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Complex systems models for causal inference in social epidemiology.

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.

SUBMITTER: Kouser HN

PROVIDER: S-EPMC8849440 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Complex systems models for causal inference in social epidemiology.

Kouser Hiba N HN Barnard-Mayers Ruby R Murray Eleanor E

Journal of epidemiology and community health 20201110 7

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 i ...[more]

PMID: 33172839

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