Section 5

**Using the model to measure the level of risk.**

### **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 was run for 10,000 iterations to produce the relative
frequency plots and statistics. It was run for 300 iterations to produce
points on the spider plots, a number sufficient to stabilize the
reported means. All models used Latin Hypercube sampling.

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~**);

```{=html}
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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

```{=html}
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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~**).

Figures 5.1a and 5.1b, displayed on the next two pages, show cumulative
uncertainty distributions. The estimates are all 'nominal mean'
estimates assessing the human health illness rates rather than the
actual number of cases there may be in a year as a result of random
chance.

![](media/image2.wmf){width="8.1in" height="6.0in"}Figure 5.1a
Confidence distributions for 1998 (heavier lines) and 1999 (lighter
lines) values for the **probabilities** described in this section for
the four different denominators representing different populations at
risk -- black squares denote expected values

> ![](media/image3.wmf){width="7.9in" height="5.688194444444444in"}

Figure 5.1b Confidence distributions for 1998 (heavier lines) and 1999
(lighter lines) values for the probabilities described in this section
(in **1 in x** format) for the four different denominators representing
different populations at risk -- black squares denote expected value

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 was discussed in the Introduction. The theory behind
them is discussed later in this section. 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 chicken 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.

![](media/image6.wmf){width="5.7in" height="4.2in"}

Figure 5.2. Estimates of **K~all~**for 1998 and 1999

![](media/image7.wmf){width="5.8in" height="3.9340277777777777in"}

Figure 5.3. Estimates of **K~res~** for 1998 and 1999

![](media/image8.wmf){width="5.8in" height="3.870833333333333in"}

Figure 5.4. Comparison of **K~all~**and **K~res~** for 1998 and 1999

#### Measuring the level of risk

The results and principles of Sections 1 to 4 of this model can be used
to measure and monitor the level of risk to the U.S. population posed by
fluoroquinolone resistant *Campylobacter* from domestically reared
broilers.

## [Measuring the level of human health impact]{.underline}

1\. Probability

The level of risk was assessed 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 5.1. 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 5.2. 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 5.1 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 5.2 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 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.

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 an antibiotic. Both they themselves and their medical
practitioner consider these people sick. This 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 *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 the
increased incremental days of illness would be a good measure of the
human health impact. The current data describing duration of diarrhea
for resistant and susceptible illnesses are not sufficiently robust to
use in this model.

**[Theory behind, and use of, the parameter K]{.underline}**

If one selects an infected item of food at some point in the production
of a food product (e.g. an infected carcass at the spin chiller of a
production plant which will contain some random number of servings),
there are any number of potential probabilistic pathways for which the
consumption of this item will result in the infection of one or more
people. The paths are probabilistic because of the inherent randomness
of the system, so there must be some (unknown) probability distributions
of the number of people that could become infected, ill, etc. from an
individual serving. The shape of this distribution cannot be known
because of the myriad ways that a person can become affected as a result
of the consumption of an infected serving. The persons affected need not
even be direct consumers of the serving: for example, they can become
affected from other food that has come into contact with the serving in
question, through contact with others who have consumed the serving, or
from pets who have consumed the product. The shape of the distribution
is a result of any remaining processing of the item, the history of its
handling during distribution, the current consumption and food handling
behavior of the consuming population, as well as the distribution of the
pathogen load among infected product and the dose-response relationships
for the various segments of the consuming population.

In the case of chickens, the number of people infected by a food
pathogen is orders of magnitude lower than the numbers of servings
infected with that pathogen, so this distribution must have a mean *k*
that is much smaller than 1 (Figure 5.5)[^1]. Moreover, the probability
of infecting two people from a serving will intuitively be considerably
less than the probability of infecting just one person.

Applying the conditional probability identity principle, we can write:

= ~res~ V~i~

where:

is the mean number of people per year who will experience an adverse
human health effect as a result of consuming a pound of fluoroquinolone
resistant *Campylobacter* contaminated broiler meat;

V~i~ is the quantity (lbs.) of fluoroquinolone resistant *Campylobacter*
contaminated broiler meat consumed in a year in the U.S.

![](media/image9.wmf)

Figure 5.5. Probability distribution of number of affected people as a
result of consuming one infected portion

Just as with the microbial pathogenicity approach to dose-response
modeling using dose-response equations, the model parameter needs to be
determined from data. In essence, this requires estimating the quantity
of infected broiler meat consumed by the public in some recent time
interval and estimating what must have been, given the number of people
experiencing the human health impacts of interest as a result of
consuming those contaminated servings.

To use the model to predict the effects of various input parameters,
K~res~ and V~i~ must be decomposed into products of the component inputs
required in deriving them. For example, V~i~=V~c~\*p~c~\*p~rc~. Then can
be modeled as a function of p~rc~ values chosen to be of interest while
other inputs may or may not be held constant to reflect conditions of
interest. The following graph displays the prediction of 3 as a function
of p~rc~ ranging from 0 up to 25%. Further refinements of the predictive
properties of the model are shown in Equation sets 1 and 2 in the
Introduction.

