Microbiological Quantitative Risk Assessment and Food Safety: An Update

Transcription

1 Veterinary Research Communications, 29(Suppl. 2) (2005) DOI: /s C Springer 2005 Microbiological Quantitative Risk Assessment and Food Safety: An Update V. Giaccone 1, and M. Ferri 2 1 Department of Public Health, comparative Pathology and Veterinary Hygiene, University of Padua, Italy; 2 Department of Prevention, Public Veterinary Services of Pescara, Italy Correspondence: valerio.giaccone@unipd.it Giaccone, V. and Ferri, M., Microbiological quantitative risk assessment and food safety: An update. Veterinary Research Communications, 29(Suppl. 2), Keywords: food hygiene, meeting, risk analysis round table, workshop Quantitative Risk Assessment is one of three components of Risk Analysis (RA); the others being Risk Management and Risk Communication. Initially, in the 1970s, risk analysis was mainly applied to financial/technological risks. Later on, it was used to address chemical hazards in food products. Since the middle of the 1990s RA has gained international acceptance as the most effective tool for managing microbiological hazards in food and has emerged as a structured model for improving food control systems with the objectives of producing safer food, and for facing the increasing incidence of foodborne illnesses, as well as facilitating domestic and international trade in food. The first official document on risk analysis was developed by FAO/WHO (1995), followed by a series of formal documents on risk management and risk communication (Codex Alimentarius Commission, 1996; FAO/WHO, 1997, 1999). The three components of risk analysis (risk assessment, risk management and risk communication) are not fully separated, but partly overlapping, due to the continuous exchange of information and data among all subjects who participate in the process. Risk assessment can be defined as a structured science-based process to estimate the likelihood and severity of risk with attendant uncertainty. Many organizations recognize four major elements of risk assessment that constitute a logical and sequential pathway: hazard identification, hazard characterization, exposure assessment and hazard characterization. Hazard identification and hazard characterization represent very important phases, but in this short manuscript, we mainly focus on exposure assessment and risk characterization. Lammerding and Fazil (2000) define exposure assessment as the probability of exposure of a individual or population to a microbiological hazard and the likely ingestion of a dose of the micro-organism. Exposure assessment (frequency and ingested dose of the microorganism) provides input for the selected dose-response models. Risk characterization, following the definition of Buchanan et al. (2000), is the overall evaluation of the likelihood that a certain population will suffer adverse effects as a result of the hazard and is the integration of the exposure and dose-response assessment. With risk characterization, which represents the final stage of microbial food safety risk assessment, we try to estimate both the probability (risk estimate) and the severity of adverse effects resulting from the ingestion of contaminated food. This probability is dependent on the 101

2 102 integration of host, pathogen, and food matrix effects (these interactions are often referred to as the infectious disease triangle ). Quantitative risk characterization is aimed at identifying both the confidence intervals associated with the risk estimates and the contribution that each step in a particular food pathway has on the risk level. Sensitivity analyses, used in simulation modelling techniques such as Monte Carlo analysis, allows this to be performed efficiently. In interpreting the results of such analyses, care must be exercised due to variability, which is inherent to biological systems, food processing technologies, food preparation methods, and human behaviour, and uncertainty due to lack of information. Risk characterization allows us to weight different risk management options and to determine the influence of different strategies for reducing the estimated risk (mitigation strategies). As mentioned above, in addition to identifying the likelihood that a hazard will have an adverse impact on the population, risk characterizations should provide an evaluation of the likely severity of that hazard. In fact, in evaluating all dose-response effects due to the ingestion of food which harbours a chemical, pathogen or toxin, all likely consequences (end-points) that might be associated with that particular event (rates of infection, morbidity, mortality rates, consequences, etc.) must be taken into account. These consequences are directly linked to clear decisions made during risk management. Regarding to the concept of hazard, a new concept for evaluating public health effects has recently been introduced. This concept is called DALY (disability adjusted life-year) as originally proposed by Murray in the global burden of disease study (Murray and Lopez, 1996) and consists of the loss of years of healthy life due to either premature mortality or morbidity. The DALY, which uses time as a unit of measurement, integrates several dimensions of the impact on public health, such as the number of affected persons and the severity and duration of adverse health effects. Finally DALY is a measure used to compare the positive and negative health effects of different strategies employed to reduce a health risk in order to find the most efficient one. With the DALY concept, meaningful comparison between widely different disease end points can be made. The loss of healthy life-years in a population is calculated as: DALY = LYL + YLD, where LYL is the number of life-years lost due to mortality and YLD is the number of years lived with a disability, weighted with a factor between 0 and 1 corresponding to the severity of the disability (Murray, 1996). LYL is calculated by accumulation over all relevant diseases (i)ofthe product of d (the number of deaths due to a particular disease) and e (the standard life expectancy at the age of death due to that disease). YLD is calculated as the accumulated product of N (the number of persons affected by a non lethal disease), L (the duration of this disease), and W (a measure of its severity). Havelaar et al. (2000a,b) estimated the annual health burden represented by Campylobacter spp. infection in the Danish population using the DALY concept. Campylobacter spp can cause acute enterocolitis, the main symptoms being fever, malaise, severe abdominal pain and watery to bloody diarrhoea. The infection may be followed by rare but severe non-gastrointestinal sequelae such as Guillain-Barré syndrome, a demyelating disorder of the peripheral nervous system leading to a form of temporary paresis paralysis with weakness of respiratory muscles and loss of reflexes that may become chronic or even mortal. Havelaar et al. (2000) reported that the less frequent consequences such as gastroenteritis-related mortality (310 DALY) and Guillain-Barré syndrome (340 DALY) contribute as much as frequent acute gastroenteritis (440 DALY) to the total health burden

