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Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms
Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms
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Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms
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Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms
Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms

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Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms
Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms
Journal Article

Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms

2018
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Overview
Objectives Alcohol misuse is a complex systemic problem. The aim of this study was to explore the feasibility of using a transparent and participatory agent-based modelling approach to develop a robust decision support tool to test alcohol policy scenarios before they are implemented in the real world. Methods A consortium of Australia’s leading alcohol experts was engaged to collaboratively develop an agent-based model of alcohol consumption behaviour and related harms. As a case study, four policy scenarios were examined. Results A 19.5 ± 2.5% reduction in acute alcohol-related harms was estimated with the implementation of a 3 a.m. licensed venue closing time plus 1 a.m. lockout; and a 9 ± 2.6% reduction in incidence was estimated with expansion of treatment services to reach 20% of heavy drinkers. Combining the two scenarios produced a 33.3 ± 2.7% reduction in the incidence of acute alcohol-related harms, suggesting a synergistic effect. Conclusions This study demonstrates the feasibility of participatory development of a contextually relevant computer simulation model of alcohol-related harms and highlights the value of the approach in identifying potential policy responses that best leverage limited resources.