Association between long-term exposure to ambient air pollution and COVID-19 severity - A prospective cohort study of confirmed SARS-CoV-2 cases in Ontario, Canada
Bibliographic record
Abstract
Background and aim: Coronavirus disease 2019 (COVID-19) is causing a tremendous health burden globally. Identification of the determinants of COVID-19 severity is important for prevention and intervention. This study aims to explore long-term exposure to ambient air pollution as a potential contributors to COVID-19 severity given its known impact on the pulmonary system. Methods: Using a cohort of all confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases aged ≥20 years and not residing in a long-term care facility in Ontario, Canada during 2020, we evaluated the association between long-term exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ground-level ozone (O3) and risk of COVID-19-related hospitalization, intensive care unit (ICU) admission and death separately. Participants’ long-term exposures to each air pollutant were ascertained based on their residential addresses from 2015 to 2019. We used logistic regression and adjusted for confounding and selection bias using various individual and contextual covariates obtained through data linkage. Results: Among the 151,105 confirmed SARS-CoV-2 cases in 2020, we observed 8,630 hospitalizations, 1,912 ICU admissions and 2,137 deaths related to COVID-19. For each interquartile range increase in exposure to PM2.5 (1.70 µg/m3), we estimated ORs of 1.06 (95% confidence interval (CI): 1.01 to 1.12), 1.09 (95% CI: 0.98 to 1.21) and 1.00 (95% CI: 0.90 to 1.11) for hospitalization, ICU admission and death, respectively. Estimates were smaller for NO2 but larger for O3. Conclusions: In this large population-based study in Ontario during 2020, we found that chronic exposure to air pollution may contribute to severe outcomes following SARS-CoV-2 infection, with stronger evidence found with O3. Keywords: COVID-19 severity, long-term exposure to air pollution, death, hospitalization, intensive care unit admission
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 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".