Risk Ranking Tool for Prioritizing Commodity and Pathogen
Combinations for Risk Assessment of Fresh Produce
Maren Anderson, PhD1; Lee-Ann Jaykus, PhD2; Steve Beaulieu1, and Sherri Dennis, PhD3
1RTI International, Department of Environmental Health and Safety; 2North Carolina State University,
Department of Food, Bioprocessing, and Nutrition Science; 3U.S. FDA, Center for Food Safety and Nutrition
Results
Abstract
Methods (continued)
Title: Risk Ranking Tool for Prioritizing Commodity and Pathogen Combinations for Risk Assessment of Fresh
Produce
• There were a total of 51 pathogen-commodity pairs determined from the foodborne outbreaks
reported to the CDC and the literature that formed the basis for the risk ranking tool:
Background: Outbreaks associated with fresh produce have increased in the past decade. There is currently no
transparent, data-driven, customizable ranking system that can be used to rapidly prioritize pathogen-commodity pairs
for more rigorous risk assessment modeling efforts.
Objective: To develop a semi-quantitative risk ranking software tool to prioritize and rank pathogen-commodity
combinations based on explicit data-driven risk criteria.
Methods: To identify candidate pathogen-commodity pairs, a database was created that included all reports of fresh
produce-associated outbreaks compiled by the CDC (1996 to 2006). Additional information was sought from peerreviewed literature and publicly accessible databases. Nine risk criteria were developed across four primary
dimensions of risk: (i) strength of epidemiological association between pathogen and commodity; (ii) severity of
disease; (iii) pathogen characteristics that influence disease outcome; and (iv) commodity characteristics that
influence pathogen prevalence, behavior, and likelihood of exposure. For each risk criterion, narrative descriptions
were developed and quantified for scoring purposes, and available data were used to score each criterion. Userspecified weights were assigned to each criterion based on the user’s judgment regarding the relative contribution to
risk. The overall risk score for any one pathogen-commodity pair is the summation of the criteria scores multiplied by
the respective criteria weights.
Results: A total of 51 pathogen-produce commodity pairs were included in the risk ranking. Ranking scores ranged
from a low of 13 to a high of 155. Scenario analyses were performed to explore the impact of user-defined weights on
the ranking results. Within the range of weights that were considered, enterohemorrhagic E. coli and leafy greens
consistently ranked first, followed by Salmonella spp. and tomatoes and Salmonella spp. and leafy greens.
Table 2. Pathogen and Commodity Pairs
General Commodity
Category
Berries
Carrots
Crucifers
Conclusions: The risk ranking tool provides a systematic, transparent, and customizable tool with which to prioritize
pathogen-commodity pairs for more rigorous risk assessment modeling efforts.
Background
The U.S. Food and Drug Administration (FDA) is responsible for ensuring the safety of all domestic and imported
fresh produce consumed in the United States. Sources of pathogen contamination in fresh produce are varied, but
contributing factors include contaminated agricultural or processing waters, the use of manure as fertilizer, the
presence of wild or domestic animals in or near fields or packing areas, worker health and hygiene, environmental
conditions, production activities, and equipment and facility sanitation. Consequently, the manner in which fresh
produce is grown, harvested, packed, processed, transported, distributed, prepared, and consumed is crucial to
minimizing the risk of microbial and chemical contamination.
Green onions
Herbs
In light of the increasing number of produce-related illnesses due to pathogen contamination, there is renewed interest
in interventions that might help prevent contamination or inactivate contaminants when they are present in food. A
logical step in implementing targeted control strategies for fresh produce is the use of microbiological risk assessment.
However, there are many potential microbiological contaminants and many different produce items, and as a result,
determining which specific pathogen-commodity combinations on which to focus is a daunting task.
