Determinants of early neonatal mortality in Afghanistan: an analysis of the Demographic and Health Survey 2015
Bibliographic record
Abstract
BACKGROUND: Neonatal mortality is declining slowly compared to under-five mortality in many developing countries including Afghanistan. About three-fourths of these deaths occur in the early neonatal period (i.e., the first week of life). Although a number of studies investigated determinants of early neonatal mortality in other countries, there is a lack of evidence regarding this in Afghanistan. This study investigated determinants of early neonatal mortality in Afghanistan. METHODS: Data from the Afghanistan Demographic and Health Survey 2015 (AfDHS 2015) were analyzed. After reporting the weighted frequency distributions of selected factors, a multilevel logistic regression model revealed adjusted associations of factors with early neonatal mortality. RESULTS: A total of 19,801 weighted live-births were included in our analysis; 266 (1.4%) of the newborns died in this period. Multivariable analysis found that multiple gestations (adjusted odds ratio (AOR): 9.3; 95% confidence interval (CI): 5.7-15.0), larger (AOR: 2.9; 95% CI: 2.2-3.8) and smaller (AOR: 1.8; 95% CI: 1.2-2.6) than average birth size, maternal age ≤ 18 years (AOR: 1.8; 95% CI: 1.1-3.2) and ≥ 35 years (AOR: 1.7; 95% CI: 1.3-2.3), and birth interval of < 2 years (AOR: 2.6; 95% CI: 1.4-4.9) had higher odds of early neonatal mortality. On the other hand, antenatal care by a skilled provider (AOR: 0.7; 95% CI: 0.5-0.9), facility delivery (AOR: 0.7; 955 CI: 0.5-0.9), paternal higher education level (AOR: 0.7; 95% CI: 0.5-1.0), living in north-western (AOR: 0.3; 95% CI: 0.1-0.6), central-western regions (AOR: 0.5; 95% CI: 0.3-0.9) and in a community with higher maternal education level (AOR: 0.4; 95% CI: 0.2-0.9) had negative association. CONCLUSIONS: Several individual, maternal and community level factors influence early neonatal deaths in Afghanistan; significance of the elements of multiple levels indicates that neonatal survival programs should follow a multifaceted approach to incorporate these associated factors. Programs should focus on birth interval prolongation with the promotion of family planning services, utilization of antenatal care and institutional delivery services along with management of preterm and sick infants to prevent this large number of deaths in this period.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".