[Emerging Infectious Diseases * Volume 3 * Number 4 * October - December 1997]

Special Issue

Quantitative Risk Assessment: An Emerging Tool for Emerging Foodborne
Pathogens

Anna M. Lammerding* and Greg M. Paoli
*Health Canada, Guelph, Ontario, Canada; and Decisionalysis Risk
Consultants, Ottawa, Ontario, Canada
---------------------------------------------------------------------------

      New challenges to the safety of the food supply require new
      strategies for evaluating and managing food safety risks.
      Changes in pathogens, food preparation, distribution, and
      consumption, and population immunity have the potential to
      adversely affect human health. Risk assessment offers a
      framework for predicting the impact of changes and trends on
      the provision of safe food. Risk assessment models facilitate
      the evaluation of active or passive changes in how foods are
      produced, processed, distributed, and consumed.

The changing epidemiology of foodborne diseases is a result of complex
interactions and changes in pathogens, foods, food distribution, food
consumption, and population immunity (1-3). Predicting the impact of a
trend in one part of the food continuum presupposes understanding of the
whole system. Aspects of the food processing and distribution system can
amplify or attenuate the trend as it grows into a potential health hazard.
While a full understanding of pathogen contamination, infection, and
survival is difficult, a systematic approach to assessing the impact of the
pathogen on health may improve the quality of public health decisions
(4,5).

Quantitative risk assessment is a possible approach for designing programs
to address emerging foodborne diseases. The use of risk assessment in
environmental toxicology illustrates the potential advantages of applying
quantitative risk assessment in a new field.

Risk Assessment Defined

The essence of microbial risk assessment is describing a system in which a
microbial hazard reaches its host and causes harm. Risk assessment consists
of four steps: hazard identification, exposure assessment, dose-response
assessment, and risk characterization (6). The knowledge in each step is
combined to represent a cause-and-effect chain from the prevalence and
concentration of the pathogen to the probability and magnitude of health
effects. In risk assessment, risk consists of both the probability and
impact of disease. In this way, risk reduction can be achieved in either
dimensionby reducing the probability of disease or by reducing its
severity.

Hazard Identification

In hazard identification, an association between disease and the presence
of a pathogen in a food is documented. The information may describe
conditions under which the pathogen survives, grows, causes infection, and
dies. Epidemiologic and surveillance data, challenge testing, and
scientific studies of pathogenicity also contribute information. Data
collected during hazard identification are later used in exposure
assessment, where the impact of processing, distribution, preparation, and
consumption of the food are incorporated.

Exposure Assessment

Exposure assessment describes the pathways through which a pathogen
population is introduced, distributed, and challenged in the production,
distribution, and consumption of food. This step differs from hazard
identification in that it describes a particular food-processing pathway.
Depending on the scope of the risk assessment, exposure assessment can
begin with pathogen prevalence in raw materials (e.g., a "farm-to-fork"
risk assessment), or it can begin with the description of the pathogen
population at subsequent steps (e.g., as input to a food-processing step).
In any case, the intent of risk assessment is to track the pathogen
population and estimate the likelihood of its being ingested by the
consumer. By completing the pathway to the consumer, we incorporate the
important issues of dose-response assessment.

Dose-Response Assessment

Dose-response assessment is used to translate the final exposure to a
pathogen population into a health response in the population of consumers.
This step is very difficult because of the shortage of data on
pathogen-specific responses and because those responses depend on the
immune status of the host (consumer). However, even limited knowledge of
the shape and boundaries of a dose-response function can be informative in
comparing the efficacy of alternate controls. The differences in response
among various susceptible populations are important features in this step
(7).

Risk Characterization

Risk characterization involves integrating the information gathered in the
previous steps to estimate the risk to a population, or in some cases, to a
particular type of consumer. In this step, by modifying the assumptions in
the parameters of previous steps, we can study the effects of these
alternate assumptions on ultimate health risk. Assumptions can be changed
to study the impact of lack of knowledge and the potential gains through
further research or to suggest the impact of a suspected trend. For this
type of analysis, risk assessments are typically done in a computer
environment to ease the computational burden and provide rapid responses to
"what-if" questions using alternate assumptions and situations. Current
spreadsheet applications and available "add-ins" allow generation of
complicated probabilistic models that had previously only been available
through expensive custom software.

Risk Assessment in Environmental Toxicology

In environmental toxicology, quantitative risk assessment has emerged as
the predominant paradigm for describing the public health consequences of
human exposure to environmental contaminants (8). Within this paradigm,
existing situations are measured and compared according to a measure of
population health risk. Similarly, proposed interventions are compared
according to the reduction in population health risk that each intervention
confers.

