It's becoming obvious that you (1) Have not read or do not understand the methods and results in this paper, (2) you refuse to cite specific sections of the article with which you disagree and (3) are not willing to bring any evidence beyond a long string of "nuh uh" responses.

I can't believe I've entertained explaining what controlling for a variable means, which is concerning. If you want to bring up specific quotes/sections or bring in counter facts, then by all means, I'll address them. I am a global health statistician, so I'm happy to help clear up some questions. I invite you to bring something substantive to the table.

If you still have a meaningful problem with the methodology of this article that was published in The Lancet, then I look forward to your publication that overturns the results./s All the data is available online.

You are still not quite grasping the reason for controlling for pop density. Policy makers ARE concerned about pop density, along with age distributions and comorbidities, some of the other factors controlled for in the study. They were controlled for because it is already known how those affect the impact of a pandemic and the researchers wanted to study decisions that could help everyone. Controlling for factors isn't ignoring them. It is factoring in their respective impact and seeing if the variables of interest are still impactful.

So, now we can see which rules and implementations would be irresponsible to apply across varying pop densities. If they analyzed a policy whose efficacy was highly contingent on pop density, the statistical confidence would drop significantly, and we would conclude that it is not a generally-applicable policy.

We know a policy aspect works across all pop densities because we controlled for it and found it to be statistically significant. It achieved exactly what you want. Does that make sense?

So don't call something stupid and irresponsible if you don't know what is going on. It's a very common method to isolate specific causes of variance in data so that proper analyses can be made. Maybe approaching it in good faith and inquiring about why it concerns you would yield a more productive conversation. Through colleagues I have over at that institution, I've heard about the peer-review responses of this paper. There were minor edits, but reviewing health economists would have pointed out a problem as glaring as "ignoring" a factor as important as population. Not controlling for population and having sweeping generalizations across pop densities would have prevented this paper from being published in the first place, because that would be poor science.

History need not repeat itself. That's why global health research tends to have two branches - metrics and implementation. We have the metrics, so now we have to hope that policymakers can cooperate with the implementation experts.

Correct me if I dont address your concern, but I believe you're mistakenly conflating "controlling for" and "ignoring" factors. Controlling for factors allows us to study the direct impact of variables that may be highly correlated with this noise. The authors had a pretty good reason, in the context/goal of the article:

"[W]e controlled for factors that have a known direct and biological connection to SARS-CoV-2 infection and COVID-19 death rates. These factors are generally outside the realm of policy makers in a crisis (eg, age profile, population density, and presence of comorbidities)."

Controlling for the data variance directly explained by things like population density allows us to learn about the policies and behaviors themselves, rather than allowing potentially correlated variables to muddy up the water. It's an extremely common and robust technique in statistical modeling.