# CE-22-008 ASTRAL (Aquatic Safety Through Research, Association, and Linkage of Data)

> **NIH ALLCDC U01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $294,848

## Abstract

Abstract
The long-term goal of the project is to understand conditions affecting morbidity and mortality from
unintentional drowning, especially in subpopulations that experience higher rates of drowning. The
specific aims are: 1) Develop an analysis database by linking data from EMS, hospital and fatality
recordsverified by syndromic surveillance data and enriched with socio-economic contextual
information pertaining to risk factors of drowning; 2a) Examine gender, racial and ethnic differences
in drowning rates and identify risk factors that account for these differences; 2b) Perform spatial-
temporal analysis of drownings in order to identify locations and time periods when these events are
most likely to occur and 3) Compute the aggregate medical and other costs associated with fatal and
non-fatal drowning by age, minority status and aquatic body.
This is a cross-sectional study of all victims or patients of unintentional drowning that occurred in
HarrisCounty, Texas between 2016-2023. We will link data from fatality reports, EMS and
hospitalization dataand add contextual information on drowning from other data sources to prepare
an analysis database. Individual and environmental information of drowning victims will be matched
at the neighborhood level. We will match demographic, exposure and outcome variables through
spatial overlap and will identify demographic, medical and drowning characteristics associated with
fatal and nonfatal drowning. Spatial regression modeling will produce a model to predict fatal and
nonfatal drowning, further broken down by gender, race, Hispanic ethnicity and other ethnic
differences.
We will examine associations of drowning with neighborhood characteristics, proximity to swimming
venues and swim lesson participation. Drowning incidents will be examined for spatial concentrations
using the Nearest Neighbor Hierarchical Cluster algorithm. To relate drownings to predictive factors,
a Markov Chain Monte Carlo Poisson-Lognormal-Conditional Autoregressive spatial regression
model willbe tested at the census block group level.
Hospital billing records will be analyzed to compute the direct medical costs, comprehensive
(lifetime) costs, and disability adjusted life years associated with drowning. This project will integrate
informationfrom multiple data sources to identify key factors that contribute to increased drowning
rates. The results will inform injury prevention efforts and help prioritize strategies to safeguard
people from drowning.

## Key facts

- **NIH application ID:** 10827481
- **Project number:** 5U01CE003504-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Rohit Shenoi
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2024
- **Award amount:** $294,848
- **Award type:** 5
- **Project period:** 2022-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10827481

## Citation

> US National Institutes of Health, RePORTER application 10827481, CE-22-008 ASTRAL (Aquatic Safety Through Research, Association, and Linkage of Data) (5U01CE003504-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10827481. Licensed CC0.

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