How COVID Has Affected Employee Healthcare and Costs
Featuring Janet Young, M.D.
Hello and welcome to Healthcare on the rocks employee benefits with a twist. I'm Jennifer Jones, population health practice leader at Springbuk.
And I'm Mike Pattengale, senior account executive for Channel Sales. Today we're continuing our mini-series on important trends, employee health care, and benefits management. In our previous episode, we talked about the growth and evolution of telemedicine over the course of the pandemic. If you haven't listened to that episode yet, head on over to springbuk.com/podcasts to check it out. Our guest in that episode was Chris Gagan, a fellow Buk and one of the co-authors of our recently published employee health trends report. Today our guest is another co-author of the trends report. And if you're keeping score at home, you know my co-host, Jenn is also a co-author of that report. And I'm only slightly hurt that they didn't invite me to contribute, but maybe next year.
I am honored to introduce our colleague, Janet Young, who is Springbuk's lead clinical scientist. Janet has more than 30 years of experience in providing clinical expertise and oversight to the development of healthcare analytics used in provider, payer, employer, and government sectors. Prior to joining Springbuk, she served as a lead clinical scientist at IBM Watson Health, guiding clinical content development related to new models, methods, and analytics using claims EMR health risk assessment and socio-demographic data. Janet received her M.D. from Yale University School of Medicine. Janet, thanks so much for joining us today.
Janet Young 1:49
Yeah, I'm very excited to be here with both of you.
So in the employee health trends report, we reported on the emergence and development of four major themes. So as you know, and since the major medical story of the past two years has been COVID, we knew we'd be taking a look at how it has impacted employee healthcare and employer benefit plans. So as you dug into the research for the topic this year, you quickly zeroed in on what was happening to patients and their cost of care after their COVID diagnosis. So what was really the impetus for that?
Yes. So early in the pandemic, we had spent a lot of time focusing on the impact of the acute disease and employer health costs. We weren't thinking about any long-term issues because COVID-19 was a new disease. But after a few months, we really started hearing more and more about people who were having ongoing issues past the acute phase of the disease, issues like brain fog, fatigue, and shortness of breath. So at that point, some of the focus turned toward understanding what kind of symptoms people are having, why people were having these symptoms, how common they were, and how to treat people with ongoing issues.
Something to note is that these post-acute symptoms aren't unique to COVID-19. There's a number of infectious diseases that also caused issues like this past the acute phase, one that people might have heard of is Lyme disease, which has some similar impact. But what makes COVID unique is really how many people have had the disease in a short period of time and the large percentage of people who are having post-acute issues.
Studies vary pretty widely in terms of the percent of individuals who are said to have long-term issues, but it's generally thought to be between 10% and 30% of people who've been infected. So we know that this is a common issue. But there's very little information out there about how these post-acute issues impact costs. So we wanted to quantify how COVID impacted costs for individuals past the acute phase.
And Janet, I understand you had data from some 30,000 COVID patients. Can you share a bit on what you discovered?
Sure. So those patients that we looked at – and you're right, we did have about 30,000 – had a diagnosis of COVID, somewhere between June of 2020 and December 2020. They had to be enrolled for at least six months after the diagnosis so that we could follow their costs past the acute period. And they also had to be enrolled in 2019 so we could have a basis for comparison. And just one note when I'm talking about cost-center spending. What I'm really talking about is the plan paid amounts.
So in this population, you know, the first thing that stuck out was that about 5% of those individuals had been hospitalized, but they accounted for about 40% of the cost. Individuals who are hospitalized were also older and more likely to have comorbid conditions like diabetes and asthma. We realized, given the difference in cost and the difference in the type of people who are hospitalized, that it would be best to divide individuals who had COVID into hospitalized and non-hospitalized to get a better understanding of what was happening within each group during that post-acute phase.
So what we found was that in both groups, the costs were higher than expected over the six months following the initial diagnosis, but the magnitude of the increase in cost over expected differed greatly. The hospitalized patients cost about 70% more than we would have expected, which amounted to an average of almost $35,000 per person more than expected over six months.
