National Center for Emerging and Zoonotic Infectious Diseases Designing Studies to Better Understand...
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Transcript of National Center for Emerging and Zoonotic Infectious Diseases Designing Studies to Better Understand...
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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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: [email protected] 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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