Indigenous Australian drinking risk: Comparing risk categorisations based on recall of recent drinking occasions to AUDIT-C screening in a representative sample
Introduction: Aboriginal and Torres Strait Islander (Indigenous) Australians have identified alcohol consumption as an area of concern. Accurate screening tools are required to help detect and assist at-risk drinkers, and to provide accurate data to policy makers. The Finnish method (determining drinking patterns based on the last two to four drinking occasions), has been proposed as a culturally appropriate and effective screening tool for detecting Indigenous Australians at risk from alcohol consumption. While it has been found to be valid and acceptable for use with Indigenous Australians, the Finnish method has not been compared to the three-item Alcohol Use Disorders Identification Test—Consumption (AUDIT-C) which is currently recommended by the Australian government for use in Aboriginal community-controlled health services. Methods: We compared the performance of the AUDIT-C and Finnish method as screening tools for detecting harms experienced from alcohol in a representative, cross-sectional, sample of Indigenous Australians. Results: AUDIT-C was substantially faster for participants to complete than the Finnish method. Metrics derived from both the AUDIT-C and Finnish method were similarly linked to the frequency of self-reported International Classification of Diseases, 11th revision dependence symptoms and harms. Discussion and Conclusions: The AUDIT-C is likely most appropriate for use in clinical settings due to its speed and ease of use. The Finnish method provides relatively detailed information about drinking and is better suited to population surveys.
Funding
This work was supported by the National Health and Medical Research Council through a Project Grant (#1087192), the Centre of Research Excellence in Indigenous Health and Alcohol (#1117198) and a Practitioner Fellowship for KMC (#1117582). We would like to acknowledge the communities who supported this project. We would also like to thank Michelle Fitts, David Warrior, Shane Bond, Dudley Ah Chee, Keith Weetra, Teagan Weatherall, Mustafa Al Ansari, Taleah Reynolds, Catherine Zheng, Monika Dzidowska and Summer Loggins for their contributions.