More comprehensive molecular and genetic sequencing could help link cases to each other and alert authorities to HIV “clusters” that otherwise might be missed.
Part of the HIV surveillance process in the United States involves collecting data about cases of infection and then classifying the specific strains of HIV involved in each case by geographic area and when those strains first entered the surveillance system. This process is already effective in helping public health officials identify areas of the country where interventions might be useful for preventing further spread of the infection. However, more comprehensive molecular and genetic sequencing could help link the cases to each other and alert authorities to HIV “clusters” that otherwise might be missed.
“[HIV transmission] is kind of like a game of telephone,” explained Sharoda Dasgupta, PhD, MPH, in her oral presentation on the topic on April 24, 2017, at the 2017 EIS Annual Conference in Atlanta, Georgia. Dr. Dasgupta went on to say that individuals living with HIV strains that are similar may have experienced closely related transmission events. “Given that HIV is spread through bodily fluids during sexual contact or contact with injection drug equipment, large outbreaks can happen [and be traced],” she said. The similar HIV sequencing data from one strain is to another strain in terms of genetic distance, the greater the likelihood that a person living with that strain of HIV was infected as part of a related transmission event.
HIV surveillance programs may track genetic sequencing, molecular sequencing, or both. However, Dr. Dasgupta and her team determined that, oftentimes, incomplete sequencing data prevents health professionals from spotting “priority clusters” that either need to be monitored on an ongoing basis or that require some form of intervention. “We wanted to look at sequence data to determine what might be missed,” Dr. Dasgupta explained.
For their research, the team analyzed HIV surveillance data from Michigan and Washington state. (Only Michigan was noted in the published abstract.) “First, we identified molecular clusters based on HIV diagnoses between 2012 and 2014 in order to [identify] molecular clusters. Second, we determined the number of priority clusters defined as those with at least two new HIV diagnoses in the last year to indicate recent, active transmission. Third, we established the number of priority clusters and calculated sensitivity for completeness level [of sequencing data],” said Dr. Dasgupta.
The team identified 2341 individuals with diagnosed infections in Michigan alone and established that 26% of those infected were in a cluster. Of those in clusters, 26% were in priority clusters. In Washington, 18% of the 148 individuals diagnosed were in a molecular cluster and 13% of those in a cluster were considered to be in priority clusters.
“We were limited because we only analyzed two jurisdictions and we based our analysis on the assumption that data is missing from these collected sequences at random,” said Dr. Dasgupta. “If the latter is incorrect, there may be systemic bias,” she added, also noting that there were some demographic factors involved in data collection that were unique to each jurisdiction. “In Michigan, HIV sequencing is less common among non-white race ethnicity as well as persons who acquired HIV though injection drug use. In Washington, HIV sequencing is less common among drug users and heterosexuals,” she said. Moving forward, the research team plans to analyze how collection factors stretch across different jurisdictions.
The team determined that as completeness of sequencing data declined, both the number of clusters identified and the sensitivity of the surveillance decreased from 100% to 37% (at 50% completeness) and 7% (at 25% completeness). “This is likely resulting in missed opportunities for public health intervention,” Dr. Dasgupta observed, adding that the problem would be best resolved by expanding jurisdictions’ efforts to “maximize sequence completeness.” She added, “With this being a new concept, the completeness of sequence data has increased over time, and we are hoping to continue that trend as we move forward [analyzing surveillance data].”