In this case, the numerator lags the denominator.

> All six cases were in women aged between 18 and 48, with symptoms appearing six to 13 days after vaccination.

Presumably, some of the 7 million people who have gotten the vaccine did so in the prior 5-12 days. It is reasonable to expect to see more cases over the next 6-13 days from the moment we pause J&J vaccination.

Also, if the effected group is only young women, the denominator isn't really the same as the total number vaccinated. Not saying it isn't still very rare, but in young people COVID deaths are also very rare. We have alternative vaccines, it makes perfect sense to advise young women against J&J for the time being while this is sorted out.

And for anyone that thinks this is just normal incidence of blood clots being blamed on the vaccine - that's what was said about AstraZeneca originally, and if these clots are anything like those it is absolutely not the case. Those are serious clots that are very rare to see period, let alone in young people, and require different treatment than normal clots (they have surprisingly low platelet count).

I suspect US agencies had good reason to want to pause J&J given all of that - the AZ vaccine issues were also disproportionately affecting women interestingly.

I guess I wasn't being specific - good correction. Reporting medical side effects can easily lag with new treatments, especially when we're not sure what we're looking for.

I suspect that, as soon as we start looking for blood clotting post-vaccine, then you'll find more cases that may have not been caught otherwise due to their not being severe enough to be noticed.

This exact thing happened with the AstraZeneca vaccine, which works very similarly to J&J. The EMA is now recognizing thrombocytopenia (not the same fatal issue but possibly related) as a "common" side effect, i.e. somewhere between 1 in 10 to 1 in 100.

https://twitter.com/HamidMerchant/status/1381797044495466504

Provided a larger sample size, doesn’t an increasing rate of an outcome suggest existence a confounding variable that the smaller previously tested sample failed to identify, and (not absolutely- but more likely than not) would apply increasingly at the larger scale? Not a statistician here- and honestly asking.