AMERIKA

Low-income patients with diabetes are more likely to experience insurance instability

Researchers find people who lose Medicaid coverage also likely to remain uninsured, especially those with the most complex needs

Oregon Health & Science University

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Nathalie Huguet, Ph.D. (OHSU)

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An Oregon Health & Science University-led study reveals that low-income adults with diabetes are more likely to go in and out of health insurance, and that insurance instability is even worse for those with complex needs.

Published in the Journal of American Family Medicine, the study examined electronic health records for over 300,000 adults, age 19 to 64, who received care in community-based health centers between 2014 and 2019. Of these, about 39,500 lost their health insurance.

Researchers used statistical models to find out how likely people were to lose insurance.

They found that patients with diabetes were 25% more likely to lose their insurance compared with those without diabetes. Among the patients, those who had uncontrolled diabetes, more complex medication plans or complications were even more likely to lose coverage.

“It was a surprise, to be honest,” said the study’s corresponding author, Nathalie Huguet, Ph.D., an associate professor of family medicine in the OHSU School of Medicine. “We thought it would be the other way around because you would think someone with diabetes would have more active participation in health insurance.”

Insurance instability, known as churn, was identified when a previously insured patient had at least two consecutive visits to a clinician without insurance.

Huguet said it was especially concerning that many patients never regained health insurance. They found that 46% of patients with diabetes who lost Medicaid were unlikely to regain health insurance, and 61% of those who lost private insurance coverage never regained insurance.

“The really important finding was, unlike previous assumptions about people who lose eligibility, most people don’t get insurance back,” she said. “This is especially important because 25 million people were recently disenrolled from Medicaid in May 2023 at the end of the public health emergency due to the pandemic, and policymakers assumed that most of those people would find other insurance.”

The study used data through 2019, but Huguet said she is planning to look at what happened to the people disenrolled after the pandemic. Her concern is that the data showed people who most need consistent care, including people with diabetes and other complex medical needs, are more vulnerable to losing their health insurance.

“I would hope policymakers would see that they need to identify ways to keep people enrolled, or if they are disenrolled, that there is direct navigation to get them another type of insurance,” she said. “Instead of mass disenrolling millions of people, consider a slower process with support to help people find other insurance.

“States such as Oregon did a good job and did not disenroll people after the pandemic,” she added. “If we want to really control costs and keep people out of emergency rooms, we need to help people keep their insurance.”

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Journal

The Journal of the American Board of Family Medicine

Method of Research

News article

Subject of Research

People

Article Title

Insurance Instability Among Community-Based Health Center Patients with Diabetes Post-Affordable Care Act Medicaid Expansion

Article Publication Date

16-Apr-2025

 

Research Spotlight: Using artificial intelligence to reveal the neural dynamics of human conversation

Jing Cai, PhD, of the Department of Neurosurgery at Massachusetts General Hospital, is the lead author of a paper published in Nature Communications, “Natural language processing models reveal neural dynamics of human conversation”

Mass General Brigham

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By combining AI with electrical recordings of brain activity, researchers were able to track the language exchanged during conversations and the corresponding neural activity in different brain regions.

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Credit: Mass General Brigham

What were you investigating?

We investigated how our brains process language during real-life conversations. Specifically, we wanted to understand which brain regions become active when we're speaking and listening, and how these patterns relate to the specific words and context of the conversation.

What methods did you use?

We employed artificial intelligence (AI) to take a closer look at how our brains handle the back-and-forth of real conversations. We combined advanced AI, specifically language models like those behind ChatGPT, with neural recordings using electrodes placed within the brain. This allowed us to simultaneously track the linguistic features of conversations and the corresponding neural activity in different brain regions.

By analyzing these synchronized data streams, we could map how specific aspects of language–like the words being spoken and the conversational context–were represented in the dynamic patterns of brain activity during conversation.

What did you find?

We found that both speaking and listening during a conversation engage a widespread network of brain areas in the frontal and temporal lobes. What's interesting is that these brain activity patterns are highly specific, changing depending on the exact words being used, the context and order of those words.

We also observed that some brain regions are active during both speaking and listening, suggesting a partially shared neural basis for these processes. Finally, we identified specific shifts in brain activity that occur when people switch from listening to speaking during a conversation.

Overall, our findings illuminate the dynamic way our brains organize themselves to produce and understand language during a conversation.

What are the implications?

Our findings offer significant insights into how the brain pulls off the seemingly effortless feat of conversation. It highlights just how distributed and dynamic the neural machinery for language is–it's not just one spot lighting up, but a network across different brain regions. The fact that these patterns are so finely tuned to the specifics of words and context shows the brain's remarkable ability to process the nuances of language as it unfolds.

