How Roadside Safety Signs Backfire—and Why Policymakers Don’t Notice
nudgesYou are driving along the highway and see an electronic sign that reads: “3,238 traffic deaths this year.” Do you think this reminder of roadside mortality would change how you drive? According to a recent, peer-reviewed study in Science, seeing that sign would make you more likely to crash. That’s ironic, given that the sign’s creators assumed it would make you safer.
The study, led by a pair of economists at the University of Toronto and University of Minnesota, examined seven years of traffic accident data from 880 electric highway sign locations in Texas, which experienced 4,480 fatalities in 2021. For one week of each month, the Texas Department of Transportation posts the latest fatality messages on signs along select traffic corridors as part of a safety campaign. Their logic is simple: Tell people to drive with care by reminding them of the dangers on the road.
But when the researchers looked at the data, they found that the number of crashes increased by 1.52 percent within three miles of these signs when compared with the same locations during the same month in previous years when signs did not show fatality information. That impact is similar to raising the speed limit by four miles or decreasing the number of highway troopers by 10 percent.
The scientists calculated that these messages contributed to 2,600 additional crashes and 16 deaths annually. They also found a social cost, meaning the financial expense borne by society as a whole due to these crashes, of $377 million per year, in Texas alone.
The cause, they argue, is distracted driving. Much like incoming texts or phone calls, these “in-your-face” messages grab your attention and undermine your focus on the road. The signs are particularly distracting and dangerous because, in communicating that many people died doing exactly what you are doing, they cause anxiety. Supporting this hypothesis, the scientists discovered that crashes increase when the signs report higher numbers of deaths. Thus, later in the year, as that total mortality figure goes up, so do the percentage of crashes.
Boomerang effects happen when those with authority, in government or business, fail to pay attention to the science. These leaders rely on armchair psychology and gut intuitions on what should work, rather than measuring what does work.
That change over time is not simply a function of changing weather, the study’s authors observed. They also found that the increase in car crashes is greatest in more complex road segments, which require greater focus to navigate.
The overall findings represent what behavioral scientists like myself call a “boomerang effect,” meaning an intervention that produces consequences opposite to those intended. Unfortunately, these effects are all too common. Between 1998 and 2004, Congress funded the $1 billion National Youth Anti-Drug Media Campaign, which famously boomeranged. Using professional advertising and public relations firms, the campaign bombarded kids aged 9 to 18 with anti-drug messaging, focused on marijuana, on TV, radio, magazines, and websites. A 2008 study funded by the National Institutes of Health found that children and teens saw these ads two to three times per week. However, more exposure to this advertising increased the likelihood that youth used marijuana. Why? Surveys and interviews suggested that young people who saw the ads got the impression that many of their peers used marijuana. As a result, they became more likely to use the drug themselves.
Boomerang effects happen when those with authority, in government or business, fail to pay attention to the science. These leaders rely on armchair psychology and gut intuitions on what should work, rather than measuring what does work.
To be clear, message campaigns—whether on electronic signs or through advertisements—can have a substantial effect on behavior. Extensive research reveals that people can be influenced by “nudges,” which shape the environment to influence their behavior in a predictable manner. For example, a successful campaign to reduce car accidents involved sending smartphone notifications that helped drivers evaluate their performance after each trip. These messages informed drivers of their personal average and best performance, as measured by accelerometers and gyroscopes. The campaign, which ran over 21 months, significantly reduced accident frequency.
Nudges work best when rigorously tested with small-scale experiments that evaluate their impact. Because behavioral scientists are infrequently consulted in creating these policies, some studies suggest that only 62 percent have a statistically significant effect. Other research reveals that up to 15 percent of desired interventions may backfire.
In the case of roadside mortality signage, the data are damning. The new research based on the Texas signs aligns with several past studies. For instance, research has shown that increasing people’s anxiety causes them to drive worse. Another, a Virginia Tech study in a laboratory setting, found that showing drivers fatality messages increased what psychologists call “cognitive load,” or the amount of information your brain is processing, with emotionally-salient information being especially burdensome and preoccupying, thus causing more distraction.
Nonetheless, Texas, along with at least 28 other states, has pursued mortality messaging campaigns since 2012, without testing them effectively. Behavioral science is critical here: when road signs are tested by people without expertise in how minds work, the results are often counterproductive. For example, the Virginia Tech research looked at road signs that used humor, popular culture, sports, and other nontraditional themes with the goal of provoking an emotional response. When they measured how participants responded to these signs, they noticed greater cognitive activation and attention in the brain. Thus, the researchers decided, the signs worked. But a behavioral scientist would note that increased attention likely contributes to the signs’ failure. As the just-published study in Science makes clear, distracting, emotionally-loaded signs are dangerous to drivers.
But there is good news. First, in most cases, it’s very doable to run an effective small-scale study testing an intervention. States could set up a safety campaign with a few electric signs in a diversity of settings and evaluate the impact over three months on driver crashes after seeing the signs. Policymakers could ask researchers to track the data as they run ads for a few months in a variety of nationally representative markets for a few months and assess their effectiveness. They could also ask behavioral scientists whether their proposals are well designed, whether similar policies have been tried previously in other places, and how these policies have worked in practice.
Everyday citizens can write to and call their elected officials to ask them to make this kind of research a priority before embracing an untested safety campaign. More broadly, you can encourage them to avoid relying on armchair psychology and to test their intuitions before deploying initiatives that might place the public under threat.
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.