Article

Social Epidemiology and complex system dynamic modelling as applied to health behaviour and drug use research

Center for Social Epidemiology and Population Health, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA.
The International journal on drug policy (Impact Factor: 3.19). 11/2008; 20(3):209-16. DOI: 10.1016/j.drugpo.2008.08.005
Source: PubMed

ABSTRACT

A social epidemiologic perspective considers factors at multiple levels of influence (e.g., social networks, neighbourhoods, states) that may individually or jointly affect health and health behaviour. This provides a useful lens through which to understand the production of health behaviours in general, and drug use in particular. However, the analytic models that are commonly applied in population health sciences limit the inference we are able to draw about the determination of health behaviour by factors, likely interrelated, across levels of influence. Complex system dynamic modelling techniques may be useful in enabling the adoption of a social epidemiologic approach in health behaviour and drug use research. We provide an example of a model that aims to incorporate factors at multiple levels of influence in understanding drug dependence. We conclude with suggestions about future directions in the field and how such models may serve as virtual laboratories for policy experiments aimed at improving health behaviour.

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    • "The advent of behavioral ILD has led to modeling efforts that aim to characterize dynamic phenomena within behavioral , social, and public health settings (Boker, 2012; Boker & Nesselroade, 2002; Ionides, Bretó, & King, 2006; Rivera, 2012; Rivera, Pew, & Collins, 2007; Tan, Shiyko, Li, Li, & Dierker, 2012; Trail et al., in press). The result includes novel descriptions of time-varying predictors of substance use (Chandra, Scharf, & Shiffman, 2011; Galea, Hall, & Kaplan, 2009; Samanta, 2011; Shiyko, Lanza, Tan, Li, & Shiffman, 2012; Timms, Rivera, Collins, & Piper, 2012), pain management (Deshpande, Nandola, Rivera, & Younger, 2011), and disease transmission within populations (Bhadra et al., 2011; Ionides et al., 2006). Increased availability of ILD also allows use of an engineering analytical approach to study self-regulation within smoking (Rivera 2012; Timms et al., 2012). "
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    • "In short, people affect others’ behavior. This idea is well supported empirically [15], [16] and is implicated in several areas of health behavior, e.g.: smoking cessation [17], mental health [18], emotions [19], suicidal ideation [20], drug use [21], and obesity [22]. The social network literature, however, does not address the possible psychological mechanisms involved in the spreading of behavior across social network ties. "
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