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Child injury: using national emergency department monitoring systems to identify temporal and demographic risk factors
  1. Karen Hughes1,
  2. Philip McHale1,
  3. Sacha Wyke2,
  4. Helen Lowey1,
  5. Mark A Bellis1
  1. 1Centre for Public Health, Liverpool John Moores University, Liverpool, UK
  2. 2Knowledge and Intelligence Team (North West), Public Health England, Liverpool, UK
  1. Correspondence to Dr K Hughes, Centre for Public Health, Liverpool John Moores University, Henry Cotton Building, 15-21 Webster Street, Liverpool L3 2ET, UK; k.e.hughes{at}ljmu.ac.uk

Abstract

Background Injury is a leading cause of death in children. Emergency department (ED) data offer a potentially rich source of data on child injury. This study uses an emerging national ED data collection system to examine sociodemographics and temporal trends in child injury attendances in England.

Methods Cross sectional examination of ED attendances for key injury types made by children aged 0–14 years between April 2010 and March 2011 (road traffic injury (RTI) n=21 670; assault n=9529; deliberate self harm (DSH) n=3066; sports injury n=88 250; burns n=22 222; poisoning n=12 446). Multivariate analyses examined the impact of demographics (age, gender, residential deprivation) and temporal events (day, month, school and public holidays) on risk of attendance for different injury types.

Results For most injury types, attendance increased with deprivation. The attendance ratio between children from the poorest and richest deprivation quintiles was greatest for assaults (4.21:1). Conversely, sports injury attendance decreased with deprivation. Males made more attendances than females for all but DSH. Age and temporal profiles varied by injury type. Assault attendances reduced at weekends while burns attendances increased. RTI and sports injury attendances were increased during school term times.

Conclusions ED data can provide a major epidemiological resource for examining both temporal and demographic risks of child injury. Emerging systems, such as the one analysed here, can already inform the targeting of prevention, and with improved data coding and use, their utility would be greatly strengthened.

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