Epidemiological characteristics of centenarian deaths in China during 2013–2020: A trend and subnational analysis
Bibliographic record
Abstract
BACKGROUND: Studies that comprehensively address the characteristics of centenarian deaths are rare. The present study aimed to depict the characteristics of centenarian deaths in China and their changing trends. METHODS: Data on centenarian deaths between 2013 and 2020 were obtained from the national mortality surveillance system of China, including date, place of death (PoD), and underlying cause of death (CoD). Descriptive analyses were performed to understand the epidemiological characteristics, and a joinpoint regression model was adopted to examine the changing trends in the proportions of different PoDs, CoDs among centenarians, and centenarian deaths accounting for all deaths and deaths among people aged 65 years and older. RESULTS: There were 46,938 registered centenarian deaths between 2013 and 2020 that included 34,311 females (73.10%) and 12,627 males (26.90%). January (12.05%), February (9.99%), and December (9.74%) were the top three months with the highest number of deaths. The proportions of deaths that occurred in homes, hospitals, and nursing homes were 81.71%, 13.63%, and 2.68%, respectively. The proportion of deaths in nursing homes increased by 9.60% (95% confidence intervals [CIs], 6.4-12.9%) from 2014 to 2020. Heart disease (35.72%) was the leading cause of death, followed by respiratory diseases (17.63%), cerebrovascular disease (15.60%), and old age (11.22%). The proportion of respiratory diseases decreased by 4.8% (95% CI, -8.8 to -0.7%), and the proportion of deaths from old age decreased by 2.3% (95% CI, -4.4 to -0.1%) per year. Shanghai had the highest proportions of deaths in hospitals (39.38%) and nursing homes (14.68%). Sichuan had the highest proportion of deaths attributed to respiratory diseases (32.30%), while Jiangsu (26.58%) and Zhejiang (23.61%) had the highest proportions of deaths from old age. CONCLUSION: Unlike other countries, centenarian deaths in China are characterized by a higher proportion of home and heart disease deaths, and this death pattern differs across provinces.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".