Epidemiology and Treatment Patterns of Patients with Vitiligo: A Real-World Analysis
Bibliographic record
Abstract
INTRODUCTION: Vitiligo, a chronic autoimmune skin depigmentation disease with an unpredictable course, has been associated with several comorbid autoimmune and psychological conditions. Our current understanding of vitiligo burden and management in the real world is limited. This real-world analysis presents data on vitiligo epidemiology, comorbidities, and treatment of patients in Israel. METHODS: This retrospective study analyzed data from the Maccabi Health Services database. Prevalent patients with vitiligo in 2021 were matched to patients in the general population on the basis of age group, gender, and socioeconomic status. Patient demographics, vitiligo incidence and prevalence, comorbidities, and treatment patterns are reported. Data are presented as percentages, mean, median, P values, and standard mean differences (SMD). RESULTS: In this analysis, 11,412 patients with vitiligo were matched to patients from the general population. Incidence and prevalence rates increased over time from 2005 to 2021. Compared to the general population, patients with vitiligo were more likely to have an immune-mediated comorbidity (29.7% vs 18.4% [P < 0.001; SMD 0.27]) or psychological comorbidity (18.7% vs 15.9% [P < 0.001; SMD 0.07]). Comorbidities included atopic dermatitis (patients with vitiligo vs general population 12.5% vs 8.4%), psoriasis (5.8% vs 3.6%), Hashimoto's thyroiditis (2.9% vs 1.1%), alopecia areata (2.2% vs 0.9%), depression (10.8% vs 9.5%), and sleep disorder/insomnia (5.9% vs 4.4%). Only 74.8% of all patients with vitiligo had ever received treatment, with topical corticosteroids (51.5%) and calcineurin inhibitors (36.5%) most commonly prescribed. At the end of 2021, 83.7% of patients were untreated. CONCLUSION: Patients with vitiligo are more likely to have various immune-related and psychological comorbidities, highlighting the significant impact of the condition on well-being. Nearly a quarter of patients had never received treatment, with many receiving only topical treatments, and medication persistence was low. This highlights the lack of adequate treatment in this population and the need for more effective management options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".