ASTHMA AND COPD EXACERBATIONS: ED VISITS AND HOSPITALIZATIONS AFTER WILDFIRE SMOKE EXPOSURE AMONG OLDER ADULTS
Bibliographic record
Abstract
Abstract Global temperatures are rising, leading to more intense wildfires, as seen in Quebec, Canada, on June 2023, where 13 million acres burned. The resulting particulate matter may severely degrade air quality, posing risks to older adults (OAs) with respiratory diseases. Studies indicate Black/African American communities may be disproportionately affected by air pollution compared to white communities. This study evaluated the impact of wildfires on emergency department (ED) visits and hospitalizations for COPD and asthma among adults 65 and older. The observational cohort study assessed 1321 patient records from two representative New York City hospitals. Findings revealed no significant change in the overall volume of ED visits, hospitalizations, and median length of stay during the smoke-affected months compared to the previous year. However, there was a significant increase in non-white OAs’ ED visits and hospitalizations due to COPD or asthma: 27.19% in 2022 to 35.87% in 2023 (p = 0.0007), and more non-white patients with Medicaid insurance (80% in 2023, 71.43% in 2022). Significantly, more Black/African Americans required acute care: 16.74%, from 11.06% in 2022 (p = 0.003). These results indicate that worsening air quality may disproportionally impact non-white OAs. Black/African Americans and those with Medicaid insurance may more likely experience COPD or asthma exacerbation, requiring ED evaluation and/or hospitalization due to smoke pollution. The escalation in global temperatures and resulting wildfires may particularly affect OAs of color, especially those facing socioeconomic disadvantages. The findings underscore the importance of targeted public health strategies to address these disparities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".