Epidemiological profile trends and cost of pediatric sickle cell disease in Brazil from 2008 to 2022
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the epidemiological trends of Pediatric Sickle Cell Disease (SCD) in Brazil over the period 2008-2022, with a focus on understanding the incidence, mortality rates, and associated healthcare costs. The study explored potential associations between patient characteristics and the occurrence of crises in pediatric SCD cases. METHODS: A cross-sectional study was conducted, analyzing national annual rates of pediatric SCD hospitalizations using data from the FioCruz platform. Descriptive and inferential analyses, including time series and ARIMA regression, were employed. Economic dimensions were assessed using cost categorization. The study followed STROBE reporting guidelines. RESULTS: Data on 81,942 pediatric SCD hospitalizations were collected, with a predominance of crisis-related cases (74.08 %). Males and children under five years old were most affected. Regional disparities were observed, with the Southwest region recording the highest hospitalization rates. ICU costs were higher for crisis-related hospitalizations. Mortality rates were significantly higher for crisis-related cases (p < 0.001), with ARIMA regression indicating a significant association between hospitalizations for crisis-related cases and mortality. CONCLUSION: This study highlights the significant burden of pediatric SCD in Brazil, particularly crisis-related cases, suggesting a need for focused interventions. By prioritizing early detection, equitable access to healthcare, and evidence-based interventions, Brazil can mitigate the burden of SCD and improve patient outcomes. These findings contribute to informing public health policies and interventions aimed at addressing the challenges of pediatric SCD management in Brazil.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".