In case you haven’t realized it yet, TARTLE is a strong advocate for the importance of data collection and evaluation when it comes to making decisions. Part of that is recognizing that it is important that we have complete data, not just data that looks complete. We also have inherent biases to account for, biases that can color how we frame, process, and evaluate our data. Or to put it another way, we tend to see what we want in the data, or only look for the data that confirms what we already believe, not realizing that there are data that goes the other way as well.
The many controversies that have arisen in the wake of the COVID-19 virus illustrate all of this perfectly.
Here are just three examples:
Masks – People on the far right reject them entirely on the basis of a few studies that say their effectiveness is limited. People on the far left advocate for wearing them 24/7 based on the same studies. In fact, it may be that the right masks in the right situation is effective, and many situations where they don’t matter regardless of the mask.
Hospital Capacity – There are many reports that hospital capacity is being challenged. While the presence of a percentage certainly implies that some data has been analyzed, the fact is that data is very incomplete. To truly understand the situation, you need to compare the capacity with similar times over the last couple of years. Another question that needs to be asked is how many of these people need to be in a room with a bed? How long are those beds occupied by a given patient? Is it one person for a week or three people in a day? How is the counting being done? This matters because there is a perverse incentive on the part of hospitals to over-treat people. The more services a patient uses and the more time is spent using them, the more the hospital can charge to the insurance company.
Case Numbers: Cases are indeed increasing but you’re not hearing a whole lot about the death rates. That’s because the numbers there are remarkably low and the average of those dying from it is within a couple years of life expectancy when looked at nationally. In some places the average age of death from COVID is over 80. Also, nearly everyone has some sort of underlying condition that made them less able to deal with the virus.
Another important example has taken a more prominent role in the headlines over the last month. That would be the recently announced COVID vaccines. There are a number of competing ones at this point, each touting an effectiveness of 90% or greater. Now, if that’s proves to be accurate that is amazing. But let’s dig just a bit into that data, at least the data we have as of this writing.
The Moderna vaccine is advertising an effectiveness of 94.5%. To get there, they had an extremely large sample size, a total of 30,000 participants, evenly split between a control group that received a placebo and a group that received the vaccine. What’s interesting is that only 90 people in the control group got COVID; 90 out of 15,000 or 0.6%. Among that small group only eleven got significantly sick. The study made no mention of any deaths.
Of the group that got the vaccine, five contracted COVID and none got significantly sick. Now based on that, we can surmise that the vaccine is effective. However, there are important points missing in the data. For example, we have no idea of the sex, age, health, and daily habits of any of the participants. These are all important factors in determining the effectiveness of the vaccine. If all of the people who contracted COVID in each groups were healthy and in their twenties, that tells us something different than if they were all in their seventies. For example, whether or not they were wearing masks would also be useful. Were they largely staying at home or working as normal? What climate were the participants in? Again, these are important variables.
All of this goes to show that having data, all of the data available in a truly open and transparent forum that anyone can look at is incredibly important. Without that, we are prey to biases, those of others and our own. We need as much unvarnished data as possible to make better decisions and counteract those biases so we can get at the truth of things.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
In case you haven’t realized it yet, TARTLE is a strong advocate for the importance of data collection and evaluation when it comes to making decisions. Part of that is recognizing that it is important that we have complete data, not just data that looks complete. We also have inherent biases to account for, biases that can color how we frame, process, and evaluate our data.
Jason Rigby (00:00):
We need a little ... little [inaudible 00:00:01].
Alexander McCaig (00:00):
Yeah.
Speaker 3 (00:07):
Welcome to Tartle Cast, with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path.
Alexander McCaig (00:25):
I'm glad I don't have to-
Jason Rigby (00:27):
You got COVID?
Alexander McCaig (00:28):
I was going to say, I'm glad I don't have to do this episode with a mask on.
Jason Rigby (00:32):
Yes, I know. Exactly. We're in the safety of a studio.
Alexander McCaig (00:35):
This is-
Jason Rigby (00:36):
Well, I think we've probably already had COVID, because you got really sick.
