Burden of Vitiligo in Canada: Retrospective Analysis of a Canadian Public Claims Database
Bibliographic record
Abstract
Background: Vitiligo is an autoimmune disease resulting in skin depigmentation. Treatment options are limited. Objectives: To examine disease burden and healthcare resource utilization (HCRU) among patients with vitiligo in Québec, Canada. Methods: In this retrospective study, data were obtained from the Régie de l’Assurance Maladie du Québec (RAMQ) databases for 125,000 random individuals from January 2010 to December 2019. The International Classification of Diseases, Ninth Revision ( ICD-9 ) diagnostic code [709.x (other skin disorders)] with vitiligo-related treatment was used to identify patients with vitiligo. Patient characteristics and treatments, including treatment type, episodes (treatments used without discontinuation), and sequences (treatment episodes ≥30 days), were assessed. Annualized HCRU and costs (2021 adjusted) included all-cause hospitalization, emergency department visits, outpatient visits, and medications among patients with vitiligo (n = 113) and age- and sex-matched non-vitiligo controls (n = 339). Results: Of patients with vitiligo (mean age, 50.0 years; 68.1% female) identified using ICD-9 code 709.x with vitiligo-related treatment, 36.3% received ≥4 treatment episodes. Treatment patterns were heterogeneous, with 43 different sequences reported. Annualized mean outpatient visits (16.1 vs 5.5) and all-cause outpatient service costs per patient were significantly higher in the vitiligo versus the control group (CAN$1037 vs CAN$523; P < .01). Total all-cause services costs were higher for patients with vitiligo in the year after versus before diagnosis (CAN$3679 vs CAN$2085; P = .04). Conclusions: Vitiligo is associated with significant burden and HCRU among patients in Québec, Canada, who were identified by ICD-9 code 709.x plus vitiligo-related treatment. Measurement of true vitiligo burden remains challenging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".