Association between socioeconomic disadvantage and severe maternal morbidity and mortality in high-income countries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Socioeconomic position (SEP) is among the most important determinants of variations in health outcomes. This systematic review aimed to summarise the association between socioeconomic disadvantage and the risk of severe maternal morbidity (SMM) and maternal mortality (MM) across high-income countries. METHODS: A comprehensive search was conducted in the MEDLINE, EMBASE, CINAHL and PsycInfo databases and Google Scholar from January 2000 to June 2023. Peer-reviewed papers from observational studies conducted in Organisation for Economic Co-operation and Development countries were included. Meta-analyses of comparable studies, a narrative summary and a harvest plot were undertaken.The risk of bias was assessed using a modified Newcastle-Ottawa tool. RESULTS: The final review included 52 papers. In the meta-analyses, compared with the least amount of neighbourhood deprivation, neighbourhood income, neighbourhood poverty and years of education, the ORs for SMM in the highest group were 1.45 (95% CI 1.13 to 1.85), 1.48 (95% CI 1.34 to 1.63), 1.61 (95% CI 0.97 to 2.66) and 1.29 (95% CI 1.22 to 1.37), respectively. Similarly, the ORs for MM among least versus highest amount of neighbourhood deprivation, unemployed versus employed, lower versus higher occupational group and years of education were 2.10 (95% CI 1.57 to 2.81), 1.86 (95% CI 0.95 to 3.66), 1.61 (95% CI 1.03 to 2.51) and 1.90 (95% CI 1.29 to 2.79), respectively. DISCUSSION: In high-income countries across the different measures of SEP, socioeconomic disadvantage is associated with increased risk for SMM and MM. There is a need for interventions across multiple societal levels that will be effective in reducing these inequitable outcomes. PROSPERO REGISTRATION NUMBER: CRD42023399267.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".