Life course epidemiology seeks to understand how determinants of health and disease interact across the span of a human life, and has made significant contributions to understanding etiological mechanisms in many chronic diseases, including schizophrenia. The life course approach is ideal for understanding depression: causation in depression appears to be multifactorial, including interactions between genes and stressful events, or between early life trauma and later stress in life; timing of onset and remission of depression varies widely, indicating differing trajectories of symptoms over long periods of time, with possible differing causes and differing outcomes; and early life events and development appear to be important risk factors for depression, including exposure to acute and chronic stress in the first years of life. To better understand etiology and outcome of depression, future research must move beyond basic epidemiologic techniques that link specific exposures to specific outcomes and embrace life course principles and methods. Time-sensitive modelling techniques that are able to incorporate multiple interacting factors across long periods of time, such as structural equation models, will be critical in understanding the complexity of causal and influencing factors from early development to the end stages of life. Using these models to identify key pathways that influence trajectories of depression across the life course will help guide prevention and intervention.
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