The Incidence and Prevalence of Multiple Sclerosis in Nova Scotia, Canada
Bibliographic record
Abstract
BACKGROUND: Estimates of incidence and prevalence are needed to determine disease risk and to plan for health service needs. Although the province of Nova Scotia, Canada is located in a region considered to have a high prevalence of multiple sclerosis (MS), epidemiologic data are limited. OBJECTIVE: We aimed to validate an administrative case definition for MS and to use this to estimate the incidence and prevalence of MS in Nova Scotia. METHODS: We used provincial administrative claims data to identify persons with MS. We validated administrative case definitions using the clinical database of the province's only MS Clinic; agreement between data sources was expressed using a kappa statistic. We then applied these definitions to estimate the incidence and prevalence of MS from 1990 to 2010. RESULTS: We selected the case definition using ≥7 hospital or physician claims when >3 years of data were available, and ≥3 claims where less data were available. Agreement between data sources was moderate (kappa = 0.56), while the positive predictive value was high (89%). In 2010, the age-standardized prevalence of MS per 100,000 population was 266.9 (95% CI: 257.1- 277.1) and incidence was 5.17 (95% CI: 3.78-6.56) per 100,000 persons/year. From 1990-2010 the prevalence of MS rose steadily but incidence remained stable. CONCLUSIONS: Administrative data provide a valid and readily available means of estimating MS incidence and prevalence. MS prevalence in Nova Scotia is among the highest in the world, similar to recent prevalence estimates elsewhere in Canada. Incidence et prévalence de la sclérose en plaques en Nouvelle-Écosse, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".