a1 Department of Primary Care and Population Sciences, University College London Medical School, UK
a2 Department of Applied Health Research, University College London Medical School, UK
a3 Department of Mental Health Sciences, University College London Medical School, UK
Background The prevention of depression is a key public health policy priority. PredictD is the first risk algorithm for the prediction of the onset of major depression. Our aim in this study was to model the cost-effectiveness of PredictD in depression prevention in general practice (GP).
Method A decision analytical model was developed to determine the cost-effectiveness of two approaches, each of which was compared to treatment as usual (TAU) over 12 months: (1) the PredictD risk algorithm plus a low-intensity depression prevention programme; and (2) a universal prevention programme in which there was no initial identification of those at risk. The model simulates the incidence of depression and disease progression over 12 months and calculates the net monetary benefit (NMB) from the National Health Service (NHS) perspective.
Results Providing patients with PredictD and a depression prevention programme prevented 15 (17%) cases of depression in a cohort of 1000 patients over 12 months and had the highest probability of being the optimal choice at a willingness to pay (WTP) of £20 000 for a quality-adjusted life year (QALY). Universal prevention was strongly dominated by PredictD plus a depression prevention programme in that universal prevention resulted in less QALYs than PredictD plus prevention for a greater cost.
Conclusions Using PredictD to identify primary-care patients at high risk of depression and providing them with a low-intensity prevention programme is potentially cost-effective at a WTP of £20 000 per QALY.
(Received September 21 2012)
(Revised July 02 2013)
(Accepted July 21 2013)
(Online publication August 15 2013)
c1 Address for correspondence: R. M. Hunter, M.Sc., Department of Primary Care and Population Sciences, University College London Medical School, Royal Free Campus, Rowland Hill Street, London NW3 2PF, UK. (Email: firstname.lastname@example.org)