The Prediction and Prevention of Suicidal Behavior: The Intersection of Developmental Psychopathology and Translational Epidemiology [electronic resource]

다국어 초록 (Multilingual Abstract)

Suicide is a major public health concern. Over the past twenty years in the United States, the suicide rates increased by over 30% in half of the states and increased in all but one state. Among adolescents, suicide is the second leading cause of death. Suicidality research is inherently difficult to conduct given statistical issues related to the low base of suicide, poor communication across the field due to a lack of standard terminology, the heterogeneity in identified risk factors, and the exclusion of individuals with suicidal behavior in prospective data collection. These issues heighten the critical need for creative approaches in suicidality research. This dissertation integrates two frameworks-developmental psychopathology and translational epidemiology-in order to conceptualize developmental processes of suicidality and use epidemiological methodological approaches. Understanding suicidality from a developmental psychopathology lens reinforces key principles that can guide suicidality research, including studying suicidality across multiple levels of analysis, developmental periods, and specific sensitive periods. The use of epidemiological data and methods are particularly advantageous when researchers are constrained, whether ethically or logistically, to collect data directly with individuals experiencing suicidality. Advanced epidemiological methods combined with large-scale datasets offer the opportunity to test causal inferences. The dissertation comprises three chapters that are situated at the intersection between developmental psychopathology and translational epidemiology approaches. Chapter 1 examined the association between sexual orientation and suicide attempt and self-harm in an adolescent, twin sample. Chapter 2 examined the association between the Affordable Care Act young adult mandate, which expanded health insurance coverage, and suicide attempt and suicide. Chapter 3 applied machine learning algorithms to predict suicide attempt and suicide after specialist mental health care treatment among adolescents in Stockholm County, Sweden.