Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil

Detalhes bibliográficos
Autor(a) principal: Werneck, Guilherme L.
Data de Publicação: 2002
Outros Autores: Maguire, James H.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Cadernos de Saúde Pública
Texto Completo: https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/1780
Resumo: Most ecologic studies use geographical areas as units of observation. Because data from areas close to one another tend to be more alike than those from distant areas, estimation of effect size and confidence intervals should consider spatial autocorrelation of measurements. In this report we demonstrate a method for modeling spatial autocorrelation within a mixed model framework, using data on environmental and socioeconomic determinants of the incidence of visceral leishmaniasis (VL) in the city of Teresina, Piauí, Brazil. A model with a spherical covariance structure indicated significant spatial autocorrelation in the data and yielded a better fit than one assuming independent observations. While both models showed a positive association between VL incidence and residence in a favela (slum) or in areas with green vegetation, values for the fixed effects and standard errors differed substantially between the models. Exploration of the data's spatial correlation structure through the semivariogram should precede the use of these models. Our findings support the hypothesis of spatial dependence of VL rates and indicate that it might be useful to model spatial correlation in order to obtain more accurate point and standard error estimates.
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spelling Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, BrazilVisceral LeishmaniasisSpatial AnalysisEcologic StudiesEpidemiologyMost ecologic studies use geographical areas as units of observation. Because data from areas close to one another tend to be more alike than those from distant areas, estimation of effect size and confidence intervals should consider spatial autocorrelation of measurements. In this report we demonstrate a method for modeling spatial autocorrelation within a mixed model framework, using data on environmental and socioeconomic determinants of the incidence of visceral leishmaniasis (VL) in the city of Teresina, Piauí, Brazil. A model with a spherical covariance structure indicated significant spatial autocorrelation in the data and yielded a better fit than one assuming independent observations. While both models showed a positive association between VL incidence and residence in a favela (slum) or in areas with green vegetation, values for the fixed effects and standard errors differed substantially between the models. Exploration of the data's spatial correlation structure through the semivariogram should precede the use of these models. Our findings support the hypothesis of spatial dependence of VL rates and indicate that it might be useful to model spatial correlation in order to obtain more accurate point and standard error estimates.A maioria dos estudos ecológicos utiliza áreas geográficas como unidades de observação. Uma vez que as áreas geograficamente próximas tendem a ser mais semelhantes do que as distantes, as estimativas da magnitude do efeito e dos intervalos de confiança devem levar em conta a auto-correlação espacial das medidas. Neste estudo demonstramos um método para modelar a auto-correlação espacial dentro de um referencial de modelo misto, utilizando dados sobre determinantes ambientais e sócio-econômicos da incidência de leishmaniose visceral (LV) na cidade de Teresina, Estado do Piauí. Um modelo com uma estrutura de covariância esférica indicou uma auto-correlação espacial significativa nos dados e produziu melhor ajuste quando comparado com outro modelo que pressupunha observações independentes. Embora ambos modelos tenham demonstrado associações positivas entre incidência de LV e residência em favelas ou áreas com vegetação verde, os valores para os efeitos fixos e erros-padrão diferiram substancialmente entre os modelos. A estrutura da correlação espacial dos dados deve ser explorada através do semivariograma, antes da utilização destes modelos. Nossos achados favorecem a hipótese da dependência espacial dos coeficientes de incidência de LV e sugerem que a modelagem da correção espacial poderia ser útil para obter estimativas pontuais e de erros-padrão mais acuradas.Reports in Public HealthCadernos de Saúde Pública2002-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlapplication/pdfhttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/1780Reports in Public Health; Vol. 18 No. 3 (2002): March/AprilCadernos de Saúde Pública; v. 18 n. 3 (2002): Maio/Junho1678-44640102-311Xreponame:Cadernos de Saúde Públicainstname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZenghttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/1780/3548https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/1780/3549Werneck, Guilherme L.Maguire, James H.info:eu-repo/semantics/openAccess2024-03-06T15:26:37Zoai:ojs.teste-cadernos.ensp.fiocruz.br:article/1780Revistahttps://cadernos.ensp.fiocruz.br/ojs/index.php/csphttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/oaicadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br1678-44640102-311Xopendoar:2024-03-06T13:02:07.347617Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)true
dc.title.none.fl_str_mv Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil
title Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil
spellingShingle Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil
Werneck, Guilherme L.
Visceral Leishmaniasis
Spatial Analysis
Ecologic Studies
Epidemiology
title_short Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil
title_full Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil
title_fullStr Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil
title_full_unstemmed Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil
title_sort Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil
author Werneck, Guilherme L.
author_facet Werneck, Guilherme L.
Maguire, James H.
author_role author
author2 Maguire, James H.
author2_role author
dc.contributor.author.fl_str_mv Werneck, Guilherme L.
Maguire, James H.
dc.subject.por.fl_str_mv Visceral Leishmaniasis
Spatial Analysis
Ecologic Studies
Epidemiology
topic Visceral Leishmaniasis
Spatial Analysis
Ecologic Studies
Epidemiology
description Most ecologic studies use geographical areas as units of observation. Because data from areas close to one another tend to be more alike than those from distant areas, estimation of effect size and confidence intervals should consider spatial autocorrelation of measurements. In this report we demonstrate a method for modeling spatial autocorrelation within a mixed model framework, using data on environmental and socioeconomic determinants of the incidence of visceral leishmaniasis (VL) in the city of Teresina, Piauí, Brazil. A model with a spherical covariance structure indicated significant spatial autocorrelation in the data and yielded a better fit than one assuming independent observations. While both models showed a positive association between VL incidence and residence in a favela (slum) or in areas with green vegetation, values for the fixed effects and standard errors differed substantially between the models. Exploration of the data's spatial correlation structure through the semivariogram should precede the use of these models. Our findings support the hypothesis of spatial dependence of VL rates and indicate that it might be useful to model spatial correlation in order to obtain more accurate point and standard error estimates.
publishDate 2002
dc.date.none.fl_str_mv 2002-06-01
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dc.language.iso.fl_str_mv eng
language eng
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https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/1780/3549
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dc.publisher.none.fl_str_mv Reports in Public Health
Cadernos de Saúde Pública
publisher.none.fl_str_mv Reports in Public Health
Cadernos de Saúde Pública
dc.source.none.fl_str_mv Reports in Public Health; Vol. 18 No. 3 (2002): March/April
Cadernos de Saúde Pública; v. 18 n. 3 (2002): Maio/Junho
1678-4464
0102-311X
reponame:Cadernos de Saúde Pública
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
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reponame_str Cadernos de Saúde Pública
collection Cadernos de Saúde Pública
repository.name.fl_str_mv Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)
repository.mail.fl_str_mv cadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br
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