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Automotive Field Data in Injury Biomechanics

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Accidental Injury

Abstract

A crucial component to understanding the biomechanics of injury is the study of real world injuries. These studies are essential to characterize the incidence and characteristics of impact injuries, to establish impact test configurations, and to evaluate the effectiveness of injury intervention measures. This chapter will describe the data sources for these evaluations, metrics of performance for injury, and representative applications, i.e., the generation of injury risk curves, measurement of societal costs of injuries, and estimating the mortality associated with specific injuries.

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References

  1. Association for the Advancement of Automotive Medicine (2001) The abbreviated injury scale: 1990 revision, Update 98

    Google Scholar 

  2. Association for the Advancement of Automotive Medicine (2008) Abbreviated injury scale 2005 (Update 2008)

    Google Scholar 

  3. Gennarelli TA, Wodzin E (2006) AIS 2005: a contemporary injury scale. Injury 37(12):1083–1091. doi:10.1016/j.injury.2006.07.009, S0020-1383(06)00419-0 [pii]

    Article  PubMed  Google Scholar 

  4. Baker SP, O’Neill B, Haddon W Jr, Long WB (1974) The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma 14(3):187–196

    Article  CAS  PubMed  Google Scholar 

  5. Osler T, Baker SP, Long W (1997) A modification of the injury severity score that both improves accuracy and simplifies scoring. J Trauma 43(6):922–925, discussion 925–926

    Article  CAS  PubMed  Google Scholar 

  6. Balogh Z, Offner PJ, Moore EE, Biffl WL (2000) NISS predicts postinjury multiple organ failure better than the ISS. J Trauma 48(4):624–627, discussion 627–628

    Article  CAS  PubMed  Google Scholar 

  7. Balogh ZJ, Varga E, Tomka J, Suveges G, Toth L, Simonka JA (2003) The new injury severity score is a better predictor of extended hospitalization and intensive care unit admission than the injury severity score in patients with multiple orthopaedic injuries. J Orthop Trauma 17(7):508–512

    Article  PubMed  Google Scholar 

  8. Gayzik FS, Moreno DP, Geer CP, Wuertzer SD, Martin RS, Stitzel JD (2011) Development of a full body CAD dataset for computational modeling: a multi-modality approach. Ann Biomed Eng 39(10):2568–2583. doi:10.1007/s10439-011-0359-5

    Article  CAS  PubMed  Google Scholar 

  9. National Center for Health Statistics (NCHS), Centers for Medicare & Medicaid Services (CMS) (2008) The international classification of diseases, vol 6th edn, 9th revision

    Google Scholar 

  10. Compton CP (2005) Injury severity codes: a comparison of police injury codes and medical outcomes as determined by NASS CDS Investigators. J Safety Res 36(5):483–484. doi:10.1016/j.jsr.2005.10.008, S0022-4375(05)00081-2 [pii]

    Article  PubMed  Google Scholar 

  11. Johns Hopkins University and Tri-Analytics Inc (2007) ICDMAP-90. Bloomberg School of Public Health, Center for Injury Research and Policy, Baltimore

    Google Scholar 

  12. Barnard RT, Loftis KL, Martin RS, Stitzel JD (2013) Development of a robust mapping between AIS 2+ and ICD-9 injury codes. Accid Anal Prev 52:133–143. doi:10.1016/j.aap.2012.11.030, S0001-4575(12)00423-X [pii]

    Article  PubMed  Google Scholar 

  13. National Highway Traffic Safety Administration (2012) Fatality analysis reporting system (FARS) analytical users manual: 1975–2011. Report DOT HS 811 693, Dec 2012

    Google Scholar 

  14. Radja G (2012) National automotive sampling system – crashworthiness data system, 2011 analytical user’s manual. Report DOT HS 811 675, Washington, DC

    Google Scholar 

  15. Zhang F, Chen C-L (2013) NASS-CDS: sample design and weights. Report no. DOT HS 811 807, Washington, DC

    Google Scholar 

  16. National Highway Traffic Safety Administration (2011) 2010 FARS/NASS GES standardization. Report number DOT HS 811 564, Washington, DC

    Google Scholar 

  17. National Highway Traffic Safety Administration (2013) National automotive sampling system (NASS) general estimates system (GES) analytical users manual 1988–2011. Report DOT HS 811 704

