Epidemiology and Comorbidity of Vitiligo in Germany: A Claims Data Analysis

Authors

  • Theresa Klinger Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany https://orcid.org/0009-0003-0760-3103
  • Matthias Augustin Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany https://orcid.org/0000-0002-4026-8728
  • Susanne Baumeister Incyte Biosciences Germany, Munich, Germany
  • Jennifer Riedel Gesundheitsforen Leipzig GmbH, Leipzig, Germany
  • Claudia Grellmann inav – Private Institute for Applied Health Services Research GmbH, Berlin, Germany
  • Anja Kamps Incyte Biosciences Germany, Munich, Germany
  • Markus Böhm Department of Dermatology, University of Münster, Münster, Germany https://orcid.org/0000-0001-7338-7734
  • Kristina Hagenström Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany https://orcid.org/0000-0002-2676-1479

DOI:

https://doi.org/10.2340/actadv.v106.44468

Keywords:

Prevalence, Incidence, Skin Diseases/epidemiology, Insurance Claim Reporting

Abstract

Vitiligo is a chronic skin disease resulting in destruction of melanocytes and loss of pigmentation. Little is known about epidemiology and comorbidity in Germany. This retrospective cohort analysis used nationwide statutory health insurance data (2016 to 2020). Disease rates were calculated using varying case definitions. Comorbidity was assessed against 3 control groups: persons without vitiligo, with atopic dermatitis, and with psoriasis (1:3 propensity score matching). In 2020, 4,631 persons were diagnosed with vitiligo in DAK-Gesundheit (DAK-G, mean age 56.93 years, ~148,437) and 5,820 persons in the German Analysis Database for Evaluation and Health Services Research (~144,855). Prevalence ranged from 0.12% (confidence interval [CI] 0.12–0.12) to 0.20% (CI 0.19–0.20), and incidence from 0.04% (CI 0.03–0.04) to 0.06% (CI 0.06–0.06). Rates increased with age. Women (mean age 58.60 years) were more frequently affected. Vitiligo was associated with alopecia areata (relative risk 7.14, CI 4.63–11.00) and systemic sclerosis (relative risk 3.20, CI 1.58–6.47). Several mental illnesses (depression: relative risk = 1.23, CI 1.15–1.32, anxiety: relative risk = 1.32, CI 1.19–1.47), were linked to skin diseases like vitiligo, atopic dermatitis, or psoriasis. Vitiligo affects a notable proportion of the population, with detected comorbidities supporting prior findings. To ensure reliable results, claims data should be validated through primary data linkage or sensitivity testing.

Downloads

Download data is not yet available.

References

Taïeb A, Picardo M. The definition and assessment of vitiligo: a consensus report of the Vitiligo European Task Force. Pigment Cell Res 2007; 20: 27–35. DOI: https://doi.org/10.1111/j.1600-0749.2006.00355.x

Krüger C, Schallreuter KU. A review of the worldwide prevalence of vitiligo in children/adolescents and adults. Int J Dermatol 2012; 51: 1206–1212. 1206–1212. DOI: https://doi.org/10.1111/j.1365-4632.2011.05377.x

Haulrig MB, Al-Sofi R, Baskaran S, Bergmann MS, Løvendorf M, Dyring-Andersen B, et al. The global epidemiology of vitiligo: a systematic review and meta-analysis of the incidence and prevalence. JEADV Clin Pract 2024; 3: 1410–1419. DOI: https://doi.org/10.1002/jvc2.526

Zhang Y, Cai Y, Shi M, Jiang S, Cui S, Wu Y, et al. The prevalence of vitiligo: a meta-analysis. PloS One 2016; 11: e0163806. DOI: https://doi.org/10.1371/journal.pone.0163806

Mohr N, Petersen J, Kirsten N, Augustin M. Epidemiology of vitiligo: a dual population-based approach. Clin Epidemiol 2021; 13: 373–382. DOI: https://doi.org/10.2147/CLEP.S304155

Abbas S, Ihle P, Köster I, Schubert I. Estimation of disease incidence in claims data dependent on the length of follow-up: a methodological approach. Health Serv Res 2012; 47: 746–755. DOI: https://doi.org/10.1111/j.1475-6773.2011.01325.x

Tang L, Li F, Xu F, Yan S, Zhou J, Li J, et al. Prevalence of vitiligo and associated comorbidities in adults in Shanghai, China: a community-based, cross-sectional survey. Ann Palliat Med 2021; 10: 8103–8111. DOI: https://doi.org/10.21037/apm-21-1738

