WRAL in Raleigh interviewed me late last week on insurance options for the recently unemployed and uninsured:
WRAL in Raleigh interviewed me late last week on insurance options for the recently unemployed and uninsured:
Cowboy Diva asked a good question this morning:
Losing your job is considered a qualifying life event on the ACA exchanges, right? Even if you get offered a replacement (ala COBRA) package by your employer, you don’t need an open season to apply for a healthcare plan on an exchange.
Because that’s the case, for a lot of people suddenly on unemployment, the current administration’s shenanigans about opening ACA don’t make a difference.
Almost but not quite.
Losing your job but not losing insurance as you were already uninsured does not open up a Healthcare.gov Special Enrollment Period (SEP).
Losing health insurance through work is a qualifying life event that opens up a regular SEP on Healthcare.gov.
Losing insurance through work is a life transition. A SEP is always opened up even if you are eligible for COBRA. We’ve talked about the trade-offs between Exchange and COBRA before:
Fundamentally, the question is what are the trade-offs between COBRA and the ACA?
This is a damn good question…
what is the best choice?
The major variables are time of the year, amount of cost sharing left on the COBRA plan, hassle/transition costs, age and eligibility for ACA subsidies.
A 21 year old who has no claims in their work insurance who is COBRA eligible in February and is eligible for big ACA subsidies is likely better off in the ACA individual market as they are likely to see a low to no premium Bronze plan that they are unlikely to use as they are statistically likely to be as healthy as a horse anyways.
A 64 year old who has already maxed out their deductible when they became COBRA eligible in February, makes too much for ACA subsidies and has a knee replacement surgery scheduled in March will likely be better off in COBRA.
If you lost your insurance through work, you can look at the ACA exchanges. There might be a good deal available. At the same time, COBRA may still be attractive if you make too much for ACA subsidies to matter and you anticipate significant medical costs for the rest of the year and have already spent your deductible.
Now if you live in a state (excluding Idaho) that runs its own Exchange/marketplace, you can get a COVID-19 general SEP that applies even if you were uninsured for the first few months of the year. However that is restricted to states that are acively engaged with their own markets.
We are now isolating ourselves, at least the non-MAGA portion of the population. The numbers of cases of Covid-19 – and deaths – are mounting. What can we expect? What happens next?
Lots of us are wondering what comes next in the SARS-CoV-2 pandemic. We already have an indication in New York City. At the same time, many states and countries are instituting lockdown measures that should decrease the numbers of sick and dead. I’ll use the Imperial College paper to discuss what is likely to come next.
A few words first about the Imperial College model, and models in general.
Neil Ferguson, one of the modeling team, testified to Parliament that he was becoming “reasonably confident” that the UK would be able to handle the peak of the epidemic in two or three weeks, with a death toll unlikely to exceed 20,000. This is being spun among those who want to send the US back to work as an indication that the model is wrong. IT IS NOT. What Ferguson is saying is that the UK is now taking measures to change the worst-case modeling scenario. THAT’S THE PURPOSE OF MODELING – to work out possibilities and recommend a path forward.
The Imperial College paper modeled the spread of the virus uncontrolled, and then investigated the effect of social distancing measures. The conclusion was that all the measures studied were required to suppress the growth of COVID-19 cases and avoid overloading the hospital system. I summarized that part of it in my earlier post.
Because the SARS-CoV-2 virus is new to the human race, nobody had immunity to it when it was first introduced. It can spread with no barriers to every human on the planet. As people are infected and recover, they become immune to the virus and cannot become ill from it again. It’s not clear how long that immunity lasts, but if it is even for a few months, it will slow down the spread of the virus.
Social distancing measures also slow down the spread of the virus, but they don’t confer immunity, leaving enough of the population susceptible to the virus that, when the distancing measures are lifted, the virus can spread again. The more successful the distancing measures are, the fewer people will become immune, and thus, the larger the secondary peak. The secondary peak can be controlled by reimposing distancing measures. A target might be set, and a series of secondary peaks at levels that will not break the hospital system would result (Figure 4 of the Imperial College paper).
