Modelling the cost-effectiveness of preventing major depression in general practice patients

Area of Science:

• Public Health
• Health Economics
• Psychiatry

Background:

• Depression prevention is a critical public health objective.
• PredictD is the first risk algorithm designed to predict the onset of major depression.
• This study evaluates the cost-effectiveness of PredictD for depression prevention within general practice settings.

Purpose of the Study:

• To model the cost-effectiveness of the PredictD risk algorithm for depression prevention in general practice.
• To compare two approaches: PredictD with a low-intensity prevention program versus a universal prevention program, against treatment as usual.
• To assess the net monetary benefit from the National Health Service perspective over a 12-month period.

Main Methods:

• A decision-analytical model was developed to simulate depression incidence and progression.
• The model compared PredictD plus prevention and universal prevention against treatment as usual.
• Cost-effectiveness was determined by calculating the net monetary benefit (NMB) and quality-adjusted life years (QALYs).

Main Results:

• The PredictD approach prevented 15% of depression cases per 1000 patients over 12 months.
• PredictD plus prevention demonstrated the highest probability of being the optimal choice at a willingness to pay of £20,000 per QALY.
• Universal prevention was less effective and more costly than the PredictD strategy, indicating strong domination.

Conclusions:

• Identifying high-risk primary care patients using PredictD and offering them a low-intensity prevention program is potentially cost-effective.
• The findings support the implementation of PredictD-guided interventions for depression prevention.
• The cost-effectiveness is demonstrated at a willingness to pay threshold of £20,000 per QALY.