Disparities in use of skilled birth attendants and neonatal mortality rate in Guinea over two decades
Bibliographic record
Abstract
BACKGROUND: Maternal mortality remains high in sub-Saharan African countries, including Guinea. Skilled birth attendance (SBA) is one of the crucial interventions to avert preventable obstetric complications and related maternal deaths. However, within-country inequalities prevent a large proportion of women from receiving skilled birth attendance. Scarcity of evidence related to this exists in Guinea. Hence, this study investigated the magnitude and trends in socioeconomic and geographic-related inequalities in SBA in Guinea from 1999 to 2016 and neonatal mortality rate (NMR) between 1999 and 2012. METHODS: We derived data from three Guinea Demographic and Health Surveys (1999, 2005 and 2012) and one Guinea Multiple Indicator Cluster Survey (2016). For analysis, we used the 2019 updated WHO Health Equity Assessment Toolkit (HEAT). We analyzed inequalities in SBA and NMR using Population Attributable Risk (PAR), Population Attributable Fraction (PAF), Difference (D) and Ratio (R). These summary measures were computed for four equity stratifiers: wealth, education, place of residence and subnational region. We computed 95% Uncertainty Intervals (UI) for each point estimate to show whether or not observed SBA inequalities and NMR are statistically significant and whether or not disparities changed significantly over time. RESULTS: A total of 14,402 for SBA and 39,348 participants for NMR were involved. Profound socioeconomic- and geographic-related inequalities in SBA were found favoring the rich (PAR = 33.27; 95% UI: 29.85-36.68), educated (PAR = 48.38; 95% UI: 46.49-50.28), urban residents (D = 47.03; 95% UI: 42.33-51.72) and regions such as Conakry (R = 3.16; 95% UI: 2.31-4.00). Moreover, wealth-driven (PAF = -21.4; 95% UI: -26.1, -16.7), education-related (PAR = -16.7; 95% UI: -19.2, -14.3), urban-rural (PAF = -11.3; 95% UI: -14.8, -7.9), subnational region (R = 2.0, 95% UI: 1.2, 2.9) and sex-based (D = 12.1, 95% UI; 3.2, 20.9) inequalities in NMR were observed between 1999 and 2012. Though the pattern of inequality in SBA varied based on summary measures, both socioeconomic and geographic-related inequalities decreased over time. CONCLUSIONS: Disproportionate inequalities in SBA and NMR exist among disadvantaged women such as the poor, uneducated, rural residents, and women from regions like Mamou region. Hence, empowering women through education and economic resources, as well as prioritizing SBA for these disadvantaged groups could be key steps toward ensuring equitable SBA, reduction of NMR and advancing the health equity agenda of "no one left behind."
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".