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Lessons learned in a data linkage project on fatal drowning.
Rohit P Shenoi1, Ned Levine2, Elizabeth A Camp3
1Division of Emergency Medicine, Department of Pediatrics, Texas Children's Hospital and Baylor College of Medicine, 6621 Fannin St., Suite A 2210, Houston, Texas, 77030, USA. rshenoi@bcm.edu.
Injury Epidemiology
|February 1, 2026
Summary
Linking multiple datasets to study drowning deaths in Houston revealed significant challenges. Documenting these lessons learned can improve future regional drowning research and prevention efforts.
Area of Science:
- Public Health
- Epidemiology
- Data Science
Background:
- Drowning is a leading cause of death for young children in the US.
- Regional drowning epidemiology is understudied due to fragmented data.
- Probabilistic data linkage offers a novel approach to comprehensive drowning research.
Purpose of the Study:
- To document lessons learned from applying probabilistic data linkage to fatal drowning data.
- To identify challenges and solutions in data curation, regulatory compliance, and data security.
- To inform future regional drowning research and prevention strategies.
Main Methods:
- Cross-sectional study of unintentional drowning deaths in metropolitan Houston (2016-2022).
- Probabilistic data linkage of multiple datasets.
- Analysis of challenges in project planning and execution phases.
Main Results:
- Eight out of twelve reviewed datasets were successfully linked.
- Key planning issues included data ownership, quality, and description variations.
- Execution challenges involved data security, sharing, and results dissemination.
Conclusions:
- Numerous data sources exist for fatal drowning.
- Probabilistic data linkage for epidemiological studies is complex and time-consuming.
- Documenting the process aids future drowning research and prevention.
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