Burden of epilepsy in Latin America and The Caribbean: a trend analysis of the Global Burden of Disease Study 1990 – 2019
Bibliographic record
Abstract
Background: The epilepsy prevalence in Latin America and the Caribbean (LAC) had remained high over the last 20 years. Data on the burden of epilepsy are needed for healthcare planning and resource allocation. However, no systematic analysis had been performed for epilepsy burden in LAC. Methods: We extracted data of all LAC countries from the Global Burden of Disease (GBD) study from 1990 to 2019. Epilepsy burden was measured as prevalence, mortality, and disability-adjusted life-years (DALYs; defined by the sum of years of life lost [YLLs] for premature mortality and years lived with disability [YLDs]), by age, sex, year, and country. Absolute numbers, rates, and 95% uncertainty intervals were reported. We performed correlational analyses among burden metrics and Socio-demographic Index (SDI). Findings: The burden of epilepsy decreased around 20% in LAC, led by YLLs reduction. In 2019, 6·3 million people were living with active epilepsy of all causes (95% UI 5·3 - 7·4), with 3·22 million (95% UI 2·21 - 4·03) and 3·11 million (95% UI 2·21 to 4·03) cases of epilepsy with identifiable aetiology and idiopathic epilepsy, respectively. The number of DALYs represented the 9·51% (1.37 million, 95% UI 0·99 -1·86) of the global epilepsy burden in 2019. The age-standardized burden was 175·9 per 100 000 population (95% UI 119·4 - 253·3), which tend to have a bimodal age distribution (higher in the youth and elderly) and was driven by high YLDs estimates. The burden was higher in men and older adults, primarily due to high YLLs and mortality. Alcohol use was associated with 17% of the reported DALYs. The SDI estimates significantly influenced this burden (countries with high SDI have less epilepsy burden and mortality, but not prevalence or disability). Interpretation: The epilepsy burden has decreased in LAC over the past 30 years. Even though, LAC is still ranked as the third region with the highest global epilepsy burden. This reduction was higher in children, but burden and mortality increased for older adults. The epilepsy burden is disability predominant; however, the mortality-related estimates are still higher than in other regions. Alcohol consumption and countries' development are important determinants of this burden. There is an urgent need to improve access to epilepsy care in LAC, particularly for older adults. Strengthening primary care with online learning and telemedicine tools, and promoting risk factors modification should be prioritized in the region. Funding: This research was self-funded by the authors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".