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

Abstract

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.

Fatores ambientais associados à ocorrência de leishmaniose visceral canina em uma área de recente introdução da doença no Estado do Rio de Janeiro, Brasil

“…These findings contradict others showing the proximity of households with dogs to slum areas as a risk factor for Leishmania infection 9 . However, one should not underestimate the risk that the disease will expand throughout the neighborhood, since the local environment shows favorable conditions for the vector's development and adaptation and maintenance of the leishmaniasis transmission cycle.…”

“…The evaluation of endemic diseases from the perspective of various elements involved in the transmission cycle, such as environmental and social determinants of the disease, has been the focus of some studies employing geoprocessing techniques 8,9,10,11,12 . Geoprocessing tools, especially remote sensing, can help identify environmental factors associated with the occurrence of VL.…”

Uso e cobertura do solo e prevalência de leishmaniose visceral canina em Teresina, Piauí, Brasil: uma abordagem utilizando sensoriamento remoto orbital

“…However, their use in the study of VL is much more recent and relatively infrequent, particularly in Brazil, where VL transmission is zoonotic 33 . Consulting the MEDLINE (PubMed) and LILACS databases on 31/Dec/2015, only 12 studies were identified that used RS to assess the relationship between land use and land cover characteristics and spatial distribution of VL in the Brazilian context 16,18,19,29,34,35,36,37,38,39,40,41 .…”

“…Nine of the ten studies used Landsat satellite images with resolution of 15 to 30 meters 16,18,19,29,34,35,39,40,41 and only one used IKONOS images with resolution of 1 to 4 meters 36 . As for environmental aspects, seven studies evaluated vegetation cover, mainly using NDVI, and three evaluated land use and land cover characteristics.…”

Biotic factors and occurrence of Lutzomyia longipalpis in endemic area of visceral leishmaniasis, Mato Grosso do Sul, Brazil

“…Using data derived from satellite images, as the NDVI, allows the identification and monitoring of the diversity of vegetation, as well as the delimitation of geographical area and the areas of risk for some endemic diseases such as VL (Werneck & Maguire 2002).…”