National and Regional Prevalence of Self‐reported Epilepsy in Canada
Bibliographic record
Abstract
PURPOSE: To assess the point prevalence of self-described epilepsy in the general population nationally, provincially, and in different groups of interest. METHODS: We analyzed data from two national health surveys, the National Population Health Survey (NPHS, N=49,000) and the Community Health Survey (CHS, N=130,882). Both surveys captured sociodemographic information, as well as age, sex, education, ethnicity, household income, and labor force status of participants. Epilepsy was ascertained with only one question in both surveys. "Do you have epilepsy diagnosed by a health professional?" (NPHS) and "Do you have epilepsy?" (CHS). Prevalences were age-adjusted by using national standard populations at the time of each survey. Exact 95% confidence intervals were obtained. RESULTS: In the NPHS, 241 of 49,026 subjects described themselves as having been diagnosed with epilepsy, yielding a weighted point prevalence of 5.2 per 1,000 [95% confidence interval (CI), 4.9-5.4]. In the CHS, 835 of 130,822 subjects described themselves as having epilepsy, yielding a weighted point prevalence of 5.6 per 1,000 (95% CI, 5.1-6.0). Trends in differences in prevalence among some Canadian provinces were observed. Prevalence was statistically significantly higher in groups with the lowest educational level, lowest income, and in those unemployed in the previous year. Prevalence also was higher in nonimmigrants than in immigrants. CONCLUSIONS: The overall and group-specific results are in keeping with those obtained in other developed countries by using different ascertainment methods. We discuss methodologic aspects related to the ascertainment of epilepsy in both surveys, and to the validity and implications of our findings.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".