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Research Article

DSM-IV, ICD-10 and FTND: Discordant Tobacco Dependence Diagnoses in Adult Smokers

Jill C Mwenifumbo² and Rachel F Tyndale^1,2*

¹Centre for Addiction and Mental Health, , 250 College Street, Toronto, Ontario, Canada

²Department of Pharmacology and Toxicology, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada

Received Date: Nov 15, 2010 / Accepted Date: Dec 09, 2010 / Published Date: Dec 10, 2017

Citation: Mwenifumbo JC, Tyndale RF (2010) DSM-IV, ICD-10 and FTND: Discordant Tobacco Dependence Diagnoses in Adult Smokers. J Addict Res Ther 2:105.DOI: 10.4172/2155-6105.1000105

Copyright: © 2010 Mwenifumbo JC, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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There are several measures that attempt to assess tobacco dependence but the most appropriate measure to use is often unclear. Tobacco dependence was assessed, in Canadian adult smokers of black African descent, with three measures: the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV), the International Classification of Diseases (ICD-10) and the Fagerström Test for Nicotine Dependence (FTND). The different measures resulted in very different tobacco dependence diagnosis rates: 91% were dependent by DSM-IV, 48% were dependent by ICD-10 and 48% were dependent by FTND (score ≥3). Although ICD-10 and FTND had the same diagnosis rates, they did not identify the same individuals as dependent (i.e., 35% of those dependent by ICD-10 were not dependent by FTND). For exploratory purposes, the dichotomous measures, DSM-IV and ICD-10, were scored as continuous scale measures, DSMC and ICDC. ICDC had the strongest agreement with FTND (ICC=0.76), followed by DSMC with ICDC (ICC=0.28) and DSMC with FTND (ICC=0.19). These exploratory analyses illustrate some limitations and strengths of the DSM-IV, ICD-10 and FTND. Moreover, we illustrate how measurement architecture and population specific variation may contribute to discordant diagnoses.

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