COVID-data stock photo of coronavirus image

Using Data and Multi-Sector Collaboration to Address Inequities During COVID-19

COVID-data stock photo of coronavirus image

The burden of COVID-19 has not been borne equally by all communities in the United States.

Why have Brockton and Chelsea, two historically working-class cities in Massachusetts, been hit harder by the COVID-19 pandemic than, say, Brookline and Cambridge? Why have people of color suffered more, overall, than White people? Dr. Carly Levy, Assistant Professor of Public Health and Director of the Master of Public Health Program at MCPHS, invited two experts to speak about answering those questions and others in the latest installment of the MCPHS COVID-19 webinar series, Revisioning the New Normal. The series examines the coronavirus in terms of basic biology, public health response, and societal implications.

The first guest to speak during the webinar, entitled “Using Data and Multi-Sector Collaboration to Address Inequities During COVID-19,” was Cristina Alonso, a Doctor of Public Health student at Harvard University’s T.H. Chan School of Public Health. Earlier in the year, Alonso and Patrice Basada of the Boston University School of Public Health had been engaged by the Chelsea Department of Public Health to gather and examine data to find patterns in the city’s COVID-19 cases from March through August of 2020. Among the questions they hoped to answer about the hardest-hit community in Massachusetts: Why Chelsea? What about the community’s characteristics led to such devastating COVID-19 numbers? Were there certain practices that led to the extensive spread?

In gathering and analyzing data, Alonso and Basada uncovered several informative trends, which they detailed in their report. Chelsea is a largely Latino city, home to many immigrants from Central and South American nations with especially difficult political environments. Often forced to flee their native countries, these Chelsea residents tend to have low education levels and English language skills, a circumstance that places many of them in essential jobs or leaving them unemployed—two categories that Alonso found to be at higher risk of a COVID-19 diagnosis. Among the report’s findings:

  • The coronavirus was in Chelsea in February and was being spread that early.
  • Hispanic essential workers and white retirement home residents were the most affected.
  • Unemployed people contracted COVID-19 more than non-essential workers.
  • Unemployed were people hospitalized at higher rates than those with jobs. This could be because they don’t have health insurance and so are delayed getting care until later.
  • The likelihood of hospitalization increased with age.
  • A third of Chelsea residents with COVID-19 didn’t have symptoms.

That last point is a crucial piece of information to act on if we want to stop the disease’s spread, says Alonso. “You don’t have to have symptoms to have COVID,” she says—so you absolutely should not need to have symptoms to get a free test.

The presentation’s second guest was Nina Sayles, candidate for joint Master of Public Health and Master of Urban Planning degrees at the T.H. Chan School of Public Health and Harvard University Graduate School of Design. Sayles volunteers with the Academic Public Health Volunteer Corps (APHVC), which has sent hundreds of volunteers to support local boards of health. The APHVC is part of an academic health department consortium formed in March 2019, when the MCPHS Master of Public Health program worked with eight area schools, the Massachusetts Department of Public Health, the Massachusetts Health Officers Association, and the Massachusetts Public Health Association to form the group. The goal was to bridge the gap between practice and public health academia. In March 2020 the governor and state worked with the consortium to help respond to COVID-19, and the APHVC for which Sayles now volunteers was created.

Sayles set out to see how mapping, or spatial analysis, can be used to address inequalities during COVID-19, and what benefits mapping can bring to data analysis. The idea, in this case, was to plot COVID-19 diagnoses on a map and look for relationships between outbreaks and other locational data. Done well, Sayles says, spatial analysis can help target intervention, protect the vulnerable, address disparities, and prioritize infrastructure investment.

She did point out that mapping presents some problems collecting data, and in the Brockton case, the mapping doesn’t provide definitive answers. Is COVID-19 outbreak most influenced by quality of housing? Race? Income? It’s all kind of piled on top of each other in Brockton, she said. But she found a few insights by layering maps. Mapping hotspots has showed that

  • Clusters occurred in Brockton around long-term-care and rehabilitation centers.
  • Density alone is not a “death sentence.”
  • Area Deprivation Index score—a measure of education, employment, income, poverty, and housing characteristics—correlates significantly with COVID-19 rates.
  • More non-white people are getting COVID than white people are.
  • Hotspots superimposed with housing unit age: hotspots line up with older homes

“You’re not ‘doomed’ if you simply live in a dense place,” said Sayles. “There are so many other factors.” An old housing structure with poor ventilation can put a resident at a higher risk of contraction, for example. And while it’s difficult to determine which single factor plays the strongest or weakest role in whether a COVID-19 outbreak is likely, it is extremely clear that social determinants are closely linked to health outcomes. “It’s not entirely necessary to know exactly which one is the determinant of COVID,” Sayles said. But “it’s clear there needs to be investment in some of these factors.”