Impact of Wildfire Smoke on Healthcare Utilization in a Single Quaternary Care Center in the Northeast United States
Bibliographic record
Abstract
Abstract RATIONALE: In 2023, Philadelphia, along with much of the Northeast United States, experienced a dramatic increase in air pollution, especially particulate matter of 2.5 microns or less (PM 2.5), due to wildfires in eastern Canada. Wildfire smoke has been associated with increased healthcare utilization, including exacerbations of asthma and COPD, and cardiovascular events. We performed a retrospective chart review study to assess the impact of wildfire smoke on healthcare utilization for cardiovascular and respiratory disease in a quaternary care center in Philadelphia, PA. METHODS: Electronic medical record was queried for emergency department and inpatient encounters from June 6, 2023 – June 14, 2023, and June 28, 2023 – July 6, 2023, as well as the same dates from 2022, 2019, and 2018. 2021 and 2020 were excluded due to COVID. Encounters for cardiovascular and respiratory diagnoses were identified by ICD-10 code. For hospital admissions for pulmonary diagnoses, charts were reviewed for in-hospital mortality, length of stay (LOS) use of non-invasive ventilation (NIV), and need for intubation. Scheduled procedures were excluded from this analysis as were patients found to present frequently for non-medical issues. Pearson chi square test of independence was used to assess for statistically significant differences between years. RESULTS: In 2023, 18.8% (946 out of 5141) of encounters were for cardiovascular or respiratory diagnoses, representing a statistically significant increase from 15.5% in 2018 (p = <0.001), 16.6% in 2019 (p = 0.004), and 16.0% 2022 (p = 0.002). In 2023, admissions to the hospital for cardiovascular or respiratory diagnoses represented 7.5% (387 out of 5141) of all encounters. This was a statistically significant increase from 6.1% in 2018 (p = 0.003), 6.1% in 2019 (p = 0.003), and 6.2% 2022 (p = 0.007). Encounters for COPD, asthma, acute coronary syndrome (ACS), and stroke were similar between years. Among patients admitted for pulmonary diagnoses in 2023 compared to 2022, in-hospital mortality, LOS, use of NIV, and need for intubation were similar. CONCLUSIONS: In the weeks following increases in air pollution from the 2023 Canadian wildfires, our hospital network saw an increase in healthcare utilization for cardiovascular and respiratory diagnoses compared to prior years. The number of encounters for asthma, COPD, ACS, and stroke were similar. Patients admitted to the hospital for pulmonary diagnoses experience no increased risk of mortality, LOS, or need for mechanical ventilation. Future analyses will investigate which disease processes drove the increase in healthcare utilization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".