The impact of non-disclosure of HIV status and antiretroviral therapy on HIV recency testing and incidence algorithms

Karin van den Berg; Edward L Murphy; Gary Maartens; Vernon J Louw; Eduard Grebe

doi:10.1111/vox.13627

The impact of non-disclosure of HIV status and antiretroviral therapy on HIV recency testing and incidence algorithms

Vox Sang. 2024 Apr 15. doi: 10.1111/vox.13627. Online ahead of print.

Authors

Karin van den Berg^{1

2

3}, Edward L Murphy^{4

5

6}, Gary Maartens⁷, Vernon J Louw², Eduard Grebe^{6

8}

Affiliations

¹ Medical Division, South African National Blood Service, Roodepoort, South Africa.
² Division of Clinical Haematology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa.
³ Division of Clinical Haematology, University of the Free State, Bloemfontein, South Africa.
⁴ Department of Laboratory Medicine, University of California, San Francisco, California, USA.
⁵ Department of Epidemiology, University of California, San Francisco, California, USA.
⁶ Vitalant Research Institute, San Francisco, California, USA.
⁷ Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
⁸ Eduard Grebe Consulting, Cape Town, South Africa.

PMID: 38622931
DOI: 10.1111/vox.13627

Abstract

Background and objectives: Accurate HIV incidence estimates among blood donors are necessary to assess the effectiveness of programs aimed at limiting transfusion-transmitted HIV. We assessed the impact of undisclosed HIV status and antiretroviral (ARV) use on HIV recency and incidence estimates using increasingly comprehensive recent infection testing algorithms.

Materials and methods: Using 2017 donation data from first-time and lapsed donors, we populated four HIV recency algorithms: (1) serology and limiting-antigen avidity testing, (2) with individual donation nucleic amplification testing (ID-NAT) added to Algorithm 1, (3) with viral load added to Algorithm 2 and (4) with ARV testing added to Algorithm 3. Algorithm-specific mean durations of recent infection (MDRI) and false recency rates (FRR) were calculated and used to derive and compare incidence estimates.

Results: Compared with Algorithm 4, progressive algorithms misclassified fewer donors as recent: Algorithm 1: 61 (12.1%); Algorithm 2: 14 (2.8%) and Algorithm 3: 3 (0.6%). Algorithm-specific MDRI and FRR values resulted in marginally lower incidence estimates: Algorithm 1: 0.19% per annum (p.a.) (95% confidence interval [CI]: 0.13%-0.26%); Algorithm 2: 0.18% p.a. (95% CI: 0.13%-0.22%); Algorithm 3: 0.17% p.a. (95% CI: 0.13%-0.22%) and Algorithm 4: 0.17% p.a. (95% CI: 0.13%-0.21%).

Conclusion: We confirmed significant misclassification of recent HIV cases when not including viral load and ARV testing. Context-specific MDRI and FRR resulted in progressively lower incidence estimates but did not fully account for the context-specific variability in incidence modelling. The inclusion of ARV testing, in addition to viral load and ID-NAT testing, did not have a significant impact on incidence estimates.

Keywords: HIV disclosure; HIV incidence estimation; HIV recency algorithms; antiretroviral agents; blood donation.

Abstract

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