Data sources and data-related updates for this week:
APM Research Lab relaunches Color of Coronavirus tracker: From April 2020 to March 2021, the American Public Media (APM) Research Lab compiled state-level data on COVID-19 deaths by race and ethnicity, in order to present a picture of which U.S. populations were most hard-hit by the pandemic. The project relaunched this week, now utilizing CDC mortality statistics instead of compiling data from states. One major finding from the updated data: “Indigenous Americans have the highest crude COVID-19 mortality rates nationwide—about 2.8 times as high as the rate for Asians, who have the lowest crude rates.”
CDC might take back hospital data reporting responsibilities from HHS: As longtime readers may remember, back in summer 2020, the Department of Health and Human Services (HHS) developed a new data system for hospitals to report COVID-19 patient numbers and other related metrics. At the time, the HHS was taking over responsibility for these data from the CDC; this inspired some political posturing and concerns about data quality, though the eventual HHS dataset turned out to be very comprehensive and useful. (This original data switch was the subject of my very first CDD issue, and I followed the HHS data system closely throughout 2020.) Now, Bloomberg reports, the CDC wants to take back hospital data reporting from the HHS. More political posturing and data quality concerns are, it seems, inevitable—this time tied to the CDC’s challenges in modernizing its data systems.
Hospitalizations among young children, by race/ethnicity during Omicron surge: Two MMWR studies that caught my attention this week: one examined hospitalization rates among young children, ages 0 to 4, between March 2020 and February 2022. This study found that COVID-19 hospitalization rates among children in this age range were five times higher at the peak of the Omicron surge compared to the Delta surge. The second report examined hospitalizations by race and ethnicity, finding that, during Omicron’s peak, hospitalization rates among Black adults were nearly four times higher than rates among white adults. Both reports clearly demonstrate who is still vulnerable to COVID-19 as the U.S. abandons safety measures.
Pfizer and Moderna both seeking EUAs for additional booster shots:POLITICO reported this week that first Pfizer, then Moderna have requested Emergency Use Authorization for fourth doses of their COVID-19 vaccines. Pfizer’s request is specifically for people age 65 and over, while Moderna’s is for all adults. Notably, Pfizer’s request is based on data from Israel suggesting that immunity from an initial booster wanes after several months—just as Pfizer’s initial case for boosters in the fall was also based on Israeli data.
Global COVID-related deaths may be three times higher than official records: Throughout the pandemic, researchers have used excess mortality (i.e. the deaths occurring in a given region and time period above what’s expected) to determine the true toll of COVID-19. A new study, published this week in The Lancet, took this approach for 191 countries and territories from January 2020 to December 2021. The researchers estimate that about 18 million people died worldwide due to the pandemic—including not just direct COVID-19 deaths but also others caused by COVID-related disruptions. That’s three times higher than the 6 million COVID-19 deaths that have been officially reported in this time period.
In last week’s issue, I mentioned that I am thinking more about preparedness: how the U.S. can improve our capacity to respond to public health threats, future COVID-19 surges and beyond. This mindset shift was brought on, in part, by a recent story I worked on at the Documenting COVID-19 project: examining the vulnerabilities in Idaho’s hospitals as a case study of the U.S.’s decentralized healthcare system.
Last summer and fall, Idaho was completely overrun by the Delta variant. State leaders implemented crisis standards of care, a practice allowing hospitals to conserve their limited resources when they are becoming overwhelmed. All hospitals in Idaho were in crisis standards for weeks, with the northern Panhandle region remaining in this crisis mode for over 100 days.
During this time, Idaho hospitals sent out 6,300 patient transfers in the span of four months. With Audrey Dutton, my reporting partner at the Idaho Capital Sun (a nonprofit newsroom covering Idaho state government), I analyzed data from the Idaho health department that showed where these patients were transferred, as well as how the crisis period compared to previous months.
This map shows all patient transfers out of Idaho hospitals between April and November 2021. Chart by Betsy Ladyzhets, published in the Idaho Capital Sun and MuckRock.
Here are the major findings from our story (borrowing some text from my Twitter thread, linked above):
More than one in three transfers went to hospitals in neighboring states, with the highest numbers going to eastern Washington.
Transfers went as far as Seattle, Salt Lake City, San Francisco, Billings, and even Phoenix. Many of these trips required air ambulances, due to Idaho’s mountainous geography.
These transfers strained Washington hospitals. Dr. Dave Chen, chief medical officer at MultiCare Deaconess Hospital in Spokane—one Washington hospital that took on a lot of Idaho patients—told me that smaller, rural facilities in his area are all “competing for the same beds and resources,” whether these facilities are based in Washington or Idaho.
Workers at facilities in the northern Idaho region, which remained in crisis standards for over 100 days, described doubling patients up in ambulances, traveling for hours to find free beds, and taking EMS staff away from their normal duties for long trips.
