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Less deadly than delta? In some states, omicron caused more deaths

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Less deadly than delta? In some states, omicron caused more deaths

Posted | Updated by Insights team:

Publication | Update:

May 2022
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When the omicron variant first emerged last November, many doctors, researchers and scientists hoped that the new strain — while more transmissible — would cause fewer deaths.

Indeed, early research suggested that though omicron spread much faster than the delta variant, it appeared to cause less severe disease.

Full coverage of the Covid-19 pandemic

But in Massachusetts, omicron had a much deadlier impact than delta — and in a shorter period of time. That's according to research published Friday in the Journal of the American Medical Association

The findings illustrate that a highly contagious virus — even if it tends to cause milder illness — can still confer a substantial amount of death, said the study's lead author, Dr. Jeremy Faust, an emergency physician at Brigham and Women’s Hospital in Boston.

"Even if we have a future wave where we are told that this version of the virus might be milder on a person-to-person basis, we cannot afford to misinterpret that as meaning it can’t be extremely detrimental," Faust said. 

In the study, researchers from Brigham and Women’s Hospital and other institutions compared the number of excess deaths during the first eight weeks of the omicron wave in Massachusetts to the entire 23-week period of the delta wave in the state. 

Excess deaths — how many more people died than would typically have been expected in a given timeframe — have been viewed as a more comprehensive measure of fatalities caused, either directly or indirectly, by the virus than looking at confirmed Covid-19 deaths alone. 

The researchers found that 2,294 excess deaths occurred during the omicron wave in Massachusetts from late December through mid-February, compared with 1,975 excess deaths in the state during the delta wave from late June through early December. 

The excess deaths were observed across every adult age group during the omicron wave, according to the researchers, though people age 65 and up had the most excess deaths of any age group. 

Faust noted that a small number of deaths during the omicron wave could have been caused by delta infections that occurred several weeks earlier. In other words, a person could've been infected with the delta variant in late November, but did not die until late December, when omicron had taken over.

The findings comes as an omicron subvariant, BA.2, and an offshoot of that strain, called BA.2.12.1, are spreading rapidly across the country, sending cases and hospitalizations up.

The research has limits: The data, which is preliminary, looked only at Massachusetts, meaning the findings may not extend to other states, where demographics and Covid vaccine rates may differ. Roughly 80 percent of the Massachusetts’s population has had two doses of a vaccine, according to state data.

And the data alone is unable to explain why Massachusetts, among the states with the highest Covid vaccination rates, experienced a higher than expected number of deaths during the omicron wave compared to delta.

Nationwide, excess deaths during the delta wave (around 266,000) still exceed those during omicron (around 143,000), said Dr. Robert Anderson, the head of the mortality statistics branch at Centers for Disease Control and Prevention’s National Center for Health Statistics. He was not involved in the research.

Anderson noted that calculating excess deaths attributed to delta versus omicron, both regionally and nationally, can be "tricky" because researchers have to decide "when do we end the delta wave and where do we start the omicron wave."

Which variant caused more excess deaths can fluctuate by region — in New England, excess deaths occurring during the delta wave tend to be lower compared to other regions of the U.S., Anderson added. 

But in the South, particularly in states like Alabama, Florida, Mississippi and Texas, excess deaths recorded during the delta wave tended to be higher, he said.

Bill Hanage, an associate professor of epidemiology at Harvard’s T.H. Chan School of Public Health who was not involved in the research, said the higher number of excess deaths during the omicron wave in Massachusetts may reflect the state's mitigation measures in place during the delta wave.

The combination of high vaccination rates and mitigation measures in Massachusetts meant that not many people got infected with delta and therefore fewer people died, he said.

But those same preventive tactics may not have been sufficient against the extremely contagious omicron variant, Hanage said. (Studies have shown that two doses of a Covid vaccine provide minimal protection against omicron infection, though a booster shot is able to restore some of that protection.)

"And this led, perhaps unsurprisingly, to a larger amount of mortality, even if each of those infections were milder than they would have been had they been delta infections," he said.

Hanage also said the extent to which omicron looks milder compared to prior variants may be exaggerated by the high level of population immunity in the U.S., which includes both vaccination and prior infections.

He published a paper in The New England Journal of Medicine in February that argued the milder symptoms seen in omicron infections is likely due to more existing immunity, rather than the virus itself. In other words, by the time omicron arrived, more Americans had either already been vaccinated or infected compared with previous waves.

Though, even if omicron is truly milder, the Massachusetts data show "that even something which people have come to think of as being pretty mild can, in fact, kill a very large number of people."

Faust, the study's author, said people — particularly those at higher risk of severe illness — should continue to practice safety measures such as wearing masks indoors.

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The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
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Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

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Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

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