Causal thinking and complex system approaches in epidemiology
Causal thinking and complex system approaches in epidemiology
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DOI：
10.1093/ije/dyp296
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发表时间：
2010-02-01
影响因子：
7.7
通讯作者：
Kaplan, George A.
中科院分区：
文献类型：
--
作者：
Galea, Sandro;Riddle, Matthew;Kaplan, George A.
在过去的半个世纪里，确定疾病的生物和行为原因一直是流行病学关注的中心问题之一。这导致了日益复杂的概念和分析方法的发展，其重点是孤立疾病状态的单一原因。然而，人们日益认识到，㈠生物、行为和群体等多个层面的因素可能影响健康和疾病，㈡这些因素之间的相互关系往往包括动态反馈和随时间的变化，这对流行病学的主导模式提出了挑战。以肥胖症为例，我们讨论如何采用复杂的系统动力学模型，使我们能够考虑到疾病的原因在多个层面，互惠关系和相互关系的原因，特征的肥胖症的因果关系。我们还讨论了一些关键的困难，该学科面临的将这些方法纳入非传染性疾病流行病学。最后，我们讨论了可能的前进方向。
Identifying biological and behavioural causes of diseases has been one of the central concerns of epidemiology for the past half century. This has led to the development of increasingly sophisticated conceptual and analytical approaches focused on the isolation of single causes of disease states. However, the growing recognition that (i) factors at multiple levels, including biological, behavioural and group levels may influence health and disease, and (ii) that the interrelation among these factors often includes dynamic feedback and changes over time challenges this dominant epidemiological paradigm. Using obesity as an example, we discuss how the adoption of complex systems dynamic models allows us to take into account the causes of disease at multiple levels, reciprocal relations and interrelation between causes that characterize the causation of obesity. We also discuss some of the key difficulties that the discipline faces in incorporating these methods into non-infectious disease epidemiology. We conclude with a discussion of a potential way forward.