For 28 days, they did not leave — sleeping and working all in one place.
In what they called a “live-in” at the factory, the undertaking was just one example of the endless ways that Americans in every industry have uniquely contributed to fighting coronavirus. The 43 men went home Sunday after each working 12-hour shifts all day and night for a month straight, producing tens of millions of pounds of the raw materials that will end up in face masks and surgical gowns worn on the front lines of the pandemic.
…Nikolich said the plants decided to launch the live-ins so employees could avoid having to worry about catching the virus while constantly traveling to and from work, and so the staff at the factory could be closed off to nonessential personnel.
The article also indicates why price increases are critical to increase supply:
They were paid for all 24 hours each day, with a built-in wage increase for both working hours and off time, the company said. It did not disclose the specific percentages.
Hat tip: Jonathan Meer.
Earlier I suggested that that we offer unemployed people jobs that could be done from home:
A 21st century jobs program would pay people to stay home and isolate, support people without work, and produce some useful output all at the same time.
Writing at Brookings, Apurva Sanghi and Michal Lokshin provide some more ideas:
Another high-potential area is document digitization: Only 10 percent of the world’s books are digitized. Even with the current level of optical character recognition (OCR) technology, for a book to be digitized, an independent person needs to check it for errors, problems with tables and images, tagging, and oversee the look of the resulting text. Handwritten documents, images, and tables, even in printed books, require manual processing, proofreading, careful checking, and quality control. A person would receive scanned images of, let’s say, old letters to decipher and type into the electronic document. Comparing the results of several independent people working on the same document would assure the quality of transcription.
I want to add one more item to the list: contact tracing. In addition to tracing apps, we are going to need hundreds of thousands of people doing contact tracing and most of it can be done with email and phone from home. Two birds, one stone.
Somehow I missed this April 6 paper by Hall, Jones, and Klenow:
This short note develops a framework for thinking about the following question: What is the maximum amount of consumption that a utilitarian welfare function would be willing to trade off to avoid the deaths associated with COVID-19? Our baseline answer is 26%, or around 1/4 of one year’s consumption.
So what does that imply for optimal policy? Will we manage to lose both? Via Ivan Werning.
Led by Danielle Allen and Glen Weyl, the Safra Center for Ethics at Harvard has put out a Roadmap to Pandemic Resilience (I am a co-author along with others). It’s the most detailed plan I have yet seen on how to ramp up testing and combine with contact tracing and supported isolation to beat the virus.
One of the most useful parts of the roadmap is that choke points have been identified and solutions proposed. Three testing choke points, for example, are that nasal swaps make people sneeze which means that health care workers collecting the sample need PPE. A saliva test, such as the one just approved, could solve this problem. In addition, as I argued earlier, we need to permit home test kits especially as self-swab from near nasal appears to be just as accurate as nasal swabs taken by a nurse. Second, once collected, the swab material is classified as a bio-hazard which requires serious transport and storage safety requirements. A inactivation buffer, however, could kill the virus without killing the RNA necessary for testing and thus reduce the need for bio-safety techniques in transportation which would make testing faster and cheaper. Finally, labs are working on reducing the reagents needed for the tests.
Understanding the choke points is a big step towards increasing the quantity of tests.
Under Lockdown Socialism:
–you can stay in your residence, but paying rent or paying your mortgage is optional.
–you can obtain groceries and shop on line, but having a job is optional.
–other people work at farms, factories, and distribution services to make sure that you have food on the table, but you can sit at home waiting for a vaccine.
–people still work in nursing homes that have lost so many patients that they no longer have enough revenue to make payroll.
–professors and teachers are paid even though schools are shut down.
–police protect your property even though they are at risk for catching the virus and criminals are being set free.
–state and local governments will continue paying employees even though sales tax revenue has collapsed.
–if you own a small business, you don’t need revenue, because the government will keep sending checks.
–if you own shares in an airline, a bank, or other fragile corporations, don’t worry, the Treasury will work something out.
This might not be sustainable.
That is from Arnold Kling. Too many of our elites are a little shy about pushing this message out there.
