US hospitals seeing different kind of COVID surge this time

https://apnews.com/article/coronavirus-pandemic-business-health-pandemics-49810a71d2ca21c4b56adb1d1092b6dd?fbclid=IwAR1KvwTCWhAHZwDlmzgzMiNL5xhBfOySbZwgzXs3IAXtWlHai_VRfni5eaQ

Registered nurse Rachel Chamberlin, of Cornish, N.H., right, steps out of an isolation room where where Fred Rutherford, of Claremont, N.H., left, recovers from COVID-19 at Dartmouth-Hitchcock Medical Center, in Lebanon, N.H., Monday, Jan. 3, 2022. Hospitals like this medical center, the largest in New Hampshire, are overflowing with severely ill, unvaccinated COVID-19 patients from northern New England. If he returns home, Rutherford said, he promises to get vaccinated and tell others to do so, too. (AP Photo/Steven Senne)

Hospitals across the U.S. are feeling the wrath of the omicron variant and getting thrown into disarray that is different from earlier COVID-19 surges.

This time, they are dealing with serious staff shortages because so many health care workers are getting sick with the fast-spreading variant. People are showing up at emergency rooms in large numbers in hopes of getting tested for COVID-19, putting more strain on the system. And a surprising share of patients — two-thirds in some places — are testing positive while in the hospital for other reasons.

At the same time, hospitals say the patients aren’t as sick as those who came in during the last surge. Intensive care units aren’t as full, and ventilators aren’t needed as much as they were before.

The pressures are nevertheless prompting hospitals to scale back non-emergency surgeries and close wards, while National Guard troops have been sent in in several states to help at medical centers and testing sites.

Nearly two years into the pandemic, frustration and exhaustion are running high among health care workers.

“This is getting very tiring, and I’m being very polite in saying that,” said Dr. Robert Glasgow of University of Utah Health, which has hundreds of workers out sick or in isolation.

About 85,000 Americans are in the hospital with COVID-19, just short of the delta-surge peak of about 94,000 in early September, according to the Centers for Disease Control and Prevention. The all-time high during the pandemic was about 125,000 in January of last year.

But the hospitalization numbers do not tell the whole story. Some cases in the official count involve COVID-19 infections that weren’t what put the patients in the hospital in the first place.

Dr. Fritz François, chief of hospital operations at NYU Langone Health in New York City, said about 65% of patients admitted to that system with COVID-19 recently were primarily hospitalized for something else and were incidentally found to have the virus.

At two large Seattle hospitals over the past two weeks, three-quarters of the 64 patients testing positive for the coronavirus were admitted with a primary diagnosis other than COVID-19.

Joanne Spetz, associate director of research at the Healthforce Center at the University of California, San Francisco, said the rising number of cases like that is both good and bad.

The lack of symptoms shows vaccines, boosters and natural immunity from prior infections are working, she said. The bad news is that the numbers mean the coronavirus is spreading rapidly, and some percentage of those people will wind up needing hospitalization.

This week, 36% of California hospitals reported critical staffing shortages. And 40% are expecting such shortages.

Some hospitals are reporting as much as one quarter of their staff out for virus-related reasons, said Kiyomi Burchill, the California Hospital Association’s vice president for policy and leader on pandemic matters.

In response, hospitals are turning to temporary staffing agencies or transferring patients out.

University of Utah Health plans to keep more than 50 beds open because it doesn’t have enough nurses. It is also rescheduling surgeries that aren’t urgent. In Florida, a hospital temporarily closed its maternity ward because of staff shortages.

In Alabama, where most of the population is unvaccinated, UAB Health in Birmingham put out an urgent request for people to go elsewhere for COVID-19 tests or minor symptoms and stay home for all but true emergencies. Treatment rooms were so crowded that some patients had to be evaluated in hallways and closets.

As of Monday, New York state had just over 10,000 people in the hospital with COVID-19, including 5,500 in New York City. That’s the most in either the city or state since the disastrous spring of 2020.

New York City hospital officials, though, reported that things haven’t become dire. Generally, the patients aren’t as sick as they were back then. Of the patients hospitalized in New York City, around 600 were in ICU beds.

“We’re not even halfway to what we were in April 2020,” said Dr. David Battinelli, the physician-in-chief for Northwell Health, New York state’s largest hospital system.

Similarly, in Washington state, the number of COVID-19-infected people on ventilators increased over the past two weeks, but the share of patients needing such equipment dropped.

In South Carolina, which is seeing unprecedented numbers of new cases and a sharp rise in hospitalizations, Gov. Henry McMaster took note of the seemingly less-serious variant and said: “There’s no need to panic. Be calm. Be happy.”

Amid the omicron-triggered surge in demand for COVID-19 testing across the U.S., New York City’s Fire Department is asking people not to call for ambulance just because they are having trouble finding a test.

In Ohio, Gov. Mike DeWine announced new or expanded testing sites in nine cities to steer test-seekers away from ERs. About 300 National Guard members are being sent to help out at those centers.

In Connecticut, many ER patients are in beds in hallways, and nurses are often working double shifts because of staffing shortages, said Sherri Dayton, a nurse at the Backus Plainfield Emergency Care Center. Many emergency rooms have hours-long waiting times, she said.

“We are drowning. We are exhausted,” Dayton said.

Doctors and nurses are complaining about burnout and a sense their neighbors are no longer treating the pandemic as a crisis, despite day after day of record COVID-19 cases.

“In the past, we didn’t have the vaccine, so it was us all hands together, all the support. But that support has kind of dwindled from the community, and people seem to be moving on without us,” said Rachel Chamberlin, a nurse at New Hampshire’s Dartmouth-Hitchcock Medical Center.

Edward Merrens, chief clinical officer at Dartmouth-Hitchcock Health, said more than 85% of the hospitalized COVID-19 patients were unvaccinated.

