Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression
Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression
Faraz Mahmood Ali 0 1 2 3 4 5
Richard Kay 0 1 2 3 4 5
Joerg Kupfer 0 1 2 3 4 5
Florence Dalgard 0 1 2 3 4 5
0 Institute of Medical Psychology, Justus Liebig University , Giessen , Germany
1 School of Pharmacy and Pharmaceutical Sciences, Cardiff University , Cardiff , UK
2 Department of Dermatology and Academic Wound Healing, Division of Infection and Immunity, School of Medicine, Cardiff University , Cardiff CF14 4XN , UK
3 Institute for Medicines Development , Cardiff , UK
4 School of Life & Medical Sciences, Department of Pharmacy, University of Hertfordshire , Hatfield , UK
5 Department of Dermatology and Venereology, Ska ̊ne University Hospital, Lund University , 20502 Malmo ̈ , Sweden
Purpose The Dermatology Life Quality Index (DLQI) and the European Quality of Life-5 Dimension (EQ-5D) are separate measures that may be used to gather health-related quality of life (HRQoL) information from patients. The EQ-5D is a generic measure from which health utility estimates can be derived, whereas the DLQI is a specialtyspecific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates, we explored an established mathematical technique known as ordinal logistic regression (OLR) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates. Methods Retrospective data from 4010 patients were randomly divided five times into two groups for the derivation and testing of the mapping model. Split-half cross-validation was utilized resulting in a total of ten ordinal logistic regression models for each of the five EQ5D dimensions against age, sex, and all ten items of the DLQI. Using Monte Carlo simulation, predicted health utility estimates were derived and compared against those observed. This method was repeated for both OLR and a previously tested mapping methodology based on linear regression. Results The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression. The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises, with an average overall difference of 0.0120 (a 1.6% underestimate, not of clinical importance). Conclusions This modeling framework developed in this study will enable researchers to calculate EQ-5D health utility estimates from a specialty-specific study population, reducing patient and economic burden.
DLQI; Mapping; Utility values life; EQ-5D; Ordinal logistic regression; Quality of
• Vincent Piguet1
‘Health-related quality of life’ (HRQoL) data can be used
to derive ‘Quality-Adjusted Life Years’ (QALYs), which
are implemented in economic analyses to aid healthcare
decision makers. The Dermatology Life Quality Index
(DLQI)  is the most commonly used
dermatologyspecific HRQoL instrument . In contrast, the European
Quality of Life-5 Dimension (EQ-5D)  is a generic
utility measure for use across all diseases  that provides
health utility estimates, for comparison of disease burden,
that has been little used in dermatology . Both measures
may be used together, though this may be burdensome, and
integrating data from multiple measures presents
challenges : it is not clear whether two types of measures
should inform the same decision .
There are several ‘mapping techniques’  involving
algorithms to predict health utility estimates from
disease-specific measures. A linear model  was used to
predict health utility estimates from the DLQI [10–12].
However, the methodology had limitations including
small sample sizes and psoriasis-only populations,
which may not be reliably used across a general
dermatology population. Subsequent mapping models were
derived using multiple linear regression  and
bivariate/multivariate analysis , though the authors
did not conduct formal validation to predict utility
values and only went as far as predicting EQ-5D VAS
or total scores. Blome et al.  pessimistically
postulated that ‘any prediction of utilities with the DLQI
and other variables regularly assessed in psoriasis
studies will be vague and not of clinical relevance.’
However, Gray et al.  succeeded in mapping the
Short-Form 12 to categorical EQ-5D responses using
ordinal logistic regression (OLR).
There is a wealth of DLQI data from clinical studies
over the last two decades without health utility estimate
outputs recorded. Therefore, deriving this information from
a dermatology-specific population would allow researchers
to compare more disease-specific economic data across all
conditions. The aim of this study was to create a mapping
model using OLR to predict EQ-5D health utility estimates
from DLQI scores, and we hypothesized that this can be
done reliably. Previous unsatisfactory or failed attempts
have used total DLQI scores to calculate health utility
estimates for a cohort of patients, whereas a key aspect of
OLR methodology is the use of data from individual DLQI
items mapped to individual EQ-5D domains. We also
aimed to produce health utility estimates utilizing the
previous linear regression method employed by Currie and
Conway  on our dataset, which is larger and more
diverse, to compare the accuracy of the two distinct
Materials and methods
The Dermatology Life Quality Index (DLQI)
The DLQI consists of ten items, with four possible
responses to each item: ‘‘Very much,’’ ‘‘A lot,’’ ‘‘A little,’’
and ‘‘Not at all.’’ If any item for the DLQI was left
unanswered, it was scored zero, following the developers’
instructions  (see Appendix). The two parts of item 7
were combined as a single item containing scores for both
parts, as routinely done in calculating total scores. This
allowed a uniform four-level ordinal response system for
all DLQI items.
