TTHealthWatch is a weekly podcast from Texas Tech. In it, Elizabeth Tracey, director of electronic media for Johns Hopkins Medicine, and Rick Lange, MD, president of the Texas Tech University Health Sciences Center in El Paso, look at the top medical stories of the week.
This week’s topics include artificial intelligence (AI) and bias; dietary calcium, protein, and falls in the elderly; prescription drugs for mental health during the pandemic; and a selective serotonin reuptake inhibitor (SSRI) for COVID-19.
0:42 Prescription drugs for mental health during the pandemic
1:44 Women had a higher rate of prescriptions
2:44 Increased risk of ED visits
3:44 Habit after pandemic?
4:01 Bias in AI
5:03 Different diagnostic and prognostic models
6:03 Used to create definition
8:10 741 patients treated and about the same control
9:10 Only second medication for those considered at high risk
10:08 Retrospective analysis of those already on SSRIs
10:25 Dietary calcium and protein in older adults
11:25 Additional milk, yogurt, and cheese
12:25 Obligatory calcium and protein requirements
Elizabeth Tracey: What’s happened to the prescription of medicines for mental health disorders during the COVID-19 pandemic?
Rick Lange: Can you change the diet in older adults in residential care to decrease the risk of hip fractures?
Elizabeth: Can a drug called fluvoxamine help to ameliorate the symptoms of COVID-19?
Rick: And learning about machine learning.
Elizabeth: That’s what we’re talking about this week on TTHealthWatch, your weekly look at the medical headlines from Texas Tech University Health Sciences Center in El Paso. I’m Elizabeth Tracey, a Baltimore-based medical journalist.
Rick: I’m Rick Lange, president of Texas Tech University Health Sciences Center in El Paso where I’m also dean of the Paul L. Foster School of Medicine.
Elizabeth: Rick, we know already and we’ve talked a number of times about the rate of mental health issues during the pandemic. Unsurprisingly, of course, they have increased tremendously.
This week we’re talking about a study that’s in JAMA Network Open that’s looking at three different classes of medicines, and what’s happened in the prescription of those during the pandemic in both U.S. men and women.
This study uses data from the Clinformatics Data Mart, which I was unfamiliar with previous to reading the study, which is one of the largest commercial health insurance databases in the U.S. They literally looked at millions of records that were deidentified to assess this issue of how many and who was prescribed benzodiazepines, Z-hypnotics, and serotonergic drugs during this pandemic.
They started with data from January 1, 2018 through March 31, 2021. The upshot of the whole thing is that women had a higher rate of prescriptions for all three drug classes and had larger changes in these prescription rates over time.
If we break them out, we can see that there was an increase in the Z-hypnotics, as well as the SSRIs and the SNRIs [serotonin and norepinephrine reuptake inhibitors] in both men and women, and an increase in benzodiazepine prescriptions in women. The authors interpret this as evidence that there is a substantial mental health impact of COVID-19, particularly for women. They cite other corroboration to that, that women were out of the workplace more often, taking care of kids a lot more often, and had a lot more stress. Of course, we know that women had that before the pandemic got here and also had a higher incidence of mental health disorders.
Rick: Elizabeth, the medications you mentioned are those that are commonly prescribed for people that have insomnia or anxiety or depressive disorders. Previously on our podcast, we have informed our listeners that there has been about a 33% increase in the prevalence of anxiety or depressive symptoms during COVID. Then, obviously, insomnia as a result of not only the mitigation factors, but insomnia is one of the side effects of COVID infection as well.
This also goes along with the fact that we have seen an increased risk of ED visits for these psychiatric symptoms. I’m not terribly surprised at these numbers. One of the great virtues of this study is they studied between 15 and 17 million people between 2018 and 2020.
Elizabeth: I guess one of the things that I’m interested in is what’s going to happen to this prescription drug use as the pandemic hopefully continues its decline. Will people give these things up? Because the other thing that’s concerning, at least to me, is the fact that once people get started on these things it can be really challenging to stop.
Rick: Essentially, because if you look at the waves, there was an increase in benzodiazepine use for women in the first wave and then an increase in the other medicines in both men and women in the second wave. We haven’t seen that in the third wave.
