Evaluating the effectiveness of risk-reduction strategies for consumer chemical products.

Communication about risks offers a voluntary approach to reducing exposure to pollutants. Its adequacy depends on its impact on behavior. Estimating those impacts first requires characterizing current activities and their associated risk levels, and then predicting the effectiveness of risk-reduction strategies. Characterizing the risks from chemical consumer products requires knowledge of both the physical and the behavioral processes that influence exposures. This article presents an integrated approach that combines consumer interviews, users' beliefs and behaviors, and quantitative exposure modeling. This model was demonstrated in the context of consumer exposure to a methylene chloride-based paint stripper, showing how it could be used to evaluate current levels of risk and predict the effectiveness of proposed voluntary risk-reduction strategies.

A deliberative method for ranking risks (I): Overview and test bed development.

Risk ranking offers a potentially powerful means for gathering public input to help set risk-management priorities. In most rankings conducted to date, the categories and attributes used to describe the risks have varied widely, the materials and procedures have not been designed to facilitate comparisons among risks on all important attributes, and the validity and reproducibility of the resulting rankings have not been assessed. To address these needs, a risk-ranking method was developed in which risk experts define and categorize the risks to be ranked, identify the relevant risk attributes, and characterize the risks in a set of standardized risk summary sheets, which are then used by lay or other groups in structured ranking exercises. To evaluate this method, a test bed involving 22 health and safety risks in a fictitious middle school was created. This article provides an overview of the risk-ranking method and describes the challenges faced in designing the middle school test bed. A companion article in this issue reports on the validity of the ranking procedures and the level of agreement among risk managers regarding ranking of risks and attributes.

A deliberative method for ranking risks (II): Evaluation of validity and agreement among risk managers.

A deliberative method for ranking risks was evaluated in a study involving 218 risk managers. Both holistic and multiattribute procedures were used to assess individual and group rankings of health and safety risks facing students at a fictitious middle school. Consistency between the rankings that emerged from these two procedures was reasonably high for individuals and for groups, suggesting that these procedures capture an underlying construct of riskiness. Participants reported high levels of satisfaction with their groups' decision-making processes and the resulting rankings, and these reports were corroborated by regression analyses. Risk rankings were similar across individuals and groups, even though individuals and groups did not always agree on the relative importance of risk attributes. Lower consistency between the risk rankings from the holistic and multiattribute procedures and lower agreement among individuals and groups regarding these rankings were observed for a set of high-variance risks. Nonetheless, the generally high levels of consistency, satisfaction, and agreement suggest that this deliberative method is capable of producing risk rankings that can serve as informative inputs to public risk-management decision making.

Traffic accident statistics and risk perceptions in Japan and the United States.

Several recent studies have concluded that Japan and the US have different risk cultures. This study examines the actual risk environments faced by citizens in the two countries, in the domain of traffic safety, as a possible source of differences in risk perceptions. The study contrasts traffic-accident risks from several points of view (e.g. car drivers, motorcyclists, bicyclists and pedestrians) and risk statistics (e.g. death rates, relative fatality risks, and accident lethality). Results clarify the traffic risks in the two countries and confirm their potential for explaining cross-national differences in risk perceptions.

Categorizing risks for risk ranking.

Any practical process of risk ranking must group hazards into a manageable number of categories. Defining such categories requires value choices that can have important implications for the rankings that result. Most risk-management organizations will find it useful to begin defining categories in terms of environmental loadings or initiating events. However, the resulting categories typically need to be modified in light of other considerations. Risk-ranking projects can benefit from considering several alternative categorization strategies and drawing upon elements of each in developing their final categorization of risks. In principle, conducting multiple ranking exercises by using different categorizations could be interesting and useful. In practice, agencies are unlikely to have either the resources or patience to do this, but other groups in society might. Done well, such additional independent rankings could add valuable inputs to democratic risk-management decision making.

Measuring decision sensitivity: a combined Monte Carlo-logistic regression approach.

Modeling of the uncertainty of multiple input variables for a complex decision problem complicates sensitivity analysis. A method of analysis comprising stochastic simulation of the model and logistic regression of the simulated dichotomous decision variable against all of the input variables yields a direct measure of the importance of input variables to the decision. This method is demonstrated on a previously analyzed clinical decision either to continue observation or to immediately treat with anticoagulants a woman presenting with deep vein thrombosis in the first trimester of pregnancy. A relative measure of the importance of each input variable in causing a change of decision is estimated by calculating the change in the log odds attributable to variation of each input variable over its range of uncertain values compared with the total change of log odds from variation of all input variables. This method is compared with alternative measures of input variable importance, and is found to be a simple yet powerful tool for gaining quantitative insight into the nuances of a decision model.