Decision curve analysis as a framework to estimate the potential value of screening or other decision-making aids

There is an increasing debate about the impact of mental health screening. We illustrate the use of a decision making framework that can be applied when there is no sufficient data to support a traditional cost-benefit analysis.

We conducted secondary analyses of data from 459 male prisoners who were screened upon intake. We compared the potential benefit of different approaches (screening, history taking, and universal interventions) to allocating treatment resources using decision curve analysis.

Screening prisoners for distress at typical levels of sensitivity (75%) and specificity (71%) were estimated to provide the greatest net benefit if between 2 and 5 false positives per detected illness are tolerable. History taking and self-harm screening provide the largest net benefit when only 1 or 2 false positives per detected illness would be tolerable. The benefits of screening were less among those without a recent psychiatric history, ethnic minorities, and those with fewer psychosocial needs.

Although screening has potential to increase detection of treatment, important subgroup differences exist. Greater consideration of responses to positive screens or alternatives to screening are needed to maximize the impact of efforts to improve detection and treatment of mental illness.