The ideal method to measure the informational value of surveillance data is a value-of-information study (Friedman and Wyatt, 1997), although it is time consuming and difficult to conduct. There are no examples of such analyses of biosurveillance data as of this writing.
A value-of-information measurement is a general technique that can be used to quantify the value of data for any purpose, including purposes other than biosurveillance. When applying this technique to assess biosurveillance data, an evaluator would compare the ability of a biosurveillance system to detect outbreaks or cases with and without the data in question using the detection algorithm method previously described. She would further estimate, using models, the reduction in morbidity and mortality and economic impact that could be attributed to the improvement in earliness of detection. For example, if the analysis found that outbreaks could be detected 24 hours earlier if the data were available, the evaluator would use a model to translate the 24-hour improvement in speed of detection into, for example, an additional 100 lives saved and the removal of 1000 people from the expensive "requires treatment with ventilator'' category to "requires prophylactic treatment with antibiotics'' category. The number of false alarms expected might be one per year. The evaluator could then compare the benefit of the data denominated in lives saved or sickness averted against the cost and effort required to build and maintain systems for their routine collection, as well as the cost of the false alarms.
Value-of-information analyses can also be taken to the level of economic analyses, which allow more direct comparisons of benefits and costs. Briefly, an economic analysis would translate "lives saved'' or "sickness averted'' into dollars. We discuss these transformations further in Chapter 31.
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