Epidemiology and costs of chronic obstructive pulmonary disease
Bibliographic record
Abstract
Worldwide, COPD is in the spotlight, since the high prevalence, morbidity and mortality present challenges for healthcare systems.The inconsistent use of terminology for COPD, and the lack of widely accepted diagnostic standards for the diseases that are included in the coding within the COPD spectrum have contributed to the inaccuracy of mortality data for COPD.The approach of using the single term COPD (rather than individual coding for chronic bronchitis, emphysema and chronic airway obstruction) is being championed in global and national guidelines, with the expectation that this will improve awareness, simplify the coding and, ultimately, lead to greater accuracy in death certification for COPD.In 2000, there were more deaths in the USA from COPD among females than males.In contrast to the trend for cardiovascular diseases, death rates from COPD have been rising steadily over the past few decades.This striking increase in COPD as a cause of death is projected to occur because of the worldwide epidemic of smoking, and the changing global demographics where more people in developing countries are living longer and, therefore, are at risk of COPD for longer.Prevalence estimates may vary widely depending on which spirometric criteria are used.The BOLD initiative, which is a programme designed to provide strictly standardised, rigorous and practical methods for estimating the prevalence and social and economic burden of COPD, is described. GLOBAL ECONOMIC COSTS AND MODELLING IN COPD SummaryIn addition to understanding the epidemiology of a disease, estimates of the disease costs are important for appreciating the overall disease burden.Estimates of the economic burden of a disease are important for informing policy decisions.Health economic models can be used to estimate the future burden of disease or to understand the value of an intervention.Importantly, the cost estimates of the components of a disease are vital for developing health economic models of diseases.The focus of this section of the paper is to review the economic burden of COPD and COPD disease-state models.Published cost estimates from Spain, Sweden, the USA, the Netherlands and Italy are reviewed.Additional attention is given to the cost of exacerbations, which is an important component of the overall cost of COPD.Finally, the use of health-state models in COPD is briefly discussed and the BOLD study model is introduced.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".