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

Jo-An Atkinson; Dylan Knowles; John Wiggers; Michael Livingston; Robin Room; Ante Prodan; Geoff McDonnell; Eloise O'Donnell; Sandra Jones; Paul S Haber; David Muscatello; Nadine Ezard; Nghi Phung; Louise Freebairn; Devon Indig; Lucie Rychetnik; Jaithri Ananthapavan; Sonia Wutzke; alcohol modelling consortium

doi:10.1007/s00038-017-1041-y

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

Int J Public Health. 2018 May;63(4):537-546. doi: 10.1007/s00038-017-1041-y. Epub 2017 Oct 19.

Authors

Jo-An Atkinson^{1

2

3}, Dylan Knowles^{4

5}, John Wiggers^{4

6

7}, Michael Livingston^{8

9}, Robin Room^{8

10}, Ante Prodan^{11

12}, Geoff McDonnell^{11

4}, Eloise O'Donnell⁴, Sandra Jones¹³, Paul S Haber^{14

15}, David Muscatello¹⁶, Nadine Ezard^{16

17}, Nghi Phung^{18

19}, Louise Freebairn^{4

20

21}, Devon Indig^{4

22}, Lucie Rychetnik^{4

21}, Jaithri Ananthapavan²³, Sonia Wutzke^{4

24}; alcohol modelling consortium

Collaborators

Affiliations

¹ Decision Analytics, Sax Institute, Sydney, Australia. Jo-An.Atkinson@saxinstitute.org.au.
² The Australian Prevention Partnership Centre, Sax Institute, Ultimo, NSW, Australia. Jo-An.Atkinson@saxinstitute.org.au.
³ Sydney Medical School, University of Sydney, Camperdown, Australia. Jo-An.Atkinson@saxinstitute.org.au.
⁴ The Australian Prevention Partnership Centre, Sax Institute, Ultimo, NSW, Australia.
⁵ Anthrodynamics Simulation Services, Saskatoon, Canada.
⁶ Hunter New England Population Health, Newcastle, NSW, Australia.
⁷ School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia.
⁸ Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia.
⁹ Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
¹⁰ Centre for Social Research on Alcohol and Drugs, Stockholm University, Sveaplan, Stockholm, Sweden.
¹¹ Decision Analytics, Sax Institute, Sydney, Australia.
¹² School of Computing, Engineering and Mathematics, Western Sydney University, Parramattu, Australia.
¹³ Centre for Health and Social Research, Australian Catholic University, Melbourne, Australia.
¹⁴ Sydney Medical School, University of Sydney, Camperdown, Australia.
¹⁵ Drug Health Services, Royal Prince Alfred Hospital, Sydney, Australia.
¹⁶ Faculty of Medicine, University of NSW, Sydney, Australia.
¹⁷ Alcohol and Drug Service, St Vincent's Hospital, Sydney, Australia.
¹⁸ Drug Health Western Sydney Local Health District, Sydney, Australia.
¹⁹ Liver Addiction Research Unit, Storr Liver Centre, Westmead Institute of Medical Research, Sydney, Australia.
²⁰ Knowledge Translation and Health Outcomes, Epidemiology Section, ACT Health, Canberra, Australia.
²¹ School of Medicine Sydney, University of Notre Dame Australia, Sydney, Australia.
²² School of Public Health, University of Sydney, Camperdown, Australia.
²³ Faculty of Health, Deakin Health Economics, Centre for Population Health Research, Deakin University, Burwood, Australia.
²⁴ Menzies Centre for Health Policy, University of Sydney, Sydney, Australia.

Abstract

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.

Keywords: Agent-based modelling; Alcohol-related harm; Evidence synthesis; Prevention policy.

MeSH terms

Alcohol Drinking / epidemiology
Alcohol Drinking / psychology*
Alcohol-Related Disorders / prevention & control*
Alcoholic Intoxication / epidemiology
Australia / epidemiology
Computer Simulation*
Decision Support Techniques*
Health Promotion / methods*
Humans
Public Policy*

Grants and funding

GNT9100001/National Health and Medical Research Council