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Complex systems models for causal inference in social epidemiology.

Hiba N Kouser1, Ruby Barnard-Mayers1, Eleanor Murray2

  • 1Epidemiology, Boston University, Boston, Massachusetts, USA.

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Complex systems models can bridge social epidemiology and causal inference. This approach can optimize COVID-19 resource distribution to reduce social inequalities.

Keywords:
Disease modellingEpidemiological methodsEpidemiologySocial epidemiology

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Area of Science:

  • Epidemiology
  • Causal Inference
  • Systems Science

Background:

  • Social epidemiology often faces challenges in capturing multi-level complexity.
  • Bridging the gap between social epidemiology and causal inference requires advanced modeling techniques.

Purpose of the Study:

  • To explore the utility of complex systems models in social epidemiology.
  • To demonstrate how systems models can enhance understanding of quantitative causal effects.
  • To illustrate the application of systems models in optimizing COVID-19 resource allocation for social equity.

Main Methods:

  • Discussion of complex systems modeling principles.
  • Application of systems thinking to social epidemiological research.
  • Case illustration using COVID-19 resource distribution.

Main Results:

  • Complex systems models offer a framework for integrating multi-level factors in social epidemiology.
  • Systems models can clarify causal pathways and quantitative effects.
  • The approach can guide equitable resource allocation during public health crises.

Conclusions:

  • Complex systems models are valuable tools for advancing social epidemiology and causal inference.
  • This methodology can inform public health policy to mitigate social inequalities.
  • Systems modeling provides a robust approach for addressing complex health challenges.