Literature DB >> 20938310

Using trauma registry data to guide injury prevention program activities.

Steven C Rogers1, Brendan T Campbell, Hassan Saleheen, Kevin Borrup, Garry Lapidus.   

Abstract

BACKGROUND: Injury prevention programs should be based on objective injury data. This study demonstrates how local injury data can be used to help guide injury prevention programs.
METHODS: We reviewed trauma registry data (2004-2006) from a Level I pediatric trauma center. Data included demographic information, anatomic location of injury, mechanism of injury, safety device utilization, Injury Severity Score (ISS), and temporal and geographic variables. The Injury Prevention Priority Score for each mechanism of injury was calculated.
RESULTS: There were 1,874 trauma patients. Most admissions were among white males, aged 11 years to 15 years (mean, 7.9 years ± 5.2 years). Most admissions occurred during summertime and on weekend evenings. Blunt injuries (92%) and fractures (56%) predominated (mean ISS, 5.9). A severe ISS >15 was highest among 11 year to 15 year and lowest among patients older than 15 years (p < 0.01). Falls, cut, or pierce, ATV, and off-road motorcycle ranked highest in the Injury Prevention Priority Score. Of the 134 motor vehicle occupants, 52% (n = 70) were restrained in car seats/seat belts. Only 15% of bicyclists, 24% of motorcyclists, and 58% of ATV riders wore helmets.
CONCLUSION: A significant percentage of injured children and adolescents were not using proven effective injury prevention devices at the time of their injury. These data identified areas for further study and will help guide community injury prevention programs at our institution.

Entities:  

Mesh:

Year:  2010        PMID: 20938310     DOI: 10.1097/TA.0b013e3181f1e9fe

Source DB:  PubMed          Journal:  J Trauma        ISSN: 0022-5282


  12 in total

1.  Pediatric injury patterns by year of age.

Authors:  Elisabeth T Tracy; Brian R Englum; Andrew S Barbas; Carolyn Foley; Henry E Rice; Mark L Shapiro
Journal:  J Pediatr Surg       Date:  2013-06       Impact factor: 2.545

2.  Motorcycle-related hospitalization of adolescents in a Level I trauma center in southern Taiwan: a cross-sectional study.

Authors:  Chi-Cheng Liang; Hang-Tsung Liu; Cheng-Shyuan Rau; Shiun-Yuan Hsu; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  BMC Pediatr       Date:  2015-08-28       Impact factor: 2.125

3.  Epidemiology of pediatric head trauma in guilan.

Authors:  Shahrokh Yousefzadeh Chabok; Sara Ramezani; Leila Kouchakinejad; Zahra Saneei
Journal:  Arch Trauma Res       Date:  2012-06-01

4.  Alcohol-related hospitalizations of adult motorcycle riders.

Authors:  Hang-Tsung Liu; Chi-Cheng Liang; Cheng-Shyuan Rau; Shiun-Yuan Hsu; Ching-Hua Hsieh
Journal:  World J Emerg Surg       Date:  2015-01-07       Impact factor: 5.469

5.  Obese motorcycle riders have a different injury pattern and longer hospital length of stay than the normal-weight patients.

Authors:  Hang-Tsung Liu; Cheng-Shyuan Rau; Shao-Chun Wu; Yi-Chun Chen; Shiun-Yuan Hsu; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2016-04-14       Impact factor: 2.953

6.  Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan.

Authors:  Pao-Jen Kuo; Shao-Chun Wu; Peng-Chen Chien; Cheng-Shyuan Rau; Yi-Chun Chen; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  BMJ Open       Date:  2018-01-05       Impact factor: 2.692

7.  Motorcycle-related hospitalizations of the elderly.

Authors:  Ching-Hua Hsieh; Hang-Tsung Liu; Shiun-Yuan Hsu; Hsiao-Yun Hsieh; Yi-Chun Chen
Journal:  Biomed J       Date:  2017-05-08       Impact factor: 4.910

8.  Differences between the sexes in motorcycle-related injuries and fatalities at a Taiwanese level I trauma center.

Authors:  Ching-Hua Hsieh; Shiun-Yuan Hsu; Hsiao-Yun Hsieh; Yi-Chun Chen
Journal:  Biomed J       Date:  2017-05-04       Impact factor: 4.910

9.  Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.

Authors:  Cheng-Shyuan Rau; Pao-Jen Kuo; Peng-Chen Chien; Chun-Ying Huang; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  PLoS One       Date:  2018-11-09       Impact factor: 3.240

10.  Bicycle-related hospitalizations at a Taiwanese level I Trauma Center.

Authors:  Hang-Tsung Liu; Cheng-Shyuan Rau; Chi-Cheng Liang; Shao-Chun Wu; Shiun-Yuan Hsu; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  BMC Public Health       Date:  2015-07-29       Impact factor: 3.295

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