10062017  Uitgave 11/2017 Open Access
Mapping of the DLQI scores to EQ5D utility values using ordinal logistic regression
 Tijdschrift:
 Quality of Life Research > Uitgave 11/2017
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Electronic supplementary material
The online version of this article (doi:10.1007/s1113601716074) contains supplementary material, which is available to authorized users.
Introduction
‘Healthrelated quality of life’ (HRQoL) data can be used to derive ‘QualityAdjusted Life Years’ (QALYs), which are implemented in economic analyses to aid healthcare decision makers. The Dermatology Life Quality Index (DLQI) [
1] is the most commonly used dermatologyspecific HRQoL instrument [
2]. In contrast, the European Quality of Life5 Dimension (EQ5D) [
3] is a generic utility measure for use across all diseases [
4] that provides health utility estimates, for comparison of disease burden, that has been little used in dermatology [
5]. Both measures may be used together, though this may be burdensome, and integrating data from multiple measures presents challenges [
6]: it is not clear whether two types of measures should inform the same decision [
7].
There are several ‘mapping techniques’ [
8] involving algorithms to predict health utility estimates from diseasespecific measures. A linear model [
9] was used to predict health utility estimates from the DLQI [
10–
12]. However, the methodology had limitations including small sample sizes and psoriasisonly populations, which may not be reliably used across a general dermatology population. Subsequent mapping models were derived using multiple linear regression [
13] and bivariate/multivariate analysis [
14], though the authors did not conduct formal validation to predict utility values and only went as far as predicting EQ5D VAS or total scores. Blome et al. [
14] 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. [
15] succeeded in mapping the ShortForm 12 to categorical EQ5D 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 dermatologyspecific population would allow researchers to compare more diseasespecific economic data across all conditions. The aim of this study was to create a mapping model using OLR to predict EQ5D 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 EQ5D domains. We also aimed to produce health utility estimates utilizing the previous linear regression method employed by Currie and Conway [
9] on our dataset, which is larger and more diverse, to compare the accuracy of the two distinct mapping techniques.
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 [
16] (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 fourlevel ordinal response system for all DLQI items.
The European Quality of Life5 Dimension
The EQ5D consists of two parts: a descriptive system and a visual analogue scale (VAS). The descriptive system contains five dimensions: “mobility,” “selfcare,” “usual activities,” “pain/discomfort,” and “anxiety/depression.” The 3level version EQ5D3L 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.
The data
Data from 4010 patients with skin diseases [
17] were used. The patient dataset was accessed from an international multicenter observational crosssectional 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 [
17]. The dataset (
n = 4010) was filtered to exclude subjects with missing age, sex, DLQI, and EQ5D data (11.7% in total). This resulted in a total of 3542 subjects. The sociodemographic characteristics for the entire patient dataset are given by Dalgard et al. [
17], 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 [
17]. Each participant was examined and the main diagnosis recorded. Patients completed several questionnaires, including the DLQI and EQ5D. This mapping study did not require additional ethics approval.
Table 1
Sociodemographic data for the complete dataset
No. of patients



Country


Belgium

222

Denmark

247

France

116

Germany

254

Hungary

171

Italy

517

Netherlands

209

Norway

468

Poland

247

Russia

269

Spain

274

Turkey

280

UK

268

Most common diagnoses


Psoriasis

484

Eczema

239

Acne

185

No. of patients

Average age (years, range)



All subjects

3542

46.29 (18–95)

Sex


Male (
n)

1558

47.76 (18–92)

Female (
n)

1984

45.14 (18–95)

Average DLQI score
^{a}

6.69

EQ5D domain (no. of patients)

No problems

Some problems

Extreme problems


Mobility

2692

839

11

Selfcare

3162

372

8

Usual activities

2615

874

53

Pain or discomfort

1604

1739

199

Anxiety or depressed

1954

1431

157

As no official European time tradeoff (TTO) values exist for EQ5D health states, we applied the UK TTO values throughout the validation process.
Conceptual correlations
We assessed the strength of the conceptual correlations between the DLQI and EQ5D 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
2.
Table 2
Key concepts that apply to each DLQI item [
1]
Section

