New open-access paper now published in @BMC_series #BMCHealthServRes, looking at hospital bed occupancy in England during the 1st #COVID19 wave

These results emphasise the importance of local knowledge in predicting bed occupancy & capacity planning

I previously wrote a thread going over the main results & implications of this work - head over there & read the paper for more details, here I will only cover a couple of key summary points

Most models predicting hosp. bed occupancy assume that patients receive care in only one bed type (eg general or critical), for an average length of stay

We suggest that this assumption + relying on national-level data is not appropriate for local bed occupancy predictions

Although ~82% of hospitalised patients in England only received care in general ward bed, many moved between general and critical care beds during their stay, with varying length of stay in each bed

Not taking this into account leads to inaccurate bed occupancy predictions

We were able to recreate bed occupancy at NHS Region & Trust level, but only by significantly varying length of stay between areas. This suggests regional heterogeneity in hospital length of stay

Again, not taking this into account leads to inaccurate predictions

Tldr: future models forecasting #COVID19 bed occupancy should adapt their assumptions using local data on previously hospitalised patients

Regional variations in patient hospital length of stay may provide insight into prevalence of risk factors or clinical care disparities

Thanks again to all co-authors @NaomiMFuller @RuthHKeogh @karlado Richard Sekula @ProfCalumSemple @katiito @mert0248 & @gmknght for their contributions to this!

Thanks also to University College Hospital, the ISARIC4C consortium and the @cmmid_lshtm COVID-19 working group!


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More from @QuentinLclrc

20 Nov 20
A few headlines popping up in the UK at the moment on "Supermarkets most common #COVID19 exposure location in England, data shows".

Yes, the data does show this, but don't be misled by incorrect interpretation!

What is this data?

It's the latest @PHE_uk surveillance report, which looks at 34,328 #COVID19 cases with a common exposure with at least 1 other case, over the period 9th - 15th November…

Here, "common exposure" means any location/event which at least 2 #COVID19 cases attended in the 2-7 days before testing positive.

If you rank these common exposures, you find supermarkets are the most often reported (18.3%).

But place of exposure ≠ place of infection !

Read 7 tweets
27 Oct 20
Currently a lot of discussions around #COVID19 superspreading events, so it's time for a thread to add some context and reply to common comments about this...
"*random setting* is not in the database! So it must safe!"

That's a risky conclusion to make. Not detecting transmission ≠ transmission didn't happen! There are a lot of biases that make it hard to identify some settings. We also have to remember that...
... many setting have been closed / reduced visitors these past few months. Hard for transmission to happen in a setting when no-one's there! And, just based on that, risky to assume there won't be any transmission when people come back.
Read 9 tweets

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