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Scandinavian Journal of Child and Adolescent Psychiatry and Psychology
Vol. 13:9-21 (2025) DOI 10.2478/sjcapp-2025-0002
Research Article Open Access
Discriminability of the Beck Depression Inventory and its Abbreviations in
an Adolescent Psychiatric Sample
Fatemeh Seifi1,*, Sebastian Therman2, Tommi Tolmunen1,3
1 Clinical Medicine Unit, Department of Medicine/Adolescent Psychiatry, University of Eastern Finland,
Kuopio, Finland
2 Mental Health, Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
3 Department of Adolescent Psychiatry, Kuopio University Hospital, Kuopio, Finland
*Corresponding author: fseifi@uef.fi
Abstract
Background: The Beck Depression Inventory (BDI) is a widely acknowledged self-report screening tool for evaluating the
presence and intensity of depressive symptoms. The BDI-IA, although an older version, is highly correlated with the updated
BDI-II, remains clinically valuable, and is widely used due to its free availability.
Aim: This study aimed to examine the psychometric properties of the BDI-IA and compare its diagnostic accuracy with the
abbreviated BDI-SF, BDI-PC, and BDI-6 versions against gold-standard research diagnoses in a representative Finnish
adolescent clinical population.
Methods: The participants were referred outpatient adolescents aged 13–20 years (N = 752, 73% female). We investigated
structural validity with item factor analysis and evaluated the criterion validity of mean scores and factor scores with various
diagnostic
measures. Sample-optimal cut-offs (criterion unweighted Cohen’s kappa) were estimated with a bootstrap procedure.
Results: The sample-optimal cut-off for the full BDI was 19, slightly higher than that suggested by the previous literature.
The abbreviations of the BDI-IA were demonstrated to be as good as the full scale in detecting depressive symptoms in all
three diagnostic categorizations.
Conclusion: The use of brief and user-friendly questionnaires such as the BDI-PC or BDI-6 is recommended to ensure
optimal depression screening and minimize the administrative burden, especially in primary care settings where clinical
decision-making and referrals often need to occur within a limited time frame.
Keywords: Adolescent, depression, screening, factor analysis, diagnostic accuracy
Introduction
In the past two decades, depression in adolescents
has been a growing mental health concern worldwide
due to its profound long-term impacts on
psychosocial and academic functioning (1). In clinical
settings, major depressive disorder (MDD) in
adolescents is an especially important issue due to its
high incidence and the high probability of
exacerbation of untreated symptoms persisting into
adulthood, and the increased risk of suicidal
behaviors (2). In addition, during the COVID-19
pandemic, adolescent depression approximately
doubled globally compared to the pre-pandemic level
(from 12.9% to 25.2%) (3). According to WHO,
Finland, for example, witnessed a substantial increase
in depressive symptoms among adolescents, with
17–19% of 15-year-old females reporting feeling
down in their everyday lives in 2022 (4).
It is well-established that valid screening instruments
could be beneficial, particularly in clinical settings
where symptomatic adolescents are seen in primary
care services within a constrained time frame (5).
Furthermore, the early detection of depressive
symptoms can facilitate the appropriate referral and
admission of adolescent outpatients to secondary
care, hence accelerating the treatment process and
preventing later harmful effects (5).
One of the most widely acknowledged self-report
screening tools for assessing the presence and
intensity of depressive symptoms is the Beck
Depression Inventory (BDI) (6). It has been noted
that one specific strength of BDI’s factor structure is
its expanded format, whereby complete sentences are
Discriminability of the BDI and its Abbreviations
10
used instead of rating scales such as the Likert
format, thus avoiding the potential confusion, errors,
and carelessness of respondents caused by negative
reverse-worded items (7), as well as subjective
interpretations of response options such as
sometimes or often. The original 21-item version of
the BDI has undergone several modifications since it
was first published in 1961. In the first revised
version, known as the BDI-IA, the wording of
several items was altered (8). Subsequently, in the
upgraded or latest version (i.e., BDI-II (9)), some
items have been updated to correspond with the
criteria for MDD in the fourth edition of the
Diagnostic and Statistical Manual of Mental
Disorders (DSM–IV) (10). The BDI-IA, although an
older version, is still of clinical value and widely
utilized due to its public availability and free use (11).
Additionally, the Finnish version of the BDI-IA is
widely accessible through Finnish medical and
nursing databases, which further facilitates its use
among clinicians and researchers in Finland (12).
Moreover, previous studies have reported reasonable
psychometric properties and high levels of internal
consistency for the earlier BDI versions across
different settings (13,14), and even with the revisions,
the BDI-IA and BDI-II have demonstrated a strong
correlation (r = 0.93) (15).
