@article{ref1,
title="Comparison of categorical alcohol dependence versus a dimensional measure for predicting weekly alcohol use in heavy drinkers",
journal="Drug and alcohol dependence",
year="2014",
author="Fazzino, Tera L. and Rose, Gail L. and Burt, Keith B. and Helzer, John E.",
volume="136",
number="",
pages="121-126",
abstract="BACKGROUND: The DSM specifies categorical criteria for psychiatric disorders. In contrast, a dimensional approach considers variability in symptom severity and can significantly improve statistical power. The current study tested whether a categorical, DSM-defined diagnosis of Alcohol Dependence (AD) was a better fit than a dimensional dependence measure for predicting change in alcohol consumption among heavy drinkers following a brief alcohol intervention (BI). DSM-IV and DSM-5 alcohol use disorder (AUD) measures were also evaluated. <br><br>METHODS: Participants (N=246) underwent a diagnostic interview after receiving a BI, then reported daily alcohol consumption using an Interactive Voice Response system. Dimensional AD was calculated by summing the dependence criteria (mean=4.0; SD=1.8). The dimensional AUD measure was a summation of positive Alcohol Abuse plus AD criteria (mean=5.8; SD=2.5). A multi-model inference technique was used to determine whether the DSM-IV categorical diagnosis or dimensional approach would provide a more accurate prediction of first week consumption and change in weekly alcohol consumption following a BI. <br><br>RESULTS: The Akaike information criterion (AIC) for the dimensional AD model (AIC=7625.09) was 3.42 points lower than the categorical model (AIC=7628.51) and weight of evidence calculations indicated there was 85% likelihood that the dimensional model was the better approximating model. Dimensional AUD models fit similarly to the dimensional AD model. All AUD models significantly predicted change in alcohol consumption (p's=.05). <br><br>CONCLUSION: A dimensional AUD diagnosis was superior for detecting treatment effects that were not apparent with categorical and dimensional AD models.<p /> <p>Language: en</p>",
language="en",
issn="0376-8716",
doi="10.1016/j.drugalcdep.2013.12.020",
url="http://dx.doi.org/10.1016/j.drugalcdep.2013.12.020"
}