I would trust sound data and transparent analyses showing age-stratified risk analyses if they existed. With all respect, what you just wrote is essentially hand-waving and talking past what I wrote. High effectiveness for elderly people (which I find plausible but also still deeply confounded by testing rate differences) in no way justifies mandates for people in their early 20s.
In some proportion of hospitals, when someone who is not vaccinated goes to hospital for any reason, they are tested for COVID-19. If found positive this is counted as a COVID-19 hospitalization. We have no access to the precise rate of these incidental hospitalizations. In some of these hospitals, it is the policy that vaccinated people who go to the hospital are NOT routinely tested unless they have symptoms. If this is the policy at a substantial number of hospitals, it could dramatically change the "effect size" of the measurements that we are talking about. The same issues essentially applies to COVID-19 deaths and cases.
Large effect size alone generally isn't convincing when you're using such fundamentally confounded sampling procedures, merged age-groups with wildly different risk profiles, and data aggregated across long time-windows with different population sizes.
These confounders are everywhere. Sort of in the way that only the dumb criminals get caught, we're probably missing many more confounding factors because our statistical analyses have limitations. Now add the stigma around questioning the vaccines political/financial pressures and you have a recipe for poor quality science.
It's not a conspiracy, people just tend to look away from things that could endanger their livelihoods. An emergent property of socioeconomic systems. A failure mode, so to speak.
In some proportion of hospitals, when someone who is not vaccinated goes to hospital for any reason, they are tested for COVID-19. If found positive this is counted as a COVID-19 hospitalization. We have no access to the precise rate of these incidental hospitalizations. In some of these hospitals, it is the policy that vaccinated people who go to the hospital are NOT routinely tested unless they have symptoms. If this is the policy at a substantial number of hospitals, it could dramatically change the "effect size" of the measurements that we are talking about. The same issues essentially applies to COVID-19 deaths and cases.
Large effect size alone generally isn't convincing when you're using such fundamentally confounded sampling procedures, merged age-groups with wildly different risk profiles, and data aggregated across long time-windows with different population sizes.