The partial overlap we saw between the brain regions involved in speaking and listening hints at an efficient neural system, potentially a shared mechanism that gets repurposed depending on whether we're sending or receiving information. This could tell us a lot about how we efficiently switch roles during a conversation.

What are the next steps?

The next step involves semantic decoding. This means moving beyond simply identifying which brain regions are active during conversation and decoding the meaning of the words and concepts being processed.

Ultimately, this level of decoding could provide profound insights into the neural representation of language. This work could contribute to the development of brain-integrated communication technologies that can help individuals whose speech is affected by neurodegenerative conditions like amyotrophic lateral sclerosis (ALS).

 

Authorship: In addition to Jing Cai, additional Mass General Brigham authors include Alex Hadjinicolaou, Angelique Paulk, Daniel Soper, Tian Xia, Alexander Wang, John Rolston, R. Mark Richardson, and senior authors Ziv Williams and Sydney Cash.

Paper cited: Cai, J et al. “Natural language processing models reveal neural dynamics of human conversation” Nature Communications DOI: 10.1038/s41467-025-58620-w

Funding: Jing Cai is supported by the Mussallem Transformative Award and American Association of University Women. Ziv Williams is supported by NIH R01DC019653 and NIH U01NS123130. Sydney Cash, Alex Hadjinicolaou, Angelique Paulk and Daniel Soper are supported by NIH U01NS098968.

Disclosures: None

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Journal

Nature Communications

DOI

10.1038/s41467-025-58620-w 

Method of Research

Observational study

Subject of Research

People

Article Title

Natural language processing models reveal neural dynamics of human conversation

Article Publication Date

19-Apr-2025

How thoughts influence what the eyes see

A surprising study could point to new approaches for AI systems

Columbia University School of Engineering and Applied Science

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Early visual areas in the brain adapt their representations of the same visual stimulus depending on what task we're trying to perform.

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Credit: Rungratsameetaweemana lab/Columbia Engineering

When you see a bag of carrots at the grocery store, does your mind go to potatoes and parsnips or buffalo wings and celery? 

It depends, of course, on whether you’re making a hearty winter stew or getting ready to watch the Super Bowl. 

Most scientists agree that categorizing an object — like thinking of a carrot as either a root vegetable or a party snack — is the job of the prefrontal cortex, the brain region responsible for reasoning and other high-level functions that make us smart and social. In that account, the eyes and visual regions of the brain are kind of like a security camera collecting data and processing it in a standardized way before passing it off for analysis. 

However, a new study led by biomedical engineer and neuroscientist Nuttida Rungratsameetaweemana, an assistant professor at Columbia Engineering, shows that the brain’s visual regions play an active role in making sense of information. Crucially, the way it interprets the information depends on what the rest of the brain is working on.

If it’s Super Bowl Sunday, the visual system sees those carrots on a veggie tray before the prefrontal cortex knows they exist. 

Published April 11 in Nature Communications, the study provides some of the clearest evidence yet that early sensory systems play a role in decision-making — and that they adapt in real-time. It also points to new approaches for designing AI systems that can adapt to new or unexpected situations.

We sat down with Rungratsameetaweemana to learn more about the research. 

What’s exciting about this new study?

Our findings challenge the traditional view that early sensory areas in the brain are simply “looking” or “recording” visual input. In fact, the human brain’s visual system actively reshapes how it represents the exact same object depending on what you’re trying to do. Even in visual areas that are very close to raw information that enters the eyes, the brain has the flexibility to tune its interpretation and responses based on the current task. It gives us a new way to think about flexibility in the brain and opens up ideas for how to potentially build more adaptive AI systems modeled after these neural strategies.

How did you come to this surprising conclusion?

Most previous work looked at how people learn categories over time, but this study zooms in on the flexibility piece: How does the brain rapidly switch between different ways of organizing the same visual information?

What were your experiments like?

We used functional magnetic resonance imaging (fMRI) to observe people’s brain activity while they put shapes in different categories. The twist was that the “rules” for categorizing the shapes kept changing. This let us determine whether the visual cortex was changing how it represented the shapes depending on how we had defined the categories.

We analyzed the data using computational machine learning tools, including multivariate classifiers. These tools allow us to examine patterns of brain activation in response to different shape images, and measure how clearly the brain distinguishes shapes in different categories. We saw that the brain responds differently depending on what categories our participants were sorting the shapes into. 

What did you see in the data from these experiments?

