Validation of medical service insurance claims as a surrogate for ascertaining vitiligo cases
Bibliographic record
Abstract
Abstract Background: The epidemiology of vitiligo, especially its disease burden on the healthcare system, can be assessed indirectly by analyzing health insurance claims data. Validating this approach is integral to ensuring accurate case identification and cohort characterization.Objectives: The primary aim of this study was to develop and validate an indirect measure of vitiligo ascertainment using health insurance claims data. These data were used secondarily to identify demographic characteristics, body site involvement, vitiligo subtypes, disease associations, and treatments. Methods: This study assessed the validity of identifying vitiligo from billing claims within a Canadian provincial universal health insurance program, versus vitiligo cases accrued from direct medical chart reviews. Claims-based algorithms combining ICD-9-CM diagnostic code 709 with treatment-specific data were derived and tested to identify vitiligo patients. This was compared against cases arising from the manual review of medical records of 606 patient with a diagnostic code for “dyschromia” (ICD-9-CM diagnostic code 709) from January 1 to December 31, 2016.Results: Based on the chart reviews, 204 (33.7%) patients were confirmed to have vitiligo. 42 separate claims-based algorithms combining ICD-9-CM diagnostic code 709 with treatment data specific to vitiligo were modelled and individually tested to evaluate their accuracy for vitiligo ascertainment. One algorithm achieved a sensitivity, specificity, PPV and NPV of 86.8% (95% CI 82.1-91.4), 92.5% (95% CI 90.0-95.1), 85.5% (95% CI 80.7-90.3), and 93.2% (95% CI 90.8-95.7), respectively. There was a 2.2 female to male ratio. The most common medical treatments were tacrolimus (74.5%) and topical corticosteroids (54.3%). Hypertension (24.2%) and hypothyroidism (19.6%) were the predominant co-morbidities associated with vitiligo. Conclusions: Health insurance claims data can be used to indirectly ascertain vitiligo for epidemiologic purposes with relatively high diagnostic performance between 85.5-93.2%.
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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 | 0.037 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".