Subnational burden estimates to find missing people with tuberculosis: wrong but useful?

Efforts to combat tuberculosis (TB) require reliable national and subnational data for planning, monitoring and evaluation. Yet, reliable subnational estimates of TB burden are hard to come by-especially at the lower levels of disaggregation such as district, community, or ward level. Several approaches have been proposed to generate subnational estimates of TB burden. However, ascertaining the accuracy of modelled estimates and ensuring their use for TB program planning remains a challenge, thereby raising questions about their usefulness. In this perspective article, we review several subnational TB models to gain insights into their accuracy, purpose and use as a starting point to reflect on their usefulness in finding the missing people with TB. We argue that despite concerns about their accuracy, subnational TB models can help pinpoint areas that deserve more programmatic attention (spatial targeting) and better understand the effectiveness of interventions (programmatic learning). Furthermore, increasing the use of these models can help improve both their accuracy and usefulness in the long run-if estimates are systematically compared against programmatic data and models are improved to better capture reality on the ground. As such, we conclude that subnational TB models represent an essential evidence-based learning tool to guide the search for the missing people with TB.