Several patients in the hospital’s COVID-19 ICU unit were on ventilators, a breathing tube down their throats. In one room, staff members made preparations for what they feared would be the final family visit for a dying patient.

One of the unvaccinated was Fred Rutherford, a 55-year-old from Claremont, New Hampshire. His son carried him out of the house when he became sick and took him to the hospital, where he needed a breathing tube for a while and feared he might die.

If he returns home, he said, he promises to get vaccinated and tell others to do so too.

“I probably thought I was immortal, that I was tough,” Rutherford said, speaking from his hospital bed behind a window, his voice weak and shaky.

But he added: “I will do anything I can to be the voice of people that don’t understand you’ve got to get vaccinated. You’ve got to get it done to protect each other.”

The data is telling a consistent story: Omicron is significantly milder.

‘Not the same’
The details of the Omicron variant are becoming clearer, and they are encouraging.
They’re not entirely encouraging, and I will get into some detail about one of the biggest problems — the stress on hospitals, which are facing huge numbers of moderately ill Covid-19 patients. But regular readers of this newsletter know that I try to avoid the bad-news bias that often infects journalism. (We journalists tend to be comfortable delivering bad news straight up but uncomfortable reporting good news without extensive caveats.)
So I want to be clear: The latest evidence about Covid is largely positive. A few weeks ago, many experts and journalists were warning that the initial evidence from South Africa — suggesting that Omicron was milder than other variants — might turn out to be a mirage. It has turned out to be real.
“In hospitals around the country, doctors are taking notice,” my colleagues Emily Anthes and Azeen Ghorayshi write. “This wave of Covid seems different from the last one.”
There are at least three main ways that Omicron looks substantially milder than other versions of the virus:
1. Less hospitalization
Somebody infected with Omicron is less likely to need hospital treatment than somebody infected with an earlier version of Covid.
An analysis of patients in Houston, for example, found that Omicron patients were only about one-third as likely to need hospitalization as Delta patients. In Britain, people with Omicron were about half as likely to require hospital care, the government reported. The pattern looks similar in Canada, Emily and Azeen note.
Hospitalizations are nonetheless rising in the U.S., because Omicron is so contagious that it has led to an explosion of cases. Many hospitals are running short of beds and staff, partly because of Covid-related absences. In Maryland, more people are hospitalized with Covid than ever.
“Thankfully the Covid patients aren’t as sick. But there’s so many of them,” Craig Spencer, an emergency room doctor in New York, tweeted on Monday, after a long shift. “The next few weeks will be really, really tough for us.”
The biggest potential problem is that overwhelmed hospitals will not be able to provide patients — whether they have Covid or other conditions — with straightforward but needed care. Some may die as a result. That possibility explains why many epidemiologists still urge people to take measures to reduce Covid’s spread during the Omicron surge. It’s likely to last at least a couple more weeks in the U.S.
2. Milder hospitalization
Omicron is not just less likely to send somebody to the hospital. Even among people who need hospital care, symptoms are milder on average than among people who were hospitalized in previous waves.
A crucial reason appears to be that Omicron does not attack the lungs as earlier versions of Covid did. Omicron instead tends to be focused in the nose and throat, causing fewer patients to have breathing problems or need a ventilator.
As Dr. Rahul Sharma of NewYork-Presbyterian/Weill Cornell told The Times, “We’re not sending as many patients to the I.C.U., we’re not intubating as many patients, and actually, most of our patients that are coming to the emergency department that do test positive are actually being discharged.”
In London, the number of patients on ventilators has remained roughly constant in recent weeks, even as the number of cases has soared, John Burn-Murdoch of The Financial Times noted.
3. And deaths?
In the U.S., mortality trends typically trail case trends by about three weeks — which means the Omicron surge, which began more than a month ago, should be visible in the death counts. It isn’t yet:
Data as of Jan. 3.Source: New York Times database
Covid deaths will still probably rise in the U.S. in coming days or weeks, many experts say. For one thing, data can be delayed around major holidays. For another, millions of adults remain unvaccinated and vulnerable.
But the increase in deaths is unlikely to be anywhere near as large as the increase last summer, during the Delta wave. Look at the data from South Africa, where the Omicron wave is already receding:
South Africa reported identification of Omicron on Nov. 24.Source: Johns Hopkins University
The bottom line
Given the combination of surging cases and milder disease, how should people respond?
Dr. Leana Wen, Baltimore’s former health commissioner, wrote a helpful Washington Post article in which she urged a middle path between reinstituting lockdowns and allowing Omicron to spread unchecked.
It’s unreasonable to ask vaccinated people to refrain from pre-pandemic activities,” Wen said. “After all, the individual risk to them is low, and there is a steep price to keeping students out of school, shuttering restaurants and retail shops and stopping travel and commerce.”
But she urged people to get booster shots, recommended that they wear KN95 or N95 masks and encouraged governments and businesses to mandate vaccination. All of those measures can reduce the spread of Covid and, by extension, hospital crowding and death.
What about elderly or immunocompromised people, who have been at some risk of major Covid illness even if they’re vaccinated?
Different people will make different decisions, and that’s OK. Severely immunocompromised people — like those who have received organ transplants or are actively receiving cancer treatment — have reason to be extra cautious. For otherwise healthy older people, on the other hand, the latest data may be encouraging enough to affect their behavior.
Consider this: Before Omicron, a typical vaccinated 75-year-old who contracted Covid had a roughly similar risk of death — around 1 in 200 — as a typical 75-year-old who contracted the flu. (Here are the details behind that calculation, which is based on an academic study.)
Omicron has changed the calculation. Because it is milder than earlier versions of the virus, Covid now appears to present less threat to most vaccinated elderly people than the annual flu does.
The flu, of course, does present risk for the elderly. And the sheer size of the Omicron surge may argue for caution over the next few weeks. But the combination of vaccines and Omicron’s apparent mildness means that, for an individual, Covid increasingly resembles the kind of health risk that people accept every day.

