Patterns and trends in burden of asthma and its attributable risk factors from 1990 to 2021 among South Asian countries: a systematic analysis for the Global Burden of Disease Study 2021
Bibliographic record
Abstract
OBJECTIVE: Asthma poses a significant health burden in South Asia, with increasing incidence and mortality despite a global decline in age-standardized prevalence rates. This study aims to analyze asthma trends from 1990 to 2021, focusing on prevalence, incidence, mortality, and disability-adjusted life years (DALYs) across South Asia. The study also assesses the impact of risk factors like high body mass index (BMI), smoking, and occupational exposures on asthma outcomes. METHOD: We extracted asthma data from the Global Burden of Disease database for South Asia (1990-2021). Joinpoint regression analysis was used to assess temporal trends in asthma burden. Total Percentage change (TPC) in age-standardized rates of incidence, mortality, and DALYs were calculated. Data were stratified by gender, and the contribution of risk factors was evaluated. RESULTS: Asthma-related mortality in South Asia decreased by 37%, from 27.78 per 100,000 (1990) to 17.54 per 100,000 (2021). The Maldives showed the most significant reduction in mortality (78.31%), while Bangladesh recorded a 47.44% reduction in prevalence and a 62.64% decrease in DALYs. High BMI, smoking, and environmental risks contributed significantly to DALYs, with environmental factors playing a major role in countries like Afghanistan (20.73%) and Bhutan (18.58%). Females, particularly those over 20, experienced higher asthma-related DALYs than males. CONCLUSION: Asthma burden in South Asia has reduced over the past three decades, yet the absolute number of cases continues to rise, driven by population growth and environmental risk factors. Targeted interventions addressing risk factors and healthcare disparities are essential for further reducing asthma burden.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.007 |
| 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".