The adoption of risk assessment was primarily a result of legal and
administrative challenges to regulatory authority during the 1970s (6).
Regulatory agencies were required to provide a clear connection between an
imposed regulation and an expected health benefit. If the expected health
benefit could be quantified, the regulatory agencies were required to
demonstrate that it was substantive. Quantitative risk assessment has since
become widespread for different reasons. It is now used proactively to
support decisions such as selection of waste treatment technologies,
contaminated site cleanup operations, and state and municipal priority
setting for public health initiatives.

The shift of environmental health issues into a framework of risk reduction
opened the field to a broader set of analytic tools and prompted a broader
spectrum of professionals to examine the complex problems in the field.
Scientific societies have emerged with the sole mission of focusing on the
general techniques of risk assessment and their role in public health
decisions. In addition, environmental health risks can be compared with
concurrent public health risks from other sources through the use of common
measures. While this type of comparison is not always performed, scrutiny
of the cost-effectiveness of various regulatory programs is increasingly
required on the basis of risk reduction. Microbial food safety, as a
relative latecomer to the field of risk assessment, can take advantage of
its successes and failures and the wealth of constructive criticisms of
frameworks, decisions, and methods for addressing pervasive uncertainties
(4,8).

Opportunity for Technology Transfer to Microbial Health Risks

Quantitative risk assessment is an emerging tool in the field of microbial
food and water safety (9-12). Recognizing the deficiencies of current
approaches to evaluating the risk for human illness from pathogens in food,
the Council for Agricultural Science and Technology recommended that risk
assessment provide the basis for establishing food safety priorities and
policies (5). Because of recent initiatives advocating the widespread
implementation of Hazard Analysis and Critical Control Points (HACCP)
systems, quantitative risk assessment has been proposed as a means of
providing health-outcomebased specification of microbial criteria for
HACCP plans (12-14). Concurrently, international trade agreements have
advocated that demonstration of increased domestic health risk (in a risk
assessment) is the only acceptable basis for barriers to international
trade in food (15-18). However, one of the most important benefits in the
adoption of quantative risk assessment is improved understanding of the
many factors that determine the safety of the food supply.

Some resistance to the adoption of risk assessment is likely. Good
manufacturing practices and standard operating procedures carry a long
history of reasonably safe production when properly applied. The return on
investment in producing a quantitative risk assessment may not be high for
an individual food company with a very conservative production process.
However, good manufacturing practices and outbreak data are not
particularly useful in predicting the impact of new products, newly
recognized pathogens, and changes in food processing or in comparing
international food systems. Whether changes in the food supply are planned
(as in refocused inspection systems and minimally processed foods) or are
occurring passively (as in changed pathogens, demographics, and consumer
behavior), tools are required to assemble the information that describes
the impact. Quantitative risk assessment may provide the only systematic
means to interpret the impact of changes or trends before they become a
source of epidemiologic data.

In a quantitative risk assessment of broad scope, there is a place for all
the data from diverse information gathering activities relevant to
microbial food safety. Recent analyses of pasteurized liquid egg (19) and
ground beef contamination (20) incorporated evidence from farm-based
studies of pathogen prevalence, technology assessments comparing
decontamination methods, process-specific parameters of lot size and raw
material mixing, growth and death models from predictive microbiology,
monitoring studies of transportation and retail temperature control, and
studies of consumption amounts and cooking preference.

By designing the quantitative risk assessment process as an intelligent
information bank, we can develop a model to accommodate the breadth of
available information. The model provides a focus for discussions among
workers from diverse disciplines: farmers, veterinarians, food-processing
experts, microbiologists, and consumer behavior experts. The model also
allows for consideration and comparison of control strategies for which
experimentation would be very difficult in a "live" environment. The
impact, for example, of an aging population or a shift in cooking practices
can be simulated by a variety of assumptions that reflect the extent of the
change. By placing all of the information together, we can delineate gaps
in knowledge and provide estimates of the benefits of proposed research.

The most obvious users for quantitative risk assessment as applied to
microbial food safety are agencies responsible for food inspection, disease
surveillance, and food standards. These agencies have the most to gain from
models that incorporate existing and new data, capture knowledge of the
relevant features of the food processing and distribution continuum, and
capture knowledge of the variability in consumer behavior and immune system
responses. If models are constantly updated and improved, decisions made to
research, monitor, and control foodborne pathogens can be made with
information that lends itself to multidisciplinary discussion and best
describes what is currently known and unknown. Without such a model, there
is little common ground for the type of collaboration often advocated for
addressing the inherent complexity of foodborne disease.

Risk Assessment Case Examples

Two case examples illustrate the prospects of using risk assessment to
support decisions regarding emerging foodborne diseases.

