In 2024, the Virginia Department of Health reported that Hampton Roads children between the ages of 5 and 14 were hospitalized for asthma at roughly double the state average, leading local organizations to utilize artificial intelligence to map and mitigate these regional inequities.
For Jessica Parrott, the most concerning part is that asthma is “100% treatable.”
“It’s really not okay that those kids are having to have so many (emergency department) visits and then potentially death from it when we can do something about it beforehand.”
explained Parrott, who serves as the director of the Pediatric Nurse Practitioner Program at Old Dominion University and works as a nurse practitioner at the Children’s Hospital of the King’s Daughters Urgent Care.
Childhood asthma can be triggered by smog, smoke, allergens like pollen, dust, mold, and pet dander. Knowing where in the region these hospitalizations occur is the first step to understanding why they happen, according to Aditya Chakraborty, assistant professor at ODU’s Joint School of Public Health.
Mapping Asthma Hotspots in Hampton Roads
The collaborative project between Old Dominion University and Children’s Hospital of the King’s Daughters aims to identify high-risk zones across Hampton Roads and Western Tidewater. Researchers plan to use local hospitalization data to pinpoint geographic concentrations.
“We plan to use local hospitalization data and use the geographical hotspot analysis to see where childhood asthma hospitalizations are concentrated across Hampton Roads and Western Tidewater, and to explore what might be contributing to those patterns,” Chakraborty told WHRO.
AI Models Analyze Anonymous Hospitalization Records
The study will deploy multiple AI models to analyze hospitalization records from Children’s Hospital of the King’s Daughters. Patients will remain anonymous, with researchers utilizing only zip codes alongside demographic information, average home age, income levels, and local healthcare provider distribution.
Using Predictive Models for Early Diagnosis
The initiative builds directly upon a study Chakraborty published in July in Frontiers in Public Health. That previous research tested machine learning models to identify children still experiencing active asthma symptoms based on medication usage history, symptom dates, and household factors like bedroom rugs or carpet.
“Our next step with the CHKD is to bring those methods closer to home,” Chakraborty said, as reported by WHRO. The models successfully demonstrated that predictive analytics can improve early diagnostic accuracy and secure timely clinical interventions.
Investigators expect access to care to serve as a major driver behind the elevated regional hospitalization rates. Parrott said that even families possessing insurance and a primary provider often struggle to secure timely appointments.
“We see that regularly with our free clinic that we run with ODU, because kids, even if they have insurance and they have a provider, they often can’t even get into their provider,” Parrott told WHRO.
Having granular data illustrating the true scale of the issue will guide providers in tailoring solutions.
| Metric / Factor | Hampton Roads Region (2024) | Statewide Benchmark |
|---|---|---|
| Pediatric Hospitalization Rate (Ages 5-14) | Nearly twice the state baseline | Virginia Department of Health baseline |
| Primary Data Inputs | Anonymized zip codes, demographics, housing age, income | State public health tracking registries |
| Projected Timeline | Results anticipated in 2027 | Ongoing surveillance |
Researchers Anticipate Final Results in 2027
Chakraborty and Parrott are currently completing data collection and securing institutional approvals. Once the project clears regulatory and administrative hurdles, researchers anticipate rapid progress, with final results expected sometime in 2027.
References
- Virginia Department of Health. Environmental Public Health Tracking: Asthma Hospitalizations. Available at: VDH Asthma Portal
- Chakraborty, A., et al. (2026). Machine learning applications in early pediatric asthma identification and symptom tracking. Frontiers in Public Health. Available at: Frontiers Research Link
