Research Summaries

This RECOVER study looked at whether the COVID-19 vaccine could help protect teenagers ages 12–17 from developing Long COVID. Researchers studied 1,231 teenagers enrolled in RECOVER who had confirmed COVID-19. Some were vaccinated before they got COVID-19 (724 teenagers), and some were not (507 teenagers). Researchers made sure the 2 groups were similar in terms of sex, date when they got COVID-19, and when they joined the study to make comparisons fair. They found that teenagers who were vaccinated in the 6 months before getting COVID-19 for the first time were about one-third less likely to get Long COVID. This study is important because it shows that COVID-19 vaccines, which were previously found to prevent getting COVID-19, can also protect against developing Long COVID in young people. 

This RECOVER study tested a new way to identify Long COVID in children. Researchers created a special computer-based tool, called a computable phenotype (CP), that scans electronic health records (EHRs) for specific codes and symptoms linked to Long COVID. To test the CP, researchers scanned records for nearly 340,000 children who had COVID-19. Doctors then checked a smaller group of 651 children’s records to see how well it worked. The study showed that the CP was able to successfully find that a child had Long COVID. But it sometimes marked symptoms that fit in with health problems someone had before getting COVID-19 (pre-existing conditions), so it wasn’t clear if those symptoms were due to COVID-19. When the researchers updated the CP to pay closer attention to pre-existing conditions, it became more accurate at identifying Long COVID from EHRs. This study is important because it provides a faster and more consistent way for researchers to identify Long COVID in large groups of children. This tool can help doctors give better care to children with Long COVID.

In this RECOVER study, researchers looked at whether getting COVID-19 a second time increases a child’s or teenager’s risk of developing Long COVID. Researchers compared the electronic health records (EHRs) of more than 400,000 children and teens who had COVID-19 once with the EHRs of about 58,000 children and teens who had COVID-19 twice during the Omicron period. They found that children and teens who had COVID-19 twice were two times more likely to be diagnosed with Long COVID compared to those who only had it once. Children and teens who got COVID-19 twice also experienced other health problems more often, like being very tired, headaches, stomach pain, and heart or kidney problems. This study is important because it shows that children and teens are much more likely to develop Long COVID after getting COVID-19 more than once. These findings show why it is important to continue learning about ways to protect children and teens from getting COVID-19.

This RECOVER study looked at how sense of smell changed over time in people who had COVID-19. Researchers gave a “scratch-and-sniff” test to a large group of people about 2 years after they had COVID-19. This test, called the University of Pennsylvania Identification Test (UPSIT), checks whether people can smell certain odors. The study found that 8 out of 10 people who felt that they had lost or had changes in smell after having COVID-19 were in fact not able to smell the odors normally. Even two-thirds of the people who thought their sense of smell was completely normal had a weaker sense of smell than they realized. These findings show that a weaker sense of smell can be a common and lasting problem after COVID-19. Because changes in smell have been linked to memory and thinking problems in other conditions, this could mean that people with changes in smell from COVID-19 could be at risk for thinking and memory issues. Doctors should use special smell tests to point out smell loss that patients may not notice to help them understand their condition and its risks, like not being able to smell smoke or spoiled food.

In this RECOVER study, researchers looked at whether Paxlovid could help prevent Long COVID. Paxlovid is a medicine commonly used to treat COVID-19 before it makes a person very sick. Researchers looked at the electronic health records (EHRs) of more than 445,000 people who had a high chance of getting very sick from COVID-19. People at higher risk included older adults (aged 50 and up) and people with certain medical conditions. To see if Paxlovid prevented people from developing Long COVID, researchers compared people who took Paxlovid right away once they got COVID-19 to people who did not. To make this a fair comparison, the researchers used a statistical method called a target trial emulation (TTE). A TTE uses EHRs to make two study groups as similar as possible, like they would be in a clinical trial. This makes the comparison fair because positive TTE results are more likely due to taking the treatment (in this study, Paxlovid) and not other differences, like a person’s age or health history. 

