April 6, 2022
Researchers at Massachusetts General Hospital, Boston Medical Center, and Georgia Tech used a simulation model to project pandemic deaths in each state between March 1 and Dec. 31, 2022, and predicted how the number of deaths might change if restrictions were lifted during different times of the year.
“In most states, no critical moment was identified after which it would be possible to lift NPIs (nonpharmacologic interventions) without expecting to see a rebounding surge in deaths,” the study says. “The message that there is no ‘magic moment’ to lift restrictions is important for both sides of the current masking debates in the U.S. Those opposed to mask mandates should recognize the adverse health outcomes related to relaxing transmission mitigation measures.”
“There is likely no amount of additional waiting time in any state after which removing NPIs will not lead to a rise in morbidity and mortality,” the study says.
Benjamin P. Linas, co-first author and a professor of medicine at Boston University School of Medicine, said the Omicron variant was the main cause of the resulting increase in deaths.
“The inevitable rebound in mortality was directly attributable to the Omicron variant — when we repeated the analysis, assuming the infectivity of the previous Alpha and Delta variants, the model did not project such rising mortality after relaxing mask mandates,” he told The Harvard Gazette.
“A difficult trade-off lies on the horizon,” co-senior author Jagpreet Chhatwal, director of MGH’s Institute for Technology Assessment, told The Harvard Gazette. “While there is ample evidence in our analysis that a March 2022 lifting date leads to rebound mortality in many states, the simulation also suggests that with the Omicron variant, whenever states do remove mandates will face the same difficult choice between increased COVID-19 mortality and the freedoms of returning to a pre-pandemic norm.
The study said that policy makers on the state level will have to make difficult decisions, weighing rising deaths against a return to normalcy.