Prevalence of Epilepsy across 25 Sub-Counties in Three Districts of Northern Uganda
Bibliographic record
Abstract
INTRODUCTION: Epilepsy is one of the most common neurological conditions worldwide, with large variation in prevalence across sub-Saharan African countries. Northern Uganda is one of the poorest areas of the country and has seen a high density of pigs and a prevalence of Taenia solium, a zoonotic tapeworm transmitted which causes neurocysticercosis in humans. The objective of this study was to estimate the population-level prevalence of active epilepsy in 25 sub-counties of northern Uganda. METHODS: This cross-sectional study was conducted in 2010-2011 in 25 sub-counties of Moyo, Adjumani, and Gulu districts, northern Uganda. Participants were sampled using a multistage cluster sampling strategy including sub-counties, parishes, villages, and households as sampling levels. Eligible individuals were interviewed using a previously validated screening questionnaire for epilepsy. Screen positive individuals were further examined by a team of neurologists for confirmation of active epilepsy. Sampling weights and post-stratification to account for sex distribution in each of the 25 sub-counties sampled based on projected 2010 population sizes were applied. RESULTS: A total of 38,303 individuals were sampled across 299 villages from 25 sub-counties. The overall weighted and post-stratified prevalence estimate of active epilepsy was 3.7% (95% confidence interval [CI]: 3.4%-3.9%). However, there was large variation across sex (4.6% (95% CI: 4.2%-5.0%) in men and 2.7% (95% CI: 2.4%-3.0%) in women) and across sub-counties ranging from 1.7% in Pece Division (Gulu District) and Moyo Town Council (Moyo District) to 6.6% in Awach (Gulu District). People aged between 10 and 19 were the most affected. CONCLUSIONS: In northern Uganda, active epilepsy was very prevalent but varied largely across sub-counties. Males were a lot more affected than women, making the use of weighted and post-stratified methods to estimate the prevalence of epilepsy essential. Implementing programs and interventions targeting the control of local risk factors of epilepsy such as neurocysticercosis and improving population health care access could help reduce the rather high prevalence of epilepsy in this area of the country.
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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 | 0.003 |
| 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".