Quote of the Day: On Understanding

“I can give you an argument, but I can’t give you an understanding.”

Samuel Johnson

U.S. hits 700,000 COVID deaths

https://www.axios.com/covid-deaths-700000-us-6dd0223d-562a-41b9-a780-ef54e646b07e.html

The U.S. surpassed 700,000 deaths from the coronavirus on Friday, according to data from Johns Hopkins University.

Why it matters: A summer of division over vaccine and masking mandates only added to the surge in cases caused by the Delta variant. The U.S. went from 600,000 deaths to 700,000 in the span of three-and-a-half months.

  • Public health experts have become increasingly frustrated as thepandemic of the unvaccinatedspread across the country.
  • Roughly 70 million eligible Americans remain unvaccinated, AP reports.

A new antiviral pill shows promise, as do vaccine mandates

https://mailchi.mp/a2cd96a48c9b/the-weekly-gist-october-1-2021?e=d1e747d2d8

Everything we know about the covid-19 coronavirus

Two pieces of hopeful news on the COVID front this week.

First, pharmaceutical manufacturer Merck announced this morning that molnupiravir, the oral antiviral drug it developed along with Ridgeback Biotherapeutics, reduced hospitalizations among newly diagnosed COVID patients by 50 percent. A five-day course of the drug was so successful in Merck’s clinical study that an independent monitoring group recommended halting the study and submitting the pill to the Food and Drug Administration (FDA) for emergency use authorization. Molnupiravir is activated by metabolism, and upon entering human cells, is converted into RNA-like building blocks, causing mutations in the COVID virus’s RNA genome and interfering with its replication. For that reason, the drug is unlikely to be prescribed during pregnancy, but otherwise the therapy seems to hold great promise in adding to the limited armamentarium available to fight the pandemic. One possible concern: the drug’s price tag. The federal government has agreed to purchase 1.7M courses of the drug at $700 per course, and with most insurance companies having returned to normal cost-sharing for COVID treatments, the drug may be out of reach for some patients. Still, a major clinical development to be celebrated, and more to come as Merck’s drug is vetted by the FDA.
 
At $20 to $40 per dose, with costs fully absorbed by the federal government, and remarkable effectiveness at preventing severe disease, hospitalizations, and deaths, vaccines remain far and away our best frontline weapon for fighting the COVID pandemic. Promising, then, that the much-debated vaccine mandates have begun to demonstrate success in increasing vaccination rates, even among those who have thus far resisted getting the shot.

Despite concerns about massive staffing shortages among hospitals resulting from the implementation of its mandate, the state of New York found that 92 percent of healthcare workers had been vaccinated by Monday, when the mandate went into effect. That was a 10-percentage-point increase from a week earlier, holding promise that the Biden administration’s planned federal mandate for healthcare workers could have the desired effect.

California’s mandate for healthcare workers went into effect yesterday, and was credited with boosting vaccination rates to 90 percent at many of the state’s health systems. Among private employers considering mandates, the experience of United Airlines may also be instructive: its employee mandate led to the vaccination of more than 99 percent of its workers, resulting in the termination of only 700 of its 67,000 employees. Of course, everyone prefers carrots to sticks, but sweepstakes and bonuses have only gotten so far in encouraging people to get vaccinated—now it appears mandates have a useful role to play as well.

With 56 percent of the population fully vaccinated, the US now ranks 43rd among nations, just ahead of Saudi Arabia and far behind most of Europe. In the next few days we’ll reach the grim milestone of 700,000 COVID deaths in this country—anything that helps stop that number from growing further should be welcome news.

The pandemic marks anothergrim milestone: 1 in 500Americans have died of covid-19

At a certain point, it was no longer a matter of if the United States would reach the gruesome milestone of 1 in 500 people dying of covid-19, but a matter of when. A year? Maybe 15 months? The answer: 19 months.

Given the mortality rate from covid and our nation’s population size, “we’re kind of where we predicted we would be with completely uncontrolled spread of infection,” said Jeffrey D. Klausner, clinical professor of medicine, population and public health sciences at the University of Southern California’s Keck School of Medicine. “Remember at the very beginning, which we don’t hear about anymore, it was all about flatten the curve.”

The idea, he said, was to prevent “the humanitarian disaster” that occurred in New York City, where ambulance sirens were a constant as hospitals were overwhelmed and mortuaries needed mobile units to handle the additional dead.

The goal of testing, mask-wearing, keeping six feet apart and limiting gatherings was to slow the spread of the highly infectious virus until a vaccine could stamp it out. The vaccines came but not enough people have been immunized, and the triumph of science waned as mass death and disease remain. The result: As the nation’s covid death toll exceeded 663,000 this week, it meant roughly 1 in every 500 Americans had succumbed to the disease caused by the coronavirus.

While covid’s death toll overwhelms the imagination, even more stunning is the deadly efficiency with which it has targeted Black, Latino, and American Indian and Alaska Native people in their 30s, 40s and 50s.

Death at a younger age represents more lost years of life. Lost potential. Lost scholarship. Lost mentorship. Lost earnings. Lost love.

Neighborhoods decimated. Families destroyed.

“So often when we think about the majority of the country who have lost people to covid-19, we think about the elders that have been lost, not necessarily younger people,” said Abigail Echo-Hawk, executive vice president at the Seattle Indian Health Board and director of the Urban Indian Health Institute. “Unfortunately, this is not my reality nor that of the Native community. I lost cousins and fathers and tribal leaders. People that were so integral to building up our community, which has already been struggling for centuries against all these things that created the perfect environment for covid-19 to kill us.”

