Economic burden of epilepsy in children: A population‐based matched cohort study in Canada
Bibliographic record
Abstract
OBJECTIVE: The economic burden of childhood epilepsy to the health care system remains poorly understood. This study aimed to determine phase-specific and cumulative long-term health care costs in children with epilepsy (CWE) from the health care payer perspective. METHODS: This cohort study utilized linked health administrative databases in Ontario, Canada. Incident childhood epilepsy cases were identified from January 1, 2003 to June 30, 2017. CWE were matched to children without epilepsy (CWOE) on age, sex, rurality, socioeconomic status, and comorbidities, and assigned prediagnosis, initial, ongoing, and final care phase based on clinical trajectory. Phase-specific, 1-year and 5-year cumulative health care costs, attributable costs of epilepsy, and distribution of costs across different ages were evaluated. RESULTS: A total of 24 411 CWE were matched to CWOE. The costs were higher for prediagnosis and initial care than ongoing care in CWE. Hospitalization was the main cost component. The costs of prediagnosis, initial, and ongoing care were higher in CWE than CWOE, with the attributable costs at $490 (95% confidence interval [CI] = $352-$616), $1322 (95% CI = $1247-$1402), and $305 (95% CI = $276-$333) per 30 patient-days, respectively. Final care costs were lower in CWE than CWOE, with attributable costs at -$2515 (95% CI = -$6288 to $961) per 30 patient-days. One-year and 5-year cumulative costs were higher in CWE ($14 776 [95% CI = $13 994-$15 546] and $39 261 [95% CI = $37 132-$41 293], respectively) than CWOE ($6152 [95% CI = $5587-$6768] and $15 598 [95% CI = $14 291-$17 006], respectively). The total health care costs were highest in the first year of life in CWE for prediagnosis, initial, and ongoing care. SIGNIFICANCE: Health care costs varied along the continuum of epilepsy care, and were mainly driven by hospitalization costs. The findings identified avenues for remediation, such as enhancing care around the time of epilepsy diagnosis and better care coordination for epilepsy and comorbidities, to reduce hospitalization costs and the economic burden of epilepsy care.
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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 | 0.000 | 0.000 |
| 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".