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

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port

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port

ion

Time

AttributionBurden

TrendAttribution

Intervention

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

• Þ Burden summarized with distributions

Estimates of US lab-confirmed Campylobacter illnesses, based on data extrapolated

from each FoodNet site, by state

Multiplicative model

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

Mean 90% credible interval

Illnesses (millions) 9.4 6.6 – 12.7

Hospitalizations 56,000 40,000 – 76,000

Deaths 1,350 700 – 2,250Unspecified Agents

Mean 90% credible interval

Illnesses (millions) 38.4 19.8 – 61.2

Hospitalizations 72,000 10,000 – 157,000

Deaths 1,700 350 – 3,350

Summary of Results:Domestically Acquired Foodborne illness

0

10

20

30

40

50

60

70

80

90

100P

erc

en

t

Hospitalizations

34.56

26.21

15.13

7.913.82

0.78

N=55945

Salm

on

ella_

NT

No

rovir

us

Ca

mp

ylo

ba

cte

r

To

xo

pla

sm

a

EcoliO

157

Cp

erf

rin

ge

ns

25

Oth

ers

source

Deaths

28.11

11.11

5.67

24.31

1.49 1.94

N=1341

Salm

on

ella_

NT

No

rovir

us

Ca

mp

ylo

ba

cte

r

To

xo

pla

sm

a

EcoliO

157

Cp

erf

rin

ge

ns

25

Oth

ers

source

Illnesses

10.95

58.18

9.00

0.92 0.67

10.29

N=9388060

Salm

on

ella_

NT

No

rovir

us

Ca

mp

ylo

ba

cte

r

To

xo

pla

sm

a

EcoliO

157

Cp

erf

rin

ge

ns

25

Oth

ers

source

0

10

20

30

40

50

60

70

80

90

100

Cu

m P

erc

en

t

Summary of Results:Domestically Acquired Foodborne illness

0

50

100

150

200

250

300

350

400

SD

Salmonella_NT

Toxoplasma

Listeria

Norovirus

Campylobacter

CperfringensEcoliO157

0 50 100 150 200 250 300 350 400

Mean

0

2000

4000

6000

8000

10000

SD

Salmonella_NT

Norovirus

Campylobacter

ToxoplasmaEcoliO157ListeriaCperfringens

0 5000 10000 15000 20000

Mean

0

500,000

1,000,000

1,500,000

SD

Norovirus

Salmonella_NT

Cperfringens

Campylobacter

ToxoplasmaEcoliO157Listeria

0 1,000,000 3,000,000 5,000,000

Mean

0

0.01

0.02

0.03

0.04

0.05

0.06

SD

CperfringensListeria

Salmonella_NT

CampylobacterEcoliO157

Toxoplasma

Norovirus

0 0.2 0.4 0.6 0.8 1

Mean

Deaths Hospitalizations

Illnesses Percent Foodborne

Links to additional information can be found at…

www.cdc.gov/foodborneburden

Outline• Aim• Estimating the burden of foodborne

illness• Foodborne illness estimates• Attribution and attributing•Attributions•Future directions

The Attribution Framework

Ground B

eef

Seafo

od

Bagged

Let

tuce

Norovirus

Salmonella

E. Coli O157

L. mono

Beef

Retai

l Bee

f Cuts

Leafy

Fruits

-Nuts

Eggs

Consumption

Preparation

Processing

Bunch

Spi

nach

Shell

Produ

cts

Production

Reservoir

Norovirus

Salmonella

L. mono

E. Coli O157

Leafy

Eggs

Seafo

od

Beef

Fruits

-Nuts

Pathogen-Vehicle Plane

Outbreak Based

Hypothetical Validity?

Hypothetical Validity?

Hypothetical Validity?

Data Dom.

Blending Hypothetical Validity?

Data Dom.

CaCo Hypothetical Validity?

Data Dom. Data Dom.

ConsumptionBased

Data Dom. Data Dom. Data Dom. Hypothetical Validity?

Hypothetical Validity?

QMRA Model Dom Model Dom Model Dom Model Dom Model Dom

Expert Elic. Data wt’d Opinion

Data wt’d Opinion

Data wt’d Opinion

Data wt’d Opinion

Data wt’d Opinion

Reservoir Production Processing Preparation Consumption

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

Foodborne Human Illness

Sporadic

Consumption-based

Danish Model Adaptation: Salmonella

CaCo Studies

Campylobacter Toxoplasma Listeria Salmonella Serotypes STEC

Outbreak

Blending Sporadic and

Outbreak Data

STEC 96 and 99

Simple Commodity Attribution

Annual MMWR

Complex Commodity Attribution

Painter Model

All Food

Aquatic Land animals Plant

Fish Shellfish Dairy Eggs Meat-Poultry Grains-beans Oils-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

AttributionsIllnesses (%)

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 0 0 7 66 0 0 0 <1 3 3 <1 <1 0 1 0 0 <1 ~80%

Complex outbreak-related <1 0 <1 34 0 0 <1 0 20 0 0 8 0 37 0 0 0 ~100%

Blended

Case/Control 0 5 0 15 0 28 0 8 58 0 0 0 - 20 0 0 0 ~139%

Consumption-based - - - - 6 29 - <1 65 - - - - - - - - ~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 SE 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

TheoryAnalysis

Data

TheoryAnalysis

Data

TheoryAnalysis

Data

TheoryAnalysis

Data

TheoryAnalysis

Data

OutbreakAttribution

BlendedAttribution

SporadicAttribution

Consumption-based

Models

ExpertElicitation

Synthesis

Communication

Reporting

Theory

JAN 2013 JAN 2016Project 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 Prevention1600 Clifton Road NE, Atlanta, GA 30333Telephone, 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348E-mail: cdcinfo@cdc.gov Web: www.cdc.gov

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…

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