Do free caesarean section policies increase inequalities in Benin and Mali?
Bibliographic record
Abstract
BACKGROUND: Benin and Mali introduced user fee exemption policies focused on caesarean sections (C-sections) in 2005 and 2009, respectively. These policies had a positive impact on access to C-sections and facility based deliveries among all women, but the impact on socioeconomic inequality is still highly uncertain. The objective of this study was to observe whether there was an increase or a decrease in urban/rural and socioeconomic inequalities in access to C-sections and facility based deliveries after the free C-section policy was introduced. METHODS: We used data from three consecutive Demographic and Health Surveys (DHS): 2001, 2006 and 2011-2012 in Benin and 2001, 2006 and 2012-13 in Mali. We evaluated trends in inequality in terms of two outcomes: C-sections and facility based deliveries. Adjusted odds ratios were used to estimate whether the distributions of C-sections and facility based deliveries favoured the least advantaged categories (rural, non-educated and poorest women) or the most advantaged categories (urban, educated and richest women). Concentration curves were used to observe the degree of wealth-related inequality in access to C-sections and facility based deliveries. RESULTS: We analysed 47,302 childbirths (23,266 in Benin and 24,036 in Mali). In Benin, we found no significant difference in access to C-sections between urban and rural women or between educated and non-educated women. However, the richest women had greater access to C-sections than the poorest women. There was no significant change in these inequalities in terms of access to C-sections and facility based deliveries after introduction of the free C-section policy. In Mali, we found a reduction in education-related inequalities in access to C-sections after implementation of the policy (p-value = 0.043). Inequalities between urban and rural areas had already decreased prior to implementation of the policy, but wealth-related inequalities were still present. CONCLUSIONS: Urban/rural and socioeconomic inequalities in C-section access did not change substantially after the countries implemented free C-section policies. User fee exemption is not enough. We recommend switching to mechanisms that combine both a universal approach and targeted action for vulnerable populations to address this issue and ensure equal health care access for all individuals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".