Key points:
- A new study looks at long-COVID symptoms and how they varied among various populations.
- The researchers found a higher degree of symptoms and risk of long-COVID in New York City compared with Florida.
- New York City’s increased risk could be due to its early wave of the pandemic and diverse population.
To better understand long-term COVID among diverse populations, a study published in Nature Communications analyzed electronic health records as part of the National Institutes of Health’s Researching COVID to Enhance Recovery (RECOVER) Initiative.
The research provides an overview of potential symptoms after acute COVID-19 and how the risk of these conditions may vary among different populations in the U.S.
For the study, a team of researchers from Weill Cornell Medicine studied electronic health records from two clinical research networks—one with data from 11 million New York-based patients, and another with nearly 17 million patients from the OneFlorida+ network, including Florida, Georgia and Alabama.
The team identified a broad list of diagnoses that occurred more frequently in patients who had recently had COVID-19 compared with non-infected individuals. The researchers also found more types of symptoms and higher risk of long-COVID in New York City than Florida. Specific conditions found across the New York City and Florida populations included dementia, hair loss, sores in the stomach and small intestine, blood clots in the lung, chest pain, abnormal heartbeat and fatigue.
Some of the differences between the results from the two populations might be explained by the fact that New York City had a more diverse patient population, endured one of the first waves of the pandemic and faced the lack of personal protective equipment such as masks, compared with Florida, said lead author Chengxi Zang, population health sciences expert at Weill Cornell.
“Our approach, which uses machine learning with electronic health records, provides a data-driven way to define long COVID and determine how generalizable our definition of the disease is,” Zang said. “Comparing records across diverse populations in regions that experienced the COVID-19 pandemic differently highlighted how variable long COVID is for patients and emphasized the need for further investigation to improve the diagnosis and treatment of the disease.”
The researchers used a machine learning approach on the electronic health records to provide data-driven on long-COVID.