Understanding suicide over the life course using data science tools within a triangulation framework

Suicide and suicidal behaviors are important global health concerns. Preventing suicide requires a nuanced understanding of the nature of suicide risk, both acutely during periods of crisis and broader variation over the lifespan. However, current knowledge of the sources of variation in suicide risk is limited due to methodological and conceptual challenges. New methodological approaches are needed to close the gap between research and clinical practice. This review describes the life course framework as a conceptual model for organizing the scientific study of suicide risk across in four major domains: social relationships, health, housing, and employment. In addition, this review discusses the utility of data science tools as a means of identifying novel, modifiable risk factors for suicide, and triangulation as an overarching approach to ensuring rigor in suicide research as means of addressing existing knowledge gaps and strengthening future research.