There are a few things to know about Charisse Madlock-Brown before you know she is working on one of the biggest clinical databases in medical history. Of course, it’s COVID-19 related. And it’s come together in less than a year.

She skipped her senior year of high school in Iowa City, Iowa, to start college at Bard’s College at Simon’s Rock in Massachusetts, where the whole idea is to give enterprising high school students an early start.

One of her best friends there earned a Ph.D. before a high school diploma.

“You miss out on too much wasting time ...” Madlock-Brown, 39, says. “I like moving from planning to action.”

Her specialty is health informatics, the whole field of IT born when hospitals and clinics put their medical records online. Suddenly, there was a way to mine all the pieces of data that once were handwritten in patient charts and filed away.

Last spring, she received her first grant from the National Institutes of Health to sift through the medical records of 70 million patients, looking specifically at those with two or more underlying conditions, noting how often the conditions occur, in what combination and what it costs to treat them.

Within weeks, COVID-19 made those skills essential when it became apparent that the people dying most often from COVID had other health issues. In Shelby County, for instance, people with heart conditions account for 52% of the COVID deaths, followed by respiratory diseases and diabetes.

The COVID study

Madlock-Brown, assistant professor at the University of Tennessee Health Science Center, is now leading a team of national data experts digging into the medical records of 2.5 million people who were tested for COVID in dozens of clinical centers across the nation.

The members are gleaning medical evidence from 147 million doctor visits, overlaying it with Google Maps and public databases on employment, food security and access to medical care, to tease out factors that may prove why some people are more vulnerable to COVID, or what life conditions hamper recovery, which could include poor air quality from neighborhood fixtures, like factories.

Medicine calls these factors social determinants of health, the key pieces of people’s daily lives that can define their health, based on where they live and work and what they do for leisure. The determinants can have a profound impact on how COVID affected them.

“What we want to do is not just come up with a general picture but look at it as the pandemic progressed. And we had things like policies that help mitigate transmission, how did they work for socially vulnerable groups?” she said.

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Her work is part of the National COVID Cohort Collaborative — NC3 for short — an open-science community focused on analyzing patient-level data from dozens of clinical centers to reveal patterns in COVID patients.

Each person in the database was tested for COVID. For every positive case, the data includes two who tested negative, says Melissa Haendel, director of the Center for Data to Health at the Oregon Clinical and Translational Research Institute at Oregon Health and Science University in Portland and one of three directors of NC3 project.

“To our knowledge, no one has ever collected this much clinical data from electronic health records and put it all in one place and made it publicly accessible. So, this is really a first,” she said.

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NC3 is a funded by the National Institutes of Health and private philanthropy and grew quickly from the reality that COVID presented enormous scientific and societal challenges that required quick and broad sharing of data.

About 75 institutions have signed on; 45 have done all the work to pass NC3’s quality assurance and harmonization processes, or what Haendel calls the “messy cleanup” to have their data included.

“What makes this unique, I would say, is the collaboration of all different types of people from all the different centers from around the country, working together on this initiative,” said Anita Walden, assistant director, also from Oregon Health and Science University.

“Everybody just came together back in March and said, ‘Let’s do this.’ We launched it,” she said.

The database is open to researchers of all kinds, including high school students, although it is accessed through a secure cloud portal.

“It takes a village, and it means that we want the people who are machine-learning experts at Google, if they want to volunteer their time, to come and help,” Haendel said.

“They’re not clinical experts, but it’s all about the country’s shared expertise and bringing these people together to try to analyze these data and reveal key patterns,” she said.

Researchers are digging into a dozen medical specialties, including cardiology, emergency medicine, neurology and pregnancy.

So far, Madlock-Brown’s work with social determinants is drawing much of the national attention, Walden said.

“There’s just a lot of things relative to health that people are really excited about,” Walden said. “We get questions about Charisse’s group all the time because of that.”

Early results

For Madlock-Brown, it is personal. As a person of color, she grew up knowing Black people did not get the same medical care as others did and certainly not as white people did.

“I remember my grandma telling me that back in her day, if you were an African-American woman, you weren’t going to get a C-section; you weren’t really going to get medication. They just expected you to give birth,” Madlock-Brown said. “She knew that. She needed a C-section, and they did not give that to women like her.

“That led me to be more interested in issues of social determinants of health. I teach a health outcomes and policy Ph.D. program with an informatics track.”

In the last two years, she says, there has been a palpable shift to taking these issues more seriously, enough that she started looking at social determinants of health as a maturing research topic.

“The reason I stepped up to say yes to lead the social determinants of health group is that I already had a personal interest, but professionally, I started to really familiarize myself with this research area.”

She will be presenting NC3’s role in data mining at an online seminar on Feb. 1, offered by the Trial Innovation Network.

Her committee is already showing that COVID in rural areas, particularly when shelter-in-place orders happened in late 2020, was more severe than in cities.

“Early on, these counties had high incidence of COVID and continued to have higher incidence, even through the summer,” she said.

It is showing similar conclusions for counties with high numbers of essential workers, including Shelby County.

Early data already shows interesting things in Shelby County, including the racial divide in who is getting the vaccine. Last week, the Shelby County Health Department said by Jan. 7, white people had received 51.9% of the vaccine (5,417 doses) compared to Black people who had received 24.4% (2,548 doses).

Black people in Shelby County represent 52% of the population; white people represent about 40%.

Vaccination rates by cities in Shelby County show that Arlington is the highest with 9,836 vaccinated per 100,000 residents, followed by Germantown with 6,362. Even though far more people in Memphis have been vaccinated (14,007), its rate is the lowest per 100,000 people at 2,163. 

Data on occupation and the percentage of people in each community living in congregate settings could shed light on the differences. People who live in communities with higher levels of immunity, due to the vaccine, could have different outcomes.

“More affluent areas tend to have greater health care access in general,” Madlock-Brown said, “and tend to be more proactive in seeking the services.”

Health records don’t show who lives next to a factory or a busy road, but Google Maps do. When combined, the data reveals much more, Madlock-Brown says, about the everyday lives of people and how their environments, including proximity to grocery stores and whether they have a car or not, affect how much COVID hurts their community.

“Data can explain things without putting the problem on disadvantaged groups,” she said. “The temptation is to say ‘Oh, they do this’ or ‘They must be genetically predisposed’ or ‘Maybe it’s their environment.’

“Data will help us not jump to general or behavioral conclusions when something else could be happening.”