The difference in value to society of getting a vaccine in May 2021 vs March 2022 is huge, but the difference in private profits is not
That is from Luis Pedro Coelho. And thus there is a great import to accelerating speed, at least in some critical matters.
Brad DeLong makes the point that if you have some downward nominal (or real?) rigidities, you should allow prices to rise in the expanding sectors all the more.
So many of the most important points of economics can be expressed succintly, which makes it well-suited for both blogs and Twitter.
In a post titled Defensive Gun Use and the Difficult Statistics of Rare Events I pointed out that it’s very easy to go wrong when estimating rare events.
Since defensive gun use is relatively uncommon under any reasonable scenario there are many more opportunities to miscode in a way that inflates defensive gun use than there are ways to miscode in a way that deflates defensive gun use.
Imagine, for example, that the true rate of defensive gun use is not 1% but .1%. At the same time, imagine that 1% of all people are liars. Thus, in a survey of 10,000 people, there will be 100 liars. On average, 99.9 (~100) of the liars will say that they used a gun defensively when they did not and .1 of the liars will say that they did not use a gun defensively when they did. Of the 9900 people who report truthfully, approximately 10 will report a defensive gun use and 9890 will report no defensive gun use. Adding it up, the survey will find a defensive gun use rate of approximately (100+10)/10000=1.1%, i.e. more than ten times higher than the actual rate of .1%!
Epidemiologist Trevor Bedford points out that a similar problem applies to tests of COVID-19 when prevalence is low. The recent Santa Clara study found a 1.5% rate of antibodies to COVID-19. The authors assume a false positive rate of just .005 and a false negative rate of ~.8. Thus, if you test 1000 individuals ~5 will show up as having antibodies when they actually don’t and x*.8 will show up as having antibodies when they actually do and since (5+x*.8)/1000=.015 then x=12.5 so the true rate is 12.5/1000=1.25%, thus the reported rate is pretty close to the true rate. (The authors then inflate their numbers up for population weighting which I am ignoring). On the other hand, suppose that the false positive rate is .015 which is still very low and not implausible then we can easily have ~15/1000=1.5% showing up as having antibodies to COVID when none of them in fact do, i.e. all of the result could be due to test error.
In other words, when the event is rare the potential error in the test can easily dominate the results of the test.
Addendum: For those playing at home, Bedford uses sensitivity and specificity while I am more used to thinking about false positive and false negative rates and I simplify the numbers slightly .8 instead of his .803 and so forth but the point is the same.
From my email box, here are perspectives from people in the world of epidemiology, the first being from Jacob Oppenheim:
I’d note that epidemiology is the field that has most embraced novel and principles-driven approaches to causal inference (eg those of Judea Pearl etc). Pearl’s cluster is at UCLA; there’s one at Berkeley, and another at Harvard.
The one at Harvard simultaneously developed causal methodologies in the ’70s (eg around Rubin), then a parallel approach to Pearl in the ’80s (James Robins and others), leading to a large collection of important epi people at HSPH (Miguel Hernan, etc). Many of these methods are barely touched in economics, which is unfortunate given their power in causal inference in medicine, disease, and environmental health.
These methods and scientists are very influential not only in public health / traditional epi, but throughout the biopharma and machine learning worlds. Certainly, in my day job running data science + ml in biotech, many of us would consider well trained epidemiologists from these top schools among the best in the world for quantitative modeling, especially where causality is involved.
From Julien SL:
I’m not an epidemiologist per se, but I think my background gives me some inputs into that discussion. I have a master in Mechatronics/Robotics Engineering, a master in Management Science, and an MBA. However, in the last ten years, epidemiology (and epidemiology forecasting) has figured heavily in my work as a consultant for the pharma industry.
[some data on most of epidemiology not being about pandemic forecasting]…
The result of the neglect of pandemics epidemiology is that there is precious little expertise in pandemics forecasting and prevention. The FIR model (and it’s variants) that we see a lot these days is a good teaching aid. Still, it’s not practically useful: you can’t fit exponentials with unstable or noisy parameters and expect good predictions. The only way to use R0 is qualitatively. When I saw the first R0 and mortality estimates back in January, I thought “this is going to be bad,” then sold my liquid assets, bought gold, and naked puts on indices. I confess that I didn’t expect it to be quite as bad as what actually happened, or I would have bought more put options.