Risk ranking, sometimes called hazard ranking or comparative risk assessment, is a technique that can be used to
identify, and thereby prioritize, the most significant risks for a given situation. The purpose of this project was to build
a risk ranking tool that could be used by the FDA to identify priority pathogen-commodity pairs, as applied to fresh
produce, based on explicit criteria that relate to risk. The risk ranking tool is based on epidemiological data from past
fresh produce outbreaks, and combines those data with other information about health outcomes and severity,
population susceptibility, prevalence of contamination, likelihood of pathogen growth, and human consumption
patterns to produce a semi-quantitative means by which to compare pathogen-commodity combinations for
prioritization purposes.
Leafy greens
Specific Commodity Category
Number of Outbreaks
Total Cases
Cyclospora cayetanensis
8
1,391
E. coli O157:H7 (EHEC)
3
28
Hepatitis A virus
4
314
Norovirus
5
194
Salmonella enterica
1
13
Salmonella enterica
1
8
2
32
Norovirus
12
444
Bacillus cereus
1
8
E. coli O157:H7 (EHEC)
2
161
Salmonella enterica
3
52
Score
Cryptosporidium parvum
1
8
1
3–5
>100
Hepatitis A virus
7
1,070
4
Very
Strong
>5
>100
Salmonella enterica
1
27
Cyclospora cayetanensis
3
836
E. coli O157:H7 (EHEC)
2
6
E. coli (other pathogenic)
1
66
Salmonella enterica
3
56
Shigella spp.
3
496
Salmonella enterica
4
145
Campylobacter jejuni
2
314
Norovirus
10
316
Shigella spp.
2
11
E. coli O157:H7 (EHEC)
16
624
12
432
E. coli O157:H7 (EHEC)
1
736
Bacillus cereus
2
• We reviewed all issues of the Morbidity and Mortality Weekly Report published during the same 10-year period
(1996–2006) to identify outbreak information to supplement the annual outbreak data.
Hepatitis A virus
• Peer Reviewed Literature was reviewed using the PubMed search engine (www.pubmed.org) with the key words
“outbreak” along with each fresh produce commodity of concern identified from the CDC outbreak database. Only
data from outbreaks occurring in the United States were included.
Specific Commodity Category
Table 4. Scoring of Criterion 2:
Disease Multiplier
Score
Category
Low
2
2
Medium
20
3
High
38
4
Very High
45
Score
Category
The epidemiological link
expresses the relative likelihood
that a general commoditypathogen pair has been
historically associated with
foodborne disease outbreaks.
Both number of outbreaks and
total cases were considered for
this ranking criterion.
Number of
Outbreaks
Total
Cases
<10%
<0.1%
Data from Mead et al. (1999)
were used along with available
FoodNet Reports (1997–2004) to
update the Mead values. A mild
foodborne illness without
hospitalization or death is of less
concern that an illness resulting
in more severe outcomes.
Table 9. Scoring of Criterion 8:
Consumption
Score
Category
% Consuming
1
Low
<1%
2
Medium
1–5%
3
High
5–10%
4
Very High
>10%
Score
1
Category
Evidence for growth
None
2
No
evidence
3
Some
Strong
Table 11. Scoring for Criterion 9:
Shelf Life
Score
Category
Shelf-Life
1
Very Short
0–7 days
Moderate
15–48 days
2
6
4
High
>50%
>1%
4
Long
Salmonella Typhi
1
16
Giardia lamblia
1
50
Shigella spp.
4
61
Campylobacter jejuni
4
106
Cyclospora cayetanensis
2
130
Score
E. coli (other pathogenic)
1
300
1
None
E. coli O157:H7 (EHEC)
12
324
No one is more susceptible
than others
Score
Category
Growth Potential
Score + Shelf-Life
Score
2
Some
Young children or the elderly
have a higher prevalence of
disease
1
>2
1
2
3–4
2
3
5–6
3
4
7–8
4
17
657
Norovirus
112
5,390
Salmonella enterica
1
10
Non-citrus fruit
Norovirus
5
132
Salmonella enterica
4
131
Campylobacter spp.
