Trends in the Prevalence and Incidence of Psoriasis and Psoriatic Arthritis in Ontario, Canada: A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence and incidence of psoriasis and psoriatic arthritis (PsA) over time in Ontario, Canada. METHODS: We performed a population-based study of Ontario health administrative data, using validated case definitions for psoriasis and PsA. We computed the crude and age- and sex-standardized cumulative prevalence and incidence of psoriasis from 2000 to 2015. RESULTS: Among the 10,774,802 individuals ages ≥20 years residing in Ontario in 2015, we identified 273,238 patients with psoriasis and 18,655 patients with PsA, equating to cumulative prevalence estimates of 2.54% and 0.17%, respectively. Correcting the prevalence estimates for imperfect sensitivity and specificity resulted in similar estimates. The male:female ratio was approximately 1.0 for both conditions. For psoriasis, the age- and sex-standardized cumulative prevalence increased from 1.74% in 2000 to 2.32% in 2015. For PsA, the age- and sex-standardized cumulative prevalence increased from 0.09% in 2008 to 0.15% in 2015. Between 2008 and 2015, annual incidence rates for psoriasis decreased, whereas those for PsA remained relatively stable. CONCLUSION: The prevalence and incidence of psoriasis and PsA in Ontario are similar to those observed in Europe and the US. The steady increase in the prevalence of psoriasis and PsA over the past decade may be due to a combination of population aging, population growth, and increasing life expectancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".