Productivity Loss and Health-Related Quality of Life Among People Living With Alopecia Areata: A Canada-Wide Cross-Sectional Study

Abstract

Background:

Alopecia areata (AA) is associated with reduced health-related quality of life (HRQOL), and patients with AA also experience psychosocial symptoms that can influence their participation in activities, including work.

Objective:

To describe work productivity loss and HRQOL in AA.

Methods:

In this cross-sectional study, we administered a web-based questionnaire to adults self-reporting AA recruited from a patient organization. AA severity was assessed using the 6-domain Alopecia and Areata Patient Priority Outcomes Questionnaire, productivity loss was evaluated using the Valuation of Lost Productivity Questionnaire and the Work Productivity and Activity Impairment Questionnaire, and HRQOL was assessed using the Veterans-Rand 12 Questionnaire. Linear regression was used to measure the association between AA severity and the outcomes.

Results:

Our final sample of 51 participants was predominantly women (n = 46), White race (n = 37), and working (n = 36). Unadjusted regressions showed that a 1-point increase in the emotional symptoms (ES) domain or activity limitations (AL) domain from AA was associated with increased total productivity loss (49.7 hours [95% CI: 11.5-87.9] and 40.4 hours [4.5-76.4]), paid productivity loss (33.8 hours [3.8-63.8] and 40.7 hours [16.1-65.3]), and worse HRQOL (−0.094 [−0.164 to −0.025] and −0.087 [−0.152 to −0.022]). Percentages of work impairment and activity impairment also increased with higher ES and AL.

Conclusions:

Greater ES and AL due to AA were associated with greater impairment in participants’ regular activities in our small sample. Further research is needed in a larger, more representative sample of working adults with AA to elucidate findings.

Keywords

alopecia areata quality of life work productivity patient-reported outcomes survey

Introduction

Alopecia areata (AA) is an autoimmune disease resulting in non-scarring hair loss.¹ AA can manifest as 1 or more patches of hair loss on the scalp (patchy/localized), complete loss of scalp hair (alopecia totalis), or complete loss of hair across the entire body (alopecia universalis).² AA is associated with several comorbid conditions such as atopic diseases (eg, atopic dermatitis [AD], asthma, and allergic rhinitis), autoimmune thyroid disease, systemic lupus, and psychiatric conditions, including anxiety and/or depressive disorders, and adjustment disorders.^3
-6 At present, there is no cure for AA, though several different off-label treatment options exist, such as topical or intralesional corticosteroids, topical immunotherapy, and oral immunosuppressants.^1,7,8 Most recently, Janus kinase inhibitors have been approved for the treatment of moderate to severe AA,⁹ though these medications present a significant financial burden for patients.

The physical and emotional consequences of AA can negatively impact mental health, health-related quality of life (HRQOL), and productivity.⁴ For example, many patients with AA withdraw socially and engage in avoidant behaviors due to social anxiety.⁴ These behaviors include declining to attend social events and staying home from school and work.^10
-12 This withdrawal from academic and occupational commitments is exacerbated by stigmatization and bullying in both schools and the workplace.^13,14

The productivity loss associated with AA has been evaluated in a few studies using the Work Productivity and Activity Impairment (WPAI) Questionnaire.^15
-17 However, existing studies using the WPAI could not convert their estimated percentage of work impairment due to health into time loss estimates.^18,19 Quantifying the work productivity loss in terms of time loss would inform the cost burden of AA and enable further economic evaluation of AA treatment from a societal perspective.²⁰ Furthermore, to the best of our knowledge, there is no Canadian evidence on productivity loss associated with AA to inform such economic evaluations. Similarly, measurements of HRQOL among patients with AA in Canada using health utilities would be valuable for future cost-effectiveness analyses. Existing Canadian studies on the burden of AA on patient QOL have largely focused on characterizing the psychosocial impact,^4,21 as well as the overall impact using the Dermatology Life Quality Index.⁴ While widely used in clinical practice and research,²² the Dermatology Life Quality Index itself does not directly yield estimates of health utility values.^23,24

Accurate evaluations of the economic burden of AA in different jurisdictions are a priority, given the emergence of costly new therapies. In this cross-sectional study, we aimed to describe work productivity loss and HRQOL in adults with AA from across Canada.