Activity in the visual system — including the primary and secondary visual cortices, which deal with data straight from the eyes — changed with practically every task. They reorganized their activity depending on which decision rules people were using, which was shown by the brain activation patterns becoming more distinctive when a shape was near the grey area between categories. Those were the most difficult shapes to tell apart, so it’s exactly when extra processing would be most helpful.

We could actually see clearer neural patterns in the fMRI data in cases when people did a better job on the tasks. That suggests the visual cortex may directly help us solve flexible categorization tasks.

What are the implications of these findings?

Flexible cognition is a hallmark of human cognition, and even state-of-the-art AI systems currently still struggle with flexible task performance. Our results may contribute to designing AI systems that can better adapt to new situations. The results may also contribute to understanding how cognitive flexibility might break down in conditions like ADHD or other cognitive disorders. It’s also a reminder of how remarkable and efficient our brains are, even at the earliest stages of processing.

What’s next for this line of research?

We’re pushing the neuroscience further by studying how flexible coding works at the level of neural circuits. With fMRI, we were looking at large populations of neurons. In a new follow-up study, we are investigating the circuit mechanisms of flexible coding by recording neurological activity inside the skull. This lets us ask how individual neurons and neuronal circuits in the human brain support flexible, goal-directed behavior.

We’re also starting to explore how these ideas might be useful for artificial systems. Humans are really good at adapting to new goals, even when the rules change, but current AI systems often struggle with that kind of flexibility. We’re hoping that what we’re learning from the human brain can help us design models that adapt more fluidly, not just to new inputs, but to new contexts.

Nuttida Rungratsameetaweemana, assistant professor of biomedical engineering.

Credit

Rungratsameetaweemana lab/Columbia Engineering

Journal

Nature Communications

DOI

10.1038/s41467-025-58707-4 

Article Title

Dynamic categorization rules alter representations in human visual cortex

Article Publication Date

18-Apr-2025

 

Male athletes need higher BMI to define overweight or obesity

Italian research identifies new cut-off points for overweight and obesity in sportsmen

European Association for the Study of Obesity

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New research to be presented at this year’s European Congress on Obesity (ECO 2025, Malaga, Spain, 11-14 May) shows that the internationally recognised body mass index (BMI) cut-off points greatly overestimate overweight and obesity in male athletes. The study, from Italy, also proposes new cut-off points for overweight and obesity in this group.

Body mass index (BMI) is a key method for measuring people’s weight status, defining whether they have normal weight, overweight or obesity. It is easily calculated by dividing an individual’s weight in kilograms by the square of their height in metres.  A BMI of 25 kg/m2 or above is in indicator of overweight and a BMI of 30 kg/m2 or above indicates obesity in white men and women of all ages, according to World Health Organisation’s (WHO) categorisation system.

However, some research has found that this classification system may not be good at identifying overweight and obesity in athletes and its use in this group has long been subject to criticism.

“BMI doesn’t distinguish between body fat and lean mass, which includes muscle,” explains Professor Marwan El Ghoch, of the Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.  “As a result, a muscular athlete with low body fat can be wrongly classified as living with overweight or obesity. Despite this, many sports organisations still rely on the traditional BMI classification system.”

In a new study, Professor El Ghoch and researchers from the University of Verona in Italy and Beirut University in Lebanon set out to determine how accurate the BMI cut-offs of 25 and 30 kg/m2 are at identifying overweight and obesity in male athletes and, if they were found to be inaccurate, to establish better cut-offs.

The cross-sectional study involved 622 males (average age of 25.7 years, BMI ≥ 20 kg/m2) who had been referred to the Department of Neurosciences, Biomedicine and Movement Sciences, of the University of Verona, Italy, and participated in sports including soccer, rugby, basketball, volleyball, cross-fit, karate and others at a competitive level.

The participants were categorised using the current BMI system and by their body fat percentage (BF%).

Using the current BMI system, more than a quarter of the individuals were categorised as living with overweight or obesity.  Some 451 (72.5%) individuals were of normal weight (BMI 18.5-24.99 kg/m²), 148 (23.8%) individuals were with overweight (25 kg/m²-29.9 kg/m²) and 23 (3.7%) were with obesity (30 kg/m² and above).

Total body fat percentage (BF%) was measured using dual X-ray absorptiometry (DXA) scans – known to be a highly accurate tool for measuring body composition – according to age- and gender-specific cut-off points.  A BF% of 21% or above was classified as overweight and a BF% of 26% or above was classified as obesity.

Using this system, fewer than 4% of the individuals were categorised as living with overweight or obesity. Some 598 (96.1%) individuals were of normal weight, 19 (3.1%) were with overweight and 5 (0.8%) were with obesity.

