Physical inactivity and non-communicable disease burden in low-income, middle-income and high-income countries
Bibliographic record
Abstract
OBJECTIVES: Physical inactivity is a risk factor for premature mortality and several non-communicable diseases. The purpose of this study was to estimate the global burden associated with physical inactivity, and to examine differences by country income and region. METHODS: Population-level, prevalence-based population attributable risks (PAR) were calculated for 168 countries to estimate how much disease could be averted if physical inactivity were eliminated. We calculated PARs (percentage of cases attributable to inactivity) for all-cause mortality, cardiovascular disease mortality and non-communicable diseases including coronary heart disease, stroke, hypertension, type 2 diabetes, dementia, depression and cancers of the bladder, breast, colon, endometrium, oesophagus, stomach and kidney. RESULTS: Globally, 7.2% and 7.6% of all-cause and cardiovascular disease deaths, respectively, are attributable to physical inactivity. The proportions of non-communicable diseases attributable to physical inactivity range from 1.6% for hypertension to 8.1% for dementia. There was an increasing gradient across income groups; PARs were more than double in high-income compared with low-income countries. However, 69% of total deaths and 74% of cardiovascular disease deaths associated with physical inactivity are occurring in middle-income countries, given their population size. Regional differences were also observed, with the PARs occurring in Latin America/Caribbean and high-income Western and Asia-Pacific countries, and the lowest burden occurring in Oceania and East/Southeast Asia. CONCLUSION: The global burden associated with physical inactivity is substantial. The relative burden is greatest in high-income countries; however, the greatest number of people (absolute burden) affected by physical inactivity are living in middle-income countries given the size of their populations.
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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 | 0.001 |
| 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".