Attempt to understand differences
/
#Covid19 experience. Data comparability is challenging, and inevitably some of this may need further scrutiny. However, I do think there are a number of issues which warrant a discussion
1/16 (sorry)


1/16 (sorry)
Comparing some key indicators (
/
):
Cases +test
220/173k
Gender
M 56/48%
W 48/52%
# ICU beds used at peak
54%~3.3k (England)/<10% ~ 2.8k
Note:
5x #ICU beds
Covid in
hospital beds at peak 20k
had 5k of Covid+ hospitalised at peak (not the same as beds)
2/16


Cases +test
220/173k
Gender
M 56/48%
W 48/52%
# ICU beds used at peak
54%~3.3k (England)/<10% ~ 2.8k
Note:

Covid in


2/16
Mortality in ICU
46/28% (!)
Ventilated in ICU
64/66%
% of all death
>70yrs 86/86%
Care home deaths
25/33%
3/16
46/28% (!)
Ventilated in ICU
64/66%
% of all death
>70yrs 86/86%
Care home deaths
25/33%
3/16
Excess death (see @ft @jburnmurdoch and @ChrisGiles_ for great analysis generally)
61%
/6% 
50k/4.8k
i.e. without Covid
would have had negative excess deaths(?) For
depends on how many of the excess deaths are actually Covid but untested
4/16
61%


50k/4.8k
i.e. without Covid


4/16
Conclusions:
has 46% higher ICU and 10x excess mortality and more patients in ICU. Age and gender distribution broadly similar.
has fewer cases even when compared to low testing levels in
making it likely that true
number much higher.
5/16




5/16
Particularly likely given that only 20% of
cases needed hospital where most
testing took place.
therefore likely to have seen much worse outbreak.
Potential hypotheses:
6/16



Potential hypotheses:
6/16
a)
contained #Cvoid19 better/earlier and had fewer cases (
has higher cases even with low testing and potentially more in hospital). One conclusion might therefore be
simply has more cases, end of it.
7/16



7/16
b)
had a more severe virus while
had milder and more asymptomatic cases β not sure there is much evidence for different severity of virus itself and we donβt yet know antibody levels at pop level though various studies underway
8/16


8/16
c)
has fewer high-risk groups - BMI (
ranks 36,
76 in world), BAME (
has hardly any) lower density (237/275 sqkm) London 4.5k/Berlin 3.9k sqkm; but
has higher per capita/km use of public transport (1.9/1.6k km/yrs)β¦
9/16





9/16
β¦no data on mortality amongst different occupational groups in
to compare with recent ONS analysis https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/causesofdeath/bulletins/coronaviruscovid19relateddeathsbyoccupationenglandandwales/deathsregistereduptoandincluding20april2020
10/16

10/16
d) differences in treatment β need to know hospitalisation rates which we donβt have (people in
and beds occupied in
) to understand ICU ratios. E.g.
has 5x ICU beds, are more patients going to ICU earlier? 11/16



However, utilisation in
at peak was only 50% of available i.e. no constraint. We also donβt have figures on nurse ratios though significant vacancy rate in
.
12/16


12/16
e) care home issue.
has a very low excess mortality rate (6%). RKI estimates at least 1/3 of deaths occur in care homes, in UK~25%. However,
&
suspect real # is higher. This doesnβt explain diff in ICU mortality and potentially relatively high hospitalisation rates.
13/16



13/16
On balance, qs comes down to why are
overall infection rates higher? Answer most likely a combination of late lockdown=more spread, ethnic mix, and susceptibility of underlying health issues (e.g BMI). Little evidence on differences in treatment. Needs deeper analysis.
14/16

14/16
All in all and given available data, UK seems to have a worse outbreak than Germany
15/16
15/16
Additional data needed mainly for
:
Covid hospitalisation
Nurse ratios in ICU
Ventilation levels
Mortality by occupation
Immunity level via antibody test
@kakape @DIVI_eV @rki_de anything you can help with?
16/16

Covid hospitalisation
Nurse ratios in ICU
Ventilation levels
Mortality by occupation
Immunity level via antibody test
@kakape @DIVI_eV @rki_de anything you can help with?
16/16
Final obs on data availability. Huge advantages in having central bodies such as @ONS and @ICNARC making downloadable data available but still gaps and quality issues. @DIVI_eV and @rki_de doing a sterling job but much harder in fragmented
system and lots we donβt have.
END

END