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Publication Profiles

Impact of molecular sequence data completeness on HIV cluster detection and a network science approach to enhance detection

Citation

Mazrouee S, Hallmark CJ, Mora R, et al. Impact of molecular sequence data completeness on HIV cluster detection and a network science approach to enhance detection. Sci Rep. 2022;12(1):19230. doi:10.1038/s41598-022-21924-8

Implementation stage:

Pre-Implementation

Implementation outcomes reported:

Not applicable

Continuum target addressed:

HIV testing, Linkage to HIV care, Engagement in treatment (including retention and re-engagement)

Geography:

Harris County-TX

Study Participant Classification:

Recipients of care

Plain Language Objectives

The researchers wanted to learn how missing HIV genetic sequences affect detection of possible transmission clusters. They also tested whether information about highly connected or influential cases could help improve detection when sequence data are incomplete.

Plain Language Abstract

Public health programs use HIV genetic sequence data to identify possible transmission clusters, but many surveillance datasets are incomplete. Researchers analyzed Houston surveillance data from about 13,000 people diagnosed with HIV between 2012 and 2019. Sequences were available for 6,852 people, or 53%. The team repeatedly removed data to simulate different levels and patterns of completeness, then compared the detected networks with the full available network. As overall completeness increased, more clusters were detected. Which records were missing also mattered. When the simulation removed nodes with the highest expected influence in the network, only 4.7% of priority clusters were found; when it removed the lowest-influence nodes, 60% were found. Thus, missing influential nodes had a much greater effect than missing less influential ones. The authors concluded that improving sequence reporting remains the best route to complete cluster detection, while network information might help programs prioritize efforts when completeness is difficult to achieve. These results come from simulations using one city’s surveillance network and depend on the network definition, available sequences, and assumptions used.

Key Takeaways

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