Volume 70 , September 2019, Pages 127-133
Fall-related traumatic brain injury in children ages 0–4 years
Author links open overlay panel JulietHaarbauer-Krupaa
https://doi.org/10.1016/j.jsr.2019.06.003 Get rights and content
Introduction: Falls are the leading cause of traumatic brain injury (TBI) for children in the 0–4 year age group. There is limited literature pertaining to fall-related TBIs in children age 4 and under and the circumstances surrounding these TBIs. This study provides a national estimate and describes actions and products associated with fall-related TBI in this age group. Method: Data analyzed were from the 2001–2013 National Electronic Injury Surveillance System–All Injury Program (NEISS–AIP), a nationally representative sample of emergency departments (ED). Case narratives were coded for actions associated with the fall, and product codes were abstracted to determine fall location and product type. All estimates were weighted. Results: An estimated 139,001 children younger than 5 years were treated annually in EDs for nonfatal, unintentional fall-related TBI injuries (total = 1,807,019 during 2001–2013). Overall, child actions (e.g., running) accounted for the greatest proportion of injuries and actions by others (e.g., carrying) was highest for children younger than 1 year. The majority of falls occurred in the home, and involved surfaces, fixtures, furniture, and baby products. Conclusions: Fall-related TBI in young children represents a significant public health burden. The majority of children seen for TBI assessment in EDs were released to home. Prevention efforts that target parent supervision practices and the home environment are indicated. Practical applications: Professionals in contact with parents of young children can remind them to establish a safe home and be attentive to the environment when carrying young children to prevent falls.
Nonfatal fall-related traumatic brain injury rates among children age 0–4years treated in US emergency departments - National Electronic Injury Surveillance System–All Injury Program, by year and age,...
Falls are the leading cause of nonfatal emergency department (ED) visits among children aged birth to 14 years, accounting for 2.4 million visits annually, and the leading cause of traumatic brain injury (TBI) ED visits for children in the 0–4 year age group (Taylor et al., 2017, Wang et al., 2013). There is limited literature describing the number and circumstances of fall-related TBI in children under 5 years old. The few TBI studies available that describe injury circumstances lack reporting
Data were acquired from the 2001–2013 NEISS–AIP, which is a nationally representative stratified probability sample of 66 hospitals having at least 6 beds and providing 24-h emergency services in the United States. NEISS–AIP is a collaborative effort between the Centers for Disease Control and Prevention (CDC) and the U.S. Consumer Product Safety Commission (2000). Data are collected daily from each participating hospital resulting in approximately 500,000 nonfatal injury-related ED visits
Fall-related TBI by child characteristics
On average, between 2001 and 2013 an estimated 139,001 children age 0–4 years were treated annually in EDs in the United States for nonfatal, unintentional fall-related TBI (Table 1).
This number increased over time going from 103,432 cases in 2001 to 182,069 in 2013. TBI in this age group was primarily coded as a diagnosis indicating an internal injury to the head (87.7%). Most children were treated and released from the ED (93%) and while the percentage of children hospitalized or transferred
This study used nationally representative data between the years 2001 and 2013 to study fall-related TBI in children ages 0–4 years. Prior research had shown that this age group had the highest rate of fall-related TBI ED visits overall (Taylor et al., 2017: Taylor, Greenspan, Xu, & Kresnow, 2015). An annualized national estimate of 139,001 ED visits per year in this age group approximates reports from other studies examining national data during this timeframe (Taylor et al., 2015). The annual
Fall-related TBI in young children represents a significant public health burden indicated by increased ED visits for fall-related TBI in an age group that has an increased opportunity for falls as well as greater risk for long-term impact from the injury. Prevention efforts that target environmental changes along with parental supervision practices to reduce fall risk are needed.
Young children are prone to falls and because of this may experience a traumatic brain injury. Professionals in contact with caregivers of young children can remind them to establish a safe home and be attentive to the environment when carrying young children to prevent falls. Reducing clutter on floors, and protecting children from hazards with barriers, such as safety gates on stairs, guards on windows above ground level, and guard rails on beds are prudent safety measures. Young children
The authors have indicated no financial relationships relevant to this article to disclose.
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
Declarations of Competing Interest
The authors have no conflicts of interest relevant to this article to disclose.
The authors wish to thank Christopher Taylor, PhD for his work identifying cases for analysis, determining available actions in the dataset, and his contributions to creating the action coding system.
Juliet Haarbauer-Krupa is a Senior Health Scientist on the Traumatic Brain Injury (TBI) Team in the Division of Unintentional Injury Prevention (DUIP) at the Injury Center. As a behavioral scientist, her role on the TBI team is to devise research projects and products to better understand trends in TBI in the US and to improve health outcomes for individuals living with a TBI. She is project lead on the Report to Congress on the Management of Traumatic Brain Injury in Children and the Return to
The American Journal of Emergency Medicine
K. Quinlan et al.
B.A. Morrongiello et al.
