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Record W4408931362 · doi:10.1016/s2214-109x(24)00507-2

Epilepsy in the Indigenous peoples in Canada, Australia, New Zealand, and the USA: a systematic scoping review

2025· article· en· W4408931362 on OpenAlexaffabout
Ngaire Keenan, Sean G Aitchison, Nathalie Jetté, Karen Parko, Pamela Roach, Angela Dos Santos, John S. Archer, Erik Andersén, Jeannine Stairmand, James Stanley, Lynette G. Sadleir

Bibliographic record

VenueThe Lancet Global Health · 2025
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of Calgary
FundersHealth Research Council of New Zealand
KeywordsIndigenousGeographyMEDLINEPolitical scienceNew Zealand studiesEthnologyHistorySociologySocial scienceBiologyEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Indigenous peoples have inequitable health access and outcomes yet are under-represented in health research and policy. The Intersectoral Global Action Plan on Epilepsy and other Neurological Disorders 2022-2031 highlights Indigenous peoples as high priority groups. We aimed to provide a summary of existing knowledge regarding epilepsy among Indigenous peoples in Canada, Australia, New Zealand, and the USA (CANZUS). METHODS: In this systematic scoping review, we searched Embase, MEDLINE, APA PsychInfo, Cochrane, Scopus, CINAHL databases and grey literature for reports published in any language between Jan 1, 1985, and April 16, 2023, using search terms related to seizures, epilepsy, and Indigenous peoples. Studies were assessed independently by three reviewers. Articles including epilepsy data in an Indigenous group were included. Articles were excluded if they combined Indigenous and non-Indigenous peoples as one population or if the outcomes did not include a separate analysis by Indigenous group. Case reports were also excluded. We extracted data on epilepsy epidemiology, access to health care, treatment, and health outcomes in Indigenous people. The methodological quality of studies was assessed through a methodological appraisal and an Indigenous perspective appraisal. This study is registered with Open Science Framework, https://doi.org/10.17605/OSF.IO/9JRHG. FINDINGS: Our search identified 2037 studies, of which 42 peer-reviewed articles and nine grey literature reports met inclusion criteria: these studies were in Canada (n=3), Australia (n=17), New Zealand (n=9), and the USA (n=22). With the exception of Māori children in New Zealand, who seem to have similar rates of epilepsy to children of European ancestry, the incidence and prevalence of epilepsy seemed to be higher in Indigenous peoples in these regions than non-Indigenous populations. In the included studies, Indigenous peoples showed a higher number of epilepsy hospital presentations, decreased access to specialists, decreased access and longer waits for antiseizure medication, and increased prescriptions for enzyme-inducing antiseizure medications when compared with non-Indigenous peoples. In Australia, the number of disability-adjusted life years among Aboriginal and Torres Strait Islander peoples with epilepsy was double that for non-Indigenous people with epilepsy. Mortality rates for Indigenous peoples with epilepsy in New Zealand and Australia were higher than in non-Indigenous people with epilepsy. INTERPRETATION: Although Indigenous people from CANZUS have unique cultural identities, this review identified similar themes and substantial disparities experienced by Indigenous versus non-Indigenous people in these nations. Concerningly, there were relatively few studies, and these were of variable quality, leaving substantial knowledge gaps. Epidemiological epilepsy research in each specific Indigenous group from CANZUS countries is urgently required to enable health policy development and minimise inequity within these countries. FUNDING: Health Research Council of New Zealand.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.724
Threshold uncertainty score0.549

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0250.029
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.051
GPT teacher head0.402
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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