Risk assessment theory, methods

There's also live online events, interactive content, certification prep materials, and more. An introduction to risk assessment that utilizes key theory and state-of-the-art applications.

With its balanced coverage of theory and applications along with standards and regulations, Risk Assessment: Theory, Methods, and Applications serves as a comprehensive introduction to the topic. The book serves as a practical guide to current risk analysis and risk assessment, emphasizing the possibility of sudden, major accidents across various areas of practice from machinery and manufacturing processes to nuclear power plants and transportation systems.

The author applies a uniform framework to the discussion of each method, setting forth clear objectives and descriptions, while also shedding light on applications, essential resources, and advantages and disadvantages. Following an introduction that provides an overview of risk assessment, the book is organized into two sections that outline key theory, methods, and applications. Introduction to Risk Assessment defines key concepts and details the steps of a thorough risk assessment along with the necessary quantitative risk measures.

Chapters outline the overall risk assessment process, and a discussion of accident models and accident causation offers readers new insights into how and why accidents occur to help them make better assessments.

Risk Assessment Methods and Applications carefully describes the most relevant methods for risk assessment, including preliminary hazard analysis, HAZOP, fault tree analysis, and event tree analysis.

Specifically, the application of estimates based on Japanese atomic bomb survivors to a U. Some recent efforts have used intermediate approaches with allowance for considerable uncertainty NIH , The theory of risk assessment, modeling, and estimation and the computational software for deriving statistically sound parameter estimates from data provide a powerful set of tools for calculating risk estimates.

Risk models provide the general form of the dependence of risk on dose and risk-modifying factors. Specific risk estimates are obtained by fitting the models estimating unknown parameters to data. The role of data in the process of risk estimation cannot be overemphasized.

Neither theory, models, nor model-fitting software can overcome limitations in the data from which risk estimates are derived. In human epidemiologic studies of radiation, both the quality and the quantity of the data available for risk modeling are limiting factors in the estimation of human cancer risks.

The quality of data, or lack thereof, and its impact on risk modeling are discussed below under three broad headings. The primary consequence of less-than-ideal data is uncertainty in estimates derived from such data. The specificity of risk models is limited by the information available in the data. Even the most extensive data sets contain, in addition to measurements of exposure, information on only a handful of predictor variables such as dose, age, age at exposure, and sex.

Consequently, models fit to such data predict the same risk of cancer for individuals having the same values of these predictor variables, regardless of other differences between the two individuals. For example, two individuals who differ with respect to overall health status, family history of cancer genetic disposition to cancer , exposure to other carcinogens, and so on, will be assigned the same estimated risk provided they were exposed to the same dose of radiation, are of the same age, and have the same age at exposure and the same gender.

Consequently, among a group of individuals having the same values of the predictor variables in the model, some will have a higher personal risk than that predicted by the model and some will have a lower personal risk.

However, on average, the group risk will be predicted reasonably well by the model. The situation is similar to the assessment of insurance risk. Not all teenage males have the same personal risk of having an automobile accident some are better drivers than others , yet as a group they are recognized as having a greater-than-average risk of accidents, and premiums are set accordingly. Radiation risk models are similar in that they adequately predict the disease experience of a group of individuals sharing common values of predictor variables in the model.

However, such estimated risks need not be representative of individual personal risks. The standard theory and methods of risk modeling and estimation are appropriate under the assumption that dose is measured accurately. Estimated radiation dose is a common characteristic of human epidemiologic data, and questions naturally arise regarding the adequacy of dose estimates for the estimation of risk parameters and the calculation of risk estimates.

These are different problems and are discussed separately. First, consider the problem of calculating risk estimates from a given risk equation.

Suppose that the risk equation has been estimated without bias and with sufficient precision to justify its use in the calculation of risks. Assume also that risk increases with dose: that is, the risk equation yields higher risks for higher doses.

Suppose that an estimate of lifetime risk is desired for an individual whose dose is estimated to be d. This is intuitive and is a consequence of the fact that risk is an increasing function of dose.

The problem of estimating risk equation parameters from data with estimated doses is a little more complicated. Errors in estimated doses can arise in a number of different ways, not all of which have the same impact on risk parameter estimation. For example, flaws in a dosimetry system have the potential to affect all or many dose estimates in the same manner, leading to systematic errors for which all or many dose estimates are too high or too low.

Errors or incomplete records in data from which dose estimates are constructed e. Systematic errors can result in biased estimates of risk equation parameters. The type of bias depends on the nature of the systematic error. Random errors in dose estimates also have the potential to bias estimated risk equations.

Random error-induced bias generally results in the underestimation of risk. That is, random errors tend to have the same qualitative effect as systematic overestimation of doses. The estimation of risk models from atomic bomb survivors has been carried out with a statistical technique that accounts for the random uncertainties in nominal doses Pierce and others To the extent that it is based on correct assumptions about the forms and sizes of dose uncertainties, it removes the bias due to random dose measurement errors.

Ideally, risk models would be developed from data gathered on individuals selected at random from the population for which risk estimates are desired. For example, in estimating risks for medical workers exposed to radiation on the job, the ideal data set would consist of exposure and health information from a random sample of the population of such workers.

However, data on specific populations of interest are generally not available in sufficient quantity or with exposures over a wide enough range to support meaningful statistical modeling. Radiation epidemiology is by necessity opportunistic with regard to the availability of data capable of supporting risk modeling, as indicated by the intense study of A-bomb survivors and victims of the Chernobyl accident.

A consequence of much significance and concern is the fact that risk models are often estimated using data from one population often not even a random sample for the purpose of estimating risks in some other population s. The potential problem it creates is the obvious one—namely, that a risk equation valid for one population need not be appropriate for another.

Just as there are differences in the risk of cancer among males and females and among different age groups, there are differences in cancer risks among different populations. For example, the disparity between baseline rates for certain cancers e. Transporting models is generally regarded as a necessity, and much thought and effort are expended to ensure that problems of model transportation are minimized.

Problems of transporting models from one population to another can never be eliminated completely. However, to avoid doing so would mean that risk estimates would have to be based on data so sparse as to render estimated risks statistically unreliable.

This book is the seventh in a series of titles from the National Research Council that addresses the effects of exposure to low dose LET Linear Energy Transfer ionizing radiation and human health. Updating information previously presented in the publication, Health Effects of Exposure to Low Levels of Ionizing Radiation: BEIR V , this book draws upon new data in both epidemiologic and experimental research.

Ionizing radiation arises from both natural and man-made sources and at very high doses can produce damaging effects in human tissue that can be evident within days after exposure.

However, it is the low-dose exposures that are the focus of this book. This book is among the first of its kind to include detailed risk estimates for cancer incidence in addition to cancer mortality. BEIR VII offers a full review of the available biological, biophysical, and epidemiological literature since the last BEIR report on the subject and develops the most up-to-date and comprehensive risk estimates for cancer and other health effects from exposure to low-level ionizing radiation.

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Empirically Based Risk Models. Model Parameter Estimation. Estimating Probabilities of Causation. There's also live online events, interactive content, certification prep materials, and more. This chapter gives a survey of the various steps in a risk analysis and describes the role of risk analysis and risk assessment in the risk management process.

The application of bow-tie diagrams and bow-tie analysis in a risk management process is outlined briefly. Different types of risk analyses are discussed together with quality requirements and the competence of the study team. Risk management is mentioned briefly, but management aspects are outside the scope of this book.