Six of Echo-Hawk’s friends and relatives — all under 55 — have died of covid.

“This is trauma. This is generational impact that we must have an intentional focus on. The scars are there,” said Marcella Nunez-Smith, chair of President Biden’s COVID-19 Health Equity Task Force and associate dean for health equity research at Yale University. “We can’t think that we’re going to test and vaccinate our way out of this deep pain and hurt.”

The pandemic has brought into stark relief centuries of entwining social, environmental, economic and political factors that erode the health and shorten the lives of people of color, putting them at higher risk of the chronic conditions that leave immune systems vulnerable to the coronavirus. Many of those same factors fuel the misinformation, mistrust and fear that leave too many unprotected.

Take the suggestion that people talk to their doctor about which symptoms warrant testing or a trip to the hospital as well as the safety of vaccines. Seems simple. It’s not.

Many people don’t have a physician they see regularly due in part to significant provider shortages in communities of color. If they do have a doctor, it can cost too much money for a visit even if insured. There are language barriers for those who don’t speak English fluently and fear of deportation among undocumented immigrants.

“Some of the issues at hand are structural issues, things that are built into the fabric of society,” said Enrique W. Neblett Jr., a University of Michigan professor who studies racism and health.

Essential workers who cannot avoid the virus in their jobs because they do not have the luxury of working from home. People living in multigenerational homes with several adult wage-earners, sharing housing because their pay is so low. Even the fight to be counted among the covid casualties — some states and hospitals, Echo-Hawk said, don’t have “even a box to check to say you are American Indian or Alaskan Native.”

It can be difficult to tackle the structural issues influencing the unequal burden of the pandemic while dealing with the day-to-day stress and worry it ignites, which, Neblett said, is why attention must focus on both long-term solutions and “what do we do now? It’s not just that simple as, ‘Oh, you just put on your mask, and we’ll all be good.’ It’s more complicated than that.”

The exacting toll of the last year and a half — covid’s stranglehold on communities of color and George Floyd’s murder — forced the country to interrogate the genealogy of American racism and its effect on health and well-being.

“This is an instance where we finally named it and talked about structural racism as a contributing factor in ways that we haven’t with other health disorders,” Neblett said.

But the nation’s attention span can be short. Polls show there was a sharp rise in concern about discrimination against Black Americans by police following Floyd’s murder, including among White Americans. That concern has eroded some since 2020, though it does remain higher than years past.

“This mistaken understanding that people have, almost this sort of impatience like, ‘Oh, we see racism. Let’s just fix that,’ that’s the thing that gives me hives,” Nunez-Smith said. “This is about generational investments and fundamental changes in ways of being. We didn’t get here overnight.”

Benjamin Franklin’s fight against a deadly virus: Colonial America was divided over smallpox inoculation, but he championed science to skeptics

Benjamin Franklin's fight against a deadly virus: Colonial America was divided  over smallpox inoculation, but he championed science to skeptics

Exactly 300 years ago, in 1721, Benjamin Franklin and his fellow American colonists faced a deadly smallpox outbreak. Their varying responses constitute an eerily prescient object lesson for today’s world, similarly devastated by a virus and divided over vaccination three centuries later.

As a microbiologist and a Franklin scholar, we see some parallels between then and now that could help governments, journalists and the rest of us cope with the coronavirus pandemic and future threats.

Smallpox strikes Boston

Smallpox was nothing new in 1721. Known to have affected people for at least 3,000 years, it ran rampant in Boston, eventually striking more than half the city’s population. The virus killed about 1 in 13 residents – but the death toll was probably more, since the lack of sophisticated epidemiology made it impossible to identify the cause of all deaths.

What was new, at least to Boston, was a simple procedure that could protect people from the disease. It was known as “variolation” or “inoculation,” and involved deliberately exposing someone to the smallpox “matter” from a victim’s scabs or pus, injecting the material into the skin using a needle. This approach typically caused a mild disease and induced a state of “immunity” against smallpox.

Even today, the exact mechanism is poorly understood and not much research on variolation has been done. Inoculation through the skin seems to activate an immune response that leads to milder symptoms and less transmission, possibly because of the route of infection and the lower dose. Since it relies on activating the immune response with live smallpox variola virus, inoculation is different from the modern vaccination that eradicated smallpox using the much less harmful but related vaccinia virus.

The inoculation treatment, which originated in Asia and Africa, came to be known in Boston thanks to a man named Onesimus. By 1721, Onesimus was enslaved, owned by the most influential man in all of Boston, the Rev. Cotton Mather.

etching of an 18th century man in white wig
Cotton Mather heard about variolation from an enslaved West African man in his household named Onesimus. Bettman via Getty Images

Known primarily as a Congregational minister, Mather was also a scientist with a special interest in biology. He paid attention when Onesimus told him “he had undergone an operation, which had given him something of the smallpox and would forever preserve him from it; adding that it was often used” in West Africa, where he was from.

Inspired by this information from Onesimus, Mather teamed up with a Boston physician, Zabdiel Boylston, to conduct a scientific study of inoculation’s effectiveness worthy of 21st-century praise. They found that of the approximately 300 people Boylston had inoculated, 2% had died, compared with almost 15% of those who contracted smallpox from nature.

The findings seemed clear: Inoculation could help in the fight against smallpox. Science won out in this clergyman’s mind. But others were not convinced.

Stirring up controversy

A local newspaper editor named James Franklin had his own affliction – namely an insatiable hunger for controversy. Franklin, who was no fan of Mather, set about attacking inoculation in his newspaper, The New-England Courant.

frontpage of a 1721 newspaper
From its first edition, The New-England Courant covered inoculation. Wikimedia Commons

One article from August 1721 tried to guilt readers into resisting inoculation. If someone gets inoculated and then spreads the disease to someone else, who in turn dies of it, the article asked, “at whose hands shall their Blood be required?” The same article went on to say that “Epidemeal Distempers” such as smallpox come “as Judgments from an angry and displeased God.”