Idaho is particularly vulnerable to transfer challenges: it has a lot of small rural hospitals without many ICU beds or specialized equipment, combined with geography that often requires an air ambulance rather than driving.
This story has implications beyond Idaho, as it shows the impact of America’s fractured health system. In our system, when hospitals in one state are in crisis, they cannot easily communicate with other hospitals that might be able to help them out—whether “communicating” means calling up hospital administrators to ask about free beds or sharing data about patient numbers and resources.
This is not just a COVID-19 problem. Consider what happens when a wildfire, hurricane, or other natural disaster hits. When hospitals in one area become overwhelmed, they should be able to easily reach out to other facilities—but our system makes this incredibly difficult.
One potential solution to this issue may be centralized transfer centers, which field calls from hospitals that need to send out their patients. Washington started such a transfer center during the pandemic, to great success: Dr. Steve Mitchell, who helps run the center, told me that it facilitated more than 3,500 patient transfers, mostly between summer 2021 and early 2022.
But there’s a kicker: Washington’s transfer center is funded by the state health department, and therefore it can only answer calls from Washington hospitals. If an Idaho hospital wants to transfer a patient into Washington, it has to call various Washington hospitals directly until finding a bed for that patient—a much more time- and resource-intensive process.
Look at how siloed our current system is! This is ridiculous! Clearly, we need transfer centers with regional—or even national—reach, coordinated by a national health agency. We also need more data sharing between hospitals, and better communication between facilities and EMS providers.
The CDC is not publicly releasing a lot of its COVID-19 data. Last weekend, New York Times reporter Apoorva Mandavilli broke the news that the CDC has withheld a lot of its COVID-19 data from the public, including information on breakthrough cases, demographic data, and wastewater data. This news was honestly not surprising to me because it follows a pattern: the CDC doesn’t like to share information unless it can control the interpretations. But I appreciated the conversation brought on by this article, with public health experts saying they’d rather have imperfect data than a complete data void. (I agree!)
BA.2 is definitely more transmissible than the original Omicron strain, but it does not appear to be significantly more severe or more capable of evading vaccines. Two recent posts, one in the New York Times COVID-19 updates page and one from Your Local Epidemiologist, share some updates on what scientists have learned about BA.2 in the past couple of weeks. In the U.S. and other countries with BA.2, this sublineage doesn’t seem to be causing a major rise in cases—at least so far.
New CDC study shows the utility of rapid testing out of isolation. More than half of patients infected with the coronavirus tested positive on rapid antigen tests between five and nine days after their initial diagnosis or symptom onset, a new CDC report found. The report includes over 700 patients at a rural healthcare network in Alaska. These findings suggest that rapid testing out of isolation is a good way to avoid transmitting the virus to others, if one has the tests available.
January saw record-high coronavirus infections in hospitals.POLITICO reporters analyzed hospitalization data from the Department of Health and Human Services (HHS), finding that: “More than 3,000 hospitalized patients each week in January had caught Covid sometime during their stay, more than any point of the pandemic.” This high number demonstrates Omicron’s high capacity to infect other people.
Hong Kong’s surge shows the value of vaccinations. Hong Kong has been a global leader in keeping COVID-19 cases low throughout the pandemic, yet Omicron has tested this territory’s strategy—causing record cases and overwhelming hospitals. One major issue for Hong Kong has been low vaccination rates, particularly among the elderly, as people did not see the need to get vaccinated when cases in the territory were practically nonexistent.
Under the new CDC guidance, about 70% of Americans live in counties where they can go unmasked in public. Chart via CDC.gov.
This past Friday, the CDC announced a major shift to its guidance for determining COVID-19 safety measures based on county-level community metrics. The new guidance is intended to replace COVID-19 thresholds that the agency developed last summer, during the Delta wave; here, the CDC is promoting a shift from using cases and test positivity for local decision-making to using metrics tied directly to the healthcare system.
This shift away from cases isn’t new: state health departments have been moving in this direction recently, as I wrote last week. Similarly, the CDC’s recommendation for when Americans should feel safe in taking off their masks aligns with recent guidance changes from state leaders.
The new guidance is essentially a lot more lenient when it comes to mask removal. Overnight, the U.S. goes from under 5% of counties in “low” or “moderate” transmission (under the old guidance) to over 60% of counties, representing 70% of the population, in a “low” or “medium” COVID-19 community level.
🚨🚨 the CDC has updated its guidance on using county-level COVID-19 metrics to drive recommended safety measures.
with the new guidance, we go from 82% of U.S. counties in "high" transmission (on the left) to only 37% of counties in high transmission (on the right). pic.twitter.com/Xz1Xj6FHDN
This shift will embolden more states, local health departments, and individual organizations to lift safety measures and change how they track COVID-19. In this post, I’ll unpack why I believe the CDC made certain choices with this new guidance, what critiques I’m seeing from public health experts, and some recommendations for thinking about your COVID-19 risk during this highly confusing pandemic era.
