Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such ho...Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such hotspots, but also their spatial patterns. We used spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters and areas in which nine malignant neoplasms are situated in Taiwan. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. We found a significant relationship between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, the geographic distribution of clusters where oral cavity cancer in males is prevalent was closely correspond to the locations in central Taiwan with serious metal pollution. In females, clusters of oral cavity cancer were closely related with aboriginal townships in eastern Taiwan, where cigarette smoking, alcohol drinking, and betel nut chewing are commonplace. The difference between males and females in the spatial distributions was stark. Furthermore, areas with a high morbidity of gastric cancer were clustered in aboriginal townships where the occurrence of Helicobacter pylori is frequent. Our results revealed a similarity between both males and females in spatial pattern. Cluster mapping clarified the spatial aspects of both internal and external correlations for the nine malignant neoplasms. In addition, using a method of logistic regression also enabled us to find differentiation between gender-specific spatial patterns.展开更多
Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiw...Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.展开更多
Dengue fever is a serious vector-borne infectious viral disease found worldwide. Dengue fever forecasting is in demand in the front line of epidemic prevention and control work. The goal of this study was to evaluate ...Dengue fever is a serious vector-borne infectious viral disease found worldwide. Dengue fever forecasting is in demand in the front line of epidemic prevention and control work. The goal of this study was to evaluate the feasibility of using only notified case home locations to predict new cases and village locations. We took the Tainan City dengue fever outbreak in 2015 as the research subject and divided it into 5 periods according to epidemic temporal change. In each period, the predicted variable was the location of the reported cases in the previous week, the previous 2 weeks, and the previous 3 weeks. In addition, we used 21 preset distances with a radius of 0 to 2000 m at intervals of 100 m to predict the villages where new cases would appear. Accounting for 4 predictors of a confusion matrix at each preset distance, these predictors were used in calculations using the Matthews correlation coefficient (MCC) as the basis for model evaluation. In the lag phase, the optimal predictor was within 1700 m in the 3-week forecast. In the exponential phase, the optimal predictor was within 300 m in the 1-week forecast. In the stationary phase, the optimal predictor was within 100 m in the 3-week forecast and within 200 m in the 2-week forecast. In the early decline phase, the optimal predictor was 0 m in the 1-week forecast. In the late decline phase, the optimal predictor was within 200 m in the 2-week forecast. According to MCC calculations and comparisons among the 5 studied periods, the best MCC score was in the exponential phase, a stage of rapid increase of new cases. The results of this study suggest that the epidemic forecasting model based on the location of notified cases has a high reference value for epidemic control and prevention.展开更多
The aim of the present study was to assess spatial features of tuberculosis prevalence and their relationships with four main ethnic communities in Taiwan. Methods of spatial analysis were clustering pattern determina...The aim of the present study was to assess spatial features of tuberculosis prevalence and their relationships with four main ethnic communities in Taiwan. Methods of spatial analysis were clustering pattern determination (such as global version of Moran’s test and local version of Gi*(d) statistic), using logistic regression calculations to identify spatial distributions over a contiguous five years and identify significant similarities, discriminant analysis to classify variables, and geographically weighted regression (GWR) to determine the strength of relationships between tuberculosis prevalence and ethnic variables in spatial features. Tuberculosis demonstrated decreasing trends in prevalence in both genders during 2005 to 2009. All results of the global Moran’s tests indicated spatial heterogeneity and clusters in the plain and mountainous Aboriginal townships. The Gi*(d) statistic calculated z-score outcomes, categorized as clusters or non-clusters, at at 5% significance level. According to the stepwise Wilks’ lambda discriminant analysis, in the Aborigines and Hoklo communities townships with clusters of tuberculosis cases differentiated from townships without cluster cases, to a greater extent than in the other communities. In the GWR models, the explanatory variables demonstrated significant and positive signs of parameter estimates in clusters occurring in plain and mountainous aboriginal townships. The explanatory variables of both the Hoklo and Hakka communities demonstrated significant, but negative, signs of parameter estimates. The Mainlander community did not significantly associate with cluster patterns of tuberculosis in Taiwan. Results indicated that locations of high tuberculosis prevalence closely related to areas containing higher proportions of the Aboriginal community in Taiwan. This information is relevant for assessment of spatial risk factors, which, in turn, can facilitate the planning of the most advantageous types of health care policies, and implementation of effective health care services.展开更多
文摘Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such hotspots, but also their spatial patterns. We used spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters and areas in which nine malignant neoplasms are situated in Taiwan. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. We found a significant relationship between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, the geographic distribution of clusters where oral cavity cancer in males is prevalent was closely correspond to the locations in central Taiwan with serious metal pollution. In females, clusters of oral cavity cancer were closely related with aboriginal townships in eastern Taiwan, where cigarette smoking, alcohol drinking, and betel nut chewing are commonplace. The difference between males and females in the spatial distributions was stark. Furthermore, areas with a high morbidity of gastric cancer were clustered in aboriginal townships where the occurrence of Helicobacter pylori is frequent. Our results revealed a similarity between both males and females in spatial pattern. Cluster mapping clarified the spatial aspects of both internal and external correlations for the nine malignant neoplasms. In addition, using a method of logistic regression also enabled us to find differentiation between gender-specific spatial patterns.
