Removing user fees to improve access to caesarean delivery: a quasi-experimental evaluation in western Africa
Bibliographic record
Abstract
INTRODUCTION: Mali and Benin introduced a user fee exemption policy focused on caesarean sections in 2005 and 2009, respectively. The objective of this study is to assess the impact of this policy on service utilisation and neonatal outcomes. We focus specifically on whether the policy differentially impacts women by education level, zone of residence and wealth quintile of the household. METHODS: We use a difference-in-differences approach using two other western African countries with no fee exemption policies as the comparison group (Cameroon and Nigeria). Data were extracted from Demographic and Health Surveys over four periods between the early 1990s and the early 2000s. We assess the impact of the policy on three outcomes: caesarean delivery, facility-based delivery and neonatal mortality. RESULTS: We analyse 99 800 childbirths. The free caesarean policy had a positive impact on caesarean section rates (adjusted OR=1.36 (95% CI 1.11 to 1.66; P≤0.01), particularly in non-educated women (adjusted OR=2.71; 95% CI 1.70 to 4.32; P≤0.001), those living in rural areas (adjusted OR=2.02; 95% CI 1.48 to 2.76; P≤0.001) and women in the middle-class wealth index (adjusted OR=3.88; 95% CI 1.77 to 4.72; P≤0.001). The policy contributes to the increase in the proportion of facility-based delivery (adjusted OR=1.68; 95% CI 1.48 to 1.89; P≤0.001) and may also contribute to the decrease of neonatal mortality (adjusted OR=0.70; 95% CI 0.58 to 0.85; P≤0.001). CONCLUSION: This study is the first to evaluate the impact of a user fee exemption policy focused on caesarean sections on maternal and child health outcomes with robust methods. It provides evidence that eliminating fees for caesareans benefits both women and neonates in sub-Saharan countries.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".