As a modeling projection goes further into the future, its results become less reliable. This is partly because of uncertainties in the parameters, but it is also because we are taking measures to control the spread of the disease. Distancing helps. If we can make testing general for both the virus itself and for immunity to it, that will change things significantly.
There are several things we don’t know that make a difference in how things will play out. We have some idea about them, but as we learn more, the model predictions may change.
How long do people spread the virus?
How many people are infected by the virus and never show any symptoms but become immune? This is important because the more people become immune this way, the lower the peaks in the model and the faster they die out. When enough people are immune, the virus cannot propagate. That’s herd immunity. We don’t know what percent of the population must be immune to establish herd immunity.
What kind of immunity do people develop after having the virus? How long does it last. Is it a strong immunity, or do some people remain susceptible to reinfection?
Does the virus becomes less infective during the summer? If it does, infections could decrease but peak again in the fall and winter. Or the virus could weaken to cause a less dangerous disease. That’s not unknown in pandemics.
The single biggest factor in filling in what we don’t know now is testing. Test people with symptoms. Test people without symptoms. Test adults. Test children. Test people near the people with symptoms. Test medical workers, them especially. Tracking cases to determine who should be quarantined.
A new test promises results within five minutes. That is a test for the virus itself. We also need serology tests, to determine if people are immune to the virus.
Testing will give individuals and their medical support more information about their next actions. It will also provide information about the course of the disease and information that can be used in modeling.
We are buying time with the measures we are taking now. We can’t shut down the economy for long, and Trump and his admirers are already pressing to open things up again. Some of them are ignoring distancing now.
The answer, eventually, will be a vaccine. Several groups are working on vaccines, and one clinical trial is in progress. Eighteen months is commonly cited as the time period, but it could take longer. We don’t know.
Cross-posted to Nuclear Diner
Martin has been kind enough to put together a Guest Post on Data Modeling in the Epidemic. Part 1 was posted at approximate 2:30 pm on Wednesday. This is Part 2.
Once again, Martin is standing by in case we have questions.
Take it away, Martin!
Questions on Data Modeling in the Epidemic: Part 2
So, how do we know if containment is working and for how long do we need to do this?
We can answer this! Well, we can get close, with a few caveats, because we can look at what happened in China, and we can do a little bit to confirm that model with what’s happening in Italy a bit ahead of us. So how do we build it?
Well, what do we have to work with, and what do we need to know? We have a few data elements – confirmed cases, fatalities, recoveries. And we have time. We know this data for each day. We know this for the whole world, for different countries, and for different cities and states. Now, the experts have a whole bunch of other data, hospitaliation, ICU cases, intubated cases, tests administered but waiting on results, etc. and all in infinitely more detail than we have.
Confirmed cases is kind of garbage. I’ve been largely ignoring it because I don’t know if it’s telling me reproduction rate R0 or testing rate. It may get reliable, but I’m not counting on it.
The most accurate bit of data is likely fatalities. Unlike determining if someone is infected or not, we’re really good at determining if someone is dead or not. And if they are dead, we can test if they’re infected, so we can probably rely at this point on that being a pretty reliable number. Time can be a bit more uncertain than you might think because when data is collected and reported in a human administrative dependent process (as opposed to an automated weather station that does things on precise and unwavering schedules) you have problems of people going to the dentist and not getting their data in until the next day. So, we’ll expect this to be a bit noisy from day to day.
Now, because we’re at the start of an epidemic, where there’s almost nothing holding spread back like herd immunity, we can probably expect to see something like a perfect exponential curve. A model for infected is more complicated because people recover. Nobody recovers in our model. And when we plot that out, that’s exactly what we get. People aren’t very good at intuiting variation from an exponential curve, or even extrapolating on an exponential curve, but if you take the logarithm of your data, you wind up with a straight line, and we’re pretty good at intuiting a linear function. Below is a plot of the log of our fatality data for the US, and that’s a pretty darn straight line. I suspect that recent uptick in the slope is due to NYC dominating the national data and having a higher R0.