Li C-Y, Dai Y-X, Chen Y-J, Chu S-Y, Chen T-J, Wu C-Y, et al. Cancer risks in vitiligo patients: a nationwide population-based study in Taiwan. Int J Environ Res Public Health 2018; 15. DOI: https://doi.org/10.3390/ijerph15091847

Morrison B, Burden-Teh E, Batchelor JM, Mead E, Grindlay D, Ratib S. Quality of life in people with vitiligo: a systematic review and meta-analysis. Br J Dermatol 2017; 177: e338–e339. DOI: https://doi.org/10.1111/bjd.15933

Wang G, Qiu D, Yang H, Liu W. The prevalence and odds of depression in patients with vitiligo: a meta-analysis. J Eur Acad Dermatol Venereol 2018; 32: 1343–1351. DOI: https://doi.org/10.1111/jdv.14739

Kussainova A, Kassym L, Akhmetova A, Glushkova N, Sabirov U, Adilgozhina S, et al. Vitiligo and anxiety: a systematic review and meta-analysis. PloS One 2020; 15: e0241445. DOI: https://doi.org/10.1371/journal.pone.0241445

Ezzedine K, Eleftheriadou V, Jones H, Bibeau K, Kuo FI, Sturm D, et al. Psychosocial effects of vitiligo: a systematic literature review. Am J Clin Dermatol 2021; 22: 757–774. DOI: https://doi.org/10.1007/s40257-021-00631-6

Fabrazzo M, Cipolla S, Signoriello S, Camerlengo A, Calabrese G, Giordano GM, et al. A systematic review on shared biological mechanisms of depression and anxiety in comorbidity with psoriasis, atopic dermatitis, and hidradenitis suppurativa. Eur Psychiatry 2021; 64: e71. DOI: https://doi.org/10.1192/j.eurpsy.2021.2249

Wilson EB. On confidence intervals. Proc Natl Acad Sci U S A 1942; 28: 88–93. DOI: https://doi.org/10.1073/pnas.28.3.88

Tsadik AG, Legesse GF, Desta DM, Assefa BT, Kidanemariam HG, Gidey MT. Clinico-epidemiological profile and treatment pattern of vitiligo in selected dermatological clinics of Mekelle City, Northern Ethiopia. Dermatol Res Pract 2020; 2020: 3625753. DOI: https://doi.org/10.1155/2020/3625753

Gandhi K, Ezzedine K, Anastassopoulos KP, Patel R, Sikirica V, Daniel SR, et al. Prevalence of vitiligo among adults in the United States. JAMA Dermatol 2022; 158: 43–50. DOI: https://doi.org/10.1001/jamadermatol.2021.4724

Radtke MA, Schäfer I, Gajur AI, Augustin M. Clinical features and treatment outcomes of vitiligo from the patients’ perspective: results of a national survey in Germany. Dermatology (Basel, Switzerland) 2010; 220: 194–200. DOI: https://doi.org/10.1159/000275657

Migayron L, Boniface K, Seneschal J. Vitiligo, from physiopathology to emerging treatments: a review. Dermatol Ther (Heidelb) 2020; 10: 1185–1198. DOI: https://doi.org/10.1007/s13555-020-00447-y

Schubert I, Ihle P, Köster I. Interne Validierung von Diagnosen in GKV-Routinedaten: Konzeption mit Beispielen und Falldefinition. Gesundheitswesen 2010; 72: 316–322. DOI: https://doi.org/10.1055/s-0030-1249688

Radtke MA, Schäfer I, Gajur A, Langenbruch A, Augustin M. Willingness-to-pay and quality of life in patients with vitiligo. Br J Dermatol 2009; 161: 134–139. DOI: https://doi.org/10.1111/j.1365-2133.2009.09091.x

Augustin M, Gajur AI, Reich C, Rustenbach SJ, Schaefer I. Benefit evaluation in vitiligo treatment: development and validation of a patient-defined outcome questionnaire. Dermatology (Basel) 2008; 217: 101–106. DOI: https://doi.org/10.1159/000128992

Choi CW, Eun SH, Choi KH, Bae JM. Increased risk of comorbid rheumatic disorders in vitiligo patients: a nationwide population-based study. J Dermatol 2017; 44: 909–913. DOI: https://doi.org/10.1111/1346-8138.13846

