Development of a disease-specific instrument to measure quality of life in patients with alopecia areata

ARTICLE

ejd.2012.1752

Auteur(s) : Yuichiro Endo¹ yendou-tky@umin.ac.jp, Yoshiki Miyachi¹, Akiko Arakawa^1,2,3

¹ Department of Dermatology, Kyoto University, 54 Kawahara-cho Shogoin, Sakyo-ku Kyoto 606-8507, Japan

² Department of Dermatology, University of Luebeck, Germany

³ Department of Dermatology, Ludwig-Maximilians-Universität, München, Germany

Reprints: Y. Endo

Alopecia areata (AA) is a common hair loss disease with a wide spectrum of severity, ranging from partial to complete hair loss of the scalp and whole body [1]. Age of onset has a peak in the second and third decades of life. Spontaneous remission can occur in up to 50% of patients within a year [1], although the rest of the patients will have recurrent or prolonged hair loss. A number of treatments can promote rapid hair regrowth, but none has been shown to improve the disease course [2]. As hair loss causes essentially psychosocial disability, not physical damage, managements include variety of options; observation, counselling, contact immunotherapy, and systemic corticosteroids [2, 3]. For severe AA, a wig or hairpiece is the most effective solution to enable patients to participate in a wider range of social activities [2].

Health-related quality of life (QoL) is a multidimensional concept covering influences on an individual's life [4], consisting of physical, psychological and social aspects. QoL measures in dermatology can be categorized into three groups; generic, dermatology-specific and disease-specific instruments [5]. The generic instruments such as the short form 8 (SF-8) [6] include questions more general in nature, to compare general health across different diseases. In contrast, dermatology-specific questionnaires, such as the Dermatology Life Quality Index (DLQI) [7], are designed to compare the QoL amongst different skin diseases. Disease-specific instruments are further calibrated to each disease to reflect disease characteristics and disease management. Accordingly, disease-specific instruments are the most informative when evaluating QoL in a single disease population [8, 9].

It is well recognized that hair loss has a dramatic effect on QoL [10-13]. AA preferentially develops during young adulthood [1], at the time when individuals may place importance on their own body image [14]. Indeed, patients with AA reportedly experience symptoms of depression and anxiety and may suffer from adjustment disorders [8, 11, 15-17]. Furthermore, some research suggested that emotional stress could trigger the onset and/or exacerbation of AA [10, 15]. However, no disease-specific instrument has been available to evaluate QoL of patients with AA. Health-related QoL and dermatology-specific questionnaires cannot be expected to reflect impaired QoL by AA. Firstly, because AA would have minimal influence on general aspects of health status. Secondly, management of AA requires specific modalities, totally different from those for general skin disease. Indeed, the AAQ contains two specific domains representing “restriction of activity” and “concealment”, which can hardly be described even by dermatology-specific questionnaires. In this cross-sectional study, we developed an AA-specific instrument designed to measure QoL within 3 minutes.

Methods and patients

New outpatients with AA were consecutively recruited to the study in our outpatients’ clinic between June 2009 and August 2010. The study was performed only in Japanese patients. Sample size was set at 120 so as to achieve a power of 0.8 in factor analyses, on the condition that the significance level is 0.05, with 20 variables and 4 factors [18]. Patients associated with the other systemic diseases were excluded. This study was approved by institutional review boards. Written informed consent was obtained from all patients.

Instrument development

Semi-structured interviews were conducted with five patients (two men and three women, age 28 ± 6.4years) to produce items for the preliminary questionnaire. The interview guide was prepared to retrieve information regarding medical circumstances. For patients to describe freely how their life is affected by AA, the interviewer (the first author) asked open-ended questions on the interview guide. The interviewer met a participant for the first time in each interview and was not engaged in the treatment thereafter so as to minimise possibility of the content of the interview getting distorted because of personal acquaintance. Each interview was recorded and carefully analysed by content analysis by the first and last author. Contents of the interviews were transcribed and these were later grouped into 31 preliminary questions using K-J method (affinity diagramming). Time frames in the question were set at one month. Patients subjectively evaluated their degree in each question on a five-point scale ranging from 0 (not at all) to 4 (very much). Two dermatologists (the first and last author) independently revised the draft questionnaire for the pilot test. Then, five newly recruited patients responded to the preliminary questions and commented on its interpretability.

