Differentiated Human Immunodeficiency Virus RNA Monitoring in Resource-Limited Settings: An Economic Analysis.
Viral load (VL) monitoring for patients receiving antiretroviral therapy (ART) is recommended worldwide. However, the costs of frequent monitoring are a barrier to implementation in resource-limited settings. The extent to which personalized monitoring frequencies may be cost-effective is unknown.
We created a simulation model parameterized using person-level longitudinal data to assess the benefits of flexible monitoring frequencies. Our data-driven model tracked human immunodeficiency virus (HIV)-infected individuals for 10 years following ART initiation. We optimized the interval between viral load tests as a function of patients' age, gender, education, duration since ART initiation, adherence behavior, and the cost-effectiveness threshold. We compared the cost-effectiveness of the personalized monitoring strategies to fixed monitoring intervals every 1, 3, 6, 12, and 24 months.
Shorter fixed VL monitoring intervals yielded increasing benefits (6.034 to 6.221 discounted quality-adjusted life-years [QALYs] per patient with monitoring every 24 to 1 month over 10 years, respectively, standard error = 0.005 QALY), at increasing average costs: US$3445 (annual monitoring) to US$5393 (monthly monitoring) per patient, respectively (standard error = US$3.7). The adaptive policy optimized for low-income contexts achieved 6.142 average QALYs at a cost of US$3524, similar to the fixed 12-month policy (6.135 QALYs, US$3518). The adaptive policy optimized for middle-income resource settings yields 0.008 fewer QALYs per person, but saves US$204 compared to monitoring every 3 months.
The benefits from implementing adaptive vs fixed VL monitoring policies increase with the availability of resources. In low- and middle-income countries, adaptive policies achieve similar outcomes to simpler, fixed-interval policies.
College of Science and Engineering, Industrial and System Engineering, University of Minnesota, Minneapolis.
Diana M
DM
Negoescu
College of Science and Engineering, Industrial and System Engineering, University of Minnesota, Minneapolis.
Zhenhuan
Z
Zhang
Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Switzerland.
Heiner C
HC
Bucher
Department of Medicine, and.|Center for Health Policy/Center for Primary Care and Outcomes Research, Stanford University, California.
Eran
E
Bendavid
D000293:Adolescent; D000328:Adult; D023241:Antiretroviral Therapy, Highly Active / Q000191:economics* / Q000379:methods / Q000706:statistics & numerical data; D018791:CD4 Lymphocyte Count; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D005260:Female; D015658:HIV Infections / Q000188:drug therapy* / Q000191:economics; D006295:Health Resources; D006801:Humans; D008297:Male; D012367:RNA, Viral / Q000097:blood*; D019562:Viral Load / Q000191:economics / Q000379:methods; D055815:Young Adult
D012367:RNA, Viral
adaptive viral load monitoring.; differentiated care