Do national health insurance schemes guarantee financial risk protection in the drive towards Universal Health Coverage in West Africa? A systematic review of observational studies
Bibliographic record
Abstract
To facilitate the drive towards Universal Health Coverage (UHC) several countries in West Africa have adopted National Health Insurance (NHI) schemes to finance health services. However, safeguarding insured populations against catastrophic health expenditure (CHE) and impoverishment due to health spending still remains a challenge. This study aims to describe the extent of financial risk protection among households enrolled under NHI schemes in West Africa and summarize potential learnings. We conducted a systematic review following the PRISMA guidelines. We searched for observational studies published in English between 2005 and 2022 on the following databases: PubMed/Medline, Web of Science, CINAHL, Embase and Google Scholar. We assessed the study quality using the Joanna Briggs Institute (JBI) critical appraisal checklist. Two independent reviewers assessed the studies for inclusion, extracted data and conducted quality assessment. We presented our findings as thematic synthesis for qualitative data and Synthesis Without Meta-analysis (SWiM) for quantitative data. We published the study protocol in PROSPERO with ID CRD42022338574. Nine articles were eligible for inclusion, comprising eight cross-sectional studies and one retrospective cohort study published between 2011 and 2021 in Ghana (n = 8) and Nigeria (n = 1). While two-thirds of the studies reported a positive (protective) effect of NHI enrollment on CHE at different thresholds, almost all of the studies (n = 8) reported some proportion of insured households still encountered CHE with one-third reporting more than 50% incurring CHE. Although insured households seemed better protected against CHE and impoverishment compared to uninsured households, gaps in the current NHI design contributed to financial burden among insured populations. To enhance financial risk protection among insured households and advance the drive towards UHC, West African governments should consider investing more in NHI research, implementing nationwide compulsory NHI programmes and establishing multinational subregional collaborations to co-design sustainable context-specific NHI systems based on solidarity, equity and fair financial contribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".