**Introduction to the risk assessment**

#### The human health impact of fluoroquinolone-resistant *Campylobacter*

Food borne diseases caused by bacteria have a major public health impact
in the United States. Recent estimates describe 5,000 deaths and 76
million cases of food borne illness annually (70).

Emergence of antimicrobial resistant and multi-drug resistant bacteria
are evident in both human and veterinary medicine (6, 124). Bacterial
food borne disease is a growing problem worldwide and has been addressed
in many reviews and reports on the topic. In industrialized countries,
the food borne pathogens, *Salmonella* and *Campylobacter* are
infrequently transferred from person to person. In these countries,
epidemiological data has demonstrated that a significant source of
antibiotic resistant food borne infections in humans is the acquisition
of resistant bacteria from animals via food (36, 92).

Although *Campylobacter* infections are usually self-limiting,
antibiotic therapy is used for patients: 1) who demonstrate symptoms of
high fever, bloody diarrhea, or more than eight stools in 24 hours; 2)
who are immunosuppressed; 3) who have bloodstream infections; or 4)
whose symptoms worsen or persist for more than 1 week (12).
Antimicrobial therapy can reduce the duration of illness (30, 42).
Empiric treatment of patients with enteric disease seeking treatment is
the norm because when treatment is delayed (e.g., until *C*. *jejuni*
infection is confirmed by a medical laboratory), therapy may not be
effective (12). Fluoroquinolone drugs are frequently used in the empiric
treatment of patients presenting to a physician with gastrointestinal
symptoms because they exhibit good activity against most enteric
pathogens (12, 86).

*Campylobacter* is the most common known cause of bacterial food borne
illness in the United States (22, 95). Campylobacteriosis has been
estimated to comprise 14.2% of total food borne illness in the United
States and represents 17.3% of total food borne disease hospitalizations
(70). Estimates attribute 99 deaths to food borne campylobacteriosis
annually, which comprises 5.5% of the total estimated deaths due to food
borne pathogens. (70). The incubation period for campylobacteriosis is 1
day to 1 week and infections usually result in mild to moderate symptoms
including diarrhea, abdominal pain and fever. Symptoms may last 1 day to
1 week or more, and in up to 20 percent of cases, illness lasts for more
than a week (12). Although most cases of campylobacteriosis are
self-limiting, some patients experience symptoms sufficiently severe to
seek care and take antibiotics for their illness. Relapses occur in
approximately 5 to 10% of untreated patients. More invasive disease such
as blood infections occur in less than 1% of patients with *C. jejuni*
infections and are more common in the elderly or very young individuals
(21). Rare manifestations of *C. jejuni* can include meningitis,
endocarditis and septic abortion. Persons with immunoglobulin
deficiencies may manifest prolonged, severe and recurrent infections
(12). Campylobacteriosis has been associated with chronic sequelae that
include reactive arthritis, inflammation of the liver and kidney and
Guillain-Barré syndrome, a disease that may result in a reversible
paralysis (12).

Antimicrobial drugs are used in food-producing animals to treat, prevent
and control disease and to improve growth and feed efficiency. In the
United States, regulatory terminology names these products "new animal
drugs." Before any new animal drug can be approved in the United States,
the drug's sponsor must demonstrate that the product is safe and
effective for its intended use. If the antimicrobial is intended for use
in food producing animals, the drug sponsor must demonstrate safety for
consumers of edible animal products, as well as safety for use in the
animal.

Selection for antimicrobial resistant and multi-drug resistant bacteria
is a hazard associated with drug use in both human and veterinary
medicine (6, 124). Animals serve as reservoirs for many food borne
pathogens, including *Salmonella* and *Campylobacter*. Antibiotic
resistant food borne pathogens may be present in or on animals as a
result of drug use in animals. When an animal is treated with an
antimicrobial drug, a selective pressure is applied to all bacteria
associated with that animal. Bacteria that are sensitive to the
antimicrobial are killed, while bacteria that have the ability to resist
the antimicrobial can persist and replace the sensitive bacteria. In
addition, bacteria can become resistant when resistance genes are passed
from a resistant bacterium to a sensitive one. Thus, antimicrobial
agents may increase the prevalence of resistant bacteria among both
target pathogens and normal bacterial flora. These resistant food borne
pathogens, like susceptible pathogens, may contaminate a carcass at
slaughter (100, 101), and can be transmitted to humans through
consumption and handling of contaminated food (32, 33, 45, 46). When
these bacteria cause an illness that needs treatment, medical therapy
may be compromised if the pathogenic bacteria are resistant to the
drug(s) used for treatment (42, 80).

