Urbanisation and common mental disorders (CMDs; ie, depressive, anxiety, and substance use disorders) are increasing worldwide. In this Review, we discuss how urbanicity and risk of CMDs relate to each other and call for a complexity science approach to advance understanding of this interrelationship. We did an ecological analysis using data on urbanicity and CMD burden in 191 countries. We found a positive, non-linear relationship with a higher CMD prevalence in more urbanised countries, particularly for anxiety disorders. We also did a review of meta-analytic studies on the association between urban factors and CMD risk. We identified factors relating to the ambient, physical, and social urban environment and showed differences per diagnosis of CMDs. We argue that factors in the urban environment are likely to operate as a complex system and interact with each other and with individual city inhabitants (including their psychological and neurobiological characteristics) to shape mental health in an urban context. These interactions operate on various timescales and show feedback loop mechanisms, rendering system behaviour characterised by non-linearity that is hard to predict over time. We present a conceptual framework for future urban mental health research that uses a complexity science approach. We conclude by discussing how complexity science methodology (eg, network analyses, system-dynamic modelling, and agent-based modelling) could enable identification of actionable targets for treatment and policy, aimed at decreasing CMD burdens in an urban context.

城市化和常见精神障碍（CMD；即抑郁、焦虑和物质使用障碍）在全球范围内正在增加。在这篇评论中，我们讨论了城市化和 CMD 的风险如何相互关联，并呼吁采用复杂的科学方法来促进对这种相互关系的理解。我们使用 191 个国家的城市化和 CMD 负担数据进行了生态分析。我们发现，在城市化程度更高的国家，CMD 患病率与较高的 CMD 患病率呈正、非线性关系，尤其是对于焦虑症。我们还回顾了关于城市因素与 CMD 风险之间关联的元分析研究。我们确定了与周围、物理和社会城市环境相关的因素，并显示了每个 CMD 诊断的差异。我们认为，城市环​​境中的因素可能作为一个复杂的系统运作，并相互影响并与城市居民个体（包括他们的心理和神经生物学特征）相互作用，以塑造城市环境中的心理健康。这些交互作用在不同的时间尺度上运行并显示反馈循环机制，呈现以非线性为特征的系统行为，随着时间的推移很难预测。我们提出了一个使用复杂科学方法的未来城市心理健康研究的概念框架。我们最后讨论了复杂性科学方法（例如，网络分析、系统动态建模和基于代理的建模）如何能够识别可操作的治疗和政策目标，旨在减少城市环境中的 CMD 负担。