Hunter R M, Nazareth I, Morris S, King M
Department of Primary Care and Population Sciences, University College London Medical School, UK.
Department of Applied Health Research, University College London Medical School, UK.
Psychol Med. 2014 May;44(7):1381-90. doi: 10.1017/S0033291713002067. Epub 2013 Aug 15.
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).
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.
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.
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.
预防抑郁症是公共卫生政策的一项关键优先事项。PredictD是首个用于预测重度抑郁症发作的风险算法。我们开展本研究的目的是建立一个模型，以评估PredictD在全科医疗中预防抑郁症的成本效益。
开发了一个决策分析模型，以确定两种方法的成本效益，将每种方法与常规治疗（TAU）在12个月内进行比较：（1）PredictD风险算法加上低强度抑郁症预防计划；（2）一种普遍预防计划，即最初不识别有风险的人群。该模型模拟了12个月内抑郁症的发病率和疾病进展情况，并从英国国家医疗服务体系（NHS）的角度计算净货币效益（NMB）。
在1000名患者的队列中，为患者提供PredictD和抑郁症预防计划在12个月内预防了15例（17%）抑郁症病例，并且在每获得一个质量调整生命年（QALY）愿意支付20,000英镑的情况下，成为最优选择的概率最高。普遍预防被PredictD加上抑郁症预防计划强烈主导，因为普遍预防导致的QALY比PredictD加上预防计划更少，而成本更高。
使用PredictD识别初级保健中抑郁症高风险患者并为其提供低强度预防计划，在每QALY愿意支付20,000英镑的情况下可能具有成本效益。
