Figure 8 shows tornado graphs for low-risk and high-risk populations corresponding to the median realization of Prevpack.
Figure 8–. Tornado graphs of sensitivity of the mean number of cases/year to various inputs in (a) low-risk population and (b) high-risk population.
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It can be seen that the sensitivity ranking of inputs is the same for both subpopulations. However, in the high-risk population, temperature and time at retail and home (TempR, timeR, TempH, timeH), and the concentration of L. monocytogenes in packages at the time of consumption (Npack-cons), have more influence on the output than in the low-risk population.
It is observed that SS is a factor strongly related to the number of cases; however, it cannot be conceived of as an RM option to reduce the disease burden. Quite to the contrary, the consumption of vegetable, RTE or fresh, is being promoted by health initiative campaigns such as the “Fruits & Veggies—More Matters™” program (Fruits & Veggies—More Matters 2010).
TempH was the 2nd input that mostly influenced the variation in the mean number of cases. Temperature is a primary factor in controlling the rate of growth of L. monocytogenes (Buchanan and others 1989). It seems to be adequate to develop programs for consumer education as a Risk management strategy. The risk assessment carried out by the HHS-FDA and USDA-FSIS (2003) identified refrigerated storage temperature as one of the 5 broad factors that affect consumer exposure to L. monocytogenes. The others were: amount and frequency of consumption of RTE food, frequency and levels of L. monocytogenes in RTE food, potential of the food to support the growth of the pathogen during refrigerated storage, and duration of refrigerated storage.
The 3rd and 4th QMRA inputs in tornado graphs were timeH and Npack-cons, respectively. They were considered as potential RM options, as they may be more feasible to be reduced, from a public perspective, than TempH. The level of contamination in the food product reported by Lindqvist and Westöö (2000) was the factor to which the PI was most sensitive. However, these authors did not model the processes taking place along the food chain, that is, temperature, time, or other factor that may affect the status of the pathogen in the product.
Risk management measures to reduce disease burden
Together with other tools, such as epidemiology-based tools and economic analysis, risk assessment can provide a sound scientific foundation for “risk-based” management systems (FAO/WHO 2006).
The QMRA model developed (baseline model) was modified in order to incorporate all RM options stated previously.
A hypothetical and ideal situation of 100% compliance with Regulation (CE) N° 2073/2005 would mean that the concentration of L. monocytogenes in the product at the time of consumption is 100 cfu/g, in other words, a value for Npack-cons of 4.30 log10 cfu/package (a package = 200 g).
By selecting only those simulated values of Npack-cons≤ 4.30, the PI mean and standard deviation would decrease until 2.72 × 10−5 and 1.28 × 10−4, respectively, for the low-risk population, and 1.43 × 10−9 and 6.76 × 10−9, respectively, for the high-risk population. These numbers are around 2 to 3 log10 units lower than those reported above in the baseline model. Lindqvist and Westöö (2000), in demonstrating this, found 2 orders of magnitude difference by using the exponential dose–response model for PI. In our work, the new PI values calculated were implemented in the QMRA model for the case of Prevpack median, resulting in a mean number of cases of 4 × 10−2 and 244 cases for low-risk and high-risk populations, respectively. If, instead of Prevpack, taking the prevalence value reported by the Spanish Zoonoses Report of 2008 (EFSA 2010) (2.1 positive vegetable samples out of 47), the estimated number of cases would be practically the same (5.75 × 10−2 and 350 cases for low- and high-risk populations, respectively). These numbers approximate the burden of the disease in Spain. Nonetheless, it should be borne in mind that listeriosis cases may be attributed to other sources (not only RTE lettuce salads), and also, the number of cases are usually underreported.
In our baseline model, the level 100 cfu/g is exceeded during the shelf-life of RTE lettuce salads, given the growth model employed, and the temperature and time data sets assumed in the 3 growth stages (retail, transport, and storage at home). The level of 100 cfu/g corresponded to the 51.6% percentile of the simulated data of Npack-cons in the baseline model.
In the light of these results, it seems that the hypothetical and ideal situation above is actually achieved in Spain. Luckily, it might be the case, and our baseline model may overestimate the risk of listeriosis due to the assumptions made (for example, the use of the growth model without modified atmosphere packaging). However, if desired to model the food chain of foodstuff at the maximum extent (from primary production to consumption), a decision has to be made in relation to data gaps, acquiring the RM measures is more important than the absolute values of risk from the baseline model.
