Costs of chronic obstructive pulmonary disease in urban areas of China: a cross-sectional study in four cities
Bibliographic record
Abstract
PURPOSE: The economic burden of COPD has not been well studied in China. This study investigated the total costs caused by COPD and the influencing factors for the high economic burden in urban areas of China. PATIENTS AND METHODS: A cross-sectional study was carried out among 678 COPD patients in four cities in China in 2011. The average annual direct medical costs (DMCs), direct nonmedical costs (DNMCs), and indirect costs (ICs) on COPD were measured by median and mean (± standard deviation). Logistic regression model was used to explore factors related to high total costs on COPD. RESULTS: The median annual DMCs, DNMCs, and ICs per COPD patient were RMB 5565 Yuan (US$ 862), 0 Yuan (US$ 0), and 0 Yuan (US$ 0), respectively, and the mean annual DMCs, DNMCs, and ICs per COPD patient were RMB 11968 (±22422) Yuan [US$ 1853 (±3472)], 539 (±2092) Yuan [US$ 83 (±324)], and 2087 (±8110) Yuan [US$ 323 (±1256)], respectively. The annual DMCs, DNMCs, and ICs for diagnosed COPD patients were RMB 195.70 billion Yuan (US$ 30.30 billion), 8.78 billion Yuan (US$ 1.36 billion), and 34.10 billion Yuan (US$ 5.28 billion), respectively, in China. Hospitalization accounted for 56.7% of the total costs. High economic burden was significantly related to age, acute exacerbations, and disease severity in COPD patients. CONCLUSION: COPD posed a heavy economic burden in China. Measures to delay the disease progression and to reduce the risks of acute exacerbation and hospitalization will help substantially lower the costs for COPD care.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".