Materials and Methods

Study Design and Participants

This was a cross-sectional study in which participants completed an online questionnaire administered using Qualtrics (Provo, UT, USA). The questionnaire was disseminated to potential participants through the Canadian Alopecia Areata Foundation, and questionnaires were completed anonymously. To be eligible, participants were required to be 19 years of age or older, a resident of Canada, self-report a health professional diagnosis of AA, and be able to understand English.

This study was developed in collaboration with a patient partner living with AA and 2 additional patient partners with chronic disease (1 living with migraines and 1 with AD). A draft of the questionnaire was piloted in 3 patients with a history of AD, 3 patients with migraines, and 1 patient with AA. Questions related to productivity loss and demographics were the same for the 3 diseases. The questions related to disease history, severity, and treatment were disease-specific. After they completed the draft questionnaire, participants were interviewed for feedback, and appropriate revisions were made.

This study was approved by The University of British Columbia Research Ethics Board (#H22-03211). Recruitment for this study occurred between February 24, 2024 and April 26, 2024. Participants provided consent electronically before initiating the questionnaire. We followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for reporting observational studies.²⁵

AA Symptom and Outcomes

Symptoms and outcomes of AA were assessed using the Alopecia Areata Patient Priority Outcome (AAPPO) tool.^26,27 The AAPPO is an 11-item measure that assesses hair loss symptoms, emotional symptoms (ES), and activity limitations (AL) due to AA. Hair loss symptoms were categorized based on areas of the body where the respondent is experiencing hair loss: scalp, eyebrows, eyelashes, and body. For each area of hair loss, the current amount of hair loss was evaluated on a 5-point scale (0 = no hair loss to 4 = complete hair loss). ES were assessed using 4 items asking participants how often they felt (1) self-conscious, (2) embarrassed, (3) sad, and (4) frustrated about their hair loss over the past week using a 5-point scale (0 = never to 4 = always). AL were assessed using 3 items asking participants how much they limited their (1) participation in outdoor activities, (2) exercise or physical activity, and (3) interactions with others because of their hair loss over the past week using a 5-point scale (0 = not at all to 4 = completely). Six independent domain scores were generated based on the AAPPO: 4 domain scores corresponding to each of the hair loss areas, a mean ES domain score (domain 5), and a mean AL domain score (domain 6).²⁷

Productivity Loss, Work and Activity Impairments, and HRQOL

Our primary measure of productivity loss was measured using the Valuation of Lost Productivity (VOLP) Questionnaire.²⁸ The VOLP consists of questions that inquire about unemployment due to health, absenteeism (number of absent work days due to health), presenteeism (hours actually taken to complete all work relative to the hours taken to complete the same work if not experiencing any health problems), and unpaid work loss (hours of paid and unpaid help received for unpaid work activities due to health).²⁸

The primary outcome variable was total hours of productivity loss as measured using the VOLP. This was calculated as the sum of the hours of paid and unpaid productivity loss in a 3 month period among those who were employed, and the sum of the work hours lost due to unemployment and unpaid productivity loss among those who reported unemployed due to health. As secondary outcomes, we separately analyzed paid productivity loss (from presenteeism and absenteeism) for those who were employed and unpaid productivity loss for employed and unemployed participants.

We included the WPAI – General Health as a second measure of work productivity loss.^29,30 The WPAI-GH consists of questions that inquire about work hours, work hours missed due to both health reasons and other reasons, and the degree to which health affected both productivity while at work and productivity in other activities. The recall period is 7 days. We analyzed the percent overall work impairment due to health and the percent activity impairment due to health. Calculations for the VOLP and WPAI outcomes followed the steps as described in Naik et al.³¹

We included the Veterans RAND 12 Item Health Survey (VR-12) as our measure of HRQOL. The VR-12 was developed from the Veterans RAND 36 Item Health Survey, which was developed and modified from the original RAND version of the 36-item Health Survey version 1.0 (also known as the “MOS SF-36”).^32,33 The VR-12 is a generic HRQOL instrument that includes 14 items. The first 12 correspond to 8 health domains (physical functioning, social functioning, role limitations due to physical problems, role limitations due to emotional problems, bodily pain, mental health, vitality, and general health), while the remaining 2 items capture change in physical and emotional health over the past year. We used Canadian preference weights to derive a health utility score (0 represents death and 1 represents best possible health) from VR-12 responses.³⁴

