Community-based management of epilepsy in Southeast Asia: Two intervention strategies in Lao PDR and Cambodia
Bibliographic record
Abstract
BackgroundEpilepsy affects more than 50 million people worldwide, 80% of whom live in low- and middle-income countries (LMICs). In Southeast Asia, the prevalence is moderate (6‰), and the main public health challenge is reducing the treatment gap, which reaches more than 90% in rural areas.MethodsThis 12-month comparative study (intervention vs. control areas) assessed the community effectiveness of two different strategies for the identification and home follow-up of people with epilepsy by Domestic Health Visitors for epilepsy (DHVes). In Lao PDR, DHVes were health center staff covering several villages via monthly visits; in Cambodia, DHVes were health volunteers living in the villages.FindingsAt baseline, the treatment gap was >95% in Lao PDR and 100% in Cambodia. After 12 months, the treatment gap in Lao PDR decreased by 5·5% (range: 4·0–12·2) in the intervention area and 0·5% (range: 0·4–0·8) in the control area (p<0·0001). In Cambodia, the treatment gap decreased by 34·9% (range: 29·0–44·1) in the intervention area and 8·1% (range: 6·7–10·2) in the control area (p<0·0001). Among the PWEs followed at home by the DHVes, the proportion adhering to drug treatment was 85·2% in Lao PDR and 78·1% in Cambodia. The cost associated with strategy implemented in Cambodia, compared with the control area, was lower than the cost associated with strategy implemented in Lao PDR.”InterpretationThe treatment gap was significantly reduced with both intervention strategies, but the effect was larger in Cambodia. The results of this cost analysis pave the way for scaling-up in rural areas of Lao PDR and Cambodia, and experimental adaptation in other LMICs.FundingThe study was funded by the Global Health Department of Sanofi and Grand Challenges Canada (grant number 0325–04).
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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) | 0.000 | 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".