Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | preprint |
Idioma: | por |
Título da fonte: | SciELO Preprints |
Texto Completo: | https://preprints.scielo.org/index.php/scielo/preprint/view/1614 |
Resumo: | Objective: The main objective of this paper is to analyze the relationship between GDP and three variables related to traffic accidents in Brazilian municipalities: traffic accident mortality, deaths per vehicle; and vehicles per inhabitant. Methods: 2005, 2010 and 2015 terrestrial traffic accident (ATT) mortality rates were estimated using a three years moving average and were standardized, then, we applied the empirical Bayes estimator (EBE). Fatality rates (deaths per vehicle) also were based on EBE. Vehicles per inhabitant considered the ratio between vehicle fleet and the population at municipal level. For every studied year, we estimated linear regression models between GDP and the interest variables. Results: Variables distribution indicates that, between 2005 and 2015, GDP and vehicles per inhabitant kept the same rising relationship. Fatality rates show a decreasing association with GDP. TA mortality distribution with GDP presented a pattern close to an inverted-U. Model coefficients practically did not change for the vehicle per inhabitant. Estimated association between deaths per vehicle and GDP kept the same sign, but diminished between 2005 and 2015. Model coefficient sign changed in 2015 for TA mortality. Conclusion: Similarly to what was observed in developed countries, the relationship between mortality from traffic accidents and GDP changed in the analyzed period. |
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Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015A relação entre PIB per capita e os acidentes de transporte nos municípios brasileiros, 2005, 2010 e 2015MortalidadeAcidentes de transportefrotaPIB per capitaMunicípiosMortalitytraffic accidentsfleetmunicipalitiesGDP per capitaObjective: The main objective of this paper is to analyze the relationship between GDP and three variables related to traffic accidents in Brazilian municipalities: traffic accident mortality, deaths per vehicle; and vehicles per inhabitant. Methods: 2005, 2010 and 2015 terrestrial traffic accident (ATT) mortality rates were estimated using a three years moving average and were standardized, then, we applied the empirical Bayes estimator (EBE). Fatality rates (deaths per vehicle) also were based on EBE. Vehicles per inhabitant considered the ratio between vehicle fleet and the population at municipal level. For every studied year, we estimated linear regression models between GDP and the interest variables. Results: Variables distribution indicates that, between 2005 and 2015, GDP and vehicles per inhabitant kept the same rising relationship. Fatality rates show a decreasing association with GDP. TA mortality distribution with GDP presented a pattern close to an inverted-U. Model coefficients practically did not change for the vehicle per inhabitant. Estimated association between deaths per vehicle and GDP kept the same sign, but diminished between 2005 and 2015. Model coefficient sign changed in 2015 for TA mortality. Conclusion: Similarly to what was observed in developed countries, the relationship between mortality from traffic accidents and GDP changed in the analyzed period.Objetivo: O artigo pretende analisar a relação entre o PIB per capita e três variáveis relacionadas aos acidentes de transporte nos municípios brasileiros: a mortalidade por acidentes de transporte terrestre (ATT); as mortes por veículo; e o número de veículos por pessoa. Métodos: As taxas de mortalidade por ATT foram estimadas (2005, 2010 e 2015) por meio do estimador bayesiano empírico (EBE). A taxa de mortalidade por veículo foi também estimada pelo EBE. O número de veículos por pessoa foi baseado na razão entre a frota de automóveis e a população residente. Para os três anos em análise, estimamos um modelo de regressão linear entre o PIB per capita municipal e as três variáveis de interesse. Resultados: A distribuição das variáveis mostra que a relação entre o PIB e número de veículos por pessoa se manteve crescente ao longo dos anos, e foi sempre negativa considerando as mortes por veículo. A taxa mortalidade por ATT apresentou distribuição próxima a um U-invertido. Os coeficientes do modelo de regressão praticamente não variaram para a relação entre PIB e os veículos por habitante. O sinal para o modelo com a taxa de mortalidade por veículo se manteve o mesmo (negativo), mas apresentou diminuição. A taxa mortalidade por ATT, por sua vez, apresentou inversão do sinal em 2015. Conclusões: De modo similar ao observado nos países desenvolvidos, parece ter havido uma inversão na relação entre mortalidade por ATT e PIB nos municípios brasileiros entre 2005 e 2015.