Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic / 한국역학회지

OBJECTIVES: This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters.METHODS: This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely high- and low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing.RESULTS: A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p < 0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p < 0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p < 0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p < 0.001).CONCLUSIONS: This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.

Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic / 한국역학회지

OBJECTIVES: This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters. METHODS: This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely high- and low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing. RESULTS: A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p < 0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p < 0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p < 0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p < 0.001). CONCLUSIONS: This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.

Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic / 한국역학회지

OBJECTIVES@#This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters.@*METHODS@#This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely high- and low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing.@*RESULTS@#A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p < 0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p < 0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p < 0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p < 0.001).@*CONCLUSIONS@#This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.

Spatial outline of malaria transmission in Iran

OBJECTIVE@#To conduct for modeling spatial distribution of malaria transmission in Iran.@*METHODS@#Records of all malaria cases from the period 2008-2010 in Iran were retrieved for malaria control department, MOH&ME. Metrological data including annual rainfall, maximum and minimum temperature, relative humidity, altitude, demographic, districts border shapefiles, and NDVI images received from Iranian Climatologic Research Center. Data arranged in ArcGIS.@*RESULTS@#99.65% of malaria transmission cases were focused in southeast part of Iran. These transmissions had statistically correlation with altitude (650 m), maximum (30 °C), minimum (20 °C) and average temperature (25.3 °C). Statistical correlation and overall relationship between NDVI (118.81), relative humidity (⩾45%) and rainfall in southeast area was defined and explained in this study.@*CONCLUSIONS@#According to ecological condition and mentioned cut-off points, predictive map was generated using cokriging method.