The children

A quarter of cases reported in the U.S. are now in children

This is what the data are showing. The talking heads tells us there is nothing to think about - it's simply because children are not vaccinated.

But there is plenty to think about:

1) Children return to school. Especially where kids are not vaccinated, schools may be testing kids regularly. As our politicians made famous, more testing leads to more cases.

2) As testing ramps up among children, the number of tests conducted in vaccinated adults has dropped because public health officials discourage vaccinated people with mild symptoms from getting tested. Vaccinated people with mild disease are inadvertently hurting our fight against Covid-19 in two ways: many are spreading the virus, and many are not getting tested, thus biasing our datasets. I'd love to see testing statistics split by vaccinated vs unvaccinated. Have anyone seen such data?

This previous post explains why it is very hard to interpret case trends when seleciton bias permeates the dataset. I present a framework to think about these issues.

3) It's also weird that we should worry about cases in children since for months, the same talking heads have said these vaccines have never been designed to prevent mild cases (a claim that is debunked just by opening the first page of any of the clinical trial protocols, or perhaps finding the videos from 2020 in which the same talking heads can be found extolling 95% efficacy which was computed using cases.). As is well known, the risk of severe disease and deaths in children is tiny so if we should be alarmed by cases in children, then we must be worried about spread. If we are worried about spread by infected children, why are we not worried about spread by infected, vaccinated adults?

4) A quick trip to the Census reveals that about a quarter of the U.S. population are under 20.

5) There are many possible reasons why children is an increasing share of cases. Many of these reasons are artifacts of selection biases that pollute all of our very poor-quality datasets. These are artifacts - decoys that confuse us. We will continue to make very bad decisions if we continue to take biased datasets and gloss over their flaws.

 

 

 

 

 

 

 

Comments

[Michael Droy]

I think you have been very hopeful if you ever thought we would get sensible comment coming out of governments.

The fall in IFR estimates from complete guesses of 5% in early 2020, to 1% guesses on the basis of 5% being too high in spring 2020 to pretty clear certainty of around 0.2%* or lower by May 2020 was never reported, not by any government, their officials or the scientist or statistics community.

An honest government would have presented the results of a QALY study that matched the cost in quality adjusted life years from the economic and health service impact of lock downs against the Covid impact from not locking down.
And yet not only were these not presented by any government, most have claimed that there has been no such study. (Ridiculous IMO, QALY calculations are precisely why governments employ so many economists and the basis for most micro and macro health service decisions.)

What governments specialise in is defensive PR to minimise criticism of policies and ministers/politicians. And Nudge theory, where great care is taken in the choice of what best should be said to achieve intended changes in behaviour.
And that is what we get.

*I'm avoiding here a discussion of what IFR is and how it is really a function acting on the characteristics of a population rather than a simple %.

[Kaiser]

MD: I'm curious how the economists justify extending a few months of a sick elderly's life usign QALY.