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Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA)
BACKGROUND: Identifying hidden patterns and relationships among the features of the Fatal Pedestrian Road Traffic Injuries (FPRTI) can be effective in reducing pedestrian fatalities. This study is thus aimed to detect the patterns among the fatally injured pedestrians due to FPRTI in East Azerbaijan...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207772/ https://www.ncbi.nlm.nih.gov/pubmed/34130665 http://dx.doi.org/10.1186/s12889-021-11212-x |
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author | Jamali-Dolatabad, Milad Sarbakhsh, Parvin Sadeghi-bazargani, Homayoun |
author_facet | Jamali-Dolatabad, Milad Sarbakhsh, Parvin Sadeghi-bazargani, Homayoun |
author_sort | Jamali-Dolatabad, Milad |
collection | PubMed |
description | BACKGROUND: Identifying hidden patterns and relationships among the features of the Fatal Pedestrian Road Traffic Injuries (FPRTI) can be effective in reducing pedestrian fatalities. This study is thus aimed to detect the patterns among the fatally injured pedestrians due to FPRTI in East Azerbaijan province, Iran. METHODS: This descriptive-analytic research was carried out based on the data of all 1782 FPRTI that occurred in East Azerbaijan, Iran from 2010 to 2019 collected by the forensic organization. Categorical Principal Component Analysis (CATPCA) was performed to recognize hidden patterns in the data by extracting principal components from the set of 13 features of FPRTI. The importance of each component was assessed by using the variance accounted for (VAF) index. RESULTS: The optimum number of components to fit the CATPCA model was six which explained 71.09% of the total variation. The first and most important component with VAF = 22.04% contained the demographic and socioeconomic characteristics of the killed pedestrians. The second-ranked component with VAF = 12.96% was related to the injury type. The third component with VAF = 10.56% was the severity of the injury. The fourth component with VAF = 9.07% was somehow related to the knowledge and observance of the traffic rules. The fifth component with VAF = 8.63% was about the quality of medical relief and finally, the sixth component with VAF = 7.82% dealt with environmental conditions. CONCLUSION: CATPCA revealed hidden patterns among the fatally injured pedestrians in the form of six components. The revealed patterns showed that some interactions between correlated features led to a higher mortality rate. |
format | Online Article Text |
id | pubmed-8207772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82077722021-06-16 Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA) Jamali-Dolatabad, Milad Sarbakhsh, Parvin Sadeghi-bazargani, Homayoun BMC Public Health Research BACKGROUND: Identifying hidden patterns and relationships among the features of the Fatal Pedestrian Road Traffic Injuries (FPRTI) can be effective in reducing pedestrian fatalities. This study is thus aimed to detect the patterns among the fatally injured pedestrians due to FPRTI in East Azerbaijan province, Iran. METHODS: This descriptive-analytic research was carried out based on the data of all 1782 FPRTI that occurred in East Azerbaijan, Iran from 2010 to 2019 collected by the forensic organization. Categorical Principal Component Analysis (CATPCA) was performed to recognize hidden patterns in the data by extracting principal components from the set of 13 features of FPRTI. The importance of each component was assessed by using the variance accounted for (VAF) index. RESULTS: The optimum number of components to fit the CATPCA model was six which explained 71.09% of the total variation. The first and most important component with VAF = 22.04% contained the demographic and socioeconomic characteristics of the killed pedestrians. The second-ranked component with VAF = 12.96% was related to the injury type. The third component with VAF = 10.56% was the severity of the injury. The fourth component with VAF = 9.07% was somehow related to the knowledge and observance of the traffic rules. The fifth component with VAF = 8.63% was about the quality of medical relief and finally, the sixth component with VAF = 7.82% dealt with environmental conditions. CONCLUSION: CATPCA revealed hidden patterns among the fatally injured pedestrians in the form of six components. The revealed patterns showed that some interactions between correlated features led to a higher mortality rate. BioMed Central 2021-06-16 /pmc/articles/PMC8207772/ /pubmed/34130665 http://dx.doi.org/10.1186/s12889-021-11212-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Jamali-Dolatabad, Milad Sarbakhsh, Parvin Sadeghi-bazargani, Homayoun Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA) |
title | Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA) |
title_full | Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA) |
title_fullStr | Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA) |
title_full_unstemmed | Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA) |
title_short | Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA) |
title_sort | hidden patterns among the fatally injured pedestrians in an iranian population: application of categorical principal component analysis (catpca) |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207772/ https://www.ncbi.nlm.nih.gov/pubmed/34130665 http://dx.doi.org/10.1186/s12889-021-11212-x |
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