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If the risk assessment has included uncertainty and not combined the uncertainty into the probability measure herbalshopcompanynet purchase 60caps cystone, we can also look at how sure we are about these estimates of probability per serving (Figure 5 empowered herbals buy discount cystone 60 caps on-line. It is difficult to herbs list buy cystone 60caps low cost graphically compare two or more second-order distributions so, whilst it is theoretically possible to produce, for example, probability distributions of the number of illnesses a person or subpopulation may endure over a period, if these are second-order distributions it will generally be far clearer to th make a comparison of an appropriate statistic (mean, 90 percentile, etc. Risk per serving the risk per serving suffers the ambiguity of What should be defined as a serving The risk is also not easily translated to any individual, as one needs to take into account the amount of that food that an individual might consume within a defined period. However, if a standard quantity (like 100 g cooked weight, or 30 g protein intake, or 1000 calories) is first set, the risk per serving measure provides an easy comparison of the risk from direct consumption of different food products. Individual risk Individual risk can be specified for a random individual within the population of concern, or for a random consumer of the product (assuming not everyone in the population consumes the 56 Quantitative risk characterization Randomness only Uncertainty and randomness (a) x = probability measure; y = confidence Single-point probability A fixed value measure (b) x = number ill people. Multiple lines show uncertainty Probability distribution (d) x = sub-group; y = probability measure (e) x, y same as (d). It can also be specified for random individual of various subpopulations when one wishes to explore the degree to which subpopulations differ in bearing the population risk. Population-level risk A population-level risk estimate considers the risk distributed over the population or sub population of interest, and might also look at the risk burden absorbed by the population as a whole. It does not distinguish between sub-groups within that population, such as by region, ethnicity, age or health status. These estimates can be produced for separate subpopulations if required, and aggregated to a single measure for the population as a whole. Risk characterization needs to consider the measurement endpoint (reported health outcome) used in developing the dose-response relationship, and may require estimating the desired risk assessment endpoint(s) from a more or less severe measurement endpoint. Infection may 58 Quantitative risk characterization be measured as the multiplication of organisms within the host, followed by excretion, or a rise in serum antibodies. A fraction of those infected will exhibit symptomatic illness (the morbidity ratio), as measured by clinical observation or reported by patients or consumer responses. Care must be taken to ensure that the implications of the case definition used in a clinical trial or epidemiological investigation are understood. For clinical trials, typical measurement endpoints include infection (as indicated by a faecal positive) or illness (as indicated by diarrhoea). Epidemiological surveys may provide information on morbidity and mortality ratios. It is conceivable that the ratios might be dose-dependent, however, and epidemiological data will not inform this relationship. In some cases, clinical trials have used a continuous dose-response measurement endpoint. As discussed earlier in relation to variability and uncertainty, if sufficient information is available to develop subpopulation-specific dose-response relationships. However, even in cases where such separate dose-response relationships cannot be specified, it may be informative to characterize risk by subpopulation. For example, there may be sufficient data to develop sub population-specific morbidity or mortality ratios. Care must therefore be taken to ensure that there is a reasoned basis for classifying consumers as members of different subpopulations, and that subpopulation definitions are consistent between the exposure and dose-response analyses. A key consideration in choosing appropriate models is the level of detail required for the assessment, consistent with the assessment objectives. The choice of quantitative model must evaluate how well the model is supported by the available data, how effective the outputs are in informing decision-makers, and how many assumptions have been made in creating the model and the robustness of those assumptions. Inevitably, the process of choosing models, selecting and analysing data, and applying the data and models to answer assessment questions, involves subjective judgement.


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