# Multifactorial spatiotemporal analyses to evaluate environmental triggers and patient-level clinical characteristics of severe asthma exacerbations in children

> **NIH NIH R21** · DUKE UNIVERSITY · 2020 · $120,750

## Abstract

Asthma is a chronic heterogeneous airway disorder characterized by inflammation, mucus hypersecretion,
airway hyperreactivity, and impaired airflow. Severe exacerbations of asthma occur frequently in children and
require immediate use of systemic steroid therapy to prevent serious outcomes such as hospitalization or death.
In addition to direct health risks, pediatric asthma exerts a substantial cost burden, as asthma exacerbations are
a leading cause of emergency department visits, hospitalization, and missed school days. Multiple environmental
factors are purported to play a role in asthma symptoms, including aeroallergens, pollutants, weather changes,
and community viral outbreaks such as influenza. Additionally, asthma prevalence is greater in children of low
socioeconomic status (SES) and in African-American and Hispanic/Latino children, suggesting both
environmental and genetic effects on asthma incidence and severity. The existence of geographical asthma
“hotspots” indicates that asthma prevalence and severity are influenced by place-based risks, including local air
quality, built environment factors, access to health care providers, socioeconomic factors, culture, and behavior.
To effectively prevent and treat pediatric asthma attacks, it is necessary to understand how patient-specific
characteristics interact with environmental factors to render an individual susceptible to severe asthma
exacerbations. Lacking sufficient power, previous studies have largely examined suspected asthma triggers in
isolation; thus, there is a significant knowledge gap regarding how environmental factors interact with each
other and with patient-level factors to promote severe asthma exacerbations in pediatric populations. We
hypothesize that a longitudinal analysis of environmental exposures and patient-level factors will elucidate new
multifactorial causes of severe asthma exacerbations. To elucidate the contributions and interactions of
environmental and patient-level factors, we will apply machine learning approaches to a longitudinal (2007-2017)
geocoded database of patient electronic health records detailing asthma-related health encounters and publicly
available, overlapping spatiotemporal environmental data. Further, we will evaluate the interactions between
person-level clinical factors, including obesity, history of premature birth/bronchopulmonary dysplasia, and atopy,
to determine their effects on susceptibility to selected environmental triggers. These analyses will 1) provide an
analysis of the relative contribution and interactions of environmental factors to pediatric asthma exacerbations, 2)
identify geographic hotspots of asthma prevalence and severity, and 3) determine how person-level clinical factors
influence susceptibility to different asthma triggers. Our findings will provide new insights into risk factors for
severe asthma exacerbations, spur new studies into the biological mechanisms that underlie the interactions
between human biol...

## Key facts

- **NIH application ID:** 9884782
- **Project number:** 5R21HL145415-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Benjamin Alan Goldstein
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $120,750
- **Award type:** 5
- **Project period:** 2019-03-04 → 2021-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9884782

## Citation

> US National Institutes of Health, RePORTER application 9884782, Multifactorial spatiotemporal analyses to evaluate environmental triggers and patient-level clinical characteristics of severe asthma exacerbations in children (5R21HL145415-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9884782. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*