Association between ambient air pollution and childhood respiratory diseases in low- and middle-income Asian countries: A systematic review
Bibliographic record
Abstract
Ambient air pollution has been associated with respiratory diseases in children. However, little is known about its impact on low- and middle-income countries (LMICs) in Asia. This systematic review investigates and summarises the associations between short- and long-term exposure to ambient air pollutants and childhood respiratory diseases. We conducted a systematic search of peer-reviewed articles on PubMed, Scopus and Ovid MEDLINE. Observation study designs consisting of the cohort, cross-sectional, case-crossover, and time series study were included. The quality assessment was conducted based on the study designs, using the AXIS (Appraisal Tool for Cross-Sectional Studies), NOS (Newcastle-Ottawa scale) and the Mustafíc criteria. Each of the included studies was appraised to evaluate the risk of bias using an adapted OHAT (Office of Health Assessment and Translation) assessment tool. A total of 41 articles with high and medium quality from 2015 to 2019 were included in this review. The studies reported exposure to a high concentration of ambient air pollution (PM10 n = 25, PM2.5 n = 22, SO2 n = 13, NO2 n = 24, CO n = 5, O3 n = 7) was significantly correlated with increased risk of childhood respiratory morbidity and mortality across different age groups and countries. However, the distribution of the studies did not cover all Asian LMICs. Due to the age group variation, it was impossible to identify specific groups susceptible to ambient air pollution's adverse effects. Short- and long-term exposure to ambient air pollution is associated with increased childhood respiratory morbidity and mortality in Asian LMICs. There is a need for more studies focused on the relationship between ambient air pollution and childhood respiratory diseases, especially in Asian countries with poor air quality and rapid urbanisation.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".