Evaluation of the Dengue Surveillance System in Khyber Pakhtunkhwa Province, Pakistan, in 2020
Notice bibliographique
Résumé
Background Installation and actualization of a disease surveillance system are prerequisites for early detection of outbreaks. Prompt response is possible when a robust surveillance system is in place. Dengue is one of the many diseases endemic to Pakistan and is potentially fatal. Objective This study aimed to assess the current dengue surveillance system and its performance and to provide recommendations to stakeholders for its actualization and improvement. Methods A cross-sectional study was conducted in 2020 to document the outcomes. The evaluation was guided by the updated Centers for Disease Control and Prevention guidelines for public health surveillance for the year 2019. A structured questionnaire was designed and piloted to estimate the simplicity, flexibility, acceptability, and stability of the current dengue surveillance system. The sample included 45 provincial- and district-level staff involved in dengue surveillance. Provincial data on dengue were analyzed to evaluate completeness, quality, positive predictive value, sensitivity, and representativeness. Field visits to districts were performed to assess data flow and timeliness. Results The reporting rate ranged from 12/100,000 in 2017 to 21/100,000 in 2019, with a total of 7641 reported cases in the province. The mean time of reporting cases was 1 day (range 0-2 days). The simplicity of the dengue surveillance system was at 90% with respect to structure and data flow. The stability of the system was at 84% because of data backup. System flexibility was at 81% and allowed the addition and modification of variables. The average completeness of the selected variables was 65%. About 59% of the staff interviewed considered the system acceptable. Data quality was suboptimal at 48%. The representativeness of the system was at 40%, and it was mainly representative of secondary and tertiary health care hospitals, particularly inpatients. The system positive predictive value for dengue was 15% and sensitivity was 14%, which were below par. The dengue surveillance system can detect dengue outbreaks early. Conclusions An immediate, collaborative, multisectoral, and transdisciplinary plan is needed to enhance reporting from all health facilities. Adequate government funding is needed to improve data quality, and a monitoring mechanism is needed at all levels for prompt functioning of the surveillance system.
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 | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».