All-Cause and Cause-Specific Burden of Asthma in a Transitioning City in China: Population Study
Bibliographic record
Abstract
Background: Understanding the impact of asthma on public health is crucial for evidence-based prevention and treatment strategies. Objective: This study aimed to identify the causes of asthma-related mortality in Pudong, Shanghai, China, offering insights for managing similar regions or countries in transition. Methods: Mortality statistics were obtained from the Vital Statistics System of Pudong for 2005-2021. Temporal patterns for the burden of asthma were examined. The crude mortality rate (CMR), age-standardized mortality rate by Segi's world standard population (ASMRW), and years of life lost (YLL) for both all-cause and asthma-specific deaths were computed. Mortality rates associating with aging and other variables were categorized using the decomposition technique. The autoregressive integrated moving average model was used to forecast the asthma-related death mortality rate by 2035. Results: A total of 1568 asthma-related deaths occurred during the follow-up period, with the CMR and ASMRW being 3.25/105 and 1.22/105 person-years, respectively. The primary underlying causes of death were chronic lower respiratory diseases, coronary heart diseases, and cerebrovascular disease. The YLL due to total asthma-related deaths added up to 14,837.76 years, with a YLL rate of 30.73/105 person-years. Male individuals had more YLL (8941.81 vs 5895.95 y) and a higher YLL rate (37.12/105 vs 24.38/105 person-years) than female individuals. From 2005 to 2021, the ASMRW declined by 3.48%, and both the CMR and YLL rate decreased in the 0-29, 70-79, and ≥80 years age groups (all P<.01). However, asthma-related deaths increased from 329 people between 2005 and 2008 to 472 people between 2017 and 2021. The proportion of the population aged 80 years and older gradually increased by 1.43% (95% CI 0.20%-2.68%; P=.03), and the mortality rates of asthma deaths attributable to population aging rose by 21.97% (95% CI, 11.58%-33.32%; P<.001) annually. Conclusions: Asthma remains a significant public health challenge in transitioning countries, requiring increased attention and resource allocation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".