The Covid-19 hospitalization risk associated with air pollution in New York state counties after the 2023 Quebec wildfires
Bibliographic record
Abstract
Background: Air pollution from the 2023 Quebec wildfires affected New York state (NY) with daily average PM 2.5 levels that peak on June 7. Increased Covid-19 hospitalizations were recorded weeks after the wildfires. This study analyses the trend of Covid-19 hospitalization in NY counties after the 2023 Quebec wildfires and estimates their association with higher PM 2.5 concentration levels, compared to 2022. Design and methods: A Bayesian spatiotemporal regression model was used to estimate the impact of wildfire smoke on Covid-19 hospitalizations. Four periods of pre/post-wildfire and 7-day post-wildfire daily hospitalization periods were considered to compare the association of daily average PM 2.5 levels, from May 1 to June 7, with daily Covid-19 hospitalization rates in NY counties in 2022 and 2023. The pre/post-wildfire and 7-day post-wildfire periods considered a lag of 2, 4, 6, and 8 weeks and 24, 48, 72, and 96 h, respectively. The model was adjusted for sociodemographic factors. Results: The Covid-19 hospitalization rate followed an increasing trend in the second, third and fourth pre/post-wildfire periods in 2023 in contrast with 2022 when no trends were identified. Each PM 2.5 unit increase was associated with a 2%; 6% and 7% Covid-19 higher hospitalization risk in periods 2, 3, and 4, respectively, in 2023 only. These findings identify a potential impact of wildfire smoke on the severity of Covid-19 morbidity after 2 weeks of the wildfires. Robust spatiotemporal analyses can be used to identify specific at-risk areas and communities to support public health decision-making and health strategies. Conclusions: This study identifies a higher risk of Covid-19 hospitalization in New York State associated with higher air pollution levels from the 2023 Quebec wildfires, in the first week and 2, 3, and 4 weeks after the wildfires. These findings concur with the increasingly investigated association of air pollution with severe Covid-19. The methodological approach of this study shows the utility of spatiotemporal epidemiological analyses and need for future research on wildfire smoke as a potential determinant of severe Covid-19. With more frequent and extreme climate events it is paramount to improve our understanding of many potential health impacts of wildfires to prepare strategies to deal with, and potentially anticipate, environmental health and healthcare responses in wildfire-prone regions.
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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 | 0.030 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".