Dropout Rate and Associated Factors in Community-Based Health Insurance in Ethiopia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".