Indicators for tracking European vulnerabilities to the risks of infectious disease transmission due to climate change.

A wide range of infectious diseases may change their geographic range, seasonality and incidence due to climate change, but there is limited research exploring health vulnerabilities to climate change. In order to address this gap, pan-European vulnerability indices were developed for 2035 and 2055, based upon the definition vulnerability = impact/adaptive capacity. Future impacts were projected based upon changes in temperature and precipitation patterns, whilst adaptive capacity was developed from the results of a previous pan-European study. The results were plotted via ArcGISTM to EU regional (NUTS2) levels for 2035 and 2055 and ranked according to quintiles. The models demonstrate regional variations with respect to projected climate-related infectious disease challenges that they will face, and with respect to projected vulnerabilities after accounting for regional adaptive capacities. Regions with higher adaptive capacities, such as in Scandinavia and central Europe, will likely be better able to offset any climate change impacts and are thus generally less vulnerable than areas with lower adaptive capacities. The indices developed here provide public health planners with information to guide prioritisation of activities aimed at strengthening regional preparedness for the health impacts of climate change. There are, however, many limitations and uncertainties when modeling health vulnerabilities. To further advance the field, the importance of variables such as coping capacity and governance should be better accounted for, and there is the need to systematically collect and analyse the interlinkages between the numerous and ever-expanding environmental, socioeconomic, demographic and epidemiologic datasets so as to promote the public health capacity to detect, forecast, and prepare for the health threats due to climate change.

Methodology for determining the appropriateness of a linear dose-response function.

Microbial food safety risk assessment models can often at times be simplified by eliminating the need to integrate a complex dose-response relationship across a distribution of exposure doses. This is possible if exposure pathways lead to pathogens at exposure that consistently have a small probability of causing illness. In this situation, the probability of illness will follow an approximately linear function of dose. Consequently, the predicted probability of illness per serving across all exposures is linear with respect to the expected value of dose. The majority of dose-response functions are approximately linear when the dose is low. Nevertheless, what constitutes "low" is dependent on the parameters of the dose-response function for a particular pathogen. In this study, a method is proposed to determine an upper bound of the exposure distribution for which the use of a linear dose-response function is acceptable. If this upper bound is substantially larger than the expected value of exposure doses, then a linear approximation for probability of illness is reasonable. If conditions are appropriate for using the linear dose-response approximation, for example, the expected value for exposure doses is two to three logs(10) smaller than the upper bound of the linear portion of the dose-response function, then predicting the risk-reducing effectiveness of a proposed policy is trivial. Simple examples illustrate how this approximation can be used to inform policy decisions and improve an analyst's understanding of risk.

Framework for microbial food-safety risk assessments amenable to Bayesian modeling.

Regulatory agencies often perform microbial risk assessments to evaluate the change in the number of human illnesses as the result of a new policy that reduces the level of contamination in the food supply. These agencies generally have regulatory authority over the production and retail sectors of the farm-to-table continuum. Any predicted change in contamination that results from new policy that regulates production practices occurs many steps prior to consumption of the product. This study proposes a framework for conducting microbial food-safety risk assessments; this framework can be used to quantitatively assess the annual effects of national regulatory policies. Advantages of the framework are that estimates of human illnesses are consistent with national disease surveillance data (which are usually summarized on an annual basis) and some of the modeling steps that occur between production and consumption can be collapsed or eliminated. The framework leads to probabilistic models that include uncertainty and variability in critical input parameters; these models can be solved using a number of different Bayesian methods. The Bayesian synthesis method performs well for this application and generates posterior distributions of parameters that are relevant to assessing the effect of implementing a new policy. An example, based on Campylobacter and chicken, estimates the annual number of illnesses avoided by a hypothetical policy; this output could be used to assess the economic benefits of a new policy. Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems.

Expert judgement in a risk assessment model for Salmonella spp. in pork: the performance of different weighting schemes.

A structured expert judgement study was carried out in order to obtain input parameters for a quantitative microbial risk assessment (QMRA) model. This model aimed to estimate the risk of human Salmonella infections associated with the consumption of minced pork meat. Judgements of 11 experts were used to derive subjective probability density functions (PDFs) to quantify the uncertainty on the model input parameters. The performance of experts as probability assessors was measured by the experts' ability to correctly and precisely provide estimates for a set of seed variables (=variables from the experts' area of expertise for which the true values were known to the analyst). Subsequently different weighting schemes or "decision makers" (DMs) were applied using Cooke's classical model in order to obtain combined PDFs as a weighted linear combination of the expert's individual PDFs. The aim of this study was to compare the performance of four DMs namely the equal weight DM (each expert's opinion received equal weight), the user weight DM (weights are determined by the expert's self-perceived level of expertise) and two performance-based DMs: the global weight DM and the item weight DM. Weights in the performance-based DMs were calculated based on the expert's calibration and information performance as measured on the set of seed variables. The item weight DM obtained the highest performance with a calibration score of 0.62 and an information score of 0.52, as compared to the other DMs. The weights of the performance-based DMs outperformed those of the best expert in the panel. The correlation between the scores for self-rating of expertise and the weights based on the experts' performance on the calibration variables was low and not significant (r=0.37, p=0.13). The applied classical model provided a rational basis to use the combined distributions obtained by the item weight DM as input in the QMRA model since this DM yielded generally more informative distributions for the variables of interest than those obtained by the equal weight and user weight DM. Attention should be paid to find adequate and relevant seed variables, since this is important for the validation of the results of the weighting scheme.

