Background: Tuberculosis (TB) is currently one of the greatest problems in public health. Mycobacterium tuberculosis infects about one third of the world's population, of whom more than 80% are living in developing countries. The incidence and prevalence of TB are very different in various parts of Iran and also throughout the world. East provinces are one of the areas in the country where the risk of TB rate is the highest because of its ethnically varied population and special location. The present research sought to identify high risk clusters of Tuberculosis with mapping using Space-time Permutation scan statistics. Exact locations of patients, primary residences at the time of diagnoses are routinely collected as part of the TB surveillance program to ability clusters and detect disease outbreaks Tuberculosis early is important in order to decrease morbidity and mortality through timely implementation of disease prevention and control measures. It has been shown for syndromic surveillance data that when exact geographic coordinates of individual patients are used, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. Materials and Methods: The present research is of descriptive type. The required data were gathered from the registered TB reports of TB Control Office in the Center for Communicable Disease of the Iran Ministry of Health and Medical Education (MOHME). The data were extracted at province level in over the time period of 2006–2013. We apply a variation of Space-time Permutation scan statistic to the TB data in which a patient location. SAT Scan software was used to analyze the data and to identify high risk clusters. ArcGIS10 was utilized to map the distribution of Tuberculosis and to demonstrate high risk clusters. Results: In the purely spatial analyses, the most likely clusters were in Sistan & Balouchestan provinces (in 2006, 2008), the Golestan, Khorasan Razavi, (in 2009), and the Khozestan (in 2010 to 2013). In the space-time analysis, the most likely clusters were Golestan and Khozestan industrial area (in 2010-2013). Accordingly, the most likely clusters and High risk regions included East and West-South provinces, particularly Golestan, Khorasan Razavi, Sistan & Balouchestan and Khozestan. It was statistically significant at the p-value below 0.05. Conclusion: The spatial and space-time Permutation scan statistics are effective ways of describing circular disease clusters. Since, in reality, infectious diseases might form other cluster types, the effectiveness of the method may be limited under actual practice. The sophistication of the analytical methodology, however, is a topic for future study.