…here are a few tentative answers about your “rude questions:”
a. As a class of scientists, how much are epidemiologists paid? Is good or bad news better for their salaries?
Glassdoor data show that epidemiologists in the US are paid $63,911 on average. CDC and FDA both pay better ($98k and $120k), as well as pharma (Merck: $94k-$115k). As explained above, most are working on cancer, diabetes, etc. So I’m not sure what “bad news” would be for them.
b. How smart are they? What are their average GRE scores?
I’m not sure where you could get data to answer that question. I know that in pharma, many – maybe most – people who work on epidemiology forecasting don’t have an epidemiology degree. They can have any type of STEM degree, including engineering, economics, etc. So my base rate answer would be average of all STEM GRE scores. [TC: Here are U. Maryland stats for public health students.]
c. Are they hired into thick, liquid academic and institutional markets? And how meritocratic are those markets?
Compared to who? Epidemiology is a smaller community than economics, so you should find less liquidity. Pharma companies are heavily clustered into few geographies (New Jersey, Basel in Switzerland, Cambridge in the UK, etc.) so private-sector jobs aren’t an option for many epidemiologists.
d. What is their overall track record on predictions, whether before or during this crisis?
CDC has been running flu forecasting challenges every year for years. From what I’ve seen, the models perform reasonably well. It should be noted that those models would seem very familiar to an econometric forecaster: the same time series tools are used in both disciplines. [TC: to be clear, I meant prediction of new pandemics and how they unfold]
e. On average, what is the political orientation of epidemiologists? And compared to other academics? Which social welfare function do they use when they make non-trivial recommendations?
Hard to say. Academics lean left, but medical doctors and other healthcare professionals often lean right. There is a conservative bias to medicine, maybe due to the “primo, non nocere” imperative. We see that bias at play in the hydroxychloroquine debate. Most health authorities are reluctant to push – or even allow – a treatment option before they see overwhelming positive proof, even when the emergency should encourage faster decision making.
…g. How well do they understand how to model uncertainty of forecasts, relative to say what a top econometrician would know?
As I mentioned above, forecasting is far from the main focus of epidemiology. However, epidemiologists as a whole don’t seem to be bad statisticians. Judea Pearl has been saying for years that epidemiologists are ahead of econometricians, at least when it comes to applying his own Structural Causal Model framework… (Oldish) link: http://causality.cs.ucla.edu/blog/index.php/2014/10/27/are-economists-smarter-than-epidemiologists-comments-on-imbenss-recent-paper/
I’ve seen a similar pattern with the adoption of agent-based models (common in epidemiology, marginal in economics). Maybe epidemiologists are faster to take up new tools than economists (which maybe also give a hint about point e?)
h. Are there “zombie epidemiologists” in the manner that Paul Krugman charges there are “zombie economists”? If so, what do you have to do to earn that designation? And are the zombies sometimes right, or right on some issues? How meta-rational are those who allege zombie-ism?
I don’t think so. Epidemiology seems less political than economy. There are no equivalents to Smith, Karl Marx, Hayek, etc.
i. How many of them have studied Philip Tetlock’s work on forecasting?
Probably not many, given that their focus isn’t forecasting. Conversely, I don’t think that Tetlock has paid much attention to epidemiology. On the Good Judgement website, healthcare questions of any type are very rare.
And here is Ruben Conner:
Weighing in on your recent questions about epidemiologists. I did my undergraduate in Economics and then went on for my Masters in Public Health (both at University of Washington). I worked as an epidemiologist for Doctors Without Borders and now work as a consultant at the World Bank (a place mostly run by economists). I’ve had a chance to move between the worlds and I see a few key differences between economists and epidemiologists:
Trust in data: Like the previous poster said, epidemiologists recognize that “data is limited and often inaccurate.” This is really drilled into the epidemiologist training – initial data collection can have various problems and surveys are not always representative of the whole population. Epidemiologists worry about genuine errors in the underlying data. Economists seem to think more about model bias.
