Global Burden of Disease Study trends for Canada from 1990 to 2016
Bibliographic record
Abstract
BACKGROUND: The Global Burden of Disease Study represents a large and systematic effort to describe the burden of diseases and injuries over the past 3 decades. We aimed to summarize the Canadian data on burden of diseases and injuries. METHODS: We summarized data from the 2016 iteration of the Global Burden of Disease Study to provide current (2016) and historical estimates for all-cause and cause-specific diseases and injuries using mortality, years of life lost, years lived with disability and disability-adjusted life years in Canada. We also compared changes in life expectancy and health-adjusted life expectancy between Canada and 21 countries with a high sociodemographic index. RESULTS: In 2016, leading causes of all-age disability-adjusted life years were neoplasms, cardiovascular diseases, musculoskeletal diseases, and mental and substance use disorders, which together accounted for about 56% of disability-adjusted life years. Between 2006 and 2016, the rate of all-cause age-standardized years of life lost declined by 12%, while the rate of all-cause age-standardized years lived with disability remained relatively stable (+1%), and the rate of all-cause age-standardized disability-adjusted life year declined by 5%. In 2016, Canada aligned with countries that have a similar high sociodemographic index in terms of life expectancy (82 yr) and health-adjusted life expectancy (71 yr). INTERPRETATION: The patterns of mortality and morbidity in Canada reflect an aging population and improving patterns of population health. If current trends continue, Canada will continue to face challenges of increasing population morbidity and disability alongside decreasing premature 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".