ABSTRACT
To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29201 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, ArcGIS, 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.
ABSTRACT
PURPOSE@#An analysis and identification of the hidden relationships between effective factors in the mortality rate caused by road accidents in Fars Province of Iran to prevent and reduce traffic accidents in the future.@*METHODS@#This cross-sectional study was conducted to integrate all the pervious researches performed on mortality rate of road traffic accidents in Fars Province from March 21, 2013 to March 20, 2017. In order to reveal the relationships between the factors affecting mortality rates of road traffic accidents, the data regarding road traffic accidents extracted from resources such as Legal Medicine Organization, Traffic Police, Accident & Emergency Department, as well as Department of Roads and Urban Development of Fars Province, then cleaned and the applicable attributes embedded in the data all aggregated for further analysis. It should be noted that the data not related to Fars Province were deleted, the data analyzed, converted and the aggregation between various attributes identified. The aggregation between these different attributes as well as the FP-growth algorithm and two indexes of support and confidence calculated and interesting and effective rules extracted. In the end, several accident-provoking factors, the degree of consecutive and interdependence of each one in road accidents identified and introduced. The statistical analysis was conducted by using Rapid Miner software.@*RESULTS@#Of the 6216 people dead due to road traffic accidents, 4865 (79.02%) were male and 1292 (20.98%) were female, 59 of them have no clear gender. The largest portion of people died of road traffic accidents belonged to married and self-employed men who collided with motorcycles in autumn. Moreover, young individuals (aged 19-40 years) with secondary educational level who died of accidents in summer at 12:00 a.m. and then 5:00 p.m. in outer city main roads of Kazerun-Shiraz, then Darab-Shiraz, Fasa-Darab and in within-city main streets had the highest mortality rates. Among women, the middle-aged group (aged 41-65 years) followed by young-aged group (aged 19-40 years) with elementary educational level and then illiterate accounted for the highest mortality rate of road traffic accidents. The automobiles involved in accidents included Pride, Peugeot 405, Peykan pickup, Samand, Peugeot Pars, other vehicles and motorcycles.@*CONCLUSION@#The high mortality rate of illiterate and low-literate in various age groups indicates that educational level plays a crucial role as a factor in road accidents, requiring related organizations such as Traffic Police and Ministry of Education to take necessary measures and policies.
ABSTRACT
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, ArcGIS, 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.
ABSTRACT
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 29177 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 5359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied time-series 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.
ABSTRACT
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 time-series models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria (P