Spatiotemporal Trends in Malnutrition-related Hospitalization and Mortality Among Brazilian Children Under Five
Bibliographic record
Abstract
Abstract This study investigates annual hospital admissions and deaths due to malnutrition among Brazilian children under five from 2008 to 2024, analyzing spatial and temporal disparities across microregions and states. Using data from Brazil’s Hospital Information System (SIH), we applied a joint Bayesian spatiotemporal model to examine trends and assess five policy scenarios projected through 2030 in the context of Sustainable Development Goal 2 (SDG2): end all forms of malnutrition by 2030. Results reveal persistent regional inequalities, with the North and Northeast bearing the highest burdens, reflecting deep-rooted structural disparities. Key risk factors included pediatric bed availability (RR 1.13, 95% CrI 1.08–1.18), illiteracy, and low income. The National Hospital Care Policy (PNHOSP) contributed to reduced hospitalizations (RR 0.94, 95% CrI 0.89–0.99), but presented a borderline association with higher odds of death (OR 1.26, 95% CrI 0.98–1.58). Projections suggest that, under current conditions, Brazil is unlikely to meet SDG2 by 2030. Targeted investments in pediatric care infrastructure, combined with broader improvements in the social determinants of health, will be essential to mitigate severe malnutrition outcomes and reduce preventable deaths.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".