    Google Scholar 

  18. National Highway Traffic Safety Administration (2012) Data modernization project: better data, safer roads. http://www.nhtsa.gov/Data/DataMod/DataMod

  19. Bellis E, Page J (2008) National motor vehicle crash causation survey (NMVCCS) SAS analytical users manual. Report DOT HS 811 053

    Google Scholar 

  20. Federal Highway Administration (2012) Model minimum uniform crash criteria guideline (MMUCC), 4th edn. Report DOT HS 811 631, June 2012

    Google Scholar 

  21. National Highway Traffic Safety Administration (2010) The crash outcome data evaluation system (CODES) and applications to improve traffic safety decision-making. NHTSA technical report DOT HS 811 181, Apr 2010

    Google Scholar 

  22. Johnson SW, Walker J (1996) The crash outcome data evaluation system (CODES). NHTSA final report DOT HS 808 338

    Google Scholar 

  23. Winston FK, Reed R (1996) Air bags and children: results of national highway traffic safety administration special investigation into actual crashes. In: Women’s travel issues second national conference, Baltimore

    Google Scholar 

  24. Elias JC, Sullivan LK, McCray LB (2001) Large school bus safety restraint evaluation. In: National Highway Traffic Safety Administration (ed) 17th international technical conference on the enhanced safety of vehicles

    Google Scholar 

  25. National Highway Traffic Safety Administration (2013) Special crash investigations. http://www-nass.nhtsa.dot.gov/BIN/logon.exe/airmislogon

  26. Elliott MR, Resler A, Flannagan CA, Rupp JD (2010) Appropriate analysis of CIREN data: using NASS-CDS to reduce bias in estimation of injury risk factors in passenger vehicle crashes. Accid Anal Prev 42(2):530–539. doi:10.1016/j.aap.2009.09.019, S0001-4575(09)00247-4 [pii]

    Article  PubMed  Google Scholar 

  27. Stitzel JD, Kilgo P, Schmotzer B, Gabler HC, Meredith JW (2007) A population-based comparison of CIREN and NASS cases using similarity scoring. Ann Proc Assoc Adv Automot Med 51:395–417

    Google Scholar 

  28. Yu MM, Danelson KA, Stitzel JD (2008) Categorical similarity comparison of CIREN and NASS. Biomed Sci Instrum 44:304–309

    PubMed  Google Scholar 

  29. National Highway Traffic Safety Administration (2013) CIREN data. http://www.nhtsa.gov/Research/Crash+Injury+Research+(CIREN)/Data

  30. National Highway Traffic Safety Administration (2006) Crash injury research engineering network coding manual, Version 1.6

    Google Scholar 

  31. National Highway Traffic Safety Administration (2010) Crash injury research engineering network coding manual, version 2.0

    Google Scholar 

  32. Schneider LW, Rupp JD, Scarboro M, Pintar F, Arbogast KB, Rudd RW, Sochor MR, Stitzel J, Sherwood C, Macwilliams JB, Halloway D, Ridella S, Eppinger R (2011) BioTab–a new method for analyzing and documenting injury causation in motor-vehicle crashes. Traffic Inj Prev 12(3):256–265. doi:10.1080/15389588.2011.560500, 938347404 [pii]

    Article  PubMed  Google Scholar 

  33. National Highway Traffic Safety Administration (1996) 1996 Pedestrian crash data study data collection, coding, and editing manual

    Google Scholar 

  34. Chidester AB, Isenberg RA (2001) Final report – the Pedestrian crash data study. In: Paper presented at the proceedings of the seventeenth international enhanced safety vehicle conference, Amsterdam

    Google Scholar 

  35. Lefler DE, Gabler HC (2004) The fatality and injury risk of light truck impacts with pedestrians in the United States. Accid Anal Prev 36(2):295–304, S0001457503000071 [pii]

    Article  PubMed  Google Scholar 

  36. Roudsari BS, Mock CN, Kaufman R (2005) An evaluation of the association between vehicle type and the source and severity of pedestrian injuries. Traffic Inj Prev 6(2):185–192. doi:10.1080/15389580590931680, G5L6613042205666 [pii]

    Article  PubMed  Google Scholar 

  37. American College of Surgeons (2007) National trauma data bank – research data system, vol RDS 7.1. American College of Surgeons Committee on Trauma, Chicago

