Stillbirth, newborn and infant mortality: trends and inequalities in four population-based birth cohorts in Pelotas, Brazil, 1982–2015
Bibliographic record
Abstract
BACKGROUND: Infant-mortality rates have been declining in many low- and middle-income countries, including Brazil. Information on causes of death and on socio-economic inequalities is scarce. METHODS: Four birth cohorts were carried out in the city of Pelotas in 1982, 1993, 2004 and 2015, each including all hospital births in the calendar year. Surveillance in hospitals and vital registries, accompanied by interviews with doctors and families, detected fetal and infant deaths and ascertained their causes. Late-fetal (stillbirth)-, neonatal- and post-neonatal-death rates were calculated. RESULTS: All-cause and cause-specific death rates were reduced. During the study period, stillbirths fell by 47.8% (from 16.1 to 8.4 per 1000), neonatal mortality by 57.0% (from 20.1 to 8.7) and infant mortality by 62.0% (from 36.4 to 13.8). Perinatal causes were the leading causes of death in the four cohorts; deaths due to infectious diseases showed the largest reductions, with diarrhoea causing 25 deaths in 1982 and none in 2015. Late-fetal-, neonatal- and infant-mortality rates were higher for children born to Brown or Black women and to low-income women. Absolute socio-economic inequalities based on income-expressed in deaths per 1000 births-were reduced over time but relative inequalities-expressed as ratios of mortality rates-tended to remain stable. CONCLUSION: The observed improvements are likely due to progress in social determinants of health and expansion of health care. In spite of progress, current levels remain substantially greater than those observed in high-income countries, and social and ethnic inequalities persist.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".