COVID in Children; Antibodies in Healthcare Workers: It’s TTHealthWatch! | #covid19 | #kids | #childern

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. A transcript of the podcast is below the summary.

This week’s topics include the COVID incubation period, rates of COVID infection in kids, how heart attacks have been impacted by the pandemic, and antibody prevalence in healthcare personnel.

Program notes:

0:46 Novel technique to estimate incubation period for COVID

1:47 Total of 1,000 in analysis

2:47 You left Wuhan and then became infected

3:47 95% or greater become viremic

4:02 Heart attacks during pandemic

5:02 ST elevation MI

6:00 Prevalence of antibody in New York healthcare personnel

7:05 Over 13% in healthcare workers

8:05 Bivariate and multivariate analyses

8:50 COVID in kids

9:50 Deaths rare

10:50 Opening schools?

11:30 Use information on infection locally

12:21 End

Transcript:

Elizabeth Tracey: What is the incubation period for COVID-19?

Rick Lange: Characteristics of children that are hospitalized with COVID infection.

Elizabeth: What is the prevalence of antibodies in healthcare workers in the New York City area to COVID-19?

Rick: And how has COVID pandemic affected people with heart attacks?

Elizabeth: That’s what we’re talking about this week on TT HealthWatch, 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: And 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: I hope it’s going to be okay with you if we start first with Science Advances. This is the study that is trying to use a novel technique to estimate the incubation period for SARS-CoV-2 before the manifestation of COVID-19.

It was done in China and what they did is used confirmed cases that were detected outside of Wuhan, with known histories of travel or residency in Wuhan, and then the ultimate development of symptoms. And so they called this an interesting construct, which I’m not familiar with and I don’t know if you were. They call it Disease Onset Forward Time and Forward Follow-Up Study.

They actually had quite a few people who were a part of this, although as they started to use exclusion criteria, that number dropped. They started out with just about 13,000 confirmed cases and then they have over 6,000 that had their dates of symptom onset. They had 3,000 who had histories of travel or residency in Wuhan and then a total of 1,000 in this final analysis. They also had detailed information including region, gender, age, date of symptom onset, date of confirmation, history of travel or residency, and the date of their departure from Wuhan.

To make a long story short, they utilized this whole bunch of data in their model to say that the incubation period, the median is 7.76 days, which is considerably longer than what most of us have been citing, and the 90th percentile is 14 to 28 days, much longer than I think many of us have been thinking.

Rick: The value of this study over previous ones is the large number of patients, over 1,000 patients that were studied.

How has this been assessed in the past? Well, when people developed symptoms, they would ask them to recall when they had been exposed to someone that may have had a COVID infection — so there’s a little bit of recall bias — and secondly is you’re not really sure who you may or may not have caught it from.

This is a different study and they didn’t look back. They just said, “Okay, you left Wuhan and then you subsequently became infected.” They developed a curve — and that is when did you develop infection from when you left Wuhan — and then what they did is a model called a Weibull distribution. They tried to assess backwards.

Is it more valid than the other estimates? I don’t particularly think so. Again, the greatest value is it has a large number of patients. It suggests that 5% to 10% of patients may have an incubation period of longer than 14 days and historically we really haven’t seen that in real life. Do I think that the incubation period is longer based upon this mathematical modeling? Not necessarily.

Elizabeth: I’m kind of glad to hear you disparage this because we already have this imposition of this 2-week quarantine period for people who travel or suspect they may have been infected or exposed, and extension of that just seems like a huge burden to me.

Rick: Right. There may be an occasional person that has a prolonged asymptomatic incubation period. But for the most part, I think what the real-life experiences have taught us is, as you said, that 95%, or 95% or greater, become symptomatic, or viremic, within 14 days of exposure.

Elizabeth: We’ll stick with that for right now, but of course we can be corrected. Got to go on record about that. Why don’t we turn to this issue of MI — of heart attacks — during the coronavirus pandemic? That’s in JAMA Cardiology.

Rick: We have a lot of people concerned that coming into the hospital may be dangerous because of exposure to COVID. It suggests that that may be true with heart attacks and strokes. What this particular study did — and it was a really good study — it’s a cross-sectional retrospective study that analyzed acute myocardial infarction — that is, heart attack — hospitalizations that occurred from December of 2018 all the way to the middle of May.

These were at 49 different hospitals in a particular hospital system and here’s what they realized. There was a substantial decrease in people presenting and that is, it decreased by about 50%. Now, you say, “Well, maybe fewer people had heart attacks.” First of all, we have a year-and-a-half data that suggests that’s not the case. The other thing we have is the mortality data and it looks like the mortality increased somewhere between 27% and 100% for people that had heart attacks, depending upon whether they presented with what’s called a non-ST elevation MI versus an ST elevation MI. That was in the early COVID period.

In the later COVID periods where we started to encourage people to receive care, hospitalizations tended to increase somewhat, but the mortality was still twice as high in people that had an ST elevation myocardial infarction, most likely because they presented late. The earlier you present, we know that the better your outcome and the lower your mortality.

Elizabeth: Of course, we’ve all been trying to drum home the point, and some, of course, might look askance at us and say that this is our vested interest, but in telling people who have chronic health conditions or who experience symptoms that are consistent with MI or stroke that they really need to come in. At least I, for one, have to be very frank and say that I feel a good deal safer in the hospital than I feel anywhere else in town.

