# Precision Assessment Algorithm for Reducing Disaster-related Respiratory Health Disparities

> **NIH NIH R43** · CASTNER INCORPORATED · 2021 · $228,344

## Abstract

ABSTRACT
Weather and climate disasters are responsible for over 13,000 deaths and $1.7 trillion additional costs over the
last 40 years in the USA. Older adults are particularly susceptible to respiratory symptoms, disease
exacerbation, unscheduled health care utilization, and decreased quality of life after disaster exposure to
particulates, mold, and flooding. Our preliminary data reveal those with Black racial identities possess fewer
resources to prepare for disaster. Profound racial disparities observed in the COVID-19 pandemic illustrate the
devastating sequelae of long-standing macro-level disparities of segregated housing and sociopolitical
networks. The long-term goal of this work is to eliminate racial disparities in large scale disaster health
outcomes. The short-term goal of this research is to identify pathways of equal opportunity and disaster
affirmative action interventions. The objective here is to create a software prototype of a machine learning
algorithm with a novel, valid and reliable assessment tool of disaster vulnerability for older adults with chronic
obstructive respiratory disease, prioritizing equality of opportunity to reduce racial bias and disparity. Our
specific aims are to 1) Empirically validate a novel assessment tool of disaster vulnerability using self-reported
items and scoring system, 2) Refine the validated instrument with a machine learning based algorithm for
precision prediction of household emergency preparedness for disaster, 3) Assess racial disparities, data and
algorithm bias for Black participants in household hazard vulnerabilities and our instrument development
process, and 4) Test interoperability with existing customer software platforms as a plug-in software add-on.
We will accomplish these aims using a mixed-methods approach, recruiting 20 expert panel members and up
to 600 potential end-user participants, working to over-sample those who reside in predominantly Black
communities and Black racial identities. The knowledge gained from this study will provide foundational work to
develop precision interventions to reduce post-disaster respiratory symptoms, disease exacerbation,
unscheduled health care utilization, and decrements in respiratory quality of life. The results of this study will
inform the next generation of electronic health record and patient reported outcomes applications, ensuring the
validity, prognostic accuracy, and machine learning models are most relevant to those at highest risk for racial
disparities: those with Black racial identities. Health care providers can use our software tool to target and
optimize disaster telehealth service lines and increase intervention precision to reduce disaster morbidity and
mortality disparities.

## Key facts

- **NIH application ID:** 10401726
- **Project number:** 1R43MD017188-01
- **Recipient organization:** CASTNER INCORPORATED
- **Principal Investigator:** Jessica Castner
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $228,344
- **Award type:** 1
- **Project period:** 2021-09-21 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401726

## Citation

> US National Institutes of Health, RePORTER application 10401726, Precision Assessment Algorithm for Reducing Disaster-related Respiratory Health Disparities (1R43MD017188-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10401726. Licensed CC0.

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