Focus on implementation: Epidemiologists expect to be part of the response and to deal with organizing data as it comes in. This isn’t a glamorous process. In addition, the government response can be well executed or poorly run and epidemiologists like to be involved in these details of planning. The knowledge here is practical and hands-on. (Epidemiologists probably could do with more training on organizational management, they’re not always great at this.)
Belief in models: Epidemiologists tend to be skeptical of fancy models. This could be because they have less advanced quantitative training. But it could also be because they don’t have total faith in the underlying data (as noted above) and therefore see fancy specifications as more likely to obscure the truth than reveal it. Economists often seem to want to fit the data to a particular theory – my impression is that they like thinking in the abstract and applying known theories to their observations.
As with most fields, I think both sides have something to learn from each other! There will be a need to work together as we weigh the economic impacts of suppression strategies. This is particularly crucial in low-income places like India, where the disease suppression strategies will be tremendously costly for people’s daily existence and ability to earn a living.
And here is from an email from epidemiologist Dylan Green:
So with that…on to the modelers! I’ll merely point out a few important details on modeling which I haven’t seen in response to you yet. First, the urgency with which policy makers are asking for information is tremendous. I’ve been asked to generate modeling results in a matter of weeks (in a disease which I/we know very little about) which I previously would have done over the course of several months, with structured input and validation from collaborators on a disease I have studied for a decade. This ultimately leads to simpler rather than more complicated efforts, as well as difficult decisions in assumptions and parameterization. We do not have the luxury of waiting for better information or improvements in design, even if it takes a matter of days.
Another complicated detail is the publicity of COVID-19 projections. In other arenas (HIV, TB, malaria) model results are generated all the time, from hundreds of research groups, and probably <1% of the population will ever see these figures. Modeling and governance of models of these diseases is advanced. There are well organized consortia who regularly meet to present and compare findings, critically appraise methods, elegantly present uncertainty, and have deep insights into policy implications. In HIV for example, models are routinely parameterized to predict policy impact, and are ex-post validated against empirical findings to determine the best performing models. None of this is currently in scope for COVID-19 (unfortunately), as policy makers often want a single number, not a range, and they want it immediately.
I hope for all of our sakes we will see the modeling coordination efforts in COVID-19 improve. And I ask my fellow epidemiologists to stay humble during this pandemic. For those with little specialty in communicable disease, it is okay to say “this isn’t my area of expertise and I don’t have the answers”. I think there has been too much hubris in the “I-told-ya-so” from people who “said this would happen”, or in knowing the obvious optimal policy. This disease continues to surprise us, and we are learning every day. We must be careful in how we communicate our certainty to policy makers and the public, lest we lose their trust when we are inevitably wrong. I suspect this is something that economists can likely teach us from experience.
One British epidemiologist wrote me and told me they are basically all socialists in the literal sense of the term. not just leaning to the left.
Another person in the area wrote me this:
Another issue that isn’t spoken about a lot is most Epidemiologists are funded by soft money. It makes them terrifyingly hard working but it also makes them worried about making enemies. Every critic now will be reviewed by someone in IHME at some point in an NIH study section, whereas IHME, funded by the Gates Foundation, has a lot of resilience. It makes for a very muted culture of criticism.Ironically, outsiders (like economist Noah Haber) trying to push up the methods are more likely to be attacked because they are not a part of the constant funding cycle.I wonder if economists have ever looked at the potential perverse incentives of being fully grant funded on academic criticism?
As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.
Here is a very good tweet storm on their methods, excerpt: “What I love about this paper is its humility in the face of uncertainty.” And: “…rather than trying to get exact answers using strong assumptions about who opts-in for testing, the characteristics of the tests themselves, etc, they start with what we can credibly know about each to build bounds on each of these quantities of interest.”
I genuinely cannot give a coherent account of “what is going on” with Covid-19 data issues and prevalence. But at this point I think it is safe to say that the mainstream story we have been living with for some number of weeks now just isn’t holding up.
