# Precision Approaches to Reduce Asthma Disparities with Electronic Health Record Data

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $812,274

## Abstract

Asthma, a chronic disease that manifests as airway hyperresponsiveness to specific environmental stimuli,
affects over 20 million American adults. Disparities in adult asthma prevalence, severity, and death in the U.S.
are well known but few approaches have significantly decreased them. Studies of real-world populations such
as those derived from Electronic Health Records (EHRs) are invaluable to guide the design of personalized care
strategies because they capture a large number of diverse and vulnerable people. Additionally, EHR data can
be leveraged to identify geospatial areas where people are at peak risk of a condition by studying the geographic
distribution of affected patients. We have identified individual- and area-level factors that are associated with
asthma using EHRs linked to rich and diverse sources of social, economic, and environmental variables, and we
have developed methods to appropriately extract information from EHR data that are heterogeneously available
across patients and reduce bias in the analysis of these imperfect data. This proposal will identify sub-groups of
adults with asthma who share common patterns of demographic, clinical, social, and environmental exposure
characteristics using EHR data augmented with data on social, economic, and environmental factors, which will
enable the design of effective precision strategies to reduce asthma exacerbations. Our aims are to: 1) develop
and validate natural language processing (NLP) algorithms to extract social, occupational and allergy information
from EHR notes; 2) determine geospatial areas that have increased asthma exacerbation risk using spatial
generalized linear mixed models and identify risk factors in these areas; and 3) use Bayesian hierarchical
clustering techniques to identify asthma sub-phenotypes that will form the basis of a clinical decision support tool
that offers precision care strategies. This project will result in the creation of a tool that can assist in the care of
adults with asthma based on actionable risk factors and motivate public health strategies to mitigate the burden of
asthma in specific regions. Our novel data integration approaches, sub-phenotyping methods, and software
developed will have broad applicability for the study of any condition using EHR or other real-world data.

## Key facts

- **NIH application ID:** 10540025
- **Project number:** 1R01HL162354-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Blanca E Himes
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $812,274
- **Award type:** 1
- **Project period:** 2022-09-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10540025

## Citation

> US National Institutes of Health, RePORTER application 10540025, Precision Approaches to Reduce Asthma Disparities with Electronic Health Record Data (1R01HL162354-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10540025. Licensed CC0.

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