Mapping complex public health problems with causal loop diagrams

Jeroen F. Uleman*, Karien Stronks, Harry Rutter, Onyebuchi A. Arah, Naja Hulvej Rod

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.

Original languageEnglish
Article numberdyae091
JournalInternational Journal of Epidemiology
Volume53
Issue number4
Number of pages7
ISSN0300-5771
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s).

Keywords

  • causal loop diagram
  • complex systems science
  • Complexity
  • feedback loops
  • group model building
  • system dynamics
  • systems thinking

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