From the Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark.
Institute of Advanced Studies, University of Amsterdam, The Netherlands.
Epidemiology. 2023 Jul 1;34(4):505-514. doi: 10.1097/EDE.0000000000001612. Epub 2023 May 30.
Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.
公共卫生和潜在的疾病过程是复杂的，通常涉及生物、社会、心理、经济和其他过程的相互作用，这些过程可能是非线性和适应性的，并且具有复杂系统的其他特征。因此，需要将公共卫生的边界推向超越单一因素数据分析的范围，并扩大研究方法的能力，以应对现实世界的复杂性。本文提出了一种将复杂系统思维应用于公共卫生的方法，特别关注流行病学方法和数据如何为此做出贡献。我们提出的框架包括三个核心维度——模式、机制和动态——通过这些维度可以对复杂系统进行概念化。这些维度涵盖了复杂系统的七个关键特征——涌现、相互作用、非线性、干扰、反馈回路、适应和进化。我们将这一框架与传统上在流行病学中使用的方法和数据的例子联系起来。我们得出结论，系统地产生关于复杂健康问题的知识可能会受益于：根据我们确定的核心维度来制定研究问题和计划，这是一种全面的方法，可以捕捉复杂系统的关键特征；将传统的流行病学方法与系统方法（如计算模拟建模）相结合；跨学科工作；以及对广泛的数据类型持续投资。我们相信，所提出的框架可以支持使用流行病学和其他学科系统地产生关于复杂健康问题的知识。这将帮助我们理解新兴的健康现象，识别弱势群体，并发现促进公共卫生的杠杆点。