Statistical Analysis

Data were summarized using descriptive, univariate statistics, that is, means and standard deviations (SDs) for continuous variables and frequencies and percentages for categorical variables. We examined the association between each of the 6 AAPPO domains and our outcome variables using unadjusted ordinary least squares (OLS) regressions. Given the small sample size, we limited covariate-adjustment in models to outcomes with sufficient sample. Covariate selection was based on the bivariate associations between potential covariates and the outcome variables, and covariates were included only in the model for the respective outcome for which it was significantly associated (P < .05). These potential covariates were age, gender, White ethnicity, marital status, education level, household income, and the number of comorbidities. We translated estimates from OLS models for the paid productivity loss hours into monetary equivalents based on the average hourly wage in Canada in 2023.³⁵

Statistical tests were 2-sided, and the threshold for significance was P < .05. Analyses were performed using R statistical software version 4.3.3 and Stata version 15.1 (StataCorp LLC, College Station, TX, USA).

Results

The characteristics of our sample are presented in Table S1. A total of 51 participants living with AA completed the questionnaire and were included in the analyses. The sample was predominantly women (n = 46, 90.2%) and White race (n = 37, 72.5%) with a mean (SD) age of 49.5 years (14.7). Among the 6 scores derived from the AAPPO tool, the average scores were highest for the hair loss on the scalp domain (mean = 3.1 [1.0]), followed by the ES domain (mean = 2.9 [1.0]); whereas, the average score was lowest for the AL domain (mean = 1.3 [1.1]).

Employment-related characteristics are presented in Table S2. Out of the 51 participants, 36 (70.6%) reported working full- or part-time and 15 (29.4%) were not working. A little over half of those working reported working from home at least a part of the time (n = 20, 55.6%). The mean (SD) total productivity loss hours over a 3 month period was 100.8 (147.7) in the full sample. Among workers, the mean 3 month paid work productivity loss hours was 67.2 (87.8), which was primarily due to presenteeism (mean = 43.6 hours [77.0]). Average percent overall work impairment and percent activity impairment in the last 7 days were similar to each other (28.5% [26.5%] and 31.8% [31.1%], respectively).

Treatment strategies and workplace supports reported by the participants are presented in Table S3. The most reported treatments were steroid injections (n = 37, 72.5%) and topical steroids (n = 36, 70.6%). Few workplace supports were reported overall.

In our unadjusted regression models, the AAPPO domains 1 to 4 for hair loss (ie, scalp, eyelashes, and body) were generally not associated with VOLP, WPAI, or health utility outcomes (Table S4). A 1-point increase in domain 5, ES, was associated with multiple productivity loss and impairment outcomes: increase in total productivity loss (49.7 hours [95% CI: 11.5-87.9], P < .05); increase in paid productivity loss (33.8 hours [3.8-63.8], P < .05); increase in percentage overall work impairment (14.2% [5.8%-22.6%], P < .01); and increase in percentage activity impairment (12.4% [4.5%-20.2%], P < .01). There were also positive and significant associations shown between domain 6, AL, and total productivity loss (40.4 hours [4.5-76.4], P < .05); paid work productivity loss (40.7 hours [16.1-65.3], P < .01); unpaid work productivity loss (12.6 hours [0.5-24.7], P < .05); percentage overall work impairment (16.1% [8.9%-23.2%], P < .001); and percentage activity impairment (12.9% [5.8%-19.9%], P < .001). One-point increase in the ES and AL score was associated with decreases in health utility (−0.094 [−0.164 to −0.025], P < .01 and (−0.087 [−0.152 to −0.022], P < .01, respectively).

We used the paid productivity loss hour models to estimate the monetary equivalent and proportion of work time lost due to ES and AL (Table S5). Assuming participants experienced ES “rarely” or AL “a little” (ie, scores = 1), the corresponding hours were 1.8 hours (0.4% of work hours) and 49.2 hours (10.3% of work hours) in a 3 month period. These hours translated into $60 and $1649 CAD of loss per worker, respectively. Conversely, a score of 4 (ES “always” and AL “completely”) would translate to $3460 and $5742 CAD of paid productivity loss.

