Liver cancer is a common and leading cause of cancer death in China.We used the cancer registry data collected from 2009 to 2011 to describe the spatial distribution of liver cancer incidence at village level in Sheng...Liver cancer is a common and leading cause of cancer death in China.We used the cancer registry data collected from 2009 to 2011 to describe the spatial distribution of liver cancer incidence at village level in Shengqiu county,Henan province,China.Spatial autocorrelation analysis was employed to detect significant differences from a random spatial distribution of liver cancer incidence.Spatial scan statistics were used to detect and evaluate the clusters of liver cancer cases.Spatial展开更多
This paper focuses on the problem of detecting the geographical cluster with the most severe status in multiple groups of population given limited medical resources.Populations are grouped based on characteristics suc...This paper focuses on the problem of detecting the geographical cluster with the most severe status in multiple groups of population given limited medical resources.Populations are grouped based on characteristics such as age,gender,and race.In the early stages of a disease,an outbreak may only present in specific population groups.Therefore,to efficiently detect the outbreak,we are particularly interested in monitoring and evaluating such groups.We define the objective of detection as the most severe cluster(MSC).Taking into account the interactions between population groups,a multivariate normal scan statistic is proposed to simultaneously determine the location and size of a significant MSC,as well as the specific population groups in which the MSC is located.The proposed method is applied to an example of lung cancer in New York State,where the MSC with the highest mortality rate at the aggregate level is detected.Further,the detection capacity of this method is evaluated using a simulation study based on the lung cancer example.展开更多
基金supported by research grants form 12th five years plan of National Science and Technology Infrastructure Program(2013BAI12B03)11th five years plan of National Science and Technology Infrastructure Program(2006BAI19B03)
文摘Liver cancer is a common and leading cause of cancer death in China.We used the cancer registry data collected from 2009 to 2011 to describe the spatial distribution of liver cancer incidence at village level in Shengqiu county,Henan province,China.Spatial autocorrelation analysis was employed to detect significant differences from a random spatial distribution of liver cancer incidence.Spatial scan statistics were used to detect and evaluate the clusters of liver cancer cases.Spatial
基金This work is supported by National Science Foundation of China[grant number 71172131 and 71325003]Ministry of Education of China[grant number NCET11-0321]Shanghai Pujiang Programme。
文摘This paper focuses on the problem of detecting the geographical cluster with the most severe status in multiple groups of population given limited medical resources.Populations are grouped based on characteristics such as age,gender,and race.In the early stages of a disease,an outbreak may only present in specific population groups.Therefore,to efficiently detect the outbreak,we are particularly interested in monitoring and evaluating such groups.We define the objective of detection as the most severe cluster(MSC).Taking into account the interactions between population groups,a multivariate normal scan statistic is proposed to simultaneously determine the location and size of a significant MSC,as well as the specific population groups in which the MSC is located.The proposed method is applied to an example of lung cancer in New York State,where the MSC with the highest mortality rate at the aggregate level is detected.Further,the detection capacity of this method is evaluated using a simulation study based on the lung cancer example.
