Department of Psychiatry, McLean Hospital, Belmont, Massachusetts.
Department of Psychiatry, Massachusetts General Hospital, Boston.
JAMA Psychiatry. 2021 Jun 1;78(6):642-650. doi: 10.1001/jamapsychiatry.2021.0089.
Several statistical models for predicting suicide risk have been developed, but how accurate such models must be to warrant implementation in clinical practice is not known.
To identify threshold values of sensitivity, specificity, and positive predictive value that a suicide risk prediction method must attain to cost-effectively target a suicide risk reduction intervention to high-risk individuals.
DESIGN, SETTING, AND PARTICIPANTS: This economic evaluation incorporated published data on suicide epidemiology, the health care and societal costs of suicide, and the costs and efficacy of suicide risk reduction interventions into a novel decision analytic model. The model projected suicide-related health economic outcomes over a lifetime horizon among a population of US adults with a primary care physician. Data analysis was performed from September 19, 2019, to July 5, 2020.
Two possible interventions were delivered to individuals at high predicted risk: active contact and follow-up (ACF; relative risk of suicide attempt, 0.83; annual health care cost, $96) and cognitive behavioral therapy (CBT; relative risk of suicide attempt, 0.47; annual health care cost, $1088).
Fatal and nonfatal suicide attempts, quality-adjusted life-years (QALYs), health care sector costs and societal costs (in 2016 US dollars), and incremental cost-effectiveness ratios (ICERs) (with ICERs ≤$150 000 per QALY designated cost-effective).
With a specificity of 95% and a sensitivity of 25%, primary care-based suicide risk prediction could reduce suicide death rates by 0.5 per 100 000 person-years (if used to target ACF) or 1.6 per 100 000 person-years (if used to target CBT) from a baseline of 15.3 per 100 000 person-years. To be cost-effective from a health care sector perspective at a specificity of 95%, a risk prediction method would need to have a sensitivity of 17.0% or greater (95% CI, 7.4%-37.3%) if used to target ACF and 35.7% or greater (95% CI, 23.1%-60.3%) if used to target CBT. To achieve cost-effectiveness, ACF required positive predictive values of 0.8% for predicting suicide attempt and 0.07% for predicting suicide death; CBT required values of 1.7% for suicide attempt and 0.2% for suicide death.
These findings suggest that with sufficient accuracy, statistical suicide risk prediction models can provide good health economic value in the US. Several existing suicide risk prediction models exceed the accuracy thresholds identified in this analysis and thus may warrant pilot implementation in US health care systems.
已经开发出了几种预测自杀风险的统计模型，但这些模型必须达到何种准确性才能在临床实践中实施尚不清楚。
确定自杀风险预测方法必须达到的灵敏度、特异性和阳性预测值的阈值，以便以成本效益的方式将自杀风险降低干预措施针对高风险个体。
设计、设置和参与者：本经济学评价将发表的关于自杀流行病学的数据、自杀的医疗保健和社会成本以及自杀风险降低干预措施的成本和效果纳入到一个新的决策分析模型中。该模型预测了美国成年人中有初级保健医生的人群在终生期间与自杀相关的健康经济结果。数据分析于 2019 年 9 月 19 日至 2020 年 7 月 5 日进行。
向高预测风险个体提供了两种可能的干预措施：积极接触和随访（ACF；自杀企图的相对风险，0.83；年医疗保健成本，96 美元）和认知行为疗法（CBT；自杀企图的相对风险，0.47；年医疗保健成本，1088 美元）。
致命和非致命自杀企图、质量调整生命年（QALYs）、医疗保健部门成本和社会成本（以 2016 年美元计）以及增量成本效益比（ICERs）（ICERs≤150000 美元/QALY 被指定为具有成本效益）。
如果特异性为 95%且灵敏度为 25%，则基于初级保健的自杀风险预测可以将自杀死亡率降低 0.5/100000 人年（如果用于针对 ACF）或 1.6/100000 人年（如果用于针对 CBT），从基线的 15.3/100000 人年开始。为了从医疗保健部门的角度具有成本效益，特异性为 95%时，风险预测方法的灵敏度必须为 17.0%或更高（95%CI，7.4%-37.3%），如果用于针对 ACF，灵敏度必须为 35.7%或更高（95%CI，23.1%-60.3%），如果用于针对 CBT。为了实现成本效益，ACF 预测自杀企图的阳性预测值需要为 0.8%，预测自杀死亡的阳性预测值需要为 0.07%；CBT 需要预测自杀企图的阳性预测值为 1.7%，预测自杀死亡的阳性预测值为 0.2%。
这些发现表明，具有足够的准确性，统计自杀风险预测模型可以在美国提供良好的健康经济效益。现有的几种自杀风险预测模型超过了本分析确定的准确性阈值，因此可能需要在美国医疗保健系统中进行试点实施。
