3.5 Discussion 81 (Yeh et al. 2005). As previously stated, this system of chilling is currently used in New Zealand.
Model estimates indicated that the procedure of evisceration had little effect on car- cass contamination. This is not consistent with some empirical studies, in which evis- ceration has been identified as a major source of surface bacterial contamination (Pearce et al. 2004, Lo Fo Wong et al. 2002, Berends et al. 1997). However, it should be noted that our models predicted highly skewed distributions of pathogens on carcasses as a re- sult of evisceration (Figure 3.6). Therefore it is possibly that the effect of this procedure in increasing carcass contamination levels is only evidenced in a relatively small propor- tion of the carcasses. The right skewed distribution indicated that although most carcasses possessed low surface bacterial levels, a small number possessed relatively high levels. It is this latter group of highly contaminated carcasses that may serve as possible sources of contamination for other carcasses as well as present possible microbial hazards to the consuming public. The level of surface bacterial contamination during evisceration is de- pendent on the degree of auto-contamination, cross-contamination, pathogen prevalence and concentration. Results from one of our quantitative targeted microbial studies, which ascertained the surface contamination of sampled carcasses in New Zealand abattoirs, in- dicated that in general, bacterial counts pertaining to the three pathogens of interest were low. (The results of this study are presented in the Appendix.) One possible explanation for this is attributed to the fact, in New Zealand there is a slower abattoir chain speed as a result of the lower numbers of carcasses processed per day in comparison to other devel- oped countries. In Spain, for example, 200 pig carcasses are processed per hour (Rivas et al. 2000), and in the USA, some 800 carcasses per hour (Yu et al. 1999) in comparison to approximately 163 – 1000 each processing day in New Zealand. Therefore, in New Zealand the operator has comparatively more time to spend on each carcass and therefore there is less prone to erroneously cause faecal spillage onto the carcass. Another possi- ble reason may stem from the fact that experienced operators are usually employed and these are less likely to incise the intestine during evisceration (Borch et al. 1996). Both of the above possible explanations result in the low levels of auto and cross-contamination observed in our study.
The median contamination level of Campylobacter from evisceration was 0.04 cfu/cm2, with 0.004 cfu/cm2 – 0.13 cfu/cm2 recorded as the 10th – 90th decile range. Yeh et al.
(2005) reported isolating mean low numbers of Campylobacter, post-evisceration (0.7cfu/cm2) in a microbial surveillance study conducted in Taiwan. This reported value was not very different from the median value predicted from our model for this organism. For storage, our model estimated the median level of Campylobacter on carcasses to be 0.005 cfu/cm2 with10th and90th deciles of 0.0001 cfu/cm2 and 0.035 cfu/cm2 respectfully. Oosterom et al. (1983) reported reductions of Campylobacter numbers on the surface of pig car- casses in the abattoir to values below detection after chilling overnight. This reduction was explained to result from the sensitivity of the organism to desiccation. Using the predicted median values from our model, an area the size of 200cm2needs to be swabbed to detect a single organism. The possibility of detecting the organism at these concen- trations is very low. It is however unexpected that the predicted level of Campylobacter is higher than the other pathogens. One reason for this finding may be attributed to the large initial concentration of this organism in pigs faeces, which was at least 2 - 18 times higher than that for Salmonella and E. coli. This high level of Campylobacter in faecal material, exceeding counts for other pathogenic organisms was also reported by Yeh et al.
(2005). Teunis et al. (2005) predicted a probability of infection ranging from 0.6 to 0.8 after exposure to 100cfu of the organism, based on a dose-response assessment informed by data from a human feeding study and two milk outbreaks.
Within Europe and North America, Salmonella has been routinely isolated from pigs and on pork carcasses (Berends et al. 1996, Snijders & Collins 2004, Vieira-Pinto et al.