Four outcomes were considered for covariate-adjustment regression models because of sufficient sample size: (1) total productivity loss hours, (2) unpaid productivity loss hours, (3) percentage activity impairment, and (4) health utility. The models for unpaid productivity loss hours were not further adjusted, as none of the covariates were significantly associated with this outcome. The models for the remaining 3 outcomes were adjusted for at least 1 covariate (Table S6).

Overall, the associations between domains 1 to 4 and total productivity loss hours, percent activity impairment, and health utility did not change appreciably in the adjusted models and remained non-statistically significant (Table S6). The associations of the ES and AL domains with total productivity loss and health utility were all attenuated after covariate adjustment and became non-statistically significant. The associations between the 2 domains and percent activity impairment were robust to adjustment and showed similar effect sizes across models (ES: 11.1% [3.9%-18.4%], P < .01; AL: 11.7% [5.4%-18.0%], P < .001).

Discussion

This cross-sectional study involving a small sample of AA patients examined the association between 6 aspects of AA patient outcomes and productivity loss, work and activity impairment, and HRQOL. Our unadjusted results suggest ES and AL due to hair loss were associated with these outcomes. In other words, hair loss itself may be viewed as a necessary but not sufficient condition for poorer outcomes since hair loss is needed to produce the consequent ES and AL that are, in turn, associated with productivity, impairment, and HRQOL outcomes. However, the associations were sensitive to covariate adjustment and were generally attenuated. Our covariate-adjusted results indicated that worsening ES and AL were associated with greater impairment in other activities than work.

Our findings regarding the association between ES and work productivity loss somewhat corroborated a similar study using AAPPO and WPAI. A cross-sectional survey conducted in the U.S. using the Adelphi AA Disease Specific Programme examined the association between the ES domain from the AAPPO and overall work impairment and activity impairment from the WPAI.¹⁵ In covariate-adjusted linear regression models, a 1-point increase in ES was associated with an increase in work impairment of 4.1% and activity impairment of 3.1%. The magnitude of the association was smaller than the association in the present study, although our finding of a larger association may be attributed to our sample of participants who had experience with, on average, more severe AA symptoms. The average ES score reported in the U.S. study was 2.0, whereas the current sample had a higher average of 2.9.¹⁵ A large majority of patients in the U.S. study (>80%) reported never being affected by hair loss of the eyebrows and eyelashes, whereas the average patient in our study reported moderate hair loss in these areas.¹⁵ Our sample also reported a greater degree of impairment on the WPAI compared to the U.S. study: 31.8% versus 13.1% and 28.5% versus 12.2% for work and activity impairment, respectively.¹⁵ The WPAI values in our study were more comparable to estimates among patients with physician-assessed moderate and severe disease.^16,17

To our knowledge, no studies have examined other domains of AAPPO and work productivity. Our unadjusted results suggested that greater AL were associated with greater productivity loss and work impairment. This finding builds upon evidence that has shown patients living with AA may retract from activities of daily life,¹⁴ including work.^14,36 Li et al reported that 21.7% of patients with AA in their study experienced workplace bullying, which included having their opinions ignored, exclusion from activities, and being targets of gossip.¹⁴ The effects of workplace bullying on work productivity have been established elsewhere: Bartlett and Bartlett conducted an integrative literature review on workplace bullying and found that workplace bullying can lead to increased absenteeism and decreased performance while at work (ie, increased work errors, decreased concentration, etc), as well as other performance-related factors such as burnout and poorer morale.³⁶ Taken together, the existing evidence suggested that patients living with AA may experience workplace bullying which, in turn, results in decreased work productivity and retraction from work.

The association between AA severity and HRQOL in the literature generally showed that more severe disease correlated with lower HRQOL^12,17,37,38; although a non-linear relationship³⁹ and underestimation of disease severity in physician-administered assessments^38,40 may help explain some inconsistent findings. The direction of our results indicated that increasing ES and AL was associated with lower HRQOL, but the associations were non-significant in covariate-adjusted models. Nonetheless, ES appeared to have a larger association with HRQOL when compared numerically to AL. In a scoping review, Muntyanu et al also found that emotional distress and poorer self-perception had negative impacts on HRQOL and that the emotional domains of HRQOL were generally more negatively affected compared to physical domains and functional domains among AA patients.¹² In terms of studies of AA severity and health utility scores, a survey-based study of dermatologists and their patients conducted in 5 European countries found that disease severity was significantly associated with lower EQ-5D-5L index scores (mild: 0.89, moderate: 0.85, and severe: 0.77).¹⁷ These are similar to estimates from Japan.¹⁶

