Impact of stillbirths on international comparisons of preterm birth rates: a secondary analysis of the <scp>WHO</scp> multi‐country survey of Maternal and Newborn Health
Bibliographic record
Abstract
OBJECTIVE: To evaluate the extent to which stillbirths affect international comparisons of preterm birth rates in low- and middle-income countries. DESIGN: Secondary analysis of a multi-country cross-sectional study. SETTING: 29 countries participating in the World Health Organization Multicountry Survey on Maternal and Newborn Health. POPULATION: 258 215 singleton deliveries in 286 hospitals. METHODS: We describe how inclusion or exclusion of stillbirth affect rates of preterm births in 29 countries. MAIN OUTCOME MEASURES: Preterm delivery. RESULTS: In all countries, preterm birth rates were substantially lower when based on live births only, than when based on total births. However, the increase in preterm birth rates with inclusion of stillbirths was substantially higher in low Human Development Index (HDI) countries [median 18.2%, interquartile range (17.2-34.6%)] compared with medium (4.3%, 3.0-6.7%), and high-HDI countries (4.8%, 4.4-5.5%). CONCLUSION: Inclusion of stillbirths leads to higher estimates of preterm birth rate in all countries, with a disproportionately large effect in low-HDI countries. Preterm birth rates based on live births alone do not accurately reflect international disparities in perinatal health; thus improved registration and reporting of stillbirths are necessary. TWEETABLE ABSTRACT: Inclusion of stillbirths increases preterm birth rates estimates, especially in low-HDI countries.
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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 | 0.029 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".