Spatiotemporal Clustering of Tuberculosis Notifications in Nepal During the COVID-19 Pandemic and Subsequent Recovery: A National Space-Time Scan Analysis, 2019–2024

The COVID-19 pandemic substantially disrupted tuberculosis (TB) prevention, diagnosis, and treatment services worldwide. Despite increased TB notifications in Nepal during the post-pandemic recovery period, the geographic distribution and temporal clustering of notifications remain poorly understood. This study examined the spatial and temporal patterns of TB notifications across Nepal during the COVID-19 recovery period. We conducted a nationwide retrospective ecological analysis of district-level TB notifications across all 77 districts of Nepal from FY 2019/20 to FY 2023/24. Space-time, purely spatial, and purely temporal scan statistics were performed using a discrete Poisson model in SaTScan. Statistical significance was assessed using Monte Carlo simulation with 999 replications. A total of 172,155 TB cases were notified during the study period. TB notification rates increased by 51%, from 92 to 139 per 100,000 population between FY 2019/20 and FY 2023/24. Four statistically significant high-notification space-time clusters were identified (all p < 0.001). The primary cluster included Kathmandu, Lalitpur, Bhaktapur, Makwanpur, Bara, and Parsa districts during FY 2021/22–FY 2023/24 (RR = 1.60). Additional clusters were identified in western Nepal, the eastern Terai, and Rupandehi district. Purely temporal analysis showed a nationwide increase in notifications during FY 2022/23–FY 2023/24 (RR = 1.26, p = 0.001). TB notifications increased substantially during Nepal’s post-pandemic recovery period, with marked geographic and temporal heterogeneity. Integrating spatial and temporal surveillance may help identify areas requiring geographically targeted TB control strategies.