NEW with @clivecookson

Hospital mortality data suggests the autumn resurgence of Covid-19 is proving less lethal than the spring, but is it that simple?

What’s going on beneath the surface, and is there cause to be optimistic?

A deep dive into the data: ft.com/content/b3801b…
1) The autumn resurgence of the virus is well underway, however you want to measure it.

Skeptics will say that we’re just seeing more cases because of more testing, so let’s head that one off at the pass.

Here are positivity rates, which are now rising across Europe and the US
2) Some might object that we’re just spotting much more mild cases of the virus now than we were in spring.

This is true (and a good thing — we’re catching more people who could infect others), BUT serious cases are also climbing, as measured by people in hospital with Covid-19
3) The critical metric is loss of life, so what do we see from deaths data?

Unfortunately deaths are rising too, but it’s useful to plot spring and autumn side by side here:

It’s taken ~a month for daily deaths to climb to 1 per million. In March the same ascent took one week.
4) Main reason for slower climb in autumn deaths is the virus spreading more slowly. Social distancing and other restrictions, and folks are being cautious regardless.

But another factor often touted is that people with serious cases are more likely to survive now

Is this true?
5) Data from UK intensive care patients looks promising:

The latest weekly figures from @ICNARC (a goldmine of data) show that 28 days after admission to ICU, mortality rates for UK Covid-19 patients have fallen from 39% in the spring/summer to 27% in the autumn
6) One possibility is hospital mortality rates may have fallen because of the shifting age composition of cases.

Indeed, data from @ISARIC1 spanning hospitals in 44 countries shows the proportion of Covid-19 patients aged 60 and above has fallen from 70% in Feb to 44% in Aug
7) But that doesn’t seem to explain all of it:

Even if limit our analysis to Covid-19 hospital patients aged 60 and older, in-hospital mortality rates have halved from 50% in early spring to 25% in August
8) @icnarc data show similar patterns in ICU, with biggest falls in mortality among younger patients. 28-day measure of mortality halved among under-70s btwn spring/summer & autumn

They also find reduced mortality for patients in especially poor condition (low blood oxygen etc)
9) However, as @GidMK told me this morning, we need to be cautious in how we interpret those numbers:

Such was the state of crisis in the spring that many patients arrived at hospital (let alone ICU) already severely ill, and healthcare resources themselves were more stretched
10) But it’s certainly plausible that improvements in critical care over the course of the pandemic have led to tangible reductions in mortality

@metadoc & @p_openshaw explained to @clivecookson how new medicines & lessons learned along the way by doctors could be playing a part
11) But many of the improvements listed here are fragile, and depend on what happens as the autumn resurgence continues

As @GidMK noted, a big factor in reduced hospital mortality could be the reduced pressure on hospitals & ICUs as case rates & bed occupancy fell over summer
12) If the virus continues to spread rapidly as autumn becomes winter, demand for hospital beds and specialist care could reach or exceed capacity, and outcomes would surely suffer as a result.
tl;dr
- Autumn resurgence more muted than spring, due to social distancing etc. Lose that & transmission accelerates
- Mortality rates down in some contexts, but due in part to reduced pressure on healthcare system
- To keep pressure down & outcomes up, gotta control transmission
14) Full story ft.com/content/b3801b…

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More from @jburnmurdoch

18 Oct
NEW: today we've published the culmination of a weeks-long @ftdata team effort, summarising everything data has shown us about the virus over the year so far, from Wuhan to the autumn resurgence ig.ft.com/coronavirus-gl…

Free to read, with a wealth of #dataviz on the key trends.
A huge amount of work from a vast cast of people went into this, over many weeks.

Consider this our rolling credits screen:

1) @caletilford & @aendrew built a beautiful interactive experience, designed by @carolinenevitt who also drew up a bespoke colour palette for the series
2) @tomhannen @BobHaslett @sdbernard created some stunning visuals, including gorgeous animations 🎥😍

3) @alekswis @martinstabe @officeofjane @theboysmithy and I dug into data and scientific papers to report out the story itself, and added charts that told that story 📖📈
Read 4 tweets
15 Oct
⚠ warning to anyone using ECDC subnational cases rates data ⚠

Figures released this morning include several errors, some very significant.

They’ve since silently uploaded a new file, but incorrect one is still online 😑

We’re checking new file for errors and will report back
Latest ECDC data appears to be correct: ecdc.europa.eu/en/publication…

You can the impact of the correction in this before (left) and after (right) duo courtesy of @sdbernard.

With the correction, all of France breathes a sigh of relief
Today has been another classic episode of the hit series "Covid data is an utter shitshow".

Data jiggery pokery going on everywhere, well beyond that ECDC slip-up.
Read 4 tweets
1 Oct
Tidied up a chart from the new Indian study that I think does a great job of showing what we mean by super-spreading:

Vast majority of confirmed cases don’t go on to infect anyone else; a small number of people account for the majority of transmission

science.sciencemag.org/content/early/… Image
This is why the Japanese approach of avoiding the three Cs (enClosed spaces, Crowded spaces, Close contact) has been so effective.

Even if someone is highly infectious, you hugely constrain the amount of spreading they can do if you limit the number of people they’re mixing with
We still don’t know exactly what causes super-spreading. Is it mainly that someone is especially infectious, or mainly that a moderately infectious person spends time in an enclosed & crowded space while infectious?

Whatever the answer is, avoiding the 3Cs can only help.
Read 4 tweets
28 Sep
I’ve noticed a lot of people slipping up on how they interpret UK Covid-19 prevalence & testing data, so here’s a very brief thread on how to interpret figures from different sources, and what caveats each source does and does not come with:
• Pillar 2 community testing: these are the bulk of cases picked up at the moment. Case and positivity rates here *could* be influenced by where and who is being tested, so e.g patterns in this data with age, deprivation etc could be skewed by who is getting tested
@ONS infection survey: these tests are random, and designed to be representative of the overall population.

Therefore trends and patterns in this data *are not* due to e.g certain locations or groups of people being more likely to get tested.
Read 5 tweets
14 Sep
NEW with @ChrisGiles_ & @DanielThomasLDN: UK’s big cities are shadows of their usual selves, but smaller urban areas have rebounded ft.com/content/203cc8…

What’s driving the divergence? Chart thread:

1/ Footfall in central London is still down 69%, but has picked up elsewhere
2/ This is driven by working patterns, but that in itself plays out in two distinct ways:

First, job type. Staff are returning to the workplace at very different rates in different sectors, and the sectors with the most remote working today are clustered in cities, esp. London
3/ Workers in retail, hospitality can’t do their jobs remotely and have returned to the workplace. They’re popping out for lunch or drinks near work and maybe shopping centrally before going home.

In big cities, office-workers are still at home, leaving the high streets empty.
Read 12 tweets
9 Sep
The most effective way to keep Covid in check and return to semblance of normality (far more so than blanket restrictions) is to have as many people as possible being tested, regularly & regardless of symptoms.

For government to be discouraging people from getting tested is wild
If there’s still a shortage of testing (or in this case test-processing) capacity, that’s a problem to be solved on the supply side, not the demand side.

Blanket restrictions (which do the most economic damage), are what countries do when their testing apparatus is inadequate.
The “overreaction to a 'casedemic' is killing our economy/cities” crowd are tilting at a false dichotomy where our only options are:

"Keep restrictions in place to limit transmission, hurting econ & cities" or "It’s overblown, let’s get back to normal and save econ/cities"
Read 5 tweets

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