Alexander McCaig (00:41):
I did.
Jason Rigby (00:41):
And then I got really sick.
Alexander McCaig (00:43):
But that was well over a year ago.
Jason Rigby (00:45):
Yeah. But I mean, that's how long it's been. And you never get sick and I never get sick, so we probably had it. Whatever.
Alexander McCaig (00:52):
But here's the interesting part. New Mexico went into a further lockdown. They said record cases. Okay, big whoop.
Jason Rigby (01:01):
Hospitals are capacitated.
Alexander McCaig (01:03):
Well, there's an issue with the hospitals, let's talk about this. Everybody doesn't need a damn bed. Just because a guy's got the flu, or whatever it is, or they got a hangnail, don't put them on a hospital bed.
Jason Rigby (01:16):
Well-
Alexander McCaig (01:17):
The reason a-
Jason Rigby (01:17):
... there's money-
Alexander McCaig (01:17):
... hospital does that-
Jason Rigby (01:18):
... with all that.
Alexander McCaig (01:19):
... is because there's money involved. It's a perverse incentive to get people to sit there. You can bill insurance the longer someone sits there and takes up that room, the more tests you can run on them, the more you're going to generate as revenue.
Jason Rigby (01:31):
COVID is just amped up our medical system problems.
Alexander McCaig (01:35):
Yeah, so when people are like, "Oh, we're running out of beds, this is ..." stop putting people in beds that don't need to be in beds. They were doing a cool thing in California where they'll put a doctor or a physician's assistant in an ambulance. Keep the people out of the hospital.
Alexander McCaig (01:48):
So if someone goes, they make a call, and the ambulance shows up with the paramedics, the doctor will be there. You don't have to drag them all the way back.
Jason Rigby (01:54):
Well, that's how usually doctors used to make visits.
Alexander McCaig (01:56):
Well, that keeps them out of the hospital. Actually decrease [inaudible 00:01:58]. A very efficient way of doing things. But my point was, New Mexico's gone down to a deeper state of lockdown. They said that there's record cases. But then if you look at the amount of deaths, well, let's look at the data here for a second.
Alexander McCaig (02:12):
Most of the people ... actually, all of them except for one person in the most recent 18 death that we've had, one person was in their late 40s. The rest of them were, on average, 80 years old with a preexisting condition that already weakened them to their state of morbidity. Like, that's it.
Jason Rigby (02:33):
Yeah, and I'm pretty sure that plus-40 person had-
Alexander McCaig (02:38):
They had a bad preexisting-
Jason Rigby (02:39):
... an underlying-
Alexander McCaig (02:39):
... underlying condition. So the cases are increasing, but the deaths aren't out of control. It's a very specific group that dies. It's people that are already weakened and they have those preexisting conditions and they're elderly. They have trouble fighting it. It's like the flu in the same sense. Actually, it's actually like it.
Jason Rigby (02:56):
Well, it's exactly like it, and that's what happened. That's what happens with the flu every single year, people with underlying conditions die of the flu.
Alexander McCaig (03:03):
The flu kills way, way more people every single year. Way more. And when we're looking at the data here, especially in this article we have from CNN Health, they were talking about the effectiveness of a vaccine, two vaccines that have come out.
Alexander McCaig (03:20):
One was the one that Pfizer had and one, Moderna. And CNN was touting that Moderna has the better one, you don't have to keep it as cold. It's got a longer shelf life, like a head of cabbage. But what's interesting here is that there's biases when we look at data.
Jason Rigby (03:40):
Yeah, and I think this is important [crosstalk 00:03:42]-
Alexander McCaig (03:42):
And this is very, very important. Because there's a lot of emotional charge around a lot of subjects, people want to skew the data to make it say whatever message they want to say that they think is correct through their perspective.
Alexander McCaig (03:56):
Data's extremely objective. It is what it is. But people will re-skew it and put it in a different hot light like we have around here to say that, I don't know, but visually this is how it should look. So let's go through this article and let's talk about some of the issues in this, and the real seriousness of how we're viewing this data and how serious is COVID, as any other virus.
