Prevalence of epilepsy: a population-based cohort study in Denmark with comparison to Global Burden of Disease (GBD) prevalence estimates
Bibliographic record
Abstract
BACKGROUND: The Global Burden of Disease Study (GBD) produces prevalence estimates for 'idiopathic epilepsy' (ie, of unknown aetiology) and 'secondary epilepsy' (ie, with known aetiology) but does not report prevalence by underlying aetiologies for 'secondary epilepsy'. METHODS: We used nationwide, population-based register data from Denmark to identify underlying causes of epilepsy and their contribution to prevalence of 'secondary epilepsy' and compared with global prevalence data from GBD 2019. We identified all persons with a hospital-based epilepsy diagnosis and a filled prescription for antiseizure medication between 1 January 2009 and 31 December 2018. Epilepsy was categorised into 'idiopathic' or 'secondary' and 'total epilepsy' as the sum of the two epilepsy categories. RESULTS: On 31 December 2018, a total of 5 784 284 individuals (49.7% males) were living in Denmark including 40 336 with epilepsy (51.5% males). Perinatal conditions, traumatic brain injury, brain tumours and stroke were prominent underlying causes of 'secondary epilepsy'. The prevalence of 'total epilepsy' in Denmark was 697 (95% CI 691 to 704) per 100 000 population (264 (95% CI 260 to 269) for 'secondary epilepsy' and 433 (95% CI 428 to 438) for 'idiopathic epilepsy'). In the GBD 2019 Study, the prevalence of 'total epilepsy' in 2018 was 682 (95% uncertainty interval (UI) 586 to 784) per 100 000 population (359 (95% UI 324-397) for 'secondary epilepsy' and 324 (95% UI 249 to 404) for 'idiopathic epilepsy'). CONCLUSIONS: Prevalence estimates of 'total epilepsy', 'idiopathic epilepsy' and 'secondary epilepsy' in Denmark align with the GBD 2019 estimates. In future studies, it is suggested to explicitly include all types of epilepsy, including 'secondary epilepsy', which is currently estimated as sequelae (consequences) of underlying diseases.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".