Elizabeth: You think this is a straw man: Don’t worry about it, people are not going to become habituated to using these medicines after the pandemic.
Rick: I think that will be the case. I mean, time will tell.
Elizabeth: Since we are talking about really ginormous datasets, why don’t we turn to The BMJ? This is a study that’s taking a look at artificial intelligence and biases that are inherent when these strategies are used to take a look at really big data sets and outcomes, and then use them as predictive models.
Rick: Elizabeth, our listeners should know that at the beginning of the week, you and I kind of throw around different studies about what we’re going to talk about. By the way, when we talk about them, it’s the first time usually we’ve discussed it. But when you picked this study, I kind of ho-hummed, like why would our listeners want to hear about machine-learning techniques? But then as I read the study, I became more convinced that it was in fact important information.
What happens is that we use prediction models in medicine. We use them to either talk about the probability someone will have a diagnosis or the risk that they will actually develop a particular outcome.
Oftentimes, it’s not just a single variable that predicts that. Well, now we can throw these into computers and use machine learning to do supervised predictions. I think we’re lulled into the fact and concept that, gosh, if we think we throw this information into a computer, we come out with an answer that’s much better than just usual statistics. We tend to believe it.
What these authors did was said, “OK, we have these different diagnostic and prognostic prediction models developed via machine learning. How reliable are they? Or how biased could they be?”
They looked at over 152 different studies. About 40% included a diagnostic prediction model and about 60% of them dealt with a prognostic prediction model.
What they found out was that almost 90% of them had a huge amount of bias. Most of it was based upon the fact that there was just a poor methodological quality that contributed to this high risk of bias. That could be due to the fact there was a small study size, poor handling of data, and a failure to deal with overfitting. That’s taking the analysis a little bit further than it needs to be.
But I was surprised at the fact that nearly 90% of these machine-learning techniques had a high risk of bias. What about you?
Elizabeth: I am so concerned about this, Rick, because everyone is using these things. Everyone is attempting to take these, as I said, ginormous data sets, analyze them and come up with something that’s going to be predictive or definitive. The fact that there is just so much bias that’s inherent in that I find it really concerning.
Rick: Well, that’s why I’m glad you brought this up. Because I think we think again, if it’s machine learning or an artificial intelligence computer, that it must be right. In fact, what it shows is that sometimes intelligence is artificial when it comes from the computer.
What the authors do is they offer different ways of analyzing each particular machine-learning prediction model, looking at the number of participants, the predictors, the outcomes, the analysis, and an overall assessment. If we do this rigorously, we can determine which of these studies are more reliable or which may have a high incidence of bias.
Elizabeth: I think it’s really great that we have identified that there is an issue with this because that’s going to bring awareness hopefully. I don’t think machine learning and AI are going anywhere. They are increasingly employed in lots of different fields, not just in medicine. I think we’re going to see more of it, not less.
Rick: I agree. That’s why it’s important to do the studies rigorously and to examine how reliable each of these prediction models are. Thanks for picking this particular study.
Elizabeth: You’re welcome. Thanks for your flexibility and openness in reading it.
Let’s turn to The Lancet. This is a study that’s taking a look at the SSRI that’s called fluvoxamine on the risk of emergency care and hospitalization among patients with COVID-19. This is part of a study that’s called TOGETHER. That’s all caps if anybody wants to look that up.
What they did here is in 11 clinical sites in Brazil, they enrolled people who had confirmed positive SARS-CoV-2 infection with a known risk factor for progression to severe disease. Patients were randomly assigned 1:1 to either fluvoxamine 100 mg twice daily for 10 days or a placebo.
Everybody was blinded. Their outcome measure was, because they didn’t have enough hospital beds in Brazil at the time, retention in a COVID-19 emergency setting or a transfer to a tertiary hospital due to COVID-19, up to 28 days post random assignment on the basis of intention to treat.
They were able to enroll 741 patients who received the fluvoxamine and 756 who received the placebo. Sure enough, the treatment with fluvoxamine, 100 mg twice daily — again, for 10 days — among high-risk outpatients with early-diagnosed COVID-19 reduced their need for hospitalization and death in comparison to the control group.
