Disability Risk in Pediatric Motor Vehicle Crash Occupants.

Background: Mortality rates among children in motor vehicle crashes (MVCs) are typically low, however non-fatal injuries can vary in severity by imposing differing levels of short- and long-term disability. To better discriminate the severity of non-fatal MVC-injuries, a pediatric-specific disability risk (DR) metric was created. Methods: The National Automotive Sampling System (NASS-CDS) 2000-2011 was used to define the top 95% most common Abbreviated Injury Scale (AIS) 2+ injuries among pediatric MVC occupants. Functional Independence Measure (FIM) scores were abstracted from the National Trauma Data Bank (NTDB-RDS) 2002-2006. Multiple imputation was employed to account for missing data. The DR and co-injury-adjusted DR (DRMAIS) of the most common AIS2+ MVC-induced injuries were calculated for 7-18 year old children by determining the proportion of those disabled after an injury to those sustaining the injury. DR and DRMAIS values ranged from 0 to 1, representing 0-100% disability risk. Results: The mean DR and DRMAIS of all injuries were 0.290 and 0.191, respectively. DR and DRMAIS were greatest for injuries to the head (DR: 0.340, DRMAIS: 0.279), thorax (DR: 0.320, DRMAIS: 0.233) and spine (DR: 0.315, DRMAIS: 0.200). The mean DR and DRMAIS increased with increasing AIS severity but there was significant variation and overlapping values across AIS severity levels. Comparison of DRMAIS to co-injury adjusted mortality risk (MRMAIS) revealed that among 118 injuries with MRMAIS of 0.000, DRMAIS ranged from 0.000 to 0.429. Conclusions: Incorporation of DR metrics into injury severity metrics may improve the ability to distinguish between the severity of different non-fatal injuries. This is especially crucial in the pediatric population where permanent disability can result in a high number of years lost due to disability (YLD). The accuracy of such severity metrics are crucial to the success of pediatric triage algorithms such as Advanced Automatic Crash Notification algorithms. Level of Evidence: Level III, Epidemiologic/prognostic study (C) 2017 Lippincott Williams & Wilkins, Inc.

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