Food Safety Performance Standards: an Epidemiologic Perspective

Transcription

1 Food Safety Performance Standards: an Epidemiologic Perspective Institute t of Medicine i Food dforum Meeting Rajal Mody, MD MPH LCDR US Public Health Service Enteric Diseases Epidemiology Branch Centers for Disease Control and Prevention June 5, 2012 National Center for Emerging and Zoonotic Infectious Diseases Division of Foodborne, Waterborne, and Environmental Diseases

2 Questions (and Answers) What methodologies should lddrive the development of performance standards? Surveillance (size of problem) Source attribution (causes of problem) How does one know if the achievement of a performance standard dwill meet a public health goal? If a standard is achieved related to a food that is a major source of illness, progress toward the public health goal is likely Confidence in the likelihood of achieving the goal depends on The accuracy with which illnesses were attributed to this food source Compliance with the performance standard, related to o Regulatory monitoring o Consequences to industry for not achieving the standard How does CDC evaluate impact of food safety ft measures? Surveillance and source attribution

3 Cycle of Public Health Prevention and Control Surveillance Interventions Investigate sources (e.g.,,p performance standards) ( attribute illnesses to sources ) Applied research

4 CDC Enteric Disease Surveillance: A Comprehensive System of Apps FoodNet FDOSS PulseNet Listeria Initiative 4 selected surveillance apps

5 CDC Enteric Disease Surveillance: A Comprehensive System of Apps FoodNet Conducts active surveillance for 9 pathogens and hemolytic uremic syndrome in 10 sites FoodNet FDOSS Foodborne Disease Outbreak Surveillance System (FDOSS) Collects data on investigated outbreaks PulseNet National Molecular Subtyping Network for Foodborne Disease Surveillance PulseNet Listeria Initiative Listeria Initiative Uniform questionnaires and laboratory methods across the country to quickly identify causes of Listeria outbreaks

6 Source Attribution Helps Identify Where to Set Performance Standards To make progress we need to know the sources of infections Among laboratory confirmed illnesses ~5% occur in outbreaks: source determined for ~50% ~95% are sporadic: sources not determined Illnesses can be attributed to foods using a range of data sources and analytic methods Outbreak data: summaries of food vehicles identified in outbreaks FoodNet studies of sporadic infections to identify likely sources Models that t use dt data on subtypes of pathogens (Hald or Danish Model)

7 Example 1: Role of Outbreak ksurveillance in Juice Regulation Foodborne Disease Outbreak Surveillance System (FDOSS) Interventions (e.g., performance standards) Investigate sources (outbreaks) Applied research

8 Reported Fruit Juice Associated Outbreaks, : Public Health Burden Burden measure Number Outbreaks 26 States 24 Reported illnesses 1384 Hospitalizations 149 HUS cases 17 Deaths 1 Outbreaks are the tip of the iceberg of illnesses caused by any source

9 Reported Fruit Juice Associated Outbreaks, : Causative Agents Agents Outbreaks Ill Salmonella E. coli O Norovirus Cryptosporidium Chemical agents 2 7 E. coli O111, Cryptosporidium Undetermined 9 142

10 Reported Juice Associated Outbreaks by Type of Fruit, United States, Apple juice or cider Orange juice Lemonade Other fruit juice Year

11 1998 FDA s JuiceLabelingRegulation: an Interim Measure Required juice processors to either Implement process control measures to achieve a 5 log pathogen reduction Or Place a warning label indicating the health risks of drinking untreated t djuice

12 Reported Juice Associated Outbreaks by Type of Fruit, United States, Apple juice or cider Orange juice Lemonade Other fruit juice Juice Labeling Regulation Year

13 2001: FDA s Final Juice Regulation Requires systems to achieve a 5 log pathogen reduction Applies to most juice processors

14 Reported Juice Associated Outbreaks by Type of Fruit, United States, Apple juice or cider Orange juice Lemonade Other fruit juice Juice Labeling Regulation Final Juice Regulation

15 Example 2: Role of Surveillance and Source Attribution in the Regulation of E. coli O157 in Beef FDOSS FoodNet Interventions e t (e.g., performance standards) Investigate sources (outbreaks & case control studies) FDOSS = Foodborne Disease Outbreak Surveillance System Applied research

16 Origin of E. coli O157 Zero Tolerance policy Initial source attribution: outbreaks in 1980s associated with ground beef The ti trigger: Jack kin the Box outbreak, ill 44 hemolytic uremic syndrome (kidney failure) 4 died Outcomes Zero tolerance in ground beef Regulations to improve inspection and hygiene in slaughter plants

17 FoodNet Established in 1996 to Monitor Impact E. coli O157 infections per 100, HACCP implementation 2002 Large ground beef recall and many companies began testing all lots of beef trim for O Declared an adulterant HACCP = Hazard Analysis Critical Control Point

18 Percent of Ground Beef Samples that Yielded E. coli O157 and Incidence of Human Infections, incidence % of samples per 100, Testing done by U.S. Department of Agriculture,

