Real-world incidence and prevalence of systemic lupus erythematosus in Alberta, Canada
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is rather uncommon than rare. The purpose of this study was to estimate the incidence and prevalence of SLE in the population of Alberta, Canada, using administrative health data. Multiple population-based data sources, including the Alberta Health Care Insurance Plan Central Stakeholder Registry (AHCIP CSR), Fee-For-Service, and Hospital Discharge Abstract Database were used. Age- and sex-specific incidence and prevalence rates, and 95% confidence intervals (CI), were computed using the AHCIP CSR mid-year population estimates as the denominator, for the period of 2000-2015. The overall incidence of SLE for all age groups was 4.43 (95% CI 3.65, 5.04) per 100,000 population. The overall incidence in male and female of all age groups was 1.26 (95% CI 0.72, 1.76) and 7.69 (95% CI 6.22, 8.81) per 100,000 population, respectively. A prevalence of 47.99 per 100,000 (male = 13.5, female = 83.2) of SLE was observed for the year 2000 and has increased to 90 (male = 25.5, female = 156.7) per 100,000 population in 2015. Over the 16-year period, the incidence of SLE in women was approximately six times higher than in men (odds ratio = 6.16). The highest and lowest incidence was recorded in 2001 and 2015, respectively. Despite the stable incidence of SLE, the findings of the study confirms that the prevalence of SLE has increased over the 16-year period. The increase in prevalence of SLE in Alberta will have an impact on health service utilizations. This finding can be used for planning and evaluating health services for this group of patients. Further studies are required to determine the economic burden of the condition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".