Rick: This particular study, or group of studies called TOGETHER, are trying to repurpose existing medications that are widely available, well understood, and have a good safety profile to see whether they can be useful in patients who have COVID.
I like this study. It’s a medicine that’s widely available. It’s relatively inexpensive. In the U.S., this 10-day course costs only $4. It can be used in countries that have limited access to vaccine or monoclonal antibodies. I think there is a lot of virtue to this particular study and these particular medications.
It’s only the second medication, oral medication, that’s available to treat individuals that are considered otherwise to be at high risk for hospitalization. That is they have underlying heart disease, diabetes, hypertension, lung disease, kidney disease, or are on steroids.
Elizabeth: I thought that that was something very informative. In fact, it comprises almost an entire paragraph of who was eligible to be enrolled in the study because they had a risk factor for more severe COVID disease. That is really way longer a list than what you’ve just cited in here.
Rick: It was about a 30% reduction in hospitalization and about a 32% reduction in death as well. This oral, over-the-counter, readily available and safe drug can be effective.
Now, the other thing we need to find out is, is this a class effect? Because there are other SSRIs that are even more readily available and less expensive.
Elizabeth: There is one study that I would really like to see if it would be possible to do this, and that would be retrospectively examine people who are already taking SSRIs. Are they at lower risk for severe COVID-19 disease?
Rick: That’s an interesting question. We haven’t talked about the mechanism by the way. These medicines are known to be anti-inflammatory and also have anti-platelet capabilities. We know that there is additional risk of thrombosis in these patients. That’s a putative explanation of why they might be beneficial.
Elizabeth: More to come. Thinking of then things that already exist and how they might be used — again, dietary sources of calcium and protein — in this case, they were looking at its impact on hip fractures and falls in older adults. That’s back to The BMJ.
Rick: When we talk about hip fractures, about 30% of all of them occur in full-time institutionalized care. These are older adults that are in a residential care setting. They oftentimes have decreased oral intake, they lose muscle mass, and they also have just bone fragility as well. This increases the risk of falls and fractures. There is some concern that extra calcium leads to kidney stones, but it doesn’t actually help prevent bone fractures.
These investigators took a different approach. Instead of giving supplements, why don’t we just change the diet, increase the dietary calcium and protein? Can we make the bones a little bit stronger and can we actually prevent some of the falls because the protein will help maintain muscle mass?
This was a great study. They looked at over 7,000 permanent residents, mean age of about 86, and they randomized 30 facilities to provide the residents with additional milk, yogurt, and cheese. Then they had 30 control facilities that didn’t change the diet at all.
What they discovered was this reduced the risk of fracture by 33%, hip fractures by 46%, and falls by 11%. It looked like in a small group of individuals they did some biochemical measurements; it looked like there was less bone resorption. There is also not a decrease in muscle mass that occurs with additional aging in these individuals.
Elizabeth: Is it possible that the increased consumption of calcium and protein at an older age when these other relative risks are potentially less significant, such as the development of kidney stones? Maybe that’s what we need to do is focus on this older age group and start supplementing more at that point. I think that it’s tempting to think about, “I need to consume more calcium and protein in my diet throughout the lifespan,” but maybe that’s not true. Maybe the time that it’s really important to switch the diet is when we’re older.
Rick: That’s a great question. I would say that there is an obligatory calcium and an obligatory protein requirement. If we don’t meet those anytime in life, then we can have bone resorption and decrease in muscle mass.
Elizabeth: I am guessing that we’re going to see an increasing number of studies that are going to focus on different periods of life on the life trajectory and what interventions are important at different times.
Rick: Yeah. No. I think that’s fair. Obviously, the dietary needs of someone in the first year of life, for example, is very different than up through puberty and then after puberty, and then certainly in the aging process.
As we age, we get into a, not an anabolic where we are building things, but a catabolic where we are beginning to break down bone and muscle mass. That’s where it’s particularly important to make sure that we have the building blocks to try to maintain both bone and muscle. The diet is a huge portion.
Elizabeth: That’s a look at this week’s medical headlines from Texas Tech. I’m Elizabeth Tracey.
Rick: I’m Rick Lange. Y’all listen up and make healthy choices.