Skydiving and COVID-19

A few friends have pointed out to me the comment going around that stopping the shelter in place orders right now is a lot like saying, “Hey, this parachute worked so well to slow me down, let me cut it away at 2,000 feet.” And I think it’s a good analogy.

But I have a different lesson I pull from my skydiving experience when dealing with this whole COVID-19 issue.

When I started skydiving I was in my early 30s, single, with a good income, no real debts I’d leave behind, no kids, no pets, and no family members that needed me to care for them. In some respects my dying would’ve been more beneficial to my family than my living, at least monetarily.

So the risk of skydiving that I perceived at the time, which was that I would die, wasn’t a big risk to me. I figured it would go fast if it happened and then it would be over. And, sure, living longer would be nice, but if that’s how things were I wasn’t too worried about it.

But as I got more into the sport, I realized that the true risk of skydiving was not dying. It was being severely injured and requiring months of rehab and depending on others to take care of me during that time.

One of my AFF instructors had a bad opening on his parachute and it fractured his pelvis, tore his aorta, and punctured his bowel. He was in the hospital for weeks and in rehab for months. Another girl I knew got caught in the prop wash from a plane that was on the tarmac and broke her leg. There’s even a term in skydiving called “femuring” because it’s common enough to hear that someone broke a femur during a bad landing. That’s the hardest bone in the body and yet skydivers break it often enough that it’s a sports term.

That was when I really had to sit down and reconsider my risk assessment. Because it wasn’t about potentially dying. It was about potentially having long-term pain. Or potentially needing in-home care when I had no one to give that care during rehab.

When I did that I also realized that I was only as safe as the stupidest person in the plane. Or the stupidest person on the jump with me.

Only so much you can do to avoid a canopy collision. And if some idiot launches wrong out of the plane or with a loose handle that leads to an early deployment that takes out the tail of that plane you’re going down with them whether you did everything right or not.

That change in my risk assessment isn’t the full reason I quit jumping. But it definitely had an impact. I was okay with dying. I was not okay with being a living burden on my family. They didn’t deserve to pay for my risky choices.

Which brings me back around to how this ties into COVID-19.

There’s been a lot of focus on the fatality rate. And on who actually dies. In Colorado over 50% of the fatalities are people over 80 years old. The death rate in Colorado for someone in their 20s is about a quarter of one percent. Pretty negligible.

Which makes it tempting for someone in their 20s to say, “The fatality rate on this thing is so small why should I stop living my life over this?”

Now, I’m not going to rant again about how overwhelming the healthcare system impacts everyone not just those with COVID-19 and how helping to spread this illness can mean that someone with an appendicitis or a stroke or a bad accident could end up not getting life-saving care, but that’s something to consider as well.

What I want to focus on instead is what happens if you get COVID-19 and don’t actually die from it.

We don’t know enough right now to know the long-term impacts of this illness. But there are a few things about it that make me think about rheumatic fever, so I want to talk about that for a second.

I am by no means claiming that the two illnesses are related. But I’m familiar with rheumatic fever because both of my parents were impacted by it when they were children.

For my father it damaged his kidneys when he was probably five or six years old. That damage was severe enough that he ultimately lost his kidneys in his early 20s which meant dialysis or transplants to stay alive. That one illness–that did not kill him–is the reason he died at 45 instead of living a long, healthy life. It also impacted everything he did. Every moment of his life from that point forward was colored by his illness.

For my mother rheumatic fever caused heart damage which may have ultimately lead to her needing open heart surgery and a valve replacement in her early 50s.

It took over a decade from that illness for my father’s kidneys to fail. And many decades for my mom to need heart surgery. But the initial damage was done by the rheumatic fever.

So turning back to COVID-19. We do not yet know what the long-term impacts of this illness are, but they could potentially be very significant.

It is clear that this illness impacts the lungs. It is also clear that for some patients they don’t even know their lungs are being affected.

Do you want to struggle with breathing for the rest of your life every time your neighbors decide to use their fireplace? Or when your neighbor engages in probably illegal home repairs that kick dust or chemicals into the air?

That could maybe happen if you get COVID-19. (Maybe not, but we don’t know enough yet to rule it out.)

Also with COVID-19 there are a non-trivial number of patients whose kidneys are affected by the illness. I’ve read more than one report of seriously ill patients who had to be dialysed because of it. Again, maybe it’s temporary. Not every patient in a hospital setting who requires dialysis requires it for life.

But what if the illness causes lasting kidney damage? Patients who receive kidney transplants do not have a full life expectancy. You get more years than dialysis in general, but not a full life. And if that kidney damage is a long-term effect of this illness, there probably won’t be enough kidneys to go around for everyone to get a transplant, which means dialysis. My dad made it 20+ years on dialysis, but the average is closer to five years.

COVID-19 has also been shown to cause clots which if they don’t kill you can cause strokes, heart attacks, and loss of limbs. The long-term effects of having a stroke can be incredibly challenging. Or what about losing a limb due to a clot. Trust me, you don’t want to go through that.

There may also be a potential for liver damage.

Again, we don’t know exactly what we’re dealing with yet. And some of these other health implications may not become clear for years. We may only see that they were COVID-19 related when we look at the incidence of X in the population prior to COVID-19 versus after.

For all we know those “asymptomatic” patients people love to talk about could just be people with lung involvement who don’t notice the symptom. We may only know they were impacted when they go in for breathing issues a year or five or ten down the road.

So don’t be binary in how you think about this illness. It is not a choice between dying or being fine. For the younger members of the population the main outcome of this could actually be long-term health impacts to lungs, kidneys, liver, and heart.

