Prevalence and Population-Attributable Risk for Chronic Airflow Obstruction in a Large Multinational Study
Bibliographic record
Abstract
Abstract Rationale The Global Burden of Disease program identified smoking and ambient and household air pollution as the main drivers of death and disability from chronic obstructive pulmonary disease (COPD). Objectives To estimate the attributable risk of chronic airflow obstruction (CAO), a quantifiable characteristic of COPD, due to several risk factors. Methods The Burden of Obstructive Lung Disease study is a cross-sectional study of adults, aged ≥40, in a globally distributed sample of 41 urban and rural sites. Based on data from 28,459 participants, we estimated the prevalence of CAO, defined as a postbronchodilator FEV1-to-FVC ratio less than the lower limit of normal, and the relative risks associated with different risk factors. Local relative risks were estimated using a Bayesian hierarchical model borrowing information from across sites. From these relative risks and the prevalence of risk factors, we estimated local population attributable risks. Measurements and Main Results The mean prevalence of CAO was 11.2% in men and 8.6% in women. The mean population attributable risk for smoking was 5.1% in men and 2.2% in women. The next most influential risk factors were poor education levels, working in a dusty job for ≥10 years, low body mass index, and a history of tuberculosis. The risk of CAO attributable to the different risk factors varied across sites. Conclusions Although smoking remains the most important risk factor for CAO, in some areas, poor education, low body mass index, and passive smoking are of greater importance. Dusty occupations and tuberculosis are important risk factors at some sites.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".