Despite the wide variety of literature evaluating the
factor structure of the BDI in general and clinical
populations across different countries (16,17), most
validity reports, including those in Finland, have
addressed the adult population (18,19). The results of
a systematic review suggest that the factor structure
of the BDI for adolescents, particularly in clinical
contexts, could be different from that for nonclinical
adolescents and adults (20). It is unclear, however,
whether some degree of multidimensionality impacts
the use of the measure for screening. Furthermore,
essential unidimensionality presents an opportunity
for abbreviation by removing the least discriminative
items. While the 21-item BDI demonstrates a broad
content coverage of depressive symptoms (21), the
use of brief screening questionnaires, such as the
abbreviated versions with 13 items (BDI-SF) (22), 7
items (BDI-PC) (23), or 6 items (BDI-6) (24), may
thus make it more user-friendly in a clinical context
without significant loss of accuracy (25).
Various BDI thresholds for suspecting the
presence of depressive disorders and the need for
referral have been suggested in studies with
adolescent samples. Nevertheless, there is a lack of
consensus regarding whether the provided optimal
cut-off values can be generalized to clinical use
among adolescents. In this respect, a recent meta-
analysis established that BDI scores of both 11 and
16 could yield favorable diagnostic accuracy for
MDD in adolescents, although the latter provided
slightly better detection (26). There are also several
studies that report BDI threshold values for the
clinical settings other than the above (27–29).
Relatively little research has been undertaken to
test the validity of the BDI-IA or the abbreviated
versions of the BDI in adolescent psychiatric
outpatients or to set thresholds for their use. One
study demonstrated that the brief self-assessment
BDI-6 had satisfactory psychometric validity in an
adolescent psychiatric context (25). However, this
abbreviated format did not incorporate the crucial
suicidality item. Hence, the present study aims to fill
the above gaps by examining and comparing the
diagnostic accuracy, validity, and psychometric
properties of the BDI-IA, BDI-SF, BDI-PC, and
BDI-6 questionnaires against the gold-standard
Structured Clinical Interview for DSM Disorders
(SCID) in a representative Finnish adolescent clinical
population. Furthermore, we aimed to apply receiver
operating characteristics (ROC) curve analysis to
propose optimal cut-off scores and a sample-
optimized BDI abbreviation and to establish
generalizable data enabling the more effective and
accurate detection of depressive symptoms among
adolescents in clinical settings. Note that the
technical term ‘diagnostic accuracy’ widely used in
the field does not imply that the questionnaires are
used to determine diagnoses, but for screening.
Methods
Participants
The data for the current investigation were derived
from the ongoing REAL-SMART project
(“Recognition and early intervention for alcohol and
substance abuse in adolescence and systemic
metabolic alterations related to different psychiatric
disease categories in adolescent outpatients”). The
participants were referred patients aged 13–20 years
who attended the adolescent psychiatric outpatient
clinic of Kuopio University Hospital (KUH) in
Finland between June 2017 and March 2022, with
breaks in clinic operations due to the COVID-19
pandemic. The reasons for non-participation were
not recorded but included declining to participate,
dropping out or being transferred to inpatient
treatment before being approached, and not being
presented with the study by clinical personnel for
various reasons. Of the adolescents having at least
one appointment in the outpatient clinic (n = 2853),
a total of 754 (26.4%) participants were enrolled in
the study, of whom the majority (73 %) were female,
which is a typical gender distribution for these clinics.
Previous or current diagnoses did not affect
recruitment. Two patients were excluded for not
filling in the BDI-IA at baseline.
The participants were first interviewed and then
autonomously completed a multi-measure
Discriminability of the BDI and its Abbreviations
11
questionnaire containing the BDI-IA on a tablet
computer later in the same session. All items were
presented one at a time, with automatic advancement
after giving a response. Although it was possible to
return to change earlier responses by swiping right on
the screen, this functionality was not advertised, and
only a few respondents used it for only a few
responses.
Prior to undertaking the investigation, the Research
Ethics Committee of Kuopio University Hospital
confirmed the study procedures and written
informed consent was obtained from all the
participants. In addition, the study complied with the
ethical principles set by the 7th revision of the
Declaration of Helsinki (30).
To enhance the transparency of the present research,
we adhered to the STARD 2015 (Standards for the
Reporting of Diagnostic Accuracy Studies) checklist
(Supplementary material Table S1), which consists of
30 items (31).
Measures
Research diagnoses
A trained psychiatric nurse conducted a Structured
Clinical Interview for DSM-IV, clinician version
(SCID-CV), with each participant (32), assigning all
comorbid diagnoses. The involvement of a trained
psychiatric nurse was in accordance with the SCID
user’s guide, which permits structured diagnostic
interviews and the assignment of diagnoses by
trained professionals who are not necessarily
psychiatrists or psychologists (32).
