Physical inactivity as a risk factor for all-cause mortality in Brazil (1990–2017)
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to estimate the mortality from all causes as a result of physical inactivity in Brazil and in Brazilian states over 28 years (1990-2017). METHODS: Data from the Global Burden of Disease (GBD) study for Brazil and states were used. The metrics used were the summary exposure value (SEV), the number of deaths, age-standardized mortality rates, and the fraction of population risk attributable to physical inactivity. RESULTS: The Brazilian population presented risk of exposure to physical inactivity of (age-standardized SEV) of 59% (95% U.I. 22-97) in 1990 and 59% in 2017 (95% U.I. 25-99). Physical inactivity contributed a significant number of deaths (1990, 22,537, 95% U.I. 12,157-34,745; 2017, 32,410, 95% U.I. 17,976-49,657) in the analyzed period. These values represented mortality rates standardized by age (per 100,000 inhabitants) of 31 (95% U.I. 17-48) in 1990 and 15 (95% U.I. 8-23) in 2017. From 1990 to 2017, a decrease in standardized death rate from all causes attributable to physical inactivity was observed in Brazil (- 52%, 95% U.I. - 54 to - 49). The Brazilian states with better socioeconomic conditions presented greater reductions in age-standardized mortality (male: rho = 0.80; female: rho 0.84) over the period of 28 years. CONCLUSIONS: These findings support the promotion of physical activity in the Brazilian population for the prevention of early mortality.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".