2006). Our microbial studies indicated that Salmonella is rare on pig carcasses and in pig faeces in the lairage. (The results of these quantitative studies are presented in the Appendix). Therefore pork may not currently be a significant vehicle for transference of this pathogen to the New Zealand population. Nevertheless, given the international importance and the possibility of it emerging as an important pathogen in New Zealand, we described pathogen propagation of this organism in our suite of models. Available published literature was used to determine model parameters and variables. Since our models allows updating, in the event that this zoonotic organism does become prevalent in pigs in New Zealand, country specific parameters can replace those currently in use.
Model outputs predicted that pig carcasses will be contaminated with a median value of 0.004 cfu/cm2post-storage. Haas et al. (1999) estimated that consumption of 2.36×104 cfu can result in a 0.5 probability of illness. This assessment was based on data from nine
3.5 Discussion 83 different non-typhoidal Salmonella strains investigated in human feeding studies.
The predicted median value of E. coli on carcasses exiting the slaughter house was 0.001 cfu/cm2. Our results are consistent with that found by Chang et al. (2003b), who identified blast chilling, currently used in New Zealand abattoirs, to be effective in re- ducing the number of E. coli on the pork skin. Chang et al. (2003b) demonstrated that pig carcasses contaminated with low numbers of E. coli pre-storage would be reduced to undetectable levels after blast chilling for 24 hours. From our estimated median value, an area of 1000cm2 will have to be swabbed to detect a single E. coli cfu. We can therefore assume that the predicted level of contamination is close to or below the detection level.
From an empirical study, Tamplin et al. (2001) reported mean values for E. coli after 24 hours post-chilling at storage, of 0.1 – 6cfu/cm2 which is much higher than the median value predicted by our model.
Our model predicted mean prevalence levels of 100%, 98% and 94% for Salmonella, E. coli and Campylobacter respectively. These values appear to be greater than others obtained from microbial studies reported in the literature (Pezzotti et al. 2003, Tamplin et al. 2001). Tamplin et al. (2001) reported prevalence values of 58% for E. coli and a mere 7% for Salmonella post-chilling. For Campylobacter, Pezzotti et al. (2003) reported a prevalence of 10.3% in pork at retail, however the storage time was unknown. The rea- son(s) for the discrepancy may stem from limitations of microbiological tests to detect very low bacteria numbers. It must be remembered that in these cited studies, the entire carcass or half carcass was not swabbed and therefore there is a chance of non-detection of bacteria. In our studies an area of 225cm2 was swabbed. This represents only ap- proximately 1.6% of the total surface area of the average pig carcass. There is also the possibility that our model over-estimates the prevalence level. This can be modified by using more precise parameters which can be obtained by conducting further microbial tests.
The model development process facilitated identification of the dominant bacterial processes of cross-contamination and inactivation. One novel feature of this suite of models is the use of a variety of different methods to model cross-contamination and inactivation. Cross-contamination in abattoirs is complex. Of the six MPRM processes, it is the most difficult to model and consequently relatively few studies have addressed this issue (Aziza et al. 2006). Cross-contamination has previously been modelled both deter-
ministically and stochastically using a variety of techniques including simple difference equations (Aziza et al. 2006, Nauta, van der Fels-Klerx & Havelaar 2005); difference equations based on the Reed-Frost model (Ivanek et al. 2004) and using fixed transfer rates (Kusumaningrum et al. 2004) to determine the effect on pathogen prevalence and level of contamination. Our models employ a mixture of techniques including differ- ential equations that incorporate the element of time when modelling continuous-time processes; difference equations when describing discrete-time processes, as well as sto- chastic transfer rates. We also describe the processes of cross-contamination occurring between two or more surfaces sequentially as well as simultaneously. It is this feature that makes our models both complex and unique, and can therefore be used as a template for future modelling of cross-contamination in food pathways.
Another aspect of our models is the mechanistic nature. Using this approach, the con- tamination level on each pig is calculated and output as it progresses through the abattoir.
Also, the number of bacteria output at the end of one module becomes the input value for the same pig as it enters subsequent modules. In this way the individual carcass contami- nation level on each pig can be followed throughout the slaughter house. A distribution of surface bacterial levels for the specific pathogens is output by our models. This method- ology allows the between carcass heterogeneity of surface contamination to be captured.
