Global tuberculosis (TB) burden estimates are aggregated at the national level, despite the likelihood of uneven distribution across and within regions in the same country. Subnational estimates are crucial to producing informed policies and informing budget allocation at more granular levels. In collaboration with the National TB Programme (NTP), we applied a simple and transparent tool to estimate the subnational TB burden in Pakistan. We tailored the SUBnational Burden Estimation for TB (SUBsET) tool to account for the district-level hierarchy of Pakistan. Districts were assigned weighted scores based on population size, level of urbanisation, households with one room, and food insecurity levels. Using the 2022 national TB incidence estimate, we first allocated the burden across administrative units based on data from the 2010-11 TB prevalence survey and subsequently refined this distribution to reflect weighted scores specific to each district. The estimated TB incidence was compared with pulmonary TB notifications to calculate the case detection rate (CDR) for each district. Utilising the updated SUBsET model, we assigned weight scores to 150 districts spanning seven provinces/regions in Pakistan. The estimated TB incidence varied significantly, ranging from 110 (95%CI: 80-145) to 462 (95%CI: 337-607) per 100,000 inhabitants per year. The provinces bearing the highest burden was Sindh (292; 95%CI: 213-384), followed by Khyber Pakhtunkhwa (269; 95%CI: 196-354) and Punjab (243; 95%CI: 177-320). The CDR was below 70% in three-quarters of the districts and over-reporting (>100%) was observed in 10 districts, primarily within Punjab, which suggests that individuals with TB may be crossing district lines to access care. The application of the SUBsET tool through active collaboration with the NTP revealed high heterogeneity in subnational TB incidence in Pakistan, urging a more granular and tailored approach to TB prevention and care. This approach ensured transparency and acceptance of the findings for wider in-country dissemination.
Copyright: © 2024 Schwalb et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.