Of course, their costs were highest in the first month when they were hospitalized. But they continued to be higher throughout the six months. So that even in the sixth month, the cost per member was about $625 more than expected. The non-hospitalized patients had an average of about $1,000 more than expected during the six-month period. So an increase of about 25%. Again, the highest spending was in the first month. It went down pretty quickly in the second month and kinda leveled off. But in the six months, they were still spending on average about $90 more than expected.
One thing that was kind of interesting in this group, though, unlike the hospitalized group, was that the increase in spending was really concentrated in about 20% of the patients. So what you're really seeing there is that some patients do return, you know, pretty much back to expected quickly. And then there's another group that have costs that were much higher than expected. One thing I think we have to remember in both groups is the costs were still higher six months after the diagnosis. And because that's where the study ended, we don't know if or when the costs returned to expected.
Yeah, that's a really good point. And interesting to hear how high above the expected amount they were, could you share with our listeners a bit around what what you think drove those costs?
Yeah, so we did take a look at what drove the costs. And in both groups, we saw an increase in spending on cardiovascular conditions, particularly chronic ischemic heart disease, atrial fibrillation, and pulmonary emboli, which are those clots that lodge in arteries to the lungs. We know that COVID has been shown to increase abnormal blood clotting. So I wasn't surprised to see this issue in hospitalized patients, but I really didn't expect to see it in the non-hospitalized. In addition to that, the patients in both groups had increased spending on encounters for symptoms, particularly symptoms like fatigue and shortness of breath.
One thing that was kind of interesting was that we saw an increase in spending related to neoplasms, but only in the non-hospitalized patients. So one potential explanation in these individuals is that there may have been people who hadn't engaged with the health care system in a while. And you know, now they've engaged because they're feeling unwell, then once they saw clinician, the clinician may have asked them about other symptoms, ordered blood tests, encouraged the individual to get screening that they delayed or avoided, which might ultimately lead to more diagnoses of conditions like cancer.
And Janet, you alluded to as far as how we were defining these patients as far as their enrollment period and having some of that historical data. But how did you or can you explain to us as far as how you determined what would have been those expected costs for each of these groups?
Yeah, I would say that determining the expected costs was probably the most challenging part of performing this analysis. Initially, we thought about using a matched cohort during the same time period. But we became concerned that individuals without COVID were likely to have artificially low costs in 2020 because so many people avoided or delayed care. And that's why we opted instead to compare costs of individuals who were diagnosed with COVID against their own average monthly costs just prior to COVID in 2019.
That's a great point. Knowing that because there was such low utilization in 2020, because I think, as you mentioned, you know, naturally using a matched cohort would make the most sense, but because that data skewed didn't necessarily have that option. So I'm gonna ask you a question that has been, I think, very topical and a point of conversation a lot is around long COVID, especially knowing that there's been a lot of new research that's been put out as far as the impact of long COVID. Why don't you tell us a little bit about how you looked into this, and how you define that, and what it means to you, and how we could help individuals as far as looking to capture what truly long COVID means.
So it's hard to know for sure whether what we're looking at is long COVID, although I think we can infer that it probably is. But there's a couple of reasons that make it a little difficult. The first is we base our conclusions on diagnoses found on medical claims. These diagnoses come in as codes, but there actually wasn't a code for long COVID until after our study. There is a code for it now that became valid on claims starting in October of 2021. But the second reason is that there's really not a universally accepted definition of long COVID. So to kind of give you an example, here in the U.S., the CDC is defining it as new returning or ongoing health problems people can experience four more weeks after first being infected with the virus. But if you look at the World Health Organization, they'd say it's a condition in individuals with a history of COVID that occurs three months from the onset with symptoms that lasts for at least two months that cannot be explained by an alternative diagnosis. So we're even talking about different timeframes here.
But if we look at the CDC definition, that I do think we can infer that much of the cost and conditions we're seeing are related to COVID. We know that fatigue is the most common symptom in people with long COVID, and it was a driver of that post-acute cost in our study. There have also been studies showing the increase in cardiovascular issues and people with long COVID, and again, that was a driver in our study of the post-acute cost.
From my understanding the study here was done before we had the Delta variant or Omicron. Would you share, or would you expect that there would be similar findings with these variants?