NUSAP method for evaluating the data quality in a quantitative microbial risk assessment model for Salmonella in the pork production chain.

The numeral unit spread assessment pedigree (NUSAP) system was implemented to evaluate the quality of input parameters in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. The input parameters were grouped according to four successive exposure pathways: (1) primary production (2) transport, holding, and slaughterhouse, (3) postprocessing, distribution, and storage, and (4) preparation and consumption. An inventory of 101 potential input parameters was used for building the QMRA model. The characteristics of each parameter were defined using a standardized procedure to assess (1) the source of information, (2) the sampling methodology and sample size, and (3) the distributional properties of the estimate. Each parameter was scored by a panel of experts using a pedigree matrix containing four criteria (proxy, empirical basis, method, and validation) to assess the quality, and this was graphically represented by means of kite diagrams. The parameters obtained significantly lower scores for the validation criterion as compared with the other criteria. Overall strengths of parameters related to the primary production module were significantly stronger compared to the other modules (the transport, holding, and slaughterhouse module, the processing, distribution, and storage module, and the preparation and consumption module). The pedigree assessment contributed to select 20 parameters, which were subsequently introduced in the QMRA model. The NUSAP methodology and kite diagrams are objective tools to discuss and visualize the quality of the parameters in a structured way. These two tools can be used in the selection procedure of input parameters for a QMRA, and can lead to a more transparent quality assurance in the QMRA.

Quantitative modeling for risk assessment of Vibrio parahaemolyticus in bloody clams in southern Thailand.

A risk assessment of Vibrio parahaemolyticus in bloody clams (Anadara granosa) consumed in southern Thailand was conducted. This study estimated the prevalence and concentration of pathogenic V. parahaemolyticus in bloody clams at harvest and retail stages; and during this process, methods to detect the total and pathogenic V. parahaemolyticus were investigated. Consumption of bloody clams and cooking efficiency were studied using interviews and on-site observation of consumers. A beta-Poisson dose-response model was used to estimate probability of illness applying estimation methods for the most likely parameter values presented by USFDA. Microbial and behavioral data were analyzed by developing a stochastic model and the simulation gave a mean number of times a person would get ill with V. parahaemolyticus by consuming bloody clams at 5.6 x 10(-4)/person/year. Sensitivity analysis demonstrated the fraction of people who did not boil the clams properly was the primary factor in increasing risk. This study serves as an example of how a microbiological risk assessment with limited data collection and international cooperation leads to valuable local insight.

A linear model for managing the risk of antimicrobial resistance originating in food animals.

A linear population risk model used by the U.S. Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) estimates the risk of human cases of campylobacteriosis caused by fluoroquinolone-resistant Campylobacter. Among the cases of campylobacteriosis attributed to domestically produced chicken, the fluoroquinolone resistance is assumed to result from the use of fluoroquinolones in poultry in the United States. Properties of the linear population risk model are contrasted with those of a farm-to-fork model commonly used for microbial risk assessments. The utility of the linear population model for the purpose for which it was used by CVM is discussed.

A Bayesian approach to quantify the contribution of animal-food sources to human salmonellosis.

Based on the data from the integrated Danish Salmonella surveillance in 1999, we developed a mathematical model for quantifying the contribution of each of the major animal-food sources to human salmonellosis. The model was set up to calculate the number of domestic and sporadic cases caused by different Salmonella sero and phage types as a function of the prevalence of these Salmonella types in the animal-food sources and the amount of food source consumed. A multiparameter prior accounting for the presumed but unknown differences between serotypes and food sources with respect to causing human salmonellosis was also included. The joint posterior distribution was estimated by fitting the model to the reported number of domestic and sporadic cases per Salmonella type in a Bayesian framework using Markov Chain Monte Carlo simulation. The number of domestic and sporadic cases was obtained by subtracting the estimated number of travel- and outbreak-associated cases from the total number of reported cases, i.e., the observed data. The most important food sources were found to be table eggs and domestically produced pork comprising 47.1% (95% credibility interval, CI: 43.3-50.8%) and 9% (95% CI: 7.8-10.4%) of the cases, respectively. Taken together, imported foods were estimated to account for 11.8% (95% CI: 5.0-19.0%) of the cases. Other food sources considered had only a minor impact, whereas 25% of the cases could not be associated with any source. This approach of quantifying the contribution of the various sources to human salmonellosis has proved to be a valuable tool in risk management in Denmark and provides an example of how to integrate quantitative risk assessment and zoonotic disease surveillance.

Risk Assessment Model for Human Infection with the Cestode Taenia saginata.

A probabilistic risk assessment model was developed to estimate the risk to human health of Taenia saginata in the New Zealand cattle population. A standardized monitoring program was established to determine the number of suspect cysts detected during postmortem inspection and the scenario set was applied to risks in both the domestic and export markets. The mean number of human infections per year as a result of consumption in the export and the domestic market was estimated as 0.50 and 1.10 respectively. Estimations for expression of specific clinical symptoms were even less. In a scenario set where postmortem inspection procedures for T. saginata were not applied, the mean number of human infections per year was estimated to increase from 0.50 to 0.61 in the export market and from 1.10 to 1.30 in the domestic market. Given that T. saginata infection in humans results in mild and readily treatable symptoms, these risk estimates are extremely low on any scale of food-borne disease and bring the value of specific postmortem inspection procedures for T. saginata in the New Zealand situation into question. The Monte Carlo model developed to calculate these probabilities is presented here in detail to illustrate the potential of Monte Carlo methods for modeling risk.