Antibiotic prescription sources and use among under-5 children with fever/cough in sub-Saharan Africa
Notice bibliographique
Résumé
BACKGROUND: Childhood febrile illness is among the leading causes of hospital admission for children <5 y of age in sub-Saharan Africa (SSA). Antibiotics have played a pivotal role in enhancing health outcomes, especially for children <5 y of age. Antibiotics prescription pattern evidence exists for SSA, however, prescription sources (either from qualified or unqualified sources) and use among children with fever or cough have not been explored. Thus the present study assessed antibiotic prescription sources and use among children <5 y of age with fever and cough in SSA. METHODS: We used Demographic and Health Survey data from 37 countries with a total of 18 866 children <5 y of age who had fever/cough. The surveys span from 2006 to 2021. The dependent variable was antibiotics taken for fever/cough based on prescriptions from qualified sources. The data were weighted using sampling weight, primary sampling unit and strata. A mixed-effects logistic regression model (both fixed and random effects) was fitted since the outcome variable was binary. Model comparison was made based on deviance (-2 log likelihood) and likelihood ratio tests were used for model comparison. Variables with p≤0.2 in the bivariable analysis were considered for the multivariable mixed-effects binary logistic regression model. In the final model, the adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p<0.05 in the multivariable model were used to declare a significant association with taking antibiotics for fever/cough prescribed from qualified sources. RESULTS: The percentage of unqualified antibiotic prescriptions among children <5 y of age who had a fever/cough and took antibiotics was 67.19% (95% CI 66.51 to 67.85), ranging from 40.34% in Chad to 92.67% in Sao Tome. The odds of taking antibiotics prescribed from unqualified sources for fever/cough among children <5 y of age living in rural areas were 1.23 times higher (AOR 1.23 [95% CI 1.13 to 1.33]) compared with urban children. The odds of taking antibiotics prescribed from qualified sources for fever/cough among children <5 y of age whose mothers had primary, secondary and higher education decreased by 14% (AOR=0.86 [95% CI 0.79 to 0.93]), 21% (AOR 0.79 [95% CI 0.72 to 0.86]) and 21% (AOR 0.79 [95% CI 0.65 to 0.95]) compared with those whose mother had no formal education, respectively. CONCLUSIONS: The study showed that the majority of the children who received antibiotics obtained them from unqualified sources in the 37 SSA countries. Our findings underscore the significance of addressing healthcare disparities, improving access to qualified healthcare providers, promoting maternal education and empowering mothers in healthcare decision-making to ensure appropriate antibiotic use in this vulnerable population. Further research and interventions targeted at these factors are warranted to optimize antibiotic prescribing practices and promote responsible antibiotic use in the management of fever and cough in children <5 y of age.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».