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Active case-finding (ACF) is an important component of the End TB Strategy. However, ACF is resource-intensive, and the economics of ACF are not well-understood. Data on the costs of ACF are limited, with little consistency in the units and methods used to estimate and report costs. Mathematical models to forecast the long-term effects of ACF require empirical measurements of the yield, timing and costs of case detection. Pragmatic trials offer an opportunity to assess the cost-effectiveness of ACF interventions within a ‘real-world´ context. However, such analyses generally require early introduction of economic evaluations to enable prospective data collection on resource requirements. Closing the global case-detection gap will require substantial additional resources, including continued investment in innovative technologies. Research is essential to the optimal implementation, cost-effectiveness, and affordability of ACF in high-burden settings. To assess the value of ACF, we must prioritize the collection of high-quality data regarding costs and effectiveness, and link those data to analytical models that are adapted to local settings.

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Highlights• Mathematical and economic modelling is being used to inform how disease control resources are allocated and what policies are adopted. These models specify complex non-linear relationships between service coverage and impact. However, functions describing the relationship between costs and service volume have typically been less sophisticated, assuming constant marginal costs of expanding service coverage. This approach runs counter to the theoretical understanding of the way costs behave when scaling up.• We propose an alternative approach. We developed a mechanistic framework to estimate total costs for inclusion in model-based economic evaluations using a combination of secondary data from small-scale costing studies and routine reporting systems. We provide a pragmatic, mechanistic framework rooted in economic theory for others facing similar data constraints to improve resource allocation models used to define packages of interventions within LMIC settings.• Using a case study of tuberculosis case detection in South Africa, we show that the functional form chosen to estimate total costs will determine the magnitude of total costs when increasing the outputs and coverage. In turn, these differences can impact policy choice and resource allocation decisions. The framework presented here is a first step towards a more transparent and empirically based cost modelling approach to better inform resource allocation processes.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.