99
8. Risk Characterization Method
The risk characterization is the final component of the risk assessment. Risk characterization integrates the hazard characterization and the exposure assessment to synthesize the probability
and severity of adverse health effects in a particular population of consumers. In this risk assessment, the output of the risk characterization is the probability of invasive listeriosis
following the consumption of a random serving of cheese by an individual in a considered subpopulation and country. Using a second-order Monte-Carlo simulation framework, the
variability and uncertainty of the risk characterization outputs are estimated as a reflection of the variability and uncertainty of the model inputs. In addition, a sensitivity analysis is used to
explore the impact of the uncertainty and variability of inputs on the risk outputs.
8.1. Output of the Risk Characterization
The main output that will be used to assess the risk of invasive listeriosis from soft-ripened cheese consumption in Canada and the U.S. is the probability of invasive listeriosis following the
consumption of a random serving of cheese by an individual of the considered subpopulation.
We will simplify this output to the: risk per serving in the particular country Canada, U.S. for
the considered population Elderly, Immunocompromised, Pregnant, General. This output is of interest because the expected number of cases of invasive listeriosis in a particular population
during a specific period of time is proportional to the mean risk per serving. The average number of cases in N
c, p
servings is
p c
s p
c p
c
R N
C
,
, ,
× =
, where N
c,p
is the number of servings consumed by population p in country c during this period and
p c
s
R
,
is the mean risk per serving for this population p during this period of time
15
. For any risk mitigation strategy indexed 1 that does not impact the number of servings consumed in a population, the proportion of avoided cases
compared to the baseline pasteurized-milk cheese baseline or raw-milk cheese baseline -- indexed 0 is then equal to:
1 1
R R
C C =
.
15
under the assumption of a binomial result for the number of cases in N
c, p
servings.
100 Other risk characterization outputs of interest are:
- the risk per contaminated serving,
p s
cs
R
,
, that is the probability of illness following the
consumption of a random contaminated serving by an individual in population p
Elderly, Immunocompromised, Pregnant or General in the country c. A contaminated serving is defined as a serving including one or more cells of L. monocytogenes;
- the prevalence of contaminated servings, P
s
, that is, the probability that a random serving of cheese contains one or more cells of L. monocytogenes.
Recall that all of these outputs are distributions that describe how the risk output varies over a reference population of interest. For simplicity, we will provide some statistics characterizing
these distributions such as the mean, the standard deviation and some quantiles.
The number of cases per year will not be provided due to the unknown number of servings in the population.
8.2. Estimator for the Risk Outputs
The risk outputs of interest cannot be extracted directly from the literature but, rather, are synthesized by using a set of mathematical models and equations that link several input
parameters to the risk outputs see Appendix, section “Model Documentation”. Stochastic, uncertain inputs then yield stochastic, uncertain outputs whose distributions can be evaluated
either analytically or by simulation.
Because the overall integration of the model to derive the final distribution of each of the risk outputs is analytically intractable, a Monte-Carlo simulation was used. Monte-Carlo simulation
is a simulation sampling method: input parameters’ values are sampled from their input distributions, thus simulating the action of sampling from the inputs’ variability distributions,
subject to our uncertainty. The modeled risk output calculated using those inputs propagates the inputs’ variability and acts as a sample from the risk output’s probability distribution, subject to
our uncertainty about the inputs.
Kategori