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Julia E Fawcett,* Anthony P Shakeshaft,† Mark F Harris,‡ Alex Wodak,§ Richard P Mattick,¶ Robyn L Richmond**
* PhD Candidate, † NHMRC Research Fellow, ¶ Director, National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW 2052; ‡,** Professors, School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW; § Director, Alcohol and Drug Service, St Vincent's Hospital, Sydney, NSW. A.ShakeshaftATunsw.edu.au
To the Editor: Revised Australian Alcohol Guidelines1 were released in 2001. Although general practitioners (GPs) can be influential in initiating and supporting behaviour change to reduce levels of alcohol misuse among their patients,2,3 the extent to which their advice remains relevant and effective depends largely on the extent to which screening tools can be modified to take account of revised versions of such guidelines.
The Alcohol Use Disorders Identification Test (AUDIT)4 is a clinical instrument used widely to screen patients for problematic alcohol use. The aims of our study were to examine the ability of AUDIT to classify general practice patients’ alcohol consumption into the categories specified in the revised Australian guidelines, and to identify any additional information needed for such classification.
Patients aged at least 16 years attending a general practice surgery in western Sydney were asked by receptionists to complete a health-related survey by means of a hand-held computer while waiting for their consultation. Items covered a number of domains, including demographics, the AUDIT, and two additional questions about consumption of specified quantities of alcohol. The use of computers ensured patients were only asked questions relevant to them.
Risk of harm in the long term: Respondents’ average number of standard drinks per week was calculated from the first two AUDIT questions, using a previously devised method.5
Risk of harm in the short term: AUDIT question 3 is not specific enough to distinguish short-term risk of harm, so additional, sex-specific questions on how many occasions in the previous 30 days the patient had consumed “7–10” and “11 or more” (men) or “5–6” and “7 or more” (women) standard drinks were asked.
Of the 115 patients who completed the survey, 62% were female; their mean age was 42 years; 10% were unemployed; 34% had had tertiary education; 65% were married or in a de facto relationship; and 80% were born in Australia. Their alcohol consumption patterns are shown in the Box.
AUDIT is a reliable and valid instrument, and is widely used as a clinical tool. However, as national guidelines are updated, clinical tools such as AUDIT need to remain consistent with them. Ideally, revisions would build on the benefits of existing tools rather than rendering them obsolete. For example, a major advantage of AUDIT is that it measures a number of drinking dimensions within the one, brief, validated instrument. This multidimensionality could be preserved while promoting AUDIT’s consistency with new guidelines by adding two items, with high face validity, to more accurately assess risk of harm in the short term. Incorp-orating the two additional consumption items we used in this study with AUDIT allows drinkers to be classified according to the guidelines as “low-risk”, “risky” or “high-risk” both in the long term and short term, with minimal additional response time.
Alcohol consumption patterns in one general practice in western Sydney, as defined by the recently revised Australian Alcohol Guidelines1
Characteristic |
Males (%) |
Females (%) |
Total (%) |
||||||||
Abstinent |
18.2 |
29.6 |
25.2 |
||||||||
Long-term harm |
|
|
|
||||||||
Low-risk |
68.2 |
67.6 |
67.8 |
||||||||
Risky |
11.4 |
1.4 |
5.2 |
||||||||
High-risk |
2.3 |
1.4 |
1.7 |
||||||||
Short-term harm |
|
|
|
||||||||
Low-risk |
61.4 |
52.1 |
55.7 |
||||||||
Risky |
9.1 |
8.5 |
8.7 |
||||||||
High-risk |
11.4 |
9.9 |
10.4 |
||||||||
Bold text represents categories that cannot be distinguished using AUDIT alone. |
|||||||||||
Acknowledgements: Thanks to receptionists and GPs in the participating practice in western Sydney, and Hugh Garsden at the Centre for Health Informatics (UNSW) for programming.
©The Medical Journal of Australia 2004 www.mja.com.au ISSN: 0025-729X
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