Causation in risk assessment and management: Models, inference, biases, and a microbial risk-benefit cause study

Cox Jr, L. A. and Ricci, P. (2005) Causation in risk assessment and management: Models, inference, biases, and a microbial risk-benefit cause study. Environment International, 31 3: 377-397. doi:10.1016/j.envint.2004.08.010


Author Cox Jr, L. A.
Ricci, P.
Title Causation in risk assessment and management: Models, inference, biases, and a microbial risk-benefit cause study
Journal name Environment International   Check publisher's open access policy
ISSN 0160-4120
Publication date 2005-04
Sub-type Article (original research)
DOI 10.1016/j.envint.2004.08.010
Volume 31
Issue 3
Start page 377
End page 397
Total pages 21
Place of publication Oxford, England
Publisher Pergamon-Elsevier Science Ltd
Collection year 2005
Language eng
Subject C1
321299 Public Health and Health Services not elsewhere classified
730299 Public health not elsewhere classified
Formatted abstract
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.
Keyword Risk
Uncertainty
Entropy of information
Causal diagrams
Anti-microbial agents
Benefits
Costs
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 07:00:56 EST