The magnitude of the public health risk associated with antimicrobial
use in animals has been debated for over thirty years. Since the
approval of fluoroquinolones for use in food producing animals, reports
have identified a relationship between the approval of fluoroquinolones
for therapeutic use in food producing animals and the development of
fluoroquinolone resistance in *Campylobacter* in animals and humans (36,
79, 92). The approval of these drugs in food-producing animals in the
Netherlands, (36, 58, 80), Spain (79, 116) and the United States (92)
temporally preceded increases in resistance in *Campylobacter* isolates
from treated animals and ill humans. Despite several restrictions placed
on the use of the two approved poultry fluoroquinolone products in the
United States, fluoroquinolone-resistant isolates were recently
identified on 24 percent of domestic retail chicken products from which
*Campylobacter* were isolated (82). Molecular subtyping revealed an
association between resistant *C*. *jejuni* strains from chicken
products and *C. jejuni* strains from domestically acquired human cases
of campylobacteriosis (92). To date, fluoroquinolone resistance has not
been observed in *Salmonella* species associated with poultry in the
U.S. (23).

Based upon emerging scientific evidence that therapeutic uses of
antimicrobials in food-producing animals, in addition to subtherapeutic
feed uses, may select for resistant bacteria of human health concern,
the FDA announced in November 1998 draft guidance for industry (GFI \#
78) on this subject. This GFI which was finalized in December 1999
(available at http://www.fda.gov/cvm/) states that FDA believes it is
necessary to consider the potential human health impact of the microbial
effects associated with all uses of all classes of antimicrobial new
animal drugs intended for use in food-producing animals when approving
such drugs. In December 1998, CVM issued a discussion document entitled
"A Proposed Framework for Evaluating and Assuring the Human Safety of
the Microbial Effects of Antimicrobial New Animal Drugs Intended for Use
in Food-Producing Animals." This document set out FDA's concept of a
regulatory system that could be used for antimicrobials for use in food
producing animals to address microbial safety concerns. To assess
microbial safety, the document discussed the need to consider both the
importance of the drug to human medicine and the potential human
exposure to resistant bacteria acquired from food producing animals that
are human pathogens or that can transfer their resistance to human
pathogens. The document articulated the need to determine acceptable
levels of resistant bacteria in animal products (thresholds) to ensure
that the effectiveness of human antimicrobials would not be compromised.

To evaluate the human health impact of antimicrobial use in animals, the
FDA Center for Veterinary Medicine (CVM) developed a quantitative risk
assessment model. The risk assessment was intended to estimate the risk
to human health from antibiotic resistant food borne pathogens
associated with the domestic use of antimicrobials in food producing
animals. Specifically, a mathematical model was derived to relate the
prevalence of fluoroquinolone resistant *Campylobacter* infections in
humans associated with the consumption of chicken to the prevalence of
fluoroquinolone resistant *Campylobacter* in chickens.

#### Rationale for this risk assessment 

The use of fluoroquinolones in chickens and the development of resistant
*Campylobacter* in chickens were of concern for several reasons. First,
chickens are reservoirs for many food borne pathogens including
*Campylobacter* and *Salmonella*. For example, broiler carcass
contamination measured in the processing plant estimates that 20% of
broiler chickens in the United States are contaminated with *Salmonella*
and over 80% are contaminated with *Campylobacter* (104). Consumption of
food contaminated with these bacteria can lead to illness in susceptible
individuals (12, 28, 45). Second, *Campylobacter* is the most common
known cause of bacterial food borne illness in the United States (21,
22, 95). Sporadic cases of *Campylobacter* account for approximately 99%
of all *Campylobacter* cases (95). Epidemiological investigations of
sporadic infections have indicated that chicken is the most common
source of human infection (3, 92, 95). Also, slaughter and processing of
chickens may result in bacterial contamination on the carcass that can
survive on retail product and result in human exposure during food
preparation and consumption (29, 101, 104). Third, *Campylobacter* has
been reported to develop resistance when fluoroquinolones are used. (57,
58, 79, 80). Finally, fluoroquinolones are used in human medicine
empirically to treat gastrointestinal infections, such as
campylobacteriosis and are important for use in many other therapeutic
indications in human medicine (86). Increasing levels of resistance
reduce the utility of fluoroquinolones in the empiric treatment of
enteric illness (12, 42, 80, 125).

The model assumes that resistant bacteria pass through the food supply,
infect humans and are treated in the same manner as susceptible
bacteria. The health risk associated with antimicrobial resistant
bacteria represents an increase in risk to consumers because resistance
to an antimicrobial used in human medicine can compromise the
effectiveness of therapy. Using this approach, the incremental human
health impact of resistant food borne disease can be determined without
assessing all the factors influencing the cause of the food borne
illness itself.

To limit the complexity of the assessment, only the human health risk
associated with the use of fluoroquinolones in chickens was assessed.
Fluoroquinolones were chosen because of their importance in treating
enteric infections in humans (86). Information from USDA and CDC on
sources of food borne disease indicated that chicken carcasses carry a
relatively high level of *Campylobacter* and are associated with a large
number of cases of food borne illness (95, 104). *Salmonella* was not
included in the model because, as indicated earlier, fluoroquinolone
resistance has not been observed in *Salmonella* species associated with
poultry.

