Manual for the Benzodiazepine Dependence Questionnaire (BDEPQ)

VALIDITY OF THE BDEPQ 43
could not be carried out with the same number of predictors used in the other
analyses of the predictive validity of the BDEPQ. Instead predictors of BZD dose
at wave two rather than change in BZD dose were examined.
The correlation between BDEPQ scores at wave one and diazepam-equivalent
dose at wave two was 0:242 (n =177�p < :05) indicating a small relationship.
A regression equation including demographic characteristics (age and sex), use of
other drugs (tobacco and alcohol), scores on the BDEPQ and other questionnaires,
BWSQ scores, and patterns of BZD use (dose, frequency and duration of use, and
half-life) accounted for 54:2% of variance in dose of BZDs taken at wave two
(R 2 =0:542�F14�127 =10:753�p < :05). Partial correlation analysis revealed that
dose of BZDs, frequency of BZD use and duration of BZD use form wave onewere
signi cant independent predictors of dose at wave two. BDEPQ scores did not
independently predict dose at wave two indicating that the correlation of 0:242
reported above is shared with other variables.
The BDEPQ as a screening test for diagnoses of dependence
So far BZD dependence has been considered as a continuum. There are many
instances were interpretation of a test is made easier by cut-o scores. It is easier
for test users to make a categorical statement from test scores than it is to consider
the respondent on a dimension. For this reason alone I analysed the ability of the
BDEPQ to act as a screening test for diagnoses of BZD dependence made using
the CIDI. For this analysis it is assumed that CIDI diagnoses of dependence are
the `Gold Standard' even though this proposition has been disputed above.
A Receiver Operating Characteristic (ROC) curve was plotted from the sensitivity
and false alarm rate by treating each score on the BDEPQ as a cut-o point.
Sixty-eight participants had complete information on both the BDEPQ and the
CIDI. Of these 68, 21 (30:9%) had either a current CIDI diagnosis of DSM3R or
ICD10 BZD dependence or had met criteria for such a diagnosis at some time in
the past. ROC analysis has two objectives: selecting an optimal cut-o score, and
testing whether the screening test predicts the `gold standard' better than chance.
Figure 2 shows a graph of the ROC curve for the BDEPQ. The obtained
sensitivity and false alarm rates (1 - speci city) are shown as points. A natural
logarithm function has been tted to this obtained data. Selected points are
labeled with the BDEPQ scores they re ect. A diagonal line has also been plotted
to repented the screening pro le of a random or chance test. Points on the
BDEPQ ROC curve farthest from this line are better cutting points. Two cutting
points emerge from inspection of Figure 2, these are scores of 23 and 34. The
choice between these points depends on the relative importance of the correctly
identifying people who are BZD dependent compared with the costs of falsely
selecting people who the CIDI would not diagnoses as BZD dependent. If correctly
identifying BZD dependence was of greater importance a cutting score of