Predicting Imminent Suicidal Thoughts and Nonfatal Attempts: The Role of Complexity
Abstract
For decades, our ability to predict suicidal thoughts and behaviors (STBs) has been at near-chance levels. The objective of this study was to advance prediction by addressing two major methodological constraints pervasive in past research: (a) the reliance on long follow-ups and (b) the application of simple conceptualizations of risk. Participants were 1,021 high-risk suicidal and/or self-injuring individuals recruited worldwide. Assessments occurred at baseline and 3, 14, and 28 days after baseline using a range of implicit and self-report measures. Retention was high across all time points (> 90%). Risk algorithms were derived and compared with univariate analyses at each follow-up. Results indicated that short-term prediction alone did not improve prediction for attempts, even using commonly cited “warning signs”; however, a small set of factors did provide fair-to-good short-term prediction of ideation. Machine learning produced considerable improvements for both outcomes across follow-ups. Results underscore the importance of complexity in the conceptualization of STBs.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 06, 2019
- Source ID
- 10.1177/2167702619838464
Entities
People
- Colin G. Walsh
- Jessica D. Ribeiro
- Kathryn P. Linthicum
- Kathryn R. Fox
- Xieyining Huang
Organizations
- Florida State University
- Harvard University
- United States Department of Defense
- Vanderbilt University