Alexander McCaig (04:20):
Viruses don't want to kill people. The body dies itself. That's just what it is. A virus needs a host like a human needs a home. They move back and forth. They don't just pick and choose certain people, they're just like, I'll go anywhere that fits. It's like a hermit with a shell.
Jason Rigby (04:37):
Yeah, little hermit crab.
Alexander McCaig (04:40):
And I want to be very objective about that, a virus has no emotion. It's just a biological thing. It's hopping and skipping around. You pick up tons of them every single day, you don't realize it.
Jason Rigby (04:47):
Yeah, and different species have different ones that they're facing. I mean, in the animal kingdom-
Alexander McCaig (04:52):
Birds.
Jason Rigby (04:52):
... there's all kinds of ... Yeah.
Alexander McCaig (04:54):
Bird death is really high right now, for whatever reason. You find it everywhere. But I think we're starting to blow things out of proportion and we need to maintain an objective look on data. And also how it's collected. So let's-
Jason Rigby (05:10):
Well, I mean, you can see that on the far left or the far right. I mean, how they use data politically. So now everybody just advice is like, "I don't need to wear a mask because masks don't do this and masks don't ... mask ..."
Alexander McCaig (05:21):
It's like polar-
Jason Rigby (05:23):
And on the far left it's like-
Alexander McCaig (05:24):
It's polarized the groups.
Jason Rigby (05:26):
... masks 24/7.
Alexander McCaig (05:27):
It's no longer a health thing anymore. It's become a political thing.
Jason Rigby (05:32):
Yeah, exactly. And so now we're trying to logically use data to fix our emotional bias.
Alexander McCaig (05:38):
That's correct. So we've become so emotionally out of whack with everything, we're all tense.
Jason Rigby (05:42):
So it's like, "Hey, I have this data to support," and then next thing we're sharing it.
Alexander McCaig (05:46):
Yeah, that's precisely right. So now let's look at something that is really popular right now, walk us through the article and we'll talk about this data.
Jason Rigby (05:56):
Mr. Fauci, which everybody knows, Dr. Anthony Fauci, top infectious disease doctor-
Alexander McCaig (06:01):
I love how they had to put that, the nation's top-
Jason Rigby (06:03):
Top infectious disease-
Alexander McCaig (06:04):
... infections disease-
Jason Rigby (06:04):
... doctor.
Alexander McCaig (06:04):
... doctor.
Jason Rigby (06:04):
Yeah, this is CNN, said that it's just as good as it gets, it's 94.5%. It's truly outstanding. So he's using ... he's already, right now, he's looked at data, Mr. Fauci's looked at data, because he's giving a number, a percentage, so when somebody starts throwing out percentages or numbers, that means there's data involved. It's that simple.
Alexander McCaig (06:24):
That's just what is it. You can't just come up with a number-
Jason Rigby (06:24):
So he's coming up with 94.5%. And he said he heard this result Sunday afternoon with members of the Data Safety and Monitoring Board. And that's an independent panel that is looking ... I don't know, that board must, may be set up by the government, I would imagine.
Alexander McCaig (06:42):
I would imagine. So, if you're creating-
Jason Rigby (06:45):
They're funneling information to him.
Alexander McCaig (06:45):
If you think about it from a risk management standpoint, you just don't want a company to come out with say, "Oh, our vaccine's 1000% effective." That's not possible. You can't be more than 100%. Or don't tout more than it really is. You need some sort of third party to come and say, "We're going to test this ourselves." We need to be independent of your profit making process, because that's what is-
Jason Rigby (07:06):
Yeah, because that's what it is.
Alexander McCaig (07:07):
That's what it is.
Jason Rigby (07:07):
That's what it is right now. So the Data Safety Monitoring Board came to him and they said that there was a genetic health assessment, and then from there, experts were saying don't expect a Coronavirus before December. But, now we've got two popping out, like you had said.
