Association between preterm birth and cigarette smoking among pregnant women in Africa: Systematic review and Meta-analysis
Bibliographic record
Abstract
Objective: Smoking during pregnancy is an important predictor for birth outcomes that have an impact on their postpartum health, cognitive abilities, and economic standing. While extensive research has been conducted on the association between smoking and preterm birth, there is a scarcity of comprehensive evidence specific to the Africa region. This review aimed at generating summarized evidence on the association between maternal exposure to cigarettes smoking and preterm birth in Africa. Method: PubMed, Google Scholar, and the African Journals Online databases were used to conduct a thorough literature search. Review inclusion was open to all designs of observational studies. The Newcastle-Ottawa Scale (NOS), which was used for case-control, cohort and cross-sectional studies, was the measure used for quality assessment. To get the pooled estimates and the associated 95% confidence interval (CI), the random-effects model was utilized. Result: A total of four studies were included in the prevalence and association meta-analysis. Among women with active smoking exposure, the pooled prevalence of preterm birth was 19.44% (95% CI [16.04, 22.84]), with moderate heterogeneity ( I 2 = 63.49%, p = .05). Among passive the prevalence was 17.5% ([10.88, 24.12]) with no observed heterogeneity ( I 2 = 0%). The overall pooled prevalence was 19.22% ([16.28, 22.16]). For association estimates, the overall pooled AOR across studies was 3.18 ([1.30, 5.06]), Conclusion: This review showed that maternal tobacco exposure, whether active or passive, is associated with an increased risk of preterm birth in African 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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".