COVID-19 represents an unprecedented health, social and economic challenge in Australia and around the world. Support Burnet’s COVID-19 emergency response today.
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.