Inequalities and factors associated with maternal healthcare services utilisation in Mozambique: evidence from the Demographic and Health Survey 2022−2023
Bibliographic record
Abstract
Background Mozambique has one of the highest maternal mortality rates in sub-Saharan Africa. While some progress has been made, further efforts are required to ensure that women in Mozambique have access to high-quality healthcare. A key strategy for reducing maternal and child mortality is to promote adequate access to and utilisation of maternal healthcare services. Methods We used the population-based, nationwide, cross-sectional Mozambique Demographic and Health Survey 2022–2023 data (n=3808). The survey employed a two-stage stratified sampling design that yielded a nationally representative sample at the household level. Four essential maternal healthcare services outcomes were defined: adequate (at least four visits) antenatal care by a skilled provider, lab-based test services (blood, urine and ultrasound), births with a skilled birth attendant and postnatal care by a skilled provider. Results Overall, 18.6% of women received all four maternal healthcare services. Maternal healthcare utilisation showed significant inequalities favouring wealthier and more empowered women. Regression model suggests that women who were classified in the highest quintile for empowerment index (adjusted OR (aOR)=2.17, 95% CI=1.41 to 3.33), women in the two highest quintiles for wealth index (richer: aOR=2.37, 95% CI=1.41 to 3.98; richest: aOR=2.60, 95% CI=1.41 to 4.79) and women residing in urban area (aOR=1.35, 95% CI=0.99 to 1.83) were significantly associated with the utilisation of all four healthcare services. Other factors like exposure to media (television/radio/newspaper), husband’s educational status, distance to the nearest health facility and province/region of residence also determined maternal healthcare services utilisation. Conclusion Our findings highlight the need for targeted interventions such as improving women’s education, healthcare infrastructure and distance barriers and promoting gender equality to ensure greater service utilisation. These findings could help advance further development and implementation of Mozambique’s national strategies, and development assistance, for community-based primary healthcare and women-centred care as they provide the latest evidence on this topic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".