For the pointer I thank David Joslin.
Here’s the latest video from MRU where I cover some interesting papers on the effect of pollution on health, cognition and productivity. The video is pre-Covid but one could also note that pollution makes Covid more dangerous. For principles of economics classes the video is a good introduction to externalities and also to causal inference, most notably the difference in difference method.
Might I also remind any instructor that Modern Principles of Economics has more high-quality resources to teach online than any other textbook.
I will be doing a Conversation with him, mostly about his ideas on Covid-19 response and testing, though we will cover other topics as well. So what should I ask him?
I was surprised when Trump won. The economy was doing well, Trump had charisma but was erratic and made what seemed like many missteps (like disparaging people in the military) that it didn’t seem plausible he could win. Yet, he plowed through the Republican primaries and gathered such a large and powerful base of support that people like Ted Cruz and Lindsey Graham, who have good reasons to hate his guts, even they kowtowed. I don’t want to revisit the debates about why Trump won but one of the reasons was that his base felt disrespected by coastal and media elites–their religion, their guns, their political incorrectness, their patriotism, their education, their jobs–all disrespected.
And now maybe it is happening again. From the point of view of the non-elites, the elites with their models and data and projections have shut the economy down. The news is full of pleas for New York, which always seemed like a suspicious den of urban iniquity, but their hometown is doing fine. The church is closed, the bar is closed, the local plant is closed. Money is tight. Meanwhile the elites are laughing about binging Tiger King on Netflix. It doesn’t feel right. I can understand that or feel that I must try to understand that.
Here’s a picture from a protest in Ohio. It wasn’t a large protest, about 100 people, but they look pretty angry. They want to reopen the economy.
Photo: Joshua Bickel.
Columbus Dispatch: Kevin Farmer of Cincinnati climbed to the top of the Statehouse steps with his bullhorn to lead the protesters in a series of chants.
“Some say that we’re actually causing havoc or putting lives in danger right now — but actually they’re putting my livelihood in danger and others because we’re laid off during this pandemic,” Farmer said to the crowd.
Farmer told The Dispatch that he has been laid off from his job at Cincinnati Metropolitan Housing, and said his employer will contact him when it is OK to return to work.
Farmer said he hoped DeWine would see the dissent caused by the demonstration, and allow Ohioans to get back to their jobs.
“Don’t Mike DeWine supposed to be a Republican (sic)? Don’t he believe in less government? Small government?” Farmer said.
“He has an obligated right to get us back to work, because if not, what do you think Americans are gonna go through?”
Farmer also led the demonstrators in a series of “When I say tyrant, you say Mike DeWine” chants, among others.
Another demonstrator, John Jenkins of Pleasantville, was bearing an upside down American flag, traditionally a distress signal.
“Ohio is currently under distress,” Jenkins said. “The United States is generally under distress.”
…Joe Marshall, who did not identify where he was from, said he was representing Anonymous Columbus Ohio.
Marshall said he chose to demonstrate against DeWine because he believes DeWine and Acton are being led astray by the World Health Organization, which he said is corrupt and peddling false information to local governments.
“Their numbers here are what these clowns are going by,” Marshall said. “Even if they are right, they don’t justify” enforcing a stay-at-home order.
“These are common sense things,” Marshall said. “The problem is, Mr. DeWine doesn’t want to do common sense things, he wants to listen to Amy [Ohio Health Director Dr. Amy Acton, AT], and Amy gets her orders from the World Health Organization.”
Another protestor from a follow-up:
Columbus Dispatch: “We have children to feed, businesses to run, employees to pay, and Ohio must end this shutdown now. Those with high-risk categories and compromised immune systems can shelter safely at home while the rest of us can exercise our constitutional liberties to work and take care of our businesses and children.
“Patriots who love and respect our liberties and the Constitution are sick and tired of the fear-mongering while the governor and (state Health Director) Dr. (Amy) Acton continue to hide the numbers from the public.”
