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Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions

Injuries have become devastating and often under-recognized public health concerns. In Canada, injuries are the leading cause of potential years of life lost before the age of 65. The geographical patterns of injury, however, are evident both over space and time, suggesting the possibility of spatia...

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Autores principales: Vaz, Eric, Cusimano, Michael D., Bação, Fernando, Damásio, Bruno, Penfound, Elissa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951915/
https://www.ncbi.nlm.nih.gov/pubmed/33705490
http://dx.doi.org/10.1371/journal.pone.0248285
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author Vaz, Eric
Cusimano, Michael D.
Bação, Fernando
Damásio, Bruno
Penfound, Elissa
author_facet Vaz, Eric
Cusimano, Michael D.
Bação, Fernando
Damásio, Bruno
Penfound, Elissa
author_sort Vaz, Eric
collection PubMed
description Injuries have become devastating and often under-recognized public health concerns. In Canada, injuries are the leading cause of potential years of life lost before the age of 65. The geographical patterns of injury, however, are evident both over space and time, suggesting the possibility of spatial optimization of policies at the neighborhood scale to mitigate injury risk, foster prevention, and control within metropolitan regions. In this paper, Canada’s National Ambulatory Care Reporting System is used to assess unintentional and intentional injuries for Toronto between 2004 and 2010, exploring the spatial relations of injury throughout the city, together with Wellbeing Toronto data. Corroborating with these findings, spatial autocorrelations at global and local levels are performed for the reported over 1.7 million injuries. The sub-categorization for Toronto’s neighborhood further distills the most vulnerable communities throughout the city, registering a robust spatial profile throughout. Individual neighborhoods pave the need for distinct policy profiles for injury prevention. This brings one of the main novelties of this contribution. A comparison of the three regression models is carried out. The findings suggest that the performance of spatial regression models is significantly stronger, showing evidence that spatial regressions should be used for injury research. Wellbeing Toronto data performs reasonably well in assessing unintentional injuries, morbidity, and falls. Less so to understand the dynamics of intentional injuries. The results enable a framework to allow tailor-made injury prevention initiatives at the neighborhood level as a vital source for planning and participatory decision making in the medical field in developed cities such as Toronto.
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spelling pubmed-79519152021-03-22 Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions Vaz, Eric Cusimano, Michael D. Bação, Fernando Damásio, Bruno Penfound, Elissa PLoS One Research Article Injuries have become devastating and often under-recognized public health concerns. In Canada, injuries are the leading cause of potential years of life lost before the age of 65. The geographical patterns of injury, however, are evident both over space and time, suggesting the possibility of spatial optimization of policies at the neighborhood scale to mitigate injury risk, foster prevention, and control within metropolitan regions. In this paper, Canada’s National Ambulatory Care Reporting System is used to assess unintentional and intentional injuries for Toronto between 2004 and 2010, exploring the spatial relations of injury throughout the city, together with Wellbeing Toronto data. Corroborating with these findings, spatial autocorrelations at global and local levels are performed for the reported over 1.7 million injuries. The sub-categorization for Toronto’s neighborhood further distills the most vulnerable communities throughout the city, registering a robust spatial profile throughout. Individual neighborhoods pave the need for distinct policy profiles for injury prevention. This brings one of the main novelties of this contribution. A comparison of the three regression models is carried out. The findings suggest that the performance of spatial regression models is significantly stronger, showing evidence that spatial regressions should be used for injury research. Wellbeing Toronto data performs reasonably well in assessing unintentional injuries, morbidity, and falls. Less so to understand the dynamics of intentional injuries. The results enable a framework to allow tailor-made injury prevention initiatives at the neighborhood level as a vital source for planning and participatory decision making in the medical field in developed cities such as Toronto. Public Library of Science 2021-03-11 /pmc/articles/PMC7951915/ /pubmed/33705490 http://dx.doi.org/10.1371/journal.pone.0248285 Text en © 2021 Vaz 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
Vaz, Eric
Cusimano, Michael D.
Bação, Fernando
Damásio, Bruno
Penfound, Elissa
Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions
title Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions
title_full Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions
title_fullStr Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions
title_full_unstemmed Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions
title_short Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions
title_sort open data and injuries in urban areas—a spatial analytical framework of toronto using machine learning and spatial regressions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951915/
https://www.ncbi.nlm.nih.gov/pubmed/33705490
http://dx.doi.org/10.1371/journal.pone.0248285
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