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Identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach

Road Traffic Accidents (RTA) are a major worldwide public health problem. The aim of this study was to use the growth mixture model for clustering countries on the basis of the mortality rate patterns of RTAs from 2007 to 2013. We obtained the data on RTA death rates from World Health Organization r...

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Autores principales: Salehi, Masoud, Mobaderi, Tofigh, Mehmandar, Mohammadreza, Dehnad, Afsaneh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382161/
https://www.ncbi.nlm.nih.gov/pubmed/30785919
http://dx.doi.org/10.1371/journal.pone.0212402
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author Salehi, Masoud
Mobaderi, Tofigh
Mehmandar, Mohammadreza
Dehnad, Afsaneh
author_facet Salehi, Masoud
Mobaderi, Tofigh
Mehmandar, Mohammadreza
Dehnad, Afsaneh
author_sort Salehi, Masoud
collection PubMed
description Road Traffic Accidents (RTA) are a major worldwide public health problem. The aim of this study was to use the growth mixture model for clustering countries on the basis of the mortality rate patterns of RTAs from 2007 to 2013. We obtained the data on RTA death rates from World Health Organization reports and Human Development Index (HDI) of United Nations Development Programme reports for the years 2007, 2010 and 2013. Simple Latent Growth Models (LGM) in 181 countries were applied to estimate overall RTA mortality rate growth trajectories and the latent growth mixture modeling utilized to cluster them. According to non-linear LGM, the overall mortality rate of RTAs showed a decrease from 2007 to 2010 followed by an increase from 2010 to 2013. The HDI covariate had a significant negative and positive effect on intercept and slope of the LGM, respectively. The extracted mixture model appeared to have seven classes with different trends in RTA mortality rates. The worldwide countries were clustered into seven classes. Further studies on each of the seven classes are suggested to provide recommendations for reducing the mortality rate of the RTAs. Additionally, increasing HDI in some countries could have a significant effect on reducing the RTA death rates.
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spelling pubmed-63821612019-03-01 Identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach Salehi, Masoud Mobaderi, Tofigh Mehmandar, Mohammadreza Dehnad, Afsaneh PLoS One Research Article Road Traffic Accidents (RTA) are a major worldwide public health problem. The aim of this study was to use the growth mixture model for clustering countries on the basis of the mortality rate patterns of RTAs from 2007 to 2013. We obtained the data on RTA death rates from World Health Organization reports and Human Development Index (HDI) of United Nations Development Programme reports for the years 2007, 2010 and 2013. Simple Latent Growth Models (LGM) in 181 countries were applied to estimate overall RTA mortality rate growth trajectories and the latent growth mixture modeling utilized to cluster them. According to non-linear LGM, the overall mortality rate of RTAs showed a decrease from 2007 to 2010 followed by an increase from 2010 to 2013. The HDI covariate had a significant negative and positive effect on intercept and slope of the LGM, respectively. The extracted mixture model appeared to have seven classes with different trends in RTA mortality rates. The worldwide countries were clustered into seven classes. Further studies on each of the seven classes are suggested to provide recommendations for reducing the mortality rate of the RTAs. Additionally, increasing HDI in some countries could have a significant effect on reducing the RTA death rates. Public Library of Science 2019-02-20 /pmc/articles/PMC6382161/ /pubmed/30785919 http://dx.doi.org/10.1371/journal.pone.0212402 Text en © 2019 Salehi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Salehi, Masoud
Mobaderi, Tofigh
Mehmandar, Mohammadreza
Dehnad, Afsaneh
Identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach
title Identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach
title_full Identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach
title_fullStr Identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach
title_full_unstemmed Identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach
title_short Identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach
title_sort identifying developmental trajectories of worldwide road traffic accident death rates using a latent growth mixture modeling approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382161/
https://www.ncbi.nlm.nih.gov/pubmed/30785919
http://dx.doi.org/10.1371/journal.pone.0212402
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