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Abstract 4368793: Economic Consequences of Increased Cardiopulmonary Clinical Encounters due to Distant Canadian Wildfire Smoke Exposure

2025· article· en· W4415791936 on OpenAlexaboutno aff
Mary Maldarelli, W DˈSouza, Bradley A. Maron, Zafar Zafarí

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeme Oxygenase-1 and Carbon Monoxide
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware deploymentMedical costsSmokeDiseaseHealth careAir quality indexHazard ratioRisk assessment

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.014
GPT teacher head0.276
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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