Items


Symptoms and feelings

1, 2

Daily activities

3, 4

Leisure

5, 6

Work and school

7

Personal relationships

8, 9

Treatment

10

For the ‘Mobility’ EQ5D 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 ‘Selfcare’ 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 ‘selfconscious’ due to their skin condition, as well as items 4, 5, 7, 9, 10. Overall, all ten DLQI items correlated strongly with the EQ5D domains, reemphasizing 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:
$$P\left( {Y = 1} \right) = \frac{1}{{1 + {\text{e}}^{{(  a_{1} + b_{1} x_{1} + b_{2} x_{2} + \cdots + b_{m} x_{m} )}} }}$$
$$P\left( {Y = 2} \right) = \frac{1}{{1 + {\text{e}}^{{(  a_{2} + b_{1} x_{1} + b_{2} x_{2} + \cdots + b_{m} x_{m} )}} }}  P(Y = 1)$$
$$P\left( {Y = 3} \right) = 1  P\left( {Y = 2} \right)  P(Y = 1)$$
‘Y’ represents the outcome of any given EQ5D domain (“mobility,” “selfcare,” “usual activities,” “pain/discomfort,” or “anxiety/depression”). The outcome categories
Y = 1, 2, and 3 represent the three possible responses for a given EQ5D domain, i.e., “no problems,” “some problems,” or “extreme problems,” respectively. Sex was coded as 0 = male and 1 = female. The
xvariables 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 EQ5D item response. The model is based on the assumption that for each EQ5D dimension there is an underlying continuous ‘latent’ variable, for example, measuring mobility. The value of the linear combination
\(b{}_{1}x_{1} + b_{2} x_{2} + \cdots + b_{m} x_{m}\) 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 <
a
_{1}, the subjects would record an outcome
Y = 1, if
a
_{1} <
Z <
a
_{2}, they would record an outcome of
Y = 2, and if
Z >
a
_{2} they would record an outcome
Y = 3.
Using all data, a series of ordinal logistic regressions were fitted for each of the five EQ5D 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 loglikelihoods with the Chisquare 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 [
18].
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 EQ5D domain
EQ5D domain

Covariates: age/sex

Covariates: DLQI

Covariates: age/sex/DLQI



−2 log likelihood

Chisquare comparing to full model

Degrees of freedom (
df)

−2 log likelihood

Chisquare comparing to full model

Degrees of freedom (
df)

−2 log likelihood


Mobility

507.4

171.9

2

1311

107.0

10

1566

Selfcare

430.8

18.7

2

862.5

172.9

10

988.7

Usual activities

610.2

36.6

2

1388.1

269.2

10

1754.1

Pain

783.8

37.5

2

1738

424.9

10

2373.3

Anxiety/depression

772.6

19

2

1787.9

284.4

10

2451.7

Model validation
Splithalf crossvalidation was employed [
19] 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 outofsample 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 [
19] 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 [
20] 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 splithalf crossvalidation.
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 (splithalf crossvalidation), 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 same set.
The proportional odds assumption was assessed using the test for parallelism within SPSS. For each domain, except mobility, this test gave a nonsignificant 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 submodel that compares categories 1 and 2 combined with category 3 is unstable and the results for the test for parallelism unreliable.
Currie and Conway method: linear regression
The methodology reported above for model derivation, splithalf crossvalidation, and Monte Carlo simulation was repeated to test the linear regression algorithm utilized by Currie and Conway [
9]. 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 × DLQI total score).
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 (MAE).
Results
Model validation
OLR method
For each of the five EQ5D domains, an ordinal model was derived and used to predict the probability of each EQ5D 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 [
21]. 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 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 psoriasisonly 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). Thirtysix 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). Thirtysix 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 finalfitted OLR models using data from the 3542 subjects are given in Table
4.
Table 4
Final model estimates (standard errors) for each EQ5D domain
Mobility

Selfcare

Usual activities

Pain/discomfort

Anxiety/depression



Threshold a
_{1}

4.500 (0.190)

4.854 (0.251)

3.574 (0.171)

2.204 (0.133)