    Google Scholar 

  38. (1998–2007) Agency for Healthcare Research and Quality, Rockville. www.hcup-us.ahrq.gov/databases.jsp

  39. EMS Performance Improvement Center (2013) EMS performance improvement center. http://www.emspic.org/

  40. Gabler HC, Hinch J, Steiner J (2008) Event Data Recorders: A Decade of Innovation, SAE International, Warrendale, PA (2008)

    Google Scholar 

  41. Osler T, Rutledge R, Deis J, Bedrick E (1996) ICISS: an international classification of disease-9 based injury severity score. J Trauma 41(3):380–386;discussion 386–388

    Article  CAS  PubMed  Google Scholar 

  42. Kilgo PD, Osler TM, Meredith W (2003) The worst injury predicts mortality outcome the best: rethinking the role of multiple injuries in trauma outcome scoring. J Trauma 55(4):599–606. doi:10.1097/01.TA.0000085721.47738.BD; discussion 606–597

    Article  PubMed  Google Scholar 

  43. Meredith JW, Evans G, Kilgo PD, MacKenzie E, Osler T, McGwin G, Cohn S, Esposito T, Gennarelli T, Hawkins M, Lucas C, Mock C, Rotondo M, Rue L, Champion HR (2002) A comparison of the abilities of nine scoring algorithms in predicting mortality. J Trauma 53(4):621–628. doi:10.1097/01.TA.0000032120.91608.52; discussion 628–629

    Article  PubMed  Google Scholar 

  44. Weaver A, Barnard R, Kilgo P, Martin R, Stitzel J (2013) Mortality-based quantification of injury severity for frequently occurring motor vehicle crash injuries. Assoc Adv Automot Med Accept 57:235–246

    Google Scholar 

  45. Kilgo PD, Weaver AA, Barnard RT, Love TP, Stitzel JD (2013) Comparison of injury mortality risk in motor vehicle crash versus other etiologies. Accid Anal Prev (in review)

    Google Scholar 

  46. Meredith JW, Kilgo PD, Osler T (2003) A fresh set of survival risk ratios derived from incidents in the National Trauma Data Bank from which the ICISS may be calculated. J Trauma 55(5):924–932. doi:10.1097/01.TA.0000085645.62482.87

    Article  PubMed  Google Scholar 

  47. Sacco WJ, MacKenzie EJ, Champion HR, Davis EG, Buckman RF (1999) Comparison of alternative methods for assessing injury severity based on anatomic descriptors. J Trauma 47(3):441–446; discussion 446–447

    Article  CAS  PubMed  Google Scholar 

  48. Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME (1989) A revision of the Trauma Score. J Trauma 29(5):623–629

    Article  CAS  PubMed  Google Scholar 

  49. Boyd CR, Tolson MA, Copes WS (1987) Evaluating trauma care: the TRISS method. Trauma Score and the Injury Severity Score. J Trauma 27(4):370–378

    Article  CAS  PubMed  Google Scholar 

  50. Brohi K (2007) TRISS: Trauma-Injury Severity Score. http://www.trauma.org/index.php/main/article/387/

  51. Malliaris AC, Hitchcock R, Hedlund J (1982) A search for priorities in crash protection. In: International congress and exposition, vol SAE 820242. SAE, Warrendale

    Google Scholar 

  52. Fildes BN, Lane JC, Lenard J, Vulcan AP (1994) Passenger cars and occupant injury: side impact crashes. Report CR 134, Canberra

    Google Scholar 

  53. Fildes BN, Monash University. Accident Research Centre, Australia. Federal Office of Road Safety (1992) Feasibility of occupant protection measures. Federal Office of Road Safety

    Google Scholar 

  54. Gabler HC, Digges K, Fildes BN, Sparke L (2005) Side impact injury risk for belted far side passenger vehicle occupants. SAE transactions, journal of passenger car – mechanical systems, vol 114, section 6, paper no. 2005-01-0287

    Google Scholar 

  55. Augenstein J, Digges K, Ogata S, Perdeck E, Stratton J (2001) Development and validation of the URGENCY algorithm to predict compelling injuries. In: Paper presented at the enhanced safety of vehicles

    Google Scholar 

  56. Augenstein J, Perdeck E, Bahouth GT, Digges KH, Borchers N, Baur P (2005) Injury identification: priorities for data transmitted. In: Paper presented at the enhanced safety of vehicles

