Estimating the Economic Consequences of Leveraging the Novel cobas HIV-1/HIV-2 Qualitative Nucleic Acid Amplification Test in Routine HIV Testing

评估在常规 HIV 检测中应用新型 cobas HIV-1/HIV-2 定性核酸扩增检测的经济效益

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Abstract

BACKGROUND: Efficient and cost-effective testing strategies are needed to reduce human immunodeficiency virus (HIV) transmission. The aim of the current study was to compare the costs of using the current Centers for Disease Control and Prevention 3-step HIV algorithm-antigen-antibody screening, differentiation immunoassay (Geenius), and nucleic acid amplification test against an alternative algorithm using a cobas HIV-1/HIV-2 qualitative nucleic acid amplification test. METHODS: A 1-year cost calculator was developed from a US payer perspective using a decision-tree approach to simulate testing in a population of 1 million individuals. An estimated 2.44% of individuals were tested for HIV with an antigen-antibody immunoassay test, of which 0.72% were reactive. Based on real-world evidence, it was estimated that 8.78% and 1.95% of those tested with Geenius and cobas, respectively, were retested after indeterminate/invalid results. HIV-1/HIV-2 prevalence was applied along with test performance and retest rates to estimate the total number of tests required and the overall costs per algorithm. RESULTS: Among 175 patients, overall payer costs with the current and alternative algorithm were $5299.37 and $6870.24, respectively, yielding $8.95 additional cost per patient. The total number of tests performed decreased by 21.29% using the alternative algorithm (191.8 vs 243.7). The total number of retests were 20.7 and 4.5 with the current and alternative algorithm, respectively. CONCLUSIONS: The study demonstrates a significant reduction in the number of cumulative tests needed to diagnose HIV accurately while remaining cost neutral to payers using the alternative algorithm. These findings suggest that the proposed alternative algorithm can streamline HIV testing without a substantial increase in healthcare costs and minimize adverse patient outcomes.

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