Prevalence and Temporal Trends of Epilepsy in Children: A Retrospective Birth Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Epilepsy is one of the most common chronic neurologic diseases in children; however, few recent studies examine the prevalence of epilepsy and its evolution over time according to birth or maternal characteristics. The aim of the study was to examine the prevalence of epilepsy in children born between 2002 and 2020 and the temporal trends by year of birth, in Ontario, Canada, overall, and according to maternal and birth characteristics. METHODS: We included all in-hospital deliveries between 2002 and 2020 (N = 2,343,482) in Ontario, Canada, using linked administrative health dataset. We estimated the overall prevalence of epilepsy diagnosed before the age of 18 years, by birth and maternal characteristics. For temporal trend analyses, we restricted our population to children born up to 2012 (N = 1,405,271) and examined the prevalence of epilepsy diagnosed by age 8 by their year of birth, using Poisson regression. RESULTS: The overall prevalence of epilepsy in our cohort was 8.1 per 1,000 live births (95% CI: 8.0-8.2). Prevalence was higher for boys, for children born preterm, with congenital malformations, from multiple pregnancies, from mothers born in Canada, and for children living in deprived areas. Epilepsy prevalence diagnosed by age 8 increased slightly between 2002 and 2012 cohorts (6.9 [95% CI: 6.2-7.6] to 7.3 [95% CI: 6.6-8.1] per 1,000 live births, respectively). Differences by gestational age as gradient and socioeconomic characteristics were persistent and stable over time, while those by pregnancy plurality and sex decreased. SIGNIFICANCE: In a large population-based birth cohort in Canada, we observed a slight increase in epilepsy prevalence over time among children born in 2002 and those born in 2012 and persistent disparities by gestational age, socioeconomic position, and maternal immigration status. This study highlights the need for continued surveillance of rates to see if this increasing trend is persistent, to understand the potential causes behind it, and to understand the persistence of these disparities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".