Preterm Birth in Black Birthing People: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
Background: Preterm birth (PTB) is a global health challenge with significant morbidity and mortality rates among affected infants driven by various factors. In Canada, approximately 8% of babies are born prematurely, contributing to infant mortality, and imposing social and financial burdens on families and society. Racial disparities in PTB are evident, particularly within Black populations in the United States, with limited data available in Canada. Our objective was to understand the epidemiology, risk factors, and racial disparities related to PTB in this population. Methods: This review was conducted in English using a combination of search terms "preterm birth", "Black women" and "Canada" in various online databases. Included and excluded articles were confirmed by each reviewer and organized using The Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The quality assessment was conducted using the Newcastle-Ottawa Scale. A multi-level meta-analysis was performed to account for inter-study correlations, focusing on three primary birth outcomes: PTB, low birth weight (LBW), and small for gestational age (SGA). Results: Over 1,000 studies were screened, 10 articles in the review, and nine in the meta-analysis. The articles discussed how the Black population experienced higher rates of PTB and other adverse pregnancy outcomes including LBW, SGA, and stillbirth. The articles also addressed the relationship between PTB and socioeconomic risk factors such as level of education, income, poverty, and social isolation. The results of the multi-level meta-analysis indicated that the associations between maternal race/ethnicity and PTB (and between maternal race/ethnicity and SGA) were the only associations found to be significant across the studies. Conclusions: Despite Canada's universal healthcare system, Black birthing people face significant disparities in maternal health, underscored by negative healthcare experiences and systemic racism within the healthcare system. Addressing these disparities requires comprehensive approaches including race-based data collection, policy advocacy, and healthcare system reforms to ensure equitable access to care and improve maternal and neonatal health outcomes. J Clin Gynecol Obstet. 2024;13(3):59-74 doi: https://doi.org/10.14740/jcgo985
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.009 | 0.010 |
| 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) | 0.004 | 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".