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IJE Advance Access published online on October 9, 2009
International Journal of Epidemiology, doi:10.1093/ije/dyp296
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2009; all rights reserved.

Causal thinking and complex system approaches in epidemiology

Sandro Galea*, Matthew Riddle and George A Kaplan

Center for Social Epidemiology and Population Health, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.

*Corresponding author. Department of Epidemiology, University of Michigan School of Public Health, 109 Observatory Street, Room 3663, Ann Arbor, MI 48109-2029, USA. E-mail: sgalea{at}umich.edu


  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.

Keywords Agent-based modelling, dynamic systems modelling, epidemiology, regression

Accepted 30 July 2009


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