Jason Rigby (07:28):
And then what he began to say, it's the second half of December, Fauci said this, "Vaccines are expected to begin with high-risk groups and be available for the rest of the population next spring." So now they took this data-
Alexander McCaig (07:41):
Okay, so they know ... what was the key thing right there? High-risk groups. What are your high-risk groups?
Jason Rigby (07:49):
The 80-plus.
Alexander McCaig (07:50):
It's the 80-plus, preexisting conditions. That is your high risk, and that right now are the people that are dying. Those are the high-risk groups. And then the outliers might be someone young with a preexisting condition. The company was Moderna and their chief medical officer was like, "Wow, this is really high efficacy." So how do they determine that efficacy?
Jason Rigby (08:20):
Yeah, because Pfizer said they were at 90. Moderna's saying they're at 94.5. And Moderna's-
Alexander McCaig (08:24):
Who cares about 4%?
Jason Rigby (08:26):
Yeah, exactly. But I mean-
Alexander McCaig (08:28):
If I get a 90 or above on a test, I'm amped up.
Jason Rigby (08:30):
So here's Moderna's trial, so let's break down the numbers, the data numbers.
Alexander McCaig (08:34):
Let's look at the data here.
Jason Rigby (08:34):
15,000 study participants were given a placebo. They always do that. Go ahead.
Alexander McCaig (08:39):
A placebo is not the actual drug, just so people know. I don't want to assume that everyone knows what a placebo is. From the mental aspect of the person receiving the shot, they don't know if they're actually getting the vaccine or they're receiving something that is really nothing going into the body. It's a saline shot. It's just saltwater. So they gave 15,000 people in the study-
Jason Rigby (09:01):
A shot of saline.
Alexander McCaig (09:02):
... a shot of saline.
Jason Rigby (09:03):
And that had absolutely no effect.
Alexander McCaig (09:05):
It does nothing.
Jason Rigby (09:06):
Now, this is funny, over several months-
Alexander McCaig (09:09):
Well, how many months?
Jason Rigby (09:10):
Several. It says several. Several could be-
Alexander McCaig (09:12):
What is several?
Jason Rigby (09:12):
We need to know. But the 15,000 study participants, 90, 9-0, not 900, 90 of them developed COVID-19.
Alexander McCaig (09:23):
Now, do you have your phone?
Jason Rigby (09:25):
Yes.
Alexander McCaig (09:26):
Just get the calculator out.
Jason Rigby (09:26):
You're going to divide 90 into 15,000?
Alexander McCaig (09:32):
No, 90 divided by 15,000. What is that?
Jason Rigby (09:37):
You know what the weirdest thing was?
Alexander McCaig (09:39):
What?
Jason Rigby (09:39):
When I opened my phone, my calculator app, it had 90 on it.
Alexander McCaig (09:43):
Interesting. It was ready for us.
Jason Rigby (09:45):
Yeah, it was ready.
Alexander McCaig (09:46):
Divide-
Jason Rigby (09:46):
Whether it's listening-
Alexander McCaig (09:46):
... 90 by 15,000.
Jason Rigby (09:52):
0.006.
Alexander McCaig (09:56):
0.006. So 0.6% of the whole group got COVID. Now, and that data also doesn't tell us that 0.6%, what age these people were at, what it might be, anything preexisting in their life. Who they are as a human being, we don't know that. So again, there's a lot of holes in this when you're receiving this information. So this episode's a lot about how we read data.
Jason Rigby (10:20):
So if we go to the doctor's office and we get a placebo but we think it's a vaccine, then we've just knocked it down to 0.006, off of saline.
Alexander McCaig (10:30):
Off of saline.
Jason Rigby (10:32):
But people don't realize, and I know you believe in this, I believe in this, the thought, the power of thought-
Alexander McCaig (10:38):
Well, they've shown-
Jason Rigby (10:39):
... and what it does to your body.
Alexander McCaig (10:40):
They've shown that placebos can be 40 to 70% as effective, within that range, of actually fixing whatever the issue might be, just because ... I don't know if it's willpower or whatever creates it.
Jason Rigby (10:54):
Well, and I don't want to get into this too heavily, but we know the body can heal itself.
