Causes and risk factors for deaths in young infants in South Asia: the ANISA prospective population-based observational cohort study
Bibliographic record
Abstract
INTRODUCTION: Strategies for reducing infant mortality require accurate, local, population-level data. We conducted a population-based observational study in three countries in South Asia to describe risk factors, causes and rates of mortality in young infants. METHODS: Pregnancies, births and pregnancy outcomes were determined through household surveillance, and cause of deaths was ascertained by verbal autopsy. Cox regression was used to identify risk factors for deaths during days 0-<3, 3-<7 and 7-<60. RESULTS: Among 73 622 pregnancy outcomes, 4638 deaths were identified, including 1669 stillbirths (36.0%), 1347 (29.0%) deaths among non-registered liveborn infants who died before the first home visit by community health workers (CHWs), and 1622 (35.0%) deaths that occurred during days 0-<60 among liveborn registered infants. Most deaths among liveborn infants (59.3%, 1757 of 2965) took place within 3 days of birth. The most common causes of death over the young infant period were infections/sepsis (32.5%, n=963 of 2,965), birth asphyxia (29.0%, n=859) and preterm birth/low birth weight (14.1%, n=418). Risk factors for mortality included early morbidity (need for resuscitation, intrapartum infection/antibiotics, multiple gestation, congenital anomalies), environmental factors (smoke exposure, maternal betel chewing) and poor maternal access to quality care (history of a prior neonatal death, lack of care seeking for labour complications). Protective factors included biology (female sex, higher birth weight), essential newborn care (immediate breastfeeding, clean cord care) and access to quality maternal and newborn care (antenatal care, facility birth, skilled birth attendant, maternal education, household wealth). CONCLUSIONS: Our population-based data highlight the importance of addressing deaths due to birth asphyxia and infections, while recognising that the relative burden of deaths due to preterm birth and congenital anomalies is increasing globally. Access to quality community-based and facility-based maternal and newborn care is critical to efforts to reduce mortality in young infants in high-mortality settings such as rural South Asia.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".