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Understanding the circumstances of paediatric fall injuries: a machine learning analysis of NEISS narratives
  1. Elise Omaki1,
  2. Wendy Shields1,
  3. Masoud Rouhizadeh2,
  4. Pamela Delgado-Barroso3,
  5. Ruth Stefanos3,
  6. Andrea Gielen1
  1. 1Center for Injury Research and Policy, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
  2. 2Department of Pharmaceutical Outcomes & Policy, University of Florida College of Pharmacy, Gainesville, Florida, USA
  3. 3Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
  1. Correspondence to Elise Omaki, Center for Injury Research and Policy, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21218, Maryland, USA; eperry{at}jhu.edu

Abstract

Objectives Falls are the leading cause of non-fatal injury among young children. The aim of this study was to identify and quantify the circumstances contributing to medically attended paediatric fall injuries among 0–4 years old.

Methods Cross-sectional data for falls among kids under 5 years recorded between 2012 and 2016 in the National Electronic Injury Surveillance System was obtained. A sample of 4546 narratives was manually coded for: (1) where the child fell from; (2) what the child fell onto; (3) the activities preceding the fall and (4) how the fall occurred. A natural language processing model was developed and subsequently applied to the remaining uncoded data to yield a set of 91 325 cases coded for what the child fell from, fell onto, the activities preceding the fall, and how the fall occurred. Data were descriptively tabulated by age and disposition.

Results Children most often fell from the bed accounting for one-third (33%) of fall injuries in infants, 13% in toddlers and 12% in preschoolers. Children were more likely to be hospitalised if they fell from another person (7.4% vs 2.6% for all other sources; p<0.01). After adjusting for age, the odds of a child being hospitalised following a fall from another person were 2.1 times higher than falling from other surfaces (95% CI 1.6 to 2.7).

Conclusions The prevalence of injuries due to falling off the bed, and the elevated risk of serious injury from falling from another person highlights the need for more robust and effective communication to caregivers on fall injury prevention.

  • Fall
  • Descriptive Epidemiology
  • Child

Data availability statement

Data are available in a public, open access repository. Data from the National Electric Injury Surveillance System are freely accessible to the public through the Consumer Product Safety Commission. Please visit https://www.cpsc.gov/Research--Statistics/NEISS-Injury-Data for details.

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Data availability statement

Data are available in a public, open access repository. Data from the National Electric Injury Surveillance System are freely accessible to the public through the Consumer Product Safety Commission. Please visit https://www.cpsc.gov/Research--Statistics/NEISS-Injury-Data for details.

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Footnotes

  • Twitter @eliseomaki

  • Contributors EO, WS and AG conceptualised the study, obtained funding, provided overall oversight and leadership of this work; MR constructed the natural language programming models; PD-B and RS reviewed the narratives, established the coding framework and labelled narratives accordingly. EO was responsible for the overall content, accepts full responsibility for the finished work, had access to the data, and controlled the decision to publish.

  • Funding This work was supported by a grant from the National Institute for Child Health and Development (grant number 1R21HD099513) and from the American Public Health Association's Injury and Violence Prevention Data Science Demonstration Programme.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.