Mortality and burden of non-communicable diseases in China
Bibliographic record
Abstract
Objective To assess the leading causes of death and disability adjusted life year (DALY) due to non-communicable disease (NCD) in China. Methods World Health Report 2004 published by the World Health Organization (WHO) was reviewed, including total deaths, deaths per 100 000, age-standardized death rate per 100 000, total DALYs, DALYs per 100 000 and age-standardized DALYs per 100 000 by cause and by member state. Diseases or injuries were assigned to three levels: communicable diseases, NCD and injuries (the first level); categories of disease or injure (the second level); specific diseases (the third level). R2.15 was used for data analysis. Results NCD causes 737.6 million deaths, 141million total DALY years, 627 age standardized mortality per 100 000, and 10 829 age-standardized DALYs per 100 000. NCD account for 79.4% and 70.3% total death or all-cause DALYs. Conclusions Cardiovascular disease, malignant neoplasm and respiratory disease were the leading causes of death, while neuropsychiatric disorder, cardiovascular disease and sense organ disease were the most important causes of DALYs. Among China, the United Kingdom, the United States, Canada, Japan, Korea and India, China ranked second in age-standardized mortality rate of chronic disease. DALYs of esophagus cancer and chronic obstructive pulmonary disease were 6 or 2 times of world average level. Besides, the increasing trend in the prevalence of diabetes mellitus remains impressive. Key words: Global burden of disease; Non-communicable disease; Disability adjusted life year; Age-standard death rate
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".