Quantitative microbiological risk assessments

Ideally, a full QRA should span the entire food production and consumption continuum and include:

• Hazard identification,

• Hazard characterisation – determining the severity of foodborne disease and ideally

including a dose-response relationship,

• Exposure assessment – determining patterns of exposure of consumers to the hazard

• Risk characterisation – estimating the “risk estimate”, or burden of illness (e.g. incidence rates and severity of disease) due to the exposure (integration of hazard characterisation and exposure assessment).

As yet, no single published analysis has completely met this challenge. Individual assessments have tended to focus primarily on either exposure assessment or hazard characterisation, or have been limited to certain sectors of food production. The relative merit of focusing on specific sectors in food production (i.e. scope of assessment) depends on the purpose of the assessment, which needs to be clearly articulated before an analysis is commenced. For logistic reasons, it is desirable to split the overall “farm to plate” continuum into several distinct modules, each of which will represent a particular stage in the continuum (Kelly et al., 2000).

It is obvious that a QRA requires intensive resource input, supported by substantial inputs of data and expert knowledge from diverse sources (Lammerding and Fazil, 2000). While the ultimate outcome of concern is human health risk, from an industry perspective exposure assessment is of major importance. QRA attempts to integrate existing knowledge about a hazard and product through a sequence of diverse environmental scenarios (e.g. farm, plant, distribution, retail, consumption). In comparison with toxicology, from which the framework for QRA was derived, there are additional difficulties when addressing living hazards in biological systems. Considerable data are required in both circumstances, but both biological variability in terms of model inputs and uncertainty (lack of precision in data due to sampling issues and measurement errors) are arguably greater with microbiological hazards. Judgement is required in deciding whether to invest resources to obtain more definitive data on model inputs, or to estimate uncertainty using modelling approaches. It is desirable in risk assessment to separate uncertainty and variability as sources of variation in model parameters (Nauta, 2000). The predicted risk might be overstated or understated, without proper accounting of uncertainty due to measurement or sampling error (Marks and Coleman, 1998).

Approaches to evaluate the impact of changes in variables included sensitivity analysis in deterministic models and use of stochastic models. Another alternative that may be applicable in some circumstances is analysis of the “worst-case” scenario. Zwietering and

van Gerwen (2000) suggested that deterministic sensitivity analysis, including analysis of the “worst-case” scenario, should proceed stochastic modelling.

2.3.1 Stochastic modelling

Ability to deal with uncertainty or variability has been enhanced by the availability of computers and software for simulation modelling. Consequently, complex models that link together food ingredients, batch processing, cross-contamination, microbial growth, cooking, recontamination, consumption, human exposure to pathogens, the dose-response relationship and the biological and economic impact components of the identified risks are conceivable (McNab, 1998; van Gerwen et al., 2000). Where data are unavailable or uncertain, probability density functions can be used to represent the known, most likely, or expected values for a parameter. Input parameters may include the prevalence of infected animals in herds, prevalence of contaminated carcasses at slaughter, or factors (e.g. temperature, water activity) that influence microbial multiplication on products during transportation or storage. Multiple iterations of models are generated, with each iteration sampling at random from the distributions specified for each model parameter. The distribution of model outputs reflects the pattern of expected results given the variability specified in the input parameters. Three somewhat distinct and complimentary applications of modelling that can be considered in QRA are:

1. Exposure assessment related to animal production and slaughter (defining the

prevalence and concentration of hazard on product),

2. Predictive microbiology – modelling the predicted growth of pathogens under

various environmental conditions (time, temperature, water activity, pH, etc) to estimate numbers following processing steps or at the point of consumption,

3. Dose-response modelling (hazard characterisation)

2.3.1.1 Exposure assessment in production and slaughter

Simulation approaches have been applied to model pathogen transmission in the farm or slaughter environments (Jordan et al., 1999; Hartnett et al., 2001). Key parameters include the prevalence of infected farms in a region, and of animals within farms, and ideally would include quantitative estimates of the concentration of organisms in infected animals. The diversity among farms of animal population dynamics, management systems and environmental conditions present considerable challenges for data collection

and modelling. The most comprehensive attempt to incorporate a production module in a

QRA has been for Campylobacter in broiler production (Hartnett et al., 2001) which

attempted to estimate the probability that a random bird from the Great Britain poultry

flock would be Campylobacter positive at the point of slaughter. This was modelled

simply as the product flock prevalence and within flock prevalence of infected birds. Broiler production is arguably the most uniform form of animal production in developed countries, and a considerable body of literature exists on the epidemiology of the organism in broilers. However, data on flock prevalence was identified an area where data were sufficient. Similarly, parameters for estimating within flock prevalence were largely based on averages of expert opinion incorporated in a triangular distribution (indicating the lowest, highest and most probable values). The authors indicated a lack of data or high level of uncertainty related to many elements of the model.

2.3.1.2 Predictive microbiology

Predictive microbiology can be used to contribute to calculation of the likely number of organisms at the time when food is consumed (Walls and Scott, 1997). Such modelling may provide an estimate of the effect of processing steps on microbial growth and product safety in food production and distribution (Zwietering and Hasting, 1997). It is known that growth and multiplication of microorganisms depends on a variety of factors, which do not act independently from each other under normal circumstances. Predictive microbiology is based on the body of knowledge about the combined and complex effects of the diverse factors and their respective interactive influence. These factors relate to growth, survival and death responses of microbes of concern in food that should be modelled with respect to main controlling factors, initially the combined effect of temperature, pH and water activity (Roberts, 1997), and fitting data to a mathematical equation. The models may be either probabilistic or kinetic. Probabilistic models are useful in obtaining an indication of the wisdom of change in product formulation or product storage, while kinetic models are focused on establishing a quantitative relationship between growth, including duration of the lag phase and controlling factors. Kinetic modelling is achieved in two stages: (1) deriving parameters by fitting a sigmoid curve to growth data, (2) using the function, commonly quadratic, to describe how the derived parameters of each curve were affected by the controlling factors (Roberts, 1997).

2.3.1.3 Dose response modelling

The infectious dose of a pathogen is not a fixed value, but depends on the susceptibility of the host and other factors. Thus, the probability of disease following exposure to a microbial hazard is particularly dependant on the numbers of the pathogen present

(dose)(Stringer, 2000)in the product at the time of consumption. The “dose response”

relationship can be empirically modelled by using beta-Poison, Weibull-gamma and Gompertz models (Stringer, 2000). A sigmoid curve relationship is seen when the log of the number of organisms ingested is plotted against the percentage of the population that becomes infected (Voysey and Brown, 2000).

Lammerding et al. (2000) described the relationship between the numbers of the

Salmonella organisms ingested if/when present in food (i.e. regardless of the food) and the public health outcome (illness). To provide a background and rationale for the use of three different dose-response models, the authors reviewed sets of data provided by various countries, including published data on Salmonella. The dose-response curve was generated by each of the models. The first model used was a beta-Poisson function. This

model was developed by the USDA/FSIS for S. Enteritidis in eggs where data from a

surrogate microorganism (i.e. Shigella dysenteriae) was used to model the Salmonella

dose-response. The second model used was the Weibull function. It was developed by Health Canada and based on volunteer studies for several pathogens, including data from two Salmonella outbreaks. The third model used a beta-Poisson distribution based on data generated by volunteer studies using faecal shedding (infection) as the dependant variable. All three models were then compared with the actual data collected during an

outbreak in a country that has a good record on Salmonella outbreaks. The conclusion

was that none of the models had advantages over others and that all three models generated reasonable estimates.