Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis(CL) in Fars province, Iran.Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. T...Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis(CL) in Fars province, Iran.Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. Time-series data were recorded from 29 201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using Sa TScan software.Results: This study resulted in statistically significant spatiotemporal clusters of CL(P < 0.05). The most likely cluster contained 350 cases from 1 July 2010 to 30 November2010. Besides, 5 secondary clusters were detected in different periods of time. Finally,statistically significant sub-clusters were found within the three large clusters(P < 0.05).Conclusions: Transmission of CL followed spatiotemporal pattern in Fars province,Iran. This can have an important effect on future studies on prediction and prevention of CL.展开更多
Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and...Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and the endemic values of cutaneous leishmaniasis were determined by retrospective clusters derived from spatiotemporal permutation modeling on a time-series design. The accuracy of the values was assessed using receiver operating characteristic(ROC) curve. SPSS version 22, Arc GIS, and ITSM 2002 software tools were used for analysis. Results: Nine statistically significant retrospective clusters(P<0.05) resulted in finding seven significant and accurate endemic values(P<0.1). These valid endemic scores were generalized to the other 18 cities based on 6 different climates in the province. Conclusions: Retrospectively detected clusters with the help of ROC curve analysis could help determine cutaneous leishmaniasis endemic values which are essential for future prediction and prevention policies in the area.展开更多
Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis(CL) or for malaria in Fars province, Iran in 2016. Methods: ...Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis(CL) or for malaria in Fars province, Iran in 2016. Methods: Using time-series data including 29 177 CL cases recorded during 2010-2015 and 357 malaria cases recorded during 2010-2015, CL and malaria cases were predicted in 2016. Predicted cases were used to verify if they followed uniform distribution over time and space using space-time analysis. To testify the uniformity of distributions, permutation scan statistics was applied prospectively to detect statistically significant and non-significant outbreaks. Finally, the findings were compared to determine whether permutation scan statistics worked better for CL or for malaria in the area. Prospective permutation scan modeling was performed using SatScan software. Results: A total of 5 359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied timeseries models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria(Pv was set to 0.1). The results indicated two significant prospective spatial-temporal outbreaks for CL(P<0.5) including Most Likely Clusters, and one non-significant outbreak for malaria(P>0.5) in the area. Conclusions: Both CL and malaria follow a space-time trend in the area, but prospective permutation scan modeling works better for detecting CL spatial-temporal outbreaks. It is not far away from expectation since clusters are defined as accumulation of cases in specified times and places. Although this method seems to work better with finding the outbreaks of a high-frequency disease; i.e., CL, it is able to find non-significant outbreaks. This is clinically important for both high-and low-frequency infections; i.e., CL and malaria.展开更多
基金the PhD dissertation(pro-posal No.12439)written by Marjan Zare and approved by Research Vice-chancellor of Shiraz University of Medical Sci-ences.
文摘Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis(CL) in Fars province, Iran.Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. Time-series data were recorded from 29 201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using Sa TScan software.Results: This study resulted in statistically significant spatiotemporal clusters of CL(P < 0.05). The most likely cluster contained 350 cases from 1 July 2010 to 30 November2010. Besides, 5 secondary clusters were detected in different periods of time. Finally,statistically significant sub-clusters were found within the three large clusters(P < 0.05).Conclusions: Transmission of CL followed spatiotemporal pattern in Fars province,Iran. This can have an important effect on future studies on prediction and prevention of CL.
文摘Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and the endemic values of cutaneous leishmaniasis were determined by retrospective clusters derived from spatiotemporal permutation modeling on a time-series design. The accuracy of the values was assessed using receiver operating characteristic(ROC) curve. SPSS version 22, Arc GIS, and ITSM 2002 software tools were used for analysis. Results: Nine statistically significant retrospective clusters(P<0.05) resulted in finding seven significant and accurate endemic values(P<0.1). These valid endemic scores were generalized to the other 18 cities based on 6 different climates in the province. Conclusions: Retrospectively detected clusters with the help of ROC curve analysis could help determine cutaneous leishmaniasis endemic values which are essential for future prediction and prevention policies in the area.
文摘Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis(CL) or for malaria in Fars province, Iran in 2016. Methods: Using time-series data including 29 177 CL cases recorded during 2010-2015 and 357 malaria cases recorded during 2010-2015, CL and malaria cases were predicted in 2016. Predicted cases were used to verify if they followed uniform distribution over time and space using space-time analysis. To testify the uniformity of distributions, permutation scan statistics was applied prospectively to detect statistically significant and non-significant outbreaks. Finally, the findings were compared to determine whether permutation scan statistics worked better for CL or for malaria in the area. Prospective permutation scan modeling was performed using SatScan software. Results: A total of 5 359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied timeseries models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria(Pv was set to 0.1). The results indicated two significant prospective spatial-temporal outbreaks for CL(P<0.5) including Most Likely Clusters, and one non-significant outbreak for malaria(P>0.5) in the area. Conclusions: Both CL and malaria follow a space-time trend in the area, but prospective permutation scan modeling works better for detecting CL spatial-temporal outbreaks. It is not far away from expectation since clusters are defined as accumulation of cases in specified times and places. Although this method seems to work better with finding the outbreaks of a high-frequency disease; i.e., CL, it is able to find non-significant outbreaks. This is clinically important for both high-and low-frequency infections; i.e., CL and malaria.
