Nonlinearity in the epidemiology of complex health and disease processes.
The challenges posed by chronic illness have pointed out to epidemiologists the multifactorial complex nature of disease causality. This notion has been referred to as a web of causality. This web extends theoretically beyond risk markers. It includes determinants of emergence/non-emergence of disease. This web of determinants is a form of complex system. Due to its complexity, the determinants within such system are not linked to each others in a linear, predictable manner only. Predictability is possible only on a short-term basis, and unpredictability sets in over the long run. Understanding such a system of determinants calls for articulation and testing of complex models which synthesize our knowledge of multiple determinants at many scales, both biological and otherwise. Given the complexity of this web and existing knowledge about the nonlinearity of such systems, the following question is posed: Can the challenge of studying causality be adequately addressed if emphasis continues to be placed on using tools and methods that are geared towards looking at such system from a linear paradigm? Or is it time to add to the epidemiologic research agenda the notion of nonlinearity and its relevant form of analytical approaches that are being tested in other disciplines? Furthermore, the question posed here applies as well to the study of determinants of health. Addressing determinants of heath adds further complexity to our task.
Philippe
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Department of Social and Preventive Medicine, Faculty of Medicine, University of Montreal, Canada.
Mansi
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D015984:Causality; D002908:Chronic Disease / Q000453:epidemiology*; D018450:Disease Progression; D006262:Health; D006801:Humans; D016014:Linear Models; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012106:Research