Time trends and sociodemographic determinants of preterm births in pregnancy cohorts in Matlab, Bangladesh, 1990–2014
Bibliographic record
Abstract
INTRODUCTION: Preterm birth is the major cause of under-five mortality. Population-based data on determinants and proportions of children born preterm are limited, especially from low-income countries. This study aimed at assessing time trends and social, reproductive and environmental determinants of preterm births based on a population-based pregnancy cohort over 25 years in rural Bangladesh. METHODS: In this cohort study in Matlab, a rural area in Bangladesh, we used data from the Health and Demographic Surveillance System from 1990 to 2014. Gestational age at birth was based on the reported last menstrual period and verified by ultrasound assessments. Preterm birth proportions were assessed within strata of social and reproductive characteristics, and time series analysis was performed with decomposition for trend and seasonality. We also determined the prevented fractions of preterm birth reduction associated with social and demographic changes during the follow-up period. RESULTS: Analyses were based on 63 063 live births. Preterm birth decreased from 29% (95% CI 28.6 to 30.1) in 1990-1994 to 11% (95% CI 10.5 to 11.6) in 2010-2014. Low education, older age and multi-parity were associated with higher proportions of preterm births across the study period. Preterm births had a marked seasonal variation. A rapid increase in women's educational level and decrease in parity were associated with the decline in preterm births, and 27% of the reduction observed from 1990 to 2014 could be attributed to these educational and reproductive changes. CONCLUSION: The reduction in preterm birth was to a large extent associated with the sociodemographic transition, especially changes in maternal education and parity. The persistent seasonal variation in the proportion of preterm birth may reflect the environmental stressors for pregnant women across the study period. Continued investments in girls' education and family planning programmes may contribute to further reduction of preterm births in Bangladesh.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".