Inequalities and trends in Neonatal Mortality Rate (NMR) in Ethiopia: Evidence from the Ethiopia Demographic and Health Surveys, 2000–2016
Bibliographic record
Abstract
BACKGROUND: Substantial inequality in neonatal mortality rates (NMR) remains in low- and middle-income countries to the detriment of disadvantaged subpopulations. In Ethiopia, there is a dearth of evidence on the extent and trends of disparity in NMR. This study assessed the socioeconomic, residence and sex-based inequalities in NMR, as well as examined its change over a sixteen year period in Ethiopia. METHODS: Using the World Health Organization's (WHO) Health Equity Assessment Toolkit (HEAT) software, data from the Ethiopia Demographic and Health Surveys (EDHS) were analyzed between 2000 and 2016. NMR was disaggregated by four equity stratifiers: education, wealth, residence and sex. In addition, absolute and relative inequality measures, namely Difference, Population Attributable Risk (PAR), Ratio, Relative Concentration Index (RCI) and Slope Index of Inequality (SII) were calculated to understand inequalities from different perspectives. Corresponding 95% Uncertainty Intervals (UIs) were computed to measure statistical significance. FINDINGS: Large educational inequalities in NMR were found in 2000, 2005, and 2011, while wealth-driven inequality occurred in 2011. Sex disparity was noted in all the surveys, and urban-rural differentials remained in all the surveys except in 2016. While socioeconomic and area-related inequalities decreased over time, sex related inequality did not change during the period of study. CONCLUSIONS: NMR appeared to be concentrated among male newborns, neonates born to illiterate and poor women and those living in rural settings. However, the inequality narrowed over time. Interventions appropriate for different subpopulations need to be designed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".