Trends and risk of recurrent preterm birth in pregnancy cohorts in rural Bangladesh, 1990–2019
Bibliographic record
Abstract
INTRODUCTION: A history of preterm birth reportedly increases the risk of subsequent preterm birth. This association has primarily been studied in high-income countries and not in low-income settings in transition with rapidly descending preterm birth figures. We evaluated the population-based trends of preterm births and recurrent preterm births and the risk of preterm birth recurrence in the second pregnancy based on prospectively studied pregnancy cohorts over three decades in Matlab, Bangladesh. METHODS: A population-based cohort included 72 160 live births from 1990 to 2019. We calculated preterm birth and recurrent preterm birth trends. We assessed the odds of preterm birth recurrence based on a subsample of 14 567 women with live-born singletons in their first and second pregnancies. We used logistic regression and presented the associations by OR with a 95% CI. RESULTS: The proportion of preterm births decreased from 25% in 1990 to 13% in 2019. The recurrent preterm births had a similar, falling pattern from 7.4% to 3.1% across the same period, contributing 27% of the total number of preterm births in the population. The odds of second pregnancy preterm birth were doubled (OR 2.18; 95% CI 1.96 to 2.43) in women with preterm birth compared with the women with term birth in their first pregnancies, remaining similar over the study period. The lower the gestational age at the first birth, the higher the odds of preterm birth in the subsequent pregnancy (test for trend p<0.001). CONCLUSION: In this rural Bangladeshi setting, recurrent preterm births contributed a sizeable proportion of the total number of preterm births at the population level. The increased risk of recurrence remained similar across three decades when the total proportion of preterm births was reduced from 25% to 13%.
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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 | 0.002 |
| 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".