Relationship between GDP per capita and traffic accidents in Brazilian municipalities, 2005, 2010 and 2015

Detalhes bibliográficos
Autor(a) principal: Pinheiro, Pedro Cisalpino
Data de Publicação: 2020
Outros Autores: Teixeira, Renato Azeredo, Ribeiro, Antonio Luiz Pinho, Malta, Deborah Carvalho
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.
id SCI-1_465b8438b9edb6ab640e9f27a8c5b37f
oai_identifier_str oai:ops.preprints.scielo.org:preprint/1614
network_acronym_str SCI-1
network_name_str SciELO Preprints
repository_id_str
spelling 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
rights_invalid_str_mv 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
dc.source.none.fl_str_mv reponame:SciELO Preprints
instname:SciELO
instacron:SCI
instname_str SciELO
instacron_str SCI
institution SCI
reponame_str SciELO Preprints
collection SciELO Preprints
repository.name.fl_str_mv SciELO Preprints - SciELO
repository.mail.fl_str_mv scielo.submission@scielo.org
_version_ 1797047821414694912