Risk factors for neonatal mortality in rural areas of Bangladesh served by a large NGO programme
Bibliographic record
Abstract
Neonatal deaths account for about half of all deaths among children under 5 years of age in Bangladesh, making prevention a major priority. This paper reports on a study of neonatal deaths in 12 areas of Bangladesh served by a large NGO programme, which had high coverage of reproductive health outreach services and relatively low neonatal mortality in recent years. The study aimed to identify the main factors associated with neonatal mortality in these areas, with a view to developing appropriate strategies for prevention. A case-control design was adopted for collection of data from mothers whose children, born alive in 2003, died within 28 days postpartum (142 cases), or did not (617 controls). Crude and adjusted odds ratios (AOR) were calculated as estimates of relative risk for neonatal death, using 'neighbourhood' controls (241) and 'non-neighbourhood' controls (376). A similar proportion of case and control mothers had received NGO health education and maternal health services. The main risk factors for neonatal death among 122 singleton babies, based on the two sets of controls, were: complications during delivery [AOR, 2.6 (95% CI: 1.5-4.5) and 3.1 (95% CI: 1.8-5.3)], prematurity [AOR, 7.2 (95% CI: 3.6-14.4) and 8.3 (95% CI: 4.2-16.5)], care for a sick neonate from an unlicensed 'traditional healer' [AOR, 2.9 (95% CI 0.9-9.5 and 5.9 (95% CI: 1.3-26.3)], or care not sought at all [AOR, 23.3 (95% CI: 3.9-137.4)]. The strongest predictor of neonatal death was having a previous sibling not vaccinated against measles [AOR, 5.9 (95% CI: 2.2-15.5) and 12.0 (95% CI: 4.5-31.7)]. The findings of this study indicate the need for identification of babies at high risk and early postpartum interventions (40.2% of the deaths occurred within 24 hours of delivery). Relevant strategies include special counselling during pregnancy for mothers with risk characteristics, training birth attendants in resuscitation, immediate postnatal check-up in the home for high-risk babies identified at delivery, advice for mothers on appropriate care-seeking for sick babies, improving the capacity of sub-district hospitals for emergency obstetric and newborn care, and promotion of institutional deliveries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".