Regional differences in the contribution of smoking, dietary and cooking behaviours to airflow obstruction in China: A population‐based case–control study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The major contributing risk factors to airflow obstruction (AO) in China remain largely unknown. We examined the environmental and lifestyle risk factors of unrecognized AO in the baseline of a population-based cohort drawn from 115 urban and rural communities across 12 provinces in China. METHODS: Amongst 46,285 adults recruited from 2005 to 2009, 3686 were identified with AO on spirometry (defined by the ratio of forced expiratory volume in the first second to forced vital capacity <0.7) and without known chronic lung disease. These cases were age- and sex-matched to 11,129 controls with normal spirometry and no chronic lung disease from the same community. Conditional multivariable adjusted OR and population attributable fraction (PAF) were calculated for each identified risk factor and their combined effect. RESULTS: Compared to controls, smoking initiation age <20 years (OR 1.22 [95% CI 1.01-1.48]), smoking duration ≥40 years (OR 1.82 [1.50-2.22]), low vegetables (OR 1.86 [1.67-2.07]) and fruits (OR 1.14 [1.02-1.29]) intake, cooking with biomass fuels (OR 2.54 [2.32-2.78]) and poor kitchen ventilation (OR 1.37 [1.19-1.58]) were significantly associated with elevated risks of unrecognized AO. The combined effect of these lifestyle factors significantly elevated the odds by 25 fold (18.6-34.3). The addition of prior tuberculosis and low socioeconomic status further increased the odds to 40.1 (28.2-57.0) and the PAF to 66.7% (51.1-78.1). CONCLUSION: Smoking, unhealthy diet, biomass cooking fuels and low socioeconomic status are strongly associated with AO. Addressing these risk factors could substantially reduce the burden of AO in China.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".