A cost-effectiveness analysis of a novel algorithm to sequentially diagnose leprosy based on manufactured tests under the SUS perspective.

Brazil has the second largest number of leprosy cases (a disease with a significant burden) in the world. Despite global and local efforts to eliminate this public health problem, inadequate or late diagnosis contribute to perpetuate its transmission, especially among household contacts. Tests such as the rapid IgM antibody detection (RT) and real-time polymerase chain reaction (RT-PCR) were developed to overcome the challenges of early diagnosis of leprosy. This study aimed to analyze the cost-effectiveness of a new diagnostic algorithm recommended by the Brazilian government to diagnose leprosy in household contacts of confirmed leprosy cases, which includes the RT and RT-PCR tests. A decision tree model was constructed and the perspective of the Brazilian Unified National Health System (SUS) and a 1-year time horizon were adopted. Only direct medical costs related to diagnostic tests were included. Effectiveness was measured as the number of avoided undiagnosed leprosy cases. Different scenarios were analyzed. The sequential use of RT, slit-skin smear (SSS) microscopy, and RT-PCR as recommended by the Brazilian Ministry of Health was compared to a base case (isolated SSS microscopy), yielding an incremental cost-effectiveness ratio of USD 616.46 per avoided undiagnosed leprosy case. Univariate sensitivity analysis showed that the prevalence of leprosy among household contacts was the variable that influenced the model the most. This is the first economic model to analyze a diagnostic algorithm of leprosy. Results may aid managers to define policies and strategies to eradicate leprosy in Brazil.