![](media/image10.wmf){width="5.454166666666667in"
height="4.029861111111111in"}

Figure 5.6. Using the risk assessment model to predict changes in on the
vertical axis, due to increases in p~rc~, on the horizontal axis.

### **Sensitivity analysis**

A sensitivity analysis was performed on the risk assessment model to
determine which parameters are contributing to the model outputs' total
uncertainty. The purpose of this exercise is to determine a) the model
parameters to which the model outputs are most sensitive, and b) where
extra information would be most useful in reducing the uncertainty about
a model parameter and thus in the model outputs.

Five model outputs were used for the uncertainty analysis: 3~T~ -- the
nominal mean number of fluoroquinolone resistant *Campylobacter* cases
attributable to chicken: 4~T~ - the nominal mean number of
fluoroquinolone-resistant *Campylobacter* cases attributable to chicken,
seeking care, treated with fluoroquinolone and therefore affected by the
fluoroquinolone resistance; V~i~ -- the total consumption of boneless,
domestically reared chicken contaminated at slaughter plant with
fluoroquinolone-resistant *Campylobacter* in U.S.(lbs); and the ratios
K~res~ and K~all~ described above.

The sensitivity analysis was carried out by fixing each model parameter
to the 5^th^, 25^th^, 50^th^, 75^th^ and 95^th^ percentiles of its
uncertainty distribution in turn, whilst leaving all other model
parameters with their uncertainty distributions. For each percentile,
the model is simulated (with 300 iterations, sufficient to stabilize the
output mean) to determine the mean output value. The result is a spider
plot (118,119). The x-axis shows the percentile used for each model
parameter and the y-axis shows the magnitude of the mean of the output
in question. The degree of influence of an input parameter equates to
the range of output mean values corresponding to the input percentiles.
For example, Figure 5.8 shows that for 1999 eliminating the uncertainty
about p~rh~ would be the most effective mean for reducing the
uncertainty in the estimate of 4~T~.

**[Sensitivity analysis for 3~T~]{.underline}**

Figure 5.7 illustrates the parameters that contribute the most to the
uncertainty in the value for 3~T~. The parameter p~rh~ produces the
greatest vertical range for both 1998 and 1999 and therefore is the most
influential input parameter. The next most important parameters are
p~ca~ and p~+~. The parameters p~rh~ and p~ca~ plot with positive
gradients so 3~T~ would be larger the larger the true value of p~rh~ and
p~ca~. The parameters p~cn~ and p~+~ plot with a negative gradient, so
the lower their true values, the higher the true value of 3~T~.

From Figure 5.7 we can conclude that, to reduce the uncertainty in the
human health impact of fluoroquinolone-resistant *Campylobacter* in
broilers, the collection of the following data would be useful (in order
of importance):

Proportion of *Campylobacter* infections from chicken that are
fluoroquinolone resistant (p~rh~);

Probability a case of campylobacteriosis is attributable to chicken
(p~ca~);

Probability that a stool will be requested and submitted from a patient
with non-bloody diarrhea (p~cn~); and

Probability that the culture will confirm *Campylobacter* given it was
tested (p~+~).

Figure 5.7. The parameters that contribute the most to the uncertainty
in the value for **3~T~**.

**[Sensitivity analysis for 4~T~]{.underline}**

Figure 5.8 illustrates the parameters that contribute the most to the
uncertainty in the value for **4~T~**. The parameter p~rh~ produces the
greatest vertical range for both 1998 and 1999 and therefore is the most
influential input parameter. The next most important parameters are
p~cn~ and p~+~.

From Figure 5.8 we can conclude that, to reduce uncertainty in the human
health impact of fluoroquinolone resistant *Campylobacter* in broilers,
collection of the following data would be useful (in order of
importance):

Proportion of *Campylobacter* infections from chicken that are
fluoroquinolone resistant (p~rh~);

Probability that a stool will be requested and submitted from a patient
with non-bloody diarrhea (p~cn~); and

Probability that the culture will confirm *Campylobacter* given it was
tested (p~+~).

![](media/image12.wmf){width="6.2in" height="3.1659722222222224in"}

Figure 5.8. The parameters that contribute the most to the uncertainty
in the value for **~T~**.

**[Sensitivity analysis for V~i~]{.underline}**

Figure 5.9 illustrates the parameters that contribute the most to the
uncertainty in the value for **V~i~**. There are only two uncertainty
parameters in determining this output, p~c~ and p~rc~, and p~rc~ (the
prevalence of fluoroquinolone-resistant *Campylobacter* among
*Campylobacter* contaminated broiler carcasses) is clearly contributing
the greatest uncertainty to the determination of **V~i~**.

![](media/image13.wmf){width="5.996527777777778in"
height="2.888888888888889in"}

Figure 5.9. The parameters that contribute the most to the uncertainty
in the value for **Vi**.

