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 - Dray, A.; Perez, P.; Moore, David; Dietze, P.; Bammer, G.; Jenkinson, R.; Siokou, Christine; Green, Rachael; Hudson, S.; Maher, L. (Elsevier BV, 2012)
Background: Agent-based simulation models can be used to explore the impact of policy and practiceon 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 dogsreduce 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.