Dsouza PA, Monteiro RC, Dias M, Prabhu SH, Bhat RM, Jayaraman J, et al. Beyond the surface: decoding pityriasis versicolor through clinical, dermoscopic and microbiological exploration. Indian J Dermatol 2025; 70: 6–10. DOI: https://doi.org/10.4103/ijd.ijd_391_24

Leung DYM. New insights into atopic dermatitis: role of skin barrier and immune dysregulation. Allergol Int2013; 62: 151–161. DOI: https://doi.org/10.2332/allergolint.13-RAI-0564

Li X, Kong L, Li F, Chen C, Xu R, Wang H, et al. Association between psoriasis and chronic obstructive pulmonary disease: a systematic review and meta-analysis. PloS One 2015; 10: e0145221. DOI: https://doi.org/10.1371/journal.pone.0145221

Klinger T, Augustin M, Leicht G, Moritz S, Hagenström K. Prevalence and comparative risk of mental health disorders in persons with vitiligo: a retrospective matched cohort study using claims data with expert-informed case validation. BMJ Open 2026; 16: e106687. DOI: https://doi.org/10.1136/bmjopen-2025-106687

Montgomery SN, Syder N, Barajas G, Elbuluk N. Psychological comorbidities of vitiligo: a retrospective cross-sectional analysis in an urban population. Arch Dermatol Res 2023; 316: 14. DOI: https://doi.org/10.1007/s00403-023-02771-7

Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41–55. DOI: https://doi.org/10.1093/biomet/70.1.41

Mei H, Xu Y, Wang J, Ma S. Evaluation of survival outcomes of endovascular versus open aortic repair for abdominal aortic aneurysms with a big data approach. Entropy (Basel) 2020; 22. DOI: https://doi.org/10.3390/e22121349

Wang C, Chen M-H, Schifano E, Wu J, Yan J. Statistical methods and computing for big data. Stat Interface 2016; 9: 399–414. DOI: https://doi.org/10.4310/SII.2016.v9.n4.a1

Wagner C. Die Population unter Risiko bei Prävalenz- und Inzidenzschätzungen – Nennerkonzepte. In: Routinedaten im Gesundheitswesen: Handbuch Sekundärdatenanalyse: Grundlagen, Methoden und Perspektiven. Bern: Hans Huber; 2014: p. 424–432.

Swart E, Gothe H, Geyer S, Jaunzeme J, Maier B, Grobe TG, et al. Gute Praxis Sekundärdatenanalyse (GPS): Leitlinien und Empfehlungen. Gesundheitswesen 2015; 77: 120–126. DOI: https://doi.org/10.1055/s-0034-1396815

Hagenström K, Protz K, Petersen J, Augustin M. Development of a model to predict closure of chronic wounds in Germany: claims data analysis. Int Wound J 2022; 19: 76–85. DOI: https://doi.org/10.1111/iwj.13599

Busse R, Blümel M, Knieps F, Bärnighausen T. Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition. Lancet 2017; 390: 882–897. DOI: https://doi.org/10.1016/S0140-6736(17)31280-1

Swart E, editor. Health care utilization research using secondary data. New York: Springer; 2014. DOI: https://doi.org/10.1007/978-1-4614-9191-0_5

Swart E, Schmitt J. Standardized Reporting Of Secondary data Analyses (STROSA) – Vorschlag für ein Berichtsformat für Sekundärdatenanalysen. Z Evid Fortbild Qual Gesundhwes 2014; 108: 511–516. DOI: https://doi.org/10.1016/j.zefq.2014.08.022

Arbeitsgruppe Epidemiologische Methoden der Deutschen Arbeitsgemeinschaft für Epidemiologie (DAE). Leitlinien und Empfehlungen – Deutsche Gesellschaft für Epidemiologie; 2023 [cited 2023 Oct 19]. Available from: https://www.dgepi.de/de/berichte-und-publikationen/leitlinien-und-empfehlungen/

Downloads

Published

2026-04-29

How to Cite

Klinger, T., Augustin, M., Baumeister, S., Riedel, J., Grellmann, C., Kamps, A., … Hagenström, K. (2026). Epidemiology and Comorbidity of Vitiligo in Germany: A Claims Data Analysis. Acta Dermato-Venereologica, 106, adv44468. https://doi.org/10.2340/actadv.v106.44468