Epidemiological profile trends and cost of sickle cell disease in Brazil from 2008 to 2022.
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate the epidemiological profile trends and economic impact of sickle cell disease (SCD) in Brazil from 2008 to 2022, focusing on incidence, mortality, and healthcare costs. METHODS: A cross-sectional analysis was conducted using data from the Fundação Oswaldo Cruz's platform, Plataforma de Ciência de Dados Aplicada à Saúde, encompassing hospitalizations related to SCD from January 2008 to December 2022. The International Classification of Diseases codes for SCD were used to retrieve data on incidence, mortality, procedures performed, and healthcare costs. RESULTS: The study included 151,535 hospitalizations for SCD, with 69.92% associated with SCD crises and 22.48% without crises. The mean annual hospitalizations were higher for crises (6,883.06) compared to those without crises (2,221.12). Mortality rates were significantly higher for patients hospitalized with crises compared to those without crises (p < 0.001). The economic impact of SCD was substantial, with annual costs exceeding 413 million USD. CONCLUSION: This study revealed a significant burden of SCD in Brazil, characterized by high hospitalization rates, particularly among younger patients, and elevated mortality rates associated with crises. Prospective studies and public health interventions are warranted to address SCD and mitigate its impact on public health.
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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 | 0.000 | 0.002 |
| 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".