Trends in the Prevalence and Determinants of Caesarean Section Delivery in Jordan: Evidence from Three Demographic and Health Surveys, 1990-2002
Bibliographic record
Abstract
This paper explores recent levels and trends in the prevalence of Caesarean section (C-section) delivery in Jordan during the period 1990--2002 and examines the impact of socio-demographic, healthcare and spatial characteristics on C-sections there. The study used three national data sets from the Jordan Demographic and Health Survey (JDHS) program conducted in 1990, 1997 and 2002. The JDHS surveys were based on large, nationally representative samples of 16,296, 7335 and 7825 households. Rates of C-section delivery were calculated based on the last birth for each woman delivering in hospitals. Associations between C-section delivery and selected covariates were estimated using Chi2 tests and odds ratios from binary logistic regression models. Hospital-based C-sections increased consistently, from 8.5% in 1990, to 12.9% in 1997, to 17.8% in 2002. The rate of increase in C-section delivery was slightly higher in private hospitals than in public ones. Multiple births, child birth weight, old age at birth and antenatal visits were important determinants of C-section. Place of residence and place of delivery were not consistently associated with C-section. Unexpectedly, mother's education was also not associated with Caesarean section. The final model, based on the three merged data sets, showed a strong association between year of the survey and C-section, reflecting the substantial increase in C-section rates over time. Also, the odds ratio of C-section from multiple births was significantly higher in 2002 compared with 1990. Halting the increase in C-section deliveries, especially in private hospitals, should become a priority for health professionals and policy makers in Jordan. Further in-depth studies are needed, however, to better monitor and analyze changes in C-section rates for identifying ways to reduce the prevalence of this surgical procedure.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".