COVID-19 risk factors change over time in Massachusetts communities
BY GINA MANTICA
As COVID-19 cases increase across the country again, public health officials grapple with how to direct their time, energy, and resources to stop the spread of the coronavirus. But targeted interventions require an understanding of which local communities are at the greatest risk for contracting the disease. A team of researchers from the BU School of Public Health, including Hariri Institute Junior Faculty Fellow Prasad Patil and Research Fellow M. Patricia Fabian, investigated how community level risk factors for COVID-19 change over time.
The team found that associations between COVID-19 case rates and risk factors changed over time from March through October 2020. The findings were published recently in BMC Infectious Diseases.
Patil and colleagues sought to understand whether the surges of positive cases in the summer and winter were because the risk factors associated with COVID-19 had changed. “We knew in general that there were phases to the pandemic with surges at different times,” said Patil, “This is why we thought of breaking our analysis down into phases.” The researchers worked with community-level data from the Massachusetts Department of Public Health for their analysis. As the biostatistician on the project, Patil helped determine what models the researchers used to study relationships between the number of positive SARS-CoV-2 tests and various population statistics including race, ethnicity, age, and occupation.
As predicted, the researchers found that the vulnerability of communities shifted over time. At the onset of the pandemic, an increase in the number of COVID-19 cases was associated with the percent of residents identifying as Black. However, this relationship diminished over time and in the fall, the association became non-significant. Similarly, in March an increase in positive cases was associated with the number of residents that identified as Latinx. But this association disappeared in the fall, and then reappeared at the start of the winter months. This suggests that fixed assumptions about vulnerabilities might need to be reassessed continuously, as new mitigation measures are put in place. “What risk factors are most heavily associated with the spread of disease is not always the same,” said Patil. It could also suggest that systemic racism or other inequities not captured in demographic datasets, like those from the United States Census, play a role in driving disparities in the occurrence of COVID-19.
Patil and colleagues also found that age and the number of long-term care facilities within a town was associated with the number of COVID-19 cases early on in the pandemic, and this relationship dissipated over time. “There were a lot of early outbreaks when there were no precautions being taken at these facilities. As stronger efforts were made to protect these locations, COVID-19 cases went down,” said Patil. This suggests that with targeted interventions, the spread COVID-19 can be controlled in vulnerable populations.
Additionally, the researchers found that the more essential workers there were living in a town, the higher the incidence of COVID-19 throughout all phases of the pandemic. This aligns with the team’s predictions that essential workers face greater risk of exposure to the novel coronavirus, and therefore have a greater risk of contracting COVID-19. The findings suggest that essential workers might be contributing to community-level spread in the towns where they live.
Research on how COVID-19 risk factors change at different phases of the pandemic enables policymakers to devise effective public health strategies for mitigating the spread of COVID-19 in local communities. By identifying the most vulnerable populations, governments can direct resources to help those most in need. “We showed our findings to the Department of Public Health, and they incorporated some of the stuff we found into their policy to help inform vaccine prioritization,” said Patil. “Identifying beforehand the most vulnerable populations at different phases of the pandemic allows policymakers to prioritize the allocation of resources to different regions and populations.”
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