There are several limitations to consider for this study. First, this was a cross-sectional study, so we cannot make causal inferences in the association between AA severity and productivity loss, work and activity impairment, and HRQOL. Second, our sample size was small and likely represents a more severe group of patients with AA. Relatedly, our sample was comprised predominantly of women, so our sample should not be taken as representative of the general AA population.⁴¹ Furthermore, we recruited study participants from a patient organization, which introduces an additional bias. As noted in another study that recruited from the Canadian Alopecia Areata Foundation,⁴ most members are referred by physicians. If many of the members who also participated in our study required greater or ongoing social support (eg, more severe), our estimates of work productivity loss and its association with AA severity might be biased relative to the general AA population, which has more diverse levels of needs. For example, these participants may have taken additional time off to attend clinic visits, support groups, or educational webinars aimed at managing ES. Conversely, individuals who are not members of the patient organization may lack access to such resources to help them cope with AA and may therefore experience even greater productivity loss. Third, the developers of the AAPPO did not recommend a composite score to describe overall disease severity, which limited the comparisons we could make to other studies’ findings of AA overall severity and its association with work productivity and HRQOL.²⁷ Finally, we did not account for several potentially important variables in our analysis that could have clarified the association between AA and work productivity loss. Although we collected information on mental health conditions, we could not determine whether they preceded the onset of AA. Prior studies have found an increased incidence of depression following AA,^42,43 which may suggest a mediating role in the association. Conversely, pre-existing mental health conditions could instead function as confounders.⁴⁴ We also did not consider 2 potential effect modifiers: concealment strategies, which we did not collect, and occupation type, which we collected but lacked the sample size to explore. Those who conceal hair loss may have lower work productivity loss, as concealment may give individuals more confidence and reduce the number of comments about their hair loss,^45,46 compared to those who do not conceal. Similarly, individuals with occupations that are more public-facing or are based outdoors may be affected by their hair loss differently than those whose occupations are less public-facing or are based indoors.^47,48

Despite the limitations, we have several strengths to highlight. First, while the AAPPO measure does not have a composite score, the tool captures both symptoms and the impact of symptoms on patients. The latter is an important component of disease severity and one that may not be adequately captured by other tools, especially physician-administered ones.^12,26,49 Second, we used both a measure that has not previously been used in patients with AA (VOLP) and a measure that has recently been used in this patient group in the United States (WPAI-GH).¹⁵ This enabled comparisons to the other study, and comparisons between measures within our study. Further, we also extended the findings of the U.S. study by considering the other domains of AAPPO in our analysis. Our finding that the hair loss domains were not associated with productivity loss corroborated the rationale for focusing on the ES, as well as AL, of AA as posited by the previous authors.¹⁵

Conclusion

In our study, greater emotional burden and retraction from social interactions due to AA were associated with greater activity impairment. Estimates suggested these symptoms may have economic impacts in terms of productivity loss and work impairment, but more research is needed in a larger, more representative sample to elucidate these effects. Future research should also explore the mechanisms by which AA may result in work productivity loss in order to identify specific supports that could be provided.

Supplemental Material

sj-docx-1-cms-10.1177_12034754261445858 – Supplemental material for Productivity Loss and Health-Related Quality of Life Among People Living With Alopecia Areata: A Canada-Wide Cross-Sectional Study

Supplemental material, sj-docx-1-cms-10.1177_12034754261445858 for Productivity Loss and Health-Related Quality of Life Among People Living With Alopecia Areata: A Canada-Wide Cross-Sectional Study by Alexander Tam, Hiten Naik, Anthony J. Gilding, Logan Trenaman, Touraj Khosravi-Hafshejani, Larry D. Lynd and Wei Zhang in Journal of Cutaneous Medicine and Surgery

Footnotes

Acknowledgements

We would like to thank the patients who provided feedback on an earlier version of the questionnaire. Hiten Naik would like to acknowledge the support of The University of British Columbia Clinician Investigator Program and CAN-TAP-TALENT and Michael Smith Health Research BC Postdoctoral Fellowship. Logan Trenaman would like to acknowledge the support of the Leo Greenawalt Endowed Professorship in Health Policy. Wei Zhang would like to acknowledge the support of the Michael Smith Health Research BC Scholar Award.

