Does #COVID19 crowd out care for non COVID patients in the #NHS? Has this led to a loss of lives? Are the numbers negligible? The short answers are: yes, yes & no!
Paper ➡️ bit.ly/33XMyHB & a long🧵on how we capture non COVID19 excess deaths & much more ⬇️ 1/n
Lets start with a headline result: we estimate that for every 30 #COVID deaths there is at least one avoidable non COVID excess death in 🏴 hospitals. To arrive at this we use cool #NHS data which makes for a great #EconTwitter#econometrics#DataScience teaching example. 2/n
The #NHS has population individual level hospital episode data (HES) linked to death certificates. For each admitted patient, they predict P(Death|X). This is an out-of-sample prediction coming from Lasso logistic regression model trained on data from the last 3 years. 3/n
The model considers individual variables such as age, mode of admission (ambulance, walk in etc.), pre-existing conditions, diagnosis, etc among others. The model does a good job at predicting out of sample accurately the # of deaths across NHS providers. At least until … 4/n
…Mar 20 from then onwards there is structure in the implicit residuals with E[Observed - Expected(X)|X] >0 suggesting an omitted variable bias in the trained model. Cumulatively this is at least 4000 excess deaths from Mar 20 to Feb 21 alone. Now, you could be worried… 5/n
…that this just captures COVID deaths. Thankfully the @NHSDigital removes ALL hospital episodes that have a #COVID19 diagnosis (which you get testing positive & note, all patients are routinely tested on admission & in hospital). Further, all deaths mentioning COVID19 on... 6/n
the death certificate are removed. This is the most comprehensive measure of COVID19 deaths. So we genuinely capture conservatively the # of excess deaths among non COVID19 hospital episodes. The 4k relative to 120k gives you the 1 in 30 but could be as high a… in 25. Now,.. 7/n
What is driving the variation in non #COVID19 excess deaths post Mar20? Surprise, surprise, pressures as e.g. measured by changes in provider-specific #COVID admissions appears an important omitted variable driving the variation. We can explore a bit what type of patient… 8/n
… ends up being more likely to die under pandemic conditions? This data is not as nice (loads lot of suppression/low case counts… but it is basically a bunch of acute conditions. Most people can relate to "acute myocardial infarcrtion = heart attacks”.. so 9/n
There is a non trivial and large increase in non COVID19 excess deaths among patients seeking help for non COVID19 reasons. You basically dont want to be having a heart attack in COVID times in an area with loads of COVID pressures. We could stop the paper here but,… 10/n
…we do MANY more exercises exploring in essence what is going on under the NHS' hood. Specifically, we look at the pandemic's effect on A&E care & waiting times; specialist referrals; diagnostic waits and… ; access & quality of cancer care…11/n
We study patterns before & after the arrival of #COVID19 & across the recurring waves. For cancer care, the long run mortality impacts are most obvious: e.g. across #NHS patients with lower gastrointestinal cancer are now 20p.p. less likely to begin treatment within 62 days…12/n
The ongoing #pandemic pressures keep cancer treatment delays up. We estimate there to be 32k missing cancer patients and more than 50k patients that had their treatment delayed by > 4 weeks which has mortality implications in the months and years to come. Now you could…13/n
Think that this may all be due to lockdowns causing pent-up demand. There is a some of that but its important that on intensive margin the recurrent demand pressures due to COVID with COVID19 induced staff absence rates among patient facing staff is making things worse…but 14/n
…the good news is that among providers with higher #Vaccination take-up, the non COVID19 excess mortality is weaker suggesting that #MandatoryVaccination may reduce this contributing factor to #NHS pressure. But best would be broad vaccination take up in community…15/n
Lowering the #COVID19 pressures to begin with. The paper iI quite dense. The important message is that COVID19 care does crowd out non COVID care which prevents the NHS to provide its usual quality of care, producing notably worse outcomes for non COVID patients that... 16/n
could be avoided if there was broad vaccination take-up. I worry that recent NHS deals with private providers make the situation not notably better (see bit.ly/3IDSa9b) as private providers suck out more FTE/resources from the NHS while cream-skimming good risks...17/n
Meaning the govt in essence just transfers cash to private providers that further cannibalise the NHS without more output being produced as human capital is the constraining factor. Have a read and happy to get feedback on the paper ➡️ bit.ly/33XMyHB ENDS.
With the "green backlash" underway in Germany and the EU (attempts to kill the EU ETS2 -- emissions trading scheme for transport and buildings -- and dilute the EU ETS1, which covers 'industry'), it strikes me as important to draw attention to social science work on the topic.
