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Long COVID is a phenomenon in which serious health effects linger well after a patient tests negative for coronavirus infection. Patients describe persistent "brain fog," fatigue, dizziness, rapid heart rate, shortness of breath and chest pain, among symptoms that affect nearly every organ system in the body and last a year or more after the infection has passed.

In a new study, data scientists at Stanford University combed a massive data set of 14.4 billion health insurance records from more than 244 million Americans and found that conventional methods may substantially overestimate the range and duration of health effects attributable to COVID-19.

"Our new study design found 98% fewer long COVID–attributable outcomes between four and 12 months after infection compared with conventional approaches applied in the same data set," said co-senior author Jure Leskovec, a professor of computer science. "This doesn't mean long COVID is 98% less common. It means that how we design these studies fundamentally changes which health effects we can reliably attribute to COVID."

The results are published in the journal Proceedings of the National Academy of Sciences.

Beyond its implications for understanding long COVID itself, this research carries an important message about how researchers can better design future population-level health studies, Leskovec said.

Improvements suggested by this work could help reduce biases that lead to skewed or unreliable results.

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