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

Abstract

Background: HIV evolves rapidly and therefore infections with similar genetic sequences are likely linked by recent transmission events. Clusters of related infections can represent subpopulations with high rates of transmission. We describe the implementation of an automated near real-time system to monitor and characterise HIV transmission hotspots in British Columbia, Canada.

Methods: In this implementation case study, we applied a monitoring system to the British Columbia drug treatment database, which holds more than 32 000 anonymised HIV genotypes for nearly 9000 residents of British Columbia living with HIV. On average, five to six new HIV genotypes are deposited in the database every day, which triggers an automated reanalysis of the entire database. We extracted clusters of five or more individuals with short phylogenetic distances between their respective HIV sequences. The system generated monthly reports of the growth and characteristics of clusters that were distributed to public health officers.

Findings: In June, 2014, the monitoring system detected the expansion of a cluster by 11 new cases during 3 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 most cases achieved suppressed viral loads. During the next 12 months, we detected 12 new cases in this cluster with reduction in the onward transmission of drug resistance.

Interpretation: Our findings show 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 real-time monitoring of new cases, and can be implemented in all settings in which HIV genotyping is the standard of care.

Funding: BC Centre for Excellence in HIV/AIDS, the Canadian Institutes for Health Research, the Genome Canada-CIHR Partnership in Genomics and Personalized Health, and the US National Institute on Drug Abuse.

Figure 1. Routine HIV resistance genotyping

The blue trend line represents the accumulation of HIV genotypes in the BC Drug Treatment Program (DTP) population database since the inception of the Laboratory Program at the BC Centre for Excellence in HIV/AIDS (CFE). To date, over 32,000 HIV genotypes have been deposited in the DTP database. The red trend line indicates the quarterly median (interquartile range indicated by shaded region) of sample processing times from test requisition to the date of entry of HIV genotype records into the population database.

Figure 2. Network diagram and growth trend for cluster index 0

This figure depicts diagrams that are generated for the phylogenetic monitoring reports. The largest phylogenetic cluster from the BC DTP database, cluster 0 is largely composed of people who use injection drugs in the Vancouver ‘Downtown Eastside’. (A) Each circle in the network diagram corresponds to a person living with HIV, sized in proportion to their most recent viral load, and colored to indicate whether any HIV genotype tests for that individual have been classified with high (red) or intermediate (orange) HIV drug resistance, or mortality (grey). This annotation scheme emphasizes parts of the network where transmissions are most likely to occur, notwithstanding individuals who have not yet been sampled. New cases within the reporting period are indicated by a double outline. Lines drawn between circles indicate that the shortest distance separating HIV sequences from the respective individuals in the phylogenies fell within the clustering threshold. (B) The blue line represents the growth of cluster 0 based on the imputed dates of HIV seroconversion. Prevalence (green) was estimated by subtracting deceased cases from the growth trend. A red circle indicates the estimated number of cases for which the most recent viral loads were at detectable levels, based on the available data at the end of the reporting period.

Figure 3. Geographic distribution of cluster 0

This simplified map of the province of BC is automatically generated by the monitoring system for every active cluster within the reporting period. The map, adapted from a cartogram published in The British Columbia Atlas of Child Development, is distorted to emphasize regions with higher population densities. In this example, cases in cluster 0 were defined as recent when their earliest database entry date was in 2014 or later. The distribution of recent cases among regions, based on the forward sortation areas of the physicians' offices where individuals have most recently accessed primary care, is indicated by the coloration of the respective polygons (see figure legend). Bold labels indicate the five regional health authorities of BC.

Figure 4. Timeline of cluster 55 outbreak

A timeline of the growth of cluster 55 is illustrated by the upper diagram where new cases are mapped to their respective dates of sample collection (circle) and HIV genotype database entry (diamond), and colored with respect to HIV drug resistance. A dashed line indicates the date that a provisional report on an outbreak of 8 new cases in cluster 55 was issued. The network diagrams in each panel below the timeline summarize the phylogenetic relationships among the new cases and their most recent viral loads by the end of the respective reporting periods. Note that the HIV genotype connecting an individual to others in the network and their most recent viral load were not necessarily derived from the same sample.