A PNAS analysis led by Stanford researchers suggests earlier estimates of long COVID’s effects may have been inflated by how studies selected their comparison groups, though the authors said the findings do not mean long-term symptoms are not real.
Here are five things to know:
- The researchers analyzed 14.4 billion insurance claims from 244.7 million U.S. patients, following 614 health outcomes for two years. They compared test-positive patients only with test-negative patients, instead of with a mix of test-negative and untested people, which they argue avoids bias from who gets tested.
- Under that design, far fewer symptoms were attributable to COVID-19. Between 30 and 119 days after infection, 43 symptoms were attributable to COVID-19, compared with 262 under the unrestricted design, an 83.5% reduction. From 120 to 359 days, five symptoms remained, versus 286, and from 360 to 719 days, none remained, versus 369.
- The authors said people who get tested often do so while seeking care for other conditions, so the long-term effects of those conditions can be misattributed to COVID-19. The unrestricted design also linked COVID-19 to injuries it could not plausibly cause, such as lightning, drowning and firearm injuries. Of 49 such “negative control” outcomes, 53.1% appeared significantly associated with COVID-19 under the unrestricted design, versus 4.1% under the test-based design.
- The symptoms most clearly tied to COVID-19 in the first four months included myopathy, hair loss, palpitations, malnutrition and fatigue. Respiratory problems such as pneumonia and shortness of breath carried the largest absolute burden. From 120 to 359 days, excess risk for most outcomes fell near zero, though myopathy, hair loss, respiratory failure and pulmonary embolism remained elevated.
- The authors cautioned that the study does not show symptoms beyond one year are not genuine, only that their attributability to COVID-19 is uncertain. They said the test-based design may modestly underestimate some effects, that claims data are built for billing and can lack clinical detail and that the analysis excludes people who used only at-home tests and did not seek care.
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