The interviewer was blind to BDI responses when
completing the SCID and assigning research
diagnoses. The following diagnoses were exclusion
criteria in discriminability and optimal cut-off
analyses, as the presence of depressive symptoms
independent of depression diagnoses were
undetermined for them: all psychotic disorders
except Major Depressive Disorder (MDD) with
Psychotic Features, Bipolar Disorder, Cyclothymic
Disorder, and mood disorders other than MDD or
Dysthymia. The SCID-based diagnoses are presented
in Table 1. To ensure the robustness of our results,
we dichotomized the presence or absence of
depression diagnoses in three different ways, with
one assigned as the main classification. In the
primary categorization, MDD vs. no depression,
patients with major depressive disorder (MDD),
either current or in partial remission, were coded as
depressed, patients with dysthymia were excluded,
and all others were coded as not depressed. Note that
MDD in full remission was coded as not depressed.
The secondary categorization, depression vs. none,
was otherwise the same, except that participants with
dysthymia were also coded as depressed. In the other
secondary categorization, MDD vs. no MDD, those
with dysthymia (without MDD) were coded as no
MDD. Note that the depressed cases were the same
for the primary categorization MDD vs. No
Depression and the secondary MDD vs. no MDD,
and the non-depressed cases were the same for the
primary categorization and the secondary
Depression vs. none.
Beck Depression Inventory I-A (BDI-IA)
Participants completed the self-report 21-item BDI-
IA (8), which was previously translated into Finnish
(12). Each item of the BDI-IA and its abbreviations
is scored from 0 to 3, with a sum score range from 0
to 63 for the BDI-IA (see the items in Table 3). We
used the sum score screening thresholds (primarily
16 or greater and secondarily 11 or greater)
recommended in a recent meta-analysis (26). The
BDI-IA and its abbreviations were scored
automatically and thus blind to the results of the
SCID.
Beck Depression Inventory – Short Form (BDI-SF)
The BDI-SF (22) consists of 13 items extracted from
the original BDI to evaluate the presence of the
depressive symptoms. This set of optimal items has
previously revealed high correlations with the total
score for the original version (22,33).
Beck Depression Inventory for Primary Care (BDI-PC)
The BDI-PC, also referred to as the BDI-FS (“Fast
Screen”), is composed of 7 non-somatic items of the
BDI for screening MDD in primary care patients
(23). Previous studies have reported high internal
consistency for the BDI-PC in adult medical
inpatients and outpatients (23,34) and adolescent
outpatients in primary care (35).
Beck Depression Inventory-6 (BDI-6)
Two brief scales, named BDI-6, comprise six items
extracted from the BDI-21 and BDI-IA. The
selected items differ in the validation studies we
found (24,25,36), and the suicidality item is not
included in either. Both BDI-6 versions
demonstrated acceptable criterion validity in the
studies mentioned above. In the current study, we
investigated the BDI-6 version presented by Blom et
al. (2012) (25), as it was based on the BDI-IA rather
than the original BDI-21.
Statistical analyses
All statistical analyses were performed in the R
software environment (version 4.4.0) (37). When
calculating BDI sum scores, missing BDI responses
were replaced with the participant’s mean for the
other responses of the respective form version.
Overall, missingness was minimal (0.5% of all
responses).
Discriminability of the BDI and its Abbreviations
12
For the nonparametric comparison of distributions,
we used the stochastic superiority index, p = (X >
Y) + 0.5 × (X = Y), also known as the common
language effect size, which indicates the probability
that a random member of subgroup X has a higher
score than a random member of subgroup Y. To test
the statistical significance of p, we used the
appropriate Brunner–Munzel test (38,39),
implemented in the brunnermunzel R package
(version 2.0) (40). All statistical tests were considered
significant at p < 0.05 unless stated otherwise.
Sample size calculations
The sample was not collected specifically to test the
BDI's diagnostic accuracy, and this study was
conceived after the data had been gathered. Thus,
data collection was not informed by the present
study's needs. However, previous studies on the
single-test accuracy of the BDI (26) had a median
sample size of 316, indicating that our study was
adequately powered (see Table S2 for detailed
information).
We estimated the required sample size for comparing
two binary diagnostic tests (the pre-specified cut-off
applied to two BDI versions) in a paired design with
the calculations suggested by Akoglu (2022) (41),
with a Type I error rate (α) of 5%, 95% power, and
conservatively applying Yates’ continuity correction.
For detecting a clinically meaningful difference of .05
in either sensitivity or specificity between versions,
the required sample size is 249 when the test
agreement is at maximum and 1908 at minimum.