If you want to play along at home, the fatality rate for the US is approximated by e0.273t where t is days since the first fatality (Feb 29).
So we have a well behaved exponential function, and that doesn’t tell us when things will change, but it does give us a sense of urgency. You can look forward and see projected fatalities that make you pucker and decide let’s make sure we don’t get there and then work backward.
Understand, we’re building a very simplified model here. Our goal isn’t to give us any real long-term predictive value of how many people may contract this, or how many people will die. Our goal is to give a good approximation of the worst case sceniario for early in this epidemic and then look for when the model breaks on the assumption that our actions will break the model before other normal factors like herd immunity does. The model gives us a sense that if we want to keep fatalities below a certain number (and we’re assuming that number is relatively small) then we need to act before a certain date. In terms of actual fatalities, the model is probably accurate to about an order of magnitude, and that’s all we’re looking for. Are we looking at thousands or tens of thousands or hundreds of thousands of fatalities? What should I emotionally try to prepare myself for, and how loudly should I scream at my governor to shut my state down now, even if things may not seem too bad locally.
What does China tell us?
China gives us some good data to work from. They did a bunch of minor things just as the US did, but they locked down Wuhan on Jan 23, and all other urban areas the next day. Jan 23 is our day 0. And China saw a nice exponential curve as well – it was a little different in magnitude (the slope of the log is different) so it might grow a bit faster or a bit slower but either way it grows incredibly fast.
The first sign their lockdown was working was on Feb 5 (day 13). That was the first day that new cases fell, and they generally continued to fall after that. That doesn’t mean that people stopped getting sick on Feb 5, it means they stopped getting sick on Jan 24, but we couldn’t measure it until 13 days later (give or take a few days, plus a few days to confirm that it’s a trend and not just an outlier). So, if we are modeling infections and we want to know if a given action had an effect, measure any change that occurs around the 13 day mark. That also tells us that any action needs to remain in place for probably around 3 weeks before we get any real sign it is working or not. But this is our inflection point for R0 going from greater than 1 to less than 1.
The next sign came on Feb 13 (day 21), the first indication that the rate of fatalities was halting. Now, the fatalities didn’t immediately fall, but it stopped growing and that’s key. Fatalities per day stayed relatively flat until Feb 24 (day 32) when it started to consistently fall. Then on March 9 (day 46) the number of daily fatalities fell to about the level of day 0.
So, what does this tell us? Well, look at that date where the fatality projection makes you pucker, go back 21 days and make sure your most aggressive mitigation action is in place by then, because if not, you will hit that number, and you may maintain that daily rate of fatalities for days.
Now, a few caveats here. The 13 day and 21 day numbers are largely a function of the virus, and not the population. Those should be roughly equally true in China as New York City as Montana. If you blow through day 13 (give or take) and have no reduction in infections, then you didn’t dream big enough, need to throw down some much more restrictive actions, and wait another 13 days (give or take).
So, does Italy validate that? Possibly. Italy quarantined their first area on Feb 23 and then did national quarantines on March 8/9. We should see some slowing of new cases on March 7 (day 13 for the smaller area) but without an infection model we can’t see that, and a larger reduction around March 21/22 (day 13), and we did see that on March 22. We should also see some sign of reduction in the fatality numbers around March 15 (day 21 for the smaller area), and we do. Their numbers are still climbing, because that wasn’t the national lockdown, but it definitely slowed right around that date. The next and larger data point should come around March 29/30.
The dates after that are largely a function of the population, the effectiveness of the actions taken on day 0 and the compliance of the population. The 11 day long plateau in the fatality rate that China saw might be shorter or longer here. The 14 days to reduce from the plateau back to day 0 might be shorter or longer here.