ORCID iD

Alexander Tam

Ethical Considerations

This study was approved by The University of British Columbia Research Ethics Board (#H22-03211).

Consent to Participate

All participants provided consent electronically before participating in the study.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant-in-aid from Pfizer Canada. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Anonymized and de-identified data are available upon reasonable request to the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

References

Sibbald

Alopecia areata: an updated review for 2023. J Cutan Med Surg. 2023;27:241-259. doi:10.1177/12034754231168839

Mostaghimi

Gao

Ray

, et al. Trends in prevalence and incidence of alopecia areata, alopecia totalis, and alopecia universalis among adults and children in a US employer-sponsored insured population. JAMA Dermatol. 2023;159:411-418. doi:10.1001/jamadermatol.2023.0002

Toussi

Barton

Agbai

Kiuru

Psychosocial and psychiatric comorbidities and health-related quality of life in alopecia areata: a systematic review. J Am Acad Dermatol. 2021;85:162-175. doi:10.1016/j.jaad.2020.06.047

Gilding

Pope

Sibbald

The burden of disease in alopecia areata: Canadian online survey of patients and caregivers. JMIR Dermatol. 2022;5:e39167. doi:10.2196/39167

Lee

W-S.

Comorbidities in alopecia areata: a systematic review and meta-analysis. J Am Acad Dermatol. 2019;80:466-477.e16. doi:10.1016/j.jaad.2018.07.013

Mostaghimi

Soliman

Barqawi

Grada

Immune-mediated and psychiatric comorbidities among patients newly diagnosed with alopecia areata. JAMA Dermatol. 2024;160:945-952. doi:10.1001/jamadermatol.2024.2404

Chu

AlJasser

Alharbi

Abahussein

McElwee

Shapiro

Benefit of different concentrations of intralesional triamcinolone acetonide in alopecia areata: an intrasubject pilot study. J Am Acad Dermatol. 2015;73:338-340. doi:10.1016/j.jaad.2015.04.049

Phan

Ramachandran

Sebaratnam

DF.

Methotrexate for alopecia areata: a systematic review and meta-analysis. J Am Acad Dermatol. 2019;80:120-127.e2. doi:10.1016/j.jaad.2018.06.064

Husein-ElAhmed

Comparative efficacy of oral Janus kinase inhibitors and biologics in adult alopecia areata: a systematic review and Bayesian network meta-analysis. J Eur Acad Dermatol Venereol. 2024;38:835-843. doi:10.1111/jdv.19797

10.

Sinclair

Eisman

Lee

CMY

Hitschfeld

Witcombe

Pereira

CdF

. Health-related quality of life of adult and adolescent patients living with alopecia areata in Australia. Australas J Dermatol. 2024;65(5):451-461. doi:10.1111/ajd.14311

11.

Mesinkovska

King

Mirmirani

Cassella

Burden of illness in alopecia areata: a cross-sectional online survey study. J Investig Dermatol Symp Proc. 2020;20:S62-S68. doi:10.1016/j.jisp.2020.05.007

12.

Muntyanu

Gabrielli

Donovan

, et al. The burden of alopecia areata: a scoping review focusing on quality of life, mental health and work productivity. J Eur Acad Dermatol Venereol. 2023;37:1490-1520. doi:10.1111/jdv.18926

13.

Christensen

Jafferany

Psychiatric and psychologic aspects of chronic skin diseases. Clin Dermatol. 2023;41:75-81. doi:10.1016/j.clindermatol.2023.03.006

14.

Reyes-Hadsall

Drake

Huang

Mostaghimi

Experiencing workplace bullying in patients with alopecia areata: a cross-sectional survey study. Skin Appendage Disord. 2023;9:258-261. doi:10.1159/000529924

15.

Gandhi

Shy

Ray

Fridman

Vaghela

Mostaghimi

The association of alopecia areata-related emotional symptoms with work productivity and daily activity among patients with alopecia areata. Dermatol Ther. 2023;13:285-298. doi:10.1007/s13555-022-00864-1

16.

