Multicentre point-prevalence survey of antibiotic use and healthcare-associated infections in Ethiopian hospitals
Notice bibliographique
Résumé
OBJECTIVE: Effective antimicrobial containment strategies such as Antimicrobial Stewardship Programs (ASPs) require comprehensive data on antibiotics use which are scarce in Ethiopia. This study sought to assess antibiotics use and healthcare-associated infections (HCAIs) in Ethiopian public hospitals. DESIGN: We conducted a cross-sectional study using the WHO point-prevalence survey protocol for systemic antibiotics use and HCAIs for low/middle-income countries. SETTING: The study was conducted among 10 public hospitals in 2021. PARTICIPANTS: All patients admitted to adult and paediatric inpatient and emergency wards before or at 08:00 on the survey date were enrolled. OUTCOME MEASURE: The primary outcome measures were the prevalence of antibiotic use, HCAIs and the hospitals' readiness to implement ASP. RESULTS: Data were collected from 1820 patient records. None of the surveyed hospitals had functional ASP. The common indication for antibiotics was for HCAIs (40.3%). Pneumonia was the most common bacterial infection (28.6%) followed by clinical sepsis (17.8%). Most treatments were empiric (96.7%) and the overall prevalence of antibiotic use was 63.8% with antibiotics prescription per patient ratio of 1.77. Ceftriaxone was the most commonly prescribed antibiotic (30.4%) followed by metronidazole (15.4%). Age, having HIV infection, ward type, type of hospital, catheterisation and intubation history had significant association with antibiotic use. Patients who were treated in paediatric surgical wards were about four times more likely to be on antibiotics compared with patients treated at an adult emergency ward. Patients on urinary catheter (adjusted OR (AOR)=2.74, 95% CI: 2.04 to 3.68) and intubation device (AOR=2.62, 95% CI: 1.02 to 6.76) were more likely to be on antibiotics than their non-intubated/non-catheterised counterparts. Patients treated at secondary-level hospitals had 0.34 times lower odds of being on antibiotics compared with those in tertiary hospitals. CONCLUSIONS: Antibiotic use across the surveyed hospitals was common and most were empiric which has both practical and policy implications for strengthening ASP and promoting rational antibiotics use.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,001 |
| 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 ».