Air pollution and long COVID: association with pulmonary function and radiological abnormalities 3–15 months post-COVID
Bibliographic record
Abstract
While studies have examined associations between air pollution and subjective long COVID outcomes such as fatigue and symptoms, no studies have focused on objective lung health measures. This study aimed to assess the impact of air pollution, examined through different exposure methods (exposures assigned via geospatial model, versus residential and personal measurements) on pulmonary function and radiological abnormalities in long COVID patients. We recruited 95 patients who attended a hospital outpatient clinic 3-6 months post-infection, during which pulmonary function was assessed via spirometry (FEV1,FVC,FEV1/FVC ratio) and diffusion capacity for carbon monoxide (DLCO), along with a chest CT. Of these, 38 patients with abnormalities in one of the assessed modalities returned for a follow-up visit approximately nine months later. Ambient levels of PM 2.5 , PM 10 , NO 2 , and O 3 was assigned using land-use regression models, while residential and personal PM 2.5 measurements were collected between the hospital visits in the participants' home environments. No associations were found between residential (assigned or measured) air pollution exposure and pulmonary function or radiological abnormalities at the first visit. Pulmonary function also typically improved between the first and second visit. However, an association between personal exposure and CT abnormalities was observed. A one IQR(12.3 μg/m 3 ) increase in personal PM 2.5 significantly increased the risk of airway abnormalities during the follow-up visit (adjusted OR:3.35, 95%CI:1.03,14.63), particularly mosaic patterns (OR:5.74, 95%CI:1.43,40.62). These findings add to evidence of exposure to air pollution playing a role in long COVID and call for mitigation measures to improve air quality.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".