3 of illnesses associated with Campylobacter in the Dutch population per year (1440 DALY). Using the same approach, Havelaar et al. (2000a,b) compared the risk of infection by Cryptosporidium parvum in drinking water to the risk or renal cell cancer as a consequence of water decontamination. In conducting risk assessment, three concepts must be clear: risk, probability and probability distribution. Several definitions of risk have been proposed: according to Notermans and Mead (1996), risk is the probability that an adverse effect will occur, whereas in the contest of QMRA and following a document of Codex Alimentarius Commission (1998), it is more precisely defined as a function of the probability of an adverse effect and severity of that effect, consequential to a hazard(s) in food. Therefore, risk can be defined either as the probability or as a quantitative estimate of the perceived probability for the occurrence of particular event (e.g., occurrence of a hazard and as the consequences of human exposure to that hazard). In food microbiology, risk is represented by the occurrence of pathogens in food, their multiplication and/or production of toxins. The different strategies for reducing the risk adopted by risk managers may foresee a reduction in the frequency of contamination and/or adverse effects on human health. In all definitions of risk, the word probability appears. But what does probability or likehood mean? In the context of QRA, the term probability plays an important role (Vose, 1998). This probability represents a quantitative measure, a number between zero and one expressing the odds of an event. If a parameter or variable can have different values, and we know how probable these values are, these probabilities can be represented by a probability distribution. The fact that they are called probabilities implies that their outcome in any given situation is subjected to uncertainty or randomness. Risk assessment models can be qualitative or quantitative. The former follow the same logical path as the latter and are used in the lack of scientific data, time and resources. Quantitative Risk assessment can be used at the beginning of the assessment process, when is important to understand if the risk is meaningful and if it requires a more in-depth analysis using a probabilistic approach. Quantitative risk assessment can be based on a deterministic or stochastic modelling approach. The deterministic model generates only single values (point-estimates) which are generally the mean for the outcome parameter or description of worst-case scenario. The stochastic model uses all available data and by the means of probability distribution describes the interval of values of specific parameters. The uncertainty and variability are ignored in deterministic modelling; whereas in stochastic modelling they play an important role. In fact, the probability distribution can be represented either by uncertainty or by variability. Uncertainty represents the lack of perfect knowledge of the parameter value which may be reduced by further measurements. In the context of microbial growth modelling, it can derive, for example, from imprecise measurements or lack of knowledge of the effects of conditions that are not included in the model. Variability, on the other hand, represents the true heterogeneity of the population that is a consequence of the physical system and irreducible by additional measurement (e.g., strain differences, variability in temperature, etc.) (Nauta, 2002a,b). The probability that an individual or population incur in a particular hazard takes the form of a distribution of risk. Several approaches have been suggested that consider the whole food chain from farm to fork. Almost all employ the stochastic model, which weights the uncertainty and variability of data with probability distribution. It may be important for microbiological QRA to 103

