Iran has one of the highest tuberculosis (TB) incidence rates in the Middle East, but a comprehensive analysis of its spatiotemporal distribution is lacking. This study aims to identify patterns of TB incidence across Iranian counties from 2014 to 2019, using advanced spatio-temporal methods, to guide targeted interventions for better disease control. This study was done by utilizing National data on confirmed TB cases in counties of Iran from 2014 to 2019. After cleaning the data and calculating the annual TB incidence rates per 100,000 population a descriptive map of annual incidence was created. The Getis-Ord Gi* and Anselin Local Moran’s I were used to determine the disease’s spatial hotspots. In addition, the spatial scan statistic was used for both spatiotemporal and spatial variation in temporal trends. The null hypothesis, which states that there are no clusters, was rejected in every case at p ≤ 0.05. Over six years, 56,748 TB cases dropped from 13.44 per 100,000 in 2014 to 9.89 in 2019. The Gi* statistic showed that eastern, southeastern, northern, and northeastern Iran had the greatest TB rates and hotspots throughout the research. Northern hotspots have grown during the research period, affecting Tehran’s bordering counties. Additionally, a temporal study showed a high-rate cluster distribution in early 2014–2015. Space-time analysis identified 7 high-rate TB clusters with a 50% scanning window size across time and locales ( p < 0.05). Spatial variation in temporal trends found additive tendency TB clusters in the research. The study found high-risk TB areas in Iran’s northern, eastern, and southeastern regions. Despite a decreasing trend in TB incidence, targeted prevention, screening, and treatment programs are crucial. Spatiotemporal analyses offer insights for optimizing TB control strategies in Iran, with significant clusters identified in Tehran’s neighboring counties. Not applicable.
Khodadoust et al. (Mon,) studied this question.