1
13
Hepatitis A virus
1
23
Salmonella enterica
20
2,149
Medium
Severity of disease increases
with age
4
Strong
Children, pregnant women,
immunocompromised
Table 7. Scoring of Criterion 6:
Infectious Dose
Green Onions
Green onions, scallions
1
High
Herbs
Basil, parsley, cilantro (no other herbs identified)
2
Medium
Leafy greens
Lettuce (unspecified), mesclun, spinach, romaine, leaf, iceberg, bagged lettuce
Score
≥49 days
Table 12. Scoring for Criterion 9:
Combined Growth Potential and
Shelf Life
Strength for Evidence
3
Category
3
Low
4
Very Low
Infectious Dose
(CFU)
≥100,001
1,001–100,000
101–1,000
1–100
Data on infectious dose was
collected from CFSAN fact
sheets and the literature. The
organisms with the lowest
infectious dose received the
highest score as they have a
higher likelihood of causing
disease at the low levels of
contamination anticipated in
naturally-contaminated produce.
• The user then determines the weights based on the importance of each risk variable from 1 to 5
(Figure 2).
• The Risk Ranking Tool will then generate a report detailing the top priority pathogen and
commodity pairs for that scenario (Figure 3).
Pathogen and Commodity Pair
RR Score Low
RR Score High
2
Tomatoes and Salmonella enterica
27
135
• The underlying database has been extensively quality assured and, although it represents a current snapshot of
a wide variety of information, it has been designed to facilitate periodic updates of the information with
relative ease. The data are not proprietary and the database structure is simple and transparent, allowing for
multiple uses of the underlying data.
Leafy greens and Salmonella enterica
27
135
Limitations of the Risk Ranking Tool
Crucifers and E. coli O157:H7 (EHEC)
26
130
Melons and Salmonella enterica
26
130
• The food categories and scoring bins were necessarily simple to promote ease-of-use and transparency.
Although the categories and scoring are generally consistent with other approaches developed by the FDA and
others, alternative schemes that could have been developed may produce different ranking results.
Melons and E. coli O157:H7 (EHEC)
26
130
Carrots and Salmonella enterica
25
125
Mixed Produce and E. coli O157:H7
(EHEC)
25
125
Crucifers and Cryptosporidium parvum
24
120
Herbs and E. coli O157:H7 (EHEC)
24
120
Green onions and
Cryptosporidium parvum
24
120
Berries and E. coli O157:H7 (EHEC)
23
115
5
• The tool does not take into account all possible pathogen-produce commodity pairs, rather, it is “trained” on
the basis of recognized foodborne disease outbreaks. This severely limits the predictive capabilities for
emerging pathogen-commodity pairs and sporadic outbreaks. This limitation is illustrated by the absence of
the combination of Salmonella enterica serovar Saintpaul and peppers (Jalapeño and Serrano), an outbreak
that occurred after the development of this tool.
• Data deficiencies were generally handled by assigning higher risk scores, essentially equating the absence of
data with greater potential for adverse health impacts. This “protective” convention was adopted because data
on all criteria were not available for all pathogens and commodities. This approach tends to bias data poor
pathogen-commodity pairs towards higher rankings, and the current version of the tool does not include the
ability to quantify this uncertainty.
•In a majority of iterations, tomatoes–Salmonella enterica and leafy greens–Salmonella enterica ranked second and
third, respectively.
• In an effort to further enhance our understanding of the model function and its predictive power,
multiple model runs were conducted in which the weights for various criteria were changed relative to
one another (similar to a sensitivity analysis). The baseline scenario for these simulations consisted of
all inputs assigned a weight of 2. For comparison purposes, we then increased the weight of one or two
of the inputs to 5 while keeping all others at 2. The results showed some degree of model sensitivity to
all criteria, but the top 10 ranked pathogen-commodity pairs remained relatively consistent, albeit their
individual rank may have increased or decreased relative to one another.
