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Causation in risk assessment and management: models, inference, biases, and a microbial risk-benefit case study [An article from: Environment International]
This digital document is a journal article from Environment International, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser. Description: Causal inference of exposure-response relations from data is a challenging aspect of risk assessment with important implications for public and private risk management. Such inference, which is fundamentally empirical and based on exposure (or dose)-response models, seldom arises from a single set of data; rather, it requires integrating heterogeneous information from diverse sources and disciplines including epidemiology, toxicology, and cell and molecular biology. The causal aspects we discuss focus on these three aspects:*Drawing sound inferences about causal relations from one or more observational studies; *Addressing and resolving biases that can affect a single multivariate empirical exposure-response study; and *Applying the results from these considerations to the microbiological risk management of human health risks and benefits of a ban on antibiotic use in animals, in the context of banning enrofloxacin or macrolides, antibiotics used against bacterial illnesses in poultry, and the effects of such bans on changing the risk of human food-borne campylobacteriosis infections. The purposes of this paper are to describe novel causal methods for assessing empirical causation and inference; exemplify how to deal with biases that routinely arise in multivariate exposure- or dose-response modeling; and provide a simplified discussion of a case study of causal inference using microbial risk analysis as an example. The case study supports the conclusion that the human health benefits from a ban are unlikely to be greater than the excess human health risks that it could create, even when accounting for uncertainty. We conclude that quantitative causal analysis of risks is a preferable to qualitative assessments because it does not involve unjustified loss of information and is sound under the inferential use of risk results by management. .
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Evolution and the New Gnosis: Anti-establishment Essays on Knowledge
Science, Religion and Causal Logic
Exposing a long-hidden logical conflict that has hindered the quest for adeeper understanding in science, religion and philosophy The unique ideasin this book shatter what are often unconscious or deeply hidden butnevertheless widely held assumptions. Whatever your world view ordiscipline, examine it anew through the window of these 22 hard-hittingessays with titles such as: Evolution as a Property of Mind, Christianityand the Old Gnosis, Isaac Newton & Harry Potter, What is Imagination? -combined with critical insights and commentaries on the works of CharlesDarwin, Noam Chomsky, Michael Polanyi, Arthur Koestler, Theodore Roszak,Owen Barfield, Rudolf Steiner and many others. Also, investigate the socialfuture as viewed in the light of an ongoing evolution of humanconsciousness..
Price: $15.77
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