Maternal and neonatal health impact of obstetrical risk insurance scheme in Mauritania: a quasi experimental before-and-after study
Bibliographic record
Abstract
A variety of health financing schemes shaped on pre-payment scheme have been implemented across Sub-Saharan Africa (SSA) to address the Millennium Development Goals (MDGs). In Mauritania, the Obstetric Risk Insurance package (ORI) focusing on maternal and perinatal health has been progressively implemented at the health district level since 2002. Here, our main objective was to assess the effectiveness of the ORI in increasing facility-based delivery rates, as well as increases in family planning, antenatal and postnatal care, caesarean delivery and neonatal health, from demographic and health survey data between 2002 and 2011. We also examined whether the effects of the ORI varied between strata of the population. The study was based on a quasi-experimental before-and-after design to assess the causal link between availability of ORI and increase in use of maternal health services and neonatal mortality. In combination with geographical information system, difference-in-differences and odd ratio approaches were used to address our objectives. Indicators of access to care for pregnant women and neonatal health and improved in both non-intervention and intervention groups during the study period. There was no global effect of the availability of ORI on facility-based delivery rates, nor on the use of antenatal and postnatal care services, except for qualified antenatal services. However, delivery rates in local health centres with ORI increased more rapidly than in those with no ORI, the contrary was shown for hospitals. Caesarean delivery and family planning decreased with ORI. Although late neonatal mortality rates remained low in the country, a significant decrease was seen in districts without ORI. Except for some strata of the population, ORI has not really met its objective of attracting more pregnant women towards facility-based health care.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".