Two Conservative Icons Gave Opposite Advice on COVID-19. Those Misinformed Died in Higher Numbers, New Study Reports.
The news sources that you consume can kill you - or save you. That's the fundamental insight of a powerful new study about the impact of watching either Sean Hannity's news show Hannity or Tucker Carlson's Tucker Carlson Tonight. One saved lives and the other resulted in more deaths, due to how each host covered COVID-19.
Carlson took the threat of COVID-19 seriously early on, more so than most media figures on the right or left.
This research illustrates the danger of falling for health-related misinformation due to judgment errors known as cognitive biases. These dangerous mental blindspots stem from the fact that our gut reactions evolved for the ancient savanna environment, not the modern world; yet the vast majority of advice on decision making is to "go with your gut," despite the fact that doing so leads to so many disastrous outcomes. These mental blind spots impact all areas of our life, from health to politics and even shopping, as a survey by a comparison purchasing website reveals. We need to be wary of cognitive biases in order to survive and thrive during this pandemic.
Sean Hannity vs. Tucker Carlson Coverage of COVID-19
Hannity and Tucker Carlson Tonight are the top two U.S. cable news shows, both on Fox News. Hannity and Carlson share very similar ideological profiles and have similar viewership demographics: older adults who lean conservative.
One notable difference, however, relates to how both approached coverage of COVID-19, especially in February and early March 2020. Researchers at the Becker Friedman Institute for Economics at the University of Chicago decided to study the health consequences of this difference.
Carlson took the threat of COVID-19 seriously early on, more so than most media figures on the right or left. Already on January 28, way earlier than most, Carlson spent a significant part of his show highlighting the serious dangers of a global pandemic. He continued his warnings throughout February. On February 25, Carlson told his viewers: "In this country, more than a million would die."
By contrast, Hannity was one of the Fox News hosts who took a more extreme position in downplaying COVID-19, frequently comparing it to the flu. On February 27, he said "And today, thankfully, zero people in the United States of America have died from the coronavirus. Zero. Now, let's put this in perspective. In 2017, 61,000 people in this country died from influenza, the flu. Common flu." Moreover, Hannity explicitly politicized COVID-19, claiming that "[Democrats] are now using the natural fear of a virus as a political weapon. And we have all the evidence to prove it, a shameful politicizing, weaponizing of, yes, the coronavirus."
However, after President Donald Trump declared COVID-19 a national emergency in mid-March, Hannity -- and other Fox News hosts -- changed their tune to align more with Carlson's, acknowledging the serious dangers of the virus.
The Behavior and Health Consequences
The Becker Friedman Institute researchers investigated whether the difference in coverage impacted behaviors. They conducted a nationally representative survey of over 1,000 people who watch Fox News at least once a week, evaluating both viewership and behavior changes in response to the pandemic, such as social distancing and improving hygiene.
Next, the study compared people's behavior changes to viewing patterns. The researchers found that "viewers of Hannity changed their behavior five days later than viewers of other shows, while viewers of Tucker Carlson Tonight changed their behavior three days earlier than viewers of other shows." The statistical difference was more than enough to demonstrate significance; in other words, it was extremely unlikely to occur by chance -- so unlikely as to be negligible.
Did these behavior changes lead to grave consequences? Indeed.
The paper compared the popularity of each show in specific counties to data on COVID-19 infections and deaths. Controlling for a wide variety of potential confounding variables, the study found that areas of the country where Hannity is more popular had more cases and deaths two weeks later, the time that it would take for the virus to start manifesting itself. By March 21st, the researchers found, there were 11 percent more deaths among Hannity's viewership than among Carlson's, again with a high degree of statistical significance.
The study's authors concluded: "Our findings indicate that provision of misinformation in the early stages of a pandemic can have important consequences for health outcomes."
Such outcomes stem from excessive trust that our minds tend to give those we see as having authority, even if they don't possess expertise in the relevant subject era.
Cognitive Biases and COVID-19 Misinformation
It's critically important to recognize that the study's authors did not seek to score any ideological points, given the broadly similar ideological profiles of the two hosts. The researchers simply explored the impact of accurate and inaccurate information about COVID-19 on the viewership. Clearly, the false information had deadly consequences.
Such outcomes stem from excessive trust that our minds tend to give those we see as having authority, even if they don't possess expertise in the relevant subject era -- such as media figures that we follow. This excessive trust - and consequent obedience - is called the "authority bias."
A related mental pattern is called "emotional contagion," in which we are unwittingly infected with the emotions of those we see as leaders. Emotions can motivate action even in the absence of formal authority, and are particularly important for those with informal authority, including thought leaders like Carlson and Hannity.
Thus, Hannity telling his audience that Democrats used anxiety about the virus as a political weapon led his audience to reject fears of COVID-19, even though such a reaction and consequent behavioral changes were the right response. Carlson's emphasis on the deadly nature of this illness motivated his audience to take appropriate precautions.
Authority bias and emotional contagion facilitate the spread of misinformation and its dangers, at least when we don't take the steps necessary to figure out the facts. Such steps can range from following best fact-checking practices to getting your information from news sources that commit publicly to being held accountable for truthfulness. Remember, the more important and impactful such information may be for your life, the more important it is to take the time to evaluate it accurately to help you make the best decisions.
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.