Abstract 4368793: Economic Consequences of Increased Cardiopulmonary Clinical Encounters due to Distant Canadian Wildfire Smoke Exposure
Bibliographic record
Abstract
Background: Wildfire smoke (WFS) events pose hazard to cardiopulmonary (CPM) health, and are expected to increase in intensity and frequency. In June 2023, western Canadian WFS drifted >2,000 miles to the Eastern US, resulting in an 18% state-wide increase in CPM disease clinical encounters across the University of Maryland Medical System (UMMS). This observation supports proactive strategies that mitigate WFS-associated disease burden; however, data on healthcare-associated cost of WFS exposure are needed to build effective resource deployment methods. Hypothesis: If there was a higher CPM clinical encounter burden in association with the 2023 Canadian WFS event, then we will identify significantly increased healthcare costs. Methods: We analyzed our previously published data identifying N=6 “hotspot” days when air quality in Maryland exceeded toxic levels due to Canadian WFS during June 2023. Using a two-part regression model and a Monte Carlo simulation, we quantified costs of increased CPM clinical visits during the 6 hotspot days in June 2023 vs. control days in 2019+2018. Results: During the N=6 hotspot days, we modeled increased costs of cardiopulmonary disease as $2,265,565 (95% credible interval [CrI]: $341,565-$4,275,182), of which $2,065,019 ($305,384-$3,892,125) (91.1%) was due to direct medical costs and $200,546 ($33,306-$383,057) (8.9%) was due to indirect costs. In a 10-year projection model of cardiopulmonary disease costs from future wildfire events, we estimated the direct and indirect costs for 5 future wildfire events of $9,205,805 ($1,361,394-$17,351,002) and $894,031 ($148,478-$1,707,659), respectively, and for 15 future wildfire events as $27,082,023 ($4,005,008-$51,043,904), and $2,630,097 ($436,798-$5,023,665), respectively. Modeling dissemination of N95 respirators to high risk patients at 95 % efficiency projected a cost reduction of 7,156,848 ($1,078,993-$13,505,164) if 15 potential future wildfires within the next 10 years. Conclusion: The estimated healthcare cost burden caused by increased CPM disease burden in Maryland occurring in association with Western Canadian WFS over a 6 day period was ~$2M. Our prediction models also anticipates a major adverse economic impact of future WFS events, ranging $9M-27M when considering events of similar magnitude as included in this study. These data emphasize the need for preventative action to reduce future WFS-associated healthcare burden and consequent economic cost.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".