Discover what the latest science from the RECOVER Initiative means for our ability to understand, diagnose, prevent, and treat Long COVID.
This page contains descriptions of findings from RECOVER research studies. These descriptions use plain language and a format that is easy to understand.
If you want to learn more about the scientific discoveries described here, you can also browse and search the complete list of RECOVER Publications.
To find another way to know if children had COVID-19, researchers compared health records of 2 groups of children: children who only had a positive antibody test and children who had a positive PCR test. A positive antibody test means a child had COVID-19 in the past, had the COVID-19 vaccine, or both. A positive PCR test means a child currently has COVID-19. Researchers used data from children’s hospitals in the National Pediatric Learning Health System (PEDSnet) network.
Antibody tests found 2,714 children who had COVID-19 and didn’t have a positive PCR test or COVID-19 vaccine. If researchers used only PCR tests, they wouldn’t have known these children had COVID-19. Knowing if children had COVID-19 is the first step to know their risk of Long COVID and if symptoms could be related to Long COVID.
Having COVID-19 can lead to new symptoms or symptoms that do not go away. This condition is called Long COVID. RECOVER researchers are working to answer questions about Long COVID in adults. To do this, they are studying more than 14,700 adults in the US to understand how common Long COVID is in adults; how the body changes when someone has Long COVID; what makes some people more likely to develop Long COVID, such as where a person lives and their age, race, and sex; and what happens in the body that might cause Long COVID. This paper is important because it can show other scientists how to do their own research on Long COVID in adults.
RECOVER researchers used data from the RECOVER observational cohort study in adults ages 18 and over. They compared symptoms reported in surveys by participants who did and did not have COVID-19. Researchers found 37 symptoms that participants who had COVID-19 reported more often after having COVID-19 compared to participants who never had COVID-19. A combination of 12 of these symptoms helped identify participants with Long COVID, including feeling tired and unwell after activity, feeling weak and tired (fatigue), and brain fog. A definition of Long COVID based on symptoms is important for future research and finding treatments. Read the Research Q&A
An electronic health record (EHR) is a digital medical chart that has health data like doctor visits, lab results, and other information. These data are useful for understanding trends in health information, including how Long COVID affects people. Because of this, EHR data from different settings can be difficult to compare and use in research.
Researchers from the National COVID Cohort Collaborative (N3C) looked at over 15 million EHRs from 75 hospitals and clinics. Their goal was to make data from different healthcare settings more compatible. To do this, they had to understand and describe how definitions of patient visits differ between healthcare settings so the EHR data from different healthcare settings would be compatible.
The researchers focused on identifying patterns in EHR data. They hoped these patterns would help them gain a better understanding of a patient’s complete care experience, including:
To detect these patterns, researchers created two sets of rules for computer processing of EHR data. These rules are called algorithms. The first algorithm allowed researchers to group EHR data in new ways to make it easier to understand how the information in an EHR is related and easier to analyze individual EHRs and to compare EHRs from different sources.
The second algorithm allowed researchers to identify when EHRs indicated that a patient had been admitted to the hospital. Better data about hospitalizations will help future researchers study COVID-19 and its long-term effects, including Long COVID.
By using algorithms, N3C researchers are trying to make large amounts of EHR data more consistent, manageable, and understandable. Algorithms like the two tested by these researchers can help other researchers enhance the quality of EHR data, making it more consistent and capable of producing important insights about conditions like Long COVID.
People who had COVID-19 may develop Long COVID, which is when someone may experience various health issues after having COVID. Many of these problems are due to a strong reaction to the SARS-CoV-2 virus by the immune system (the body’s defense system that fights infections). This reaction can happen a long time after the virus is no longer in the body.
A healthy immune system can tell the difference between our body’s healthy cells and the harmful ones that can make us sick. However, an overactive immune system can make mistakes and cause harm because it may attack healthy organs and tissues. The immune system response to diseases like COVID is complex and different for each person.
It is important to understand how the immune system works after having COVID for many different people, and this may require large, long-term studies. This paper reviews researchers’ current knowledge and the gaps in their understanding about the immune response after a COVID infection and how that may lead to both short-term and long-term problems.
This paper summarizes some work of the RECOVER Mechanistic Pathways Task Force. This group does research on what happens in body systems when people have Long COVID. In this study, the authors reviewed existing research about why and for how long SARS-CoV-2, the virus that causes COVID-19, stays in the body. The authors also reviewed research on whether SARS-CoV-2 might become dormant (inactive) and then reactivate later as part of its natural lifecycle. They discuss whether the amount of time the virus stays in the body, also known as the “persistence” of the virus, could be related to whether or not people develop Long COVID. They also describe what is known so far about this topic, what we still need to find out, and the types of research studies that may help answer these questions.
RECOVER researchers wanted to understand whether being vaccinated before having COVID-19 lowered the chance of developing Long COVID. They used electronic health records (EHR) to study the effect of vaccination using EHR for two groups of people who had COVID-19. One group was based on clinic data and represented more than 47,000 people. In this group, 695 were diagnosed with Long COVID in clinics and more than 26,000 were fully vaccinated. The other group represented almost 200,000 individuals who had COVID-19. The researchers used a computer program to estimate who might have Long COVID in this group based on their medical and symptom information. In this group, more than 86,000 people represented were fully vaccinated. The researchers made sure that the people in the vaccinated and unvaccinated groups were as similar to each other as possible in terms of the same sex, age, race, and medical history.
To test whether vaccination lowered the chance of developing Long COVID, they made comparisons within each group. They used several different definitions of Long COVID and several different statistical tests to figure out whether vaccination status affected Long COVID. For both of the study groups and for all definitions of Long COVID and each statistical test in the analysis, the researchers got the same answer: people who were vaccinated before having COVID-19 were less likely to develop Long COVID.
RECOVER researchers wanted to find environmental risk factors of Long COVID by comparing the environment of people who had Long COVID to people who didn’t have Long COVID. Environmental risk factors are things about where a person lives that raise their chance of having a health condition, such as air pollution and poor housing conditions. The researchers looked at data from electronic health records (EHRs) of more than 100,000 people in the New York City area and Florida.
The researchers found that people who lived in areas with higher levels of air pollution, more poverty, and less access to healthy food had a higher chance of getting Long COVID. People in the New York City area had different environmental risk factors than people in Florida.
RECOVER researchers analyzed electronic health records (EHR) in order to define Long COVID. Researchers found up to 25 different symptoms that patients who had COVID-19 were more likely to have than those who didn’t have COVID-19. The symptoms were related to many different organs, such as memory loss, hair loss, and feeling tired. They found that certain types of Long COVID symptoms were more likely to happen in patients who were older, had more severe COVID-19, or had more health problems before they had COVID-19.
This research is important because the findings show that Long COVID affects many organs, and types of Long COVID symptoms differ between certain groups of patients. However, EHR findings are limited in that they can only look at data from the patients' past. In order to confirm these findings, future studies that follow patients' symptoms over time, into the future, are needed.
