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Comment by tomesch1982 | Hacker News Reader

tomesch1982

Sounds a bit like survivor bias. People who have already recovered once (and not died) are more likely to recover a second time (and not die). In the control group, people who have never had the virus, some have a harder time with it.

I hope this gets taken into account.

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The story talks about the study finding not only reduced risk of hospitalization but also increased risk of breakthrough infection (i.e., getting it a second time). It doesn't mention death or mortality at all as far as I can see.

What's the basis for your claim that it's about death rates?

You don't have to be looking for death rates to be affected by survivorship via.

If the pool of people you're studying is people who got it a second time, they cannot have died when they contracted it the first time by definition (i.e you simply cannot die and then continue living and participate in a subsequent study)

That said, you can account for this confounding factor and perhaps the study did account for that (didn't read it). I'm just pointing out that the question cannot be dismissed just by looking at whether death rates are part of the study or not

> You don't have to be looking for death rates to be affected by survivorship via.

I didn't say you did, but the post I was replying to said it the study here sounded like it. It didn't at all really considering the death rate is vastly lower than the infection rate and the infection rate differences were so huge.

> That said, you can account for this confounding factor and perhaps the study did account for that (didn't read it). I'm just pointing out that the question cannot be dismissed just by looking at whether death rates are part of the study or not

I didn't dismiss it, on the contrary I gave the poster a chance to substantiate their claim.

Thankfully quite a small percentage of covid patients die. Certainly not enough for a 16x difference.

You cannot conclude anything about the 16x difference (TFA says 13x, by the way) from the number of dead patients.

As a made up example to illustrate this point, assume that people are either "lucky" or "unlucky". Unlucky people die when they get the virus. Lucky people never die. Assume that one person per million is unlucky, and assume that the vaccine does absolutely nothing. Then this experiment on one million people would find one death in the vaccinated and zero deaths in the control group, inferring that natural immunity is infinitely better.

Why do you believe "a small percentage of COVID-19 patients die" (which is only true compared to, say, Ebola) has any relevance to the results of this study?

The argument I was replying to is the numbers are skewed by survivorship bias, but given >98% of people survive that's only going to skew the numbers 2% or so not 600% or whatever.

It doesn't have to do with the percentage who die, but rather the variation in susceptibility.

If 2% of people are 80% likely to die from COVID, and the rest have a baseline 0.1% risk. Assume prior infection provides no protection:

* 1000 (of whom 20 are particularly vulnerable) people are naturally infected; .001 * 990 =~ 1 people of average susceptibility die; 16 maximally susceptible people die. Total of 17 deaths.

* Then, you are left with 983 people, (of whom 4 are particularly vulnerable). Upon reinfection, .001 * 983 =~ 1 person of average susceptibility dies; 3.2 people of high susceptibility die. There's a total of ~4 deaths.

This is a 4x reduction in deaths even if there's no protection from prior infection.

Exactly - “for those who didn’t die”

A death rate of .5% is not going to change any of these statistics. They have 99.5% of the data.

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