On the scale that micromorts work, that isn't something that it measures well. Micromorts work best for the acute risk "do X and this is your chance of dying." Adding in things that are chronic risks doesn't factor into micromorts well.

Consider "traveling 230 miles by car is 1 micromort"... but that doesn't account for impaired driving or any of the other factors known to influence likelihood or survival of a car crash.

The numbers would likely change if you further specified them, but they are useful for the broad comparisons and risk analysis.

If you've got someone going skydiving every weekend, what does that do to their life insurance rates? Well, skydiving is 8 micromorts per jump. So that's 400 micromorts per year. And that goes into some insurance premium cost calculations.

From Wikipedia:

> Micromorts for future activities can only be rough assessments, as specific circumstances will always have an impact. However, past historical rates of events can be used to provide a ball park, average figure.

Another part of this is a study about risk acceptance.

> An application of micromorts is measuring the value that humans place on risk. For example, a person can consider the amount of money they would be willing to pay to avoid a one-in-a-million chance of death (or conversely, the amount of money they would receive to accept a one-in-a-million chance of death). When offered this situation, people claim a high number. However, when looking at their day-to-day actions (e.g., how much they are willing to pay for safety features on cars), a typical value for a micromort is around $50 (in 2009). This is not to say the $50 valuation should be taken to mean that a human life (1 million micromorts) is valued at $50,000,000. Rather, people are less inclined to spend money after a certain point to increase their safety. This means that analyzing risk using the micromort is more useful when using small risks, not necessarily large ones.

That last sentence is an important one when looking at this.

Would you be willing to be paid $150 to get vaccinated?

Please don't. What you are doing is called "P-hacking". Yes, if you keep selecting for subsets of a dataset you'll eventually find one that, just due to random chance, looks like a statistically significant result that proves whatever it is you want to see. That's not the way you do science.

As always, xkcd explains it best: https://www.explainxkcd.com/wiki/index.php/882:_Significant

No that's not P-hacking. The majority of US residents have been infected at this point. The sizes of those population groups both with and without serious pre-existing conditions are so huge that there is minimal chance of an incorrect random result.

Your link is not relevant to this issue so I don't know what point you're trying to make.

To be clear I support vaccination. But patients should make their own decisions free of coercion based on informed consent with full knowledge of the risks on both sides. Don't presume to patronize them with spurious, bad faith comments about "P-hacking". Let's see the actual data.

Right, you have a hypothesis. But you don't have data. Trying to tease out evidence for your hypothesis from existing data by slicing the data set up is the very definition of p-hacking. If you refuse to believe existing work on the basis of an unmeasured hypothesis, well, that's a different sort of logical fallacy on top of the bad statistics.

And FWIW, if you want a more complete (but much less amusing) explainer on the subject, wikipedia always comes through: https://en.wikipedia.org/wiki/Data_dredging

This is not at all correct. By your definition looking at anything in greater detail is P hacking.

For example, peanut allergies don't exist because most people don't have reactions to them. You absolutely can ask the question for if peanuts are good or bad for a subpopulation.

The alternative is just sticking your head in the sand.

The difference is in the motivations for asking the question and what's done with the answer (so agenda). People are assuming and agenda of others in this thread, and are crafting responses to head of that perceived agenda without actually answering the question, which is rarely productive, IMO. Better to ask than assume, or if the answer is known, provide it with a caveat that it doesn't actually support the specific cases you're worried it might be used for erroneously.

Wrong, I do not have a hypothesis (or any sort of preconceived notion). I am just curious to see what the data shows about risk stratification across age groups with and without common risk factors. Do you have any references to recent peer reviewed studies on that topic?

I'm not sure why you're trying to derail the discussion by bringing up data dredging. That would only potentially be relevant as a possible criticism of the methodology in a particular study. So what's your point?

I would not call "P-Hacking" wishing to know the stats of the group you're in, especially if there are significant differences between groups.

The global risk of death from childbirth is X.

It isn’t p-hacking for a dude to inquire what the chances are for the male subgroup.