Researchers found that Paxlovid did not stop most people who were at high risk of getting very sick from COVID-19 from getting Long COVID. However, people who took Paxlovid were slightly less likely to develop some symptoms linked to Long COVID like fatigue (feeling very tired) and having trouble thinking. Researchers also found that Paxlovid seemed to improve the symptoms of people aged 65 and older a little bit more than it did for younger people. This study is important because it shows that taking Paxlovid is not a way to prevent Long COVID in most people, but it may help reduce symptoms in some higher-risk groups.

In this RECOVER study, researchers created a new computer tool to help doctors identify Long COVID more quickly and accurately. The tool, called a hybrid natural language processing (NLP) pipeline, quickly scans patients’ electronic health records (EHRs) to find descriptions of symptoms and figure out if a patient has them. Researchers tested this tool across 11 US health systems and found it was very accurate at identifying the right Long COVID symptoms. Patient EHRs contain a lot of important information, but it can take a long time to find specific symptoms across thousands of records. Researchers can now look at large amounts of health records from thousands of patients at once using the hybrid NLP pipeline technology. This study is important because it gives researchers a faster, more accurate way to identify Long COVID.

In this RECOVER study, researchers wanted to update a smart computer program, called a machine learning pipeline, to better identify people with Long COVID. In 2021, the first version of the program, called LCM 1, was created to identify people with or likely to have Long COVID. LCM 1 depended on people having a COVID-19 diagnosis date in their electronic health records (EHR). This meant LCM 1 could miss people who may have taken a COVID-19 test at home. LCM 1 also did not look at information about whether people got COVID-19 more than once. To improve the program and create a new version called LCM 2, researchers used more than 5 million EHRs from a large set of data called the National COVID Cohort Collaborative (N3C). They taught the program to look at a person’s health information over many years, not just starting from their first recorded COVID-19 diagnosis. Researchers found that LCM 2 was very accurate. They used it to estimate that about 1 in 10 people in the database who had COVID-19 went on to develop Long COVID. This study is important because it shows that older machine learning models, like LCM 1, can be updated to keep up with the way an illness is tracked and diagnosed over time. This can help other researchers improve their machine learning models to produce more accurate findings.

In this RECOVER study, researchers wanted to find out if getting COVID-19 made young children more likely to get sick later. Some researchers believe that the immune system (the body’s defense system) won’t be able to fight off germs well after someone has had COVID-19. They also thought that children might be more likely to catch the cold-like virus that spread widely in 2022, called respiratory syncytial virus (RSV), or other lung infections after having COVID-19. To learn more, RECOVER researchers looked at the electronic health records (EHRs) of thousands of young children (those less than 5 years old) across 27 US health systems. They compared more than 133,000 young children who had COVID-19 to more than 37,000 young children who had the flu and more than 46,000 young children who had other respiratory infections. Researchers checked for RSV and other respiratory infections for 6 months in each group. They found the opposite of what was believed about the body’s immune system: young children who had COVID-19 were less likely to get RSV or other respiratory infections than young children in the other groups. This study is important because it suggests that getting COVID-19 may not make young children more likely to get RSV or other respiratory illnesses afterward.

This RECOVER study looked at how social determinants of health (SDoH) affect the risk of developing Long COVID after getting COVID-19. SDoH are the living and working conditions that affect a person’s health, such as how safe a neighborhood is, access to education, and how easy it is to get healthcare. Between October 2021 and November 2023, RECOVER researchers studied adults from 33 states, Washington, DC, and Puerto Rico who recently had COVID-19. These adults filled out surveys about their social situations, health conditions, and pregnancy status. The researchers followed them for 6 months to see who developed symptoms of Long COVID. They looked at SDoH including money problems, not having enough food, level of education, problems getting health care, having friends or family for support, and where someone lives. Out of 3,787 participants, 418 people (about 11%) developed Long COVID. Researchers found that people with money problems, not enough food, less education, trouble getting healthcare, and little social support had a higher risk of experiencing Long COVID. The researchers suggest that future studies explore whether addressing SDoH-related needs can help lower the chance of developing long-term effects of COVID-19.