4 104 separate uncertainty and variability, by using, for example, second order Monte Carlo analysis. Among all proposed models of microbiological QRA, there is a clear requirement that they reflect specific conditions existing in different countries and are able to adapt to a national context. This may be important when addressing exposure assessment, due to national or regional differences in the way food is produced; distributed, in the occurrence of a specific microorganism; and how food is prepared and consumed. The different approaches of microbiological QRA reflect the different purposes of the study as they are defined by the risk manager who triggers the whole process of risk management. It is the risk managers who, for end points assessment, determine which relevant stage of the farm to fork chain has to be included in the QRA study, along with the key processes and all details necessary to estimate the probability and the likely level of exposures. The Codex guidelines include a list of principles and definitions but do not refer to a specific methodology. Roberts et al. (1995) proposed the Event Tree Model, which describes a scenario from an initiating event to a defined endpoint of assessment. This approach allows identification of the high risk phases that lead to contamination and subsequent disease, along with risk variables in need of further data or modelling. In contrast to the event tree, the Fault Tree (Roberts, 1995) begins with the occurrence of an hazard and from there describes the events that must have occurred for the hazard to be present. Mark et al. (1998) introduced the Dynamic Flow Tree (DFT) model, in which the dynamic nature of bacterial growth is emphasized. This model incorporates predictive microbiology using data from statistical analysis. The model proposed by Cassin et al. (1998) and adopted by Lammerding and Fazil (2000) is called the Process Risk Model and focuses on the integration of predictive microbiology and scenario analysis to provide an assessment of the hygienic characteristics of a manufacturing process. The importance of this microbiological QRA model relies on the capacity to identify the interventions able to mitigate or reduce risk at key phases in the whole food manufacturing, but it does not provide in itself a quantitative assessment of the risk, i.e. the final risk estimate. Using the Process Risk Model, the pathogen is followed along the whole food manufacturing process using two parameters: prevalence and concentration. Prevalence refers to the frequency of contamination in a specific commodity, or fraction of contaminated units. The concentration is the number of microorganisms per unit. The probability distribution of these parameters are taken into account in each phase of the manufacturing process. If the purpose of QRA is to estimate the risk to a population from a food-pathogen combination, it may suffice to structure the model so as to use data and information as close to the consumption point as possible. An example is the recent microbiological QRA study carried out in the USA (US-FDA/FSIS, 2000) for 20 different ready-to-eat foods. A further example is the Swedish study focusing on determination of the risk represented by Listeria monocytogenes in smoked trout and salmon (Lindqvist and Westwöö, 2000). The result is that in Sweden 2.8 person/100,000 has the probability to eat a portion of the above products containing 10 4 cfu/g of Listeria monocytogenes, considering that food with 10 4 cfu/g of L. monocytogenes may cause infection in an at risk population. Nauta (2001) recently proposed the Modular Process Risk models (MPRM) for quantitative exposure assessment. In this framework both the consumption stage and processes in primary production are not explicitly considered. This model is a variant of the approach of the Cassin s Process Risk Model, with a modular structure. The idea behind the MPRMs is that all processing steps in

5 105 any food pathway can be identified as one of the six basic processes (modules): either one of two microbial processes (growth and inactivation) or one of four product handling processes (mixing, partitioning, removal and cross contamination). In each step the number of microorganisms per unit (e.g. carcass, bottle of milk, a package of ground beef) is estimated. The size of the units may change during the food pathway being described, for instance as result of mixing, partitioning, or removal. The difference between the PRM and MPRM is that MPRM describes the transmission of pathogens along the food manufacturing chain by using the six basic processes and secondly the Monte Carlo simulation that permits identification/separation of uncertainty and variability. Neglecting the difference between these may lead to an improper risk estimate (Nauta, 2001) and/or incomplete understanding of the results. In conclusion, the food industry and the national authorities responsible for food safety have different aims when applying microbiological QRA, HACCP or predictive modelling. Use of HACCP as a qualitative system for risk management has helped food manufacturers and food caterers to improve hygiene standards and to ensure a higher level of security in the final food product. HACCP is well adapted to defined and controlled production and preparation processes. However, for public heath authorities microbiological QRA may serve as a means to quantify the risk attributable to certain food products. The quantification of risk is much more important when the QRA studies are linked to public health objectives. The quantitative approach using public health measures, may allow comparison between the defined risk and other health risks and thus evaluation and implementation of the best risk control strategies proposed during the risk management phase. Finally, it is clear that industry, when implementing HACCP, may utilize the results of both microbiological QRA and predictive modelling for assessing whether a food product will be safe at the time of consumption. For the purpose of national health authorities, microbiological QRA is not so much about the production of safe food, but an evaluation of the health status of the population. REFERENCES Buchanan, R.L., Smith, J.L. and Wesley, L., Microbial risk assessment: Dose-response relations and risk characterization. International Journal of Food Microbiology, 58, CAC (Codex Alimentarius Commission), Principles and guidelines for the Application of Microbiological Risk Assessment. CX/FH 96/ FAO, Rome Italy CAC (Codex Alimentarius Commission), Draft principles and guidelines for that conduct of microbiological risk assessment. FAO/WHO document CX/FH/3, July 1998 Cassin, M.H., Lammerding, A.M., Todd, E.C.D., Ross, W. and Coll, R.S., Quantitative risk assessment for Escherichia coli 0157:H7 in ground beef hamburgers. International Journal of Food Microbiology 41, FAO/WHO, Application of risk analysis to food standard issues. Report of the Joint FAO/WHO expert consultation. WHO. Geneve. WHO/FNU/FOS/95.3 FAO/WHO, Risk management and Food Safety. Report of the Joint FAO/WHO expert consultation. Rome Italy January, FAO Food and Nutrition Paper 65, Rome FAO/WHO, Risk Assessment of Microbiological Hazard in Foods. Report of the Joint FAO/WHO Expert Consultation, Geneva, Switzerland, March, 1999 Havelaar, A.H., et al., 2000a. Health burden in the Netherlands due to infection with thermophilic Campylobacter spp. Epidemiology and Infection, 125(3), Havelaar, A.H., et al., 2000b. Balancing the risks and benefits of drinking water disinfection: Disability adjusted life-years on the scale. Environmental Health Perspectives, 108,

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