School of Public Health, University of Alberta, Edmonton, Alberta.
Can J Psychiatry. 2010 Oct;55(10):622-32. doi: 10.1177/070674371005501002.
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.
生命历程流行病学旨在了解健康和疾病的决定因素如何在人类生命的跨度内相互作用，并为理解许多慢性疾病（包括精神分裂症）的病因机制做出了重大贡献。生命历程方法非常适合理解抑郁症：抑郁症的病因似乎是多因素的，包括基因与应激事件之间的相互作用，或早期生活创伤与后来生活中的压力之间的相互作用；抑郁症的发病和缓解时间差异很大，表明在很长一段时间内症状有不同的轨迹，可能有不同的原因和不同的结果；早期生活事件和发展似乎是抑郁症的重要危险因素，包括生命最初几年暴露于急性和慢性压力。为了更好地理解抑郁症的病因和结果，未来的研究必须超越将特定暴露与特定结果联系起来的基本流行病学技术，而采用生命历程原则和方法。时间敏感的建模技术能够在长时间内纳入多个相互作用的因素，例如结构方程模型，对于理解从早期发展到生命末期的因果和影响因素的复杂性至关重要。使用这些模型来确定影响抑郁症轨迹的关键途径将有助于指导预防和干预。
