The impact of the obstetrical risk insurance scheme in Mauritania on maternal healthcare utilization: a propensity score matching analysis
Bibliographic record
Abstract
In Mauritania, obstetrical risk insurance (ORI) has been progressively implemented at the health district level since 2002 and was available in 25% of public healthcare facilities in 2015. The ORI scheme is based on pre-payment scheme principles and focuses on increasing the quality of and access to both maternal and perinatal healthcare. Compared with many community-based health insurance schemes, the ORI scheme is original because it is not based on risk pooling. For a pre-payment of 16-18 USD, women are covered during their pregnancy for antenatal care, skilled delivery, emergency obstetrical care [including caesarean section (C-section) and transfer] and a postnatal visit. The objective of this study is to evaluate the impact of ORI enrolment on maternal and child health services using data from the Multiple Indicator Cluster Survey (MICS) conducted in 2015. A total of 4172 women who delivered within the last 2 years before the interview were analysed. The effect of ORI enrolment on the outcomes was estimated using a propensity score matching estimation method. Fifty-eight per cent of the studied women were aware of ORI, and among these women, more than two-thirds were enrolled. ORI had a beneficial effect among the enrolled women by increasing the probability of having at least one prenatal visit by 13%, the probability of having four or more visits by 11% and the probability of giving birth at a healthcare facility by 15%. However, we found no effect on postnatal care (PNC), C-section rates or neonatal mortality. This study provides evidence that a voluntary pre-payment scheme focusing on pregnant women improves healthcare services utilization during pregnancy and delivery. However, no effect was found on PNC or neonatal mortality. Some efforts should be exerted to improve communication and accessibility to ORI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".