Higher Cesarean Delivery Rates are Associated with Higher Infant Mortality Rates in Industrialized Countries
Bibliographic record
Abstract
BACKGROUND: Recent data indicate that more than half of high-income industrialized countries have a cesarean delivery rate of > 25 percent, which is higher than the appropriate level considered by most health professionals worldwide. METHODS: Data for 31 high-income industrialized countries in 2010 (or the nearest year) obtained from the World Health Organization, Organization for Economic Cooperation and Development, World Bank, and individual countries were analyzed in this study. We examined the correlation between cesarean delivery rate and infant mortality rate with Pearson correlation coefficient analysis, and examined the independent effect of cesarean delivery on infant mortality with multiple linear regression analyses. RESULTS: The cesarean delivery and infant mortality rates varied substantially among the included countries: from 15.6 to 50.0 percent and from 1.9 per to 6.8 per 1,000 live births, respectively. Cesarean delivery rates were positively correlated with infant mortality rates (Pearson correlation coefficient: 0.41, p < 0.05). The association remained after adjustment for maternal age, infant sex, per capita GDP, and the Gini index (p < 0.03), but disappeared after further adjustment for preterm birth (p = 0.07). In a sensitivity analysis, the results were not appreciably affected by excluding births at < 22 weeks of gestation, by weighting the data by the number of births in each country, or by excluding data from particular countries with possible measurement issues (USA, Greece). CONCLUSIONS: A higher cesarean delivery rate is associated with higher infant mortality rate among these high-income industrialized countries. One of the mechanisms by which cesarean delivery affects infant mortality is through iatrogenic prematurity.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".