Prioritizing Additional Data Collection to Reduce Decision Uncertainty in the HIV/AIDS Response in 6 US Cities: A Value of Information Analysis

• Published in: Value in health 2020-12, Vol.23 (12), p.1534-1542
• Main Authors: Zang, Xiao, MSc, Jalal, Hawre, MD, PhD, Krebs, Emanuel, MA, Pandya, Ankur, PhD, Zhou, Haoxuan, Enns, Benjamin, MA, Nosyk, Bohdan, PhD
• Format: Article
• Language: English
• Subjects: Acquired immune deficiency syndrome; Adolescent; Adult; AIDS; Ambition; Antiretroviral drugs; Cities; Cost analysis; Cost-Benefit Analysis; Data collection; Data Collection - economics; Data Collection - methods; Decision making; Decision Making, Organizational; decision uncertainty; Deep learning; Disease Eradication - economics; Disease Eradication - methods; Disease Eradication - organization & administration; Disease prevention; Disease transmission; Epidemics; expected value of partial perfect information; expected value of perfect information; Female; Health care expenditures; Health education; Health services; HIV; HIV combination implementation strategies; HIV Infections - economics; HIV Infections - prevention & control; Human immunodeficiency virus; Humans; Internal Medicine; Learning; Male; metamodel; Middle Aged; Models, Statistical; Prioritizing; Prophylaxis; Public health; Risk behavior; Uncertainty; United States - epidemiology; Urban Population - statistics & numerical data; Value; Young Adult
• ISSN: 1098-3015; 1524-4733
• DOI: 10.1016/j.jval.2020.06.013

AbstractObjectivesThe ambitious goals of the US Ending the HIV Epidemic initiative will require a targeted, context-specific public health response. Model-based economic evaluation provides useful guidance for decision making while characterizing decision uncertainty. We aim to quantify the value of eliminating uncertainty about different parameters in selecting combination implementation strategies to reduce the public health burden of HIV/AIDS in 6 US cities and identify future data collection priorities. MethodsWe used a dynamic compartmental HIV transmission model developed for 6 US cities to evaluate the cost-effectiveness of a range of combination implementation strategies. Using a metamodeling approach with nonparametric and deep learning methods, we calculated the expected value of perfect information, representing the maximum value of further research to eliminate decision uncertainty, and the expected value of partial perfect information for key groups of parameters that would be collected together in practice. ResultsThe population expected value of perfect information ranged from $59 683 (Miami) to $54 108 679 (Los Angeles). The rank ordering of expected value of partial perfect information on key groups of parameters were largely consistent across cities and highest for parameters pertaining to HIV risk behaviors, probability of HIV transmission, health service engagement, HIV-related mortality, health utility weights, and healthcare costs. Los Angeles was an exception, where parameters on retention in pre-exposure prophylaxis ranked highest in contributing to decision uncertainty. ConclusionsFunding additional data collection on HIV/AIDS may be warranted in Baltimore, Los Angeles, and New York City. Value of information analysis should be embedded into decision-making processes on funding future research and public health intervention.