1. Why was it possible to introduce the EU ETS1 in the first place? See this paper by @edenhofer_jacob and @ChFlachsland
Mass level:
Shifting climate policy to the EU diffused responsibility, made it harder to assign blame, and drew less voter attention. The EU Commission—not electorally accountable—could thus pass stricter policies.
Elite level:
Moving policymaking to the EU reduced policy uncertainty, alleviated competitiveness concerns, presented carbon-intensive lobbies with a more difficult collective action problem, and lowered audience costs. osf.io/preprints/soca…
But, perhaps, the most important factor were the free allowances that were used to placate business opposition; the create concentrated benefits ("rents") and diffuse costs -- which means they present a Stiglerian policy (see next tweet). wires.onlinelibrary.wiley.com/doi/10.1002/wc…
Glad to see that my evidence submission to UK Parliament Committee on Use of AI in Government has been quoted in todays report. There is much said and some case study examples in the piece. I want to use this 🧵to sketch out policy implications of expanded use of AI in govt...
Implication 1: With AI + digital payment infrastructure, tax filing/compliance cost cost can vastly decrease allowing for a broader fiscalisation that could be implemented with a reduction in the VAT registration threshold that is very high in international comparison in UK.
Implication 2: Citizens perceive poor front-line services and bloated middle management that can be automated. Significant need to reallocate workforce a) out of offices to front line services & b) to become integral part of the innovation ecosystem.
@EconMaett @infornomics I think also tagging @edenhofer_jacob here. I think the big challenge continues to be that, of course, foreign interference is entirely possible on social media in the absence of (digital) ID. We simply dont know if it is humans or bots or networks of bots operating accounts.
The problem is that the issue of ID is a transnational one. States are traditionally providing identity layers; issue currencies; etc. The right of free speech is vital, but it does not come with the right to an audience. And in social media space, you can easily "fake" an audience. This is part of the hybrid attack on institutions. But the US is caught up in wanting to protect the economic interests of its tech companies and has used this as a wedge issue within Europe finding varying coalitions. In the end there is no way around it that some form of capital controls will come back as these are now technologically feasible.
the "attack" on some business models of large tech companies that basically enable structural CIT tax evasion (think: transfer pricing) is an EU case and so its not surprising that tech companies covered and protected by US policy (as happened with Trump v1 tax cuts) fight back and it may well be that their best strategy is to break up the EU.
This is a longer thread to connect some dots. It is so obvious to some to understand what "went wrong" in 2000s/2010s but as I keep saying deeds matter more than words. I will try to relate some academic work that speaks to this. This is also about understanding...
The above is a status report on the G20 Data Gaps Initiative (DGI) tackling data deficiencies to improve policy-making. The Phase 3 started in, surprise surprise, 2022. Focus areas are 1️⃣ Climate Change 2️⃣ HH Distributional Info 3️⃣ Fintech/Fin Inclusion 4️⃣ Data Access/Sharing
Anybody who is somewhat on top of things would recognize these as immediate priorities to allow mechanisms like CBAM, carbon credit trading, compensation etc. to work and to embed this into a national accounting framework. Informational capacity needs to be explicitly be built...
How do spatially skewed economic shocks deepen gender employment gaps? New research by @sarthak_joshi who is on the market this year reveals that rising Chinese imports reshaped labor demand in India, disproportionately restricting women’s access to urban non-farm jobs...
due to gendered commuting frictions.
Main finding: Improving transport infrastructure for women could have:
✅ Mitigated female labor force declines by 30% (2001–2011)
✅ Boosted total output by 0.4%
A stark case for policy intervention to empower women and grow economies.
This is obviously relevant to all the great research that has document the distributional implications of trade shocks more broadly highlighting the role of fiscal policy accommodating and shaping these shocks to cushion or shape their impact. In the context of the UK & the US...
Here are the more "meta" reflections that I wish to share on this joint work with @j_schneebacher and @PalmouChristina. The paper title is quite innocent, but I wish to highlight the direction of travel and ultimately, the foundational questions that this work raises around...
the role of data as a public resource. The paper, in essence, studies the impact of the recent large energy price shock through the lense of very granular high frequency survey and administrative data. Such a large shock has to leave its mark on the economy. But where it falls...
is an open question. When studying economic data the question is always one of the underlying resolution e.g. at the sector (think agriculture, manufacturing, services), industry (think 2 digit, 3 digit, 4 digit, ...), at the firm or company level or even the product level,...