Sequencing HIV Diagnostic Samples to Detect Genetic Clusters and Assess Sequence Coverage Gaps.

Abstract

Background: HIV molecular cluster detection in the United States relies on HIV sequences obtained from drug resistance testing during clinical care ("routine care sequences"). This approach misses people who are not linked to care or who receive care but have uncollected or unreported sequences.

Methods: We collected "HIV test sequences" from remnant serum samples of people testing newly positive from 2018 through 2021 by a large public health laboratory in North Carolina. We incorporated the HIV test sequences into a statewide molecular cluster analysis and assessed impact on "active cluster" detection (≥5 members newly diagnosed). We described data gaps filled by HIV test sequences, comparing (1) the extent of care sequence missingness due to gaps in care linkage vs sequence collection or reporting and (2) the characteristics of people with an HIV test sequence who had a care sequence, care but no care sequence, or no evidence of care.

Results: Of 19 770 people included in the cluster analysis, 847 had an HIV test sequence, one-third of whom had no routine care sequence. We identified 13 additional active clusters (a 33% relative increase) and 40 larger active clusters after incorporating HIV test sequences. Most people with an HIV test sequence but no care sequence (78%) had another care indicator, suggesting sequence undercollection or underreporting, but a fifth (22%) had no evidence of care.

Conclusions: Higher sequence coverage can improve cluster detection. While increased routine care sequence collection and reporting could fill many data gaps, sequencing remnant HIV test samples could include people without care linkage.