Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil

Detalhes bibliográficos
Autor(a) principal: Costa,Silmery da Silva Brito
Data de Publicação: 2021
Outros Autores: Branco,Maria dos Remédios Freitas Carvalho, Vasconcelos,Vitor Vieira, Queiroz,Rejane Christine de Sousa, Araujo,Adriana Soraya, Câmara,Ana Patrícia Barros, Fushita,Angela Terumi, Silva,Maria do Socorro da, Silva,Antônio Augusto Moura da, Santos,Alcione Miranda dos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista da Sociedade Brasileira de Medicina Tropical
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822021000100336
Resumo: Abstract INTRODUCTION: Dengue, chikungunya, and Zika are a growing global health problem. This study analyzed the spatial distribution of dengue, chikungunya, and Zika cases in São Luís, Maranhão, from 2015 to 2016 and investigated the association between socio-environmental and economic factors and hotspots for mosquito proliferation. METHODS: This was a socio-ecological study using data from the National Information System of Notifiable Diseases. The spatial units of analysis were census tracts. The incidence rates of the combined cases of the three diseases were calculated and smoothed using empirical local Bayes estimates. The spatial autocorrelation of the smoothed incidence rate was measured using Local Moran's I and Global Moran's I. Multiple linear regression and spatial autoregressive models were fitted using the log of the smoothed disease incidence rate as the dependent variable and socio-environmental factors, demographics, and mosquito hotspots as independent variables. RESULTS: The findings showed a significant spatial autocorrelation of the smoothed incidence rate. The model that best fit the data was the spatial lag model, revealing a positive association between disease incidence and the proportion of households with surrounding garbage accumulation. CONCLUSIONS: The distribution of dengue, chikungunya, and Zika cases showed a significant spatial pattern, in which the high-risk areas for the three diseases were explained by the variable "garbage accumulated in the surrounding environment,” demonstrating the need for an intersectoral approach for vector control and prevention that goes beyond health actions.
id SBMT-1_72477ecc096724860e777acfa367a60e
oai_identifier_str oai:scielo:S0037-86822021000100336
network_acronym_str SBMT-1
network_name_str Revista da Sociedade Brasileira de Medicina Tropical
repository_id_str
spelling Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern BrazilDengueChikungunyaZikaSpatial analysisSocio-environmental factorsEconomic factorsAbstract INTRODUCTION: Dengue, chikungunya, and Zika are a growing global health problem. This study analyzed the spatial distribution of dengue, chikungunya, and Zika cases in São Luís, Maranhão, from 2015 to 2016 and investigated the association between socio-environmental and economic factors and hotspots for mosquito proliferation. METHODS: This was a socio-ecological study using data from the National Information System of Notifiable Diseases. The spatial units of analysis were census tracts. The incidence rates of the combined cases of the three diseases were calculated and smoothed using empirical local Bayes estimates. The spatial autocorrelation of the smoothed incidence rate was measured using Local Moran's I and Global Moran's I. Multiple linear regression and spatial autoregressive models were fitted using the log of the smoothed disease incidence rate as the dependent variable and socio-environmental factors, demographics, and mosquito hotspots as independent variables. RESULTS: The findings showed a significant spatial autocorrelation of the smoothed incidence rate. The model that best fit the data was the spatial lag model, revealing a positive association between disease incidence and the proportion of households with surrounding garbage accumulation. CONCLUSIONS: The distribution of dengue, chikungunya, and Zika cases showed a significant spatial pattern, in which the high-risk areas for the three diseases were explained by the variable "garbage accumulated in the surrounding environment,” demonstrating the need for an intersectoral approach for vector control and prevention that goes beyond health actions.Sociedade Brasileira de Medicina Tropical - SBMT2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822021000100336Revista da Sociedade Brasileira de Medicina Tropical v.54 2021reponame:Revista da Sociedade Brasileira de Medicina Tropicalinstname:Sociedade Brasileira de Medicina Tropical (SBMT)instacron:SBMT10.1590/0037-8682-0223-2021info:eu-repo/semantics/openAccessCosta,Silmery da Silva BritoBranco,Maria dos Remédios Freitas CarvalhoVasconcelos,Vitor VieiraQueiroz,Rejane Christine de SousaAraujo,Adriana SorayaCâmara,Ana Patrícia BarrosFushita,Angela TerumiSilva,Maria do Socorro daSilva,Antônio Augusto Moura daSantos,Alcione Miranda doseng2021-09-21T00:00:00Zoai:scielo:S0037-86822021000100336Revistahttps://www.sbmt.org.br/portal/revista/ONGhttps://old.scielo.br/oai/scielo-oai.php||dalmo@rsbmt.uftm.edu.br|| rsbmt@rsbmt.uftm.edu.br1678-98490037-8682opendoar:2021-09-21T00:00Revista da Sociedade Brasileira de Medicina Tropical - Sociedade Brasileira de Medicina Tropical (SBMT)false
