Academic journal article Environmental Health Perspectives

Risk Management and Precaution: Insights on the Cautious Use of Evidence

Academic journal article Environmental Health Perspectives

Risk Management and Precaution: Insights on the Cautious Use of Evidence

Article excerpt

Risk management, done well, should be inherently precautionary. Adopting an appropriate degree of precaution with respect to feared health and environmental hazards is fundamental to risk management. The real problem is in deciding how precautionary to be in the face of inevitable uncertainties, demanding that we understand the equally inevitable false positives and false negatives from screening evidence. We consider a framework for detection and judgment of evidence of well-characterized hazards, using the concepts of sensitivity, specificity, positive predictive value, and negative predictive value that are well established for medical diagnosis. Our confidence in predicting the likelihood of a true danger inevitably will be poor for rare hazards because of the predominance of false positives; failing to detect a true danger is less likely because false negatives must be rarer than the danger itself. Because most controversial environmental hazards arise infrequently, this truth poses a dilemma for risk management. Key words: complacency, false negatives, false positives, futility, positive predictive value, zero risk. Environ Health Perspect 111:1577-1581 (2003). doi: 10.1289/ehp.6224 available via[Online 10 June 2003]


Risk management has been widely advocated as a rational means for environmental decision making, assisting us in dealing with the wide array of dangers that we face in our uncertain world, ranging from pathogens in drinking water to terrorist attacks. Done well, risk management is inherently precautionary in the sense that it should make use of effective risk assessment to predict, anticipate, and prevent harm, rather than merely reacting when harm arises.

Some of the insights that have supported the movement toward making better use of available evidence for medical decision making, particularly in the field of diagnostic screening, have important but usually overlooked insights for environmental decision making. In particular, the use of four key concepts used for judging the quality of evidence in medical diagnosis--sensitivity, specificity, positive predictive value, and negative predictive value--are relevant to the assessment of environmental hazards, especially those that have low probabilities of occurrence. Applying these concepts rigorously allows us to see more clearly both the value and the limitations of the precautionary approach, as well as to reveal more quantitatively the logical flaw in the notion of "zero risk." The key question, we suggest, is not whether to be precautionary, but how precautionary we ought to be in specific cases, in relation to the quality of our screening evidence.

Interpreting Evidence about Hazards

Our premise can be illustrated by considering an analogy with airport security. Suppose that we have acquired impressive new scanning technology with the following detection capabilities: a) when someone is carrying a dangerous weapon, 99.5% of the time it will respond positively, and b) when someone is not carrying such a weapon, 98% of the time it will respond negatively. If our best intelligence indicates that about 1 in 10,000 passengers screened will be carrying a detectable, dangerous weapon, we can ask how well the screening evidence will allow us to manage this risk. In particular, we can ask, if we get a positive result how likely is that detection to be correct? Given the properties described, common intuition will lead us to expect that this detection should be reliable.

The answer to our question depends on considering that, on average, we will need to screen 9,999 unarmed passengers to find the 1 who is

carrying a weapon. The characteristics described provide for a false-positive rate of 2% (98% of the time unarmed passengers will show up as negative). This means that, on average, we will get 199.98 or, effectively, 200 false positives detected for every true positive. …

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