COVID-19 vaccine acceptance and its associated factors in Ethiopia: A systematic review and meta-analysis
Notice bibliographique
Résumé
Background: COVID-19 vaccination is considered as an effective intervention for controlling the burden of the pandemic. However, vaccine hesitation is increasing and hindering efforts targeting to reduce the burden of the COVID-19 disease. Hence, determining COVID-19 vaccine acceptance and identifying determinants that would hinder people to vaccinate against COVID-19 is crucial to effectively improve COVID-19 vaccine uptake. In Ethiopia, the pooled proportion of COVID-19 vaccine acceptance and its determinants is not well known. Thus, the aim of this study is to estimate the pooled proportion of COVID-19 vaccine acceptance and its determinants in Ethiopia. Methods: A systematic search of articles was conducted from PubMed, Scopus, Web of Science, MEDLINE, CINAHL, Science Direct and Cochrane Library. Data were extracted using a data extraction tool which was adapted from the Joanna Briggs Institute. The quality of each included primary studies was evaluated using the Newcastle-Ottawa scale tool. Data analysis was performed using STATA 14. Heterogeneity in studies was assessed using Cochrane Q and I 2 test. Publication bias was assessed using visual inspection of funnel plots and Egger's test. A random effects model was applied to determine the pooled estimates if heterogeneity was exhibited; otherwise, a fixed-effects model was used. Results: A total of 14 studies involving 6373 participants were included for the final analysis. The pooled proportion of COVID-19 vaccine acceptance in Ethiopia was 56.02% (95% CI: 47.84, 64.20). The likelihood of COVID-19 vaccine acceptance was higher among participants who had history of chronic disease (AOR = 1.33, 95% CI: 1.09, 2.97), good knowledge (AOR = 2.13, 95% CI: 1.59, 4.97), positive attitude (AOR = 2.23, 95% CI: 1.21, 4.66), good COVID-19 preventive practice (AOR = 1.97, 95% CI: 1.82, 2.12), and high perceived seriousness of COVID-19 (AOR = 3.21, 95% CI: 2.32, 5.98). Conclusion: More than half participants were willing to accept COVID-19 vaccine. Thus, awareness creation battles about the efficacy and safety of the COVID-19 vaccine should be provided to the community. Besides, policy-makers, health planners and other stakeholders should encourage COVID-19 vaccine uptake behaviors by providing trusted information. Systematic review and meta-analysis registration: PROSPERO CRD42021264708.
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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 | 0,046 | 0,136 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,020 | 0,002 |
| Bibliométrie | 0,000 | 0,002 |
| 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 | 0,001 | 0,001 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».