Since the compared BDI versions were various
summations of nearly the same responses, test
agreement can be expected to be close to the
maximum, and the present sample size was,
therefore, more than sufficient.
As we secondarily report differences in BDI
distributions between the depressed and non-
depressed groups, we also estimated the required
sample size for these comparisons. We are not aware
of a specific power calculation formula for the
Brunner–Munzel test, but it is at least as powerful as
the Wilcoxon–Mann–Whitney U-test in most
situations (39). We, therefore, estimated the two-
sided power of the U-test with the formulas of
Noether (1987) (42), as implemented in the rankFD
package (version 0.1.1) (43) for R. At an α of 5% and
95% power, detecting even a modest effect of p =
0.6 required only 217 participants each in the two
compared groups. The sample was thus clearly
sufficient, even though the estimation was slightly
optimistic in not accounting for ties.
Factor analysis
The latent structure of the BDI-IA was investigated
with a confirmatory factor analysis of the a priori
single-dimensional model, treating the items as
ordinal (DWLS estimator), with the cfa function in
the lavaan package (version 0.6-17) (44), using all
pairwise-available data and otherwise standard
settings. Factor scores were computed with the
lavPredict function using the default empirical Bayes
modal approach. Factor scores for the abbreviated
forms were calculated using the parameters from the
full model, thus keeping parameters identical across
the form versions and conceptually treating missing
items in an abbreviated version as missing responses
of the full BDI.
Discriminability
Basic accuracy ratios, Cohen’s kappa (κ), the
diagnostic odds ratio (DOR), and the number needed
to diagnose (NND; reciprocal of the Pierce skill
score, a.k.a. Youden’s J index) for each BDI version
in detecting gold-standard depression diagnoses were
computed with the epi.tests function of the epiR
package (version 2.0.74) (45). The NND can here be
interpreted as the number of individuals that need to
be screened to correctly detect one person with a
diagnosis (46). The primary full-length BDI cut-off
of 0.76 for these analyses was the mean score
equivalent to a sum of 16 or greater, as described
above, and the secondary mean score cut-off was
0.52. For the abbreviated forms, apparent
prevalences were higher at these cut-offs due to
higher scores for the included items. For
comparability, we therefore matched cut-offs with
the closest equivalent in sensitivity; when two cut-off
candidates were equal in their sensitivity difference,
the cut-off with the smallest difference in specificity
was selected.
To compare the diagnostic performance of the BDI-
IA with the BDI-SF, BDI-PC, and BDI-6 in a paired
design, the discriminability of the mean scores of the
shortened forms was compared with those of the full
21-item form using the statistical procedure of
Roldán-Nofuentes (2020) (47) implemented in the R
software package testCompareR (version 1.0.3) (48).
First, a global test jointly compared sensitivity and
specificity for detecting the gold-standard diagnosis,
and if this test was statistically significant, sensitivity
and specificity differences were tested separately.
Multiple tests within the same form pair were
corrected with Holm’s method, the package default.
Due to cut-off matching by sensitivity, this
procedure mainly compared specificity but
considered the sensitivity discrepancies arising from
imperfect matching due to coarse sum score
distributions.
As sum scores have a simpler measurement model,
which is potentially less accurate than factor scores,
we compared the diagnostic categorization by factor
scores with categorization by the full BDI-IA sum
Discriminability of the BDI and its Abbreviations
13
score, in the same manner as the comparisons with
the mean scores of the abbreviations. Again, the
factor score cut-off was selected to match the
sensitivity of the sum score cut-off, and accuracy was
compared with testCompareR as above. The strength
of association between the binary categorizations by
mean scores and factor scores was expressed as a
correlation (equivalent to Stuart’s τc).
Optimal cut-offs
As an exploratory addition to the main results, we
determined sample-optimal cut-offs for all the BDI
versions using the R package cutpointr (version
1.1.2) (49) with the bootstrapped version of the
cutpointr function using default settings, the chosen
cut-off being validated in 1000 out-of-bag samples.
The main criterion for comparison was Cohen’s
unweighted κ, estimated jointly and for genders
separately, with secondary criteria being
misclassification cost with a) false positives and false
negatives being weighted equally or b) with triple
weight on the latter. Discriminability across the
whole score range of each form was assessed with
TABLE 1. Characteristics and presence of SCID-based diagnoses by gender.
