Taxas de Mortalidade por Doenças Cardiovasculares e Câncer na População Brasileira com Idade entre 35 e 74 Anos, 1996-2017
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVD) and cancer are the main causes of death worldwide. These diseases share many risk factors. Control of traditional risk factors for CVD was associated with lower incidence of cancers. OBJECTIVE: To analyze CVD and cancer mortality rate trends in Brazilian population aged 35-74 years from 1996 to 2017. METHODS: Crude and age-adjusted death rate trends were analyzed for all causes of death, CVD, and cancer. Data were obtained from mortality database of the Ministry of Health. Joinpoint Regression Program performed analysis of trends and adjustments in death rates. The degree of changes was determined by the average annual percent change (AAPC). Level of statistical significance was set at p <0.05. RESULTS: Mortality from all causes of death (AAPC=-1.6%; p<0.001), CVD (AAPC=-2.3; p<0.001), ischemic heart disease (IHD) (AAPC=-1.6; p<0.001) and stroke (AAPC=-3.7; p<0.001) declined. Same trends were observed for CVD (p<0.001) in men and women. Death rates from all causes of cancer (AAPC=-0.1; p=0.201), in men (AAPC=-0.1; p=0.193) and in women (AAPC=-0.1; p=0,871) remained unchanged. In 2002, mortality from cancer exceeded the sum of deaths from IHD and stroke. If trends continue, cancer mortality will also exceed mortality from CVD by 2024. In women, death rates from breast, lung and colon cancer increased, and from cervical and gastric cancers decreased. In men, mortality from lung, stomach and esophagus cancer decreased, and from prostate cancer remained unchanged. CONCLUSION: CVD are currently the leading cause of death in Brazil, but death rates from cancer will exceed those from CVD in a few years.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".