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.
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...
Interesting to read Iversen and Rosenbluth's book, especially in light of more recent research on gender equality. I'll highlight some of my favourites below.
Just to echo Jacob's point: Read @PikaGoldin!!!
Goldin, Claudia. 2006. ‘The Quiet Revolution That Transformed Women’s Employment, Education, and Family’. American Economic Review 96(2): 1–21.
--. 2014. ‘A Grand Gender Convergence: Its Last Chapter’. American Economic Review 104(4): 1091–1119. doi:10.1257/aer.104.4.1091.
--. 2023. ‘Why Women Won’. doi:10.3386/w31762.
Here are some fascinating papers by economists:
@kuhnmo et al
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Daniel makes an important point. Do check out his great work with @melissaleesands.
Let add some thoughts: while the conventionel Meltzer-Richard model leads us to expect the demand for redistribution to decline with income, exposure to the negative nature.com/articles/s4158…
externalities of inequality - notably crime - is an important reason why support for redistribution among the rich is often fairly high. Indeed, this is what Rueda and Stegmueller argue in their nice @AJPS_Editor piece and their '19 book. onlinelibrary.wiley.com/doi/abs/10.111…
More recent research has, however, qualified this argument in an important way. The exposure to the negative externalities of inequality depends on the degree of residential segregation. When the latter is high, the rich's exposure is low, even though rich and poor cambridge.org/core/books/who…
We have to talk about something. Something that has been missing for much of the short 2024 General Election campaign in the UK.
I know you had enough of it all. You had enough of red busses raining down cash on the NHS as you wait for an appointment.
You had enough of it, because, well, had they not literally said they would "get it done"? And then, "got it done". It is finished, dont want to talk about it anymore.