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

Abstract

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.

KEYWORDS:

• Race disparities
• seasonal variation
• machine learning
• electronic healthcare records
• EHR

Acknowledgments

We would also like to acknowledge the University of Cincinnati’s Center for Health Informatics for making the data available to us for this study.

Declaration of interest

The authors report no conflicts of interest.

Additional information

Funding

This work was supported by the National Institutes of Health (NIH) grant R01HL132344.