Observed trends in the magnitude of socioeconomic and area-based inequalities in use of caesarean section in Ethiopia: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: In Ethiopia, there is a paucity of studies on inequality in caesarean section using methodologically rigorous and well-established approaches. In this study, we showed extent and the overtime dynamics of inequality in caesarean section in Ethiopia following rigorous methodologies. METHODS: The data for analysis came from Ethiopia Demographic and Health Surveys (EDHS) conducted between 2000 and 2016. We used the World Health Organization's (WHO) Health Equity Assessment Toolkit (HEAT) to analyze the data. Caesarean delivery was disaggregated by four equity stratifiers, namely education, wealth, residence and regions. Relative and absolute summary measures were calculated for each equity stratifier to capture inequality from different perspectives. 95% Uncertainty Interval was calculated around a point estimate to measure statistical significance. RESULTS: We found large socioeconomic and area-based inequalities in use of caesarean section in all study surveys. The inequalities have occurred in favour of socioeconomically advantaged women and those living in urban areas and certain regions such as Addis Ababa. While area-related inequality had generally increased with time, socioeconomic inequality showed fluctuation. Adoption of different measures in the study for the inequality analysis has caused the emergence of mix of patterns in caesarean section inequality over time. CONCLUSIONS: In all the surveys, wealthy and more educated women, and those residing in urban areas had higher chance of obtaining caesarean delivery. Policy makers should work to ensure caesarean section that is in the accepted normal range. More emphasis should be drawn to subpopulation with under use of caesarean section while at the same time, discouraging unjustified use of it.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".