Variables

For finalisation, 122 patients were recruited. The questionnaire contained demographic background (age, gender, occupation), the SF-8 [6], State-Trait Anxiety Inventory-Form (STAI) [19], and the Center for Epidemiologic Studies Depression Scale (CES-D) [17]. The SF-8 measures physical and mental health, where higher scores indicate a more favourable quality of life. The STAI evaluates individual's level of anxiety. Higher scores of the STAI represent greater tendency to feel anxiety. The CES-D is a scale to screen depressive symptoms. Higher scores of the CES-D indicate higher level of depression. These scales were adopted based on the literature mentioned above reporting anxiety and distress among patients with AA.

AA severity was assessed by both researcher and by patients, independently. The percentage of scalp hair loss was evaluated photographically at the time of study enrolment according to Olsen's AA investigational assessment guideline [20]. Area of hair loss is graded ranging from S0 (0% hair loss) to S5 (100% hair loss). AA severity was evaluated exclusively by the last author so as to eliminate inter-rater error. All patients also rated their disease severity scored on a 4-point scale.

Statistical analysis

The AAQ was finalised via item analysis and exploratory factor analysis. Items were appraised in terms of score distribution and redundant items were removed from the measure. After their removal, the remaining items were considered for exploratory factor analysis using principal component analysis with Varimax rotation. Items, own factor loading values of which were less than 0.5, were deleted from the model. The minimum requirement of eingen value was set at 1. Probability less than 0.1 was considered as significant.

After the model construction, statistical performance of the AAQ was assessed in terms of reliability and validity. Cronbach's alpha coefficients were calculated with optimal values ranging between 0.6-0.9. Construct validity of the AAQ was tested by correlation analysis and confirmatory factor analysis. Firstly, Pearson's correlation coefficients were calculated between the AAQ and the SF-8, the STAI, the CES-D and disease severity. Then, confirmatory factor analysis was preformed using maximum likelihood estimation [21]. Common measures of overall goodness of fit were used to test construct validity; Chi square value, Goodness of fit index (GFI), adjusted goodness of fit (AGFI), comparative fit index (CFI), and root-mean-square error of approximation (RMSEA). With respect to GFI, AGFI and CFI, values above 0.95 indicate that the data fit well to the proposed model [22]. The theoretical range of GFI, AGFI and CFI is 0 to 1. RMSEA values below 0.05 are usually considered indices of good fit.

To assess whether the factor structure above fitted the data, a confirmatory factor analysis was conducted. As ‘adaptation’ was not significantly correlated with other domains, these correlation coefficients were set at 0. The results are given in figure 1 and table 1. Chi-square value was 11.9 (degree of freedom 12, p = 0.45), which was sufficiently larger than the conventional value of 0.05. The GFI was 0.97 and the AGFI was 0.94, and the CFI 0.99, and the RMSEA value was 0.01. All of these values meant the data fit well to the supposed model. Standardised regression coefficients, except two, ranged from 0.49 to 0.97, which were considered to be satisfactory.

Table 1 Goodness-of-fit measurement results

Statistical analysis was calculated using SPSS version 12.0 (SPSS Inc., Chicago, IL, USA) and AMOS version 5.0 (SmallWaters, Chicago, IL, USA). Cases with missing values were removed from each analysis.

Results

Demographic backgrounds

Demographic characteristics are shown in table 2. Sixty-six percent of the participants were women, with a mean age of 38.3 ± 16.5. Forty six percent of the participants were graded as S1, limited hair loss, while 28% were scored as S4b or S5, almost total baldness.

