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Meta-analysis: Are 3 Questions Enough to Detect Unhealthy Alcohol Use?

Levente Kriston, MA; Lars Hölzel, MA; Ann-Kristin Weiser, BA; Michael M. Berner, MD; and Martin Härter, MD, PhD
[+] Article and Author Information

From the University Medical Center Freiburg, Freiburg, and University Medical Center Hamburg-Eppendorf, Hamburg, Germany.


Acknowledgment: The authors thank Sarah Mannion for copyediting the manuscript.

Grant Support: In part by a research grant (01EB0111) from the German Federal Ministry of Education and Research.

Potential Financial Conflicts of Interest: None disclosed.

Requests for Single Reprints: Levente Kriston, MA, Section Clinical Epidemiology and Health Services Research, Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Hauptstrasse 5, D-79104, Freiburg, Germany; e-mail, levente.kriston@uniklinik-freiburg.de.

Current Author Addresses: Mr. Kriston, Mr. Hölzel, Ms. Weiser, and Dr. Berner: Section Clinical Epidemiology and Health Services Research, Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Hauptstrasse 5, D-79104, Freiburg, Germany.

Dr. Härter: Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, D-20246, Hamburg, Germany.


Ann Intern Med. 2008;149(12):879-888. doi:10.7326/0003-4819-149-12-200812160-00007
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Findings are reported according to the QUOROM (Quality of Reporting of Meta-Analysis) statement (22).

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Figures

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Figure 1.
Study flow diagram.

AUDIT = Alcohol Use Disorders Identification Test; AUDIT-C = Alcohol Use Disorders Identification Test–Consumption.

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Figure 2.
Reviewer judgments about methodological quality items, reported as percentages across all included studies.
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Appendix Figure 1.
Receiver-operating characteristic curves from the primary studies for each reference standard.

AUDIT = Alcohol Use Disorders Identification Test; AUDIT-C = Alcohol Use Disorders Identification Test–Consumption. A. Risky drinking. B. Alcohol use disorders. C. Alcohol dependence. D. Unhealthy alcohol use.

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Figure 3.
Summary receiver-operating characteristic curves for the Alcohol Use Disorders Identification Test (AUDIT) and the Alcohol Use Disorders Identification Test–Consumption (AUDIT-C).

Dotted lines indicate 95% prediction regions and pairs of connected points indicate single studies. A. Accuracy for risky drinking in primary care. B. Accuracy for alcohol use disorders in primary care. C. Accuracy for unhealthy alcohol use in primary care. D. Accuracy for risky drinking in the general population.

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Appendix Figure 2.
Summary point estimates for the Alcohol Use Disorders Identification Test (AUDIT) and the Alcohol Use Disorders Identification Test–Consumption (AUDIT-C).

Solid circles indicate summary point estimates, dotted lines indicate 95% CIs, and pairs of connected points indicate single studies. A. Accuracy for risky drinking in primary care. B. Accuracy for alcohol use disorders in primary care. C. Accuracy for unhealthy alcohol use in primary care. D. Accuracy for risky drinking in the general population.

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Appendix Figure 3.
Funnel plots of accuracy against effective sample size to test for possible small-study effects.

AUDIT = Alcohol Use Disorders Identification Test; AUDIT-C = Alcohol Use Disorders Identification Test–Consumption; ess = effective sample size. A. Risky drinking in primary care. B. Alcohol use disorders in primary care. C. Unhealthy alcohol use in primary care. D. Risky drinking in the general population.

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Comments

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One Question Enough?
Posted on December 19, 2008
David O. Parrish
Bayfront Family Medicine Residency
Conflict of Interest: None Declared

I read with interest the AUDIT Meta-analysis article. Simply asking how long the patient has been an alcoholic can be useful. This puts the patient and physician in the position of determining how long, rather than whether, and allows planning for treatment to commence. This can be used in hospitalized patients where it is a bit easier and more appropriate than in an outpatient setting. The PSQT can be useful too.

Reference:

1. Parrish DO Single Question Screening for Problem Drinking. The Journal of Family Practice,

2001. Vol. 50, No. 8

Conflict of Interest:

None declared

Reporting meta-analyses of diagnostic accuracy studies: take a walk on the wild side
Posted on January 13, 2009
Levente Kriston
University Medical Center Hamburg-Eppendorf, Germany
Conflict of Interest: None Declared

TO THE EDITOR: In a recent meta-analysis we compared the diagnostic accuracy of two tests for detecting unhealthy alcohol use (1). We applied the currently recommended hierarchical summary receiver operating characteristics curve (HSROC) analysis to synthesize findings of primary research (2). However, we were confronted with uncertainties how to present our findings.

First, for a comprehensive research report the choice of what should be reported is essential. Our analysis provided numerous results, from which a manageable amount had to be selected. Point estimates, such as pooled sensitivity and specificity, may be preferred by clinicians. Furthermore, under certain assumptions they can easily be converted into predictive values, which are even more familiar to several practitioners. But as the included studies applied different thresholds for defining test positivity, these estimates are affected by the primary authors' subjective trade-off between sensitivity and specificity, which showed substantial heterogeneity. Therefore, we focused our interpretation mainly on the characteristics of summary receiver operating characteristics (SROC) curves.

Second, to describe SROC curves for a general medical readership did not prove to be straightforward. It was unclear how to weight information on the statistical model, estimation procedure, numeric output, SROC curve forms, precision of the curves, crossing of two curves, and the extent of overlapping prediction regions. Our decision to report as much information as possible may satisfy biostatisticians but is likely to impair comprehensibility and ease of communication to a more general readership.

Third, although the two investigated tests did show differences in their diagnostic accuracy, these differences rarely reached statistical significance, were frequently opposite for different measures, and the assessment of their clinical significance remained subjective. Due to missing reporting guidelines our way of presentation is only one of several possible alternatives, and it is unclear how it affects interpretation of the findings by the reader.

In summary, we faced several challenges while presenting the results of a meta-analysis of diagnostic accuracy studies. In accordance with recent publications we feel that there is still more development needed in this area (3, 4). We welcome the work of the Cochrane Diagnostic Test Accuracy Working Group on a detailed guideline dealing with how to perform meta-analyses of diagnostic accuracy studies (4, 5) and hope that standardized reporting guidelines soon become available.

References

1. Kriston L, Hölzel M, Weiser A-K, Berner MM, Härter M. Meta-analysis: are 3 questions enough to detect unhealthy alcohol use? Ann Intern Med. 2008;149:879-88.

2. Rutter CM, Gatsonis CA. A hierarchical regression approach to meta -analysis of diagnostic test accuracy evaluations. Stat Med. 2001;20:2865-84.

3. Cornell J, Mulrow CD, Localio AR. Diagnostic test accuracy and clinical decision making. Ann Intern Med. 2008;149:904-6.

4. Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM; Cochrane Diagnostic Test Accuracy Working Group. Systematic reviews of diagnostic test accuracy. Ann Intern Med. 2008;149:889-97.

5. Deeks JJ, Bossuyt PM, Gatsonis C (editors). Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 1.0.0. Oxford, UK: The Cochrane Collaboration, 2009. [Forthcoming]. Avaliable from: http://srdta.cochrane.org/en/authors.html.

Conflict of Interest:

None declared

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