Disparities in caesarean section prevalence and determinants across sub-Saharan Africa countries
Bibliographic record
Abstract
BACKGROUND: Access to safe Cesarean section (C-section) in resource-constrained settings such as sub-Sahara Africa (SSA) region is a foremost approach to reduce maternal mortality. C-section is an obstetric operative procedure used appropriately to improve delivery outcomes. However, errors in the procedure have enormous potential harm that may outweigh the benefits. This study assessed the prevalence and determinants of C-section in several SSA countries. This study examined the prevalence and determinants associated with cesarean delivery in SSA countries. METHODS: Secondary data of women of reproductive age (15-49 years) from the current Demographic and Health Survey (DHS) in 34 SSA countries was utilized in this study. The mode of delivery among women was the primary outcome variable. Percentage and descriptive statistics were used to conduct univariate analyses. Furthermore, multivariable multilevel logistic regression was used to investigate correlates of C-section among SSA women. RESULTS: Results showed disparities in the percentage of C-section among women from 34 SSA countries. C-section at public healthcare settings ranged from 3% in Burkina Faso to 15.6% in Ghana. However, in private healthcare settings, C-section ranged from 0% in Sao Tome and Principe to 64.2% in Rwanda. Overall, C-section was 7.9% from public healthcare and 12.3% from private healthcare facilities respectively. In the adjusted regression model; women aged 35-49 had increase in the odds of C-section, while a unit increase in the number of children ever born had 17 and 20% significant reduction in the odds of C-section in public and private healthcare respectively. Assessing public healthcare settings; women from richer/richest households, male and large size children at birth had increase in the odds of C-section, while those from rich neighbourhood had reduction in the odds of C-section. In private healthcare settings, women with high decision making power and multiple births had increase in the odds of C-section, while those who attended ANC visits had significant reduction in the odds of C-section. CONCLUSION: The findings from this study would help formulate health policies and implement actions that would improve the outcome of C-section care. Monitoring of emergency obstetric care services is necessary to address issues connected to poor C-section outcomes in resource-constrained settings. Also training of medical personnel including midwives and nurses in emergency obstetric care, ensuring accessibility to life-saving drugs and supplies should be encouraged in health care system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".