My assumption is everything will be longer in the US than China. Despite Wuhans high population density, China has an unprecedented ability to control their population and an unusually high level of compliance by the public. The US is struggling with compliance, and has very little control. That doesn’t mean it won’t work, it just means we probably won’t see that nice sharp inflection that China had. It’ll probably be messier and slower, possibly much slower. Italy should give us a little more insight in how much things can vary. Their lockdown was national, but Italians are notoriously defiant of government guidelines, so they should look closer to US efforts.
Days 0 in the US:
Bay Area: March 16
California: March 19
New Jersey: March 21
NYC: March 22
Bay area is already showing some evidence of improvement, presumably from their work from home, public gathering orders back in early March. We’d expect to see real new case declines on or just after March 29. CA as a whole, April 2, New Jersey April 4, NYC April 5. We’d expect to see fatality growth halt in the Bay Area on or after April 7, CA April 10, NJ April 12, NYC April 13.
If nothing else, we’re trying to establish the importance of acting quickly because once fatalities starts to go, it goes fast. And while we’re in this state just waiting for something to happen, roughly when we can expect to see results and where to look.
Additional information added at 3pm, based on new information and an additional model:
So, I wrote this yesterday, and some new information has come out, and some new models have been made.
Specifically, I built a model of NY. To start, it’s a bad model since there are only 13 days of fatality data. A model built on 13 datapoint is going to suck. It can change wildly with just one more data point. To give an example, a model built of Washington State built after 13 days would look apocalyptic because all of the data was dominated by a single nursing home, that you would expect to have a vastly higher mortality rate than the general population. The mode suggests a trend that simply can’t hold, mainly because everyone in the nursing home was pretty much accounted for. You can’t grow fatalities in a 150 person facility above 150, but the model is too primitive to reflect that. So that’s just a limitation we need to keep in mind. And sure enough, after some more data was collected, a more reasonable and less apocalyptic trend developed. We just had to be aware that there were a lot of unknowns and wait to see how things shook out. That how this kind of field data modeling works. It’s messy and limited but can help to tell us where to look or can change the urgency of a decision.
As to New York, their numbers look pretty apocalyptic right now. But New York isn’t getting the kind of detailed reporting that Washington did. For all we know, most of these fatalities came out of a single project in the city and once it tears through the few thousand people that live there, the underlying general population trend will emerge. I’m hoping that’s the case, but we just have to wait. I want my model to break, because that provides us with a new bit of information, and we can then go and try and figure out why it broke. We have a theory for when lockdowns will break the model, but there can be other causes as well – as happened in Washington state. That’s not a bad thing. In fact, that’s sort of the point.
Another key bit of information is out of Italy that they may be significantly underreporting their fatalities. That was a concern of mine. Any human collected dataset has to deal with this stuff, so you just asterisk everything. The testing data is kind of garbage because everyone has different access to tests, and different criteria for testing, but it also changes for a given location. When the 10th person this hour walks in presenting the same set of Covid symptoms, do you really take the time to give the test, or do you just get them in a bed ASAP. Of course testing is good information, but you may no longer feel you have time to do it. So testing data just gets that much more unreliable. Same goes for fatalities. When 13 people a day are dying in your hospital, the effort you put into determining cause of death is going to change. Do you run the Covid test so you can list that as cause of death, or do you just put down what you know, respiratory failure, and move on.
Italy’s fatality data seemed to fall off faster than I expected. I attributed that to the smaller quarantine in northern Italy having an oversized effect on the data (they quarantined for a reason, so it’s not unreasonable to assume they’d dominate the data) but now it’s looking like it may (also?) be a lack of attribution of fatalities to the disease. We just have to deal with that. China’s data saw a similar pattern, possibly for a similar reason. Maybe fatalities don’t drop off at day 21, hospitals simply get too overwhelmed to count them accurately, and the real dropoff is day 32. More likely it would be somewhere in between. So, we’ll look more closely at Italy’s data as it continues to come in and see if we can figure that out. California may provide a better data point. We locked down earlier than Italy in terms of number of fatalities, so I don’t expect CA will get quite as overwhelmed as Italy. That should give us somewhat more consistent data.
