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Complex systems models for causal inference in social epidemiology
  1. Hiba N Kouser,
  2. Ruby Barnard-Mayers,
  3. Eleanor Murray
  1. Epidemiology, Boston University, Boston, Massachusetts, USA
  1. Correspondence to Eleanor Murray, Department of Epidemiology, Boston University, Boston, MA 02445, USA; ejmurray{at}bu.edu

Abstract

Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.

  • Disease modelling
  • Epidemiological methods
  • Epidemiology
  • Social epidemiology

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Footnotes

  • Twitter Eleanor Murray @epiellie, Hiba Kouser @h_kouser and Ruby Barnard-Mayers @rubymaei.

  • Acknowledgements This project was supported by the NICHD (R21HD098733).

  • Contributors All authors contributed to the conceptualization and drafting of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Commissioned; externally peer reviewed.

  • Data availability statement Data sharing not applicable as no datasets generated and/or analysed for this study.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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