Exploring spatial patterns in the associations between local AIDS incidence and socioeconomic and demographic variables in the state of Rio de Janeiro, Brazil

Abstract

Access to antiretroviral therapy (ART), universally provided in Brazil since 1996, resulted in a reduction in overall morbidity and mortality due to AIDS or AIDS-related complications, but in some municipalities of Rio de Janeiro, AIDS incidence remains high. Public health surveillance remains an invaluable tool for understanding current AIDS epidemiologic patterns and local socioeconomic and demographic factors associated with increased incidence. Geographically Weighted Poisson Regression (GWPR) explores spatial varying impacts of these factors across the study area focusing attention on local variations in ecological associations. The set of sociodemographic variables under consideration revealed significant associations with local AIDS incidence and these associations varied geographically across the study area. We find the effects of predictors on AIDS incidence are not constant across the state, contrary to assumptions in the global models. We observe and quantify different local factors driving AIDS incidence in different parts of the state.

Winter Is Coming: A Socio-Environmental Monitoring and Spatiotemporal Modelling Approach for Better Understanding a Respiratory Disease

“…Although OLS and GLM often fit non-spatial data well, when used with spatial data the methods can be problematic due to violations of several assumptions such as the normal distribution and homogeneity of residuals, and their independence (lack of autocorrelation). Researchers also assume that the relationships are constant over the space meaning the associations are spatially stationary [48]. However, spatial data often bring these behaviours as a result of their nature, in other words, the data can be (auto) correlated, introduce heterogeneity into linear models and the relations can be nonstationary in the study area.…”

“…Also, as this study aims to explore local variations of the effect of independent variables on the outcome, the global modelling methods such as (generalised) linear regression models missing the necessary detail (local focus) since they focus mostly on the description of general global relations. The family of Geographically Weighted Regression (GWR) models [49] has been developed in order to capture the spatial non-stationarity and spatially varying associations, and it has been utilised in quantitative social and health sciences [48,50,51].…”

“…The GWPR model enables the investigation of local spatial variations with regard to the number of COPD hospitalisation events in individual CAUs in the study areas. The GWPR model is defined by (Equation (1)) [48,55]:…”

“…Following the technique of GWR, one can calibrate this model using a kernel regression methodology in which smoothed geographical variations of parameters are estimated with a spatial weighting kernel [55]. In this study, the adaptive bi-square kernel weighting function (Equation (2)) was used as it has a clear-cut range where kernel weights are non-zero, and it is more suitable for when one seeks a definitive local extent for model fitting [48].…”

“…where w ij is the geographical weight assigned to the i-th observation at a regression based at point j in the study area (referred to as the j-th regression location), d ij is the Euclidean distance between observation i and regression location j, and d is a non-linear parameter (bandwidth) size defined by a number of neighbours [48,51]. The choice of bandwidth size is of great importance since it controls the level of smoothing of the outputs as larger bandwidth size generally produces more smoothed estimates of coefficients [51].…”