Table 2 Demographics of patients, given as mean ± standard deviation or no (%) (n = 122^b)

^a Calculated by AA investigative assesment guideline(Olsen et al., 2004 21)

^b The total number of some variables does not correspond to 122 because patients who provided missing values were excluded from the table.

Completion of the AAQ

Five items were excluded from 31 preliminary questions, since the same values were selected by more than half of patients. Ten items were additionally removed, because of poorer commonality values, in the light of past works [13]. As a result of exploratory factor analysis, three-factors with seven items were adopted, accounting for 75% of the total variance (table 3), ‘restriction of activity’, ‘concealment’ and ‘adaptation’. The employed scoring system was a simple summation in each domain. Scores for ‘restriction of activity’ and ‘concealment’ were reversed so as to achieve a high score representing a high QoL by a simple summation.

Table 3 Factor validity and internal consistency of the 7-item AAQ (n =122)

Factors were analyzed using principal component analysis with varimax rotation. Standardized coefficients were shown above (lower than 0.3 were not shown).

Alpha represents Cronbach's alpha coefficient.

The AAQ must not be reproduced or used without permission. Anyone wishing to use the measure should contact Dr. Yuichiro Endo at Kyoto University (E-mail: yendou-tky@umin.ac.jp).

Reliability

Cronbach's alpha coefficients for each subscale in the AAQ were 0.81 for “restriction of activity”, 0.77 for “concealment” and 0.59 for “adjustment” (table 3). The value for adjustment was slightly low to acceptable overall.

Validity

Subscales for “restriction of activity” and “concealment” were reasonably correlated with the SF-8 MCS, the CES-D and the STAI, in spite of no correlation with physical health assessed by SF8 PCS (table 4). “Adaptation” was negatively correlated with depression (the CES-D) and anxiety (the STAI , table 4).

Table 4 Construct validity of the AAQ (n = 46)

All entries without special indication represent Pearson's correlation coefficients.

*** p<0.001, ** p<0.01, * p<0.05, † p<0.1

^a SF8 PCS: The Medical Outcome Study 8-Item Short-Form Health Survey Physical Component Summary

^b SF8 MCS: The Medical Outcome Study 8-Item Short-Form Health Survey Mental Component Summary

^c STAI: State-Trait Anxiety Inventory-Form JYZ; Trait Anxiety scale

^d CES-D: The Center for Epidemiologic Studies Depression Scale

^e Calculated by AA investigative assesement guideline(Olsen et al., 2004 20)

In contrary to our expectations, there was no correlation between the AAQ with demographic variables and objective severity, such as disease extension and disease duration (table 4). However, subjective severity scored as patient's Global Assessment was significantly correlated with “restriction of activity” and “concealment”, but not with “adaptation” (table 4, AAQ1; r = -0.41, AAQ2; r = -0.27, AAQ3; r = 0.03). The AAQ were not associated with any demographic variables, besides the significant correlation between ‘adaptation’ and age.

Additionally, we examined how AAQ changed between the patients using wigs and the patients who did not use wigs. There were no differences in the SF-8, the STAI, and the CES-D between these groups, however, levels of “restriction of activity” and “concealment” were significantly lower in the wig group, compared to the non-wig group (table 5. AAQ1; 8.14 ± 3.4 vs. 5.65 ± 3.1, p = 0.001. AAQ2; 5.22 ± 2.83 vs. 3.41 ± 2.45 p = 0.002). Instead, “adaptation” showed a higher tendency in the wig group, though the difference did not reach significance (table 5. AAQ3; 3.84 ± 2.37 vs. 4.85 ± 2.2, p = 0.061). Therefore, the AAQ was more sensitive than these other measures and possessed sufficient construct validity.

Table 5 The effect of wig usage on QoL

Comparison was made using Mann-Whitney's U test.