    Google Scholar 

  57. Augenstein J, Perdeck E, Stratton J, Digges K, Bahouth G (2003) Characteristics of crashes that increase the risk of serious injuries. Ann Proc Assoc Adv Automot Med 47:561–576

    Google Scholar 

  58. Augenstein J, Perdeck E, Stratton J, Digges K, Steps J, Bahouth G (2002) Validation of the urgency algorithm for near-side crashes. Ann Proc Assoc Adv Automot Med 46:305–314

    CAS  Google Scholar 

  59. Bahouth G, Digges K, Schulman C (2012) Influence of injury risk thresholds on the performance of an algorithm to predict crashes with serious injuries. Ann Adv Automot Med 56:223–230

    PubMed Central  PubMed  Google Scholar 

  60. Bahouth GT, Digges KH, Bedewi NE, Kuznetsov A, Augenstein JS, Perdeck E (2004) Devleopment of URGENCY 2.1 for the prediction of crash injury severity. Top Emerg Med 26(2):157–165

    Article  Google Scholar 

  61. Champion H, Augenstein J, Blatt A, Cushing B, Digges K, Flanigan M, Hunt R, Lombardo L, Siegal J (2005) New tools to reduce deaths and disabilities by improving emergency care: URGENCY software, occult injury warnings, and air medical services database. In: Paper presented at the enhanced safety of vehicle conference, Washington, DC

    Google Scholar 

  62. Malliaris AC, Digges KH, DeBlois JH (1997) Relationships between crash casualties and crash attributes. In: Paper presented at the SAE international congress & exposition, Detroit

    Google Scholar 

  63. Rauscher S, Messner G, Baur P, Augenstein J, Digges K, Perdeck E, Bahouth G, Pieske O (2009) Enhanced automatic collision notification system – improved rescue care due to injury prediction – first field experience. In: Paper presented at the enhanced safety of vehicle conference, Stuttgart

    Google Scholar 

  64. Kononen DW, Flannagan CA, Wang SC (2011) Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accid Anal Prev 43(1):112–122. doi:10.1016/j.aap.2010.07.018, S0001-4575(10)00206-X [pii]

    Article  PubMed  Google Scholar 

  65. Kusano KD, Gabler HC (2013) Comparison of logistic regression and ensemble machine learning algorithms injury risk models for advanced automated crash notification algorithms. In: Proceedings of the 2013 road safety and simulation international conference, Rome

    Google Scholar 

  66. Centers for Disease Control and Prevention (2008) Recommendations from the expert panel: advanced automatic collision notification and triage of the injured patient. Centers for Disease Control and Prevention, Atlanta

    Google Scholar 

  67. Gabauer DJ, Gabler HC (2008) Comparison of roadside crash injury metrics using event data recorders. Accid Anal Prev 40(2):548–558. doi:10.1016/j.aap.2007.08.011, S0001-4575(07)00139-X [pii]

    Article  PubMed  Google Scholar 

  68. Stigson H, Kullgren A, Rosen E (2012) Injury risk functions in frontal impacts using data from crash pulse recorders. Ann Adv Automot Med 56:267–276

    PubMed Central  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Biomedical Engineering, Virginia Tech, Center for Injury Biomechanics, Virginia Tech-Wake Forest University, Blacksburg, VA, USA

    Hampton C. Gabler Ph.D.

  2. Biomedical Engineering, Wake Forest University, Center for Injury Biomechanics, Virginia Tech-Wake Forest University, Winston-Salem, NC, USA

    Ashley A. Weaver Ph.D. & Joel D. Stitzel Ph.D.

Authors
  1. Hampton C. Gabler Ph.D.
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  2. Ashley A. Weaver Ph.D.
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  3. Joel D. Stitzel Ph.D.
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Corresponding author

Correspondence to Hampton C. Gabler Ph.D. .

Editor information

Editors and Affiliations

  1. Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA

    Narayan Yoganandan

  2. School of Medicine, University of California at San Diego, San Diego, USA

    Alan M. Nahum

  3. Tandelta Inc., Ann Arbor, USA

    John W. Melvin

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Gabler, H.C., Weaver, A.A., Stitzel, J.D. (2015). Automotive Field Data in Injury Biomechanics. In: Yoganandan, N., Nahum, A., Melvin, J. (eds) Accidental Injury. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1732-7_2

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  • DOI: https://doi.org/10.1007/978-1-4939-1732-7_2

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