TRA2-2 ESTIMATING THE COST-EFFECTIVENESS OF XPERT MTB/RIF: APPLYING A BAYESIAN CALIBRATION APPROACH TO A DYNAMIC TB-HIV EPIDEMIC MODEL

Thursday, October 18, 2012: 10:48 AM
Regency Ballroom C (Hyatt Regency)
INFORMS (INF), Applied Health Economics (AHE)
Candidate for the Lee B. Lusted Student Prize Competition

Nicolas A. Menzies, MPH, Harvard University, Boston, MA, Ted Cohen, PhD, Division of Global Health Equity, Brigham and Women's Hospital, Boston, MA, Hsien-ho Lin, PhD, Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan, Megan Murray, PhD, Department of Epidemiology, Harvard School of Public Health, Boston, MA and Joshua A. Salomon, PhD, Harvard School of Public Health, Boston, MA

Purpose: The Xpert MTB/RIF test enables rapid detection of tuberculosis and rifampicin resistance. The World Health Organization recommends this recently developed test for initial diagnosis in people suspected of having multi-drug resistant TB or HIV-associated-TB, and many national TB programs are moving quickly to adopt Xpert.  As roll-out proceeds, it is essential to understand the potential health impact and cost-effectiveness of Xpert-based diagnostic strategies.

Method: We evaluated potential consequences of Xpert adoption in five southern African countries—Botswana, Lesotho, Namibia, South Africa, and Swaziland—where drug resistance and TB-HIV coinfection are prevalent. Analyses were conducted using a dynamic mathematical model of TB epidemiology, designed to account for the development and propagation of TB drug resistance, and the influence of epidemic HIV on TB natural history.  Prior information on many TB natural history parameters is poor, and to characterize uncertainty we adopted a Bayesian estimation approach, probabilistically calibrating the model to reported data on TB prevalence, incidence, and MDR-TB prevalence by country. Using the calibrated model, we compared the status quo diagnostic algorithm, which emphasizes sputum smear, to an algorithm incorporating Xpert for initial diagnosis.

Result: Compared to status quo, implementation of Xpert would avert an estimated 132 [95% posterior interval: 55 – 284] thousand TB cases and 182 [97 – 302] thousand TB deaths in southern Africa over the 10 years following introduction, and reduce prevalence by 20-30% by 2022, with more modest reductions in incidence. Health system costs are projected to increase substantially with Xpert, requiring an additional $US 460 [294-699] million over 10 years. Antiretroviral therapy for HIV represents a substantial fraction of these additional costs, a consequence of improved survival in TB/HIV-infected populations through better TB case-finding and treatment.  Relative to status quo, the Xpert strategy has an estimated cost-effectiveness of US$959 [$633-$1,485] per DALY averted over 10 years following introduction. Across the five examined countries, cost-effectiveness ratios over the same period range from $792 [$482-$1,785] in Swaziland to $1,257 [$767-$2,276] in Botswana.

Conclusion: Adoption of Xpert has potential to produce substantial changes in TB morbidity and mortality, and offers high value for money based on conventional benchmarks for cost-effectiveness in resource-limited settings. However, the additional financial burden of adoption would be substantial, including significant increases in HIV treatment costs.