• A score for each pathogen, commodity, or pathogen-commodity combination was
assigned for each of the nine criteria. Thereafter, a model was constructed so that
the scores for each of the nine criteria could be combined to produce a single score
for each pathogen-commodity pair for the purposes of risk ranking. It was assumed
that, more often than not, the user would consider one or more of the individual
criteria more important than others. Therefore, each of the nine criteria was
assigned an ordinal number weight from 1–5. For example, if a death outcome is a
more important consideration than low infectious dose, the user can assign a higher
weighting to death rate.
• To generate an overall rank per pathogen-commodity pair that incorporates all nine
criteria scores, an algorithm was developed that balances the score for each
criterion with the weight of that criterion. The result is an overall numerical score
for each pathogen-commodity pair that is produced by first multiplying each
variable’s score by its weight and then adding each of these nine values:
Rank   Scorei Weighti
•For the rest of the pathogen-commodity pairs, the risk ranking tool was sensitive to changes in the weighting scheme,
which can be modified based on the priorities of the user.
•The risk ranking tool provides an easy to use, customizable, systematic, and data-driven means by which to prioritize
pathogen-produce commodities for more rigorous quantitative microbial risk assessment efforts.
• An abbreviated Monte Carlo simulation was also applied to the model. Specifically, a random number
generator was used to determine the weights (ordinal numbers ranging from 1 to 5) for each of the nine
criteria of the Risk Ranking Tool; these randomly selected weights were used in a single simulation.
The weights were randomly selected and the model rerun 100 times. The relative ranks for each
specific pathogen-commodity pair (from 1 through 11+) are summarized over the 100 runs. In all 100
simulations, leafy greens–E. coli O157:H7 (EHEC) ranked first, further supporting its choice as the
top-ranked pathogen-commodity pair, regardless of parameter weight. Salmonella enterica in tomatoes
and leafy greens formed a cluster which could be considered 2nd in importance. Third in importance
would be the cluster of Salmonella enterica in melons, and E. coli O157:H7 (EHEC) in crucifers and
melons. This simulation exercise confirms a consistent output for identifying and prioritizing the top
pathogen-commodity pairs for further quantitative risk assessment efforts (Table 14).
Pathogen
1
2
3
4
5
6
7
8
9
10
11
100
0
0
0
0
0
0
0
0
0
0
Leafy greens
E. coli
O157:H7
(EHEC)
Tomatoes
Salmonella
enterica
0
46
27
6
7
4
6
2
1
1
0
Leafy greens
Salmonella
enterica
0
31
36
12
9
6
4
0
1
1
0
Melons
Salmonella
enterica
0
2
6
32
11
13
19
6
4
3
4
Crucifers
E. coli
O157:H7
(EHEC)
0
16
12
22
26
15
5
3
1
0
0
Melons
E. coli
O157:H7
(EHEC)
0
0
13
11
17
30
19
3
6
0
1
Mixed
produce
E. coli
O157:H7
(EHEC)
0
4
3
6
8
9
21
11
13
10
15
Carrots
Salmonella
enterica
0
C. parvum
0
Crucifers
Acknowledgments
Special thanks to Ms. Megan Tulloch of RTI for designing and building the Risk Ranking Tool, and to our expert
panelists for their timely advice and council on the ranking methodology, including Trevor Suslow, Scott Brooks,
Larry Beuchat, Meg Barth, Bob Gravani, Dave Gombas, and Jim Gorny.
References
Figure 3. Risk Ranking Tool Report Output
Table 14. Pathogen-Commodity Pair Rank Outcomes
from the Monte Carlo Simulation
Commodity
Conclusions
•For all iterations of the risk ranking tool, the leafy greens–E. coli O157:H7 (EHEC) combination ranked first.
Sensitivity Analysis
Ranking Algorithm
• The tool relies on well-established, peer-reviewed sources of information, combining foodborne disease
outbreak (epidemiological) data with information on disease severity, population susceptibility, prevalence of
contamination, likelihood of pathogen growth, and human consumption patterns. All of the information in the
database is documented according to the original source (e.g., database, journal article).