Although the predominant feature of this risk assessment is to quantify
the risk to human health, it is important that the level of risk be
viewed in context of the data used to model the risk. This risk
assessment has provided insight into the strengths and limitations of
the data available to quantify the impact of fluoroquinolone resistant
*Campylobacter* associated with consumption of chicken on human health.
Data used in the risk assessment were relevant to the model design and
were selected based upon the robustness and validity of the scientific
methods used by the investigators. The data met high standards for
validity of associations/relationships and were selected based upon a
strong body of scientific evidence, consistent across studies. While
assembling the data to be used, some limitations were raised and were
addressed as data gaps and assumptions. Where feasible, the use of data
requiring an assumption is evaluated and the impact of that use is
stated and discussed. In addition, with inclusion of 1999 data some
assumptions needed for the draft version were no longer necessary.
Benefits of conducting this risk assessment include a review of
surveillance data collection methods and recommendations for enhancing
the relevance of data collection for the quantification of the impact of
resistant food borne pathogens on human health (See Sections 3 and 5).
Significantly, this risk assessment has quantitatively demonstrated that
resistance development in bacteria from food-producing animals presents
a risk to human health.

The major strengths of this model are its mathematical simplicity and
ease with which it can be updated as new data become available. The
model provides a quickly and continuously updateable method of
estimating the current human health impact. Given a projection of future
prevalence of resistance in poultry carcasses for example, or
projections of any other modeled parameter, it will allow a prediction
of a future human health impact.

The model assumes that the presence of resistant C*ampylobacter* on the
animal carcass was due to antimicrobial drug use. Because of data
supporting the linkage between antimicrobial drug use and antimicrobial
resistance in animals in studies and surveillance, this assumption is
considered to be scientifically sound (36, 58, 79, 92, 116). The model
quantifies the level of risk due to consumption of chicken and has not
quantified the impact of the spread of the pathogen from chicken to
other food sources due to lack of data. This can occur from cross
contamination of other foods by chicken (29) or from the spread from
chicken sources to other animal reservoirs of human exposure more
proximate to the farm.

#### Modeling method used

While the safety assessments for food additives, veterinary drugs and
pesticides are very standardized and accepted internationally, microbial
risk assessments are relatively new, with no formal procedures.
Microbial food safety problems are generally extremely complicated and
assessment requires a great deal of data. To date, about a half-dozen
microbial risk assessment models have been published that attempt a full
quantitative assessment of the public health risks of microbial
contamination[^1]. These models use only very specific products and very
limiting assumptions and have not been used by regulatory agencies to
set limits on the amount of bacterial contamination permitted in food.
Under the President's Food Safety Initiative, the charge to government
agencies with respect to risk assessment is to develop better data and
modeling techniques to help characterize the nature and size of risks to
human health associated with foodborne hazards (5).

The risk assessment developed for FDA estimates the relationship between
the level of fluoroquinolone resistance in poultry and the human health
impact that results.

The model achieves the following goals:

-   Assessment of the human health impact of fluoroquinolone-resistant
    *Campylobacter* from broilers;

-   Provision of a transparent and robust assessment, based on published
    and, where necessary, regularly revised data to the extent that it
    is available;

-   Allowance of future important changes in the system being modeled

This section provides an overview of the modeling approach FDA has taken
in assessing this risk issue. It explains why a more traditional
microbial risk assessment was not adopted and how the model that has
been developed can be used as a predictive tool for evaluating future
human health impact resulting from fluoroquinolone resistant
*Campylobacter* in poultry.

##### Comparing a more traditional 'farm-to-fork' risk assessment with the FDA-CVM approach

The approach used by the FDA model is innovative. The approach that has
more typically been taken in addressing a microbial food safety problem
has been to model the microbial pathways at all stages from production
of the animal to final ingestion and any resultant illness (69A). Thus,
the approach used here has been misunderstood and questions have been
raised as to why a full microbial risk assessment, or some of its
components, were not developed. This sub-section explains why the FDA
approach was taken, its advantages and disadvantages.

A food safety microbial risk assessment typically tracks the prevalence
and level of bacterial contamination of food products from the farm to
the table (69A). These risk assessments consider various
cross-contamination and microbial growth and reduction events during
every stage of the farm to table process, for example slaughtering,
processing, transportation, storage, retail and food handling prior to
consumption. They take considerable time and effort to complete. The
models are necessarily quite complex but still make very general
assumptions. For example, it is extremely difficult to model
inter-individual variability (differences between elements at each stage
of the process, e.g. due to flock sizes, carcass sizes, variations in
processing methods and their interactions), stochastic variability
(randomness) and uncertainty (lack of complete knowledge of the values
of the model's parameters). Including all three correctly requires a
three-dimensional model, which would be enormously complex and
impractical to either write or run. Simplification is therefore
necessary: for example calculating mean values throughout rather than
inter-individual or stochastic distributions and simulating only the
model parameter uncertainty. Nonetheless, these models are useful
because they model the entire process and can therefore provide some
information on the effect on microbial contamination of changes in
practice or conditions at any point in the process.