The European Quality of Life-5 Dimension
The EQ-5D consists of two parts: a descriptive system and
a visual analogue scale (VAS). The descriptive system
contains five dimensions: ‘‘mobility,’’ ‘‘self-care,’’ ‘‘usual
activities,’’ ‘‘pain/discomfort,’’ and ‘‘anxiety/depression.’’
The 3-level version EQ-5D-3L was used, for which there
are three possible responses: ‘‘no problems,’’ ‘‘some
problems,’’ and ‘‘extreme problems.’’ In our analysis, these
outcomes were scored 1, 2, and 3.
Data from 4010 patients with skin diseases  were
used. The patient dataset was accessed from an
international multicenter observational cross-sectional study
examining the association between depressive symptoms
and dermatological conditions ranging from benign and
malignant skin lesions to chronic inflammatory diseases
such as psoriasis and lupus erythematosus . The
dataset (n = 4010) was filtered to exclude subjects with
missing age, sex, DLQI, and EQ-5D data (11.7% in total).
This resulted in a total of 3542 subjects. The
socio-demographic characteristics for the entire patient dataset are
given by Dalgard et al. , and have been summarized
in Table 1. These patients were referred to outpatient
dermatology clinics at various centers across Europe
between 2011 and 2013. The full methodology has been
previously described . Each participant was examined
and the main diagnosis recorded. Patients completed
several questionnaires, including the DLQI and EQ-5D.
This mapping study did not require additional ethics
As no official European time trade-off (TTO) values
exist for EQ-5D health states, we applied the UK TTO
values throughout the validation process.
We assessed the strength of the conceptual correlations
between the DLQI and EQ-5D and found that several key
themes were significantly associated (i.e., p \ 0.05). The
key concepts that apply to each DLQI item are shown in
Table 1 Socio-demographic data for the complete dataset
No. of patients
Table 2 Key concepts that
apply to each DLQI item 
No. of patients
Average age (years, range)
(no. of patients)
a DLQI total score range is 0–30, 0 indicating no impairment and 30
indicating maximum impairment of quality of life
For the ‘Mobility’ EQ-5D domain, DLQI items 3, 7, and
10 were most strongly correlated which cover the concepts
of ‘daily activities,’ ‘work and school,’ and ‘treatment.’
The ‘Pain’ domain was strongly correlated with almost all
key concepts of the DLQI including items 1, 3, 6, 8, 9, and
10. It correlated most with Item 1 of the DLQI, in
particular, which asks about pain and soreness of the patient’s
skin condition. The ‘Self-care’ domain correlated most
strongly with item 10 (treatment), as well as items 1, 3, and
7. ‘Usual activities’ correlated strongly with item 3 (daily
activities) as expected, as well as items 1, 5, 6, 7, and 10.
Finally, the ‘Anxiety’ domain was most strongly correlated
to item 2, which enquires about ‘embarrassment’ and
whether patients feel ‘self-conscious’ due to their skin
condition, as well as items 4, 5, 7, 9, 10. Overall, all ten
DLQI items correlated strongly with the EQ-5D domains,
re-emphasizing the strong conceptual correlation between
the two questionnaires.
Ordinal regression modeling algorithm
Ordinal models produce a set of probabilities for each
possible outcome category, as given by the equations:
PðY ¼ 1Þ ¼ 1 þ eð a1þb1x1þb2x2þ þbmxmÞ
PðY ¼ 3Þ ¼ 1 PðY ¼ 2Þ PðY ¼ 1Þ
‘Y’ represents the outcome of any given EQ-5D domain
(‘‘mobility,’’ ‘‘self-care,’’ ‘‘usual activities,’’
‘‘pain/discomfort,’’ or ‘‘anxiety/depression’’). The outcome
categories Y = 1, 2, and 3 represent the three possible
responses for a given EQ-5D domain, i.e., ‘‘no problems,’’
‘‘some problems,’’ or ‘‘extreme problems,’’ respectively.
