Engaging schools in diagnosis and treatment of malaria: Evidence of sustained impact on morbidity and behavior
Notice bibliographique
Résumé
Background: In low and middle income countries (LMICs) teachers send home children found sick in class devolving subsequent care to parents; where malaria is endemic, morbidity is high as the most parents fail to access WHO-endorsed rapid diagnostic testing (RDT and prompt treatment with artemisinin combination therapy (ACT). Consequently malaria is the principal reason a child misses school; so, we trained teachers to use RDT to evaluate all sick pupils and give ACT promptly to those positive.Aims: Pre, intra and post intervention evaluation of impact of using the WHO Health Promoting School (HPS) model to empower teachers to provide RDT and ACT and engage and inform pupils about malaria in 4 schools in rural Uganda.Methods: Documenting duration of absence from school as a surrogate measure for morbidity and change in children’s knowledge and reported behaviors regarding malaria. Pre-intervention (year 1) baseline evaluation of days of absence and children’s malaria knowledge/behavior; Intervention (year 2) trained teachers administered RDT in all sick children and treated those positive with ADT; Post-intervention (end of year 3) after schools independently continued RDT/ACT and education on malaria.Results: Pre-intervention <1:5 pupils had basic knowledge about malaria (caused by mosquitos; can be prevented; requires rapid diagnosis and prompt medication). In year 1: 953 of 1764 pupils were sent home due to illness. Mean duration of absence was 6.5 (SD 3.17) school days. In year 2: 1066 of 1774 pupils were sick, all had RDT, 765/1066 (68%) tested positive and received ACT; their duration of absence fell to 0.59 (SD 0.64) school days (p<0.001). By year 2 all children knew the signs and symptoms of malaria and had essential epidemiological knowledge. Twelve months post intervention the universality of this knowledge had been sustained and the whole-school focus on malaria continued. Children reported better health, more consistent attendance and improved academic achievement, and had become proactive in prevention strategies; 6% fewer tested positive for malaria; and key health knowledge was being passed to new pupils.Conclusion: Teacher administered RDT/ACT reduced child morbidity from malaria significantly; essential knowledge was generated and new health practices acquired that changed behaviors. Our WHO HPS model is applicable to other LMICs where malaria is endemic and morbidity high.
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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 | 0,002 | 0,000 |
| 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 | 0,001 | 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 ».