Utilisation and associated socio-demographic factors related to the maternal continuum of care in sub-Saharan Africa: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Maternal continuum of care (MCC) is the utilisation of maternal health care services, including 4+ antenatal care (ANC) visits, skilled birth attendants (SBAs), and postnatal care (PNC). This systematic review and meta-analysis assessed the pooled proportion of MCC utilisation among women in sub-Saharan Africa (SSA) and its association with selected sociodemographic factors. Methods: We identified keywords and MeSH terms related to the condition (MCC), the context (SSA), and population (women with history of childbirth) to search for published or unpublished observational studies. We used the Joanna Briggs Institute tool to extract data and the Newcastle Ottawa Scale for quality assessment. Meta-analysis was used to compute pooled estimates (MCC utilisation and odds ratio (OR) associates) with 95% confidence intervals (CI) using Stata 17. Results: Of 45 402 studies identified, we included 23 involving 320 353 women. The pooled estimate of MCC utilisation across SSA was 18.72% (95% CI = 14.51, 22.93), showing a significant increase (P < 0.05) from 2015 to 2022. Southern Africa had the highest MCC utilisation (38%; 95% CI = 36.59, 39.41), while East Africa had the lowest (17.5%; 95% CI = 12.22, 22.75). Maternal continuum of care utilisation was associated with maternal age 25-34 years (pooled odds ratio (POR) = 1.27), urban residence (POR = 2.69), richer/richest wealth status (POR = 1.68), as well as higher level of education and employment (POR = 1.32). Conclusions: MCC utilisation in SSA remains low, with significant variation across the sub-regions and sociodemographic strata. Context-specific interventions targeting identified factors are essential to enhance MCC utilisation in SSA. Registration: PROSPERO: CRD42021272708.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| 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".