Sex was coded as 0 = male and 1 = female. The
x-variables are indicator variables derived from DLQI scores,
age, fitted as a linear term, and sex, while the b’s are the
regression coefficients. The b’s are essentially ‘weights’
attached to each indicator of each DLQI item score, age,
and sex and they are used to calculate estimated
probabilities of each EQ-5D item response. The model is based
on the assumption that for each EQ-5D dimension there is
an underlying continuous ‘latent’ variable, for example,
measuring mobility. The value of the linear combination
b1x1 þ b2x2 þ þ bmxm provides a predicted score, Z, on
this continuum. If we assume that these scores Z follow a
logistic distribution, then the OLR model follows from
assuming that if Z \ a1, the subjects would record an
outcome Y = 1, if a1 \ Z \ a2, they would record an
outcome of Y = 2, and if Z [ a2 they would record an
outcome Y = 3.
Using all data, a series of ordinal logistic regressions
were fitted for each of the five EQ-5D dimensions against
the ten individual items of the DLQI, as well as age and sex
using SPSS version 22. All ten DLQI items were included
for each domain model in order to capture all the
correlations induced by each DLQI item. Regressions were run
with age and sex alone, DLQI items alone, as well as age
and sex combined with DLQI items (Table 3) in order to
evaluate the contribution of age and sex, and collectively
the ten DLQI items. Model comparisons were undertaken
by comparing twice the absolute difference in the
maximized log-likelihoods with the Chi-square distribution with
degrees of freedom equal to the difference in the number of
model terms being evaluated. Note that age and sex were
chosen as additional variables as these data are invariably
recorded and therefore accessible and have been shown to
significantly impact on QoL .
Split-half cross-validation was employed  whereby the
dataset was randomly split five times into separate
estimation and validation sets using the random number
generator in SPSS version 22. The estimation set was used to
derive the mapping models, whilst the out-of-sample
validation set was utilized for validating the fitted models.
This process was repeated with each of the five
estimation/validation sets after which the sets were reversed,
resulting in a total of 10 complete models.
Bootstrapping has been suggested as an alternative
approach to model validation  although that technique
was evaluated in a somewhat simpler setting than the one
considered here, namely with a single binary outcome
variable and a single logistic model rather than with five
ordinal outcomes and a separate logistic model in each
case. As these authors note, however, bootstrapping is
likely to offer relevant advantages in datasets with small
sample sizes. The issue of small sample sizes and
bootstrapping is discussed further in relation to model
validation  when predictor selection techniques have
been employed. In our case, the sample size is sufficiently
large and there is no predictor selection, supporting the use
of split-half cross-validation.
The model was tested on each validation dataset to
produce three predicted probabilities per subject per
EQ5D domain (Y = 1, 2, or 3). Using these predicted
probabilities, a Monte Carlo simulation was run for each subject
resulting in predicted domain responses and consequently
health utility estimates. This was repeated five times for
each random split to ensure the model output was stable.
The five estimation and validation sets were then switched
and the process was repeated (split-half cross-validation),
resulting in a total of ten models. The average predicted
health utility estimate for each validation set was then
compared with the observed health utility estimate of the
The proportional odds assumption was assessed using
the test for parallelism within SPSS. For each domain,
except mobility, this test gave a non-significant result
supporting the assumption for proportional odds. For
mobility, the p value of 0.01 did indicate some departure
from this assumption but this can be explained by the small
number of subjects (n = 11) in the dataset who have a
mobility outcome category of 3. As a consequence, the
sub-model that compares categories 1 and 2 combined with
category 3 is unstable and the results for the test for
Currie and Conway method: linear regression
The methodology reported above for model derivation,
split-half cross-validation, and Monte Carlo simulation
was repeated to test the linear regression algorithm
utilized by Currie and Conway . This method uses the
total DLQI scores and correlates it directly with the final
health utility estimates resulting in a linear regression
formula in the format: Utility = a - (b 9 DLQI total
Table 3 The significance of the DLQI items and age and sex compared to the model containing age, sex, and the DLQI items for each EQ-5D
The average difference between observed health utility
estimates and predicted health utility estimates was
calculated for both OLR and linear regression methods, as
well as mean square error (MSE) and mean absolute error
For each of the five EQ-5D domains, an ordinal model was
derived and used to predict the probability of each EQ-5D
response for each subject in each validation set, and
subsequently the health utility estimates, using Monte Carlo
simulation. The model was shown to be highly predictive,
and repeated data splits demonstrated a stable model. In
each case, the predicted mean health utility estimate was a
slight underestimate of the observed mean health utility
estimate and across the ten validation sets, the difference
between predicted mean health utility estimates and
observed mean health utility estimates ranged from
-0.0024 to -0.0239, with an mean overall difference of
-0.0120. This 1.59% underestimate represents a clinically
unimportant effect . The MSE across all ten splits
ranged from 0.0728 to 0.0818 with an average MSE of
0.0766. The MAE across all ten splits ranged from 0.1873
to 0.2009 with an average MAE of 0.1934.
