Causal thinking and complex system approaches in epidemiology

TLDR

Epidemiology has long sought to identify biological and behavioural causes of disease, but the growing recognition that multiple levels of factors interact dynamically over time challenges the traditional single‑cause paradigm. The authors aim to illustrate how complex systems dynamic models can incorporate multi‑level, reciprocal, and time‑varying causal relationships, using obesity as a case study, and to highlight the challenges of adopting such methods in non‑infectious disease epidemiology. They employ complex systems dynamic modeling to account for biological, behavioural, and group‑level factors and their interrelations in obesity causation. The paper concludes by proposing a potential pathway forward for integrating complex systems approaches into epidemiology.

Abstract

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.