Health insurance coverage and access to child and maternal health services in West Africa: a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: According to the United Nations, the third Sustainable Development Goal, 'Ensure Healthy Lives and Promote Well-Being at All Ages', set numerous targets on child and maternal health. Universal health insurance is broadly seen as a solution to fulfil these targets. West Africa is known to have the most severe maternal mortality and under-five mortality rates in the world. This review seeks to understand whether health insurance provides increased access to services for mothers and children in this region. METHODS: The protocol for this review is registered in the International Prospective Register of Systematic Reviews database (CRD42020203859). A search was conducted in the MEDLINE Complete, Embase, CINAHL Complete and Global Health databases. Eligible studies were from West African countries. The population of interest was mothers and children and the outcome of interest was the impact of health insurance on access to services. Data were extracted using a standardized form. The primary outcome was the impact of health insurance on the rate of utilization and access to services. The Joanna Briggs Institute Critical Appraisal Tool was used for methodological assessment. RESULTS: Following screening, we retained 49 studies representing 51 study settings. In most study settings, health insurance increased access to child and maternal health services. Other determinants of access were socio-economic factors such as wealth and education. CONCLUSIONS: Our findings suggest that health insurance may be a viable long-term strategy to alleviate West Africa's burden of high maternal and child mortality rates. An equity lens must guide future policy developments and significant research is needed to determine how to provide access reliably and sustainably to services for mothers and children in the near and long term.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".