As Tyler put it yesterday, “America is a democracy, and the median voter will not die of coronavirus.” Solve for the equilibrium.
Addendum: In an excellent historical piece, Jesse Walker at Reason notes that cholera riots were common in Europe in the 19th century. Respect also played a role:
The more high-handed the ruling classes were, the more likely they were to be targeted by rumors and revolt. The riots persisted longest, Cohn writes, “where elites continued to belittle the supposed ‘superstitions’ of villagers, minorities, and the poor, violated their burial customs and religious beliefs, and imposed stringent anti-cholera regulations even after most of them had been proven to be ineffectual. Moreover, ruling elites in these places addressed popular resistance with military force and brutal repression.
Ryan Peterson, Flexport Founder and CEO, has an excellent piece on Why There Aren’t Enough Masks, and How to Get More. One part of the problem is a lack of working capital brought about in part by a fear of raising prices:
Typically, buyers of PPE, whether hospitals or medical distributors, expect to place purchase orders and only pay for products upon delivery, or even later.
But when demand surges by 20x, vendors simply don’t have the money required to scale production. Factories need money to add production lines, buy raw materials, and hire workers. They need down payments so they can move.
Buyers prefer to pay upon receipt of goods for two reasons. The first is to ensure quality: They can refuse payment if the goods they receive don’t meet their standards. The second reason is they prefer to keep cash on their balance sheets, rather than paying vendors in advance.
In ordinary times, sellers will accept this. But with the entire world desperate to buy PPE, manufacturers know they can ask for a down payment and get it. Other more aggressive entities are paying down payments, so if US buyers won’t, they don’t get the supply.
American medical distributors, governments, and even hospital chains, by contrast, have been less willing, or less able, to adapt to the new reality of paying vendors upfront, at higher prices than they’d contracted.
At the same time, US distributors can’t pass higher prices through to hospitals in the midst of the crisis, for fear of being accused of profiteering. Foreign governments and healthcare systems have been less encumbered by this, showing a willingness to pay more and pay faster to get first in line.
There was a recent debate on twitter about so-called price-gouging. It was said that the argument for raising prices is weak when the elasticity of supply is low. That’s not necessarily true. First, in an emergency even a small increase in quantity can be very valuable so high prices can have high utility payoffs. Second, vendors face credit market frictions and capital constraints. Borrowing in an emergency is often not possible–this means that asset balances matter and transferring wealth from buyers to firms can ease financial constraints. Put another way, it’s the short run increase in price which allows long run elasticities to increase. Elasticity is endogenous to pricing.
Hat tip: Paul Graham.
Under Swiss law, every resident is required to purchase health insurance from one of several non-profit providers. Those on low incomes receive a subsidy for the cost of cover. As early as March 4, the federal health office announced that the cost of the test — CHF 180 ($189) — would be reimbursed for all policyholders.
The U.S. government will nearly double the amount it pays hospitals and medical centers to run Abbott Laboratories’ large-scale coronavirus tests, an incentive to get the facilities to hire more technicians and expand testing that has fallen significantly short of the machines’ potential.
Abbott’s m2000 machines, which can process up to 1 million tests per week, haven’t been fully used because not enough technicians have been hired to run them, according to a person familiar with the matter.
In other words, we have policymakers who do not know that supply curves slope upwards (who ever might have taught them that?).
The same person who sent me that Swiss link also sends along this advice, which I will not further indent:
“As you know, there are 3 main venues for diagnostic tests in the U.S., which are:
1. Centralized labs, dominated by Quest and LabCorp
2. Labs at hospitals and large clinics
3. Point-of-care tests
There is also the CDC, although my understanding is that its testing capacity is very limited. There may be reliability issues with POC tests, because apparently the most accurate test is derived from sticking a cotton swab far down in a patient’s nasal cavity. So I think this leaves centralized labs and hospital labs. Centralized labs perform lots of diagnostic tests in the U.S. and my understanding is this occurs because of their inherent lower costs structures compared to hospital labs. Hospital labs could conduct many diagnostic tests, but they choose not to because of their higher costs.