At the end of a full microbial food safety risk assessment, one has to
translate the prevalence and level of microbial contamination of the
food consumed, including any cross-contamination of otherwise unrelated
food products, into the resultant human health impact. This necessitates
a good knowledge of both the consumption patterns of the population at
risk and the appropriate dose-response relationships. The dose-response
relationship describes the probability of being infected (or becoming
ill, or suffering various degrees of illness, or death) given some
ingested dose. Each individual consumption event has associated with it
some dose-response relationship. This is because for any specific number
of organisms ingested the probability of infection, etc. depends on the
age, size, health status, etc. of the exposed person, as well as the
immediate circumstances surrounding the ingestion event, (e.g. did the
person consume the product in a small or large meal? what condition were
the bacteria in at the moment of ingestion? was the product consumed in
a fatty matrix? had the person recently undergone any antibiotic
therapy? etc.).

Data, if available for the dose-response part of a microbial risk
assessment, usually come from old dose-response experiments on a small
number of students or armed forces personnel, with little background
information on the condition and medical history of the participants.
Occasionally, one can learn from outbreaks where the food source has
been preserved, but the lack of control (knowing how much each person
consumed, etc.) makes the data difficult to analyze. The type of
dose-response model to use, the uncertainty in the available data and
the use of other bacteria as a surrogate where no directly relevant data
are available, can add orders of magnitude of uncertainty to the risk
assessment model.

The FDA-CVM model described in this report is not an attempt at a full
microbial food safety risk assessment. The modeling approach we have
used has been designed to address the effect of specific risk management
actions, while also providing the facility to take into account the
effect of the most important future changes in the physical system, for
example changes in: consumption volumes; the prevalence of contaminated
food; the microbial load on contaminated product and the fluoroquinolone
prescription rate. For most parameters relevant *Campylobacter* data
were available and there was thus little need to use surrogate data. The
approach we have used does not model the many processes between farm and
table for which we have little or no information. Instead, it relies on
connecting what has been observed in the human population with the
contaminated food to which they were exposed.

One must be cautious in using a sequential (i.e. one step following on
from the previous) risk assessment model like a more usual microbial
risk assessment that follows bacteria from 'farm to fork' when it is not
possible to verify the accuracy of the intermediary steps. To
illustrate, suppose that the model assumed to apply in a given situation
is:

x x \* a x \* a \* b

where x and y =x \* a \* b are quantities that can be observed. Once the
parameters a and b have been quantified, we can observe a value for x
and predict the value of y. One can later check that the predicted and
observed values of y correspond. Now, based on the assumed model, if
something were done that reduced b to half its value, the value of y
would be expected to correspondingly drop by a factor of 2. But if the
true relationship had been:

x x \* a x \* a \^ b

then by reducing b to half its value, the value of y would actually drop
by a factor of a \^ (b/2) rather than by the factor of 2 predicted under
the incorrectly assumed model. If we were able to observe an
intermediary step, like x \* a, we would be able to check that the model
was reasonable, but without any intermediary data we cannot.

It is therefore very helpful to have constructed a model whose parameter
values and moreover whose structural assumptions can be readily verified
by comparing predicted and observed values. The level of complexity of a
full microbial food safety risk assessment means that it is at best
difficult, and frequently impossible without considerably more data, to
validate a model in terms of both its structure and model parameter
values. The FDA-CVM model on the other hand has a very simple logic with
one fundamental model assumption (that the amount of contaminated meat
is roughly proportional to the number of people who become ill from
consuming it), which can be tested. It is unlikely that this assumption
is statistically exact, but it makes logical sense and is likely
accurate..

#### *Using the FDA model to predict future human health impact*

*Accounting for future changes in medical practice, patient behavior,
and resistant Campylobacter prevalence in poultry*

The ratios 3~n~/V~i~, 3~b~/V~i~ and 3~i~/V~i~, which have the labels
K~n~, K~b~, and K~i~ respectively, estimate per pound of contaminated
meat the expected number of people who would suffer non-bloody and
bloody enteric infections and invasive fluoroquinolone resistant
*Campylobacter* infections respectively. These ratios can then be used
to predict the expected number of cases of fluoroquinolone resistant
*Campylobacter* human infection that would seek care and be prescribed a
fluoroquinolone in the future as follows:

4~n~(t) = K~n~ \* V~i~(t) \* p~mn~(t) \*p~an~(t) \* p~FQ~(t)

4~b~(t) = K~b~ \* V~i~(t) \* p~mb~(t) \*p~ab~(t) \* p~FQ~(t) Equation
set 1.

