Prevalence and incidence of epilepsy in Latin America and the Caribbean: A systematic review and meta‐analysis of population‐based studies
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to perform an updated systematic review and meta-analysis to estimate the pooled prevalence and incidence of epilepsy in Latin America and the Caribbean (LAC), describing trends over time, and exploring potential clinical and epidemiological factors explaining the heterogeneity in the region. METHODS: Observational studies assessing the incidence or prevalence of epilepsy in LAC countries up to March 2020 were systematically reviewed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Meta-analyses and cumulative analyses were performed using random-effects models. We assessed between-study heterogeneity with sensitivity, subgroup, and meta-regression analyses. Moreover, the quality of the included studies and the certainty of evidence were evaluated using the GRADE (grading of recommendation, assessment, development, and evaluation) approach. RESULTS: Overall, 40 studies (from 42 records) were included, 37 for prevalence analyses and six for incidence (312 387 inhabitants; 410 178 person-years). The lifetime prevalence was 14.09 per 1000 inhabitants (95% confidence interval [CI] = 11.72-16.67), for active epilepsy prevalence was 9.06 per 1000 individuals (95% CI = 6.94-11.44), and the incidence rate was 1.11 per 1000 person-years (95% CI = .65-1.70). These high estimates have been constant in the region since 1990. However, substantial statistical heterogeneity between studies and publication bias were found. The overall certainty of evidence was low. Methodological aspects (sample size) and countries' epidemiological characteristics such as access to sanitation services and child and adult mortality rates explained the high heterogeneity. Finally, the prevalence of epilepsy associated with neurocysticercosis (NCC) in the general population was high, and the proportion of NCC diagnosis among people living with epilepsy was 17.37%. SIGNIFICANCE: The epilepsy prevalence and incidence in LAC are higher than worldwide estimates, being constant since 1990 and strongly influenced by NCC. We identified high between-study heterogeneity and significant methodological limitations (e.g., heterogeneous definitions, lack of longitudinal studies). The region needs upgraded research using standardized definitions and diagnostic methods, and urgent action against preventable causes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".