Risk Factors for Suicidal Thoughts and Behaviors: A Meta-Analysis of 50 Years of Research
Risk Factors for Suicidal Thoughts and Behaviors: A Meta-Analysis of 50 Years of Research
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DOI：
10.1037/bul0000084
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发表时间：
2017-02-01
影响因子：
22.4
通讯作者：
Nock, Matthew K.
中科院分区：
文献类型：
--
作者：
Franklin, Joseph C.;Ribeiro, Jessica D.;Nock, Matthew K.
自杀念头和行为（STBs）是几十年来没有明显下降的主要公共卫生问题。改善性传播感染的预防和治疗的首要步骤之一是确定风险因素（即纵向预测因素）。为了总结目前对危险因素的了解，我们对试图纵向预测特定stb相关结果的研究进行了荟萃分析。这包括过去50年的365项研究（3428个总风险因素效应大小）。目前的随机效应荟萃分析产生了几个意想不到的发现：在优势比、风险比和诊断准确性分析中，所有结果的预测仅略好于机会；没有一个大的分类或小的分类准确地预测远高于概率水平；在50年的研究中，预测能力并没有提高；研究很少检查多种危险因素的综合影响；随着时间的推移，风险因素一直是同质的，5大类别占所有风险因素测试的近80%；研究的平均时间是近10年，但更长的研究并没有产生更好的预测。现有研究的同质性意味着，目前的荟萃分析只能在非常狭窄的方法范围内讨论STB风险因素的关联，这些方法范围不允许进行接近大多数STB理论的测试。因此，当前的荟萃分析强调了未来研究需要的几个基本变化。特别是，这些发现表明需要将重点从风险因素转移到基于机器学习的风险算法上。
Suicidal thoughts and behaviors (STBs) are major public health problems that have not declined appreciably in several decades. One of the first steps to improving the prevention and treatment of STBs is to establish risk factors (i.e., longitudinal predictors). To provide a summary of current knowledge about risk factors, we conducted a meta-analysis of studies that have attempted to longitudinally predict a specific STB-related outcome. This included 365 studies (3,428 total risk factor effect sizes) from the past 50 years. The present random-effects meta-analysis produced several unexpected findings: across odds ratio, hazard ratio, and diagnostic accuracy analyses, prediction was only slightly better than chance for all outcomes; no broad category or subcategory accurately predicted far above chance levels; predictive ability has not improved across 50 years of research; studies rarely examined the combined effect of multiple risk factors; risk factors have been homogenous over time, with 5 broad categories accounting for nearly 80% of all risk factor tests; and the average study was nearly 10 years long, but longer studies did not produce better prediction. The homogeneity of existing research means that the present meta-analysis could only speak to STB risk factor associations within very narrow methodological limits-limits that have not allowed for tests that approximate most STB theories. The present meta-analysis accordingly highlights several fundamental changes needed in future studies. In particular, these findings suggest the need for a shift in focus from risk factors to machine learning-based risk algorithms.