A Nature News report makes the case that, even with the scads of data on so-called "long COVID" available to researchers, the picture on what the condition actually is or how to treat it is not much clearer than in the early days of the pandemic.
For instance, Nature reported, clinical epidemiologist Ziyad Al-Aly at Missouri's VA St. Louis Healthcare System and colleagues recently published a paper in Nature Medicine that examined tens of thousands of EMRs from the US Department of Veterans Affairs and came to the conclusion that previous vaccination for COVID-19 only reduced the risk of developing long COVID after infection by about 15 percent — findings that surprised some researchers, as some other previous reports had placed the number at about 50 percent.
At the heart of the problem, Nature reports, is that the definition and diagnosis of long COVID is still highly inconsistent. Big data can help address this issue, but it is not a sure thing, as EMRs themselves can be highly inconsistent. "People mistake the size of the study with its quality and its validity," Walid Gellad, a physician who studies health policy at the University of Pittsburgh in Pennsylvania, told Nature.
Further muddying these waters are differences in demographics and symptom identification and recording among physicians.
Still, the more data that is collected on long COVID, the better chance scientists have at unraveling its mysteries by identifying trends that will eventually emerge, Al-Aly said. "You search for the common thread," he told Nature. "The common thread here is that vaccines are better than no vaccines."