Escherichia coli O157:H7 in Ground Beef

A model of E. coli O157:H7 in ground beef has been developed to support
comparative assessment of control strategies (20). The model describes the
pathogen population from the production of ground beef (including carcass
processing) to consumer cooking and consumption. The variability and
uncertainty in the model are accommodated through the use of probabilistic
representations for many of the parameters. To generate a representative
distribution of risk, the model is simulated many times with different
values selected from the probability distributions. This is a technique
known as Monte Carlo simulation (20-22).

While the direct output of the model is a distribution of health risk from
eating ground beef hamburger patties, a more important use of the model is
to describe the changes in health risk associated with changes in various
parameters. By changing parameters describing, for example, pathogen
prevalence and concentration in raw material, temperature abuse in
transportation and retail, consumer cooking preference, infectious dose,
and size of susceptible populations, we can study the impact of trends in
disease risk factors. Because this model includes the farm-to-fork
continuum, it is possible to assess the efficacy of interventions that
would otherwise not be compared in the same analysis. In addition, the
importance of improved data at different points in the process can be
estimated.

Toxoplasmosis

A probabilistic model describing the incidence of toxoplasmosis was
generated (23). While this model did not begin at the raw material level,
valuable insights were gained in studying the impact of trends in exposure
to Toxoplasma gondii. In congenital toxoplasmosis, the impact of maternal
exposure to T. gondii depends on whether the mother has previously been
infected (24). If this is the first exposure, the impact further depends on
the trimester of pregnancy. If detected at an early stage and treated with
certain drug therapies, the infection may have a smaller impact.

With such a model, the impact of varying risk factors can be studied. Since
the most serious consequences of toxoplasmosis occur during pregnancy, a
key variable is seroprevalence as a function of age. The protection offered
by prior infection complicates disease therapy; a reduction in exposure to
T. gondii could increase incidence of congenital toxoplasmosis by reducing
the prevalence of immune women of childbearing age. This may be further
complicated by changes in the age profile of pregnancy since younger women
are less likely to have been exposed. In addition to the complexities of
the population immunity profile, various trends in risk factors can be
simulated, such as trends in cat ownership, consumption of implicated
products, and the age distribution of pregnancy. The emergence of
toxoplasmosis as one of the leading causes of death in the human
immunodeficiency viruspositive population can be studied concurrently. The
effectiveness of mitigation strategies (e.g., education and screening
programs designed for pregnant women) can be compared to food-processing
strategies intended to reduce overall exposure.

The model of T. gondii infection provides insight into the importance of
detailed hazard identification to understand the complex mechanisms of
disease, exposure modeling to understand the time-dependent nature of
exposure, and intervention modeling to understand the potential negative
consequences of a reduction in overall exposure. Moreover, the results
underline the importance of performing all of the above tasks in the same
overall exercise if the implications of trends and interventions are to be
fully understood. It is unlikely that a sound decision could be made
without a full microbial risk assessment involving model
ing of the complex
nature of population immunity and exposure.

Conclusions

One of the key benefits of quantitative risk assessment is the development
of models describing the complex nature of pathogen populations in the food
supply. Improved understanding of the efficacy of pathogen reduction is the
most important side effect of this approach. Studies assessing the health
impact of a foodborne pathogen often include extensive documentation of
pathogen levels at unconnected points in the food and consumer pathway. In
contrast, a microbial risk assessment based on a model provides a
repository of knowledge describing health risk outcomes and control
strategies. The model improves with each new related study and each
critical review as more and more relevant data are uncovered. Furthermore,
when a decision is required, a description of the system is already
available in which assumptions and proposed interventions can be tested.

Initially, models can be expected to be crude. However, as a base for
discussion, a model can be very effective at soliciting input from experts
in the food industry and the public health community. Input from
epidemiologists, microbiologists, and industry safety managers can be
merged into the model until it represents the best available understanding
of the interacting features of the food supply and their effect on the
distribution of health risk. Once the model has been developed, the impact
of various control strategies and trends can be simulated. Our current
inability to compare control strategies at different points of the food
supply chain is evidence of the need for a system-level understanding that
will improve decision-making capacity.

Decisions to address foodborne pathogens cannot wait for scientific
certainty. Large degrees of uncertainty require that decisions be made with
great caution; however, there is no excuse for not making the best decision
on the basis of available information. Model-based quantitative risk
assessment can provide the decision-maker additional insights not typically
evident in "piece-meal" considerations of data. The ability to represent
the essentially probabilistic nature of emerging foodborne disease is
another risk assessment attribute not typically achieved by traditional
approaches.

Many gains in decision support can be achieved through model-based risk
assessment. Given that many current concerns are focused on emerging
pathogens, it may be timely to adopt risk assessment as a tool that is well
equipped for studying changes and interventions in the race against
pathogens.

Address for correspondence: A. M. Lammerding, Health Canada, Health of
Animals Laboratory, 110 Stone Road West, Guelph, Ontario, Canada N1G 3W4;
fax: 519-822-2280; e-mail: anna_lammerding@inet.hwc.ca.

