International comparisons of preterm birth: higher rates of late preterm birth are associated with lower rates of stillbirth and neonatal death
Bibliographic record
Abstract
OBJECTIVE: To examine international rates of preterm birth and potential associations with stillbirths and neonatal deaths at late preterm and term gestation. DESIGN: Ecological study. SETTING: Canada, USA and 26 countries in Europe. POPULATION: All deliveries in 2004. METHODS: Information on preterm birth (<37, 32-36, 28-31 and 24-27 weeks of gestation) and perinatal deaths was obtained for 28 countries. Data sources included files and publications from Statistics Canada, the EURO-PERISTAT project and the National Center for Health Statistics. Pearson correlation coefficients and random-intercept Poisson regression were used to examine the association between preterm birth rates and gestational age-specific stillbirth and neonatal death rates. Rate ratios with 95% confidence intervals were estimated after adjustment for maternal age, parity and multiple births. MAIN OUTCOME MEASURES: Stillbirths and neonatal deaths ≥ 32 and ≥ 37 weeks of gestation. RESULTS: International rates of preterm birth (<37 weeks) ranged between 5.3 and 11.4 per 100 live births. Preterm birth rates at 32-36 weeks were inversely associated with stillbirths at ≥ 32 weeks (adjusted rate ratio 0.94, 95% CI 0.92-0.96) and ≥ 37 weeks (adjusted rate ratio 0.88, 95% CI 0.85-0.91) of gestation and inversely associated with neonatal deaths at ≥ 32 weeks (adjusted rate ratio 0.88, 95% CI 0.85-0.91) and ≥ 37 weeks (adjusted rate ratio 0.82, 95% CI 0.78-0.86) of gestation. CONCLUSIONS: Countries with high rates of preterm birth at 32-36 weeks of gestation have lower stillbirth and neonatal death rates at and beyond 32 weeks of gestation. Contemporary rates of preterm birth are indicators of both perinatal health and obstetric care services.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".