Variable
Males
Females
Total
Participants
202
550
752
Age, mean
16.74
16.47
16.55
Age, SD
1.67
1.62
1.64
Major depressive disorder
84 (42%)
294 (53%)
378 (50%)
Dysthymia
24 (12%)
87 (16%)
111(15%)
Other depressive disorder
7 (3.5%)
29 (5%)
36 (5%)
Exclusion diagnosis
6 (3%)
61 (11%)
67 (9%)
SCID = Structured Clinical Interview for DSM-IV; SD = Standard deviation. Note: an individual may have diagnoses in more than one category. Exclusion
diagnoses: psychotic disorders other than major depressive disorder (MDD) with psychotic features, bipolar disorder, and cyclothymic disorder
FIGURE 1. Flow diagram of participants based on the primary diagnostic categorization (MDD vs. no depression). Note:
MDD vs. no depression diagnosis refers to the main comparison categorization used in the analysis. For more details,
see Methods section: Research Diagnoses.
Discriminability of the BDI and its Abbreviations
14
receiver operating characteristic (ROC) curves and
accompanying area under the curve (AUC) values, as
recommended in the STARD 2015 guidelines (50).
For details of the interpretation of AUC values, see
Mallet et al. (2012) (51).
Results
A participant flow diagram is provided in Figure 1.
The demographic and diagnostic distributions of the
participants are presented in Table 1. BDI score
distributions and comparisons thereof between the
diagnostic groups are reported in Table 2. As
expected, there was little overlap in BDI scores
between those with a diagnosis of depression and
those without, p being around 0.9 across diagnostic
groups and BDI versions, which corresponds to a
standardized mean difference of 1.8.
Factor analysis
The fit of the one-dimensional factor model of the
BDI-IA was sufficient, as the scaled comparative fit
index (CFI) was 0.949, the scaled root mean square
error of approximation (RMSEA) was 0.089, and the
standardized root mean square residual (SRMR) was
0.060. Items 19 Weight loss and 20 Somatic
preoccupation had the weakest factor loadings (0.24
and 0.42, respectively), while the core items 1, 3, 4, 5,
7, and 8 pertaining to depressive mood and negative
self-view had loadings of 0.80 or above. Thresholds
(standard scores corresponding to the cumulative
response probability within an item) had a wide
range: for example, the highest score of 3 on item 10
Crying was as frequent as the score of 1 on item 19
Weight loss. See the standardized parameters of the
factor model in Table 3 and BDI factor score
distributions by diagnostic category in Table S3.
Discriminability
The main screening discriminability results are
presented in Table 4 and Table 5. The primary cut-
off (16 or higher on the full BDI) turned out to be
relatively low in this highly symptomatic sample, with
a sensitivity of 0.94 but a specificity of only 0.64. In
the global test, the mean scores of all the abbreviated
versions were as good at discriminating between
MDD and no depression as the full BDI-IA.
Although the differences were not statistically
significant, the BDI-6 had a higher negative
predictive value (NPV) than the BDI-IA at the same
positive predictive value (PPV), which was also
reflected in having the lowest NND and a higher
DOR. The 7-item BDI-PC was the only abbreviation
to have a lower κ and worse NND than the BDI-IA,
although, again, these differences were not
statistically significant, and the DOR was the highest
of all the versions. These differences were largely
attributable to a higher threshold due to a lack of
perfectly matched cut-off values. The 13-item BDI-
SF performed nearly identically to the BDI-IA. The
results of the robustness analysis using the two
alternate diagnostic categorizations were largely the
same (Tables S4 and S5).
At the secondary cut-off of 11 or higher, the
diagnostic accuracy of the form versions did not
differ, probably due to the sensitivities being extreme
(0.98 for the BDI-IA) at such a low cut-off (Tables
S6 and S7). ROC curves for the BDI-IA version are
displayed in Figures 2a and 2b, and all the form
versions are provided in Supporting information
Figure S1.
Factor scores were extremely strongly correlated (r
0.98 to 0.99) with mean scores in all BDI versions
(Figures 3 and S2). Screening assignments were also
TABLE 2. BDI sum score distributions by version and diagnostic category.
Diagnosis +
Diagnosis -
Brunner–Munzel test
Stochastic Superiority
Condition*
Version
n
M
SD
n
M
SD
Statistic
p
Estimate
CI
MDD vs. no depr.
BDI-1A
351
30.22
9.64
239
12.13
9.49
31.7
< 0.001
0.902
[0.88, 0.93]
MDD vs. no depr.
BDI-SF
359
20.24
6.60
243
7.75
6.56
32.3
< 0.001
0.902
[0.88, 0.93]
MDD vs. no depr.
BDI-PC
361
11.58
3.91
245
4.54
3.91
29.5
< 0.001
0.892
[0.87, 0.92]
MDD vs. no depr.
