The Burden of Air Pollution on Skin Health: a Brief Report and Call to Action
Bibliographic record
Abstract
INTRODUCTION: Air pollution in North America has intensified due to wildfires in recent years. In 2023, the wildfires in the Canadian province of Quebec caused a southward spread of pollutants, negatively affecting air quality and thereby aggravating certain health conditions in northeastern USA. This study examines the impact of air pollution on atopic dermatitis (AD) and skin health and how wildfires can exacerbate the burden of disease. METHODS: Carbon monoxide levels measured by the U.S. Environmental Protection Agency (EPA) in the Boston region during the months following the Canadian wildfires of 2023 were collected from the U.S. EPA Outdoor Air Quality webpage. Patient records on dermatology clinic visits for dermatitis and eczema at the Mass General Brigham (MGB) hospital system, 300 miles from the wildfires, were examined, and the data compared with data from the corresponding months in 2019-2022 for historical control. No individual patient data were collected. RESULTS: A notable rise and atypical summer peak in carbon monoxide (CO) levels in the Boston region during 2023 correlated with a spike in AD, dermatitis, and eczema-related dermatology clinic visits within the MGB hospital system, as compared with the prior 4 years. CONCLUSION: The synchronized atypical peaks of CO levels and AD-related visits during the summer of 2023 highlight the potential impact of acute air pollution events such as wildfires on air quality and the consequences for skin health. Air pollution, exacerbated by wildfires, can damage the skin through the smoke and chemicals utilized for extinguishing fires, which contain multiple potential allergens and irritants to the skin, such as CO, particulate matter (PM2.5), and ammonium phosphates, triggering airway and skin inflammation and flares of AD. This issue disproportionately affects vulnerable populations, including low-income communities and the geriatric and pediatric populations. Healthcare professionals and government agencies must work together to improve air quality and purification policies and initiatives to lower the burden of skin disease, especially for vulnerable communities.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".