thanks, i’m reading this link and looking for data about suicide rates. this report is talking about a collection of self-reported data about suicidal thoughts, which many people can have and fortunately not go through with it.
I also see a statistically significant correlation, and i’m still looking for a reliable causation and data on suicide rates. how do we know if the lack of gender affirming care directly leads to increased suicides in a systemic pattern? perhaps the same people who cannot access it also are likely to have other things in life that could cause terrible suicidal thoughts or actions. i’m wondering how we can rule this out.
You want causation? How about a study that looked at the suicide attempts per year before and after anti-trans state laws were passed? https://doi.org/10.1038/s41562-024-01979-5
good for them for having such a large sample size. i admit i’m confused though, the results are an increase “by 7–72%.”? i wonder what is up with this huge range. how can we have confidence in this?
i wish the abstract explained what types of anti-trans laws were passed, cause of course different laws end up having different effects. that could explain the uncertainty in the results range. in this case we’re concerned with how a lack of gender affirming care would directly influence systematic suicide rates, so I’m still looking out for more evidence on that topic.
Well, confidence interval and p value aren’t the same thing. They’re related, but different. You’ve identified that there’s a big confidence interval. But p value is what’s really important, because that tells you if the results are statistically significant. Now here’s a maths trick: if the confidence interval of the null hypothesis overlaps with the confidence interval of the result, then it won’t be significant. But if the confidence intervals don’t overlap, then your p value is smaller than 0.05; it’s significant.
Now here’s the data from the study:
The black circles represent years where the suicide attempt rate was not significantly different from baseline. The white circles are years where there was a significant difference to the baseline rate. So you can see that before these laws are passed, suicide rates are pretty much holding steady, and then on the second anniversary of the law’s enactment, it’s way up.
Now here’s the trick. That 7-72% is not a confidence interval. So it’s not actually related to significance. See, in the first year after the anti-trans laws were passed, for the teenage sample group, there was actually a significant effect. a 7% rise. Just very barely. You can see how the confidence interval line goes nearly all the way down to baseline. Second year, that’s way up. 72% up. So that’s the 7-72%. 7% is the first year, 72% is the second year.
So yeah, we’re pretty fucking sure this is because of the laws.
Yeah, sure. Here’s some research by the government that assessed the impact of gender affirming care and legal recognition on suicidal thoughts. More healthcare meant fewer suicidal thoughts. https://www.aihw.gov.au/suicide-self-harm-monitoring/population-groups/lgbtqia-sb-people/gender-affirmation
thanks, i’m reading this link and looking for data about suicide rates. this report is talking about a collection of self-reported data about suicidal thoughts, which many people can have and fortunately not go through with it.
I also see a statistically significant correlation, and i’m still looking for a reliable causation and data on suicide rates. how do we know if the lack of gender affirming care directly leads to increased suicides in a systemic pattern? perhaps the same people who cannot access it also are likely to have other things in life that could cause terrible suicidal thoughts or actions. i’m wondering how we can rule this out.
You want causation? How about a study that looked at the suicide attempts per year before and after anti-trans state laws were passed? https://doi.org/10.1038/s41562-024-01979-5
good for them for having such a large sample size. i admit i’m confused though, the results are an increase “by 7–72%.”? i wonder what is up with this huge range. how can we have confidence in this?
i wish the abstract explained what types of anti-trans laws were passed, cause of course different laws end up having different effects. that could explain the uncertainty in the results range. in this case we’re concerned with how a lack of gender affirming care would directly influence systematic suicide rates, so I’m still looking out for more evidence on that topic.
Well, confidence interval and p value aren’t the same thing. They’re related, but different. You’ve identified that there’s a big confidence interval. But p value is what’s really important, because that tells you if the results are statistically significant. Now here’s a maths trick: if the confidence interval of the null hypothesis overlaps with the confidence interval of the result, then it won’t be significant. But if the confidence intervals don’t overlap, then your p value is smaller than 0.05; it’s significant.
Now here’s the data from the study:
The black circles represent years where the suicide attempt rate was not significantly different from baseline. The white circles are years where there was a significant difference to the baseline rate. So you can see that before these laws are passed, suicide rates are pretty much holding steady, and then on the second anniversary of the law’s enactment, it’s way up.
Now here’s the trick. That 7-72% is not a confidence interval. So it’s not actually related to significance. See, in the first year after the anti-trans laws were passed, for the teenage sample group, there was actually a significant effect. a 7% rise. Just very barely. You can see how the confidence interval line goes nearly all the way down to baseline. Second year, that’s way up. 72% up. So that’s the 7-72%. 7% is the first year, 72% is the second year.
So yeah, we’re pretty fucking sure this is because of the laws.