19 FoodNet Documented Attainment of Public Health Objective E. coli O157 infections per 100,000 What are the major sources? Healthy People 2010 Objective: 1.0 per 100,000 Healthy People 2020 Objective: 0.6 per 100,000

20 Food Commodities Implicated in Foodborne E. coli O157 Outbreaks, United States, * 2008 Commodity Number of outbreaks (%) Beef 46 (28) Despite dramatic reduction Leafy vegetable 13 (8) Dairy 9 (5) Other meat and game 4 (2) Sprouts 4 (2) Fruits nuts/other 3 (2) Unreported/multiple 86 (52) Total 165 (100) * Heiman 2011 IDSA #615, data is preliminary and may change in incidence, likely as a result of less contaminated ground beef, beef continues to be an important target for intervention BUT source attribution based on outbreak data alone may not be representative of sources for all infections.

21 Risk Factors Identified in FoodNet Case Control Studies (population attributablepercentages) E. coli O157 infections per 100, Eating at a table service restaurant (20%) Pink hamburger at home e(8%) Exposure to cattle farms (6 8%) Pink hamburger at restaurant (7%) Exposure to cattle farms (8%) Pink hamburger at home (6%) Drinking untreated surface water (5%) Pink hamburger at restaurant (2%) Healthy People 2010 Objective: 1.0 per 100,000 Healthy People 2020 Objective: 0.6 per 100,000

22 Example 3: Role of Surveillance and Source Attribution in Listeriosis Control FDOSS FoodNet PulseNet Listeria Initiative Interventions (e.g., performance standards) Investigate sources (outbreak series & case control studies) Applied research

23 Originof the Listeria monocytogenes (Lm) Zero Tolerance Policy in Ready to Eat Meats Initial source attribution (1988): case control study of risk factors for sporadic infection Consumption of undercooked poultry and non reheated hot dogs* The ti trigger (1989): Single case associated itdwith eating contaminated turkey franks *Schwartz B, et al. Lancet. 1988

24 Listeria Outbreaks,

25 Listeria Outbreaks and Percentage of Ready to Eat Meat and Poultry Products Positive for Listeria Number of Outbreaks Single state outbreak Multistate outbreak % RTE products Lm positive (FSIS data) % of RTE products that are Lm positive Before Pulsenet PulseNet Era Listeria Initiative Era FSIS data courtesy of Dr. Kristin Holt; data can be found at

26 nd Incidence e per 1,000,0 000 No. of Outbreaks a Listeria Outbreaks, Incidence, and Percentage of Ready to Eat FSIS Regulated ProductsPositive Positive for Listeria Single state outbreak Multistate outbreak Incidence % RTE products Lm positive (FSIS data) Healthy People 2010 objective: 2.4 per million Healthy People 2020 objective: 2.0 per million % of RTE products that are Lm positive Before Pulsenet PulseNet Era Listeria Initiative Era

27 nd Incidence e per 1,000,0 000 No. of Outbreaks a Listeria Outbreaks, Incidence, and Percentage of Ready to Eat FSIS Regulated ProductsPositive Positive for Listeria Incidence % RTE products Lm positive (FSIS data) Strongly correlated Source attribution required to help understand divergence % of RTE products that are Lm positive Before Pulsenet PulseNet Era Listeria Initiative Era

28 Foods implicated in Foodborne Listeria outbreaks, United States, & * Number of Number of outbreaks outbreaks (%) (%) Ready to eat deli meats 5 (38) 1 (9) Frankfurters 3 (23) 0 Cheese 2 (15) 3 (27) Other foods 1 (8) 5 (45) Unknown 2 (15) 2 (18) Total 13 (100) 11 (100) *Preliminary data, subject to change

29 FoodNet Case Control Study of Risk Factors for Sporadic Listeria Infections, Risk factors identified: Eating melons at commercial establishments Eating hummus prepared in commercial establishments

30 Summary Surveillance provides the data needed to estimate the magnitude of the public health issue and is gateway to the data need to attribute illness to specific sources Source attribution ti is needed d to guide food safety interventions (e.g., performance standards) Ongoing surveillance and periodic source attribution can be g g p used monitor impact of interventions

31 Questions (and Answers) What methodologies should lddrive the development of performance standards? Surveillance (size of problem) Source attribution (causes of problem) How does one know if the achievement of a performance standard dwill meet a public health goal? If a standard is achieved related to a food that is a major source of illness, progress toward the public health goal is likely Confidence in the likelihood of achieving the goal depends on The accuracy with which illnesses were attributed to this food source Compliance with the performance standard, related to o Regulatory monitoring o Consequences to industry for not achieving the standard How does CDC evaluate impact of food safety ft measures? Surveillance and source attribution

32 Thank You! Enteric Diseases Epidemiology Branch The findings and conclusions in this presentation do not necessarily represent the official position of the Centers for Disease Control and Prevention.