Genomic epidemiology as a tool for understanding drivers of hepatitis A community outbreaks in Massachusetts and New Hampshire

Hepatitis A is a virus that spreads when tiny amounts of fecal matter from an infected person accidentally enter another person's mouth. This usually happens through contaminated food or water, but it can also spread easily between people who live in crowded conditions or lack access to clean sanitation. While a safe and effective vaccine has existed for decades, many adults in the United States were never vaccinated as children and have never been infected, leaving them vulnerable. When large groups of these unvaccinated adults face poor living conditions, the virus can ignite community-wide outbreaks that are difficult to stop.

In recent years, health officials in Massachusetts and New Hampshire watched as hepatitis A cases surged, particularly among people experiencing homelessness and those who use drugs. To understand how the virus was moving, researchers turned to a powerful tool called genomic epidemiology. This approach involves reading the entire genetic code of the virus found in patients. By comparing these genetic codes, scientists can see how closely related different cases are, effectively tracing the family tree of the virus to see who likely infected whom and how the outbreak traveled across the landscape.

A team of scientists from public health departments and research institutes in Massachusetts and New Hampshire set out to map the hepatitis A outbreaks that occurred between 2018 and 2024. They collected blood samples from hundreds of people who had been infected and generated complete genetic sequences for 241 of them. Their goal was to determine if the outbreak that flared up in 2023 was a continuation of the massive wave that hit the region in 2018, or if it was a fresh start with a different virus. They also wanted to see if the virus was spreading primarily within specific groups of people or jumping between different communities.

The researchers found that the two outbreaks were caused by completely different strains of the virus. The wave that struck in 2018 and 2019 was driven by a specific genetic version of the virus that spread rapidly through the region. By the time cases began to rise again in 2023, that original strain had largely disappeared. The new surge was caused by a different genetic version of the virus, indicating that the 2023 outbreak was a separate event, not a lingering remnant of the earlier one. This distinction is crucial because it means public health officials cannot assume that stopping one outbreak automatically prevents the next; each requires its own investigation and response.

Within the 2018 outbreak, the genetic data revealed how the virus moved through the community. The researchers saw that people who were experiencing homelessness or using drugs were central to the spread. The virus traveled most frequently between individuals who shared these risk factors, creating tight clusters of infection. However, the data also showed that the virus did not stay locked inside these groups. It jumped from people with these risk factors to others who did not report them, and it crossed state lines, moving between Massachusetts and New Hampshire. In fact, the genetic evidence suggested the outbreak in New Hampshire was likely seeded by cases from neighboring Massachusetts.

The study also highlighted a concerning trend in vaccination. While targeted efforts to vaccinate adults helped bring down the number of cases after the initial 2018 surge, overall vaccination rates for adults dropped significantly after the start of the COVID-19 pandemic. These rates never recovered to their previous levels during the study period. This decline left a large portion of the adult population unprotected, creating a fertile ground for the virus to return when conditions allowed. The researchers noted that people who use drugs or are homeless are not static groups; new individuals constantly enter these situations, bringing with them a lack of immunity that keeps the risk of future outbreaks high.

To make sense of this complex data, the team developed new, open-source tools that allow other scientists to analyze hepatitis A genomes quickly and accurately. They compared their method of reading the entire viral genome against the older, standard method that only reads a small snippet of the virus's code. They found that reading the whole genome provided a much clearer picture of transmission, allowing them to distinguish between cases that looked identical under the old method but were actually different. This higher resolution helped them confirm that people who lived near each other and shared risk factors were indeed infecting one another.

The researchers also discovered that the virus often traveled with other infections. By using sequencing methods that could detect multiple viruses at once, they found that many patients with hepatitis A were also infected with hepatitis C, a virus that spreads through similar routes. This co-infection likely contributed to the severe illness and higher death rates seen during the outbreaks, as people with existing liver damage were less able to fight off the new infection.

Ultimately, the study demonstrated that combining genetic data with traditional health investigations provides a much sharper view of how diseases spread. The researchers showed that while risk factors like homelessness and drug use are strong drivers of transmission, the virus does not respect social boundaries. It moves between people with and without these risk factors, and it crosses geographic borders with ease. The findings suggest that to stop future outbreaks, health officials need to maintain strong vaccination programs for adults, especially in community settings like health centers that serve vulnerable populations. They also need to improve hygiene infrastructure, such as access to clean restrooms and hand-washing stations, to break the chain of transmission. By using these new genetic tools, public health teams can now see the invisible pathways of the virus and target their interventions more precisely, potentially saving lives and preventing future waves of infection.