Group model building: a participatory approach to understanding and acting on systems.

With mounting appreciation of the complexity of chronic disease, there is a growing need to understand the systemic causes of current health trends. This will support the development of a prevention system and the use of systems thinking to achieve better, more equitable and more sustainable health outcomes. With new language and a need to change our thinking, the push towards systems practice in preventive health is challenging, and calls for a method to support its application. Group model building (GMB) is a participatory approach that is widely used to build the capacity of practitioners to think in a systems way. However, it is a resource-intensive approach that requires high-level buy-in and the investment of time. We discuss the evidence, including a systematic review of the literature examining the effectiveness of GMB approaches across a wide range of contexts. The results of the review are generally positive and suggest that GMB improves problem understanding, increases engagement in systems thinking, builds confidence in the use of systems ideas and develops consensus for action among diverse stakeholders.

SimAmph: an agent-based simulation model for exploring the use of psychostimulants and related harm amongst young Australians.

BACKGROUND: Computer simulations provide a useful tool for bringing together diverse sources of information in order to increase understanding of the complex aetiology of drug use and related harm, and to inform the development of effective policies. In this paper, we describe SimAmph, an agent-based simulation model for exploring how individual perceptions, peer influences and subcultural settings shape the use of psychostimulants and related harm amongst young Australians. METHODS: We present the conceptual architecture underpinning SimAmph, the assumptions we made in building it, the outcomes of sensitivity analysis of key model parameters and the results obtained when we modelled a baseline scenario. RESULTS: SimAmph's core behavioural algorithm is able to produce social patterns of partying and recreational drug use that approximate those found in an Australian national population survey. We also discuss the limitations involved in running closed-system simulations and how the model could be refined to include the social, as well as health, consequences of drug use. CONCLUSION: SimAmph provides a useful tool for integrating diverse data and exploring drug policy scenarios. Its integrated approach goes some way towards overcoming the compartmentalisation that characterises existing data, and its structure, parameters and values can be modified as new data and understandings emerge. In a companion paper (Dray et al., 2011), we use the model outlined here to explore the possible consequences of two policy scenarios.

Are drug detection dogs and mass-media campaigns likely to be effective policy responses to psychostimulant use and related harm? Results from an agent-based simulation model.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Extending drug ethno-epidemiology using agent-based modelling.

AIMS: To show how the inclusion of agent-based modelling improved the integration of ethno-epidemiological data in a study of psychostimulant use and related harms among young Australians. METHODS: Agent-based modelling, ethnographic fieldwork, in-depth interviews and epidemiological surveys. SETTING: Melbourne, Perth and Sydney, Australia. PARTICIPANTS: Club drug users in Melbourne, recreational drug users in Perth and street-based injecting drug users in Sydney. Participants were aged 18-30 years and reported monthly or more frequent psychostimulant use. FINDINGS: Agent-based modelling provided a specific focus for structured discussion about integrating ethnographic and epidemiological methods and data. The modelling process was underpinned by collective and incremental design principles, and produced 'SimAmph', a data-driven model of social and environmental agents and the relationships between them. Using SimAmph, we were able to test the probable impact of ecstasy pill-testing on the prevalence of harms--a potentially important tool for policy development. The study also navigated a range of challenges, including the need to manage epistemological differences, changes in the collective design process and modelling focus, the differences between injecting and non-injecting samples and concerns over the dissemination of modelling outcomes. CONCLUSIONS: Agent-based modelling was used to integrate ethno-epidemiological data on psychostimulant use, and to test the probable impact of a specific intervention on the prevalence of drug-related harms. It also established a framework for collaboration between research disciplines that emphasizes the synthesis of diverse data types in order to generate new knowledge relevant to the reduction of drug-related harms.