Using health insurance administrative data to explore patch testing utilization in Ontario, Canada—An untapped resource
Bibliographic record
Abstract
BACKGROUND: Patch testing is the key diagnostic test for diagnosing allergic contact dermatitis, but there is limited information on the use of patch testing at the population level. OBJECTIVES: To utilize Ontario Health Insurance Plan (OHIP) data to analyse trends in the rate of patch testing in Ontario. METHODS: Patch testing billing data submitted to the OHIP between 1992 and 2014 were analysed. Two patch test billing codes were investigated: one for work-related testing (G198), and one for non-work-related testing (G206). Rates of patch testing overall were calculated, and trends over time were described. RESULTS: There were 51 576 patch test billings: 48 416 non-work-related and 3160 work-related. The annual rate of non-work-related patch testing (G206) ranged from 11.9 per 100 000 people to 25.9 per 100 000 people, increasing over time. The rate of work-related patch testing (G198) ranged from 0.17 to 2.32 per 1 000 000 people, and was relatively stable. The overall distribution of billing by specialty was 70% dermatology, 19% other medical subspecialties, and 10% paediatrics and family medicine. CONCLUSIONS: Administrative health data can contribute to a more complete understanding of patch test utilization at the population level and, over time, can be used to track patch testing practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".