References

  1. Altekruse SF, Cohen ML, Swerdlow DL. Emerging foodborne diseases.
     Emerg Infect Dis 1997;3:285-93.
  2. Hedberg CW, MacDonald KL, Osterholm MT. Changing epidemiology of
     food-borne disease: a Minnesota perspective. Clin Infect Dis
     1994;18:671-82.
  3. Smith JL, Fratamico PM. Factors involved in the emergence and
     persistence of food-borne diseases. Journal of Food Protection
     1995;58:696-708.
  4. Rodricks JV. Risk assessment, the environment, and public health.
     Environ Health Perspect 1994;102:258-64.
  5. Foegeding PM, Roberts T, Bennett JM, Bryan FL, Cliver DO, Doyle MP, et
     al. Foodborne pathogens: risks and consequences. Ames (IA): Council
     for Agricultural Science and Technology (CAST) 1994; Task Force Report
     No.:122.
  6. National Research Council. Risk assessment in the federal government:
     managing the process. Washington (DC): National Academy Press; 1983.
  7. Gerba CP, Rose JB, Haas CN. Sensitive populations: who is at the
     greatest risk? Int J Food Microbiol 1996;30:113-23.
  8. Commission on Risk Assessment and Risk Management. Framework for
     environmental health risk management. Washington (DC): The
     Commission;1997.
  9. Jaykus L-A. The application of quantitative risk assessment to
     microbial food safety risks. Crit Rev Microbiol 1996;22:279-93.
 10. Rose JB, Sobsey MD. Quantitative risk assessment for viral
     contamination of shellfish and coastal waters. Journal of Food
     Protection 1993;56:1043-50.
 11. ILSI Risk Science Institute Pathogen Risk Assessment Working Group. A
     conceptual framework to assess the risks of human disease following
     exposure to pathogens. Risk Anal 1996;16;841-8.
 12. Rose JB, Haas CN, Gerba CP. Linking microbial criteria for foods with
     quantitative risk assessment. In: Sheridan JJ, Buchanan RL, Montville
     TJ, editors. HACCP: an integrated approach to assuring the
     microbiological safety of meat and poultry. Trumbull (CT): Food
     Nutrition Press;1996.p.159-70.
 13. Notermans S, Gallhoff G, Zwietering MH, Mead GC. The HACCP concept:
     specification of criteria using quantitative risk assessment. Food
     Microbiol 1995;12:81-90.
 14. Buchanan RL. The role of microbiological criteria and risk assessment
     in HACCP. Food Microbiol 1995;12:421-4.
 15. World Health Organization. Application of risk analysis to food
     standards issues. Report of the Joint FAO/WHO Expert Consultation
     1995.WHO/FNU/FOS/Report No.95.3.
 16. Hathaway S. Harmonization requirements under HACCP-based control
     systems. Food Control 1995;6:267-76.
 17. Dawson RJ. The role of the Codex Alimentarius Commission in setting
     food standards and the SPS agreement implementation. Food Control
     1995;6:261-5.
 18. Hathaway SC, Cook RL. A regulatory perspective on the potential uses
     of microbial risk assessment in international trade. Int J Food
     Microbiol 1997;36:127-33.
 19. Whiting RC, Buchanan RL. Development of a quantitative risk assessment
     model for Salmonella enteritidis in pasteurized eggs. Int J Food
     Microbiol 1997;36:111-26.
 20. Cassin MH, Lammerding AM, Todd ECD, Ross W, McColl S. Quantitative
     risk assessment of Escherichia coli O157:H7 in ground beef hamburgers.
     Int J Food Microbiol. In press 1997.
 21. Thompson KM, Burmaster DE, Crouch EAC. Monte Carlo techniques for
     quantitative uncertainty analysis in public health risk assessments.
     Risk Anal 1992;12:53-63.
 22. Vose D. Quantitative risk analysis: a guide to Monte Carlo simulation
     modelling. New York: John Wiley & Sons, Inc.;1996.
 23. Cassin MH, Lammerding AM, Paoli GM, McColl RS. Population response and
     immunity to Toxoplasma gondii [abstract]. In: Proceedings of the
     Annual Conference of the Society for Risk Analysis and International
     Society for Exposure Analysis; 1996 Dec 8-12; New Orleans, LA. MacLean
     (VA):Society for Risk Analysis. p. 129.
 24. Dixon BR. Prevalence and control of toxoplasmosis - a Canadian
     perspective. Food Control 1992;3:68-75.

---------------------------------------------------------------------------

Emerging Infectious Diseases
National Center for Infectious Diseases
Centers for Disease Control and Prevention
Atlanta, GA

URL: ftp://ftp.cdc.gov/pub/EID/vol3no4/ascii/lammer.txt