AUDIT and its abbreviated versions in detecting heavy and binge drinking in a general population survey

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Abstract

Background

The aim of this study was to define optimal cut points for the Alcohol Use Disorders Identification Test (AUDIT) and its abbreviated versions (AUDIT-C, AUDIT-QF, and AUDIT-3), and to evaluate how effectively these questionnaires detect heavy drinking in the general population.

Methods

The study population consisted of a sub-sample of the National FINRISK Study. A stratified random sample of 3216 Finns, aged 25–64, was invited to a health check. Of these, 1851 (57.6%) completed the AUDIT and participated in person in the Timeline Followback (TLFB) interview regarding their alcohol consumption. The TLFB-based definition of heavy drinking was used as a primary gold standard (for males ≥16 standard drinks average in a week or ≥7 drinks at least once a month; for females, respectively, ≥10 and ≥5 drinks). Areas under receiving operating characteristics curves (AUROCs), sensitivities and specificities were used to compare the performance of the tests.

Results and conclusions

The AUDIT and its abbreviated versions are valid for detecting heavy drinking also in a general population sample. However, performance seems to vary between the different versions and accuracy of each test is achieved only by using tailored cut points according to gender. The AUDIT and AUDIT-C are effective for both males and females. The optimal cut points for males were found to be ≥7 or 8 for AUDIT and ≥6 for AUDIT-C. Among females the optimal cut points were found to be ≥5 for AUDIT and ≥4 for AUDIT-C. The study also indicates that AUDIT-QF among females and AUDIT-3 among males are relatively effective. The cut points for detecting all heavy drinkers (including binge drinkers without exceeding weekly thresholds) were lower than for detecting heavy drinkers excluding those who are only binge drinkers.

Introduction

The Alcohol Use Disorders Identification Test (AUDIT) was developed to detect heavy drinkers, including those who are in the early stages without significant alcohol-related harms (Saunders et al., 1993). This was an important development, because at least half of heavy drinkers are in the early stages (Halme et al., 2008).

The AUDIT was primarily developed for use in the primary health care setting, and there is an extensive literature citing its effectiveness in detecting heavy drinking in this setting (Berner et al., 2007, Reinert and Allen, 2007, Saunders et al., 1993). There is a need for a short validated questionnaire to detect heavy drinking in general population surveys. This type of instrument would be one option to provide an estimate of the prevalence of heavy drinking and its trends over time. Although research supports the use of the AUDIT in the primary care setting, there is still very limited evidence of the AUDIT's performance in general population surveys (Dawson et al., 2005, Rumpf et al., 2002) as well as uncertainty regarding the gender-specific cut points for detection of heavy drinkers (Reinert and Allen, 2007).

The AUDIT is a self-administered structured questionnaire with 10 questions. The first three questions query amounts of alcohol consumed, and the remaining seven questions regard experienced harms caused by alcohol. Although the AUDIT consists of only 10 questions, it is sometimes too long. This is especially the case in large population surveys. Therefore, several abbreviated versions have been developed. The AUDIT-C consists of the first three questions that identify amount of alcohol consumed (Bush et al., 1998). The AUDIT-QF includes the two first questions regarding quantity consumed and frequency of drinking (Aalto et al., 2006). The AUDIT-3 consists of only the third question from the original AUDIT regarding binge drinking (Bush et al., 1998). Encouraging results have been obtained from these abbreviated versions, indicating that they may be as effective as the original AUDIT (Aalto et al., 2006, Reinert and Allen, 2007).

The aim of the present study was to evaluate the effectiveness of the AUDIT and the abbreviated versions in detecting heavy drinking in a general population sample. An additional purpose was to define the optimal gender-specific cut points for these questionnaires.

Section snippets

Sample and study procedure

The present study population consisted of a sub-sample of subjects studied within the National FINRISK Study in the year 2007 (Peltonen et al., 2008). The study was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa. The total sample size was 11 953 persons in the age group 25–74 years. The sample was a random sample from the Finnish Population Information System, stratified according to sex, 10-year age groups, and the six geographical areas. The

Gold standards

In the TLFB, the mean weekly reported alcohol consumption for males (n = 840) was 81.7 g (S.D. = 97.3, range = 0–900 g) and for females (n = 1011) 33.0 g (S.D. = 47.0, range = 0–495 g). There were 113 (13.5%) males and 211 (20.9%) females, who reported not drinking at all during the past 28 days.

The threshold for heavy drinking was met by 89 males (74 also binge drinking) and 51 females (48 also binge drinking). In addition, another 227 males and 199 females only met the threshold of binge drinking. Thus, 89

Discussion

The AUDIT and its abbreviated versions seem to be good options for detecting heavy drinking in a general population sample. However, the effectiveness of questionnaires is achieved only if the cut points are different for males and females. Also there seems to be variation in performance of the different abbreviated versions.

This study used two gold standards. The primary gold standard was HBD including all heavy and binge drinkers. The secondary gold standard was HD excluding those binge

Role of funding source

This work received no external funding.

Contributors

Mauri Aalto managed the literature searches and summarized of previous related work. Mauri Aalto, Hannu Alho and Kaija Seppä designed the study and wrote the protocol. Jukka Halme undertook the statistical analysis. Mauri Aalto wrote the first draft of the manuscript and all the other authors contributed to the development of it. All authors contributed to and have approved the final manuscript.

Conflict of interest

All other authors declare that they have no conflicts of interest.

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