In contrast to Mather and Boylston’s research, the Courant’s articles were designed not to discover, but to sow doubt and distrust. The argument that inoculation might help to spread the disease posits something that was theoretically possible – at least if simple precautions were not taken – but it seems beside the point. If inoculation worked, wouldn’t it be worth this small risk, especially since widespread inoculations would dramatically decrease the likelihood that one person would infect another?

Franklin, the Courant’s editor, had a kid brother apprenticed to him at the time – a teenager by the name of Benjamin.

Historians don’t know which side the younger Franklin took in 1721 – or whether he took a side at all – but his subsequent approach to inoculation years later has lessons for the world’s current encounter with a deadly virus and a divided response to a vaccine.

Independent thought

You might expect that James’ little brother would have been inclined to oppose inoculation as well. After all, thinking like family members and others you identify with is a common human tendency.

That he was capable of overcoming this inclination shows Benjamin Franklin’s capacity for independent thought, an asset that would serve him well throughout his life as a writer, scientist and statesman. While sticking with social expectations confers certain advantages in certain settings, being able to shake off these norms when they are dangerous is also valuable. We believe the most successful people are the ones who, like Franklin, have the intellectual flexibility to choose between adherence and independence.

Truth, not victory

etching of Franklin standing at a table in a lab
Franklin matured into a well-known scientist and statesman, with many successes aided by his open mind. Universal History Archive/Universal Images Group via Getty Images

What happened next shows that Franklin, unlike his brother – and plenty of pundits and politicians in the 21st century – was more interested in discovering the truth than in proving he was right.

Perhaps the inoculation controversy of 1721 had helped him to understand an unfortunate phenomenon that continues to plague the U.S. in 2021: When people take sides, progress suffersTribes, whether long-standing or newly formed around an issue, can devote their energies to demonizing the other side and rallying their own. Instead of attacking the problem, they attack each other.

Franklin, in fact, became convinced that inoculation was a sound approach to preventing smallpox. Years later he intended to have his son Francis inoculated after recovering from a case of diarrhea. But before inoculation took place, the 4-year-old boy contracted smallpox and died in 1736. Citing a rumor that Francis had died because of inoculation and noting that such a rumor might deter parents from exposing their children to this procedure, Franklin made a point of setting the record straight, explaining that the child had “receiv’d the Distemper in the common Way of Infection.”

Writing his autobiography in 1771, Franklin reflected on the tragedy and used it to advocate for inoculation. He explained that he “regretted bitterly and still regret” not inoculating the boy, adding, “This I mention for the sake of parents who omit that operation, on the supposition that they should never forgive themselves if a child died under it; my example showing that the regret may be the same either way, and that, therefore, the safer should be chosen.”

A scientific perspective

A final lesson from 1721 has to do with the importance of a truly scientific perspective, one that embraces science, facts and objectivity.

19th-century photo of a smallpox patient
Smallpox was characterized by fever and aches and pustules all over the body. Before eradication, the virus killed about 30% of those it infected, according to the U.S. Centers for Disease Control and Prevention. Sepia Times/Universal Images Group via Getty Images

Inoculation was a relatively new procedure for Bostonians in 1721, and this lifesaving method was not without deadly risks. To address this paradox, several physicians meticulously collected data and compared the number of those who died because of natural smallpox with deaths after smallpox inoculation. Boylston essentially carried out what today’s researchers would call a clinical study on the efficacy of inoculation. Knowing he needed to demonstrate the usefulness of inoculation in a diverse population, he reported in a short book how he inoculated nearly 300 individuals and carefully noted their symptoms and conditions over days and weeks.

The recent emergency-use authorization of mRNA-based and viral-vector vaccines for COVID-19 has produced a vast array of hoaxes, false claims and conspiracy theories, especially in various social media. Like 18th-century inoculations, these vaccines represent new scientific approaches to vaccination, but ones that are based on decades of scientific research and clinical studies.

We suspect that if he were alive today, Benjamin Franklin would want his example to guide modern scientists, politicians, journalists and everyone else making personal health decisions. Like Mather and Boylston, Franklin was a scientist with a respect for evidence and ultimately for truth.

When it comes to a deadly virus and a divided response to a preventive treatment, Franklin was clear what he would do. It doesn’t take a visionary like Franklin to accept the evidence of medical science today.

More than 99% of US Covid-19 deaths are among unvaccinated patients

Almost All of the Current COVID-19 Deaths Are Among Those Unvaccinated

As the delta variant of the coronavirus spreads, especially among the unvaccinated, the Biden administration is gearing up for a new push to vaccinate the so-called “movable middle”—and some public health experts say FDA could advance that goal by fully approving Covid-19 vaccines.

Analysis reveals toll of US Covid-19 deaths among unvaccinated patients

According to an analysis by the Associated Press, nearly all recent Covid-19 deaths have occurred in unvaccinated individuals.

The AP analysis is based on data from CDC, although CDC has not itself released estimates of the share of Covid-19 deaths among unvaccinated patients.

According to the AP analysis, just 0.8% of Covid-19 deaths in May were among the fully vaccinated. Meanwhile, the share of hospitalized patients who were fully vaccinated was just 0.1% in May, with fewer than 1,200 fully vaccinated people hospitalized out of more than 853,000 hospitalizations.

Meanwhile, according to CDC, 54% of the U.S. population, including 66% of American adults, have received at least one dose of a Covid-19 vaccine, while 46.1% of the total population and 56.8% of American adults have received all required doses.

