Prevalence of Epilepsy across 25 Sub-Counties in Three Districts of Northern Uganda
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».