Epidemiology and Medication Pattern Change of Psoriatic Diseases in Taiwan from 2000 to 2013: A Nationwide, Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To analyze the trend of prevalence and incidence rates for psoriatic arthritis (PsA) and psoriasis in Taiwan, and to determine the changes in medication patterns. METHODS: Data were collected from the Taiwan National Health Insurance Research Database, which covered at least 95% of the population from 2000 to 2013. International Classification of Diseases, 9th edition (ICD-9) was used to identify PsA (ICD-9 696.0) and other psoriasis (ICD-9 696.1). Medications were identified by Anatomical Therapeutic Chemical Classification code. We calculated the annual age standardized prevalence and incidence rate of PsA and psoriasis in individuals aged ≥ 16 years from 2000 to 2013, and used the Poisson regression to test the trends by Wald chi-square statistic. RESULTS: The prevalence (per 100,000 population) of psoriatic diseases between 2000 and 2013 increased from 11.12 to 37.75 for PsA, and from 179.2 to 281.5 for psoriasis. The incidence (per 100,000 person-yrs) increased from 3.64 to 6.91 in PsA, while there was no significant change in psoriasis. Prevalence and incidence in PsA were more rapidly increased than in psoriasis. Sex ratio (men to women) of PsA decreased from 2.0 to 1.5 in 2000 and 2013, respectively. There was an increase in the use of disease-modifying antirheumatic drugs (DMARD), especially biologics, which is significantly different from topical therapies. CONCLUSION: The prevalence and incidence rates of psoriatic disease, especially PsA, were increasing in Taiwan. The medication pattern showed an increase in DMARD and biologics, while use of topical therapies decreased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".