The partisan divide in coronavirus vaccinations is widening

One hesitates to elevate obviously bad arguments, even to point out how bad they are. This is a conundrum that comes up a lot these days, as members of the media measure the utility of reporting on bad faith, disingenuous or simply bizarre claims.

If someone were to insist, for example, that they were not going to get the coronavirus vaccine solely to spite the political left, should that claim be elevated? Can we simply point out how deranged it is to refuse a vaccine that will almost certainly end an international pandemic simply because people with whom you disagree think that maybe this is a good route to end that pandemic? If someone were to write such a thing at some attention-thirsty website, we certainly wouldn’t want to link to it, leaving our own readers having to figure out where it might be found should they choose to do so.

In this case, it’s worth elevating this argument (which, to be clear, is actually floating out there) to point out one of the myriad ways in which the effort to vaccinate as many adults as possible has become interlaced with partisan politics. As the weeks pass and demand for the vaccine has tapered off, the gap between Democratic and Republican interest in being vaccinated seems to be widening — meaning that the end to the pandemic is likely to move that much further into the future.

Consider, for example, the rate of completed vaccinations by county, according to data compiled by CovidActNow. You can see a slight correlation between how a county voted in 2020 — the horizontal axis — and the density of completed vaccinations, shown on the vertical. There’s a greater density of completed vaccinations on the left side of the graph than on the right.

If we shift to the percentage of the population that’s received even one dose of the vaccine, the effect is much more obvious.

This is a relatively recent development. At the beginning of the month, the density of the population that had received only one dose resulted in a graph that looked much like the current density of completed doses.

If we animate those two graphs, the effect is obvious. In the past few weeks, the density of first doses has increased much faster in more-Democratic counties.

If we group the results of the 2020 presidential contest into 20-point buckets, the pattern is again obvious.

It’s not a new observation that Republicans are less willing to get the vaccine; we’ve reported on it repeatedly. What’s relatively new is how that hesitance is showing up in the actual vaccination data.

A Post-ABC News poll released on Monday showed that this response to the vaccine holds even when considering age groups. We’ve known for a while that older Americans, who are more at risk from the virus, have been more likely to seek the vaccine. But even among seniors, Republicans are significantly more hesitant to receive the vaccine than are Democrats.

This is a particularly dangerous example of partisanship. People 65 or older have made up 14 percent of coronavirus infections, according to federal data, but 81 percent of deaths. That’s among those for whom ages are known, a subset (though a large majority) of overall cases. While about 1.8 percent of that overall group has died, the figure for those aged 65 and over is above 10 percent.

As vaccines have been rolled out across the country, you can see how more-heavily-blue counties have a higher density of vaccinations in many states.

This is not a universal truth, of course. Some heavily Republican counties have above-average vaccination rates. (About 40 percent of counties that preferred former president Donald Trump last year are above the average in the CovidActNow data. The rate among Democratic counties is closer to 80 percent.) But it is the case that there is a correlation between how a county voted and how many of its residents have been vaccinated. It is also the case that the gap between red and blue counties is widening.

Given all of that, it probably makes sense to point out that an argument against vaccines based on nothing more than “lol libs will hate this” is an embarrassing argument to make.

In scramble to respond to Covid-19, hospitals turned to models with high risk of bias

In scramble to respond to Covid-19, hospitals turned to models with high  risk of bias - MedCity News

Of 26 health systems surveyed by MedCity News, nearly half used automated tools to respond to the Covid-19 pandemic, but none of them were regulated. Even as some hospitals continued using these algorithms, experts cautioned against their use in high-stakes decisions.

A year ago, Michigan Medicine faced a dire situation. In March of 2020, the health system predicted it would have three times as many patients as its 1,000-bed capacity — and that was the best-case scenario. Hospital leadership prepared for this grim prediction by opening a field hospital in a nearby indoor track facility, where patients could go if they were stable, but still needed hospital care. But they faced another predicament: How would they decide who to send there?

Two weeks before the field hospital was set to open, Michigan Medicine decided to use a risk model developed by Epic Systems to flag patients at risk of deterioration. Patients were given a score of 0 to 100, intended to help care teams determine if they might need an ICU bed in the near future. Although the model wasn’t developed specifically for Covid-19 patients, it was the best option available at the time, said Dr. Karandeep Singh, an assistant professor of learning health sciences at the University of Michigan and chair of Michigan Medicine’s clinical intelligence committee. But there was no peer-reviewed research to show how well it actually worked.

Researchers tested it on over 300 Covid-19 patients between March and May. They were looking for scores that would indicate when patients would need to go to the ICU, and if there was a point where patients almost certainly wouldn’t need intensive care.

“We did find a threshold where if you remained below that threshold, 90% of patients wouldn’t need to go to the ICU,” Singh said. “Is that enough to make a decision on? We didn’t think so.”

But if the number of patients were to far exceed the health system’s capacity, it would be helpful to have some way to assist with those decisions.

“It was something that we definitely thought about implementing if that day were to come,” he said in a February interview.

Thankfully, that day never came.

The survey
Michigan Medicine is one of 80 hospitals contacted by MedCity News between January and April in a survey of decision-support systems implemented during the pandemic. 
Of the 26 respondents, 12 used machine learning tools or automated decision systems as part of their pandemic response. Larger hospitals and academic medical centers used them more frequently.

Faced with scarcities in testing, masks, hospital beds and vaccines, several of the hospitals turned to models as they prepared for difficult decisions. The deterioration index created by Epic was one of the most widely implemented — more than 100 hospitals are currently using it — but in many cases, hospitals also formulated their own algorithms.

They built models to predict which patients were most likely to test positive when shortages of swabs and reagents backlogged tests early in the pandemic. Others developed risk-scoring tools to help determine who should be contacted first for monoclonal antibody treatment, or which Covid patients should be enrolled in at-home monitoring programs.

