Physiological and biological predictors of length of stay and recovery in adults with acute asthma: An observational cohort study

Carbon footprint and associated costs of asthma exacerbation care among UK adults

INTRODUCTION

Asthma exacerbations are a primary driver of costs and health impacts from asthma. Despite research suggesting that asthma care has a disproportionate carbon footprint, emissions costs are not considered when evaluating its societal burden. To advance the understanding of greenhouse gas (GHG) emissions associated with asthma, we estimated the carbon footprint and associated costs of asthma exacerbation care by severity level among UK adults.

METHODS

Guidelines for asthma exacerbation treatment in UK adults were reviewed by severity level: mild, moderate, and severe/life-threatening. Components of care for each severity were evaluated for GHG emission potential and key drivers were identified. Carbon dioxide equivalent (CO₂e) emissions of drivers were sourced from published literature and combined to estimate the carbon footprint per exacerbation, by severity level. Emissions were scaled up to the annual UK adult population based on the annual number of exacerbations at each severity. Costs associated with emissions were estimated using the UK government's 2020 nontraded price of carbon, at ₤71 per tonne CO₂e (tCO₂e).

RESULTS

Overall, emissions drivers for exacerbations were medical services, including patient-travel, and quick-relief inhalers. The annual number of mild, moderate, and severe/life-threatening asthma exacerbations among UK adults were 118.9 M, 5.5 M, and 2.4 M. Associated annual carbon footprints were estimated to be 83,455 tCO₂e, 192,709 tCO₂e, and 448,037 tCO₂e for mild, moderate, and severe/life-threatening exacerbations, respectively, with a total of 724,201 tCO₂e. Total annual emissions costs from exacerbation care were £51.3 M; ₤5.9 M, ₤13.6 M, and ₤31.7 M for mild, moderate, and severe/life-threatening exacerbations, respectively.

CONCLUSION

GHG emissions from asthma exacerbation management were the highest for severe/life-threatening events, followed by moderate exacerbations. Treatment to reduce the severity and occurrence of exacerbations, such as effective, long-term control therapy via lower-emission dry powder inhalers (DPIs), can help mitigate asthma care emissions. For mild exacerbations, the use of DPIs can eliminate associated emissions.

Unraveling racial disparities in asthma emergency department visits using electronic healthcare records and machine learning

OBJECTIVE

Hospital emergency department (ED) visits by asthmatics differ based on race and season. The objectives of this study were to investigate season- and race-specific disparities for asthma risk, and to identify environmental exposure variables associated with ED visits among more than 42,000 individuals of African American (AA) and European American (EA) descent identified through electronic health records (EHRs).

METHODS

We examined data from 42,375 individuals (AAs = 14,491, EAs = 27,884) identified in EHRs. We considered associated demographic (race, age, gender, insurance), clinical (smoking status, ED visits, FEV1%), and environmental exposures data (mold, pollen, and pollutants). Machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and decision tree (DT) were used to build and identify race- and -season-specific predictive models for asthma ED visits.

RESULTS

Significant differences in ED visits and FEV1% among AAs and EAs were identified. ED visits by AAs was 32.0% higher than EAs and AAs had 6.4% lower FEV1% value than EAs. XGB model was used to accurately classify asthma patients visiting ED into AAs and EAs. Pollen factor and pollution (PM2.5, PM10) were the key variables for asthma in AAs and EAs, respectively. Age and cigarette smoking increase asthma risk independent of seasons.

CONCLUSIONS

In this study, we observed racial and season-specific disparities between AAs and EAs asthmatics for ED visit and FEV1% severity, suggesting the need to address asthma disparities through key predictors including socio-economic status, particulate matter, and mold.