Distance to emergency obstetric services and early neonatal mortality in <scp>E</scp>thiopia
Bibliographic record
Abstract
OBJECTIVES: To assess the effect of distance to emergency obstetric and newborn care (EmONC) services on early neonatal mortality in rural Ethiopia and examine whether proximity to services contributes to socio-economic inequalities in early neonatal mortality. METHODS: We linked data from the 2011 Ethiopian Demographic and Health Survey with facility data from the 2008 Ethiopian National EmONC Needs Assessment based on geographical coordinates collected in both surveys. Health facilities were classified based on the performance of nine EmONC signal functions (e.g. neonatal resuscitation, Caesarean section). We used multivariable logistic regression to assess the relationship between distance to services and early neonatal mortality. A decomposition approach was used to quantify the relative contributions of distance to EmONC services and other determinants to overall and socio-economic inequality in early neonatal mortality. RESULTS: In general, closer proximity to EmONC services and higher level of care were associated with lower early neonatal mortality. Living more than 80 km from the nearest comprehensive EmONC facility able to perform all nine signal functions compared to living within 10 km was associated with an increase of 14.4 early neonatal deaths per 1000 live births (95% CI: 0.1, 28.7). Closer proximity to a substandard EmONC facility compared with no facility was not associated with lower early neonatal mortality. Distance to EmONC services was an important determinant of early neonatal mortality, although it did not make a significant contribution to explaining socio-economic inequality. CONCLUSIONS: Our results suggest that recent initiatives by the Ethiopian government to improve geographical access to EmONC services have the potential to reduce early neonatal mortality but may not affect inequalities.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".