Edson-Heredia

Aranishi

Isaka

Anderson

Marwaha

Piercy

Patient and physician perspectives on alopecia areata: a real-world assessment of severity and burden in Japan. J Dermatol. 2022;49:575-583. doi:10.1111/1346-8138.16360

17.

Vañó-Galván

Blume-Peytavi

Farrant

, et al. Physician- and patient-reported severity and quality of life impact of alopecia areata: results from a real-world survey in five European countries. Dermatol Ther. 2023;13:3121-3135. doi:10.1007/s13555-023-01057-0

18.

Zhang

Gignac

MAM

Beaton

Tang

Anis

; Canadian Arthritis Network Work Productivity Group. Productivity loss due to presenteeism among patients with arthritis: estimates from 4 instruments. J Rheumatol. 2010;37:1805-1814. doi:10.3899/jrheum.100123

19.

Zhang

Bansback

Boonen

Severens

Anis

AH.

Development of a composite questionnaire, the valuation of lost productivity, to value productivity losses: application in rheumatoid arthritis. Value Health. 2012;15:46-54. doi:10.1016/j.jval.2011.07.009

20.

Zhang

Tocher

L’Heureux

Sou

Sun

Measuring, analyzing, and presenting work productivity loss in randomized controlled trials: a scoping review. Value Health. 2023;26:123-137. doi:10.1016/j.jval.2022.06.015

21.

Aldhouse

NVJ

Kitchen

Knight

, et al. “‘You lose your hair, what’s the big deal?’ I was so embarrassed, I was so self-conscious, I was so depressed”: a qualitative interview study to understand the psychosocial burden of alopecia areata. J Patient Rep Outcomes. 2020;4:76. doi:10.1186/s41687-020-00240-7

22.

Vyas

Johns

Ali

, et al. A systematic review of 454 randomized controlled trials using the Dermatology Life Quality Index: experience in 69 diseases and 43 countries. Br J Dermatol. 2024;190:315-339. doi:10.1093/bjd/ljad079

23.

Rencz

Baji

Gulácsi

, et al. Discrepancies between the Dermatology Life Quality Index and utility scores. Qual Life Res. 2016;25:1687-1696. doi:10.1007/s11136-015-1208-z

24.

Aggio

Dixon

Law

Randall

Price

Lloyd

. Estimation of health utility values for alopecia areata. Qual Life Res. 2024;33:1581-1592. doi:10.1007/s11136-024-03645-9

25.

von Elm

Altman

Egger

Pocock

Gøtzsche

Vandenbroucke

JP.

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370:1453-1457. doi:10.1016/S0140-6736(07)61602-X

26.

Winnette

Martin

Harris

Deal

LS.

Development of the alopecia areata patient priority outcomes instrument: a qualitative study. Dermatol Ther. 2021;11:599-613. doi:10.1007/s13555-021-00508-w

27.

Wyrwich

Winnette

Bender

, et al. Validation of the Alopecia Areata Patient Priority Outcomes (AAPPO) Questionnaire in adults and adolescents with alopecia areata. Dermatol Ther. 2021;12:149-166. doi:10.1007/s13555-021-00648-z

28.

Zhang

Bansback

Kopec

Anis

AH.

Measuring time input loss among patients with rheumatoid arthritis: validity and reliability of the Valuation of Lost Productivity Questionnaire. J Occup Environ Med. 2011;53:530. doi:10.1097/JOM.0b013e318218abf1

29.

Reilly

Zbrozek

Dukes

EM.

The validity and reproducibility of a work productivity and activity impairment instrument. Pharmacoeconomics. 1993;4:353-365. doi:10.2165/00019053-199304050-00006

30.

Zhang

Bansback

Boonen

Young

Singh

Anis

AH.

Validity of the Work Productivity and Activity Impairment Questionnaire – General Health version in patients with rheumatoid arthritis. Arthritis Res Ther. 2010;12:R177. doi:10.1186/ar3141

31.

Naik

Tam

ACT

Trenaman

Lynd

Zhang

Productivity loss associated with disability from migraine: a Canada-wide cross-sectional study. Can J Neurol Sci. 2025;52:680-687. doi:10.1017/cjn.2024.337

32.

