Modeling the Health and Economic Burden of Chronic Obstructive Pulmonary Disease in China From 2020 to 2039: A Simulation Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it