BDI-6
368
10.02
3.04
246
4.00
3.29
32.7
< 0.001
0.899
[0.88, 0.92]
Depr. vs. none
BDI-1A
389
29.13
10.04
239
12.13
9.49
28.3
< 0.001
0.884
[0.86, 0.91]
Depr. vs. none
BDI-SF
397
19.53
6.87
243
7.75
6.56
28.7
< 0.001
0.885
[0.86, 0.91]
Depr. vs. none
BDI-PC
399
11.20
4.03
245
4.54
3.91
26.8
< 0.001
0.876
[0.85, 0.90]
Depr. vs. none
BDI-6
406
9.74
3.12
246
4.00
3.29
29.8
< 0.001
0.886
[0.86, 0.91]
MDD vs. no MDD
BDI-1A
351
30.22
9.64
275
13.04
9.55
30.7
< 0.001
0.892
[0.87, 0.92]
MDD vs. no MDD
BDI-SF
359
20.24
6.60
279
8.42
6.63
30.9
< 0.001
0.891
[0.87, 0.92]
MDD vs. no MDD
BDI-PC
361
11.58
3.91
281
4.95
3.96
28.1
< 0.001
0.880
[0.85, 0.91]
MDD vs. no MDD
BDI-6
368
10.02
3.04
282
4.40
3.35
30.2
< 0.001
0.883
[0.86, 0.91]
MDD = Major Depressive Disorder; BDI-IA= Beck Depression Inventory (revised version); BDI-SF = Beck Depression Inventory – Short Form; BDI-PC =
Beck Depression Inventory for Primary Care; BDI-6 = Beck Depression Inventory-6; MDD = Major Depressive Disorder; Depr = Depression; M = Mean;
SD = Standard deviation; P = P-value; CI = Confidence interval. Note: Missingness of BDI responses was minimal, with only 85 responses of the 752
respondents missing (0.5%). Note: *Condition refers to the comparison categorizations used in the analysis. For more details, see Methods section
2.2.1: Research Diagnoses.
Discriminability of the BDI and its Abbreviations
15
highly similar (r 0.96 to 0.98 in the primary
condition), with only 1–1.5% of respondents
classified differently with the two methods (Table
S8). Factor scores did not differ in discriminability
from the BDI-IA sum/mean scores.
Optimal cut-offs
The BDI cut-offs optimized for agreement with
diagnoses (κ) in the whole sample were slightly higher
than the primary a priori value and approximately
corresponded for the full BDI-IA to a sum score of
19 or greater (Figure 2a). This difference was mostly
due to females, as the value was 17½ when estimated
in the male subsample (Figure 2b). When the
optimization criterion was an equal misclassification
cost for false positives and negatives, the BDI-IA
sum score equivalent was 18, and when false
negatives were deemed three times as costly, this cut-
off was approximately 13 (Figure S3). As with
matched cut-offs, optimized cut-offs were higher for
the abbreviated versions due to the higher scores for
the included items than the excluded ones (Table 6).
Discussion
The present study was designed to compare the
diagnostic accuracy of the BDI and its abbreviated
forms in an adolescent clinical population. We found
the BDI-IA to be acceptably unidimensional in our
adolescent psychiatric sample. This finding is
consistent with previous studies suggesting that for
adequately capturing depressive symptoms, the BDI
total score might be preferable over the dimension-
specific subscales (52,53). The unidimensional
factorial structure of the BDI-IA in our study makes
abbreviated versions of the questionnaire
meaningful, as the items measure the same latent
construct, and the items included in the
abbreviations also had the highest factor loadings.
All the abbreviations of the BDI-IA were determined
to be as good as the full scale in detecting those
adolescents with diagnosed depression, with a trend
towards being even better. This might be explained
by the abbreviated versions focusing on the core
symptoms of depression, which are most indicative
of the overall construct, as defined in diagnostic
systems and shown by their factor loadings. The
excluded somatic items may also be less diagnostic
among adolescents.
The BDI-6 was at the top in diagnostic agreement
and the diagnostic odds ratio across diagnostic
groupings despite being the shortest scale. Values
obtained for the one item longer BDI-PC, which also
includes the suicide item, were practically as good,
and both can be expected to be equally good in
screening adolescents, cutting the number of items
by two-thirds compared to the full BDI.