Jones

Kazis

Lee

, et al. Health status assessments using the Veterans SF-12 and SF-36: methods for evaluating outcomes in the Veterans Health Administration. J Ambul Care Manag. 2001;24:68-86. doi:10.1097/00004479-200107000-00011

33.

Selim

Rogers

Fleishman

, et al. Updated U.S. population standard for the Veterans RAND 12-item Health Survey (VR-12). Qual Life Res. 2009;18:43-52. doi:10.1007/s11136-008-9418-2

34.

Bansback

Trenaman

Mulhern

, et al. Estimation of a Canadian preference-based scoring algorithm for the Veterans RAND 12-Item Health Survey: a population survey using a discrete-choice experiment. CMAJ Open. 2022;10:E589-E598. doi:10.9778/cmajo.20210113

35.

Statistics Canada. Table 14-10-0064-01 Employee Wages by Industry, Annual (Internet). Statistics Canada; 2024. doi:10.25318/1410006401-eng

36.

Bartlett

ME.

Workplace bullying: an integrative literature review. Adv Dev Hum Resour. 2011;13:69-84. doi:10.1177/1523422311410651

37.

Abedini

Hallaji

Lajevardi

Nasimi

Karimi Khaledi

Tohidinik

HR.

Quality of life in mild and severe alopecia areata patients. Int J Womens Dermatol. 2018;4:91-94. doi:10.1016/j.ijwd.2017.07.001

38.

Senna

Glashofer

, et al. Predictors of QOL in patients with alopecia areata. J Invest Dermatol. 2022;142:2646-2650.e3. doi:10.1016/j.jid.2022.02.019

39.

Gelhorn

Cutts

Edson-Heredia

, et al. The relationship between patient-reported severity of hair loss and health-related quality of life and treatment patterns among patients with alopecia areata. Dermatol Ther. 2022;12:989-997. doi:10.1007/s13555-022-00702-4

40.

Reid

Haley

Borovicka

, et al. Clinical severity does not reliably predict quality of life in women with alopecia areata, telogen effluvium, or androgenic alopecia. J Am Acad Dermatol. 2012;66:e97-e102. doi:10.1016/j.jaad.2010.11.042

41.

Jeon

Jung

S-W

Kim

, et al. Global, regional and national epidemiology of alopecia areata: a systematic review and modelling study. Br J Dermatol. 2024;191:325-335. doi:10.1093/bjd/ljae058

42.

Macbeth

Holmes

Harries

, et al. The associated burden of mental health conditions in alopecia areata: a population-based study in UK primary care*. Br J Dermatol. 2022;187:73-81. doi:10.1111/bjd.21055

43.

Vallerand

Lewinson

Parsons

, et al. Assessment of a bidirectional association between major depressive disorder and alopecia areata. JAMA Dermatol. 2019;155:475-479. doi:10.1001/jamadermatol.2018.4398

44.

Foo

Redler

Forstner

, et al. Exploring the overlap between alopecia areata and major depressive disorder: epidemiological and genetic perspectives. J Eur Acad Dermatol Venereol. 2023;37:1547-1555. doi:10.1111/jdv.18921

45.

Montgomery

White

Thompson

A mixed methods survey of social anxiety, anxiety, depression and wig use in alopecia. BMJ Open. 2017;7:e015468. doi:10.1136/bmjopen-2016-015468

46.

Creadore

Manjaly

, et al. Evaluation of stigma toward individuals with alopecia. JAMA Dermatol. 2021;157:392-398. doi:10.1001/jamadermatol.2020.5732

47.

Cuthrell

Jiménez

LA.

Alopecia areata’s psychological impact on quality of life, mental health, and work productivity: a scoping review. Int Neuropsychiatr Dis J. 2024;21:48-58. doi:10.9734/indj/2024/v21i1420

48.

Gupta

Goyal

Mahendra

Quality of life assessment in patients with androgenetic alopecia. Int J Trichol. 2019;11:147. doi:10.4103/ijt.ijt_6_19

49.

Mostaghimi

Napatalung

Sikirica

, et al. Patient perspectives of the social, emotional and functional impact of alopecia areata: a systematic literature review. Dermatol Ther. 2021;11:867-883. doi:10.1007/s13555-021-00512-0

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB