- I second the desire to see bias magnitude grouped by age, is it looks much bigger in the ages that matter most.
- Can you clarify in what department(s) the "future" recorded vaccinations occurred? Or do you not distinguish? Mind that vaccination in the ED could be a proximal cause of hospitalization.
- I'm not clear on why you limited to before vaccine rollout. Couldn't it be better to instead limit to after vaccine rollout? I would think that unvaccinated and "admission before dose 1" would better reflect different base health status in the time period we actually need to know what the level of bias was.
- When the goal is to quantify bias, my first impression is it would be best to only look at those who are still unvaccinated after hospitalized, and after rollout, and then make two groups. Though a problem with that and maybe other setups/questions is that getting vaccination in the system would indicate that person presently didn't have covid or probably any respiratory virus.
- Not sure if you have baked in a type of survival bias as of yet. The longer you stay in the system (i.e. get hospitalized), the more chance you have to get a vaccination. Are you in essence comparing people with longer stays to people with shorter stays?
- Would it be possible to do a formal test-negative study on this data against covid specifically (comparing unvaccinated to "admission before dose 1")? This would in effect report the effectiveness of a placebo vaccine that test-negative studies are likely to regurgitate.
Age banding, whilst easy on the mind, is problematic in terms of sample size reduction which is why I derive propensity scores (risk scores) that do the job seamlessly.
We have no idea where vaccination took place, only when. Since we don't have dates of discharge we can't infer vaccination during the current ED admission or subsequent hospitalisation.
A study looking at ED admissions before an individual's decision to vaccinate is in the pipeline.
Since there is no survival data in terms of length of stay or status at discharge (alive/dead) then we can't adjust for survivorship bias, though health status at point of admission is not affected by this.
Looking at COVID only and non-COVID cases is a great idea but the CDS 010 data doesn't code for COVID!
Work continues using the ECDS enhanced dataset (4 more reports in preparation). At some point I’ll flip to survival analysis and start looking at time frames.
Really interesting article - thank you.
I find the graphical representation of the data particularly helpful.
I eagerly await the next exciting installment of vax roulette!
I hope you will have sufficient data to show the age banded unbiassed effect.
Ta muchly! There’s nowt like a colourful graph to clear the murk of staged multivariate logistic regression. Lots more to come…
- I second the desire to see bias magnitude grouped by age, is it looks much bigger in the ages that matter most.
- Can you clarify in what department(s) the "future" recorded vaccinations occurred? Or do you not distinguish? Mind that vaccination in the ED could be a proximal cause of hospitalization.
- I'm not clear on why you limited to before vaccine rollout. Couldn't it be better to instead limit to after vaccine rollout? I would think that unvaccinated and "admission before dose 1" would better reflect different base health status in the time period we actually need to know what the level of bias was.
- When the goal is to quantify bias, my first impression is it would be best to only look at those who are still unvaccinated after hospitalized, and after rollout, and then make two groups. Though a problem with that and maybe other setups/questions is that getting vaccination in the system would indicate that person presently didn't have covid or probably any respiratory virus.
- Not sure if you have baked in a type of survival bias as of yet. The longer you stay in the system (i.e. get hospitalized), the more chance you have to get a vaccination. Are you in essence comparing people with longer stays to people with shorter stays?
- Would it be possible to do a formal test-negative study on this data against covid specifically (comparing unvaccinated to "admission before dose 1")? This would in effect report the effectiveness of a placebo vaccine that test-negative studies are likely to regurgitate.
Age banding, whilst easy on the mind, is problematic in terms of sample size reduction which is why I derive propensity scores (risk scores) that do the job seamlessly.
We have no idea where vaccination took place, only when. Since we don't have dates of discharge we can't infer vaccination during the current ED admission or subsequent hospitalisation.
A study looking at ED admissions before an individual's decision to vaccinate is in the pipeline.
Since there is no survival data in terms of length of stay or status at discharge (alive/dead) then we can't adjust for survivorship bias, though health status at point of admission is not affected by this.
Looking at COVID only and non-COVID cases is a great idea but the CDS 010 data doesn't code for COVID!
"We have no idea where vaccination took place, only when."
Right. This is key.
Work continues using the ECDS enhanced dataset (4 more reports in preparation). At some point I’ll flip to survival analysis and start looking at time frames.
Thanks.
Re: "At this point in time nobody had got jabbed..."
True, if you're only talking about the COVID shot... :)
Well now, that’s a whole new packet of spaghetti!
with meatballs!!
as a paid subscriber to this substack i would very much like to get the free admission to the climate substack.
Your wish is my command! Comp on its way... Thank you for your support.
thank you!