Near real-time monitoring of HIV transmission hotspots from routine HIV genotyping: an implementation case study

Abstract

BackgroundDue to the rapid evolution of HIV, infections with similar genetic sequences are likely to be related by recent transmission events. Clusters of related infections can represent subpopulations with high rates of HIV transmission. Here we describe the implementation of an automated “near real-time” system using clustering analysis of routinely collected HIV resistance genotypes to monitor and characterize HIV transmission hotspots in British Columbia (BC).MethodsA monitoring system was implemented on the BC Drug Treatment Database, which currently holds over 32000 anonymized HIV genotypes for nearly 9000 residents of BC living with HIV. On average, five to six new HIV genotypes are deposited in the database every day, which triggers an automated re-analysis of the entire database. Clusters of five or more individuals were extracted on the basis of short phylogenetic distances between their respective HIV sequences. Monthly reports on the growth and characteristics of clusters were generated by the system and distributed to public health officers.FindingsIn June 2014, the monitoring system detected the expansion of a cluster by 11 new cases over three months, including eight cases with transmitted drug resistance. This cluster generally comprised young men who have sex with men. The subsequent report precipitated an enhanced public health follow-up to ensure linkage to care and treatment initiation in the affected subpopulation. Of the nine cases associated with this follow-up, all had already been linked to care and five cases had started treatment. Subsequent to the follow-up, three additional cases started treatment and the majority of cases achieved suppressed viral loads. Over the following 12 months, 12 new cases were detected in this cluster with a marked reduction in the onward transmission of drug resistance.InterpretationOur findings demonstrate the first application of an automated phylogenetic system monitoring a clinical database to detect a recent HIV outbreak and support the ensuing public health response. By making secondary use of routinely collected HIV genotypes, this approach is cost-effective, attains near realtime monitoring of new cases, and can be implemented in all settings where HIV genotyping is the standard of care.FundingThis work was supported by the BC Centre for Excellence in HIV/AIDS and by grants from the Canadian Institutes for Health Research (CIHR HOP-111406, HOP-107544), the Genome BC, Genome Canada and CIHR Partnership in Genomics and Personalized Health (Large-Scale Applied Research Project HIV142 contract to PRH, JSGM, and AFYP), and by the US National Institute on Drug Abuse (1-R01-DA036307-01, 5-R01-031055-02, R01-DA021525-06, and R01-DA011591).

Social and Genetic Networks of HIV-1 Transmission in New York City

“…Unfortunately, the time delay between HIV diagnosis/interview and genotype acquisition by the DOHMH (i.e., 6 months in our study, though this delay has shrunk since 2012) represents an impediment to prompt network-based intervention. Point-of-diagnosis-genotyping coupled with real-time genetic network analysis to identify potential transmission partners could help interdict ongoing transmission and target prevention, linkage to care, and treatment more effectively (as recent suggested in [18]).…”

Impacts and shortcomings of genetic clustering methods for infectious disease outbreaks

“…Nearly all uses of genetic clustering to infectious diseases have occurred in the context of retrospective studies, in which clusters are identified at a fixed point in time and seldom revisited in subsequent studies. In settings where there is widespread access to routine HIV genotyping, however, it is possible to prospectively track the appearance of new infections in clusters in real time (Little et al 2014; Poon et al 2016). Regardless of the inherent biases of genetic clustering methods, observing a rapid succession of new infections in a predefined cluster may represent an important source of evidence of a localized outbreak.…”

“…A sequence was classified as clustered if its shortest patristic distance to another sequence was below the cutoff in 80% or more of the bootstrap trees. This is the same clustering method being used for near real-time monitoring of HIV hotspots in British Columbia, Canada (Poon et al 2016). …”

“…More recently, there has been a surge of interest in the use of genetic clustering to identify and characterize localized outbreaks of an infectious disease. By detecting subpopulations exposed to high rates of transmission, clustering may potentially facilitate a more impactful and cost-effective deployment of public health resources (Little et al 2014; Novitsky et al 2015; Poon et al 2016), such as point-of-care testing or pre-exposure prophylaxis (Pillay et al 2015). There are now many different genetic clustering methods that have been developed for this purpose, predominantly in the field of HIV (e.g.…”

Treatment as Prevention: Characterization of Partner Infections in the HIV Prevention Trials Network 052 Trial

“…Phylogenetic methods can be used to study HIV transmission networks and hotspots within communities and populations 8–10 and to assess the genetic linkage of specific transmission events. 4,11 In this study, we used phylogenetic and statistical methods to identify genetically-linked partner infections in the HPTN 052 trial, which were the basis of the primary endpoint analysis for this landmark study.…”