-
Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014-2017 using a hierarchical Bayesian method and the spatial scan statistic
-
Erfan Ayubi, Mohammad Barati, Arasb Dabbagh Moghaddam, Ali Reza Khoshdel
-
Epidemiol Health. 2018;40:e2018032. Published online July 13, 2018
-
DOI: https://doi.org/10.4178/epih.e2018032
-
-
14,693
View
-
266
Download
-
5
Web of Science
-
7
Crossref
-
 Abstract
 PDF Supplementary Material
-
Abstract
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 highand 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.
-
Summary
-
Citations
Citations to this article as recorded by  - High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020
Neda Firouraghi, Robert Bergquist, Munazza Fatima, Alireza Mohammadi, Davidson H. Hamer, Mohammad Reza Shirzadi, Behzad Kiani
Infectious Diseases of Poverty.2023;[Epub] CrossRef - Spatio-temporal visualisation of cutaneous leishmaniasis in an endemic, urban area in Iran
Neda Firouraghi, Alireza Mohammadi, Davidson H Hamer, Robert Bergquist, Sayyed Mostafa Mostafavi, Ali Shamsoddini, Amene Raouf-Rahmati, Mahmoud Fakhar, Elham Moghaddas, Behzad Kiani
Acta Tropica.2022; 225: 106181. CrossRef - Spatial patterning of occupational stress and its related factors in Iranian critical care nurses using a hierarchical Bayesian technique
Morteza Kazemi, Kiavash Hushmandi, Amir Vahedian-Azimi, Majid Moayyed, Leila Karimi, Mohammad Ali Sheikh Beig Goharrizi, Mahmood Salesi, Karim Parastouei, Mehdi Raei
Work.2022; 72(4): 1409. CrossRef - Socio-Economic Characteristics of Urban Tuberculosis Areas in Petaling, Selangor: A Current Spatial Exploratory Scenario
N.N.N Mohd Zaini, A.R. Abdul Rasam, C.B. Ahmad
IOP Conference Series: Earth and Environmental Science.2022; 1067(1): 012041. CrossRef - Cutaneous Leishmaniasis Based on Climate Regions in Iran (1998-2021)
Mehri Rejali, Nadia Mohammadi Dashtaki, Afshin Ebrahimi, Asieh Heidari, Mohammad Reza Maracy
Advanced Biomedical Research.2022; 11(1): 120. CrossRef - Molecular Study of Cutaneous Leishmaniasis Species among Soldiers with Dermal Ulcers in Zahedan, Iran
Sina Sekandarpour, Minoo Shaddel, Zahra Sadat Asadi
Military Caring Sciences.2021; 7(4): 310. CrossRef - Sensitivity of disease cluster detection to spatial scales: an analysis with the spatial scan statistic method
Meifang Li, Xun Shi, Xia Li, Wenjun Ma, Jianfeng He, Tao Liu
International Journal of Geographical Information Science.2019; 33(11): 2125. CrossRef
|
KSE
