Maternal health service disparities across incomes and implications on prevention of mother-to-child transmission service coverage: current context in Sub-Saharan Africa
Bibliographic record
Abstract
About 69% of people living with HIV globally and over 90% of the children who acquired HIV infection are in Sub-Saharan Africa. Despite this, promising results have been observed, especially over the last decade - for example, a 25% decline in new HIV infections as compared to 2001 and a 38% decline in the number of children newly infected by HIV in 2012 as compared to 2009. However, the Global Plan and the Global Fast-Track Commitments of eliminating new HIV infections among children require addressing impediments to service expansion. In this regard, this paper attempts to draw attention to the extent to which disparities across income in using antenatal care (ANC) services may constrain the prevention of mother-to-child transmission (PMTCT) service expansion in Sub-Saharan Africa. The analysis is based on ANC service coverage data from Demographic and Health Surveys conducted between 2008 and 2015 in 31 Sub-Saharan African countries; and PMTCT coverage data from UNAIDS datasets released in 2016. Our analysis found that women in the highest wealth quintile are about three times more likely to frequently use ANC services (at least four visits) as compared to those in the lowest wealth quintile (95%CI: 1.7-5.7, P<0.0001). A regression analysis shows that one-quarter of the PMTCT service coverage can be explained by the disparity in ANC use associated with income; and the higher the disparity in ANC use, the lower the PMTCT service (P<0.05). The findings suggest that achieving the ambitious plan of zero new HIV infections among children while keeping their mothers alive will require on-going PMTCT/ANC service integration and ensuring that programs reach women who are most in need; specifically those in the poorest income categories.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".