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Global road traffic injury statistics: Challenges, mechanisms and solutions
High-quality data are the foundation to monitor the progress and evaluate the effects of road traffic injury prevention measures. Unfortunately, official road traffic injury statistics delivered by governments worldwide, are often believed somewhat unreliable and invalid. We summarized the reported...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451583/ https://www.ncbi.nlm.nih.gov/pubmed/32680705 http://dx.doi.org/10.1016/j.cjtee.2020.06.001 |
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author | Chang, Fang-Rong Huang, He-Lai Schwebel, David C. Chan, Alan H.S. Hu, Guo-Qing |
author_facet | Chang, Fang-Rong Huang, He-Lai Schwebel, David C. Chan, Alan H.S. Hu, Guo-Qing |
author_sort | Chang, Fang-Rong |
collection | PubMed |
description | High-quality data are the foundation to monitor the progress and evaluate the effects of road traffic injury prevention measures. Unfortunately, official road traffic injury statistics delivered by governments worldwide, are often believed somewhat unreliable and invalid. We summarized the reported problems concerning the road traffic injury statistics through systematically searching and reviewing the literature. The problems include absence of regular data, under-reporting, low specificity, distorted cause spectrum of road traffic injury, inconsistency, inaccessibility, and delay of data release. We also explored the mechanisms behind the problematic data and proposed the solutions to the addressed challenges for road traffic statistics. |
format | Online Article Text |
id | pubmed-7451583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-74515832020-08-31 Global road traffic injury statistics: Challenges, mechanisms and solutions Chang, Fang-Rong Huang, He-Lai Schwebel, David C. Chan, Alan H.S. Hu, Guo-Qing Chin J Traumatol Serial Report on Traffic Medicine High-quality data are the foundation to monitor the progress and evaluate the effects of road traffic injury prevention measures. Unfortunately, official road traffic injury statistics delivered by governments worldwide, are often believed somewhat unreliable and invalid. We summarized the reported problems concerning the road traffic injury statistics through systematically searching and reviewing the literature. The problems include absence of regular data, under-reporting, low specificity, distorted cause spectrum of road traffic injury, inconsistency, inaccessibility, and delay of data release. We also explored the mechanisms behind the problematic data and proposed the solutions to the addressed challenges for road traffic statistics. Elsevier 2020-08 2020-06-19 /pmc/articles/PMC7451583/ /pubmed/32680705 http://dx.doi.org/10.1016/j.cjtee.2020.06.001 Text en © 2020 Chinese Medical Association. Production and hosting by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Serial Report on Traffic Medicine Chang, Fang-Rong Huang, He-Lai Schwebel, David C. Chan, Alan H.S. Hu, Guo-Qing Global road traffic injury statistics: Challenges, mechanisms and solutions |
title | Global road traffic injury statistics: Challenges, mechanisms and solutions |
title_full | Global road traffic injury statistics: Challenges, mechanisms and solutions |
title_fullStr | Global road traffic injury statistics: Challenges, mechanisms and solutions |
title_full_unstemmed | Global road traffic injury statistics: Challenges, mechanisms and solutions |
title_short | Global road traffic injury statistics: Challenges, mechanisms and solutions |
title_sort | global road traffic injury statistics: challenges, mechanisms and solutions |
topic | Serial Report on Traffic Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451583/ https://www.ncbi.nlm.nih.gov/pubmed/32680705 http://dx.doi.org/10.1016/j.cjtee.2020.06.001 |
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