文摘Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.
文摘Dengue fever is a serious vector-borne infectious viral disease found worldwide. Dengue fever forecasting is in demand in the front line of epidemic prevention and control work. The goal of this study was to evaluate the feasibility of using only notified case home locations to predict new cases and village locations. We took the Tainan City dengue fever outbreak in 2015 as the research subject and divided it into 5 periods according to epidemic temporal change. In each period, the predicted variable was the location of the reported cases in the previous week, the previous 2 weeks, and the previous 3 weeks. In addition, we used 21 preset distances with a radius of 0 to 2000 m at intervals of 100 m to predict the villages where new cases would appear. Accounting for 4 predictors of a confusion matrix at each preset distance, these predictors were used in calculations using the Matthews correlation coefficient (MCC) as the basis for model evaluation. In the lag phase, the optimal predictor was within 1700 m in the 3-week forecast. In the exponential phase, the optimal predictor was within 300 m in the 1-week forecast. In the stationary phase, the optimal predictor was within 100 m in the 3-week forecast and within 200 m in the 2-week forecast. In the early decline phase, the optimal predictor was 0 m in the 1-week forecast. In the late decline phase, the optimal predictor was within 200 m in the 2-week forecast. According to MCC calculations and comparisons among the 5 studied periods, the best MCC score was in the exponential phase, a stage of rapid increase of new cases. The results of this study suggest that the epidemic forecasting model based on the location of notified cases has a high reference value for epidemic control and prevention.
文摘The aim of the present study was to assess spatial features of tuberculosis prevalence and their relationships with four main ethnic communities in Taiwan. Methods of spatial analysis were clustering pattern determination (such as global version of Moran’s test and local version of Gi*(d) statistic), using logistic regression calculations to identify spatial distributions over a contiguous five years and identify significant similarities, discriminant analysis to classify variables, and geographically weighted regression (GWR) to determine the strength of relationships between tuberculosis prevalence and ethnic variables in spatial features. Tuberculosis demonstrated decreasing trends in prevalence in both genders during 2005 to 2009. All results of the global Moran’s tests indicated spatial heterogeneity and clusters in the plain and mountainous Aboriginal townships. The Gi*(d) statistic calculated z-score outcomes, categorized as clusters or non-clusters, at at 5% significance level. According to the stepwise Wilks’ lambda discriminant analysis, in the Aborigines and Hoklo communities townships with clusters of tuberculosis cases differentiated from townships without cluster cases, to a greater extent than in the other communities. In the GWR models, the explanatory variables demonstrated significant and positive signs of parameter estimates in clusters occurring in plain and mountainous aboriginal townships. The explanatory variables of both the Hoklo and Hakka communities demonstrated significant, but negative, signs of parameter estimates. The Mainlander community did not significantly associate with cluster patterns of tuberculosis in Taiwan. Results indicated that locations of high tuberculosis prevalence closely related to areas containing higher proportions of the Aboriginal community in Taiwan. This information is relevant for assessment of spatial risk factors, which, in turn, can facilitate the planning of the most advantageous types of health care policies, and implementation of effective health care services.
