Global Trends and Forecast of Cardiovascular Diseases, Cancer, and Shared Risk Factors: Insights From the GBD 2021
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) and cancer share multiple risk factors and are the leading causes of morbidity and death worldwide. This study aims to examine historical and projected trends of the combined CVD-cancer global estimates and their shared risk factors. METHODS: Death and disability-adjusted life years (DALYs) associated with CVDs, cancers, and their risk factors were obtained from the GBD (Global Burden of Disease) 2021 study. Historical estimates from 1990 to 2021 and forecast data from 2025 to 2050 were examined across age groups, regions, sociodemographic index, and sex. RESULTS: In 2021, CVD-cancer resulted in age-standardized DALY rates of 8009 per 100 000 population (63% contributed by CVD) and age-standardized mortality rates of 352 per 100 000 population (67% by CVD) globally. While age-standardized DALYs of the CVD-cancer burden declined by 30% (33% in CVD and 26% in cancer), crude DALYs increased by 46% from 1990 to 2021. CVD-cancer resulted in higher age-standardized DALY rates in men (9654 per 100 000 population) than women (6556 per 100 000 population). While low-middle sociodemographic index regions had the highest CVD-related age-standardized DALY rates (6744 per 100 000 population), high-middle sociodemographic index regions had the highest cancer-related rates (3388 per 100 000) in 2021. The top risk factors driving CVD-cancer morbidity burden in 2021 were high systolic blood pressure (largely CVD contribution), dietary risks (89% by CVD), and tobacco (60% by CVD), persisting to 2050. CONCLUSIONS: Global improvements in CVD-cancer age-standardized morbidity will be offset by the ever-rising crude CVD-cancer morbidity burden. Preventive efforts should prioritize key shared risk factors of hypertension, dietary risks, and tobacco.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".