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Hiding Serious Illness as a Coping Mechanism

Defense Secretary Lloyd J. Austin III was criticized for hiding his prostate cancer diagnosis and treatment, but experts say secrecy is a common coping strategy for patients facing serious illness, especially in the first days following a diagnosis. “I see it with my patients all the time,” said palliative care specialist Andrew E. Esch, senior education adviser at the Center to Advance Palliative Care in New York City, in a New York Times article. “It’s very human to not want to have yourself sort of flayed open for the world to see.” Compartmentalizing aspects of their lives, including communicating with others, can help patients cope with their illness, according to psychiatrist Itai Danovitch of Cedars-Sinai in Los Angeles. Some patients struggle with the vulnerability that serious illness can bring and keep their illness secret to deal with that emotion. “Some people view being healthy and physically capable as being very central to their role or identity,” said psychologist Steven A. Meyers at Roosevelt University in Chicago. “Those people will have a lot more difficulty in publicly acknowledging feeling diminished in their estimation. Those people will also be much more concerned with being a burden to others.” Cultural background and age may also play a role in how open patients are about their sickness, with younger people often being more open than older people, said psychiatrist Jesse R. Fann of the Fred Hutchinson Cancer Center in Seattle.

Cancer Rates Are Up Among Those Under 50

Cancer, a disease typically associated with aging, is striking younger people more often, and experts are scrambling to understand why. In the U.S., diagnosis rates for people under 50 rose 12.8% from 2000 to 2019, according to a story in the Wall Street Journal. A study in BMJ Oncology revealed a dramatic increase globally in cancer rates, with the biggest jumps occurring in North America, Western Europe and Australia. Doctors and researchers want to understand what is causing the increases and how to identify young people most at risk. Among factors thought to play a role are less physical activity, eating ultraprocessed foods and exposure to toxins. “It’s likely some environmental change, whether it’s something in our food, our medications or something we have not yet identified,” said gastrointestinal oncologist Andrea Cercek of Memorial Sloan Kettering Cancer Center in New York City. Gastrointestinal cancers are among the fastest-growing cancers among younger people. In 2019, one in five new colorectal cancer patients in the U.S. was under 55, nearly double the proportion in 1995. “If we’re not understanding what it is now, there’s another whole generation that’s going to be dealing with this,” said medical oncologist Kimmie Ng of Dana-Farber Cancer Institute in Boston, who was among the first oncologists to investigate the rise in colorectal cancer rates in younger people.

Generative AI Could Help Identify Patients’ Social Needs

Researchers have successfully used artificial intelligence (AI) to help identify patients’ social determinants of health (SDoH). These are factors outside the health care system—housing, financial stability, access to transportation, community support and other issues—that could affect a patient’s health and recovery. Even if patients are screened for these social needs, the information can be lost in the extensive documentation that accompanies every patient’s treatment history. Researchers using generative AI were able to sift through massive amounts of data to pinpoint references to social factors in a study published in the journal npg Digital Medicine. Radiation oncologist Danielle Bitterman of Mass General Brigham in Boston and a study author told STAT that as a physician “you’re trying to do a needle in a haystack type of search for clinical information. Patients oftentimes have thousands of notes.” Bitterman and her colleagues used several large language models to identify references to social needs in the visit notes for 770 cancer patients treated with radiation. Forty-eight patients had mentions hidden in their records, and the best of the AI tools uncovered 45 of those patients. But Bitterman stressed that these systems need significant work before they can be used in the clinic. One problem is that biases embedded in the data used to train AI systems can skew results. For example, in the study, AI models generated different results when a patient’s race or gender was mentioned.