontario 13 million 36 health units RD s reported to 36 HU s, then to PHO Enteric RD s are interviewed by 36 HU s 16

17 Minnesota Ontario Centralized Interviewing Decentralized interviewing 17

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19 Minnesota Ontario Centralized Interviewing Sporadic Cases Interviewers purposefully interview similar cases (e.g., similar PFGE patterns) Outbreak Cases Decentralized Interviewing Sporadic Cases 36 health units Outbreak Cases Standardized questionnaire Field Epidemiologist 19

20 Constraints of Decentralized Interviewing During Outbreaks Different questionnaires used Questionnaires completed inconsistently Concept of provincially visible vs. locally invisible outbreaks Completing the questionnaire may not be a priority Numerous interviewers doesn t allow for; Identifying commonality via informal discussion during open-ended section of the interview Timely follow-up on hypotheses generated Identifying items not listed on the questionnaire (e.g., almonds) 20

21 Constraints of Decentralized Interviewing During Outbreaks Analysis of questionnaire data; Consists of basically running frequencies of food items Open-ended questions poorly analyzed When required, the transition from Decentralized Interviewing to Centralized interviewing is constrained by; Time lost in the transition, Info collected prior to centralized interviewing is not used efficiently, Re-interview of cases may be required 21

22 Centralized Interviewing Improves: The response time The response consistency The feedback loop during the investigation The overall response capacity Improves the identification of the source of the outbreak 22

23 How Might Centralized Interviewing Be Utilized? Apply to all enteric cases? Outbreaks? Sporadic cases? 23

24 Outbreaks? The most useful scenario for which consideration for implementation of Centralized Interviewing would be when there is strong suspicion that the item causing the illness is distributed to the population residing in several jurisdictions. For this scenario, the cause of the outbreak is almost exclusively a food item. 24

25 Sporadic Cases? Pick one pathogen which; Is frequently increased above expected amounts Is likely to generate an outbreak Little is known about the risk factors Rotate the pathogens over time. If a case-control study is used, this approach can greatly assist with developing laboratory capacity for sub-typing (e.g., MLVA, optical mapping, SNP analysis, whole genome sequencing). This approach is more consistent with surveillance. 25

26 Questions 26