Disparities in stillbirth rates according to municipal deprivation levels: a nationwide study in Brazil
Bibliographic record
Abstract
BACKGROUND: Investigating the relationship between stillbirth and deprivation is essential to guide healthcare improvements, as evidence is scarce in LMIC contexts. This study estimated the stillbirth rate (SBR) and the odds ratios (OR) of stillbirth in Brazil's municipal deprivation context. METHODS: This observational study included births in Brazil registered in the SINASC and SIM databases, employing two epidemiological designs. First, a cross-sectional analysis assessed the association between stillbirths and municipal deprivation, using data from January 1 to December 31, 2018. Logistic regression was used to estimate OR for stillbirths across deprivation levels, adjusting for sociodemographic, gestational, and fetal variables, with 95% confidence intervals. Deprivation was classified into quintiles based on Brazilian deprivation index (IBP) levels 1 to 5. Second, an ecological analysis examined time trends in SBR by deprivation level from January 1, 2000, to December 31, 2018. Time trends in SBR from 2000 to 2018 were analyzed using Prais-Winsten regression, both overall and stratified by IBP level. RESULTS: In 2018, the OR of stillbirth, including both antepartum and intrapartum cases, increased with higher levels of deprivation. Compared to the least deprived areas, level 2 had a 9% greater OR of stillbirth (95%CI: 1.03-1.15), level 3 had a 30% higher OR (95% CI: 1.23-1.27), level 4 showed a 34% increase (95%CI: 1.27-1.41), and level 5 had the highest OR, with a 68% increase (95%CI: 1.60-1.77). From 2000 to 2018, SBR in Brazil declined by 1.1% per year (p < 0.001). Significant declines were observed across deprivation levels 1 (-1.6% per year; p < 0.001) to 4 (-1.5% per year; p < 0.001), while level 5 showed persistently high stillbirth rates with no significant improvement (p ≥ 0.05). CONCLUSION: These results highlight the stark inequalities in stillbirth chances across Brazil. Targeted action is needed to close the gap in the most deprived municipalities and reduce stillbirth rates and perinatal health disparities.
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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 | 0.004 |
| 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 | 0.000 |
| 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".