Dropout Rate and Associated Factors in Community-Based Health Insurance in Ethiopia: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
ABSTRACT Background: Ethiopia is working on community-based health insurance that involves risk sharing and pooling in order to provide quality care and overcome catastrophic out-of-pocket costs. Households are enrolled in the insurance by paying a premium fee, and membership is renewed every year. The community-based health insurance program is still in its early stages of development, and coverage is still low in Ethiopia. Even if initial enrollment and uptake of CBHI is important, a high dropout rate threatens the sustainability of CBHI and exacerbates the current enrollment challenges. This systematic review and meta-analysis aimed at determining the pooled prevalence of the CBHI dropout rate and systematically reviewing its associated factors. Methods: A comprehensive search of studies was made by using PubMed, Web of Science, EBSCO, Cochrane, Google Scholar, institutional repositories, and preprint healthcare research archives. All papers published up until February 15, 2023 were included in the analysis. The risk of bias of the included studies was assessed using the Newcastle-Ottawa scale and the Joanna Briggs Institute Critical Appraisal Checklist. The pooled estimates for the dropout rate for the CBHI was calculated using a weighted random effect model and displayed using a forest plot using STATA V.17.0 software. The presence of publication bias was assessed using a funnel plot. A systematic review of the selected studies was made to identify the associated factors, and the results are presented in relevant categories based on the thematic analysis. Results: Nine studies were eligible for this systematic review and meta-analysis, with a total of 4651 study participants. The overall pooled prevalence of CBHI dropout in Ethiopia was 40.3% (95% CI: 27–54). Factors including female household head, educational level, occupation, family size, presence of chronic illness, knowledge about CBHI, attitude, coverage of benefit package, perceived service quality, year of enrollment, affordability, lack of trust, lack of availability of medication and functional laboratory equipment, household income, distance to health facility and waiting time were all found to be major factors associated with CBHI dropout. Conclusions: The overall prevalence of CBHI dropout in Ethiopia is high, with demographics, socioeconomic status, access to healthcare, and perception of service quality all playing a role. Healthcare decision makers should work to improve the quality of health care services by raising awareness about CBHI, taking into account the affordability of the scheme and the convenient timing and frequency of premium fee payment, the availability of drugs and the functionality of laboratory instruments, and improving community trust. Key words: Community-Based Health Insurance, Dropout rate, Systematic Review and Meta-Analysis, Random effect model, Ethiopia
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 | 0,026 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,017 | 0,036 |
| Bibliométrie | 0,009 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».