Review Article What Risk Assessments of Genetically Modified Organisms Can Learn from Institutional Analyses of Public Health Risks S. Ravi Rajan and Deborah K. Letourneau Department of Environmental Studies, University of California, Santa Cruz, CA 95006, USA Correspondence should be addressed to S. Ravi Rajan, [email protected] and Deborah K. Letourneau, [email protected] Received 29 March 2012; Revised 22 May 2012; Accepted 12 July 2012 Academic Editor: Joel W. Ochieng Copyright © 2012 S. R. Rajan and D. K. Letourneau. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The risks of genetically modified organisms (GMOs) are evaluated traditionally by combining hazard identification and exposure estimates to provide decision support for regulatory agencies. We question the utility of the classical risk paradigm and discuss its evolution in GMO risk assessment. First, we consider the problem of uncertainty, by comparing risk assessment for environmental toxins in the public health domain with genetically modified organisms in the environment; we use the specific comparison of an insecticide to a transgenic, insecticidal food crop. Next, we examine normal accident theory (NAT) as a heuristic to consider runaway effects of GMOs, such as negative community level consequences of gene flow from transgenic, insecticidal crops. These examples illustrate how risk assessments are made more complex and contentious by both their inherent uncertainty and the inevitability of failure beyond expectation in complex systems. We emphasize the value of conducting decision-support research, embracing uncertainty, increasing transparency, and building interdisciplinary institutions that can address the complex interactions between ecosystems and society. In particular, we argue against black boxing risk analysis, and for a program to educate policy makers about uncertainty and complexity, so that eventually, decision making is not the burden that falls upon scientists but is assumed by the public at large
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