Martin has been kind enough to put together a Guest Post on Data Modeling in the Epidemic. Part 2 will be posted on Thursday afternoon.
Martin is standing by in case we have questions.
Take it away, Martin!
Questions on Data Modeling in the Epidemic: Part 1
So, like Cheryl (such good company I find myself in!) I thought I’d provide some information on data modeling this grand experiment we find ourselves all in. I do have a statistics background and do a fair bit of data modeling of population behavior. I’m not an epidemiologist, but good data modeling always requires some degree of understanding the context the model operates in so that means spending some time becoming a ratchet epidemiologist – knowing just enough to understand how to make the model work.
Epidemiologists arm us with a bit of information regarding the behavior of this particular virus. We have the R0 (r naught) or the reproduction number that says that each person who is infected will infect R0 other people. That is determined by the infectious period (how long you can give it to someone else), the mode of transmission (air, water, touch, etc.), and the contact rate (how many people you are likely to interact with). We also know how long before symptoms appear, how long before fatality is likely, how long to recovery. These are statistical values and vary from person to person. Early in an epidemic you have fairly small sample sizes so it often just looks like chaos, but later when sample sizes are much larger those statistical values start to really show up.
Why is this such a big thing?
Well, there’s a few things going for it. An incomplete list:
1) Nobody has natural immunity, so for the contact rate above, every person an infected person comes in contact with is a potential new infection. One reason why the flu doesn’t do this is that depending on the strain of flu, there’s people walking around immune, so it doesn’t spread through them. That’s what herd immunity does – if enough people are immune, an infected person can’t find enough others to pass it along to for it to explode through the population. (This is a good place to ask Cheryl about how nuclear reactions and nuclear moderators work – basically the same process)
2) Nobody has artificial immunity. No vaccine. Again for the flu, that flu shot not just protects you personally, but helps add to the herd immunity.
3) Good modes of transmission – inhalation, contact, and fecal transmission. Hong Kong had some apartment blocks spread it through the sewer system, even when people were locked down. This is a combo that is notoriously hard to protect against.
4) Durability – it can hang in the air for hours and on surfaces for days, boosting that contact rate. It can live long enough that if the stocker at the grocery store spread it to the jug of milk overnight, it can still be there when you put it in your refrigerator.
5) It looks like other diseases – early symptoms are flu-like, so rather than rush off to the doctor, we tend to keep acting like it’s the flu, treating it like the flu, and protecting ourselves like it’s the flu. But it’s not the flu, so those things don’t work sufficiently. Ebola struggles to spread because it’s in the ‘holy fuck this guy is dying’ category, not the ‘take some Advil and go to work’ category.
6) It doesn’t affect everyone the same way. This is more important than it might seem. The 1918 flu killed young people more than old people. That allowed old people to unknowingly spread it much further than if they had fallen ill at the same rates. Covid is the opposite. Where that really matters is with testing. If you only test people who are really sick, you leave a whole bunch of people only slightly sick to run around and spread it. And it’s hard to get people to stay home sick if they don’t feel sick.
R0 is important because it tells us roughtly just how fucked we are. An R0 value of 1 means that everyone who gets it will give it to one other person. That leaves you with a fairly constant population of sick people which is very easy to deal with. An R0 value that is less than one will eventually die out on its own, or at least shrink to the size that it’s no more than a nuisance. An R0 greater than 1 means that the population of people who get infected will grow expoentially until you see things like herd immunity show up and start to knock that rate down by shrinking R0 by slowing the contact rate. An R0 value well above 1 means we’re going to go through some shit. The estimates out of China was that Covid had an R0 of around 2.7.