Interestingly, these two abbreviations share only the
first Mood item, and the BDI-6 is the only
TABLE 3. Version membership and standardized one-factor model parameters of BDI-IA items
Item
Content
Item included in version
Factor
loading
Item thresholds
BDI-SF
BDI-PC
BDI-6
1
2
3
1
Mood
✓
✓
✓
0.80
-0.51
0.64
1.52
2
Pessimism
✓
✓
–
0.75
-0.83
0.25
1.12
3
Sense of failure
✓
✓
–
0.84
-0.74
0.45
1.11
4
Lack of satisfaction
✓
✓
–
0.83
-0.55
0.44
1.46
5
Guilty feeling
✓
–
✓
0.88
-0.46
0.28
1.00
6
Sense of punishment
–
–
–
0.65
0.07
0.74
1.27
7
Negative self-view
✓
✓
–
0.84
-0.69
0.32
0.96
8
Self-accusations
–
✓
–
0.81
-0.90
-0.21
0.31
9
Self-destructiveness
✓
✓
–
0.70
-0.68
1.12
2.11
10
Crying
–
–
–
0.71
-0.48
0.26
0.69
11
Irritability
–
–
✓
0.66
-0.92
0.32
1.31
12
Social withdrawal
✓
–
–
0.74
-0.36
0.64
1.82
13
Indecisiveness
✓
–
✓
0.75
-0.68
0.05
1.60
14
Negative body image
✓
–
–
0.73
-0.13
0.36
0.63
15
Work inhibition
✓
–
✓
0.71
-0.91
-0.03
1.49
16
Sleep disturbance
–
–
–
0.56
-0.67
0.81
1.34
17
Fatigability
✓
–
✓
0.78
-0.78
0.08
1.26
18
Loss of appetite
✓
–
–
0.58
-0.05
0.73
1.54
19
Weight loss
–
–
–
0.24
0.68
1.19
1.73
20
Somatic preoccupation
–
–
–
0.42
0.25
1.48
1.93
21
Loss of libido
–
–
–
0.58
0.20
0.93
1.62
MDD = Major Depressive Disorder; BDI-IA= Beck Depression Inventory (revised version); BDI-SF = Beck Depression Inventory – Short Form; BDI-PC
= Beck Depression Inventory for Primary Care; BDI-6 = Beck Depression Inventory-6.
Discriminability of the BDI and its Abbreviations
16
Discriminability of the BDI and its Abbreviations
17
Discriminability of the BDI and its Abbreviations
18
abbreviation that includes irritability, which is a core
feature of adolescent depression, as opposed to adult
depression (54). In accordance with our findings, two
previous studies have revealed that the BDI-PC
could accurately detect MDD in pediatric care,
yielding relatively high sensitivity and specificity rates
(91%) (35), and a sensitivity of 81% and specificity of
90% (55).
The sensitivity analyses using two alternative
categorizations of the diagnoses, which included
dysthymias and assigned them to either depressed or
non-depressed groups, produced practically identical
results regarding relative diagnostic accuracy. Values
for the various indices were necessarily slightly
poorer, as these diagnostically intermediate cases
increased overlap in both diagnostic categories and
BDI scores.
Using factor scores did not improve detection,
despite a sample-optimized measurement model.
This may, for example, be due to rarer symptoms not
being indicative of greater depression, as assumed by
the model with item-specific thresholds. More
precise reasons need to be explored in a separate
study, along with other possible alternatives to the
sum score.
The sample-optimal cut-off for the full BDI was 19,
which is slightly higher than the value suggested by
the previous literature. Setting screening cut-offs is
always a balance between sensitivity and specificity;
there is no objective method to optimize
discriminability without assigning a relative cost to
misses and false alarms. The DOR, NND, and
unweighted kappa measures used in the present study
all implicitly assign such a cost. With our
misclassification cost analyses, we demonstrated one
way of adjusting for unequal consequences, but
determining the appropriate weights, for instance, to
minimize the societal burden or treatment resource
use is outside the scope of our paper.
Strengths and limitations
Our study had several strengths. The sample was
representative of patients in the adolescent patient
population, thus enabling the generalizability of the
TABLE 6. Optimal cut-off results.
