Risk and Injury Severity of Obese Child Passengers in Motor Vehicle Crashes

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DESCRIPTION (provided by applicant): The prevalence of childhood obesity has been substantially increasing thus it is considered an important public health issue in the United States. Motor vehicle crashes (MVCs) are the single greatest risk and continue to be the leading cause of death for children worldwide. Considerable studies have been conducted on child occupant injury mechanisms in MVCs using standard size child dummies. Consequentially, current child restraint systems were optimized for the standard size of children. However, obese children are too heavy for age-specific child safety seats. Moreover, risk and injury mechanisms of obese children in MVCs are still unknown. The goal of the proposed study is to investigate the risk and injury severity of obese children following MVCs through computational modeling and simulation. We hypothesize that injury levels of obese children will be greater than those of non-obese children. We will develop biofidelic computational models to represent 3- and 6-years-old obese children in various BMI ranges using state-of-the-art modeling techniques and medical imaging processing. We will then perform computational crash simulations with the obese and standard dummy models under a variety of MVC scenarios. The expected outcomes are quantitative risk assessments of regional body injuries including head, neck, and thorax. The results from obese child dummies will be compared to those from the standard dummies to examine the effects of childhood obesity on MVC injuries. PUBLIC HEALTH RELEVANCE: Application of the age- and body habitus-specific computational models will reveal the regional body injuries of obese children under dynamic loading conditions. This effort will advance our understanding of the injury mechanisms for obese children in MVCs. The outcomes from the model simulations would be used to assess and design child restraint devices to improve road safety for the rapidly increasing obese child population.