But R0 is not a property just of the virus but also of the population and their behavior. We calculate it for the population at large but every context will be different. A highly dense city like NYC will almost certainly have a higher R0 than a less dense city like Des Moines. When I lived in NYC we lived in a 5 story walk-up. There was no elevator, so you had to climb the stairs to our 4th story apt. There were about 100 people living in that building, all using the same front door handle, all touching the same mailboxes, all running their hands along the same handrails. That’s a lot of contact. I now live in a detached single family house. Only 4 people ever touch the front door or the railings. Much lower contract rate. When my wife comes home with a week to two weeks worth of groceries everyone heads out to unload the car and bring them in. But when you live in a 5 story walk up, you don’t do multiple trips to haul in 2 weeks of groceries, instead you maximize each trip. You know you’re going to climb those stairs at the end of every work day so you bring a single load of groceries with you every time – about a days worth. That means you’re going to the store roughly every day, interacting with others, etc. Again, that’s a lot of contact. In a university, a 500 seat lecture hall has roughly 10x more contact than a 30 seat K-12 classroom. That’s why universities moved to online instruction so early. Our R0 value was going to be massively higher than your kids 2nd grade classroom. Nurses have more contact than accountants, etc.
Why are some places blowing up worse than others?
Well, for NYC, the population density and community behavior of more regular store trips, more frequent eating out, and so on means that their R0 value could be much higher than 2.7, while a lot of California which saw the virus earlier than NYC has a value that might be somewhat lower than 2.7. Italy is denser than the US on average, so they’re having a bit harder time with it. Canada is lower than the US, so they’re having a bit easier time.
A containment strategy basically means to identify and isolate the infected so that their R0 reaches 0. For ebola that might mean a perimeter around the village so nobody goes in or out until nobody is infectious. You’re going to knock down the contact rate to 0 until the infection period is over, and it can’t be spread. For measles, it means isolating the person’s home the same way. But it relies on being able to identify who is infected, and then tracing where they’ve been, who they’ve been in contact with and prophylactically isolating those people before they become infectious and only releasing them after testing.
This strategy failed in the US because we couldn’t test and isolate as fast as the infection spread. It succeeded in South Korea because they could. It failed in Wuhan for a few reasons – a little bit of denial at first, but that was only a few days, but then a lack of a test.
The goal of containment is R0 = 0.
When containment fails, you mitigate. You’re giving up on R0 = 0. You can’t do it. But you can do R0 < 1, so you’re trying to get the number of cases to shrink to a small enough value that you can either maintain that state until something like a vaccine shows up, or you get the problem to be small enough that you can try to make containment work again.
That’s what China did. They mitigated, shrank the problem, and outside of Wuhan appears to have achieved containment. Jury is still out on whether they can get there for Wuhan as well.
That’s what the US is doing – reduce the contact rate. What is needed? Well, probably more for NYC than for Des Moines. Social distancing and hand washing might work in Montana but not Des Moines. A lockdown sufficient for Des Moines might not be sufficient for NYC. And if people don’t follow the rules, you might need to do more.
The goal of mitigation is R0 < 1
So, how do we know if containment is working and for how long do we need to do this?
That’s where the modeling helps us in Part 2.
Last week, Butch asked a great question:
How are income and subsidy calculated if you’ve been laid off?
The short answer is that it depends.
Now let’s get to the longer answer.
For Medicaid and CHIP eligibility (Expansion and non-expansion) income is calculated on a monthly basis. If you got laid off in March and were paid through the 15th, you would use your last paycheck plus any unemployment insurance you can collect for the month of March. If you apply in April, you’ll just use any unemployment insurance that you collect.
Now if you are going onto the Exchange for ACA subsidized insurance, income is determined on an annual basis for “modified adjusted gross income (MAGI)”. MAGI does not include Supplemental Security Income (SSI) from Social Security. MAGI is calculated on an annual basis. Subsidies are allocated based on estimated MAGI at the start of the year. At the end of the year, the IRS will reconcile actual income versus projected income. If you overestimate your income and made too little, the IRS will give you a bigger tax refund in the spring of the following year. If you estimate too low and make more than you thought, the IRS will clawback some or all of the excess subsidies.
