Cost of Utilising Maternal Health Services in Low- and Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Cost is a major barrier to maternal health service utilisation for many women in low- and middle-income countries (LMICs). However, comparable evidence of the available cost data in these countries is limited. We conducted a systematic review and comparative analysis of costs of utilising maternal health services in these settings. METHODS: We searched peer-reviewed and grey literature databases for articles reporting cost of utilising maternal health services in LMICs published post-2000. All retrieved records were screened and articles meeting the inclusion criteria selected. Quality assessment was performed using the relevant cost-specific criteria of the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. To guarantee comparability, disaggregated costs data were inflated to 2019 US dollar equivalents. Total adjusted costs and cost drivers associated with utilising each service were systematically compared. Where heterogeneity in methods or non-disaggregated costs was observed, narrative synthesis was used to summarise findings. RESULTS: Thirty-six studies met our inclusion criteria. Many of the studies costed multiple services. However, the most frequently costed services were utilisation of normal vaginal delivery (22 studies), caesarean delivery (13), and antenatal care (ANC) (10). The least costed services were post-natal care (PNC) and post-abortion care (PAC) (5 each). Studies used varied methods for data collection and analysis and their quality ranged from low to high with most assessed as average or high. Generally, across all included studies, cost of utilisation progressively increased from ANC and PNC to delivery and PAC, and from public to private providers. Medicines and diagnostics were main cost drivers for ANC and PNC while cost drivers were variable for delivery. Women experienced financial burden of utilising maternal health services and also had to pay some unofficial costs to access care, even where formal exemptions existed. CONCLUSION: Consensus regarding approach for costing maternal health services will help to improve their relevance for supporting policy-making towards achieving universal health coverage. If indeed the post-2015 mission of the global community is to "leave no one behind," then we need to ensure that women and their families are not facing unnecessary and unaffordable costs that could potentially tip them into poverty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".