4~i~(t) = K~i~ \* V~i~(t) \* p~FQ~(t)

4~T~(t) = 4~n~(t) + 4~b~(t) + 4~i~(t)

Where:

> Subscript (t) represents an estimation of the parameter value at some
> year t;
>
> p~mn~(t), p~an~(t), p~FQ~(t), etc. are the model parameter values
> (with the (t) subscript added), described in Section 3, estimated for
> year t. (p~mi~(t) and p~ai~(t) are equal to 1.) These can be updated
> if there are any changes in medical practice and willingness to seek
> health care between now and year t or left at the estimates used in
> the current model otherwise.

This model can therefore estimate the level of human health impact from
fluoroquinolone-resistant *Campylobacter* from poultry with new
predicted levels of contamination of the food, changes in quantity of
food consumed, plus any significant changes in the health practice. The
number of actual affected *Campylobacter* cases in year t is thus
calculated using the Poisson distribution, i.e. = Poisson(4~n~(t)),
Poisson(4~b~(t)), and Poisson(4~i~(t)) for non-bloody and bloody
diarrhea and invasive case respectively, and a total number of cases
given by:

Total affected cases = Poisson(4~n~(t) + 4~b~(t) + 4~i~(t))

####### Accounting for changes in the number of U.S. citizens

Changes in the number of U.S. citizens are irrelevant to the problem
except to the degree it affects the quantity of poultry meat that is
consumed, which is accounted for in the estimation of V~i~.

####### Accounting for changes in the bacterial load of contaminated carcasses

Adjustments can be made in the model for changes in the bacterial load
of contaminated carcasses. For example, if irradiation was to be
introduced into some plants that processed the fraction q of all
domestically reared poultry, and if this irradiation effectively killed
all bacteria on carcasses that were so processed, the model would be
revised as follows:

4~n~(t) = K~n~ \* (1-q) \* V~i~(t) \* p~mn~(t) \*p~an~(t) \* p~FQ~(t)

4~b~(t) = K~b~ \* (1-q) \* V~i~(t) \* p~mb~(t) \*p~ab~(t) \* p~FQ~(t)
Equation set 2.

4~i~(t) = K~i~ \* (1-q) \* V~i~(t) \* p~FQ~(t)

4~T~(t) = 4~n~(t) + 4~b~(t) + 4~i~(t)

Other *changes in farm and slaughterhouse practices* that reduced the
microbial load on contaminated carcasses can be taken into account in an
approximate way. We can do this by making use of a property of the
logexponential distribution. If a random variable is logexponentially
distributed, dividing that variable by some factor greater than 1 has
the effect of simply shifting the distribution to the left, which means
that the resultant distribution, conditional on the variable being
greater than zero, is identical to the original distribution.

Let us assume that the *Campylobacter* load L on contaminated carcasses
is logexponentially distributed (i.e. L = 10\^Expon() ) and that some
improvement in production practices has decreased the load on
contaminated carcasses by some factor d. Then the new load L takes the
form:

Log~10~ (L ) = Expon() -- Log~10~(d)

The probability that L is less than 1 (i.e. there are no bacteria on the
carcass) is given by:

P(L\<1) = P(Log~10~(L) \< 0)

= P(Expon() \< Log~10~ (d))

= 1 - Exp(-Log~10~(d)/)

Thus to correct for a reduction in microbial load of carcasses in some
year t, we would first fit a logexponential distribution to data on past
carcass load to determine a value for . Then, we would make a correction
to our estimate of human health impact by reducing the effective
prevalence of contaminated carcasses to p~p~ \* Exp(-Log~10~(d)/), where
p~p~ is the model parameter described in Section 4. Our predictive
estimate of human health cases would be as in equation set 2, except
that now V~i~(t) = p~p~ \* Exp(-Log~10~(d)/)

#### *FDA model as a generic method*

This risk assessment was developed to address a significant risk issue,
but had several other goals:

-   To evaluate how results from survey programs and laboratory tests
    and other data sources can be used most effectively in risk
    assessments and to identify collection methods that would maximize
    the value of data for risk assessment. This point is addressed in
    Discussion of Results below.

-   To develop a methodology that could be used across a range of
    antimicrobial resistance issues.

The purpose of the last goal is to allow the maximum amount of
transparency and consistency between all risk assessments FDA undertakes
to address microbial resistance issues. The modeling approach used here
achieves that goal because it requires the minimum amount of data to
perform the assessment, as well as making as few assumptions as
possible. The concepts of the approach should be applicable wherever one
can:

-   Identify source items of contamination and estimate their number;

-   Identify and estimate the level of impact that these contaminated
    items result in; and

-   Identify risk management options that FDA can take, and estimate the
    level to which they will reduce or contain the impact.

Despite having developed a generic approach, each risk assessment will
nonetheless require sufficient amounts of data to estimate the necessary
parameters before a quantitative assessment like the one carried out in
this report can be accomplished.

#### Discussion of results

This risk assessment model has provided a quantitative estimate of the
human health impact resulting from fluoroquinolone-resistant
*Campylobacter* on poultry. 1998 and 1999 were modeled side-by-side in
an \@RISK/Excel spreadsheet simulation model. Any parameter that was
common to both years was modeled in one cell and referred to wherever
necessary, which ensured consistency between model iterations.