**[Sensitivity analysis for (K~all~)]{.underline}**

Figure 5.10 illustrates the parameters that contribute the most to the
ratio K~all~. The parameters p~ca~ and p~cn~ produce the greatest
vertical range and therefore are the most influential input parameters.
While the parameter p~c~ is shown on the graphs for both 1998 and 1999,
it does not add to the uncertainty in K~all~, as indicated by the
relative flatness of the line for p~c~. The parameter p~ca~ is the only
significant parameter plotted that contributes to the uncertainty from
modeling contamination of chicken meat, i.e. all the other parameters
correspond to determining the human health impact which means that we
have more uncertainty about the human health side than the broiler side.

![](media/image14.wmf){width="6.4in" height="3.4145833333333333in"}

Figure 5.10. The parameters that contribute the most to the uncertainty
in the value for **K~all~**.

**[Sensitivity analysis for (K~res~)]{.underline}**

Figure 5.11 illustrates the parameters that contribute the most to the
ratio **K~res~**. The parameters p~rh~ and p~cn~ produce the greatest
vertical range and therefore are the most influential input parameters.
The parameters p~rc~ and p~ca~ are the only significant parameters
plotted that contribute to the uncertainty from the poultry side, i.e.
all the other parameters correspond to determining the human health
impact which means that we have more combined uncertainty on the human
health side than the broiler side.

![](media/image15.wmf){width="6.5in" height="4.159722222222222in"}

Figure 5.11. The parameters that contribute the most to the uncertainty
in the value for **K~res~**.

**[Sensitivity Analysis Summary]{.underline}**

Quantitatively assessing the uncertainty for the parameters modeled
indicates that there is more combined uncertainty on the human health
side than the broiler side. Qualitative issues in assessing the data
used in the risk assessment were raised in the Sections and given as
limitations, assumptions and data gaps and are collectively listed in
Appendix B. Other qualitative and methodological issues raised are
described in the respective sections of the document. Consideration of
both quantitative uncertainty and qualitative aspects of data are
important in the collection of data useful for risk assessment.

**[Effect of considering clustering of isolates by state in the
estimation of resistance among *Campylobacter* isolates in
poultry]{.underline}**

Chicken NARMS isolate susceptibility test results for 1999 were obtained
for states with federally inspected poultry plants representing over 95%
of chicken production. We obtained these data to allow us to assess
whether an estimate of the resistance among *Campylobacter* isolates in
poultry would be significantly different if we were able to weight
isolate test results by the production in pounds of chicken for each
state.

The data are shown in the spreadsheet model of Figure 5.12, which also
illustrates the crude estimate (based on aggregating all isolate test
results) and the estimate weighted by state production volume.

The results of this analysis are shown in Figure 5.13. The
state-weighted estimate has almost the same degree of uncertainty
(spread) but estimates the prevalence to be approximately 1.7% higher
than the crude aggregate estimate. It would therefore be more accurate
in the risk assessment model to use state-weighted estimates. However,
since the state-by-state data were only obtainable for 1999, it was
decided to use the crude method to estimate both years' prevalence to
maintain consistency. If 1998 data became available, broken down by
state, we would be able to update both years' estimates of the model
parameter p~rc~ and therefore update estimates of K~res~.

*Effect on model of underestimating total prevalence of Campylobacter
among broiler carcasses p~rc~*

The estimate of human health impact for the years 1998 and 1999 produced
by the risk assessment model are unaffected by the consistent
underestimation of p~rc~. However, the model output K~res~ is inflated
by a factor that is approximately 12%/10.4% = 1.15. This makes it
difficult to validate the estimate for K~res~ by comparison with the
estimate for K~all~. Predicting any future human health impact is
essentially unaffected since the inflation factor is a constant through
the model and cancels out.

![](media/image16.wmf)

![](media/image17.wmf){width="6.0in" height="7.966666666666667in"}

Figure 5.12. Spreadsheet model containing isolate test data and methods
of estimating prevalence (spreadsheet values in column F show a single
random realization of the model) (Ref. 112)

![](media/image18.wmf){width="6.0in" height="4.0625in"}

Figure 5.13. Estimates of fluoroquinolone resistance prevalence amongst
*Campylobacter* contaminated poultry for 1999: Line labeled 10.3% --
crude estimate aggregating all isolate test results; Line labeled
12.0% - estimate weighted by production volume from state of origin of
each isolate.

[^1]: When K~res~ is much less than 1, the unknown parameter K~res~ can
    be interpreted as approximately equal to the probability that a
    random consumer will experience the human health impact by consuming
    1lb of contaminated broiler meat. The relationship described by K
    essentially takes the role of the more traditional dose-response
    model, excepting that one has implicitly included some
    cross-contamination among people who have also consumed chicken,
    variations in pathogen load among infected servings and variation in
    organism-host interaction.