Reducing the Global Burden of Cardiovascular Disease, Part 1
Bibliographic record
Abstract
Current global health policy goals include a 25% reduction in premature mortality from noncommunicable diseases by 2025. In this 2-part review, we provide an overview of the current epidemiological data on cardiovascular diseases (CVD), its risk factors, and describe strategies aimed at reducing its burden. In part 1, we examine the global epidemiology of cardiac conditions that have the greatest impact on CVD mortality; the predominant risk factors; and the impact of upstream, societal health determinants (eg, environmental factors, health policy, and health systems) on CVD. Although age-standardized mortality from CVD has decreased in many regions of the world, the absolute number of deaths continues to increase, with the majority now occurring in middle- and low-income countries. It is evident that multiple factors are causally related to CVD, including traditional individual level risk factors (mainly tobacco use, lipids, and elevated blood pressure) and societal level health determinants (eg, health systems, health policies, and barriers to CVD prevention and care). Both individual and societal risk factors vary considerably between different regions of the world and economic settings. However, reliable data to estimate CVD burden are lacking in many regions of the world, which hampers the establishment of nationwide prevention and management strategies. A 25% reduction in premature CVD mortality globally is feasible but will require better implementation of evidence-based policies (particularly tobacco control) and integrated health systems strategies that improve CVD prevention and management. In addition, there is a need for better health information to monitor progress and guide health policy decisions.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".