The model produced a number of outputs for both 1998 and 1999:

-   Estimates of the probability a person would be affected by the risk
    in question for various U.S. sub-populations. Probabilities were
    provided as fractions and 1 in x estimates;

-   Estimates of nominal mean number of *Campylobacter* cases in U.S.
    population (2~T~);

-   Estimates of nominal mean number of fluoroquinolone resistant
    *Campylobacter* cases attributable to chicken (3~T~);

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-   Estimates of nominal mean number of fluoroquinolone-resistant
    *Campylobacter* cases attributable to chicken, seeking care, treated
    with fluoroquinolone and therefore affected by the fluoroquinolone
    resistance (~T~); and

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-   Estimates of total consumption of boneless, domestically reared
    chicken contaminated at slaughter plant with
    fluoroquinolone-resistant *Campylobacter* in U.S. in pounds
    (**V~i~**).

These estimates all attempted to assess the recent level of the problem,
and are discussed in Sections 2 to 4.

##### 

##### K~all~ and K~res~

Aside from the probabilities, two 'K' values were calculated, K~all~ and
K~res~, which represent the potential of poultry meat contaminated with
*Campylobacter* and fluoroquinolone resistant *Campylobacter*
respectively to result in human illness. These parameters are calculated
as follows:

K~all~ = [Nominal mean number of *Campylobacter* cases attributable to
chicken]{.underline}

Estimated amount of *Campylobacter*-contaminated chicken meat consumed

K~res~ = [Nominal mean number of fluoroquinolone resistant
*Campylobacter* cases from chicken]{.underline}

Estimated amount of fluoroquinolone resistant
*Campylobacter*-contaminated chicken meat consumed

The K values can be thought of as the probability that a pound of
*Campylobacter* contaminated chicken meat (in general, and resistant)
will result in a case of campylobacteriosis (in general and resistant).
If the distributions of the total number of *Campylobacter* that reside
on resistant and susceptible *Campylobacter*-contaminated carcasses are
the same, and if resistant and susceptible *Campylobacter* have similar
survivability and virulence, it is reasonable to assume that these
values will be roughly equivalent. The importance of these K-values as a
predictive tool has been discussed in this section and will be again in
Section 5 where the theory behind them is presented. Figures 5.2 to 5.4
plot these K estimates. There is strong agreement between years: i.e.,
the differences between the 1998 and 1999 distributions for both
parameters are very small compared to the total uncertainty being
described by the distributions' ranges. The difference in the spread of
the 1998 and 1999 K ~all~ distributions noted in Figure 5.2 is due to
the increase in the catchment population and the concomitant decrease in
uncertainty.There is also reasonable overlap between K~res~ and K~all~,
though K~res~ is consistently estimated as larger than K~all~. Two of
the most logical reasons for this difference are that the prevalence
estimate of fluoroquinolone resistant-*Campylobacter* on carcasses is
too small (about half of what it should be) because:

1.  The estimate used in this analysis came from an unweighted analysis
    of NARMS chicken isolate test results. An analysis that weighted the
    state prevalence by the production in pounds of chicken gives a
    significantly higher result (12.0% for the weighted modeled result
    vs. 10.3% for the unweighted modeled result in 1999).

2.  NARMS testing procedures take one isolate from a cultured dish, and
    test that isolate for resistance. This would provide a good estimate
    of resistance prevalence if all *Campylobacter* on a fluoroquinolone
    resistant-contaminated carcass were resistant. However, if there are
    also susceptible *Campylobacter* present, the isolate selected from
    a cultured dish may be a susceptible *Campylobacter* mixed in a
    population of resistant *Campylobacter*. So, for example, if a
    carcass contaminated with resistant-*Campylobacter* had, on average,
    a 50% mix of resistant and susceptible *Campylobacter*, the observed
    resistance prevalence from NARMS isolates would be about half the
    true prevalence. Data are not currently available on the
    distribution of ratio between susceptible and resistant
    *Campylobacter* on a carcass, but would be extremely useful to get a
    clearer picture of the risk issue.

In addition to the two reasons for underestimation of K~res~ above, it
may also be that the assumptions, i.e. same distribution of number of
*Campylobacter* reside on resistant and susceptible
*Campylobacter*-contaminated carcasses, and resistant and susceptible
*Campylobacter* have similar survivability and virulence, in comparing
the two K values may need to be reevaluated. If differences are observed
in K~res~ or K~all~, when making comparisons between years, these
differences may be explained by changes in the: 1) prevalence of
resistance in travelers, 2) prevalence of resistance on imported food or
3) use of the drug in other food animal species and many other factors.

