Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil.
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1017/S095026882000134X http://hdl.handle.net/11449/200676 |
Resumo: | Even though the impact of COVID-19 in metropolitan areas has been extensively studied, the geographic spread to smaller cities is also of great concern. We conducted an ecological study aimed at identifying predictors of early introduction, incidence rates of COVID-19 and mortality (up to 8 May 2020) among 604 municipalities in inner São Paulo State, Brazil. Socio-demographic indexes, road distance to the state capital and a classification of regional relevance were included in predictive models for time to COVID-19 introduction (Cox Regression), incidence and mortality rates (Zero-Inflated Binomial Negative Regression). In multivariable analyses, greater demographic density and higher classification of regional relevance were associated with both early introduction and increased rates of COVID-19 incidence and mortality. Other predictive factors varied, but distance from the State Capital (São Paulo City) was negatively associated with time-to-introduction and with incidence rates of COVID-19. Our results reinforce the hypothesis of two patterns of geographical spread of SARS-Cov-2 infection: one that is spatial (from the metropolitan area into the inner state) and another which is hierarchical (from urban centres of regional relevance to smaller and less connected municipalities). Those findings may apply to other settings, especially in developing and highly heterogeneous countries, and point to a potential benefit from strengthening non-pharmaceutical control strategies in areas of greater risk. |
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spelling |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil.Even though the impact of COVID-19 in metropolitan areas has been extensively studied, the geographic spread to smaller cities is also of great concern. We conducted an ecological study aimed at identifying predictors of early introduction, incidence rates of COVID-19 and mortality (up to 8 May 2020) among 604 municipalities in inner São Paulo State, Brazil. Socio-demographic indexes, road distance to the state capital and a classification of regional relevance were included in predictive models for time to COVID-19 introduction (Cox Regression), incidence and mortality rates (Zero-Inflated Binomial Negative Regression). In multivariable analyses, greater demographic density and higher classification of regional relevance were associated with both early introduction and increased rates of COVID-19 incidence and mortality. Other predictive factors varied, but distance from the State Capital (São Paulo City) was negatively associated with time-to-introduction and with incidence rates of COVID-19. Our results reinforce the hypothesis of two patterns of geographical spread of SARS-Cov-2 infection: one that is spatial (from the metropolitan area into the inner state) and another which is hierarchical (from urban centres of regional relevance to smaller and less connected municipalities). Those findings may apply to other settings, especially in developing and highly heterogeneous countries, and point to a potential benefit from strengthening non-pharmaceutical control strategies in areas of greater risk.Department of Infectious Diseases Botucatu School of Medicine São Paulo State University (UNESP), Av. Prof. Mário Rubens Guimarães Montenegro, s/nDepartment of Geography Faculty of Science and Technology São Paulo State University (UNESP)Department of Biostatistics Botucatu Institute of Biosciences São Paulo State University (UNESP)Department of Infectious Diseases Botucatu School of Medicine São Paulo State University (UNESP), Av. Prof. Mário Rubens Guimarães Montenegro, s/nDepartment of Geography Faculty of Science and Technology São Paulo State University (UNESP)Department of Biostatistics Botucatu Institute of Biosciences São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)Fortaleza, C. M.C.B. [UNESP]Guimarães, R. B. [UNESP]De Almeida, G. B. [UNESP]Pronunciate, M. [UNESP]Ferreira, C. P. [UNESP]2020-12-12T02:13:06Z2020-12-12T02:13:06Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1017/S095026882000134XEpidemiology and Infection.1469-44090950-2688http://hdl.handle.net/11449/20067610.1017/S095026882000134X2-s2.0-85087253472802252746836945920527496982046170000-0002-9925-53740000-0002-9404-6098Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEpidemiology and Infectioninfo:eu-repo/semantics/openAccess2021-11-18T17:39:08Zoai:repositorio.unesp.br:11449/200676Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:44:08.712667Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. |
title |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. |
spellingShingle |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. Fortaleza, C. M.C.B. [UNESP] |
title_short |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. |
title_full |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. |
title_fullStr |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. |
title_full_unstemmed |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. |
title_sort |
Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. |
author |
Fortaleza, C. M.C.B. [UNESP] |
author_facet |
Fortaleza, C. M.C.B. [UNESP] Guimarães, R. B. [UNESP] De Almeida, G. B. [UNESP] Pronunciate, M. [UNESP] Ferreira, C. P. [UNESP] |
author_role |
author |
author2 |
Guimarães, R. B. [UNESP] De Almeida, G. B. [UNESP] Pronunciate, M. [UNESP] Ferreira, C. P. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Fortaleza, C. M.C.B. [UNESP] Guimarães, R. B. [UNESP] De Almeida, G. B. [UNESP] Pronunciate, M. [UNESP] Ferreira, C. P. [UNESP] |
description |
Even though the impact of COVID-19 in metropolitan areas has been extensively studied, the geographic spread to smaller cities is also of great concern. We conducted an ecological study aimed at identifying predictors of early introduction, incidence rates of COVID-19 and mortality (up to 8 May 2020) among 604 municipalities in inner São Paulo State, Brazil. Socio-demographic indexes, road distance to the state capital and a classification of regional relevance were included in predictive models for time to COVID-19 introduction (Cox Regression), incidence and mortality rates (Zero-Inflated Binomial Negative Regression). In multivariable analyses, greater demographic density and higher classification of regional relevance were associated with both early introduction and increased rates of COVID-19 incidence and mortality. Other predictive factors varied, but distance from the State Capital (São Paulo City) was negatively associated with time-to-introduction and with incidence rates of COVID-19. Our results reinforce the hypothesis of two patterns of geographical spread of SARS-Cov-2 infection: one that is spatial (from the metropolitan area into the inner state) and another which is hierarchical (from urban centres of regional relevance to smaller and less connected municipalities). Those findings may apply to other settings, especially in developing and highly heterogeneous countries, and point to a potential benefit from strengthening non-pharmaceutical control strategies in areas of greater risk. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:13:06Z 2020-12-12T02:13:06Z 2020-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1017/S095026882000134X Epidemiology and Infection. 1469-4409 0950-2688 http://hdl.handle.net/11449/200676 10.1017/S095026882000134X 2-s2.0-85087253472 8022527468369459 2052749698204617 0000-0002-9925-5374 0000-0002-9404-6098 |
url |
http://dx.doi.org/10.1017/S095026882000134X http://hdl.handle.net/11449/200676 |
identifier_str_mv |
Epidemiology and Infection. 1469-4409 0950-2688 10.1017/S095026882000134X 2-s2.0-85087253472 8022527468369459 2052749698204617 0000-0002-9925-5374 0000-0002-9404-6098 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Epidemiology and Infection |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1808128409392381952 |