Modeling the Health and Economic Burden of Chronic Obstructive Pulmonary Disease in China From 2020 to 2039: A Simulation Study
Bibliographic record
Abstract
OBJECTIVES: Despite a growing prevalence of respiratory diseases in recent decades in China, limited evidence is available on the health and economic burden of chronic obstructive pulmonary disease (COPD). We estimated the 20-year health and economic burden of COPD in China from 2020 to 2039. METHODS: We created a probabilistic dynamic open-cohort Markov model of COPD for the Chinese population aged ≥40 years. Projections of population growth and urbanization rates were obtained from the United Nations Population Division. Other parameter inputs including smoking prevalence, COPD prevalence and severity distributions, disease-related costs, and utility weights were obtained from the most recent published literature. We modeled number of COPD patients, excess mortality due to COPD, exacerbations, COPD-attributable losses of quality-adjusted life-years, and direct and indirect COPD costs over the 20 years. RESULTS: The number of COPD patients was projected to increase from 88.3 million in 2020 to 103.3 million in 2039. The projected total losses of quality-adjusted life-years and the excess mortality due to COPD were, respectively, estimated to be 253.6 million and 3.9 million over the 20 years. The projected 20-year total discounted direct and indirect costs of COPD were, respectively, $3.1 trillion and $360.5 billion. The projected health and economic burden was higher in males and urban areas. CONCLUSIONS: COPD is projected to inflict a substantial burden to the society and the health care system in China. Effective strategies for prevention and early management of COPD are needed to mitigate the forthcoming disease burden.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".