3. seasonality in various hospi. data curves #COVID19
2/n
4. demographics !
yep we have lots of baby-boomers, particularly in Western Europe: #COVID19
3/n
5. so how does "health policy" outcomes have evolved since around 2000 as demographics is something you can factor in right ?
let's look at Western Europe using @EU_Eurostat data: #COVID19
4/n
6. Hospitals beds / 100k. hab.:
rather flat...
notice most of depicted countries are below the EU27 average....
hopefully they use them more efficiently (?) #COVID19
7. curative beds / 100k. hab. and the occupancy rate:
curative beds follow the same trend as hospital beds: from flat to decreasing, most countries below EU average
occupancy rate is also rather flat
Netherlands champ ! #COVID19
8. so where is the money ?
total healthcare expenditures in € / hab. and as % of GDP:
again rather flat, most countries above EU average
so where is that money going ? how efficiently it is used ? #COVID19
9. is the money going into "human capital" ?
let check medical doctors and nurses
again it looks rather flat, especially for nurses
so where is the money going ? #COVID19
10. let check the specialists: i focus on respiratory medecine, microbiology-bacteriology and pathology:
for respiratory medecine it's a positive trend
for micro-bio rather flat with a bump in BEL while FRA is a champ
flat for pathology with a champ in PRT #COVID19
11. let have a look at Physicians...
all flat, except Germany followed by Spain & UK
a shock in Italy (?) #COVID19
12. how old are these physicians ?
64-75: Italy again (?), followed by France, Germany, Spain... rest rather flat
>75: Italy, France up, Switzerland also... #COVID19
13. final graph: average length of stay in days of patients:
Finland could teach something !
rest is rather flat, except maybe Italy #COVID19
14. these parameters must be taken into account regarding any health or "sanitary" policies as demographics are what they are and you cannot escape that...
(disclaimer: unfortunately data on BMI, smoking, etc. from @EU_Eurostat are available for 2014 only so i didnt use that)
#thread follow up to previous thread below
i repeat the same excercise with positivity rate instead of reproduction rate vs stringency index by adding country, week and both effects in the panel regressions #COVID19 1/n
the graphs:
(notice higher positive rate when stringency stronger)
benchmark: within R² = 5.82%
+country dummies: same
+week dummies = 47.36% (~ x 100 w/r benchmark)
+country & week dum. = as above
again "seasonality" explains most of what is going on in the data #COVID19 2/n
and so again one can ask if they #lockdown during colder seasons and this makes actually things worse ? #COVID19
3/3
#thread
Following recent success of this thread (thanks @FatEmperor i guess :)just wanted to get back to this
i will just take the first graph as exemple: it's reproduction rate vs stringency index
panel (country-time in days) regression with random effects #COVID19 1/n
i put the initial graph as "benchmark" and i add 3 other graphs
panel regression with random effects
with countries dummies included
with weeks dummies included
with countries & weeks dummies included
and we will compare the R² as these are the only changing in this excercise 2/n
in panel regression what is most important is the R² within
adding country effects changes nothing
adding weeks (i.e. time trend or "seasonality" in this context) increases a lot the R² within (~ x100)
adding countries & weeks => same results as with weeks added only
3/n
Things that makes you go hmm
Reproduction rate vs Stringency index in Europe
overall R² = 2.36% #Covid_19
Hmm continues
Positive rate vs vs Stringency index in Europe
R²=3.99%
notice fitted values higher for positive rate when stringency index higher #Covid_19
another hmm
New cases per million vs Stringency index in Europe
R²=1.09% #Covid_19