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Handbook of Systems and Complexity in Health pp 147–156Cite as

Modeling Illness and Recovery with Nonlinear Dynamics

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Abstract

People like simple explanations, even if they are wrong or misleading. The single-cause model is the simplest epidemiological logic: “If condition X exists, then Y has an elevated likelihood of occurrence.” By implication, “If X is removed, Y does not happen.” The limitations of the reasoning become apparent when research reports pile up. By the early 1980s we had the popular quip, “Everything causes cancer,” which was indicative that something was wrong with our information about cancer. By 1990 public service messages were convincing the general public that cigarette smoking caused everything, and by implication if tobacco were to disappear, so would the major causes of death. However, even though tobacco consumption in the USA in 2006 had dropped to 38 % of its 1967 level [1] (Fig. 9.1) and users dropped from 26 % of the adult population in 1991 to 20.5 % in 2006, healthcare costs have skyrocketed during the same period, and cancer incidence rates have dropped by only a small amount [2] (Fig. 9.2), which might be attributed to medical advances instead.

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Author information

Authors and Affiliations

  1. Marquette University, 1881, Milwaukee, WI, 53201-1881, USA

    Stephen J. Guastello

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  1. Stephen J. Guastello
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Correspondence to Stephen J. Guastello .

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Editors and Affiliations

  1. , Department of General Practice, Monash University, PO Box 3010, Wamberal, 2260, New South Wales, Australia

    Joachim P. Sturmberg

  2. Northern Ontario School of Medicine, Merchandise Mart Plaza, Suite 1212 222, Chicago, 60654, Illinois, USA

    Carmel M. Martin

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Guastello, S.J. (2013). Modeling Illness and Recovery with Nonlinear Dynamics. In: Sturmberg, J., Martin, C. (eds) Handbook of Systems and Complexity in Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4998-0_9

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  • DOI: https://doi.org/10.1007/978-1-4614-4998-0_9

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