I tried to find this data, and went through 10 studies before I found one with actual death rates (source). The study is based on Switzerland, and concludes an actual suicide rate of ~40 per 100,000 people years in gender dysphoric (GD) people, compared to 10 per 100,000 people in Switzerland.

At first glance, this seems disproportionately higher for GD people. What is confusing though, is looking at this suicide heat map (source), half the counties in Arizona have that 40/100,000 suicide rate. Lower populated counties often have the higher suicide rates, which tells me there is a lot of variability in the data, and a small population can often lead to higher numbers. The linked study follows a population of 8000 GD, compared to counties of 100,000s-1,000,000s of people with comparable increases in suicide rates.

I won't make any claim about causality between suicide and gender dysphoria. What I will say is that the conflict in data here illustrates this is a complicated statistic to call "causal." It is not an "easily googled statistic."

I read that the studies most relied on to come to this conclusion show that people with gender dysphoria are more likely to answer "yes" to "Have you ever considered suicide?"

This has been interpreted as "gender dysphoric people are more likely to commit suicide."

It makes me wonder how much of this increased interest in suicide is driven by the normalization of suicidal thoughts as a response to medical policy (by the media). Would this study have the same results if we hadn't told young people that they are more likely to commit suicide by being trans?

If you have a source for actual higher rates of suicide, I would be curious to read it. If you're curious for a source on the "commit" vs. "consider", I can dig it up.

I believe OC is saying it's not a clear choice.

It seems to me like you're making a great example of the issue OC refers to.

The decision is between accepting study A (small particle assumption) and study B (larger particle assumption).

In the environment where this decision is being made, CDC is giving direction to both clean surfaces and mask up. Their priority is public health. If study A is correct, and they back it, they continue to protect against both potential pathways. If study A is correct and they back study B, they are giving people direction to stop cleaning surfaces - people will stop cleaning, and then die.

They stuck with the most risk-adverse option while the assumptions around particle size developed.

Also, it's worth noting this: scientists that study Stokes law (the physics behind where a particle will float or sink, and how long it will take to do so) aren't necessarily experts at HVAC design. Stokes law is a force balance between drag force and particle weight. Drag force is different outside, vs. inside, from home to home, even floor to floor. It can flip if you're heating a building versus cooling a building, and even flip in the atmosphere (look up atmosphere inversion).

It is a complex issue. It's not just a bunch of particle scientists realizing that they were wrong about one equation (Stokes law) in a eureka moment. It's a group of different fields coming together to refine assumptions about what the average drag force is in a social setting. No model is perfect. If science and engineering waited for models to be perfect before applying data to real world, every industry would just stop.

Yet, some of us look at the issue as black/white. "Scientists were lying to us!" "The government knew the whole time and CHOSE not to tell us!" Even in the r/science community, we can all have emotional responses to data.