Alexander McCaig (10:59):
Well, because it's designed to heal. You cut your skin, it heals. So can we do things internally? Possibly. I don't see why not.
Jason Rigby (11:04):
They need to get a book by Dr. Joe Dispenza called The Placebo Effect.
Alexander McCaig (11:08):
Yeah, it's cool. But so, again, we don't know what the preexisting conditions of the people are, but we say that 90, so 0.6%, less than a percent of the 15,000 study participants that got the placebo, a very small portion of them received COVID.
Jason Rigby (11:24):
And this is off of their study. Now, this is interesting, 11 developed severe forms of the disease. They didn't say how many died. And so 11 people out of 15 developed severe forms of the disease. So we don't know what that means, severe forms.
Alexander McCaig (11:37):
Yeah, that's exactly right. So, and then what percent is that? So, what's 11 divided by 90?
Jason Rigby (11:52):
11, that's 8%.
Alexander McCaig (11:55):
Okay, so 8% of our group, so less than 10% of our group of people that were sick were really sick. And again, we don't know what their age was, we don't know what their preexisting conditions are-
Jason Rigby (12:06):
Yes, exactly.
Alexander McCaig (12:06):
... we don't know how they operate. None of this stuff in here is telling you what the data. So did the natural holes in how the information is put into a specific lens when you're reading this information, it's missing a lot of stuff. So you need to be critical how you read these numbers, especially how that data's analyzed. Because however someone puts it out there, they want to do it for their benefit. They're trying to shine light to make themselves look very positive.
Jason Rigby (12:26):
Yeah, and then so they took, it says another 15,000 participants and they were given the vaccine, the actual vaccine. So this is what happened. Only five of them developed COVID-19, none of the five became severely ill. So obviously, the data's showing that their-
Alexander McCaig (12:43):
So we have an 85 person difference.
Jason Rigby (12:45):
Yes.
Alexander McCaig (12:46):
So the five that developed the COVID, again, what age were they at? What were their preexisting conditions? Were the people that developed it ones that were truly very elderly?
Jason Rigby (12:59):
Well, if I did 15,000 study participants and I gave them a placebo and they were 80 and older, and then I gave 15,000 participants that were 20, 21, 22, and gave them the vaccine, I'm only going to have five people ... do you see what I'm saying?
Alexander McCaig (13:12):
Yeah. For that other group, were they all the same age?
Jason Rigby (13:15):
Are-
Alexander McCaig (13:16):
Were they all the same age?
Jason Rigby (13:17):
Yes.
Alexander McCaig (13:17):
Or was it the first group, I chose people that were older, because we-
Jason Rigby (13:20):
Or if I want to show people that they get COVID but they recover really quickly, I would just have 20 year olds.
Alexander McCaig (13:28):
So you got to have very critical eyes when you're looking at data, whether it's with your own algorithms or you're looking at third-party analysis, there's a lot of logical holes. And you don't want to be comparing apples to oranges, I'm not saying that a lot of these people aren't professionals, but when you go through and you read something that gives a headline like this, we have to be like, "Okay, I'm glad it's 94% effective, how big was the group? What do the people look like?"
Alexander McCaig (13:55):
When you analyze data, you need all of the information objectively. You can't take pieces of it and then say, "Oh, this is what it is," because there's a lot of underlying factors, the factors that are really not seen. And the only way to do that is to collect more and observe more. But right now, this isn't telling us something that is truly effective for us to be like, "Oh, this truly is the future."
Jason Rigby (14:17):
Yeah, and so that's what they've already ... Fauci's already said it will be end of April, everybody will have it before that, it's going to be healthcare workers, elderly, and people-
Alexander McCaig (14:29):
Well, what is that, healthcare workers and elderly? So they know that the elderly are the most immunocompromised out of the gate. So then that would tell me that, I would hope, a large portion of their 30,000 total participants-
Jason Rigby (14:42):
Were that. Yes, exactly.
