Cost-effectiveness of TB diagnostic technologies in Ethiopia: a modelling study

Abstract

Background: Tuberculosis (TB) is a major threat to public health, particularly in countries where the disease is highly prevalent, such as Ethiopia. Early diagnosis and treatment are the main components of TB prevention and control. Although the national TB guideline recommends the primary use of rapid TB diagnostics whenever feasible, there is limited evidence available that assess the efficiency of deploying various diagnostic tools in the country. Hence, this study aims to evaluate the cost-effectiveness of rapid TB/MDR-TB diagnostic tools in Ethiopia.

Methods: A hybrid Markov model for a hypothetical adult cohort of presumptive TB cases was constructed. The following TB diagnostic tools were evaluated: X-pert MTB/RIF, Truenat, chest X-ray screening followed by an X-pert MTB/RIF, TB-LAMP, and smear microscopy. Cost-effectiveness was determined based on incremental costs (USD) per Disability-adjusted Life Years (DALY) averted, using a threshold of one times Gross Domestic Product (GDP) per capita (856 USD). Data on starting and transition probabilities, costs, and health state utilities were derived from secondary sources. The analysis is conducted from the health system perspective, and a probabilistic sensitivity analysis is performed.

Result: The incremental cost-effectiveness ratio for X-pert MTB/RIF, compared to the next best alternative, is 276 USD per DALY averted, making it a highly cost-effective diagnostic tool. Additionally, chest X-ray screening followed an X-pert MTB/RIF test is less cost-effective, with an ICER of 1666 USD per DALY averted. Introducing X-pert MTB/RIF testing would enhance TB detection and prevent 9600 DALYs in a cohort of 10,000 TB patients, with a total cost of 3,816,000 USD.

Conclusion: The X-pert MTB/RIF test is the most cost-effective diagnostic tool compared to other alternatives. The use of this diagnostic tool improves the early detection and treatment of TB cases. Increased funding for this diagnostic tool will enhance access, reduce the TB detection gaps, and improve treatment outcomes.