Discussion

We have developed a disease-specific instrument to measure AA patients’ QoL (AAQ). The AAQ is a self-reported measure specifically designed to evaluate quality of life in AA in the past one month. The AAQ contains 7 items regarding “restriction of activity”, “concealment” and “adaptation”. This scale is short enough to be completed within a couple of minutes. Statistical analysis revealed that the AAQ has good reliability for use in daily clinical practice and research trials.

The AAQ seems superior to dermatology-specific and generic QoL scales when assessing how the disease affects patients because disease-specific scales contain only items directly concerning the circumstances which are involved. For example, an item of the DLQI concerning skin symptoms like itch and pain seems quite irrelevant for patients with AA because AA does not accompany such symptoms unless the responder uses a wig. As a result, responses to that item of the DLQI tend to converge to “none” (0 point), which is not very informative to researchers. Therefore, the AAQ collects data about the contribution of AA to QoL more efficiently and accurately. However, disease-specific QoL scales and dermatology-specific QoL scales should not compete, rather be complementary to each other. The combination of both types of scale enables multi-faceted understanding of the consequences of AA.

As has been expected [8, 11, 15–17], this study highlighted that patients with AA were subject to a large amount of distress. For instance, the average CES-D score was 16 points, the traditional cut-off score to screen for depressive individuals. When applying this threshold, 40% of the participants had a risk for depressive disorders. In addition, the mean score of MCS of the SF-8 were much lower when compared with scores in the Japanese population [6] (41.6 vs. 50.1, t = -5.59, p<0.001). As these two scales were negatively correlated with AAQ1 (restriction of activity) and AAQ2 (concealment), the AAQ reasonably reflects the psychological status of the AA patients. Moreover, unlike in the other cutaneous diseases that show close correlation between objective severity and impairment of QoL, such as psoriasis and atopic dermatitis [23, 24], the AAQ and other QoL scales did not show any significant correlation with the objective severity in AA. This result emphasizes the need to pay attention to the psychological disturbance of patients, even if the clinical symptoms seem trivial.

“Adaptation” is one of the important characteristics of the AAQ. This subscale appears to reflect a positive attitude toward AA, while the other two evaluate negative effects of hair loss. We can find this unconventional kind of item in recently developed QoL questionnaires, for example, in a QoL scale for stroke survivors [25]. It might be possible to support each individual in a more suitable way based on an understanding of not only their damaged QoL, but also their positive attitude. When complete recovery cannot be expected, a secondary goal of disease management would support adaptation of the patients to their medical situation [2].

The present study is limited by several factors. Firstly, the study sample consisted of only outpatients of one university hospital. We cannot deny a possible sampling bias. Secondly, the AAQ was originally developed in Japan, so that we have to accumulate additional data to adapt the AAQ in other countries.

In conclusion, this study developed and validated a self-administered measure to evaluate QoL in patients with AA. The AAQ has been revealed to possess acceptable reliability and satisfactory validity. This study illustrated severely damaged QoL in AA. Quantitative and qualitative studies using AAQ will provide additional evidence to improve the management of AA in the future.

Disclosure

Acknowledgements: We thank for Dr. Ewan Langan and Dr. Yosuke Yamamoto for their critical reading and helpful comments. We also thank the anonymous reviewers for valuable advice. Funding sources: none. Conflicts of interest: none

Anyone wishing to use the measure should contact Dr. Yuichiro Endo at Kyoto University < yendou-tky@umin.ac.jp>

References

1. Alkhalifah A, Alsantali A, Wang E et al. Alopecia areata update: part I. Clinical picture, histopathology, and pathogenesis. J Am Acad Dermatol 2010; 62: 177-88, quiz 89-90.

2. MacDonald Hull SP, Wood ML, Hutchinson PE, et al. Guidelines for the management of alopecia areata. Br J Dermatol 2003 ; 149 : 692-9.

3. Stuck AE, Minder CE, Frey F.J. Risk of infectious complications in patients taking glucocorticosteroids. Rev Infect Dis 1989 ; 11 : 954-63.