Rick: You and I both know all the safety measures they’ve put in at the hospital and there have been reports that emergency room visits and hospitalizations have gone down, but this is among the few that ties that to mortality.

Elizabeth: Let’s turn to a JAMA research letter. This is taking a look at the prevalence of SARS-CoV-2 antibodies in healthcare personnel in the New York City area. This was part of the Northwell Health system. It’s the largest in New York state and it wanted to look at, “All right, how often are people becoming exposed if you’re part of the healthcare system?”

They decided to address this by offering voluntary antibody testing to all healthcare workers as part of their system. They used PCR initially and then they followed that up with free voluntary antibody testing, regardless of symptoms. The PCR was only for people who were thought to be symptomatic or thought they were exposed.

They offered this to all of their people and I thought it was interesting that 65% of them accepted and were tested by the third week in June. It seems to me that, gosh, wouldn’t you come forward and be tested if you could be? I would be.

Overall, 13.7% of their workers were seropositive. Those who were working in COVID-19 units or in intensive care units were associated with seroprevalence, with that conversion, not when they did it in multivariable analysis, but in bivariate analysis. Not surprising, I think, in a lot of ways, but pretty high level.

Rick: You need to compare that to the general population. The incidence of antibodies in the general population in New York City is 14%, so the interesting thing is this is no higher in healthcare workers than it was in the general population.

Now, the other thing is when people talk about healthcare workers having antibodies, they assume that they actually got the infection in a hospital setting. What this would suggest is that’s probably not the case. Because if they did, you’d expect a higher rate of antibodies there than the general population.

I don’t think you can take this to the bank. I do think it’s a preliminary study. I do think it’s informative, but it’s not as rigorous as other studies where we use a single test and we know the sensitivity and specificity of the testing.

Elizabeth: Right. You and I are going to agree to differ on this and history — hopefully we’re going to be around to reflect on — is going to tell us about the risk to healthcare workers and how they seroconvert compared to others.

Rick: So Elizabeth, again, when you talked about the analysis, there is the bivariate analysis that says “yes” or “no,” and a multivariate, and that takes into account a number of different things. When they looked at this, you would predict that people that worked in the COVID unit or people that worked in the ICU would have a higher antibody rate than people in other hospital settings, but that doesn’t seem to be the case in this particular trial.

Why is that? It’s because I think we’re really fastidious about using PPE [personal protective equipment], especially in those settings. That leads credence to the fact that a lot of the positivity that occurs in healthcare settings is due to pre-existing social conditions and exposures, not necessarily to exposure in the healthcare setting.

Elizabeth: Let’s go to your final one in Morbidity and Mortality Weekly Report, a look at, “Hmm, what about COVID in kids?”

Rick: This is an important report because most COVID infections in kids, individuals under 18 years of age, are usually mild or asymptomatic. But this is a large report of 576 children that were reported to the COVID-19-Associated Hospital Surveillance Network.

What they determined is that the rate of hospitalizations among kids is about 8 per 100,000. Let’s compare that to adults. It’s a 164 per 100,000. If you take a look at the population that are admitted, about 20% of them are under the age of 2 and their rate of hospitalizations is three times higher than kids that are over the age of 2.

Now, how did they fare? A third of the kids that were hospitalized ended up in the intensive care unit, only about 6% on a ventilator. As has been shown in the adult population, the same is true for kids, there’s a higher incidence of hospitalizations for Hispanics, Latinos, and Blacks compared to the white or Caucasian population. Fortunately, deaths were rare. There was only one reported death among the kids that were in the COVID network.

Elizabeth: When you speculate on the reasons that a disproportionate number of Blacks and Latino children are also being infected, what are your thoughts on that?

Rick: Elizabeth, there may be some social reasons. Interestingly enough, about 40% of these kids had underlying conditions, the major condition being obesity. We know that the rate of obesity is higher in that same population as well. Again, this wasn’t a definitive study. It couldn’t sort that out, but those are two possible reasons.

Elizabeth: I think the other thing is that we’re looking really carefully at this issue of infection among children with this specter of, “Are you going to reopen your school system or not?” hanging over everyone. Based on this particular study, what would you say about that?

Rick: We’re probably not going to have a cookie-cutter answer. But I do think we need to assess what the rate of COVID infection is in the general population, and if it’s low and/or decreasing, that’s a situation where you might consider opening schools, again, with face masks and social distancing and proper hand hygiene. But if the rates are going up or are very high, it’s a situation where you may not want to open schools.

I think it’s going to be very fluid as many of the restrictions and many of the measures we’ve talked about are imposed — again, trying to monitor what goes on — and the most important thing will be to monitor the situation.

Elizabeth: Remember, we talked about monitoring also and about what’s represented for that, how challenging it can be to do that.

Rick: It can. You don’t have to monitor 100% of the population. You could use a sample size. It’s not perfect, but we already have information about whether cases are increasing or decreasing in counties around the United States, and that’s information that the schools should use to decide how best to provide an education.

On the one hand, none of us wants to expose kids. On the other hand, we realize is that their education suffers. Many kids don’t have access to computers, don’t have access to reliable wifi, aren’t in a setting where they have support of their parents or other people to help them learn, and many times both parents are working. It’s a very difficult situation all the way around. We want to obviously educate the children, but do it in a way that is safe as possible.

Elizabeth: That’s a look at this week’s medical headlines from Texas Tech. I’m Elizabeth Tracey.

Rick: And I’m Rick Lange. Y’all listen up and make healthy choices.
Last Updated August 14, 2020


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