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-12-15info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/161410.1590/1980-549720210017.supl.1porhttps://preprints.scielo.org/index.php/scielo/article/view/1614/2550Copyright (c) 2020 Pedro Cisalpino Pinheiro, Renato Azeredo Teixeira, Antonio Luiz Pinho Ribeiro, Deborah Carvalho Maltahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessPinheiro, Pedro Cisalpino Teixeira, Renato Azeredo Ribeiro, Antonio Luiz Pinho Malta, Deborah Carvalho reponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-12-14T20:16:14Zoai:ops.preprints.scielo.org:preprint/1614Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-12-14T20:16:14SciELO Preprints - SciELOfalse |
dc.title.none.fl_str_mv |
Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015 A relação entre PIB per capita e os acidentes de transporte nos municípios brasileiros, 2005, 2010 e 2015 |
title |
Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015 |
spellingShingle |
Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015 Pinheiro, Pedro Cisalpino Mortalidade Acidentes de transporte frota PIB per capita Municípios Mortality traffic accidents fleet municipalities GDP per capita |
title_short |
Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015 |
title_full |
Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015 |
title_fullStr |
Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015 |
title_full_unstemmed |
Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015 |
title_sort |
Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015 |
author |
Pinheiro, Pedro Cisalpino |
author_facet |
Pinheiro, Pedro Cisalpino Teixeira, Renato Azeredo Ribeiro, Antonio Luiz Pinho Malta, Deborah Carvalho |
author_role |
author |
author2 |
Teixeira, Renato Azeredo Ribeiro, Antonio Luiz Pinho Malta, Deborah Carvalho |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Pinheiro, Pedro Cisalpino Teixeira, Renato Azeredo Ribeiro, Antonio Luiz Pinho Malta, Deborah Carvalho |
dc.subject.por.fl_str_mv |
Mortalidade Acidentes de transporte frota PIB per capita Municípios Mortality traffic accidents fleet municipalities GDP per capita |
topic |
Mortalidade Acidentes de transporte frota PIB per capita Municípios Mortality traffic accidents fleet municipalities GDP per capita |
description |
Objective: The main objective of this paper is to analyze the relationship between GDP and three variables related to traffic accidents in Brazilian municipalities: traffic accident mortality, deaths per vehicle; and vehicles per inhabitant. Methods: 2005, 2010 and 2015 terrestrial traffic accident (ATT) mortality rates were estimated using a three years moving average and were standardized, then, we applied the empirical Bayes estimator (EBE). Fatality rates (deaths per vehicle) also were based on EBE. Vehicles per inhabitant considered the ratio between vehicle fleet and the population at municipal level. For every studied year, we estimated linear regression models between GDP and the interest variables. Results: Variables distribution indicates that, between 2005 and 2015, GDP and vehicles per inhabitant kept the same rising relationship. Fatality rates show a decreasing association with GDP. TA mortality distribution with GDP presented a pattern close to an inverted-U. Model coefficients practically did not change for the vehicle per inhabitant. Estimated association between deaths per vehicle and GDP kept the same sign, but diminished between 2005 and 2015. Model coefficient sign changed in 2015 for TA mortality. Conclusion: Similarly to what was observed in developed countries, the relationship between mortality from traffic accidents and GDP changed in the analyzed period. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-15 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/preprint info:eu-repo/semantics/publishedVersion |
format |
preprint |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/preprint/view/1614 10.1590/1980-549720210017.supl.1 |
url |
https://preprints.scielo.org/index.php/scielo/preprint/view/1614 |
identifier_str_mv |
10.1590/1980-549720210017.supl.1 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/article/view/1614/2550 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
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SciELO |
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SCI |
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SciELO Preprints |
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SciELO Preprints - SciELO |
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