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: 2.54). 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, Oct 06, 2015
24 Reads
  • 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.
    PLoS ONE 05/2013; 8(5):e62490. DOI:10.1371/journal.pone.0062490 · 3.23 Impact Factor
  • 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.
    PLoS ONE 08/2012; 7(8):e41452. DOI:10.1371/journal.pone.0041452 · 3.23 Impact Factor
  • Source
    • "Finally, it should be noted that SimAmph has been constructed using an iterative and interactive modelling process, drawing upon the empirical framework provided by our ethno-epidemiological work (Moore et al., 2009). Indeed, these incremental processes in model development, and the empirical base from which we have developed model content , go some way to addressing some of the issues identified by Galea et al. (2009) in parameterising agent-based models. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Agent-based simulation models can be used to explore the impact of policy and practice on drug use and related consequences. In a linked paper (Perez et al., 2011), we described SimAmph, an agent-based simulation model for exploring the use of psychostimulants and related harm amongst young Australians. In this paper, we use the model to simulate the impact of two policy scenarios on engagement in drug use and experience of drug-related harm: (i) the use of passive-alert detection (PAD) dogs by police at public venues and (ii) the introduction of a mass-media drug prevention campaign. The findings of the first simulation suggest that only very high rates of detection by PAD dogs reduce the intensity of drug use, and that this decrease is driven mainly by a four-fold increase in negative health consequences as detection rates rise. In the second simulation, our modelling showed that the mass-media prevention campaign had little effect on the behaviour and experience of heavier drug users. However, it led to reductions in the prevalence of health-related conditions amongst moderate drug users and prevented them from becoming heavier users. Agent-based modelling has great potential as a tool for exploring the reciprocal relationships between environments and individuals, and for highlighting how intended changes in one domain of a system may produce unintended consequences in other domains. The exploration of these linkages is important in an environment as complex as the drug policy and intervention arena.
    The International journal on drug policy 07/2012; 23(2):148-53. DOI:10.1016/j.drugpo.2011.05.018 · 2.54 Impact Factor
Show more