Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , |
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 |