A similar mechanistic approach was conducted in other studies. One such as example is by Nauta, van der Fels-Klerx & Havelaar (2005) in which a quantitative risk assessment of Campylobacter in poultry in the Netherlands was performed.
During model development several data gaps were identified. The most important was the lack of quantitative data on pathogen prevalence and contamination levels on pig carcasses in abattoirs in New Zealand. Also, there is less quantitative information in the published literature in comparison with prevalence values. This dearth of New Zealand specific data affected parameter estimation which proved to be challenging and very time consuming. As far as possible, data from the literature was used and targeted observational and microbial studies were undertaken in order to determine parameters applicable to New Zealand. However, results from our microbial studies demonstrated that the contamination levels of pork, with the organisms of interest in the abattoir were relatively low, thus making bacterial enumeration challenging. (The results are presented in the Appendix.) Additionally, there were difficulties in obtaining some parameters from
3.5 Discussion 85 the abattoir, particularly with respect to scalding and dehairing. The arrangement of the processing chain in abattoirs makes sampling of carcasses post-killing and pre-scalding difficult. Also, the physical arrangement of the machinery makes access to carcasses between scalding and dehairing virtually impossible.
For simplicity, our definition of the environment did not include the air in the abattoir and consequently our models failed to incorporate carcass contamination with aerosolised bacteria. These bacteria can contribute to carcass contamination levels particularly with respect to Salmonella, leading to increased carcass contamination levels (Pearce et al.
2006).
Separation of uncertainty and variability results in the production of more mathemat- ically correct risk estimates (Wu & Tsang 2004, Vose 2000). Second order modelling demonstrated that the relative contribution of parameter variability was greater than para- meter uncertainty, in the final model output, that is the pathogen levels on carcasses at the end of storage.
In our model we assumed the rate of addition of pigs to the scald tank was approx- imately one per minute. If however, this rate were decreased, then it is possible that carcass contamination levels on carcasses exiting the scald tank would be lower than cur- rently predicted. Correspondingly, it is expected that increasing the frequency of scalding so that more than one pig is introduced into the scald tank per minute may result in a higher level of contamination than that currently predicted.
Unfortunately, severely limited resources and time did not allow model validation.
The data required for model validation is currently unavailable in New Zealand. For model validation, data from several abattoirs regarding pathogen prevalence and contam- ination levels on pig carcasses both before and after multiple abattoir procedures would be needed. The model can then be evaluated by comparing model predictions to col- lected empirical data. In the absence of model validation, model predictions should be interpreted with caution although some model outputs are congruent with other studies.
In the event that additional time and resources are allocated to this study, priority should be placed on model validation. The model could have been further optimised by using database software packages instead of the spreadsheet application. Alternatively other programming languages such as Visual Basic, C++ and others which facilitate code com- pilation may also reduce model computation time. Some degree of model optimisation
was performed within the model code in an attempt to expedite model execution time.
The suite of models did not include an abattoir procedural step called polishing. This omission may affect the ability of the models to be generalised to all abattoirs in New Zealand. However, it is not possible to include in the models all the unique aspects of all abattoirs and therefore the most practical approach was taken. Furthermore, it must be remembered that inherently, models are simplified, incomplete mathematical representa- tions of realistic systems.
In conclusion, the semi-stochastic, mechanistic quantitative risk models of pork process- ing provide insight into the effect of the different abattoir stages on carcass contamination levels, which is not readily obtained from microbiological prevalence data. The abattoir processes of dehairing and evisceration contribute the most to increased carcass surface pathogen load. The models predict bacteria levels on every pig in every module and also outputs distributions of these contamination levels for every module. It is this aspect of our models, in addition to the fact that a distribution of pathogen prevalence is also pro- duced, that enables their applicability in quantitative microbial risk assessment models.
Further, our models can be adapted for use in quantitative exposure assessments of other species.