Caesarean delivery and neonatal mortality rates in 46 low- and middle-income countries: a propensity-score matching and meta-analysis of Demographic and Health Survey data
Bibliographic record
Abstract
BACKGROUND: Previous research on the association between caesarean delivery (CD) and neonatal mortality has had methodological limitations and given conflicting results. We conducted a study to: (i) estimate the association between CD at the individual level and neonatal mortality rates (NMR) in 46 countries; and (ii) examine whether this association varies among countries according to country-level rates of CD or gross domestic product (GDP). METHODS: We obtained data from nationally representative Demographic and Health Surveys of women aged 15-49 years and their children aged 0-59 months (N = 392 883). Propensity-score matching, meta-analysis, and meta-regression were used to address the study objectives. RESULTS: The pooled odds ratio (OR) for the association between individual level CD and NMR in 46 countries was 1.67 (95% confidence interval (CI) 1.48-1.89), with moderate heterogeneity (I(2) = 39%). A meta-analysis of subgroups indicated that CD at the individual level was positively associated with NMR in countries with low (OR = 1.99, 95% CI 1.71-2.33, I(2) = 8.5%) and medium (OR = 1.53, 95% CI 1.29-1.82, I(2) = 24%) rates of CD. There was substantial heterogeneity of the effects of CD among countries with high rates of CD (I(2) = 63%). Results of meta-regression showed that the association of individual-level CD with NMR depended upon country-level rates of CD. Compared with countries with high rates of CD, the OR of the NMR associated with individual-level CD in countries with low rates of CD was estimated to increased by a factor of 1.48 (95% CI 1.09-1.97). CONCLUSIONS: Studies are needed to better understand the risks posed by CD in countries with low and medium rates of CD and to identify possible reasons for the heterogeneity in effects of CD among countries with high rates of CD.
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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 | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.035 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".