Global Inequality in Maternal Health Care Service Utilization: Implications for Sustainable Development Goals
Bibliographic record
Abstract
Purpose: Globally, low-middle-income countries continue to account for almost all of the pregnancy-related mortalities that are largely preventable through adequate utilization of essential maternal health care services such as antenatal care (ANC) and skilled birth assistance (SBA). Promoting the use of ANC and SBA services are hindered by numerous policy- and capacity-related barriers along with widespread inequality in utilization of the existing services that further exacerbates the scenario. In an attempt to better understand the geography of inequality in service utilization, we conducted this brief descriptive study by using World Health Organization (WHO) data on ANC and SBA utilization among the member states. Methods: This was a descriptive study based on open access data on ANC and SBA use between 2012 and 2015 available through the Global Health Observatory of WHO. Country-level data were collected for Asia (41 countries), Africa (35 countries), Europe (35 countries), North America (10 countries), Latin America and the Caribbean (25 countries), and Oceania (16 countries). Cross-country and continent comparisons were made using dot and bar charts. Results: The overall prevalence of ANC and SBA use were, respectively, 78.17% and 88.33%. Considerable disparities were found in terms of ANC and SBA use across the continents, especially in Asia and Africa. Globally, the poorest performing countries included Afghanistan, Somalia, and South Sudan where more than three-quarters of the women remain deprived of ANC and SBA services during the period of 2012 and 2015. The greatest inequality in ANC use was observed in Africa (9.4% in Somalia and 99.9% in Libya), whereas that of SBA use was observed in Asia (17.8% in Afghanistan vs. 100% in Bahrain). Europe was the most equal of all regions in terms of both ANC (66.8% in Albania vs. 99.7% in Belarus) and SBA (94.4% in Denmark vs. 100% in Lithuania) use. Conclusion: Although in the majority of countries more than three-quarters of the women receive ANC and SBA services, the extent of intraregional inequality remains overwhelming especially for Asia and Africa. Progress toward maternal health-related targets should be interpreted in terms of the disparities to ensure a more even and sustainable outcome at both national and global level.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".