Burden of asthma, dyspnea, and chronic cough in South Asia
Bibliographic record
Abstract
BACKGROUND: Asthma, dyspnea, and chronic cough are well-established risk factors of COPD and often associated with exacerbation of the disease, which is a leading cause of morbidity and mortality in South Asian countries. OBJECTIVE: The aims of this study were to, 1) measure the prevalence of asthma, dyspnea, and chronic cough, and 2) assess the relationship between these respiratory problems and self-reported health status among South Asians. METHODS: Data for this research came from the World Health Survey (2002-2003) conducted by the World Health Organization. Subjects were 35,929 men and women, aged 18 years and older, selected from Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Crude prevalence rates of asthma, dyspnea, and chronic cough were presented as percentages, and the results of their association with subjective health status were presented as odds ratios and corresponding 95% CIs. RESULTS: Prevalence of daily smoking was highest in Bangladesh (39.9%) and lowest in Sri Lanka (14.1%). Prevalence of asthma was highest in India (6.3%), while Nepal had the highest prevalence of dyspnea (11.3%) and chronic cough (15.3%). Overall prevalence of asthma and dyspnea was higher among women, while that of chronic cough was higher among men. Significant differences were observed in the prevalence rates of all the conditions among regular, occasional, and nonsmokers. A majority of the men and women who had asthma, dyspnea, and chronic cough had higher likelihood of reporting poor health status compared to those who did not have these diseases. CONCLUSION: Findings suggest that prevalence rates of asthma, dyspnea, and chronic cough were considerably high in all the countries and were significantly associated with poor subjective health. Being a high COPD-prone region, programs targeted to address these diseases could help reduce the burden of COPD and respiratory disease-related mortalities in South Asia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".