Regional variations and socio-economic disparities in neonatal mortality in Angola: a cross-sectional study using demographic and health surveys
Bibliographic record
Abstract
BACKGROUND: Inequalities in neonatal mortality rates (NMRs) in low- and middle-income countries show key disparities at the detriment of disadvantaged population subgroups. There is a lack of scholarly evidence on the extent and reasons for the inequalities in NMRs in Angola. OBJECTIVE: The aim of this study was to assess the socio-economic, place of residence, region and gender inequalities in the NMRs in Angola. METHODS: The World Health Organization Health Equity Assessment Toolkit software was used to analyse data from the 2015 Angola Demographic and Health Survey. Five equity stratifiers: subnational regions, education, wealth, residence and sex were used to disaggregate NMR inequality. Absolute and relative inequality measures, namely, difference, population attributable fraction (PAF), population attributable risk (PAR) and ratio, were calculated to provide a broader understanding of the inequalities in NMR. Statistical significance was calculated at corresponding 95% uncertainty intervals. FINDINGS: We found significant wealth-driven [PAR = -14.16, 95% corresponding interval (CI): -15.12, -13.19], education-related (PAF = -22.5%, 95% CI: -25.93, -19.23), urban-rural (PAF = -14.5%, 95% CI: -16.38, -12.74), sex-based (PAR = -5.6%, 95% CI: -6.17, -5.10) and subnational regional (PAF = -82.2%, 95% CI: -90.14, -74.41) disparities in NMRs, with higher burden among deprived population subgroups. CONCLUSIONS: High NMRs were found among male neonates and those born to mothers with no formal education, poor mothers and those living in rural areas and the Benguela region. Interventions aimed at reducing NMRs, should be designed with specific focus on disadvantaged subpopulations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".