A systematic review and meta-analysis of the effect of landscape fire smoke particulate matter (PM) on asthma-related outcomes
Bibliographic record
Abstract
TPS 681: Short-term health effects of air pollutants 1, Exhibition Hall, Ground floor, August 26, 2019, 3:00 PM - 4:30 PM Background: Asthma-related outcomes are among the most reported from human exposure to landscape fire smoke. Robust summary estimates are required to adequately inform health protection policy. Objective: To obtain summary estimates of the association between fine particulate matter (PM2.5) from landscape fire smoke and asthma-related outcomes. Methods: We conducted a systematic review and meta-analysis following PRISMA guidelines and registered the protocol in PROSPERO. Four databases (PubMed, Medline, EMBASE and Scopus) and reference lists of recent fire smoke and health reviews were searched. The Newcastle-Ottawa Scale was used to evaluate the quality of case-crossover and cohort studies, and a previously validated quality assessment framework was used for time series and ecological studies. Summary estimates were obtained for hospitalisations and emergency department (ED) visits. A descriptive analysis was done for physician visits and medication use. Publication bias was assessed using funnel plots and Begg’s Test. The trim and fill method was used when there was evidence of publication bias. Sensitivity and influence analyses were conducted on all endpoints to test robustness of estimates. Analyses were conducted in R version 3.5.1. Results: From an initial 181 articles (after duplicate removal), eighteen studies were included for the quantitative assessment. Fire smoke PM2.5 concentrations were positively associated with asthma hospitalisations (RR= 1.06, 95%CI: 1.03-1.09) and ED visits (RR=1.07, 95%CI: 1.03-1.09). Subgroup analyses found larger positive associations in adults aged over 65 years but not children for hospital admissions; and for all females, and adults aged over 65 years for ED visits. High heterogeneity between studies was observed, but results were robust to sensitivity analysis. Conclusions: Results for all ages and both genders are positive, but these seem to be driven by strong positive associations for all females and adults aged over 65 years. Risk estimates were higher than those reported for exposure to urban PM2.5.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".