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Updated: Feb 3, 2026

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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
DrowningEpidemiologyLessons learnedProbabilistic data linkage

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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.