155
Figure 2. Risk Ranking Tool Input Screen: Weights
The criterion designated growth
potential/shelf-life is intended to
describe the likelihood and extent
of growth of a particular
pathogen in a contaminated
general produce commodity,
keeping in mind that this
characteristic is actually a
function of how likely the agent
is to grow in the commodity,
along with how long the
commodity remains available in
the food chain to support growth
of the pathogen.
• The conceptual model is relatively simple and intuitive, and the user interface is easy to use with minimal
training. The tool is flexible, allowing the user to choose both the criteria and weights that reflect specific
preferences, and includes straightforward reporting capabilities. In addition, the tool was designed to support
the inclusion of additional criteria, multiple weighting schemes, and new pathogen-commodity pairs.
31
6
Shelf life data was collected from
the USDA Agricultural
Handbook 66 and the U. of CA
Agriculture and Natural
Resources Publication 3311. If
the shelf lives differed within the
commodity groups, then the
individual shelf lives were
compiled and averaged for the
group. For example, mixed
produce was assigned “very
short” due to its variable nature.
Benefits of the Risk Ranking Tool
Leafy greens and E. coli O157:H7
(EHEC)
3
Data was compiled from the
literature to determine the
strength of evidence that any one
pathogen can grow in any one
general commodity.
Likely growth at room
temperature (22–24°C)
3
Salmonella enterica
• The tool is simple, transparent and customizable where the user can first choose the value of the
bins for each risk variable (Figure 1).
• The primary purposes of this project were to (1) build a relational database of information relevant to ranking
risks for pathogens and categories of fresh produce, and (2) create a simple, transparent tool that could be used to
rapidly identify priority pathogen-commodity pairs based on risk criteria and user-specified weighting
preferences.
1
4
Some evidence that bacteria
may grow (e.g., higher pH or
bruising/damage), includes
conflicting studies
0.5–1
Category
Data from the NHANES
database (3 day dietary recall,
CDC, 2008) was used to
calculate the % of the population
that consumes each general
category on a daily basis using
the first day of the diet data
(NHANES 2003-2004).
Lack of evidence that
bacteria may grow, includes
conflicting studies
20–50%
Data on susceptible populations
was collected from CDC fact
sheets and the literature.
• The risk ranking tool was built as a Microsoft Access database application, which can be run
using MS Access 2000, 2003, or 2007.
Organism does not grow or
may be inactivated
Medium
High
Table 6. Scoring of Criterion 5:
Population Susceptibility
Risk Ranking Tool
Table 13 presents the top-ranked pathogen-commodity pairs and their associated risk rank scores (RR
Score) with all weights set at low (1) or high (5). Given the ranking algorithm, the order of risk rankings
will remain the same whether all weightings are set at 1 or all weightings are set at 5 and, therefore,
these results may be considered as “un-weighted”. As shown in Table 13, the leafy greens and E. coli
O157:H7 (EHEC) pair had the largest possible risk ranking score. Under these weighting specifications,
the scores for the 51 pathogen-commodity pairs ranged from 13 to 155. Different rankings can be
produced depending on the user preferences for specific criterion weights.
Figure 1. Risk Ranking Tool Input Screen: Bins
Table 10. Scoring for Criterion 9:
Growth Potential
Data from Mead et al. (1999)
were used as a proxy for underreporting of diseases. The disease
multiplier is a pathogen-specific
value that is multiplied by the
number of cases to account for
unreported cases. Less severe
diseases have higher multipliers.
Un-weighted Rankings
Pair Rank
4
Table 5. Scoring of Criterions 3 and
4: Hospitalization and Death Rates
Cabbage, coleslaw, broccoli (no other crucifers identified)
Tomatoes (unspecified), Roma, cherry, grape
Multiplier
1
Crucifers
Tomatoes
>5%
3
866
Carrots (no other root vegetables identified)
High
6
1
Carrots
4
Prevalence data values from the
Microbiological Data Program
(USDA) and the literature for
each specific commodity were
combined into the general
categories using a weighted
average approach that used the
total number of positive samples
divided by the total samples
across all relevant studies.