4. Finlay A.Y. Quality of life measurement in dermatology: a practical guide. Br J Dermatol 1997 ; 136 : 305-14.

5. Skoet R, Zachariae R, Agner T. Contact dermatitis and quality of life: a structured review of the literature. Br J Dermatol 2003 ; 149 : 452-6.

6. Fukuhara S, Suzukamo Y. Manual of the SF-8 Japanese Version. Kyoto : Institute for Health Outcome & Process Evaluation Research, 2004.

7. Finlay AY, Khan G.K. Dermatology Life Quality Index (DLQI)--a simple practical measure for routine clinical use. Clin Exp Dermatol 1994 ; 19 : 210-6.

8. Gupta MA, Gupta AK, Watteel G.N. Stress and alopecia areata: a psychodermatologic study. Acta Derm Venereol 1997 ; 77 : 296-8.

9. Wiebe S, Guyatt G, Weaver B, et al. Comparative responsiveness of generic and specific quality-of-life instruments. Journal of clinical epidemiology 2003 ; 56 : 52-60.

10. Manolache L, Benea V. Stress in patients with alopecia areata and vitiligo. J Eur Acad Dermatol Venereol 2007 ; 21 : 921-8.

11. Ruiz-Doblado S, Carrizosa A, Garcia-Hernandez M.J. Alopecia areata: psychiatric comorbidity and adjustment to illness. Int J Dermatol 2003 ; 42 : 434-7.

12. Schmidt S, Fischer TW, Chren MM, et al. Strategies of coping and quality of life in women with alopecia. Br J Dermatol 2001 ; 144 : 1038-43.

13. Dolte KS, Girman CJ, Hartmaier S, et al. Development of a health-related quality of life questionnaire for women with androgenetic alopecia. Clin Exp Dermatol 2000 ; 25 : 637-42.

14. Marcoux D. Cosmetics, skin care, and appearance in teenagers. Semin Cutan Med Surg 1999 ; 18 : 244-9.

15. Brajac I, Tkalcic M, Dragojevic DM, et al. Roles of stress, stress perception and trait-anxiety in the onset and course of alopecia areata. J Dermatol 2003 ; 30 : 871-8.

16. Liakopoulou M, Alifieraki T, Katideniou A, et al. Children with alopecia areata: psychiatric symptomatology and life events. J Am Acad Child Adolesc Psychiatry 1997 ; 36 : 678-84.

17. Picardi A, Pasquini P, Cattaruzza MS, et al. Psychosomatic factors in first-onset alopecia areata. Psychosomatics 2003 ; 44 : 374-81.

18. Marsh W HK-T, Balla R, Grayson, D Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research 1998; 33: 181-220.

19. Iwata N, Mishima N, Shimizu T, et al. Positive and negative affect in the factor structure of the State-Trait Anxiety Inventory for Japanese workers. Psychol Rep 1998 ; 82 : 651-6.

20. Olsen EA, Hordinsky MK, Price VH, et al. Alopecia areata investigational assessment guidelines–Part II. National Alopecia Areata Foundation. J Am Acad Dermatol 2004 ; 51 : 440-7.

21. Bollen K. Structural equations with latent variables. New York : John Wiley & Sons, 1989.

22. Bentler PM MA. Choice of structural equation models via parsimony: a rational based on precision. Psychol Bull 1989; 106: 315-7.

23. Kondo-Endo K, Ohashi Y, Nakagawa H, et al. Development and validation of a questionnaire measuring quality of life in primary caregivers of children with atopic dermatitis (QPCAD). Br J Dermatol 2009 ; 161 : 617-25.

24. Touw CR, Hakkaart-Van Roijen L, Verboom P, et al. Quality of life and clinical outcome in psoriasis patients using intermittent cyclosporin. Br J Dermatol 2001 ; 144 : 967-72.

25. Bode RK, Heinemann AW, Butt Z, et al. Development and validation of participation and positive psychologic function measures for stroke survivors. Arch Phys Med Rehabil 2010 ; 91 : 1347-56.