The predictive ability of the model at an individual
subject level was also examined using histograms to
display the difference between predicted health utility
estimates and the observed health utility estimates for each
simulation at the individual subject level. The results from
a typical split sample are displayed in Fig. 1. The plot
Fig. 1 Histogram displaying the difference between predicted and
observed health utility estimates for a typical split sample
depicts a centrality around ‘0’ which indicates the strong
predictive collective capability of the OLR models. On
average, 37% of the individual health utility estimates were
predicted to lie within 0.1 of the observed values, while
62% were predicted to lie within 0.2 and 81% within 0.3
over all 10 validation exercises.
To further evaluate its reliability, the OLR mapping
method was also applied to different subsets of the study
population. A model was derived from psoriasis-only
patients (n = 484) and tested on patients with all other skin
conditions (n = 3058). The average difference between the
observed and predicted health utility estimates was 0.05
(MSE 0.0844, MAE 0.2037). Thirty-six percent of the
individual health utility estimates were predicted to lie
within 0.1 of the observed values, while 61% were
predicted to lie within 0.2 and 78% within 0.3.
Similarly, the model performance was tested on
different geographical groups of patients. As a test exercise, a
model derived from patients in Italy (n = 517) was tested
on patients from Norway (n = 468). The average health
utility estimate difference for the Norway patients was 0.06
(MSE 0.09. MAE 0.21). Thirty-six percent of the
individual health utility estimates were predicted to lie within 0.1
of the observed values, while 59% were predicted to lie
within 0.2 and 78% within 0.3.
Despite the small sample sizes for the model building
exercise in these two cases, these evaluations support the
reliability and robustness of the modeling framework.
Details of the final-fitted OLR models using data from
the 3542 subjects are given in Table 4.
Currie and Conway method
For the Currie and Conway linear regression model, the
average difference between the observed and predicted
estimates was -0.0007. The MSE across all ten splits
ranged from 0.0438 to 0.05 with an average MSE of
0.0469. The mean absolute error (MAE) across all ten
splits ranged from 0.1527 to 0.1616 with an average MAE
of 0.1566. On average, 38% of the individual health utility
estimates were predicted to lie within 0.1 of the observed
estimates, while 78% were predicted to lie within 0.2 and
89% within 0.3 over all 10 validation exercises.
There is increasing interest in correlating and mapping
DLQI scores into generic measures, such as the EQ-5D, for
cost-analysis and to provide more accurate disease-specific
data which generic measures are unable to capture. Schmitt
et al.  correlated the Work Limitations Questionnaire
with the DLQI (r = 0.47, p \ 0.0001) to derive a model to
Table 4 Final model estimates (standard errors) for each EQ-5D domain
The 10 DLQI questions are represented in order by DLQI 1, DLQI 2, etc
a Sex was coded male = 0, female = 1
calculate work productivity in psoriasis. Moller et al. 
state that ‘disutility among psoriasis patients are within the
ranges of other chronic diseases.’ There is, therefore, a
need to accurately represent and compare data from
dermatology with health utility estimates from other
conditions. Furthermore, there are several inherent
disadvantages with generic measures  such as the
EQ5D or Short-Form 36 (SF-36), e.g., they contain irrelevant
questions for patients with severe inflammatory skin
conditions, resulting in the inability to perform imputation due
to systematically missing responses in the questionnaires.
Patients may also develop ‘questionnaire fatigue’ from
repeated completions. Focusing on one specialty- or
disease-specific questionnaire, from which health utility
estimates may be predicted, provides a perception of relevance
encouraging thorough careful completion by patients
whilst also reducing study time and costs for researchers.
