Socioeconomic inequalities in the use of caesarean section delivery in Ghana: a cross-sectional study using nationally representative data
Bibliographic record
Abstract
BACKGROUND: Inappropriate use of Caesarean Section (CS) delivery is partly to blame for Ghana's high maternal mortality rate. However, previous research offered mixed findings about factors associated with CS use. The goal of this study is to examine use of CS in Ghana and the socioeconomic factors associated with it. METHODS: Data from the nationally representative 2014 Ghana Demographic and Health Survey (GDHS) was used after permission from the Monitoring and Evaluation to Assess and Use Results (MEASURE) Demographic and Health Survey (DHS) program. Univariable and multivariable logistic regression models were fitted to examine the socioeconomic inequalities in CS use. The independent variables included maternal age, marital status, religion, ethnicity, education, place of residence, wealth quintile, and working status. Concentration index (CI) and rate-ratios were computed to ascertain the level of CS inequalities. RESULTS: Out of the 4294 women, 11.4% had CS delivery. However, the percentage of CS delivery ranged from 5% of women in the poorest quintile to 27.5% of women in the richest qunitle. Significant associations were detected between CS delivery and maternal age, parity, education, and wealth quintile . CONCLUSIONS: This study revealed that first, even though Ghana has achieved an aggregate CS rate consistent with WHO recommendations, it still suffers from inequities in the use of CS. Second, both underuse of CS among poorer women in Ghana and overuse among rich and educated women are public health concerns that need to be addressed. Third, the results show in spite of Ghana's free maternal care services policies, wealth status of women continues to be strongly and signtificantly associated with CS delivery, indicating that there are indirect health care costs and other reasons preventing poorer women from having access to CS which should be understood better and addressed with appropriate policies.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".