Depression is a global concern that puts a financial, social, and professional burden on communities and individuals. People with depression often experience more than one episode, and are at increased risk for other negative outcomes, including diminished quality of life, physical health problems, and other psychological issues. Many times, symptoms of depression exist for a long period of time before they are recognized and diagnosed.
General practitioners, therefore, may play a critical role in identifying those at risk for depression as they are often the first point of contact at risk individuals have with a health care professional. Although there are some tools that can help general practitioners identify depression, they are lengthy, time consuming, and often not cost effective. Further, they are usually only administered if doctors or patients have concerns about depression.
The need for a screening tool that can be easily and routinely applied in general practice is evident. In order to fill this void, Michael B. King, PhD, of the Faculty of Brain Sciences of the University College London Medical School recently collaborated with colleagues on the development of such a tool known as the predictD. This risk algorithm was designed to examine 10 specific risk factors: family history, mental health, physical health, education level, sex, age, work history, discrimination, and country of origin.
In a previous study, predictD proved to be quite effective at predicting depression over a 12-month period in a general practice participant sample. King recently extended his original findings by applying predictD to 4,190 participants over a 24-month period.
The results from this predictD study revealed that, similar to the findings from the first experiment, predictD was almost equally effective at predicting depression at 24 months. This result is promising and shows the potential clinical utility of predictD. Because the participants did not demonstrate symptoms of depression at screening, King believes this clearly shows that predictD can provide valuable predictive information for depression in a low-risk sample.
King added, “It may be useful as a strategy to identify those at risk in prevention efforts in general medical settings.” He believes future work should test predictD in other health care environments that provide opportunities for early identification and diagnosis.
King, M., et al. (2013). Predicting onset of major depression in general practice attendees in Europe: Extending the application of the predictD risk algorithm from 12 to 24 months. Psychological Medicine 43.9 (2013): 1929-39. ProQuest. Web.
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