Suicidal thoughts and behaviors (STBs) are major public health problems that have not declined appreciablyin several decades. One of the first steps to improving the prevention and treatment of STBs is to establish riskfactors (i.e., longitudinal predictors). To provide a summary of current knowledge about risk factors, weconducted a meta-analysis of studies that have attempted to longitudinally predict a specific STB-relatedoutcome. This included 365 studies (3,428 total risk factor effect sizes) from the past 50 years. The presentrandom-effects meta-analysis produced several unexpected findings: across odds ratio, hazard ratio, anddiagnostic accuracy analyses, prediction was only slightly better than chance for all outcomes; no broadcategory or subcategory accurately predicted far above chance levels; predictive ability has not improvedacross 50 years of research; studies rarely examined the combined effect of multiple risk factors; risk factorshave 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. Thehomogeneity of existing research means that the present meta-analysis could only speak to STB risk factorassociations within very narrow methodological limits-limits that have not allowed for tests that approximatemost STB theories. The present meta-analysis accordingly highlights several fundamental changes needed infuture studies. In particular, these findings suggest the need for a shift in focus from risk factors to machinelearning-based risk algorithms.
All Science Journal Classification (ASJC) codes
- Risk factors
- Suicidal behavior