1. Evaluate potential limitations with current foodborne illness source attribution estimates obtained from outbreak reports. Neal Golden, Ph.D. January 31 st, 2012 1

2. Overview 2  Definitions  Purpose  Background  Project Description  Illustrative Examples  Summary  Timeline

3. Definitions  Outbreak  Cases of illness that share a common cause - any two or more cases of illness that are connected to a known common cause  FDOSS - Foodborne Disease Outbreak Surveillance System  Sporadic illness  An isolated case of illness - when someone becomes ill and there is no known connection to another person’s illness  FoodNet - Foodborne Diseases Active Surveillance Network  LEDS - Laboratory-based Enteric Disease Surveillance system (replaced PHLIS) 3

4. Purpose 4  The purpose of this project is to:  Assess degree of confidence in the use of outbreak data to estimate foodborne illness source attribution  Assist in developing criteria that prioritizes pathogens for which outbreak data may be sufficient  Contribute to an analysis of uncertainty  The purpose is not to estimate foodborne illness source attribution

5. Background 5 Methods to attribute illnesses to foods SurveillancePopulation Foodborne illnesses FoodNet: Sporadic illnesses (~95%) Case-control studies FDOSS: Outbreak illnesses (~5%) Analyze data from outbreaks with implicated foods  Source attribution generally requires two key pieces of illness information: 1. the pathogen that caused the illness, and 2. the contaminated food at the point of consumption  Sporadic illness data, such as FoodNet, and outbreak data, FDOSS, provide the pathogen, but only outbreak data, provides both the pathogen and the implicated food  So what is the problem of focusing on outbreaks only?  FDOSS (outbreaks) is an overall 5% of known confirmed illnesses  Therefore, the food source is implicated for only a small fraction of known illnesses

6. Outbreaks represent a fraction of total illnesses 6  When calculating a source attribution figure by using outbreak data, we need to assume exposure pathways are similar to those that led to sporadic illnesses  Is this a valid assumption? Numbers above bars represent the number of sporadic illnesses observed for each observed outbreak illness

7. Is foodborne illness source attribution derived from outbreaks representative of sporadic illnesses? 7  Very difficult to answer the above question!  Ideally we would have a representative set of cases of sporadic illness that we know were caused by exposure to specific contaminated foods  However, this is not the case  Therefore, a major source of uncertainty is  The validity of the assumption that the distribution of pathogens and their implicated food vehicles in outbreak reports reflects the relevant foodborne pathways of exposure in the general population

8. Project Description - General  Evaluate similarities in the distributions of outbreak data vs. sporadic illnesses data for the following four pathogens:  Salmonella spp.,  E. coli O157:H7,  Campylobacter spp., and  Listeria monocytogenes  If outbreak cases look like sporadic cases across an array of epidemiologically factors, the suggests that the causal exposure pathways are similar in identity and degree of incidence  The workgroup is considering the following comparisons: 8  Age  Gender  Geography  Hospitalization  Monthly  Serotype (for Salmonella)  Yearly

9. Project Description - Limitations 9  Data limited by the FDOSS database given fewer outbreak cases identified compared with lab-based surveillance (sporadic illnesses)  Lack of variables for few direct comparisons  National (FDOSS) vs. Regional (FoodNet)  FDOSS outbreaks are national however, FoodNet is composed of 10 states representing about 15% of the U.S. population  LEDS is nationally representative, but passive lab-based surveillance

10. Illustrative Examples 10

11. Illustrative Examples – Temporal 11  Databases compared  Outbreak illnesses  FDOSS  Sporadic illnesses  FoodNet (active lab-based surveillance)  Etiology  Salmonella  Data years  1998 to 2009

12. Comparison of Salmonella outbreak (FDOSS) and sporadic (FoodNet) illnesses by month 12

13. Comparison of Salmonella outbreak (FDOSS) and sporadic (FoodNet) illnesses by year 13 vertical line - year FoodNet catchment stabilized

14. Illustrative Examples – Gender 14  Databases compared  Outbreak illnesses  FDOSS  Sporadic illnesses  FoodNet (active lab-based surveillance)  Etiology  Salmonella  Data years  1998 to 2009

15. Comparison of Salmonella outbreak and sporadic illnesses by gender 15  Some year-to-year variability  Averaging suggests the fraction of female outbreak cases is very similar to the fraction of female sporadic cases  Outbreak: 51.7%  Sporadics: 51.5% vertical line - year FoodNet catchment stabilized

16. Evaluate potential limitations with current foodborne illness source attribution  Timeline  Project plan approved in Fall 2011  Databases have been acquired  Following comparisons of the six epidemiologic factors for the four pathogens, determine if additional analyses are required, Winter 2012  Project completion in Spring 2012 16