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Improvements in the accuracy and completeness of cause-specific mortality data have the potential to better identify leading causes of death (CODs) in a population, guiding health systems in resourcing decisions for mortality reduction. In many low- and middle-income countries, cause-specific mortality surveillance in a population is most often drawn from verbal autopsies (VAs). VAs assign COD with less accuracy than clinical autopsy methods, such as minimally invasive tissue sampling (MITS). However, when MITS is used on a subset of cases alongside VA, the clinical- and interview-based data can be used in machine learning algorithms which enhance the accuracy of COD data at the population level. Insight into the costs and cost dynamics to implement this approach inform investment decisions by policymakers aiming to improve mortality surveillance data. This study demonstrates the potential costs of routine mortality surveillance using data from VAs and MITS at varying scales and surveillance objectives. Costs were modeled using data from four sites across sub-Saharan Africa and South Asia.