1.469 (0.128)

Threshold a
_{2}

9.506 (0.368)

9.074 (0.438)

7.231 (0.237)

6.052 (0.178)

4.775 (0.162)

Age

0.051 (0.003)

0.033 (0.004)

0.027 (0.003)

0.025 (0.002)

0.003 (0.002)

Sex
^{a}

0.046 (0.089)

−0.213 (0.120)

0.133 (0.087)

0.177 (0.073)

0.465 (0.073)

DLQI 1

0.087 (0.055)

0.176 (0.074)

0.270 (0.052)

0.685 (0.047)

0.035 (0.044)

DLQI 2

0.013 (0.061)

0.052 (0.079)

−0.114 (0.059)

0.014 (0.049)

0.378 (0.048)

DLQI 3

0.209 (0.068)

0.278 (0.085)

0.351 (0.063)

0.199 (0.060)

0.107 (0.057)

DLQI 4

0.071 (0.058)

0.053 (0.072)

0.051 (0.055)

0.097 (0.050)

−0.099 (0.048)

DLQI 5

0.113 (0.075)

0.064 (0.095)

0.209 (0.070)

−0.122 (0.064)

0.205 (0.062)

DLQI 6

0.116 (0.060)

0.014 (0.071)

0.215 (0.055)

0.310 (0.054)

−0.075 (0.052)

DLQI 7

0.251 (0.053)

0.236 (0.063)

0.283 (0.049)

−0.048 (0.046)

0.186 (0.044)

DLQI 8

−0.008 (0.076)

−0.013 (0.091)

−0.081 (0.071)

0.163 (0.066)

0.121 (0.064)

DLQI 9

−0.094 (0.065)

0.002 (0.075)

0.068 (0.060)

0.132 (0.057)

0.194 (0.054)

DLQI 10

0.233 (0.061)

0.478 (0.071)

0.210 (0.057)

0.245 (0.054)

0.155 (0.052)

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.
Discussion
There is increasing interest in correlating and mapping DLQI scores into generic measures, such as the EQ5D, for costanalysis and to provide more accurate diseasespecific data which generic measures are unable to capture. Schmitt et al. [
22] correlated the Work Limitations Questionnaire with the DLQI (
r = 0.47,
p < 0.0001) to derive a model to calculate work productivity in psoriasis. Moller et al. [
23] 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 [
24] such as the EQ5D or ShortForm 36 (SF36), 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 diseasespecific 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 EQ5D data, from which health utility estimates were calculated. The model reliably predicts EQ5D scores, in particular at a group level, demonstrated through a splithalf crossvalidation 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 EQ5D items. Mapping is more likely to be successful where conceptual overlap between two measures exists [
25]. This is so for the DLQI and EQ5D; 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 further testing.
The linear regression model utilized by Currie and Conway [
9] 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 [
9]. However, there is one structural advantage in the use of the ordinal model over the linear model [
9]. 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 countryspecific health utility estimates [
32]. This was not possible using the previous linear model [
9], 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 EQ5D were sometimes inconsistent within the same subject, e.g., one subject answered 1 on every EQ5D 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. [
35] 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 [
15]. 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 EQ5D health states and therefore we applied the UK TTO values throughout the validation process. Further sensitivity analysis may be conducted using preference valuesets 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 [
2]. 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 ‘splithalf crossvalidation,’ which is a valid technique for large sample sizes [
19]. 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 psoriasisonly 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 EQ5D. 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 create.
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 diseasespecific populations without having to implement numerous questionnaires. Though OLR has previously been used for converting measures [
15], as far as we are aware this is first time it has been used to convert a specialtyspecific instrument into a generic measure. A stepbystep guide is provided to implement the OLR model (Supplementary material) in the particular setting of mapping the DLQI scores to EQ5D health utility estimates. An excel spreadsheet is also available upon request with preprogrammed formulae to enable EQ5D 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 EQ5D 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 [
17]. The other ESDaP participants were Uwe Gieler, Department of Dermatology, Justus Liebig University, Giessen, Germany; Lucia TomasAragones, 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 notforprofit 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 interest.
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.
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