I analyse an anonymised data dump of 1.9 million admissions records to the emergency departments of an undisclosed NHS Trust for the period June 2017 – September 2021
1. Maybe do the same things you did in parts 2 and 3 (i.e. look for disproportionately of occurrence around covid time), but do it brute-force across all diagnoses and all treatments (or at least the most common ones). In other words, don't assume what covid looks like a priori, but see if something can imply what covid looks like. I doubt going by code groupings would work for this, as opposed to isolated codes.
2. In the above, see if all the presumable negative controls (e.g. laceration) don't proportionally spike around covid.
3. Make a group of diagnoses composed of the highest hits from the above (i.e cherry pick) and see if it's proportionality is totally nonsensical or not.
4. Look at rates of death and hospitalization for every diagnosis. See how those rates changed around covid.
I would posit that a simultaneous spike in both proportionately and in fatality rate (or admission rate) of a given diagnosis could indicate covid. Maybe this could form a code grouping as well.
Thank you! It occurred to me that the vast majority of readers will get their idea of A&E from TV drama, and it is upon this distorted image that the government nudge department preyed. Working in A&E gives an entirely different perspective, for example, seeing someone in scrubs hurrying down a corridor usually meant that somebody had brought in a cake.
Some ideas:
1. Maybe do the same things you did in parts 2 and 3 (i.e. look for disproportionately of occurrence around covid time), but do it brute-force across all diagnoses and all treatments (or at least the most common ones). In other words, don't assume what covid looks like a priori, but see if something can imply what covid looks like. I doubt going by code groupings would work for this, as opposed to isolated codes.
2. In the above, see if all the presumable negative controls (e.g. laceration) don't proportionally spike around covid.
3. Make a group of diagnoses composed of the highest hits from the above (i.e cherry pick) and see if it's proportionality is totally nonsensical or not.
4. Look at rates of death and hospitalization for every diagnosis. See how those rates changed around covid.
I would posit that a simultaneous spike in both proportionately and in fatality rate (or admission rate) of a given diagnosis could indicate covid. Maybe this could form a code grouping as well.
Great ideas... already analysed and coming up in parts 5 through 7 that are ready to roll! Parts 8 - 10 along the same lines are in prep.
sublime come sempre !!!!♥
Thank you! It occurred to me that the vast majority of readers will get their idea of A&E from TV drama, and it is upon this distorted image that the government nudge department preyed. Working in A&E gives an entirely different perspective, for example, seeing someone in scrubs hurrying down a corridor usually meant that somebody had brought in a cake.
Had to look up the meaning but agree!
Please hook me up with comps to Private Passion and Climate Corner. First six = aubuch
Comps are on their way!