Epidemiological insight into the possible drivers of Lassa fever in an endemic area of Southwestern Nigeria from 2017 and 2021
Notice bibliographique
Résumé
ABSTRACT Background Lassa fever (LF) is a viral disease transmitted between animals and humans, commonly found in West Africa, including Nigeria. The region experiences an estimated annual total of about 2 million LF cases in humans, leading to 5,000 to 10,000 deaths. Strikingly, up to 80% of LF-infected individuals show no symptoms, making its true incidence hard to determine in endemic populations. We investigated LF distribution, mortality, survival patterns, and contributing factors during a local outbreak in Nigeria, from 2017 to 2021. Method Data from the Integrated Disease Surveillance and Response weekly line list for 2017 to 2021 were extracted. The survival pattern of LF patients was visualized with the Kaplan-Meier curve, binary logistic regression model was employed to explore LF-associated factors and level of statistical significance (α) was set at 5%. Result Overall, 4,554 participants were recruited between 2017 and 2021. Their average age varied from 31.82 ± 20.0 to 37.85 ± 17.89. LF-positive patients decreased from 26.9% in 2017 to 17.7% in 2021, paralleling the mortality trend. In 2021, patient survival ranged from 5 to 30 days. Male patients had lower survival odds in the initial 10 days of hospitalization, improved chances from days 10 to 20, and reduced probabilities beyond day 20. Residence location and age were significant factors (p<0.05) associated with LF in Ondo State. Conclusion The decline in LF cases in 2021 could be attributed to the ongoing intervention by Nigerian Centre for Disease Control or the disruption caused by the COVID-19 pandemic in 2020. To address LF challenges in hotspot areas, we propose Community Action Networks that would operate using the One Health approach involving local stakeholders sustainably to promote Early Warning/Early Response system in high-risk settings and mitigate LF-related issues. SUMMARY Lassa fever (LF) is an important disease of global public health concern that is endemic in West Africa. In Nigeria, the disease constitutes a major health challenge with outbreaks being recorded on an annual basis despite efforts channeled towards combating it by the government at various levels. This study analysed a five years data of LF in Ondo State southwestern Nigeria. The results identified age and location were identified as important factors associated with infection and mortality among LF patients as the incidence and case fatality rates were highest among adults (≥ 45 years), while the highest number of suspected, confirmed and dead cases was recorded in Owo Local Government Area. Furthermore, we identified drying of food items by the roadside where rodent vectors can access them, presence of a local market, poor and unsafe sewage disposal, and proximity of refuse dumps to residential areas as possible socio-ecological factors/practices fueling the endemicity and seasonal outbreak of LF. These findings emphasize the need for active involvement of community members in the already established national LF surveillance network to facilitate prompt case identification, and early reporting and response in the LF-endemic areas 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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».