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


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.

Download full-text


Available from: George A Kaplan
    • "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). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Self-regulation, a key component of the addiction process, has been challenging to model precisely in smoking cessation settings, largely due to the limitations of traditional methodological approaches in measuring behavior over time. However, increased availability of intensive longitudinal data (ILD) measured through ecological momentary assessment facilitates the novel use of an engineering modeling approach to better understand self-regulation. Dynamical systems modeling is a mature engineering methodology that can represent smoking cessation as a self-regulation process. This article shows how a dynamical systems approach effectively captures the reciprocal relationship between day-to-day changes in craving and smoking. Models are estimated using ILD from a smoking cessation randomized clinical trial. A system of low-order differential equations is presented that models cessation as a self-regulatory process. It explains 87.32% and 89.16% of the variance observed in craving and smoking levels, respectively, for an active treatment group and 62.25% and 84.12% of the variance in a control group. The models quantify the initial increase and subsequent gradual decrease in craving occurring postquit as well as the dramatic quit-induced smoking reduction and postquit smoking resumption observed in both groups. Comparing the estimated parameters for the group models suggests that active treatment facilitates craving reduction and slows postquit smoking resumption. This article illustrates that dynamical systems modeling can effectively leverage ILD in order to understand self-regulation within smoking cessation. Such models quantify group-level dynamic responses in smoking cessation and can inform the development of more effective interventions in the future.
    No preview · Article · Sep 2013 · Nicotine & Tobacco Research
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual's pre-existing belief structure and the beliefs of others in the individual's social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.
    Full-text · Article · May 2013 · PLoS ONE
  • Source
    • "The causal web is made up of fuzzy factors such as context, social influence and life habits. Quantitative measurements of these types of factors rely on “social determinants” or “subjective health” indicators that have been criticized elsewhere [5], [6]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Rule-based Modeling (RBM) is a computer simulation modeling methodology already used to model infectious diseases. Extending this technique to the assessment of chronic diseases, mixing quantitative and qualitative data appear to be a promising alternative to classical methods. Elderly depression reveals an important source of comorbidities. Yet, the intertwined relationship between late-life events and the social support of the elderly person remains difficult to capture. We illustrate the usefulness of RBM in modeling chronic diseases using the example of elderly depression in Belgium. We defined a conceptual framework of interactions between late-life events and social support impacting elderly depression. This conceptual framework was underpinned by experts' opinions elicited through a questionnaire. Several scenarios were implemented successively to better mimic the real population, and to explore a treatment effect and a socio-economic distinction. The simulated patterns of depression by age were compared with empirical patterns retrieved from the Belgian Health Interview Survey. Simulations were run using different groupings of experts' opinions on the parameters. The results indicate that the conceptual framework can reflect a realistic evolution of the prevalence of depression. Indeed, simulations combining the opinions of well-selected experts and a treatment effect showed no significant difference with the empirical pattern. Our conceptual framework together with a quantification of parameters through elicited expert opinions improves the insights into possible dynamics driving elderly depression. While RBM does not require high-level skill in mathematics or computer programming, the whole implementation process provides a powerful tool to learn about complex chronic diseases, combining advantages of both quantitative and qualitative approaches.
    Full-text · Article · Aug 2012 · PLoS ONE
Show more