Assessment of the burden of diseases and injuries attributable to risk factors in Canada from 1990 to 2016: an analysis of the Global Burden of Disease Study
Bibliographic record
Abstract
BACKGROUND: An understanding of the risk factors contributing to disease burden is critical for determining research priorities and informing national health policy. We aimed to identify the risk factor trends in Canada. METHODS: As part of the Global Burden of Disease (GBD) study (1990-2016), we conducted an analysis of country-level estimates for Canada to assess the burden of diseases and injuries attributable to risk factors. For both 1990 and 2016, metabolic, environmental and behavioural risk factors were ranked according to their contribution to disability-adjusted life years (healthy years of life lost), total deaths and years lived with disability. RESULTS: In 2016, the risk factors accounting for the largest percentage of disability-adjusted life years in Canada were (1) tobacco, (2) diet, (3) high body mass index, (4) high fasting plasma glucose, (5) high systolic blood pressure, (6) alcohol and drug use, (7) occupational risks, (8) high total cholesterol, (9) impaired kidney function and (10) air pollution. Risk factor rankings remained similar from 1990 to 2016 despite some substantial declines in burden, including a 47% (± 3%) decline in the age-standardized disability-adjusted life years rate attributable to tobacco since 1990. Risk factors with an increasing contribution to disability-adjusted life years rates from 1990 to 2016 included high body mass index, high fasting plasma glucose and alcohol and drug use. INTERPRETATION: Metabolic and behavioural risk factors, including modifiable factors such as tobacco use and diet, remain the leading risk factors contributing to the burden of diseases and injuries in Canada. This work identifies priorities and targets for reducing premature death and disability burden in Canada.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".