Growth of HIV-1 Molecular Transmission Clusters in New York City

Abstract

Background: HIV-1 genetic sequences can be used to infer viral transmission history and dynamics. Throughout the United States, HIV-1 sequences from drug resistance testing are reported to local public health departments.

Methods: We investigated whether inferred HIV transmission network dynamics can identify individuals and clusters of individuals most likely to give rise to future HIV cases in a surveillance setting. We used HIV-TRACE, a genetic distance-based clustering tool, to infer molecular transmission clusters from HIV-1 pro/RT sequences from 65736 people in the New York City surveillance registry. Logistic and LASSO regression analyses were used to identify correlates of clustering and cluster growth, respectively. We performed retrospective transmission network analyses to evaluate individual- and cluster-level prioritization schemes for identifying parts of the network most likely to give rise to new cases in the subsequent year.

Results: Individual-level prioritization schemes predicted network growth better than random targeting. Across the 3600 inferred molecular transmission clusters, previous growth dynamics were superior predictors of future transmission cluster growth compared to individual-level prediction schemes. Cluster-level prioritization schemes considering previous cluster growth relative to cluster size further improved network growth predictions.

Conclusions: Prevention efforts based on HIV molecular epidemiology may improve public health outcomes in a US surveillance setting.

Figure 1.

New York City HIV-1 surveillance molecular transmission network. Clusters with ≥5 cases (ie, nodes) are depicted. Links (ie, edges) indicate genetic distance ≤0.015 substitutions/site. Color indicates transmission risk factor and shape indicates birth sex. Abbreviations: Hetero, heterosexual; MSM, men who have sex with men; PWID, persons who use injection drugs.

Figure 2.

Map of New York City (NYC) depicting the proportion of cases clustered and the number of cases with reported genotypes in each ZIP code. White zones represent parks and ZIP codes with no associated HIV resistance genotypes in the surveillance database. Darker shading indicates increased number of total case with reported genotypes. Cooler blue colors indicate low proportion clustering and hotter red colors indicate higher proportion clustering. The insert identifies the 5 boroughs of NYC: Brooklyn (purple), the Bronx (blue), Manhattan (red), Staten Island (orange), and Queens (green).

Figure 3.

Performance of individual-level growth prediction schemes. Numbers of newly diagnosed and genotyped cases per prioritized cases in the 12 months following prioritization are shown for (A) cases diagnosed in the previous 12 months and (B) cases prioritized by LASSO regression model. The light gray block represents number of newly diagnosed and genotyped cases linked to randomly selected cases, and the dark gray block represents number of newly diagnosed and genotyped cases linked to randomly selected clusters. All schemes prioritized at least 500 cases. Line denotes individual-level prioritization schemes.

Figure 4.

Performance of cluster-level growth prediction schemes. Numbers of newly diagnosed and genotyped cases per prioritized cases in the 12 months following prioritization are shown for (A) cases in the largest clusters, (B) cases in clusters that experienced the greatest growth in the previous 12 months, (C) cases in clusters that experienced the greatest growth in the previous 12 months relative to cluster size at time of prioritization, and (D) cases in clusters that experienced the greatest growth in the previous 12 months relative to the square root of cluster size at time of prioritization. The light gray block represents number of newly diagnosed and genotyped cases linked to randomly selected cases, and the dark gray block represents number of newly diagnosed and genotyped cases linked to randomly selected clusters. All schemes prioritized at least 500 cases. Line denotes cluster-level prioritization schemes.

Figure 5.

Performance of combined individual- and cluster-level growth prediction schemes. Numbers of newly diagnosed and genotyped cases per prioritized cases in the 12 months following prioritization are shown for cases prioritized by the LASSO regression model (blue), cases in clusters that experienced the greatest growth in the previous 12 months relative to the square root of cluster size at time of prioritization (red), and cases prioritized by a LASSO regression model that includes the square root relative growth metric (purple). The light gray block represents number of newly diagnosed and genotyped cases linked to randomly selected cases, and the dark gray block represents number of newly diagnosed and genotyped cases linked to randomly selected clusters. All schemes prioritized at least 500 cases.