We've all sorted worldometer's table by deaths/1m, seen the data in the blue bars (to Feb6), and thought, wow 🇬🇧's pretty bad.

But, we're all getting our comparisons so wrong.
i.e. red bars are d/1m just for 50-59yr olds.

Why so different?

Follow me to open your eyes.

1/10 Image
Main cause, 2🔑factors:
▶️Demographic: a pop's d/1m figure is still wgted by age, so fewer 80+ it's lower
▶️Policy: e.g. 🇩🇪's just managed spread better

Re-ranking most2least aging, a pattern emerges.

For a younger pop to have a high d/1m, it's had more spread & deaths.

2/10 Image
So how much do demographics vary?

Here for:
▶️the countries so far
▶️adding a few more
▶️plus, the world
Are the props. each 10yr age band make up.

50% marks median age, so:
Italy 🇮🇹 47
Eng🏴󠁧󠁢󠁥󠁮󠁧󠁿&Wal🏴󠁧󠁢󠁷󠁬󠁳󠁿 41
🌍 30
Nigeria🇳🇬 just 18

So expecting similar deaths overall is silly.

3/10 Image
It matters as, per pic,🌍serology studies show risk goes up 3x with each band.

So, a countries IFR is demographic dependent.

Ignoring healthcare/comorbidity for a sec, it means we can est each countries IFR.

If you infected everyone, Italy will have 10x Nigeria's deaths.

4/10 ImageImage
So how do deaths compare?

Here to Feb6 are props. by age in original set of places.

So in terms of death >80:
▶️Eng&Wal: 61%
▶️NY state: 44%
▶️Moldova: 13%
▶️Peru: 18%

Big diffs, but, it is already clear, that there are other factors at play, Norway 64%, Urkaine 20%?

5/10 Image
Adding to the pop IFR ests:
▶️Wgted IFR of actual deaths:
👉⬆️than est, skew to old
👉⬇️to young
▶️% dead
and Norway/SK/etc, become clear. Low deaths mean carehome outbreaks look worse.

But, with deaths, protecting old hard:
Austria: worst
Ukraine: best
Eng&Wal: not bad

6/10 Image
Other factors?

Next biggest reporting.
🥇🌍similar
🥈🌍per bit.ly/3a8HZe3 Brazil in places 1/5, Nigeria likely 1/10

15% in UK hosps die, 50% need O2, so outside, 3x.

But, key is that🥈🌍have not published all-cause yet, so we're only comparing hosp deaths atm.

7/10 Image
Where it is about factors like:
▶️healthcare: ICU beds nos, quality, etc
▶️comorbidity rates: e.g. obesity, OECD 4x Pakistan, but we're talking only 15% of pop.
▶️lifestyle impacts: elderly at homeOR in carehomes?

Many cancel out.
e.g. 1st🌍better healthcare, but fatter.

8/10 Image
For sure 2nd🌍all-cause when pub-ed will change this.

But, till then let's est:
▶️% of poss. deaths
▶️% spread

Shows IFRs in Brazil/Peru likely higher, but they've defo more spread.

100% impossible, but even if IgA/Tcell immunity still real NY shows we could be worse.

9/10 Image
Finally, a resolution buster! Ratio of deaths/1m by age to 🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁷󠁬󠁳󠁿:
▶️NY: worse, more dense & slower to LD?
▶️Brazil/Peru: higher in all ages
▶️NYC&Brazil similar, except <20, where far higher?
▶️Ukraine: similar, but protected old best
▶️EU: more LD, bad at CHs
▶️SK: best

10/10 Image
Long form available here!
medium.com/pragmapolitic/…

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More from @sinichol

14 Jan
Most 🌍 comparisons for C19 go wrong as they don't considering demographics.

For countries I've managed to src detailed death data for, here's total d/1m numbers.

Sure,🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁷󠁬󠁳󠁿 look similar to 🇧🇷🇵🇪, but just look at the differences <60, 🇵🇪 4x larger.

So what does this mean?

1/9 Image
Basically, fewer >80, means they've had⏫spread, and ⏫younger deaths, but👀equal.

Here ranking⏫2⏬by age are:
▶️20 countries
▶️NY city
▶️The World
That I've 👀at so far.

50% marks the median age, e.g.
🇮🇹 47
🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁷󠁬󠁳󠁿41
🌍30
🇳🇬18

So expecting similar deaths overall is silly.

2/9 Image
So how have deaths actually played out?

Here are the props. by age to late Dec for places with detailed data.

Looks like NYC, 🇧🇷 & 🇵🇪 have seen far more in the young.

Now these all have younger pops. so is spread the same? Are diff just down to demographics?

3/9 Image
Read 10 tweets
11 Jan
Trend to Mar was dashed blue in🥇📈, since accruing 75k excess deaths.

But, as🥈📊from bit.ly/3nCSOsh covers, accelerated deaths will've been missed in🥇🌊, and accrued deaths will be lowering nonC19 ones now.

So how many were accelerated if this an underestimate?

1/7
With testing now⏫the compounding effect we missed in🥇🌊is much clearer in the🥈.

NonC19 (orange) are dropping as wks progress, likely due to many of🥈🌊deaths only accelerating expected ones by a few wks.

This likely happened🥇🌊, but⏬testing meant we couldn't track it.

2/7
Looking at these excess to trend deaths, we had 13k nonC19🥇🌊.

Sure, some LD induced, but🥈🌊has none? So likely many were untested C19.

It's what happens to cum. nonC19 excess next that's crucial, dropping 15.5k suggests at least this many C19 deaths have reverted since.

3/7
Read 7 tweets

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