June 21, 2022
Long COVID remains a puzzle that scientists are trying to piece together through widespread research efforts. There are still many unknowns, but researched published this month has provided a clearer picture of the condition.
A study published Tuesday found that women are more likely to develop long COVID than men. That came on the heels of research that suggests, among vaccinated people, long COVID is less likely following an omicron infection.
Long COVID is a syndrome in which COVID-19 symptoms linger for months, or even years, after the infection.
Not only does the latest research indicate that women are at greater risk, but the study also found they experience different symptoms. Women experienced a wide variety of symptoms, including fatigue; ear, nose and throat issues; and mood, neurological, skin, gastrointestinal and rheumatological disorders.
Men were more likely to develop endocrine disorders, such as diabetes, and kidney disorders.
"Differences in immune system function between females and males could be an important driver of sex differences in long COVID syndrome," the researchers said. "Females mount more rapid and robust innate and adaptive immune responses, which can protect them from initial infection and severity. However, this same difference can render females more vulnerable to prolonged autoimmune-related diseases."
The analysis was based on data from nearly 1.3 million patients from academic papers published between December 2019 and August 2020 for COVID-19 and January 2020 to June 2021 for long COVID.
A study published last week found that the chance of a vaccinated person developing long COVID after an omicron infection is 4.4%. The risk after a delta infection was 10.8%.
But considering how much more contagious the omicron variant is, that still means a large number of people are developing long COVID.
"The caveat is that the omicron variant has spread very rapidly through our populations, and therefore a very much larger number of people have been affected," one of the researchers, Dr. Claire Steves told NPR. "So the overall absolute number of people who are set to go on to get long COVID, sadly, is set to rise. So it's certainly not a time for us to reduce services for long COVID."
The findings, published in The Lancet, included 56,003 people who were infected with omicron between Dec. 20 and March 9. Their data was compared with 41,361 people who had a delta infection between June 1, 2021 and Nov. 27.
While each study offers the scientific community a better understanding of long COVID, some experts remain concerned about the discordant results between many of them. That makes answering even the most basic questions hard to answer.
For instance, one recent study found that being vaccinated only reduced the risk of developing long COVID by about 15% – a significantly lower percentage than previous studies had estimated.
But forming any definitive conclusions about long COVID is difficult, some experts told Nature, due to differences in how long COVID is defined, the kinds of data used to study it, and how the data are analyzed.
There is still no agreement on how to define or diagnose long COVID, partly because the syndrome has been linked to more than 200 symptoms of wide ranging degrees of severity. The World Health Organization tried to nail down a definition in 2021, but most researchers continue to use a range of criteria to define it.
No one has been able to determine how common the syndrome is either. Its prevalence has been estimated to be as low as 5% and as high as 50%.
Some experts say the types of methodology used in long COVID research could impact the quality of study results. Methods that relying on large databases of insurance claims data and using self-reported data each have their weaknesses.
For others, including clinical epidemiologist Ziyad Al-Aly, these discrepancies are a part of the scientific process. He explained that it is common for epidemiologists to cobble together different sources of data and methods of analysis. The important thing is to identify common threads in all the data.