Using OLR, this study has succeeded in mapping DLQI
scores to EQ-5D data, from which health utility estimates
were calculated. The model reliably predicts EQ-5D
scores, in particular at a group level, demonstrated through
a split-half cross-validation process resulting in very close
health utility estimate predictions. The model is shown also
to provide close prediction of health utility estimates at an
individual subject level.
There are strong conceptual associations between the
DLQI and EQ-5D items. Mapping is more likely to be
successful where conceptual overlap between two
measures exists . This is so for the DLQI and EQ-5D; many
studies have reported a strong association [26–31], which is
reaffirmed by this study. Although overall predictions were
strongly correlated to the observed scores at a group level,
the individual predicting power of the model requires
The linear regression model utilized by Currie and
Conway  provided better predictive accuracy when fitted
on this study’s dataset (average difference between
predicted and observed health utility estimates = 0.00065,
compared to OLR = 0.0120). This was also reflected in the
respective MAE (linear regression = 0.16, OLR = 0.19)
and MSE (linear regression = 0.05, OLR = 0.08) values.
It is therefore plausible that this mapping method performs
better when fitted on a larger and dermatologically diverse
dataset, compared to its previous validation study which
was limited to a small sample size and to psoriasis patients
in the UK . However, there is one structural advantage
in the use of the ordinal model over the linear model .
Since the DLQI total score always takes a positive value,
the maximum utility value derived from the linear
regression equation has an upper bound of ‘a.’ In a typical
application, the value of the constant ‘a’ will approach 1
but will never be equal to 1 and a predicted health utility
estimate of ‘1’ (‘perfect health’) cannot be obtained. In the
OLR model and the associated Monte Carlo simulation
such an outcome can be achieved. Both models’ estimates
are derived from a European dataset of over 3500 patients
with various dermatological conditions, and the predicted
responses may be used to calculate country-specific health
utility estimates . This was not possible using the
previous linear model , derived from a UK dataset,
because of differing health utility estimate tariffs between
countries [33, 34]. Thus the proposed ordinal model, as
well as the revised linear regression model, may be used as
mapping tools in other European countries.
There are some limitations that apply to both models.
The observed scores for the DLQI and the EQ-5D were
sometimes inconsistent within the same subject, e.g., one
subject answered 1 on every EQ-5D domain (‘perfect
health’) but 29 on the DLQI (very poor health). This could
be due to poor understanding of the items, the reliability or
validity of the instruments, or due to random errors.
Though these data were included to avoid bias, Van Hout
et al.  argue that analysis should be restricted to
logically consistent responses. Perhaps including more
sociodemographic variables in the OLR model, other than age
and sex, may improve its predictive performance, though
this may result in only marginal improvements that would
not outweigh the complexity of running the model .
The UK TTO values were used in the derivation of both
models; it is worth considering that these health states were
elicited in 1993 and therefore may not be up to date with
current health valuations. Furthermore, no official
European TTO values exist for EQ-5D health states and
therefore we applied the UK TTO values throughout the
validation process. Further sensitivity analysis may be
conducted using preference value-sets from different
countries. However, these were not accessible for this
study, but would be a useful consideration for future
studies. Though there may be cultural variation influencing
HRQoL and utility responses, we have not been able to test
this specific question in detail. However, when the OLR
model was created using only Italian patients and tested on
a Norway population, it performed almost as well as the
model derived from the complete dataset. Our experience
suggests that within the European context there is some
uniformity of attitudes, cultural norms, and responses, as
the DLQI has undergone over one hundred validated
translations, with a significant number in European
countries . We believe the methodology remains intact and
consistent, regardless of the TTO values utilized.
Though bootstrapping may indeed be the best approach
for testing such models, this would require some additional
theoretical considerations to extend existing methodology
for the binary logistic model to the ordinal setting. We
were able to bypass this approach by using ‘split-half
cross-validation,’ which is a valid technique for large
sample sizes . Nevertheless, this study presents the
opportunity for further statistical research.