Table 13. Risk Ranking Ranges for Top-Ranked Commodities Using
Minimum and Maximum Weighing Schemes
7–14 days
Shigella spp.
Mushrooms (unspecified)
>1–≤5%
Short
Pineapple, mango (no other non-citrus fruit identified)
Mushrooms
Medium
2
Non-citrus fruit
Mixed vegetables, mixed fruit, green beans, celery, green peppers
3
0.1–0.5
369
Mixed Produce
<1%
10–20%
8
Salad (lettuce, vegetable or fruit based, garden, green, house, chef, cucumber)
Low
Medium
Norovirus
Mixed Produce
2
2
Strawberries, raspberries, blackberries, blueberries, red and green grapes
Watermelon, cantaloupe, honeydew, musk melon
Unknown, poorly
characterized
Low
Berries
Melons
Unknown
1
Mushrooms
Tomatoes
>100
Strong
Salmonella enterica
General Commodity Category
1–2
3
417
Table 1. Specific and General Commodity Categories
Moderate
106
11
• In an effort to consolidate the data, produce categories were designated after consultation with the literature and in
keeping with general botanical designations. For example, watermelon, cantaloupe, honeydew, and musk melons
were all included into the “melon” category, and the “leafy greens” category includes the lettuce (unspecified),
mesclun, spinach, romaine, leaf, iceberg, and bagged lettuce.
≤100
2
Norovirus
• Initial inclusion criteria included all “fresh” produce items, defined as produce that is not preserved by canning,
dehydration, or freezing. This was further refined by the addition of a number of exclusion criteria. Specifically,
outbreaks that included multiple foods in addition to fresh produce (e.g., tuna salad, chicken tacos) were excluded
from the analysis because clear source attribution could not be determined. Salads that consisted exclusively of
fruit and vegetable ingredients were categorized as “mixed produce.” Items that are commonly served cooked (e.g.,
potatoes, squashes, turnips, rutabagas) were also excluded.
any
Cryptosporidium parvum
56
Mixed produce
Weak
Category
106
1
• CDC’s Morbidity and Mortality Weekly Reports (1996 – 2006)
Total
Cases
2
Shigella spp.
• Primary data source. Only used outbreaks associated with fresh produce of confirmed etiology. Data only available
through 2006.
Number of
Outbreaks
Cryptosporidium parvum
15
• CDC’s Annual Listing of Foodborne Disease Outbreaks (1996-2006)
Table 3. Scoring of Criterion 1:
Epidemiological Link
2
1
1
Each of these dimensions was further characterized by two or more criteria, for a total
of nine criteria. For each of these nine criteria, four bins were defined into which the
data could be categorized. The descriptions for each bin were assigned a numerical,
ordinal score from 1 to 4.
Hepatitis A virus
Campylobacter jejuni
Category
• Production/Processing: Commodity characteristics that affect pathogen prevalence,
pathogen behavior, and likelihood of exposure by the consuming public (Prevalence
of Contamination, Consumption, Growth Potential/Shelf Life)
80
Weighted
Average
Prevalence
Score
• Agent: Pathogen characteristics that affect disease risk or severity (Population
Susceptibility, Infectious Dose)
2
41
Data Sources and Criteria
• Health: Severity of disease (Hospitalization, Death rates)
Norovirus
3
Melons
The general modeling approach was a relative risk ranking that took into account the
following four risk ranking dimensions:
Table 8. Scoring of Criterion 7:
Prevalence of Contamination
• Epidemiological Association: Strength of the epidemiological association between
the pathogen and the commodity (Epidemiological Link, Disease Multiplier)
Cyclospora cayetanensis
Methods
Risk Ranking Tool Model Approach
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Dr. Maren Anderson
Department of Environment, Health and Safety
Phone: 919.485.2740
E-mail: andersonm@rti.org
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RTI International
3040 Cornwallis Road, PO Box 12194
Research Triangle Park, NC 27709-2194 USA
www.rti.org
RTI International is a trade name of Research Triangle Institute.