Cardiorespiratory diseases are a serious public health problem worldwide. Identification of spatial patterns in health events is an efficient tool to guide public policies in environmental health. However, only few st...Cardiorespiratory diseases are a serious public health problem worldwide. Identification of spatial patterns in health events is an efficient tool to guide public policies in environmental health. However, only few studies have considered spatial pattern analysis which is considered the evaluation of spatial autocorrelation, degree of autocorrelation and dependence behavior in terms of distances. Therefore, the objective of this study is to propose a set of procedures to evaluate the spatial patterns of cardiorespiratory diseases in the Federal District, Brazil. Specifically, our proposal will be based on four questions: a) is the spatial distribution of all patients clustered, random or dispersed? b) what is the degree of clustering for either high values or low values of patients? c) what is the spatial dependence behavior? d) considering the spatial variation, at what distance does the type of distribution (cluster, random or disperse) begin to change? We chose four methods to answer these questions Global Moran’s I (question “a”);Getis-Ord General G (question “b”);semivariogram analysis (question “c”);and multi-distance spatial cluster-K-function (question “d”). Our results suggest that there is a different behavior for people up to 5 years old (cluster, p < 0.01), especially in distances below 2.5 km. For people above 59 years old, cluster is significant just in short distances (<200 m). For other age groups, the spatial distribution is basically random. Our study showed that it was possible to capture evidences of health disparities in the Federal District.展开更多
文摘Cardiorespiratory diseases are a serious public health problem worldwide. Identification of spatial patterns in health events is an efficient tool to guide public policies in environmental health. However, only few studies have considered spatial pattern analysis which is considered the evaluation of spatial autocorrelation, degree of autocorrelation and dependence behavior in terms of distances. Therefore, the objective of this study is to propose a set of procedures to evaluate the spatial patterns of cardiorespiratory diseases in the Federal District, Brazil. Specifically, our proposal will be based on four questions: a) is the spatial distribution of all patients clustered, random or dispersed? b) what is the degree of clustering for either high values or low values of patients? c) what is the spatial dependence behavior? d) considering the spatial variation, at what distance does the type of distribution (cluster, random or disperse) begin to change? We chose four methods to answer these questions Global Moran’s I (question “a”);Getis-Ord General G (question “b”);semivariogram analysis (question “c”);and multi-distance spatial cluster-K-function (question “d”). Our results suggest that there is a different behavior for people up to 5 years old (cluster, p < 0.01), especially in distances below 2.5 km. For people above 59 years old, cluster is significant just in short distances (<200 m). For other age groups, the spatial distribution is basically random. Our study showed that it was possible to capture evidences of health disparities in the Federal District.