There may be concerns regarding the use of these
models in different diseases and whether single disease
models would provide more accurate utility data. This
study includes a wide range of the most common different
skin diseases from a wide range of different European
countries, giving the models additional strength in terms of
universality. However, we successfully derived a model
from psoriasis-only patients and tested this on patients with
all other conditions, with the predicted results reassuringly
similar to the original OLR model validation exercise. Two
limitations of this exercise were the sample size of
psoriasis patients, which was relatively small (n = 484) and that
none of the patients had answered ‘extreme’ for the
selfcare domain of the EQ-5D. Given the overall sample size
from which the OLR model was created, our view is
therefore that the model may be implemented successfully
across different conditions, limiting the need for
conditionspecific modeling, which may be practically difficult to
Though we initially hypothesized that OLR will
improve upon previous attempts at predicting health utility
estimates, we have identified that both of the existing
templates may be used as a road map across other medical
disciplines in instances where similar needs exist. Both
methodologies will therefore be useful for researchers
interested in deriving generic HRQoL data, including
descriptive information, from disease-specific populations
without having to implement numerous questionnaires.
Though OLR has previously been used for converting
measures , as far as we are aware this is first time it has
been used to convert a specialty-specific instrument into a
generic measure. A step-by-step guide is provided to
implement the OLR model (Supplementary material) in the
particular setting of mapping the DLQI scores to EQ-5D
health utility estimates. An excel spreadsheet is also
available upon request with pre-programmed formulae to
enable EQ-5D domain probability calculations for a cohort
of patients, from which health utility estimates may be
predicted using Monte Carlo simulation. The DLQI is the
most commonly reported outcome measure in dermatology
[2, 36], and therefore there are many datasets from which
generic EQ-5D and health utility data can now be
predicted, using either OLR or linear regression.
Acknowledgements We wish to thank Dr. M.K.A. Basra, Dr. Paul
Kamudoni, Mr. Pedro Cruz, and Ms. Sue Wei Chong for their
contributions to the early development of this study in Cardiff. We also
wish to acknowledge and thank the European Society of Dermatology
and Psychiatry (ESDaP) Study Group who collected and validated the
patient data for this study . The other ESDaP participants were
Uwe Gieler, Department of Dermatology, Justus Liebig University,
Giessen, Germany; Lucia Tomas-Aragones, Department of
Psychology, University of Zaragoza, Zaragoza, Spain; Lars Lien, Department
of Public Health, Hedmark University College, Elverum, Norway;
Francoise Poot, Department of Dermatology, Universite Libre de
Bruxelles, Brussels, Belgium; Gregor B E Jemec, Department of
Clinical Medicine, University of Copenhagen, Copenhagen,
Denmark; Laurent Misery, Department of Dermatology, University
Hospital of Brest, Brest, France; Csanad Szabo, Department of
Dermatology, University of Szeged, Szeged, Hungary; Dennis Linder,
Department for Dermatology, Padua University Hospital, Padua,
Italy; Francesca Sampogna, Health Services Research Unit, Istituto
Dermopatico dell’Immacolata, Rome, Italy; Andrea W M Evers,
Institute of Psychology Health, University of Leiden, Leiden, the
Netherlands; Jon Anders Halvorsen, Department of Dermatology,
University of Oslo, Oslo, Norway; Flora Balieva, Department of
Dermatology, Stavanger University Hospital, Stavanger, Norway;
Jacek Szepietowski, Department of Dermatology, Wroclaw Medical
University, Wroclaw, Poland; Dmitry Romanov, Department of
Psychiatry and Psychosomatic Medicine, Sechenov First Moscow
State Medical, Moscow, Russia; Servando E Marron, Department of
Dermatology, Alcaniz Hospital, Alcaniz, Spain; Ilknur K Altunay,
Department of Dermatology, Sisli Etfal Teaching and Research
Hospital, Istanbul, Turkey. Finally we would like to thank the journal
editors and the reviewers for their insightful comments. These led to
considerable improvements in the manuscript.
Funding This research received no specific grant from any funding
agency in the public, commercial, or not-for-profit sectors.
Compliance with ethical standards
Conflict of interest AY Finlay is joint copyright holder of the DLQI.
Cardiff University and AYF receive royalties from its use. Authors
FA, RK, VP, FD, JK, and SS declare that they have no conflict of
Ethical approval This article does not describe any new studies with
human participants or animals performed by any of the authors: it
describes additional analyses of previously reported data.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://crea
tivecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
The Dermatology Life Quality Index [1, 16]*
*Footnote: how the DLQI is scored [1, 16]
The scoring of each question is as follows
Question 7: ‘‘prevented work or studying’’
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