Deciphering Trends in Cancer Mortality: A Comprehensive Analysis of Brazilian Data From 1979 to 2021 With Emphasis on Breast and Prostate Cancers
Bibliographic record
Abstract
Background: This study examined cancer mortality trends in Brazil from 1979 to 2021, emphasizing breast and prostate cancers. Methods: Utilizing data from the Brazilian Mortality Information System and the Brazilian Institute of Geography and Statistics, it analyzed cancer deaths nationally and regionally, highlighting gender-specific and regional disparities. Results: The research finds that cancer death rates have been growing at an average of 12% per year, contrasting with the population growth rate of 2.2%. This trend is more pronounced in the southern and southeastern regions of Brazil. A comparison of cancer mortality rates between Brazil, the USA, and China reveals that while the Brazilian and Chinese rates exhibit slower growth, the US rate shows a continuous decline since the 1990s. Conclusions: The study adopts a novel approach by focusing on growth rates and employing polynomial interpolation, revealing a deceleration in cancer death growth over the last 15 years across all malignant neoplasms. The study also contextualizes these findings within Brazil's cancer control policies, tracing the evolution of preventive measures and treatment advancements. It highlights the significant role of the National Cancer Institute and the Unified Health System in implementing effective strategies. The decreasing trend in cancer mortality rates in Brazil, despite population growth, illustrates the effectiveness of comprehensive cancer control and prevention measures, underlining their importance in public health policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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".