Professor El Ghoch, who led the study, says: “This demonstrates that the current BMI cut-off points are clearly flawed in determining weight status in athletes, as many of the athletes were misclassified as living with overweight or obesity, where in reality, very few had body fat levels in this range.” 

The researchers went on to use statistical modelling to identify more accurate BMI cut-off points for young male athletes.  The new cut-offs, which take into account athletes’ lower BF%, are 28.2 kg/m2 for overweight and 33.7 kg/m2 for obesity.

Study co-author, Professor Chiara Milanese, of the University of Verona, explains: “Although DXA scans measure body composition accurately, they are not always available in sports settings.  In contrast, weight and height, the two measures needed to calculate BMI, are easy to obtain and, with the new BMI cut-offs that we identified, BMI could be a highly useful tool in sports clubs, both at training grounds and in competitions.

“A direct assessment of body composition remains the gold standard but, in its absence, we encourage sports organisations and committees to adopt the new BMI classification system.”

The authors add that several further pieces of research are needed. These include identifying new cut-offs for female athletes and, potentially, specific cut-offs for different sports, particularly those were not included in the current study.

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Journal

Nutrition

 

Could opioid laws help curb domestic violence? New USF research says yes

The research suggests a correlation between opioid misuse and domestic violence, offering broader public health implications beyond addiction

University of South Florida

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Andrei Barbos, University of South Florida

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Credit: USF

TAMPA, Fla. (April 18, 2025) – A new study led by the University of South Florida reveals opioid control policies may offer broader public health benefits, including reducing instances of domestic violence. As policymakers continue to grapple with the opioid epidemic, this study highlights the power of research to inform effective public policy.

The research conducted by USF doctoral student Minglu Sun and Andrei Barbos, associate professor of economics, underscores how opioid abuse can cause a powerful ripple effect across society.

Published in Health Economics, the study analyzes the impact on the prevalence of domestic violence in Mandatory Access Prescription Drug Monitoring Programs. These opioid control programs require health care providers to consult a centralized database before prescribing opioids, helping prevent patients from obtaining multiple prescriptions from multiple doctors. In the early 2010s, states began passing laws that mandated the use of these databases after the Centers for Disease Control and Prevention and other agencies said they were a key tool to combat misuse of opioids.

“The staggered rollout of these programs across the country offered a unique opportunity to analyze their effects over time and across different regions, which allowed us to isolate the effects of these opioid control programs on the prevalence of domestic violence from other factors, such as economic cycles, concurrent policy changes or broader crime trends,” Barbos said.

With data from the National Incident-Based Reporting System, they compiled reports from 31 states between 2007 and 2019 to create controlled models for dynamic variables including demographics, income, unemployment, health status, insurance coverage and overlapping regulations, such as legalized marijuana.

Sun and Barbos found that these opioid control programs not only curb misuse but contributed to a 10% reduction in the prevalence of simple assaults, which account for nearly 75% of domestic violence incidents in the data. Simple assaults are generally defined as an attempt to cause physical harm to another person that does not involve a weapon or result in serious injury.

The effect was strongest in states with the highest opioid prescription rates. According to the CDC, southern states consistently exhibit higher rates – reinforcing the connection between opioid access and domestic violence.

“Earlier public health literature documented a correlation between opioid consumption and domestic violence, but correlation does not imply a causal relationship,” Barbos said. “This article establishes a causal relationship and provides policy makers with evidence of an additional positive spillover of opioid control policies, which may also be relevant for the policy design surrounding the current fentanyl crisis.”

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About the University of South Florida 

The University of South Florida, a high-impact research university dedicated to student success and committed to community engagement, generates an annual economic impact of more than $6 billion. Across campuses in Tampa, St. Petersburg, Sarasota-Manatee and USF Health, USF serves approximately 50,000 students who represent nearly 150 different countries. U.S. News & World Report has ranked USF as one of the nation’s top 50 public universities for six consecutive years and, for the second straight year, as the best value university in Florida. In 2023, USF became the first public university in Florida in nearly 40 years to be invited to join the Association of American Universities, a group of the leading 3% of universities in the United States and Canada. With an all-time high of $738 million in research funding in 2024 and a ranking as a top 20 public university for producing new U.S. patents, USF is a leader in solving global problems and improving lives. USF is a member of the American Athletic Conference. Learn more at www.usf.edu.

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Journal

Health Economics

Method of Research

Content analysis

Subject of Research

People

Article Title

Opioid Control Policies Can Also Reduce Domestic Violence