BDI
version
Optimization criterion
Subgroup
Optimal
cut-off
κ
Sensitivity
Specificity
AUC
DOR
NND
BDI-1A
Cohen's kappa
-
0.89
0.638
0.895
0.731
0.902
–
1.60
BDI-SF
Cohen's kappa
-
1.01
0.619
0.845
0.775
0.903
–
1.61
BDI-PC
Cohen's kappa
-
1.10
0.634
0.861
0.771
0.893
–
1.58
BDI-6
Cohen's kappa
-
1.04
0.615
0.866
0.743
0.900
–
1.64
BDI-1A
Cohen's kappa
Male
0.86
0.682
0.821
0.860
0.910
–
1.47
BDI-1A
Cohen's kappa
Female
0.90
0.611
0.927
0.654
0.896
–
1.72
BDI-SF
Cohen's kappa
Male
0.79
0.629
0.869
0.763
0.912
–
1.58
BDI-SF
Cohen's kappa
Female
1.07
0.597
0.886
0.699
0.896
–
1.71
BDI-PC
Cohen's kappa
Male
1.01
0.646
0.738
0.903
0.893
–
1.56
BDI-PC
Cohen's kappa
Female
1.12
0.605
0.896
0.692
0.890
–
1.70
BDI-6
Cohen's kappa
Male
0.95
0.650
0.857
0.796
0.895
–
1.53
BDI-6
Cohen's kappa
Female
1.14
0.623
0.910
0.692
0.899
–
1.66
BDI-1A
Misclassification cost 1:1
-
0.86
0.638
0.895
0.731
0.902
–
1.60
BDI-SF
Misclassification cost 1:1
-
0.89
0.606
0.893
0.699
0.903
–
1.69
BDI-PC
Misclassification cost 1:1
-
1.05
0.634
0.861
0.771
0.893
–
1.58
BDI-6
Misclassification cost 1:1
-
1.02
0.615
0.866
0.743
0.900
–
1.64
BDI-1A
Misclassification cost 1:3
-
0.62
0.599
0.962
0.602
0.902
–
1.77
BDI-SF
Misclassification cost 1:3
-
0.68
0.611
0.979
0.594
0.903
–
1.75
BDI-PC
Misclassification cost 1:3
-
0.72
0.594
0.949
0.614
0.893
–
1.77
BDI-6
Misclassification cost 1:3
-
0.74
0.565
0.957
0.574
0.900
–
1.88
κ = Cohen's kappa; AUC = Area under the curve; DOR = Diagnostic odds ratio; NND = Number needed to diagnose
Figure 3. BDI mean scores versus factor scores with fit
lines. Note: The higher and lower horizontal cut-off
lines correspond to BDI-IA sums 16 and 11, respectively,
and the factor score cut-offs to the matched
sensitivities.
Discriminability of the BDI and its Abbreviations
19
findings to adolescent outpatient psychiatric care.
Sensitivity and specificity are independent of
prevalence, and the high values suggest that the
abbreviated questionnaires are also suitable for
screening in primary care. The use of gold-standard
research diagnoses over register diagnoses
maximized the reliability of the screening reference.
In addition, our diagnostically naturalistic and
heterogeneous sample was relatively large, making it
sufficient in statistical power. Moreover, a paired
comparative diagnostic accuracy design was used,
providing greater statistical power than unpaired
designs.
Regarding the limitations of this study, although
SCID is widely used in clinical settings, including in
Finland, and a portion of our sample consisted of
older adolescents (i.e., ages 17-20), we acknowledge
that this instrument may not adequately capture the
developmental nuances, particularly in younger
adolescents. Furthermore, the BDI-IA, being an
older version, may have limitations, such as failing to
differentiate between increases and decreases in
depression-related vegetative symptoms (i.e., sleep
and appetite) (14), despite its high correlation with
the BDI-II (15). However, recent psychometric
studies continue to support the utility of older
versions of the BDI (56,57).
Additionally, our results naturally depend on the
employed definition of depression and its
operationalization as diagnostic criteria. However,
there is an ongoing debate on these definitions,
etiology, and the relevant pathological period. Thus,
if the diagnosis had a greater emphasis on somatic
symptoms, the full BDI might prove superior.
Moreover, according to Finnish treatment guidelines,
people with mild depression should be treated in
primary health care. However, there were a few
participants with mild depression in our sample who
might have been referred to the outpatient clinic not
primarily because of their depression but due to the
presence of other comorbid conditions. Finally,
although sensitivity and specificity are, in principle,
independent of prevalence, our findings cannot
necessarily be generalized to primary health care or
general population samples. Future studies
replicating our results in primary healthcare settings
are therefore recommended.
Conclusion
In our study, the abbreviations of the BDI-IA proved
equally effective as the full scale in detecting
depressive symptoms among adolescent psychiatric
outpatients. These results support earlier findings on
the applicability of brief and user-friendly
questionnaires to ensure optimal depression
screening and minimize the administrative burden,
especially in primary care settings where clinical
decision making, and appropriate referrals often need
to take place within a limited time frame. Since we
examined the abbreviated form items embedded in
the full version of BDI-IA, additional research is
required to assess the clinical utility and
discriminative capacity of the abbreviated forms
when used as standalone questionnaires in clinical
settings. In future investigations, the BDI-PC and
BDI-6 as optimal abbreviated forms of the BDI
should be compared with the other brief depression
screening tools in adolescent psychiatry.
Ethical considerations
The study complied with the ethical principles set by the
7th revision of the Declaration of Helsinki. The Research
Ethics Committee of Kuopio University Hospital
approved the study procedures (238/2017).
Funding
Researcher Tommi Tolmunen was supported by the
Strategic Research Council within the Academy of Finland
(SchoolWell, grant number 352509, work package
352511).
Conflict of interests
The authors declare that they have no conflict of interest.
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