Yeah, that's a great question, Mike. So with each variant, there's a few factors we have to think about. The first is the severity of disease caused by the variant because that will impact how many people are hospitalized. And additionally, more severe disease is associated with a higher likelihood of post-acute symptoms.
The second thing we have to consider is how contagious the variant is. That goes without saying that that impacts how many people get the disease. But you can have a variant that is less serious overall, but so many people get it that it still causes a lot of hospitalizations. And then the third factor is really the part of the population that has some immunity to the disease through vaccination or prior infection at the time that the variant is circulating.
So getting back to your question with Delta, I think we knew that it caused probably more severe disease than previous variants. Many people were already vaccinated or possibly had COVID before when it occurred. In terms of people who are vaccinated, there's some evidence it's not definitive, that they're less likely to get long COVID. But anyhow, among those who are unvaccinated and were infected with the Delta variant, I would expect similar patterns in terms of costs over the six-month period as what we saw in our study.
But Omicron is kind of interesting because it's caused milder disease in many people, particularly people who had some immunity. And then among people who were hospitalized, a smaller percentage were in the ICU and average length of stays were shorter. So I think what we're gonna see there is that for those who are hospitalized, the cost of hospitalizations are likely to be lower than what we saw with other variants.
But in terms of the post-acute conditions and long COVID, we really don't know. There's optimism that a smaller percentage of individuals infected will end up with long COVID because the disease is milder. But when you think about it, so many people have been infected, that a small percent of a very large number of people could still result in many individuals with long COVID.
Now, that's great. And I know there's a lot to unpack just within this part of the employee health trends, but even throughout this episode. So I was curious, Janet, can you share some of those key takeaways that you would put into the employee health trends for for our listeners today?
Yeah. So I think COVID-19 can be costly to employer health plans for many months past the acute phase of the disease. And that's particularly in members who have been hospitalized. I would recommend that employers look at their data to get an understanding of how COVID-19 is impacting costs beyond the initial diagnosis.
There's a few other things I think that employers can do, in terms of individuals who do have long COVID. Some of them have some pretty complicated health issues. And there are multidisciplinary clinics that can help people with complex issues. So it could be beneficial to help members navigate options that are in network, where they're available.
And then I think a lot of people who have these post-acute issues also have mental health issues, not too surprisingly. So they can also benefit from information about mental health options they have, like visits using telemedicine. We know the old saying about an ounce of prevention is worth a pound of cure, so trying to be sure that people don't get COVID in the first place is important. Encouraging vaccinations and booster shots would be part of that strategy.
And then there are also a few things I think that a lot of people don't think about in terms of members who might be at risk at high risk of severe disease. It would be important that they understand that they should be tested as soon as they have symptoms and contact a health care provider, because there are treatments that greatly reduce the risk of hospitalization, but they need to be taken within a few days of developing symptoms.
For individuals with weakened immune systems, there's also a drug that can be administered to prevent getting COVID. But again, individuals, you know, they have to be aware that this option exists so that they can talk to their health care provider. And then finally, I would say, you know, programs that promote better health habits and members, you know, they not only reduce the likelihood of developing a chronic disease or progressing, but they may also help individuals reduce the risk for severe COVID If they become infected.
Yeah, if only there was some sort of platform that would bring all this data together for employers to see some of these things in an easy-to-use manner. But, you know, if I think of one I'll be sure to let our listeners know. But, Janet, this has been very interesting and informative. Thank you so much for talking with us. For our listeners out there, I want to remind you that you can actually download the full Employee Health Trends report at springbuk.com/resources. This will cover what we had shared last week with Chris Gagan as well as this episode here and the others in our mini-series.
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Until next time, cheers!
Meet the Author: Janet Young, M.D.
With more than 30 years of experience, Janet Young has provided clinical expertise and oversight to the development of healthcare analytics used in provider, payer, employer, and government sectors. Prior to her role at Springbuk, Young served as a lead clinical scientist at IBM Watson Health, guiding clinical content development related to new models, methods and analytics using claims, EMR, Health Risk Assessment, and socio-demographic data. Young received her M.D. from Yale University School of Medicine.