Influence of age and fall type on head injuries in infants and toddlers
International Journal of Developmental Neuroscience
Unintentional fall injuries amoung US children: A study based on the National Emergency Department Sample
International Journal of Injury Control and Safety Promotion
Injuries among infants treated in emergency departments in the United States, 2001-2004
Rates of pediatric injuries by 3-month intervals for children 0 to 3 years of age
B.A. Morrongiello et al.
Understanding unintentional injury-risk in young children I: The nature and scope of caregiver supervision at home
Journal of Pediatric Psychology
Centers for Disease Control and Prevention
Report to Congress on the Management of Traumatic Brain Injury in Children
Motor development as a context for understanding parent safety practices
Nonfatal playground-related traumatic brain injuries among children, 2001-2013
Understanding in-home injuries: II. Examining parental strategies, and their efficacy for managing child injury risk
Journal of Pediatric Psychology
V.G. Coronado et al.
Trends in sports- and recreation-related traumatic brain injuries treated in US emergency departments: The National Electronic Injury Surveillance System-All Injury Program (NEISS-AIP) 2001-2012
The Journal of Head Trauma Rehabilitation
Head injury from falls in children younger than 6 years of age
Archives of Disease in Childhood
Timing of traumatic brain injury in childhood and intellectual outcome
Journal of Pediatric Psychology 2012
View more references
Cited by (4)
Infliximab Can Improve Traumatic Brain Injury by Suppressing the Tumor Necrosis Factor Alpha Pathway
2021, Molecular Neurobiology
2020, Seminars in Speech and Language
Recommended articles (6)
Journal of Safety Research, Volume 70, 2019, pp. 97-103
Introduction: Employers engaged in similar business activities demonstrate a range of workers' compensation claim rates. Workplace injuries and illnesses could be prevented if employers with high claim rates achieved the claim rates of their safer peers. Methods: We used Washington workers' compensation claims data for years 2013–2015 to calculate rates of compensable claims (eligible for disability or time loss benefits, if unable to work four days after injury) and total accepted claims (compensable plus medical-aid only claims) for each employer. We estimated the number and cost of claims to occur if employers with high claim rates reduced them to the rates of employers at the 25th percentile, adjusted for insurance risk class, employer size, and injury type. To evaluate the impact of setting more or less ambitious goals, we also estimated reductions based on claim rates at the 10th and 50th percentiles. Results: Over 43% of claims and claim costs would be prevented if employers with higher claim rates lowered them to the 25th percentile using either total accepted or compensable claim rates as the benchmark outcome. The estimated claim cost savings from benchmarking to compensable claims was nearly as great as the estimate based on benchmarking to total accepted claims ($308.5 mil annually based on compensable claims vs. $332.4 mil based on total accepted claims). Restaurants and Taverns had the greatest number of potentially prevented compensable claims. Colleges and Universities and Wood Frame and Building Construction had the greatest potential reduction in compensable claim costs among larger and smaller employers, respectively. Conclusion: Substantial reductions in workers' compensation claims and costs are possible if employers achieve the injury rates experienced by their safer peers. Practical application: Evaluating the range of workplace injury rates among employers within industry groups identifies opportunities for injury prevention and offers another approach to resource allocation.
Journal of Safety Research, Volume 70, 2019, pp. 159-167
Introduction: Responsibility analysis allows the evaluation of crash risk factors from crash data only, but requires a reliable responsibility assessment. The aim of the present study is to predict expert responsibility attribution (considered as a gold-standard) from explanatory variables available in crash data routinely recorded by the police, according to a data-driven process with explicit rules. Method: Driver responsibility was assessed by experts using all information contained in police reports for a sample of about 5000 injury crashes that occurred in France in 2011. Three statistical methods were used to predict expert responsibility attribution: logistic regression with L1 penalty, random forests, and boosting. Potential predictors of expert attribution referred to inappropriate driver actions and to external conditions at the time of the crash. Logistic regression was chosen to construct a score to assess responsibility for drivers and riders in crashes involving one or more motor vehicles, or involving a cyclist or pedestrian. Results: Cross-validation showed that our tool can predict expert responsibility assessments on new data sets. In addition, responsibility analyses performed using either the expert responsibility or our predicted responsibility return similar odds ratios. Our scoring process can then be used to reliably assess responsibility based on national police report databases, provided that they include the information needed to construct the score.
Journal of Safety Research, Volume 70, 2019, pp. 71-77
Introduction: Violence-related events and roadway incidents are the leading causes of injury among taxi drivers. Fatigue is under-recognized and prevalent in this workforce and is associated with both injury outcomes. We describe the association of individual, business-related, and work environment factors with driving tired among taxi drivers in two very different cities. Method: We developed a comprehensive survey for licensed taxi drivers. We trained surveyors to administer the 30-min survey using systematic sampling among taxi drivers waiting for fares in two large U.S. cities: the Southwest (City 1) and the West (City 2). A driving tired scale of the Occupational Driver Behavior Questionnaire was the outcome. Multivariate logistic models described driving tired behavior in city-specific models using adjusted Odds Ratios (ORadj). Results: City 1 and City 2 had 496 and 500 participants, respectively. Each driving tired behavior was significantly more prevalent in City 2 than City 1 (p