Analysis of morbidity due to dementia in elderly individuals in Southeast Brazil (2008-2024): an ecological study
Bibliographic record
Abstract
Background: Dementia is a syndrome of degeneration of higher cortical functions, involving memory, thinking, comprehension, language, and judgment. It has become increasingly important as a public health problem due to the growth of the elderly population, projecting that these individuals may reach 23.8% by 2040, thus nearly a quarter of the Brazilian population. Objective: To analyze the morbidity and mortality due to dementia in the elderly population in the Southeast Region from January 2008 to March 2024. Methods: This is an ecological, cross-sectional, descriptive study with a quantitative approach, conducted in May 2024, using data collected from the Hospital Information System of SUS (SIH/SUS - Sistema de Informações Hospitalares do Sistema Único de Saúde), available from the Department of Informatics of the Unified Health System (DATASUS - Departamento de Informática do Sistema Único de Saúde). The variables used were hospitalizations, total diagnostic value, deaths, and mortality rate. The collected data were organized in Microsoft Excel spreadsheets and analyzed using descriptive statistics. Results: Mortality rates, deaths, hospitalizations, and total diagnostic value were observed, respectively, for each state in the Southeast Region. In Minas Gerais, it was 19.04% (626 deaths and 3,288 hospitalizations) totaling R$15,725,524.48; in Espírito Santo, it was 7.38% (9 deaths and 122 hospitalizations) totaling R$323,552.44; in Rio de Janeiro, it was 15.11% (950 deaths and 6,289 hospitalizations) totaling R$38,420,414.65; in São Paulo, it was 7.18% (511 deaths and 7,114 hospitalizations) totaling R$40,252,064.51. Hence, it was possible to verify that the mortality rate in the state of Rio de Janeiro is lower than that of Minas Gerais; additionally, it has an approximately 50% higher total value allocated compared to the number of hospitalizations that occurred in the state of Minas Gerais. Thus, there is an unequal distribution of funds in Brazil, resulting in avoidable deaths. Conclusion: The data examined highlight the discrepancy in resources allocated to Minas Gerais compared to Rio de Janeiro. Therefore, the lack of support professionals such as nurses, psychologists, and physiotherapists for this population exacerbates this situation, consequently requiring more investment in dementia prevention measures and care for diagnosed patients with comprehensive services. For example, early recognition of the disease and differential diagnosis to mitigate the impacts of this disease, improve the patients quality of life, and increase their survival.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".