dc.title.none.fl_str_mv Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
title Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
spellingShingle Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
Costa,Silmery da Silva Brito
Dengue
Chikungunya
Zika
Spatial analysis
Socio-environmental factors
Economic factors
title_short Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
title_full Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
title_fullStr Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
title_full_unstemmed Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
title_sort Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
author Costa,Silmery da Silva Brito
author_facet Costa,Silmery da Silva Brito
Branco,Maria dos Remédios Freitas Carvalho
Vasconcelos,Vitor Vieira
Queiroz,Rejane Christine de Sousa
Araujo,Adriana Soraya
Câmara,Ana Patrícia Barros
Fushita,Angela Terumi
Silva,Maria do Socorro da
Silva,Antônio Augusto Moura da
Santos,Alcione Miranda dos
author_role author
author2 Branco,Maria dos Remédios Freitas Carvalho
Vasconcelos,Vitor Vieira
Queiroz,Rejane Christine de Sousa
Araujo,Adriana Soraya
Câmara,Ana Patrícia Barros
Fushita,Angela Terumi
Silva,Maria do Socorro da
Silva,Antônio Augusto Moura da
Santos,Alcione Miranda dos
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Costa,Silmery da Silva Brito
Branco,Maria dos Remédios Freitas Carvalho
Vasconcelos,Vitor Vieira
Queiroz,Rejane Christine de Sousa
Araujo,Adriana Soraya
Câmara,Ana Patrícia Barros
Fushita,Angela Terumi
Silva,Maria do Socorro da
Silva,Antônio Augusto Moura da
Santos,Alcione Miranda dos
dc.subject.por.fl_str_mv Dengue
Chikungunya
Zika
Spatial analysis
Socio-environmental factors
Economic factors
topic Dengue
Chikungunya
Zika
Spatial analysis
Socio-environmental factors
Economic factors
description Abstract INTRODUCTION: Dengue, chikungunya, and Zika are a growing global health problem. This study analyzed the spatial distribution of dengue, chikungunya, and Zika cases in São Luís, Maranhão, from 2015 to 2016 and investigated the association between socio-environmental and economic factors and hotspots for mosquito proliferation. METHODS: This was a socio-ecological study using data from the National Information System of Notifiable Diseases. The spatial units of analysis were census tracts. The incidence rates of the combined cases of the three diseases were calculated and smoothed using empirical local Bayes estimates. The spatial autocorrelation of the smoothed incidence rate was measured using Local Moran's I and Global Moran's I. Multiple linear regression and spatial autoregressive models were fitted using the log of the smoothed disease incidence rate as the dependent variable and socio-environmental factors, demographics, and mosquito hotspots as independent variables. RESULTS: The findings showed a significant spatial autocorrelation of the smoothed incidence rate. The model that best fit the data was the spatial lag model, revealing a positive association between disease incidence and the proportion of households with surrounding garbage accumulation. CONCLUSIONS: The distribution of dengue, chikungunya, and Zika cases showed a significant spatial pattern, in which the high-risk areas for the three diseases were explained by the variable "garbage accumulated in the surrounding environment,” demonstrating the need for an intersectoral approach for vector control and prevention that goes beyond health actions.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822021000100336
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822021000100336
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0037-8682-0223-2021
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Medicina Tropical - SBMT
publisher.none.fl_str_mv Sociedade Brasileira de Medicina Tropical - SBMT
dc.source.none.fl_str_mv Revista da Sociedade Brasileira de Medicina Tropical v.54 2021
reponame:Revista da Sociedade Brasileira de Medicina Tropical
instname:Sociedade Brasileira de Medicina Tropical (SBMT)
instacron:SBMT
instname_str Sociedade Brasileira de Medicina Tropical (SBMT)
instacron_str SBMT
institution SBMT
reponame_str Revista da Sociedade Brasileira de Medicina Tropical
collection Revista da Sociedade Brasileira de Medicina Tropical
repository.name.fl_str_mv Revista da Sociedade Brasileira de Medicina Tropical - Sociedade Brasileira de Medicina Tropical (SBMT)
repository.mail.fl_str_mv ||dalmo@rsbmt.uftm.edu.br|| rsbmt@rsbmt.uftm.edu.br
_version_ 1752122162658934784