No one expected million person lay-off weeks at the start of the year.
So if you are applying for a Special Enrollment Period for an ACA plan, add up your January through lay-off income. Then estimate what you think you’ll get from unemployment for the rest of the year until you think you can go back to work at whatever wage is available. I have no idea how to estimate when people will be going back to work and at what wage levels. It is a fraught calculation. As soon as your situation changes (hopefully for the better), update your income on either your state based marketplace or Healthcare.gov account so that your subsidy will reflect your reality as best you can guess.
If you have questions, let’s talk in the comments.
A pandemic is the personification of a situation that is out of control, and it’s easy to feel anxious or helpless. Information is the antidote. Correction: Good information. Accurate information.
Today we have a Guest Post from Suzanne, our resident Architect Extraordinaire. She specializes in buildings related to healthcare, and she is here
to tell us everything she knows about hospital design to share a bit of her practical knowledge and expertise in this area, and to answer our questions.
With that, I give you Suzanne!
Good afternoon, everyone. With all that is going on in the world, there have been a lot of questions about the built environment of healthcare facilities, and I thought it might be helpful if I gave a high-level primer on the issue.
For those of you who don’t know, I am a licensed architect and planner practicing in the healthcare market. I have been practicing for ten years and have been licensed for six years. I don’t want to talk about my company, clients, or projects specifically, but I am happy to share what I know here based on my professional experience. I hope it answers many of the questions you have. The two questions that I’ve heard the most are Why don’t we have more intensive care beds available? and Why don’t we just take some other building and turn it into a hospital? Bear with me here… there are reasons.
First off, there’s a few things to understand about buildings in general. There’s an array of codes and regulations that govern the built environment. These vary by municipality, county, and/or state, and there are also federal laws such as the ADA (Americans with Disabilities Act) and CMS (Center for Medicare and Medicaid Services) regulations that apply to buildings. However, most building codes in the US and Canada are based on the International Code (I-Code) series. For example, California has the California Building Code (CBC), but it’s really the International Building Code with some elements added and changed to apply to conditions specific in that state. The Building Code is where you find most of the governance for architectural and structural elements, most notably types of construction, allowable occupancy, means of emergency egress (this means how to exit the building), and so on. There are also codes and regulations around building systems, such as mechanical systems (HVAC), electrical systems, energy performance, plumbing, civil engineering, and more. Then there’s zoning regulations, which is another enormous—and boring–topic for another day.
I am not a code expert, and there’s no need to get into the particulars, but the essential thing to remember here is that the codes are written around a concept that the larger a building may be (either in height or area), and the more difficult it is to exit, and the more important it is to the functioning of society… that building is designed to be more robust. Buildings that are small, easily replaced, and don’t have a lot of occupants—such as houses and small multi-family residential buildings—are not really designed to survive events like fires. In contrast, a building like a 70-story high-rise office tower or an international airport terminal needs to remain structurally sound for a relatively long period in order to get everyone out safely in the event of an emergency. Hospitals tend to be large buildings, with a lot of risks present (like bulk oxygen). Also, they contain a lot of people, many of whom cannot evacuate under their own power, because they’re in wheelchairs, or have broken bones, or are under anesthesia, or are in the throes of dementia, etc. And in the event of an emergency such as a natural disaster, it is critical to the entire city/town around the facility that it remains operational. So the architecture and structural design of these buildings is significantly different than, say, a low-rise apartment building or strip retail center. I hope this sheds light on a lot of the questions people have about why one type of building is not easily repurposed into another.
