Impact of antenatal care quality on neonatal mortality in Ethiopia: Evidence from DHS (2000 to 2019)
Bibliographic record
Abstract
Abstract Background Globally, 2.3 million children died, with an average of 6,300 neonatal deaths every day. Sub-Saharan Africa (SSA) has the world’s highest infant death rate and has made the slowest progress in reducing neonatal mortality. Studies on the impact of antenatal care quality on neonatal mortality in Ethiopia have been limited. Method This study analyzed five years of Ethiopian demographic and health survey data, examining 51,730 live births from the five years prior to the survey. It assessed the quality of antenatal care through six key components. Utilizing multivariate logistic regression, the study explored the relationship between antenatal care quality scores, ANC visits, service components, and neonatal mortality. Factors with p-values below 0.05 were deemed statistically significant. Results The quality of prenatal care was 2.69 in 2016, ranging from 2.25 in the Somali region to 6.35 in Addis Ababa, 5.36 in higher education, 4.12 among the wealthiest individuals, and 4.7 in urban subpopulations. Neonatal mortality decreased by 16.3% for each unit increase in prenatal care quality. The reduction in neonatal mortality was associated with AN4+ (AOR=0.46, 95%CI 0.33, 0.64), good quality of antenatal care (AOR=0.56, 95% CI 0.42, 0.74), blood pressure measurements (AOR= 0.42, 95% CI 0.23, 0.75), and counseling about pregnancy complications (AOR= 0.58, 95% CI 0.35, 0.95). Conclusion The Quality of antenatal care varies across regions, and is more advantageous in the subpopulations. Reducing neonatal mortality is associated with pregnant women receiving quality ANC, attending at least four ANC visits, having blood pressure measurements, and receiving advice on pregnancy-related difficulties.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".