Health insurance coverage and antenatal care services utilization in West Africa
Bibliographic record
Abstract
BACKGROUND: In recent decades, there has been a significant focus towards the improvement of maternal mortality indicators in low-and middle-income countries. Though progress has been made around the world, West Africa has maintained an elevated burden of diseases. One proposed solution to increasing access to primary care services is health insurance coverage. As limited evidence exists, we sought to understand the relationship between health insurance coverage and at least four antenatal care (ANC) visits in West Africa. METHODS: Demographic and Health Survey data from 10 West African countries were weighted, cleaned, and analysed. The total sample was 79,794 women aged 15 to 49 years old were considered for the analysis. Health insurance coverage was the explanatory variable, and the outcome variable was number of ANC visits. The data were analysed using binary logistic regression. The results were presented using crude and adjusted odds ratio (aOR) at 95% confidence interval. RESULTS: Approximately 86.73% of women who were covered by health insurance had four or more ANC visits, compared to 55.15% for women without insurance. In total, 56.91% of the total sample attended a minimum of four ANC visits. Women with health insurance coverage were more likely to make the minimum recommended number of ANC visits than their non-insured-peers (aOR [95% CI] =1.55 [1.37-1.73]). CONCLUSION: Health insurance is a significant determinant in accessing primary care services for pregnant women. Yet, very few in the region are covered by an insurance scheme. In the wake of the COVID-19 pandemic, policy makers should prioritize rapid solutions to provide primary care while setting the infrastructure for long-term and sustainable options such as publicly run health insurance schemes.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".