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Using matrix frame to present road traffic injury pattern
BACKGROUND: Although many epidemiological studies have presented road traffic injuries (RTIs) according to the victim’s mode of transport, very few have mentioned the mode of transport of the victim’s counterparts. We sought to use matrix frame to present the pattern of RTIs based on the Internation...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5911434/ https://www.ncbi.nlm.nih.gov/pubmed/29682683 http://dx.doi.org/10.1186/s40621-018-0154-y |
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author | Wang, Chien-Hsing Hsieh, Wan-Hua Liang, Fu-Wen Lu, Tsung-Hsueh |
author_facet | Wang, Chien-Hsing Hsieh, Wan-Hua Liang, Fu-Wen Lu, Tsung-Hsueh |
author_sort | Wang, Chien-Hsing |
collection | PubMed |
description | BACKGROUND: Although many epidemiological studies have presented road traffic injuries (RTIs) according to the victim’s mode of transport, very few have mentioned the mode of transport of the victim’s counterparts. We sought to use matrix frame to present the pattern of RTIs based on the International Classification of Diseases, Tenth Revision (ICD-10) codes. METHODS: Patients admitted to Hualien Tzu Chi Hospital, Taiwan, for RTIs from January 1, 2013 to December 31, 2016 were included. The numbers and proportions of various crash types of RTIs were presented using a matrix frame. The row margin of the matrix is the second character of ICD-10 codes V00–V79 (victim’s mode of transport), and the column margin of the matrix is the third character of ICD-10 codes V00–V79 (mode of transport of victim’s counterpart), constituting a 80-cell grid. RESULTS: In total, 2727 patients were included. The cell with the highest proportion in the matrix grid was ICD-10 code V23 “motorcycle rider injured in collision with car, pick-up truck or van” (27.0%, 737/2727), followed by that of V27 “motorcycle rider injured in collision with fixed or stationary object” (12.5%, 342/2727) and V28 “motorcycle rider injured in noncollision transport accident” (12.2%, 334/2727). The matrix pattern of RTIs differed with sex and age. CONCLUSIONS: By using the matrix frame, we can easily understand the RTI pattern for different demographic groups and identify the priority crash types. |
format | Online Article Text |
id | pubmed-5911434 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-59114342018-04-30 Using matrix frame to present road traffic injury pattern Wang, Chien-Hsing Hsieh, Wan-Hua Liang, Fu-Wen Lu, Tsung-Hsueh Inj Epidemiol Original Contribution BACKGROUND: Although many epidemiological studies have presented road traffic injuries (RTIs) according to the victim’s mode of transport, very few have mentioned the mode of transport of the victim’s counterparts. We sought to use matrix frame to present the pattern of RTIs based on the International Classification of Diseases, Tenth Revision (ICD-10) codes. METHODS: Patients admitted to Hualien Tzu Chi Hospital, Taiwan, for RTIs from January 1, 2013 to December 31, 2016 were included. The numbers and proportions of various crash types of RTIs were presented using a matrix frame. The row margin of the matrix is the second character of ICD-10 codes V00–V79 (victim’s mode of transport), and the column margin of the matrix is the third character of ICD-10 codes V00–V79 (mode of transport of victim’s counterpart), constituting a 80-cell grid. RESULTS: In total, 2727 patients were included. The cell with the highest proportion in the matrix grid was ICD-10 code V23 “motorcycle rider injured in collision with car, pick-up truck or van” (27.0%, 737/2727), followed by that of V27 “motorcycle rider injured in collision with fixed or stationary object” (12.5%, 342/2727) and V28 “motorcycle rider injured in noncollision transport accident” (12.2%, 334/2727). The matrix pattern of RTIs differed with sex and age. CONCLUSIONS: By using the matrix frame, we can easily understand the RTI pattern for different demographic groups and identify the priority crash types. Springer International Publishing 2018-04-23 /pmc/articles/PMC5911434/ /pubmed/29682683 http://dx.doi.org/10.1186/s40621-018-0154-y Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Contribution Wang, Chien-Hsing Hsieh, Wan-Hua Liang, Fu-Wen Lu, Tsung-Hsueh Using matrix frame to present road traffic injury pattern |
title | Using matrix frame to present road traffic injury pattern |
title_full | Using matrix frame to present road traffic injury pattern |
title_fullStr | Using matrix frame to present road traffic injury pattern |
title_full_unstemmed | Using matrix frame to present road traffic injury pattern |
title_short | Using matrix frame to present road traffic injury pattern |
title_sort | using matrix frame to present road traffic injury pattern |
topic | Original Contribution |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5911434/ https://www.ncbi.nlm.nih.gov/pubmed/29682683 http://dx.doi.org/10.1186/s40621-018-0154-y |
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