MedCity News also interviewed hospitals on their processes for evaluating software tools to ensure they are accurate and unbiased. Currently, the FDA does not require some clinical decision-support systems to be cleared as medical devices, leaving the developers of these tools and the hospitals that implement them responsible for vetting them.

Among the hospitals that published efficacy data, some of the models were only evaluated through retrospective studies. This can pose a challenge in figuring out how clinicians actually use them in practice, and how well they work in real time. And while some of the hospitals tested whether the models were accurate across different groups of patients — such as people of a certain race, gender or location — this practice wasn’t universal.

As more companies spin up these models, researchers cautioned that they need to be designed and implemented carefully, to ensure they don’t yield biased results.

An ongoing review of more than 200 Covid-19 risk-prediction models found that the majority had a high risk of bias, meaning the data they were trained on might not represent the real world.

“It’s that very careful and non-trivial process of defining exactly what we want the algorithm to be doing,” said Ziad Obermeyer, an associate professor of health policy and management at UC Berkeley who studies machine learning in healthcare. “I think an optimistic view is that the pandemic functions as a wakeup call for us to be a lot more careful in all of the ways we’ve talked about with how we build algorithms, how we evaluate them, and what we want them to do.”

Algorithms can’t be a proxy for tough decisions
Concerns about bias are not new to healthcare. In a paper published two years ago
, Obermeyer found a tool used by several hospitals to prioritize high-risk patients for additional care resources was biased against Black patients. By equating patients’ health needs with the cost of care, the developers built an algorithm that yielded discriminatory results.

More recently, a rule-based system developed by Stanford Medicine to determine who would get the Covid-19 vaccine first ended up prioritizing administrators and doctors who were seeing patients remotely, leaving out most of its 1,300 residents who had been working on the front lines. After an uproar, the university attributed the errors to a “complex algorithm,” though there was no machine learning involved.

Both examples highlight the importance of thinking through what exactly a model is designed to do — and not using them as a proxy to avoid the hard questions.

“The Stanford thing was another example of, we wanted the algorithm to do A, but we told it to do B. I think many health systems are doing something similar,” Obermeyer said. “You want to give the vaccine first to people who need it the most — how do we measure that?”

The urgency that the pandemic created was a complicating factor.  With little information and few proven systems to work with in the beginning, health systems began throwing ideas at the wall to see what works. One expert questioned whether people might be abdicating some responsibility to these tools.

“Hard decisions are being made at hospitals all the time, especially in this space, but I’m worried about algorithms being the idea of where the responsibility gets shifted,” said Varoon Mathur, a technology fellow at NYU’s AI Now Institute, in a Zoom interview. “Tough decisions are going to be made, I don’t think there are any doubts about that. But what are those tough decisions? We don’t actually name what constraints we’re hitting up against.”

The wild, wild west
There currently is no gold standard for how hospitals should implement machine learning tools, and little regulatory oversight for models designed to support physicians’ decisions, resulting in an environment that Mathur described as the “wild, wild west.”

How these systems were used varied significantly from hospital to hospital.

Early in the pandemic, Cleveland Clinic used a model to predict which patients were most likely to test positive for the virus as tests were limited. Researchers developed it using health record data from more than 11,000 patients in Ohio and Florida, including 818 who tested positive for Covid-19. Later, they created a similar risk calculator to determine which patients were most likely to be hospitalized for Covid-19, which was used to prioritize which patients would be contacted daily as part of an at-home monitoring program.

Initially, anyone who tested positive for Covid-19 could enroll in this program, but as cases began to tick up, “you could see how quickly the nurses and care managers who were running this program were overwhelmed,” said Dr. Lara Jehi, Chief Research Information Officer at Cleveland Clinic. “When you had thousands of patients who tested positive, how could you contact all of them?”

While the tool included dozens of factors, such as a patient’s age, sex, BMI, zip code, and whether they smoked or got their flu shot, it’s also worth noting that demographic information significantly changed the results. For example, a patient’s race “far outweighs” any medical comorbidity when used by the tool to estimate hospitalization risk, according to a paper published in Plos One.  Cleveland Clinic recently made the model available to other health systems.

Others, like Stanford Health Care and 731-bed Santa Clara County Medical Center, started using Epic’s clinical deterioration index before developing their own Covid-specific risk models. At one point, Stanford developed its own risk-scoring tool, which was built using past data from other patients who had similar respiratory diseases, such as the flu, pneumonia, or acute respiratory distress syndrome. It was designed to predict which patients would need ventilation within two days, and someone’s risk of dying from the disease at the time of admission.

Stanford tested the model to see how it worked on retrospective data from 159 patients that were hospitalized with Covid-19, and cross-validated it with Salt Lake City-based Intermountain Healthcare, a process that took several months. Although this gave some additional assurance — Salt Lake City and Palo Alto have very different populations, smoking rates and demographics — it still wasn’t representative of some patient groups across the U.S.

“Ideally, what we would want to do is run the model specifically on different populations, like on African Americans or Hispanics and see how it performs to ensure it’s performing the same for different groups,” Tina Hernandez-Boussard, an associate professor of medicine, biomedical data science and surgery at Stanford, said in a February interview. “That’s something we’re actively seeking. Our numbers are still a little low to do that right now.”

Stanford planned to implement the model earlier this year, but ultimately tabled it as Covid-19 cases fell.

‘The target is moving so rapidly’
Although large medical centers were more likely to have implemented automated systems, there were a few notable holdouts. For example, UC San Francisco Health, Duke Health and Dignity Health all said they opted not to use risk-prediction models or other machine learning tools in their pandemic responses.