## *Measuring the human health impact*

1\. Probability

First of all, we can assess the level of risk by calculating the ratio
of the nominal mean number of fluoroquinolone-resistant *Campylobacter*
cases attributable to chicken, seeking care, treated with
fluoroquinolone and therefore affected by the fluoroquinolone resistance
each year (4~T~), to the size of the population at risk. There are
various options one may select as the population at risk, shown in the
table below:

**Table I.1**: Confidence intervals for estimates of **probability** of
being affected by fluoroquinolone resistant *Campylobacter* for various
groups

  -------------------- ------------ --------- ------------ ------------ --------- ------------
                       **1998**                            **1999**               

  Exposed group        5^th^        Mean      95^th^       5^th^        Mean      95^th^
                       percentile             percentile   percentile             percentile

  U.S. citizens        0.0018%      0.0032%   0.0053%      0.0019%      0.0034%   0.0056%

  U.S. citizens with   0.31%        0.50%     0.72%        0.44%        0.68%     0.98%
  campylobacteriosis                                                              

  U.S. citizens with   1.38%        2.11%     2.95%        1.94%        2.89%     3.99%
  campylobacteriosis                                                              
  seeking care                                                                    

  U.S. citizens with   3.01%        4.49%     6.17%        4.24%        6.15%     8.28%
  campylobacteriosis                                                              
  seeking care and                                                                
  prescribed                                                                      
  antibiotic                                                                      
  -------------------- ------------ --------- ------------ ------------ --------- ------------

**Table I.2**: Confidence intervals for estimates of 1 in **x** of being
affected by fluoroquinolone resistant *Campylobacter* for various groups

  --------------------- ------------ -------- ------------ ------------ -------- ------------
                        **1998**                           **1999**              

  Exposed group         5^th^        Mean     95^th^       5^th^        Mean     95^th^
                        percentile            percentile   percentile            percentile

  U.S. citizens         56,795       34,945   18,808       52,166       32,912   17,792

  U.S. citizens with    319          214      139          227          156      102
  campylobacteriosis                                                             

  U.S. citizens with    72           50       34           51           36       25
  campylobacteriosis                                                             
  seeking care                                                                   

  U.S. citizens with    33           23       16           24           17       12
  campylobacteriosis                                                             
  seeking care and                                                               
  prescribed antibiotic                                                          
  --------------------- ------------ -------- ------------ ------------ -------- ------------

Table I.1 gives estimates of the probability, with confidence intervals,
that an individual randomly chosen from the selected denominator
population at risk in 1998 and 1999 would have numbered among those for
whom fluoroquinolone resistant *Campylobacter* in broilers resulted in a
health impact (4~T~). Table I.2 offers an alternative expression of the
probability as 1 in x that many people find easier to interpret. The
tables show mean estimates and the uncertainty around these values.

The size of the risk may be viewed differently depending on an
individual's personal circumstances. For the average U.S. citizen, the
risk may well be perceived presently as being very small: we have
estimated that 1 in 34,945 people were affected in 1998 and 1 in 32,912
in 1999, for example. On the other extreme, people with reduced immunity
who may be more likely to seek medical help, may perceive the risk as
quite significant. The results are presented with four different
denominators.

The first denominator distributes the risk among the entire U.S.
population. The great majority of the U.S. population consumes chicken,
and the consumption of a fluoroquinolone resistant *Campylobacter*
contaminated chicken product, or consumption of another food item
contaminated by chicken (e.g. salad) is a random process. Thus, the
great majority of people are exposed to the risk and the randomness of
the process means that most people are not in full control of that risk.
They may consume the food at a restaurant, other type of food outlet or
the home of someone else. Considering only those people in the U.S.
population who consume chicken could refine this denominator a little.

The second denominator distributes the risk among people who contract
campylobacteriosis from any source. These people will potentially seek
medical care and may be prescribed a fluoroquinolone. This denominator
puts the risk from fluoroquinolone resistant *Campylobacter* from
broilers into context with the total sources of *Campylobacter*
infections. Thus, one can make statements like "0.68% of people
contracting campylobacteriosis in 1999 were affected by the risk".

The third denominator distributes the risk among those people who
contract campylobacteriosis from any source and then seek some medical
care. These people are sufficiently ill that they decide they need help.
This denominator includes consideration of those people who may be more
susceptible to *Campylobacter* than most.

The fourth denominator distributes the risk among those people who
contract campylobacteriosis from any source, seek some medical care and
are prescribed a fluoroquinolone. Both they themselves and their medical
practitioner consider these people sick. The definition represents the
group that is most seriously at risk from the failure of fluoroquinolone
therapy.

2.  Number of cases

The level of human health burden may alternatively be measured simply as
the number of people who contract fluoroquinolone resistant
campylobacteriosis in a year where the *Campylobacter* is associated
with domestically reared broilers (4~T~).

