Trends in Population‐Based Incidence and Prevalence of Juvenile Idiopathic Arthritis in Manitoba, Canada
Bibliographic record
Abstract
OBJECTIVE: To estimate the incidence and prevalence of juvenile idiopathic arthritis (JIA) in children ages <16 years in the province of Manitoba, Canada, and to determine changes in estimates between 2000 and 2012. METHODS: JIA cases were ascertained from the administrative health data of Manitoba, using a validated case-finding algorithm. Annual incidence and prevalence rates were estimated using a generalized linear model with generalized estimating equations (GEEs), adjusting for sociodemographic characteristics. Changes in estimates were tested using piecewise regression models. RESULTS: A total of 455 cases of JIA prevalence met the inclusion criteria. Sex- and age-adjusted incidence estimates were 14.01 (95% confidence interval [95% CI] 13.52, 14.53) in 2000/2001 and 9.18 (95% CI 8.56, 9.85) in 2010/2011. Prevalence estimates were 65.33 (95% CI 63.87, 66.83) in 2000/2001 and 59.61 (95% CI 58.17, 61.08) in 2010/2011. A linear piecewise model provided the best fit to the data. There was a significant decrease in prevalence over the study period (-0.18 [95% CI -0.35, -0.02]; P = 0.0292) but no statistically significant change in incidence (-0.46 [95% CI -0.94, 0.01]; P = 0.0571). Sex-stratified models revealed a decrease for males in both prevalence (estimate -0.54 [95% CI -0.84, -10.25]; P = 0.0003) and incidence (estimate -1.02 [95% CI -2.02, -0.04]; P = 0.0439); there were no changes for females. CONCLUSION: Few population-based longitudinal epidemiologic studies of JIA have been conducted. Our findings suggested a decrease in overall JIA prevalence, and in incidence and prevalence in men. Further research to validate these findings in other cohorts and to explore factors that contribute to this change will benefit future health care planning for JIA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".