Alexander McCaig (14:42):
... were that. But we don't know that. And a lot of that data is missing visually for us. So, if you're going to read news articles critically or you're going to be purchasing data and analyzing it yourself, make sure that there's a balance in how that data's delivered, and then also you continue to share all of that information so you're totally transparent. This article, I'm going to call it out right now, lacks a massive amount of transparency.
Jason Rigby (15:07):
Yeah. I mean-
Alexander McCaig (15:08):
It is frankly-
Jason Rigby (15:09):
Well, unfortunately, it's our news system.
Alexander McCaig (15:11):
Yeah, I know. Frankly, it's untruthful and it's emotionally charged.
Jason Rigby (15:14):
Yes, and it's biased towards a party, whether it's-
Alexander McCaig (15:17):
It's biased towards a party.
Jason Rigby (15:17):
... Fox News or CNN, doesn't matter.
Alexander McCaig (15:19):
And you can read this and it's obviously very revenue driven.
Jason Rigby (15:21):
Yeah, I did find it interesting though, and we can close on this, but Pfizer's, the 75 degree Celsius. Pfizer's, it has to be at -75 degrees.
Alexander McCaig (15:30):
That's freaking cold.
Jason Rigby (15:31):
So what you're looking at, and the article points this out, which I thought was interesting, this was probably the most interesting out of the article, is that now you have to have hospitals and everybody buy these special freezers. So now, I mean, how many tens of thousands of these freezers do you got to buy so that they're holding at -75? Where Moderna's vaccine is-
Alexander McCaig (15:47):
Minus 20.
Jason Rigby (15:52):
Yeah, so you're-
Alexander McCaig (15:53):
Which most pharmacies already have.
Jason Rigby (15:55):
Yeah, exactly.
Alexander McCaig (15:56):
Just basic vaccine storage at that point.
Jason Rigby (15:58):
Yeah. I mean, this war has started once COVID hit, the vaccine ... whoever wins this is going to make billions.
Alexander McCaig (16:08):
And you and I are looking at this non-dogmatically. There's no emotion behind it. We're just looking at the numbers. You and I focus on numbers, that's ... it's missing so much.
Jason Rigby (16:18):
Yes. But I think the takeaway from this, for me, is don't get emotionally charged, like you said, a virus doesn't have emotion. A virus doesn't say, "Oh, I swing left," or, "I swing right." There's none of that.
Alexander McCaig (16:33):
Yeah, I don't care if you wear a mask or not.
Jason Rigby (16:33):
If you can remove your emotion from it and look at things logically, and then take that data and get directly to the source-
Alexander McCaig (16:42):
Correct.
Jason Rigby (16:43):
... with pure logic, now you have something that you can look at. And how would we go directly to the source?
Alexander McCaig (16:47):
You can go directly to the source to understand preexisting conditions, the real populous of people by going to Tartle.
Jason Rigby (16:53):
Healthcare records.
Alexander McCaig (16:53):
Healthcare records. You can purchase healthcare records, genome sequences, directly from the individual. So if using the RMNA, whatever that genomic sequence is-
Jason Rigby (17:04):
Yeah, it's controversial.
Alexander McCaig (17:06):
... you can actually purchase all that information from the people. So you can go deeper on your studies, you can better understand the populous ahead of time. And people, which is great, that are on Tartle, are more driven, more altruistic to share that information. This article was not being altruistic, it was not sharing everything that had to be there.
Alexander McCaig (17:25):
People want cures. They want people to be healthier. People want to see their family members live longer, even themselves, and they're more willing to share that information. So if you want to go retrieve information from the source, very granular, in depth healthcare information, genomic behavioral data, all of that stuff in a combination, that's going to give you the most optimal study and you can only do it by going to Tartle.
Jason Rigby (17:49):
Yes, Tartle.co. Sign up. You can purchase data or you can sell your data.
Alexander McCaig (17:53):
Yeah, be objective. And critical.
Jason Rigby (17:56):
And critical. Exactly.
Alexander McCaig (17:57):
See you.
Jason Rigby (18:03):
[inaudible 00:18:03].
Speaker 3 (18:05):
Thank you for listening to Tartle Cast, with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path. What's your data worth?