“It’s pretty wild out there and I’ll be honest with you —  the dynamics are changing so rapidly,” said Dr. Erich Huang, chief officer for data quality at Duke Health and director of Duke Forge. “You might have a model that makes sense for the conditions of last month but do they make sense for the conditions of next month?”

That’s especially true as new variants spread across the U.S., and more adults are vaccinated, changing the nature and pace of the disease. But other, less obvious factors might also affect the data. For instance, Huang pointed to big differences in social mobility across the state of North Carolina, and whether people complied with local restrictions. Differing social and demographic factors across communities, such as where people work and whether they have health insurance, can also affect how a model performs.

“There are so many different axes of variability, I’d feel hard pressed to be comfortable using machine learning or AI at this point in time,” he said. “We need to be careful and understand the stakes of what we’re doing, especially in healthcare.”

Leadership at one of the largest public hospitals in the U.S., 600-bed LAC+USC Medical Center in Los Angeles, also steered away from using predictive models, even as it faced an alarming surge in cases over the winter months.

At most, the hospital used alerts to remind physicians to wear protective equipment when a patient has tested positive for Covid-19.

“My impression is that the industry is not anywhere near ready to deploy fully automated stuff just because of the risks involved,” said Dr. Phillip Gruber, LAC+USC’s chief medical information officer. “Our institution and a lot of institutions in our region are still focused on core competencies. We have to be good stewards of taxpayer dollars.”

When the data itself is biased
Developers have to contend with the fact that any model developed in healthcare will be biased, because the data itself is biased; how people access and interact with health systems in the U.S. is fundamentally unequal.

How that information is recorded in electronic health record systems (EHR) can also be a source of bias, NYU’s Mathur said. People don’t always self-report their race or ethnicity in a way that fits neatly within the parameters of an EHR. Not everyone trusts health systems, and many people struggle to even access care in the first place.

“Demographic variables are not going to be sharply nuanced. Even if they are… in my opinion, they’re not clean enough or good enough to be nuanced into a model,” Mathur said.

The information hospitals have had to work with during the pandemic is particularly messy. Differences in testing access and missing demographic data also affect how resources are distributed and other responses to the pandemic.

“It’s very striking because everything we know about the pandemic is viewed through the lens of number of cases or number of deaths,” UC Berkeley’s Obermeyer said. “But all of that depends on access to testing.”

At the hospital level, internal data wouldn’t be enough to truly follow whether an algorithm to predict adverse events from Covid-19 was actually working. Developers would have to look at social security data on mortality, or whether the patient went to another hospital, to track down what happened.

“What about the people a physician sends home —  if they die and don’t come back?” he said.

Researchers at Mount Sinai Health System tested a machine learning tool to predict critical events in Covid-19 patients —  such as dialysis, intubation or ICU admission — to ensure it worked across different patient demographics. But they still ran into their own limitations, even though the New York-based hospital system serves a diverse group of patients.

They tested how the model performed across Mount Sinai’s different hospitals. In some cases, when the model wasn’t very robust, it yielded different results, said Benjamin Glicksberg, an assistant professor of genetics and genomic sciences at Mount Sinai and a member of its Hasso Plattner Institute for Digital Health.

They also tested how it worked in different subgroups of patients to ensure it didn’t perform disproportionately better for patients from one demographic.

“If there’s a bias in the data going in, there’s almost certainly going to be a bias in the data coming out of it,” he said in a Zoom interview. “Unfortunately, I think it’s going to be a matter of having more information that can approximate these external factors that may drive these discrepancies. A lot of that is social determinants of health, which are not captured well in the EHR. That’s going to be critical for how we assess model fairness.”

Even after checking for whether a model yields fair and accurate results, the work isn’t done yet. Hospitals must continue to validate continuously to ensure they’re still working as intended — especially in a situation as fast-moving as a pandemic.

A bigger role for regulators
All of this is stirring up a broader discussion about how much of a role regulators should have in how decision-support systems are implemented.

Currently, the FDA does not require most software that provides diagnosis or treatment recommendations to clinicians to be regulated as a medical device. Even software tools that have been cleared by the agency lack critical information on how they perform across different patient demographics. 

Of the hospitals surveyed by MedCity News, none of the models they developed had been cleared by the FDA, and most of the external tools they implemented also hadn’t gone through any regulatory review.

In January, the FDA shared an action plan for regulating AI as a medical device. Although most of the concrete plans were around how to regulate algorithms that adapt over time, the agency also indicated it was thinking about best practices, transparency, and methods to evaluate algorithms for bias and robustness.

More recently, the Federal Trade Commission warned that it could crack down on AI bias, citing a paper that AI could worsen existing healthcare disparities if bias is not addressed.

“My experience suggests that most models are put into practice with very little evidence of their effects on outcomes because they are presumed to work, or at least to be more efficient than other decision-making processes,” Kellie Owens, a researcher for Data & Society, a nonprofit that studies the social implications of technology, wrote in an email. “I think we still need to develop better ways to conduct algorithmic risk assessments in medicine. I’d like to see the FDA take a much larger role in regulating AI and machine learning models before their implementation.”

Developers should also ask themselves if the communities they’re serving have a say in how the system is built, or whether it is needed in the first place. The majority of hospitals surveyed did not share with patients if a model was used in their care or involve patients in the development process.

In some cases, the best option might be the simplest one: don’t build.

In the meantime, hospitals are left to sift through existing published data, preprints and vendor promises to decide on the best option. To date, Michigan Medicine’s paper is still the only one that has been published on Epic’s Deterioration Index.

Care teams there used Epic’s score as a support tool for its rapid response teams to check in on patients. But the health system was also looking at other options.

“The short game was that we had to go with the score we had,” Singh said. “The longer game was, Epic’s deterioration index is proprietary. That raises questions about what is in it.”