Secondly, and specific to hospitals, there are also spatial and operational codes and standards that are meant to protect public health, safety, and welfare. These vary somewhat by state, but are all mostly written to address the same concerns. There are a few big issues present in hospitals that have led to significant regulation. First and foremost is the exceedingly high rate of hospital-acquired infections. This has led to big changes in the way hospitals are designed. Again, keeping this high-level… we now have to have more and separate spaces for patients. Other than in a few very specific situations, we don’t do shared hospital inpatient rooms anymore, and we almost never do open wards. We build more bathrooms. We build negative-pressure rooms for airborne infection isolation. We have more spatial clearance around operating tables. We install handwashing sinks all over the place. We use interior materials that are resistant to bacteria and viruses. Another significant issue has been accessibility for the disabled and patients of size. Essentially, people are bigger than they used to be, and modern hospitals are designed to accommodate those patients. A third issue of significance has been trying to reduce injuries and distances traveled by nursing staff by improving visibility and designing their space to their workflow. There are also issues around reducing medication errors, increasing security (think drugs and guns), protecting patient privacy, and more.
All of this is to explain why hospitals are such specialized environments, and as such, why they are so expensive and time-intensive and complicated to build.
In understanding why hospitals are built the way that they are, it’s also important to consider the economic environment. Healthcare buildings are incredibly expensive, because healthcare is incredibly expensive. Most hospitals are owned by corporations (either non-profit or for-profit), not the public, and it is a struggle to remain financially solvent. Certain service lines in hospitals are more profitable than others, and the business case for a hospital is written around that reality. A couple of generations ago, hospitals were fairly low-tech places, and you could check yourself in for a few days if you felt a little bit under the weather. That is obviously no longer the case. Patients have to meet criteria for admission, and length-of-stay is watched closely by insurance companies. Medicare is very strict about reimbursement. Ergo, health systems generally want to offer certain kinds of care and sometimes want to avoid others. Surgery tends to be very profitable. Behavioral health, especially prior to the passage of Obamacare, is not. The typical acute-care hospital room is relatively cheap to build and operate. Intensive care is not. The dream for a hospital system is to give you an expensive surgery after taking some expensive images and then send you home to be cared for by a home health worker, or your own family.
So, when wondering why the country is now facing a dramatic shortage of intensive care beds, the answer is: because they’re really expensive to build, they’re expensive to staff, and if we built enough to handle a crisis like COVID, they would mostly sit empty once the crisis is over. When building a hospital, every dollar has to support a business case. Beds have to be occupied a certain percentage of the time in order to justify their expense. Every square foot that I design has to return value, as it is an investment of capital. The shortage that we are facing is the result of thinking of hospitals as businesses that need to be self-supporting rather than investments in the health of a whole population.
I don’t want to make health systems sound greedy or nefarious. Most of the people I interact with are devoted to good patient care. Often, the C-suite people have clinical or research experience and truly want to do the right thing with the resources they have. The developers and project managers, for the most part, take the responsibility of building a place where lives are saved incredibly seriously. But the reality that they live in produces the results that we have.
With respect to COVID, it’s not going to be easy to ramp up intensive care capacity in time to meet this challenge. What is likely more feasible is going to be identifying infected people earlier in their disease process and hopefully giving them treatment to manage their symptoms before they need that ventilator. In the meantime, hospitals are going to end up using every available space they have for patients, such as pre-operative and post-anesthesia care areas (or maybe even labor and delivery areas, oh my God), in order to accommodate as many patients as they can. I believe that they’ll also try to use temporary facilities for less-sick people in order to reserve the hospital resources (medical gases, equipment, emergency power) for the sickest people.
If health systems can get their hands on more ventilators, I can imagine some field-hospital-esque scenario if it gets to a last resort. But a big open ward in, say, a high school gymnasium is really not a good environment for people who are infectious, and it certainly isn’t going to be good enough for the next pandemic. But there’s not any great options here. Workers who can build big, complicated buildings like a hospital are a limited resource. The supply chain for building materials is long. These buildings are expensive and take a long time to construct, and health systems don’t really build for these types of surges. There’s not enough healthcare staff out there, anyway. COVID is a 100-year event, and there are very few times we do anything in society for 100-year events. Flattening that curve and finding some effective treatment or a vaccine is what we need to do.
I’ll hang around for a bit to answer questions in the comments. I hope this has been informative and un-boring. Cheers.