Research Paper Title
Spatial modeling of Cutaneous Leishmaniasis among Iranian Army Units during 2014-2017 using hierarchical Bayesian method and spatial scan statistic.
This study aimed to map the incidence and identify possible spatial clusters of Cutaneous Leishmaniasis (CL) among Iranian Army Units (IAUs).
This ecological study investigated incidence cases of CL between 2014 and 2017. CL data were extracted from the CL registry at health deputy of AJA Military University of Medical Sciences. Standardised Incidence Ratio (SIR) of CL were computed with Besag, York and Mollié model (BYM). Purely spatial scan statistic was employed to detect the most likely high and low rate clusters and obtain observed to expected (O/E) ratio for each detected cluster. The statistical significance of the clusters were assessed using log likelihood ratio (LLR) test and Monte Carlo hypothesis testing.
A total of 1144 new CL cases were occurred among IAUs with incidence rate of 260 per 100,000 from 2014 to 2017. The provinces Isfahan and Khuzestan were found to have the higher than expected CL cases in the all studied years (SIR>1), however, the provinces Kermanshah, Kerman and Fars were observed as high risk areas in some year of the study period. The most likely significant high CL cluster involved Kermanshah province (O/E=67.88, LLR=1200.62, P-value<0.001) following by clusters Isfahan (O/E=6.02, LLR=513.24, P-value<0.001) and Khuzestan (O/E=2.35, LLR=73.71, P-value<0.001), while low rate cluster were located in the northeast areas and involved Razavi Khorasan, North Khorasan, Semnan, Golestan (O/E=0.03, LLR=95.11, P-value<0.001).
This study highlighted truly high risk areas for CL that can be considered for public health implications and planning of control interventions among IAUs.
Ayubi, E., Barati, M., Dabbagh-Moghaddam, A. & Khoshdel, A. (2018) Spatial modeling of Cutaneous Leishmaniasis among Iranian Army Units during 2014-2017 using hierarchical Bayesian method and spatial scan statistic. Epidemiology and Health. doi: 10.4178/epih.e2018032. [Epub ahead of print].
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