If you won’t limit your activities because someone else might die, then limit them because you might be permanently impacted if you get this. My dad had a good life, but I’m pretty sure he would’ve rather had a life without kidney disease if he’d been given the choice.

 

 

You Can’t See What You Don’t Track or Look At

One of the key points I tried to make in Data Principles for Beginners is that if you want to work with data you first need to track the right information. Some data can never be recovered if you don’t track it up front. And some is just impossibly difficult to obtain after the fact.

I want to say that the the example I used in that book, since I’m a writer, was how many hours it takes me to write each title I publish. This is crucial for me because it takes far less time to write a non-fiction book about Excel than it does to write a 120K-word YA fantasy novel. So if I earn the same amount on those two titles it turns out my time is much better spent writing another non-fiction book than another YA fantasy novel because I get the same return with far less time spent to get there.

The reason I bring this up today is because this COVID-19 situation is a perfect example of how important data analysis is to understanding the situation. And many of the concepts I discussed in that book are playing out right now in real life.

For example, it looks like it may be important how those who analyze fatality data bucket age groups. Here, for example, is a chart from New York state:

NY State Fatality Data

Here is similar data from Colorado:

CO fatality data 20200410 morning

Note how Colorado groups anyone over the age of 80 into one bucket whereas New York splits out those over 90 into their own category? And note how in New York that seems to be important. I haven’t run a statistical analysis on those numbers to see if the difference in fatality rate between those two groups is material or not, but it looks like it might be.

Of course, then you need to figure out why that difference exists. Maybe there was a virus that circulated for those 90+ when they were children that has given them partial immunity. Or there’s some commonality among those who live to 90+ that makes them more resilient when dealing with this. Or maybe when you’re 90+ you only bother to go to the hospital for treatment of something like this if you’re generally more healthy, and if we were to account for those who died at home during the same period the difference would go away.

But there’s no way to see that difference if that data isn’t, first, collected and, second, used for analysis. This is why it’s often very important to chart data before you create your categories so you can visually see what you’re dealing with. (I believe in the book the example I used revolved around annual income categories for bank customers. If you’re dealing with high net worth individuals using a top category of $100,000+ isn’t going to work well.)

Now maybe what we’re seeing above is just a quirk in the New York data and if you were to separate out the 90+ age range from the 80-89 age range in Colorado there’d be no difference. But the key is to be able to do so if needed (which means setting the right ranges for your dataset) and then actually attempting to do so.

(There’ve been articles about potential racial difference in outcomes as well. But without information on living situation, health care status, neighborhood pollution levels, income, etc. it’s hard to say whether it’s because of economic disadvantage, systemic racism, or something genetic. Same with the fact that more men than women seem to be dying. Without information on things like smoking history, which was one of the early suggestions that I think has since been disproven, you can’t parse out the actual cause for the differences.)

Another issue I’ve noted is the problem of comparing apples to oranges. I admire Johns Hopkins for what they’ve been doing with their dashboard but it also makes me want a strong drink. Here it is as of this morning:

Johns Hopkins 20200410

What annoys me about it is the Total Confirmed numbers on the left-hand side cannot be readily compared to the Total Deaths numbers on the right-hand side. If you look at the bottom of the total confirmed numbers you’ll see Admin0, Admin1, Admin2. These used to be better labeled. What they do is allow you to toggle between a country-level view and a more granular level of data.

By default for confirmed cases you get country-level case data.

Problem is that the death values on the right-hand side are NOT country-level data. You can now see this clearly when you look at the fifth entry in the image above which is not even for New York state, but is instead for New York city. Scroll down further and you’ll see additional entries for New York state.

There is no easy way to find the total values for the U.S. nor for the most-impacted states. It’s very frustrating. And until CNN published their U.S. tracker and Stat News published theirs (and got it working so it’s current and not weirdly delayed) I was highly annoyed by this situation. Because the data was there but it was being presented in a very ineffective and perhaps even misleading manner. (Most people don’t dig into the data they’re shown, they just take what they see on the surface so it was easy to look at the death values and assume the U.S. wasn’t as high up on the list as it actually was.)

It should be easy enough to put the same Admin0, Admin1, Admin2 category options on the death data as it was to put it on the confirmed cases data. And then the user could easily compare cases to deaths with just a glance.

Of course, as I’ve discussed before, we’re not testing enough for this data to actually be a full picture of what’s happening anyway.

There are people who have died at home who were never tested so are not part of the fatality data. There are people who very clearly have had it who also were never tested. There are people who are going to die from something else because they will either choose to stay home rather than seek care or because they won’t able to get the care they need to save their lives.

At some point in time someone with good data skills is going to have to go back and look at baseline fatality levels for a similar timeframe over say the last five years, adjust for the current year trend for the last six months or so before the virus hit, and then extrapolate the number of direct and indirect deaths caused by COVID-19 to give us a legitimate picture of the actual impact of the virus. (And of course if we’re going to give the virus blame for the indirect deaths due to lack of care we also need to give it credit for lower traffic fatalities, etc.)

Whoever does that will then have to probably back into total infection numbers once we have some idea of infection vs. fatality/hospitalization rates by region. If that’s even possible.

Of course, no good data, no good analysis. The key starting point to be able to do any of that is the data. Data is key. You have to collect the right information and in the right format. And then you have to use it effectively and ask the right questions. (Which is why one of the first chapters in that book was also about how you need subject matter experts who understand the data you’re working with not just smart people who can run a regression analysis.)

Anyway. Data and how you use it matters.

For anyone looking for the sources I referenced above:

New York

Colorado

Johns Hopkins

CNN Tracker

Stat News Tracker