In this context, my assumption is that the relatively poor CMS reimbursement of COVID-19 tests of around $40 per test, means that only the centralized labs are able to test at volume and not lose money in the process. Even in the case of centralized labs, they may have issues, because I don’t think they are set up to test deadly infection diseases at volume. I’m guessing you read the NY Times article on New Jersey testing yesterday, and that made me aware that patients often sneeze when the cotton swab is inserted in their noses. Thus, it may be difficult to extract samples from suspected COVID-19 patients in a typical lab setting. This can be diligence easily by visiting a Quest or LabCorp facility. Thus, additional cost may be required to set up the infrastructure (e.g., testing tents in the parking lot?) to perform the sample extraction.
Thus, if I were testing czar, which I obviously am not, I would recommend the following steps to substantially ramp up U.S. testing:
1. Perform a rough and rapid diligence process lasting 2 or 3 days to validate the assumptions above and the approach described below, and specifically the $200 reimbursement number (see below). Importantly, estimate the amount of unused COVID-19 testing capacity that currently exists in U.S. hospitals, but is not being used because of a shortage of kits/reagents and because of low reimbursement. This number could be very low, very high or anywhere in between. I suspect it is high to very high, but I’m not sure.
2. Increase CMS reimbursement per COVID-19 tests from about $40 to about $200. Explain to whomever is necessary to convince (CMS?…Congress?…) why this dramatic increase is necessary, i.e., to offset higher costs for reagents, etc. and to fund necessary improvements in testing infrastructure, facilities and personnel. Explain that this increase is necessary so hospital labs to ramp up testing, and not lose money in the process. Explain how $200 is similar to what some other countries are paying (e.g., Switzerland at $189)
3. Make this higher reimbursement temporary, but through June 30, 2020. Hopefully testing expands by then, and whatever parties bring on additional testing by then have recouped their fixed costs.
4. If necessary, justify the math, i.e., $200 per test, multiplied by roughly 1 or 2 million tests per day (roughly the target) x 75 days equals $15 to $30 billion, which is probably a bargain in the circumstances.
5. Work with the centralized labs (e.g., Quest, LabCorp., etc.), hospitals and healthcare clinics and manufactures of testing equipment and reagents (e.g., ThermoFisher, Roche, Abbott, etc.) to hopefully accelerate the testing process.
6. Try to get other payors (e.g., HMOs, PPOs, etc.) to follow CMS lead on reimbursement. This should not be difficult as other payors often follow CMS lead.
Just my $0.02.”
TC again: Here is a Politico article on why testing growth has been slow.
One silver lining of the crisis is that the country has been getting rid of a lot of regulations that slow things down. CA, however, has decided to slow things down even more.
Included in the council’s rules was a blanket extension of deadlines for filing civil actions until 90 days after the current state of emergency ends. Ominously for housing construction, this extended statute of limitations applies to lawsuits filed under the California Environmental Quality Act (CEQA).
That law requires local governments to study proposed developments for potentially significant environmental impacts. CEQA also gives third parties the power to sue local governments for approving a construction project if they feel that a particular environmental impact wasn’t studied enough.
The law has become a favored tool of NIMBYs and other self-interested parties to delay unwanted developments or to extract concessions from developers. Anti-gentrification activists use CEQA to stop apartment buildings that might cast too much shadow. Construction unions use the law as leverage to secure exclusive project labor agreements.
Under normal circumstances, these CEQA lawsuits have to be filed within 30 or 35 days of a project receiving final approval.
Notice that the law doesn’t say the NIMBYs get an extra 30 or 35 days to file. It says that NIMBYs get to file until 90 days “after the current state of emergency ends.” In other words, no one can know when they are free to build so the law could put every CA construction project that hasn’t already past CEQA review into limbo.
“If I’m a builder I can’t move forward with my project until the [CEQA] statute of limitations has expired. The reason why I can’t do that is because if you do move forward, courts have the authority to order you tear down what you’ve built,” Cammarota tells Reason, explaining that “lenders today are unwilling to fund those loans for construction until the statute of limitations has expired.”
Hat tip: Carl Danner.