The public health benefits from implementation of the RM measures adopted in this work are presented in Table 10. Reductions in the number of cases in low-risk and high-risk populations were practically equal, except for the 1st and 3rd RM measure. In the latter case, “Prevent high-risk consumers from consumption of RTE lettuce salads,” the reason is obvious; in the former, it is explained below.
Table 10–. Ranking of risk management measures according to the reduction of burden of listeriosis in the population of Spain.
|RM measures adopted in this work||Reduction percentage (%)|
|Low-risk population||High-risk population||Total population|
| 1. Use of specific mixture of gases||66||95||95|
| 2. Reduction of shelf-life: 4-d timeH||85||84||84|
| 3. Prevent high-risk consumers from consumption of RTE lettuce salads|| 0||75||75|
| 4. Reduction of shelf-life: 5-d timeH||64||62||62|
| 5. Microbiological criterion at primary production: n = 30; c = 0; absence in 25 g||44||44||44|
| 6. Microbiological criterion at primary production: n = 20; c = 0; c = 0; absence in 25 g||42||43||43|
| 7. Reduction of shelf-life: 6-d timeH||42||40||40|
| 8. Reduction of shelf-life: 7-d timeH||26||24||24|
| 9. Reduction of shelf-life: 8-d timeH||13||11||11|
|10. Microbiological criterion at primary production: n = 10; c = 0; c = 0; absence in 25 g|| 6|| 8|| 8|
|11. Reduction of shelf-life: 9-d timeH|| 5|| 4|| 4|
The RM measure which produced the major reduction in the number of cases was the first measure adopted, namely, the use of a specific mixture of gases, where Npack-cons values decreased. Among the different gases that could be employed, carbon dioxide has been widely studied as an inhibitor of the growth of L. monocytogenes (Bennik and others 1996; Francis and O'Beirne 1998).
The challenge test carried previously by our research group (Carrasco and others 2008) showed that the effect of the mixture of gases applied retarded the growth (lower maximum growth rate) and extended the MPD. The maximum growth rate applied in this RM measure (0.019 log10 cfu/h at 13 °C) resulted in a distribution of Npack-cons with generally lower values than those of the baseline model; for example, the percentage of values below 4.30 log10 cfu/package (100 cfu/g) were 85.9% and 51.6% for the modified model and the baseline model, respectively. Notwithstanding, given the new MPD applied in the modified model, certain percentages of Npack-cons values (1.5%) were greater than the MPD in the baseline model at 13 °C (5.6 log10 cfu/g). This percentage was responsible for <1% of doses, higher in the modified model (between 5.6 and 6.9 log10 cfu/serving) than in the baseline model. These levels of high doses are located in the right zone of the W-G model (Farber and others 1996) whose curve is different for low-risk and high-risk populations; for the low-risk, the dose–response curve is, at these dose levels, in the exponential form, while in the case of the high-risk population it has already reached a “plateau” near the maximum PI. In this way, despite an important reduction is achieved in both subpopulations by the “Use of specific mixture of gases,” the proportion of high doses in the modified model resulted in a relatively important risk increase in the low-risk population, yielding a lower net reduction of the number of cases than in the high-risk population.
The reduction of timeH by shortening the shelf-life has shown to be very important in reducing the number of cases (Table 10).
The application of microbiological criteria at primary production allowed the decrease of initial prevalence, as those “lots” (data sources from Table 2) resulting in lower confidence level than 95% for the value representing the 95% percentile in the microbiological criteria were rejected. The procedure followed in this work to test how microbiological criteria in primary production could affect a risk assessment output is of high importance for sanitary public health authorities, and, to our concern, no attempt has been made to show how to evaluate microbiological criteria in terms of public health.
Garrido and others (2010) showed that, in general terms, the most relevant scenario to reduce the burden of listeriosis in different RTE products (sliced-cooked meat and smoked fish) was the combination short time–low temperature storage. However, they did not test other RM measures such as the use of modified atmosphere packaging, prevention of consumption, or microbiological criteria application.
A number of limitations can be identified in the present QMRA model, which has been summarized below:
To date, all risk assessment performed has unavoidable limitations due to the scarcity of data and uncertainty about parameter values. Thus, it appears that the effects derived from application of RM measures are more valuable than the absolute value of QMRA outputs.
An important issue when evaluating different RM options is the cost of implementation. Todd and Roberts (1996) listed issues which need consideration for estimating the costs of foodborne illnesses. It is a matter of the competent authorities to balance the cost of both illness and implementation of RM measures, and then make a decision.