Population-Level Distribution, Risk Factors, and Burden of Mortality and Disability-Adjusted Life Years Attributable to Major Noncommunicable Diseases in Western Europe (1990-2021): Ecological Analysis
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVDs) and neoplasms are leading causes of mortality worldwide. Objective: This study aims to provide a comprehensive analysis of the mortality burden and disability-adjusted life years (DALYs) attributable to CVDs and neoplasms in Western Europe, investigate associated risk factors, and identify regional disparities. Additionally, the study evaluates the effectiveness of the Action Plan for the Prevention and Control of Non-Communicable Diseases (NCDs) in promoting healthier lives in the region. Methods: The study collected data on mortality and DALYs due to CVDs and cancers from 24 Western European countries using the Global Burden of Disease Study 2021. The analysis explored age, sex, and country-specific patterns, as well as risk factors contributing to these deaths. Additionally, the study examined time trends by calculating the annual percent change in mortality rates from 1990 to 2021 by region and cause. Results: In 2021, CVDs and neoplasms accounted for 27.8% and 27.1% of total deaths in Western Europe, with age-standardized death rates of 106.8 and 125.8 per 100,000, respectively. The top two CVDs in this region were ischemic heart disease and stroke, with age-standardized death rates of 47.27 (95% uncertainty interval [UI] 50.42-41.45) and 27.06 (95% UI 29.17-23.00), respectively. Similarly, the top two neoplasms were lung cancer and colorectal cancer, with age-standardized death rates of 26.4 (95% UI 27.69-24.47) and 15.1 (95% UI 16.25-13.53), respectively. Between 1990 and 2021, CVD mortality rates decreased by 61.9%, while cancer rates decreased by 28.27%. Finland had the highest CVD burden (39.5%), and Monaco had the highest rate of cancer-related deaths (34.8%). Gender differences were observed, with males experiencing a higher burden of both CVDs and cancer. Older individuals were also more at risk. Smoking had a stronger impact on CVD mortality and DALYs in males, while a higher Human Development Index was associated with increased cancer deaths and DALYs in females. Conclusions: The study findings highlight the substantial burden of NCDs, particularly CVDs and cancer, in Western Europe. This underscores the critical need for targeted interventions and effective implementation of the Action Plan for the Prevention and Control of NCDs to achieve the goal of ensuring healthy lives for all.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".