How We Can Return to Normal Life in the COVID-19 Era
I was asked recently when life might return to normal. The question is simple but the answer is complex, with many knowns, lots of known unknowns, and some unknown unknowns. But I'll give it my best shot.
To get the fatality rate down to flu-like levels would require that we cut Covid-19 fatalities down by a factor of 5.
Since I'm human (and thus want my life back), I might be biased toward optimism.
Here's one way to think about it: Is there another infection that causes sickness and death at levels that we tolerate? The answer, of course, is 'yes': influenza.
According to the Centers for Disease Control, from 2010 to 2019, an average of 30 million Americans had the flu each year, leading to an annual average of 37,000 deaths. This works out to an infection-fatality rate, or IFR, of 0.12 percent. We've tolerated that level of illness death from influenza for a century.
Before going on, let's get one thing out of the way: Back in March, Covid-19 wasn't, as some maintained, "like the flu," and it still isn't. Since then, the U.S. has had 3.9 million confirmed Covid-19 cases and 140,000 deaths, for an IFR of 3.6 percent. Taking all the cases — including asymptomatic patients and those with minimal symptoms who were never tested for Covid-19 — into account, the real IFR is probably 0.6 percent, or roughly 5 times that of the flu.
Nonetheless, even a partly effective vaccine, combined with moderately effective medications, could bring Covid-19 numbers down to a tolerable, flu-like, threshold. It's a goal that seems within our reach.
Chronic mask-wearing and physical distancing are not my idea of normal, nor, I would venture to guess, would most other Americans consider these desirable states in which to live. We need both now to achieve some semblance of normalcy, but they're decidedly not normal life. My notion of normal: daily life with no or minimal mask wearing, open restaurants and bars, ballparks with fans, and theaters with audiences.
My projection for when we might get there: perhaps a year from now.
To get the fatality rate down to flu-like levels would require that we cut Covid-19 fatalities down by a factor of 5, via some combination of fewer symptomatic cases and a lower chance that a symptomatic patient will go on to die. How might that happen?
First, we have to make some impact on young people – getting them to follow the public health directives at higher rates than they are currently. The main reason we need to push younger people to stay safe is that they can spread Covid-19 to vulnerable people (those who are older, with underlying health problems). But, once the most vulnerable are protected (through the deployment of some combination of effective medications and a vaccine), the fact that some young people aren't acting safely – or maybe won't take a vaccine themselves – wouldn't cause so much concern. The key is whether the people at highest risk for bad outcomes are protected.
Then there's the vaccine. The first principle: We don't need a 100 percent-effective vaccine injected into 320 million deltoid muscles (in the U.S. alone). Thank God, since it's fanciful to believe that we can have a vaccine that's 100 percent effective, universally available by next summer, and that each and every American agrees to be vaccinated.
How are we doing in our vaccine journey? We've been having some banner days lately, with recent optimistic reports from several of the vaccine companies. In one report, the leading candidate vaccine, the one effort being led by Oxford University, led to both antibodies and a cellular immune response, a very helpful belt-and-suspenders approach that increases the probability of long-lasting immunity. This good news comes on the heels of the positive news regarding the American vaccine being made by Moderna earlier in July.
While every article about vaccines sounds the obligatory cautionary notes, to date we've checked every box on the path to a safe and effective vaccine. We might not get there, but most experts are now predicting an FDA-approvable vaccine (more than 50 percent effective with no show-stopping side effects) by early 2021.
It is true that we don't know how long immunity will last, but that can be a problem to solve later. In this area, time is our friend. If we can get to an effective vaccine that lasts for a year or two, over time we should be able to discover strategies (more vaccine boosters, new and better medications) to address the possibility of waning immunity.
All things considered, I'm going to put my nickel down on the following optimistic scenario: we'll have one, and likely several, vaccines that have been proven to be more than 50 percent effective and safe by January, 2021.
If only that were the finish line.
Once we vaccinate a large fraction of high-risk patients, having a moderate number of unvaccinated people running around won't pose as much threat.
The investments in manufacturing and distribution should pay off, but it's still inconceivable that we'll be able to get vaccines to 300 million people in three to six months. For the 2009 Swine Flu, we managed to vaccinate about 1 in 4 Americans over six months.
So we'll need to prioritize. First in line will likely be the 55 million Americans over 65, and the six to eight million patient-facing healthcare workers. (How to sort priorities among people under 65 with "chronic diseases" will be a toughie.) Vaccinating 80-100 million vulnerable people, plus clinicians, might be achievable by mid-21.
If we can protect vulnerable people with an effective vaccine (with the less vulnerable waiting their turn over a subsequent 6-12 month period), that may be enough to do the trick. (Of course, vulnerable people may also be least likely to develop immunity in response to a vaccine. That could be an Achilles' heel – time will tell.)
Why might that be enough? Once we vaccinate a large fraction of high-risk patients, having a moderate number of unvaccinated people running around won't pose as much threat. Since they're at lower risk, they have a lower chance of getting sick and dying than those who received the vaccine first.
We're likely to have better meds by then, too. Since March, we've discovered two moderately effective medications for Covid-19 — remdesivir and dexamethasone. It seems likely that we'll find others by next summer, perhaps even a treatment that prevents patients from getting ill in the first place. There are many such therapies, ranging from zinc to convalescent plasma, currently being studied.
Moreover, we know that hospitals that are not overrun with Covid-19 have lower mortality rates. If we've gotten a fairly effective vaccine into most high-risk people, the hospitals are unlikely to be overwhelmed – another factor that may help lower the mortality rate to flu-like levels.
All of these factors – vaccination of most vulnerable people, one or two additional effective medications, hospitals and ICU's that aren't overwhelmed – could easily combine to bring the toll of Covid-19 down to something that resembles that of the flu. Then, we should be able to return to normal life.
Whatever the reason, if enough people refuse the vaccine, all bets are off.
What do I worry about? There's the growing anti-vaxxer movement, for one. On top of that, it seems that many Americans worry that a vaccine discovered in record speed won't be safe, or that the FDA approval process will have been corrupted by political influences. Whatever the reason, if enough people refuse the vaccine, all bets are off.
Assuming only high-risk people do get vaccinated, there will still be cases of Covid-19, maybe even mini-outbreaks, well into 2021 and likely 2022. Obviously, that's not ideal, and we should hope for a vaccine that results in the complete eradication of Covid-19. But the point is that, even with flu-like levels of illness and death, we should still be able to achieve "normal."
Hope is not a strategy, as the saying goes. But it is hope, which is more than we've had for a while.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.