3\. Incremental days of illness

A third option is to measure the human health impact as the number of
extra people-days of illness that occur as a result of fluoroquinolone
resistant *Campylobacter* associated with domestically reared broilers.
This would potentially recognize that those people with invasive
infection would have a much larger incremental duration of illness than
those with enteric infection. However, problems arise in the definition
of duration. In addition, there is no substantial evidence to suggest
that people with enteric infection and bloody diarrhea will be ill
longer than those with enteric infection and non-bloody diarrhea. Since
some 99.6% of estimated cases of campylobacteriosis are enteric
infections, calculating the number of incremental days of illness would
amount to multiplying the number of enteric infections by some constant
factor which was a difference of two medians, equivalent to a 3 day
difference (92) or a mean difference of 2 days in the CDC
*Campylobacter* Case Control Study (28).

If fluoroquinolone--resistant *Campylobacter* were demonstrated to
induce more severe or longer illness than susceptible strains, then
incremental days of illness would become a more relevant measure of the
human health impact. We have not included this measurement of human
health impact in the report.

***Using the model to determine the adverse human health impact***

This risk assessment estimates the human health impact arising from the
observed fluoroquinolone-resistant *Campylobacter* prevalence in broiler
carcasses. It effectively derives a ratio (given the label K~res~
described below) between the number of affected people (4~T~ in the
model) and the volume of contaminated meat (V~i~ in the model). The
model as it stands provides a quickly and continuously updateable method
of estimating the current human health impact. There is considerable
uncertainty in estimating the ratio K~res~ because of imperfect data,
but further data and more years of monitoring would improve this
estimate.

The parameter K~res~ relates the *current* (i.e., year of human illness
data used) ability of a pound of fluoroquinolone-resistant
*Campylobacter* contaminated poultry meat to cause human illness. It
implicitly takes into account the variety of paths that a quantity of
poultry meat may take, including being thrown away, being well-cooked,
cross-contaminating some other food product, etc. Change outside of the
defined parameters of the model would make a past value of K~res~
irrelevant. However, approximate corrections can be made to K~res~ to
take account of such effects.

The model discussed here can be improved by continuously collecting data
on fluoroquinolone resistant and susceptible *Campylobacter* human
health impacts. This will have two benefits:

1.  one can verify that the model is working as it should (i.e. it is
    probabilistically predicting the observed infections)

2.  knowledge of the value for K~res~ will improve with more data

#### Contents of this report

This risk assessment consists of this introduction, an overview, five
sections describing outputs, two appendices and a list of references.

The Overview describes this report and the model structure.

Section 1 explains the process of determining the estimated number of
reportable cases to the CDC's active surveillance system in the FoodNet
catchment area from the total number of culture confirmed cases reported
in a given year. It also details how the total number of
culture-confirmed cases is apportioned into confirmed cases of invasive
or enteric campylobacteriosis.

Section 2 uses the estimated number of reportable cases in the
catchment, calculated in Section 1, to estimate the predicted total
number of *Campylobacter* cases in the U.S. For 1999 the model gives a
mean estimate number of 1.38 million expected cases of
campylobacteriosis and 5^th^ and 95^th^ percentile estimates of 0.867
and 2.16 million cases. The large degree of uncertainty in the estimates
reflects the compounding uncertainty from each parameter of the model.

Section 3 estimates the number of individuals that acquire
fluoroquinolone-resistant infections associated with consuming chicken
and subsequently receive fluoroquinolone treatment. The results of this
section showed that in 1999 about 9,261 people were expected to be
infected with fluoroquinolone resistant *Campylobacter* from consuming
chicken and received fluoroquinolones as therapy. The model gives 5^th^
and 95^th^ percentile estimates of 5,227 and 15,326 cases. It was
assumed that all individuals with a fluoroquinolone resistant infection
would experience a longer illness when treated with a fluoroquinolone
due to a decrease in effectiveness of the drug. The fairly broad
confidence interval is reflective of the lack of certainty in the
various parameters used in the model in this section.

Section 4 estimates the pounds of boneless product carrying
fluoroquinolone resistant *Campylobacter* consumed in a year. The mean
value in 1999 for this estimate is 1,243,017,872 with 5^th^ and 95^th^
percentile estimates of the distribution of 968,000,000 and
1,540,000,000.

Section 5 proposes options for measuring the risk. In Section 5, the
human health risk is assessed for different population bases. A
description of the calculation of the parameter K, relating human health
impact to quantity of contaminated product consumed, is provided. An
example of how K is used for prediction of human health impacts in light
of changes in model inputs is also given. Properties of the model are
explored. In particular, sensitivity analyses are presented. Graphs
display the relative effects of uncertainty in the model input
parameters on the uncertainty in the key model output parameters.

Appendix A describes Bayesian and frequentist approaches to uncertainty.
Appendix B lists assumptions used in the model.

[^1]: Examples of microbial risk assessments are available at USDA-Food
    Safety Inspection Service
    ([http://www.fsis.usda.gov](http://www.fsis.usda.gov/search/query.htm)[)]{.underline}
    and FDA- Center for Food Safety and Nutrition
    ([http://vm.cfsan.fda.gov).]{.underline}