SETAC Globe - Environmental Quality Through Science
  August 2010
Volume 11 Issue 8

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Enhancing the credibility of science-based decisions

Keith Solomon, University of Guelph and Michelle Embry, ILSI Health & Environmental Sciences Institute

Recognizing the need to enhance the credibility of science-based decisions, the ILSI Health & Environmental Sciences Institute convened a tripartite committee, including SETAC members Michelle Embry and Keith Solomon, and tasked it with examining existing guidance and recommendations related to transparency in risk assessment. SETAC has long recognized the importance of transparency as a principle for producing influential science in support of sustainable environmental quality & ecosystem integrity. The practice of publishing supplemental information in the SETAC journals is an example of the importance SETAC places on transparency and documentation.

The ILSI Health & Environmental Sciences Institute committee found that transparency and documentation of the decision process are at the core of a credible risk assessment and are essential in the presentation of a weight of evidence (WoE) based approach (Schreider et al.20101). It found that lack of clarity in what information was (or was not) used, limited availability of the underlying data, and too few details on the methodology used to reach a final disposition can all undermine risk assessment and risk management credibility. Credible risk assessment and risk management has three components, credibility of the data, credibility of the risk assessment process and credibility of the resulting decision making. The loss of credibility at any one step cannot be regained in another step and will decrease the overall credibility of any resulting decision. 

If underlying data are inaccessible, even the most transparent of risk assessment processes cannot ensure credibility. Historically, regulatory agencies have placed a higher weight on well-designed, standardized and validated laboratory studies conducted by industry under Good Laboratory Practice (GLP) requirements to drive regulatory decision making. In these cases, the raw data are available for review by risk assessors, but not necessarily to the public or risk assessment readers, to help ensure the conclusions reached are supported by the underlying data. Clearly, non-GLP studies, and in particular those conducted by academic researchers and reported in the peer-reviewed scientific literature, should also be considered in an overall WoE evaluation of risks. Studies published in peer-reviewed scientific journals are gaining importance for reaching regulatory decisions. They have the advantage of being broadly available to the public but are also problematic because raw data and other details on study design and conduct are often limited or absent, thus significantly reducing the ability of reviewers and readers to fully ascertain the quality and integrity of the results and conclusions. 

Increasing the availability of raw and supporting data in the published, peer-reviewed literature would increase the utility of these studies as foundational data in regulatory decision-making.  Schreider et al. (2010) recommend the following steps to increase the credibility of science-based conclusions and decisions:

  • Existing guidance concerning criteria elements of transparency related to the risk assessment process must be more widely disseminated and applied.
  • Raw data for studies used in human health and environmental risk assessment must be more widely available.
  • The decision-making process in risk management must be better documented and a guidance framework established for both the process itself and its communication to the public.

In particular, credible scientific data are the foundation for risk assessment and risk management. The issue of the availability of raw data for human health and environmental risk assessment is at a critical juncture; critical not only to ensure that a balanced and objective process is used, but critical because the very core of scientific methodology and analysis assumes complete transparency of the data upon which assessments and decisions are made.

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