Thread by @QuentinLclrc

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

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bmchealthservres.biomedcentral.com/articles/10.11…

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

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https://twitter.com/QuentinLclrc/status/1351084851257081856

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

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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

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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

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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

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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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