National Center for Emerging and Zoonotic Infectious Diseases Designing Studies to Better Understand Food Source Attribution Division of Foodborne, Waterborne, and Environmental Diseases Mike Hoekstra

Attribution of illness to food commodity is a simple process of relating episodes of human illness through consumption or handling of foods to instances of commodity contamination…except that the available data on human illness, food consumption, and contamination are nowhere configured to make relating them simple. The totality of agents that cause illness is not known. Surveillance for the agents that are known is not complete. Surveillance reports rarely come with food specified as the cause, much less the commodity. Outbreak investigations can produce cases of human illness that are tightly linked to specific food exposures, but such tight links exist for only a fraction of reported outbreak cases, and outbreak cases are, in turn, only a small fraction of all cases. Case control studies are typically aimed at attributing illness to causal food exposures in the much larger population of sporadic illness. These studies link multiple food exposures to cases, but do so in a very noisy fashion. The actual causal exposures are in turn inferred from control food exposures, also noisy and with different potential biases. Consumption models, like that of Hald, link counts of human illness aggregated by type to commodity contamination levels by type, through food consumption estimates, yielding ecological associations. Further, commodity contamination levels can depend on the point in the food chain that they are measured, creating potentially different attributions. Quantitative microbiological risk assessment offers another route to attribution, building causal pathways from reservoir to consumption via probabilistic models applied to the food chain. These are examples of existing ways to relate illness to contaminated food. They are diverse, not exhaustive, and no single method can be deemed definitive given the large inherent uncertainties in the data and in the model structures themselves. We present design considerations for each these examples along with a paradigm for synthesizing an understanding of their collective food source attribution outputs. Abstract

Outline Aim and Background Estimating the burden of foodborne illness Foodborne illness estimates Attribution and attributing Attributions Future directions

Aim Estimate the “burden” of human illness caused by contaminated food –at the individual pathogen/agent level and in the aggregate –where burden may be defined in terms of severity (eg. illness vs. hospitalizations) Estimate the proportion of that burden caused by specific food commodities –where commodities are tied to regulation –where burden may be specific to subpopulation or illness outcome

Aim Intervene to reduce illness at point(s) informed by estimated burden and attribution Measure changes in amount of illness –where power to detect change depends on effect size and data stream Measure change in the proportion of illness caused by specific food commodities

Cycle of public health action AttributionBurden Trend Attribution

Outline Aim and Background Estimating the burden of foodborne illness Foodborne illness estimates Attribution and attributing Attributions Future directions

Estimating illnesses Multiplicative models Data summarized with distributions Factors summarized with distributions  Burden summarized with distributions

Estimates of US lab-confirmed Campylobacter illnesses, based on data extrapolated from each FoodNet site, by state

Multiplicative model

Estimated distribution of Campylobacter Illness Burden

Outline Aim Estimating the burden of foodborne illness Foodborne illness estimates Attribution and attributing Attributions Future directions

Annual estimate of domestically acquired foodborne illnesses, hospitalizations and deaths 31 Known Pathogens Mean90% credible interval Illnesses (millions) – 12.7 Hospitalizations56,00040,000 – 76,000 Deaths1, – 2,250 Unspecified Agents Mean90% credible interval Illnesses (millions) – 61.2 Hospitalizations72,00010,000 – 157,000 Deaths1, – 3,350

Summary of Results: Domestically Acquired Foodborne illness

DeathsHospitalizations IllnessesPercent Foodborne

Links to additional information can be found at…

Outline Aim Estimating the burden of foodborne illness Foodborne illness estimates Attribution and attributing Attributions Future directions

The Attribution Framework Ground Beef Seafood Bagged Lettuce Norovirus Salmonella E. Coli O157 L. mono Beef Retail Beef Cuts Leafy Fruits-Nuts Eggs Consumption Preparation Processing Bunch Spinach Shell Products Production Reservoir

Norovirus Salmonella L. mono E. Coli O157 Leafy Eggs Seafood Beef Fruits-Nuts Pathogen-Vehicle Plane Outbreak Based Hypothetical Validity? Data Dom. Blending Hypothetical Validity? Data Dom. CaCo Hypothetical Validity? Data Dom. Consumption Based Data Dom. Hypothetical Validity? QMRA Model Dom Expert Elic. Data wt’d Opinion ReservoirProductionProcessingPreparationConsumption Building Blocks in Framework

Outline Aim Estimating the burden of foodborne illness Foodborne illness estimates Attribution and attributing Attributions Future directions

Human Illness Data Sources and Related Attribution Methodologies

All Food AquaticLand animalsPlant FishShellfishDairyEggsMeat-Poultry Grains-beansOils-sugars Crustaceans Mollusks Meat Poultry Beef Game Pork Produce Fruits-nuts Vegetables Fungi Leafy Root Sprout Vine-stalk Yellow boxes identify 17 commodities Painter et al, J Food Protection 2009 Food Commodity Hierarchy

Attributions Illnesses (%) Campylobacter Finfish Crustaceans Mollusks Dairy Eggs Beef Game Pork Poultry Grains-Beans Oils-Sugars Fruits-Nuts Fungi Leafy Root Sprout Vine-Stalk Total Simple outbreak- related < ~80% Complex outbreak- related < < ~100% Blended Case/Control ~139% Consumption-based < ~100% QMRA/Other????????????????? Expert elicitation?? Weighted average??? 100%

Outline Aim Estimating the burden of foodborne illness Foodborne illness estimates Attribution and attributing Attributions Future directions

N S E W NW NE SW SE

Synthesis: Issues Categories Partition < 100% Partition > 100% Missing values Incomplete classification Non-quantitative knowledge Weighting/combining information

Synthesis: Resolutions Expert elicitation EE/BMA hybrid Bayesian model averaging Integrated blending model (?)

Project 3 Theory Analysis Data Theory Analysis Data Theory Analysis Data Theory Analysis Data Theory Analysis Data Outbreak Attribution Blended Attribution Sporadic Attribution Consumption-based Models Expert Elicitation Synthesis Communication Reporting Theory JAN 2013 JAN 2016 Project 0 Project 6 Project 7 Project 5 Project 4 Project 2 Project 10 Summary description based on existing data and understanding Summary description based on revised data and understanding Project 9 Project 8

For more information please contact Centers for Disease Control and Prevention 1600 Clifton Road NE, Atlanta, GA Telephone, CDC-INFO ( )/TTY: Web: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases Division of Foodborne, Waterborne, and Environmental Diseases

In case you were thinking outbreaks can solve all your problems…