Abstract：
This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.
摘要：
本文提出了因果循环图（CLD）作为研究复杂公共卫生问题的工具，例如健康不平等。这些问题通常涉及反馈回路-复杂系统的特征未完全集成到主流流行病学中。CLD是可视化系统变量之间连接的概念模型。它们通常是通过文献综述或与利益相关者团体的参与式方法开发的。这些图通常揭示跨尺度变量之间的反馈循环(例如，生物，心理和社会)，促进跨学科见解。我们通过涉及睡眠问题和抑郁症状之间的反馈循环的案例示例来说明它们的使用。我们概述了在流行病学中开发CLDs的典型逐步过程。这些步骤定义了一个特定的问题，确定所涉及的关键系统变量，映射这些变量并分析CLD，以找到新的见解和可能的干预目标。在整个过程中,我们建议在不同的证据来源之间进行三角测量，包括领域知识，科学文献和经验数据。还可以通过揭示知识差距来评估CLD，以指导政策变化和未来研究。最后,随着新证据的出现，CLD可以迭代地完善。我们主张更广泛地使用复杂的系统工具，像CLD一样，更好地理解和解决复杂的公共卫生问题。