Rationale for the CDC’s new guidance
With this new framework, the CDC is essentially telling Americans to watch hospitalization numbers—not case numbers—as the most important metric to inform how hard COVID-19 is hitting their community. One piece of their logic is, I suspect, that case numbers are less reliable in this pandemic era than they have been since March 2020.
That lack of reliability largely stems from the rise of at-home rapid antigen tests, which gained popularity during the Omicron surge and are now largely unconstrained by supply issues. (For example: iHealth Labs, one major at-home test provider, now allows shoppers to buy up to 50 test kits per person, up from a limit of 10 during Omicron’s peak.)
Unlike PCR test results, which are systematically processed in labs and reported to public health agencies, at-home test results typically do not travel beyond a patient’s trash can. And while a few local jurisdictions (like D.C.) have given residents options to self-report their antigen tests, the majority have opted not to take on this challenge. As a result, current case numbers for almost everywhere in the U.S. are not very reflective of actual infections in the community.
In previous pandemic eras, researchers could use PCR test positivity as an indicator of how reliable case numbers might be for a particular jurisdiction: higher test positivity usually means that more cases are going unreported. But in the era of widespread rapid tests, test positivity is also less reliable, because rapid tests aren’t accounted for in the test positivity calculations either.
Case numbers do still have some utility, because people who have COVID-19 symptoms or need a test result to travel will continue seeking out PCR tests. The CDC guidance reflects this by keeping cases as one factor of its COVID-19 community level calculation. But cases are no longer the star of the show here.
Instead, the CDC is focusing on hospitalizations: specifically, new COVID-19 admissions per 100,000 people and the share of inpatient beds occupied by COVID-19 patients. New hospital admissions are a more reliable—and more timely—metric than the total number of patients hospitalized with COVID-19, because admissions reflect only the people coming in with symptoms that recently started, not the people who have been hospitalized for days or weeks.
The share of inpatient beds occupied by COVID-19 patients, meanwhile, reflects the strain that this disease is currently putting on a hospital system. The CDC is choosing to include all COVID-19 patients here, not only those who are hospitalized for COVID-specific symptoms (the correct choice, in my view). Agency director Dr. Rochelle Walensky gave a good explanation for this at a media briefing on Friday:
We are considering anybody in a hospital bed with COVID, regardless of the reason for admission, and the reason that we landed there is multifold. First, many jurisdictions can’t differentiate, so that was important for us to recognize and realize. Second, whether or not a patient is admitted with COVID or for COVID, they increase the hospital capacity and they’re resource intensive. They require an isolation bed. They require PPE. They probably require a higher staff ratio. And so they are more resource intensive and they do take a COVID bed potentially from someone else.
Interestingly, as well, as we have less and less COVID in certain communities, the amount of people who are coming into the hospital with COVID will necessarily decrease. We will not have as many people walking around asymptomatically because there will just be less disease out there. So increasingly, as we have less disease in the community, we anticipate that more of the people who are coming into the hospital are going to be coming in because of COVID.
And then finally, as we have even less disease in the community, we anticipate that not every hospital is going to screen every patient for COVID as they walk in the door, especially if we have less and less disease in the community. And when that happens, we won’t actually be able to differentiate. In fact, people who are coming in, who are tested will necessarily be coming in for COVID. So for all of those reasons, comprehensively, we decided to stay with anybody coming in with a COVID diagnosis.
Also, a note on wastewater: I’ve seen some commentators express surprise that the CDC didn’t include wastewater in its new guidance, as this sewage surveillance can be a useful leading indicator for COVID-19 that’s more reliable than cases. The problem here is, wastewater surveillance is not available in much of the country—just look at all the empty space on this map. To use wastewater for decisionmaking, a county or state needs to have enough wastewater collection sites actually collecting these data, and most states are not there yet.
Critiques of the new guidance
While hospitalizations are a more reliable COVID-19 metric than cases, especially in our rapid testing era, they come with a few major issues. First, hospitalizations are a lagging indicator, meaning that they start to rise a couple of weeks after a new surge has started. If we rely on hospitalizations as a signal to put mask requirements or other safety measures in place, those changes will come weeks delayed.
As Boston University epidemiologist Dr. Ellie Murray put it in a recent Twitter thread: “Using lagging indicators like hospitalizations could be okay for turning *off* precautions IF we are sure that no new surge has begun. But that means we need leading indicators, like infection surveillance to guide turning *on* precautions.”
The CDC seems to have forgotten the basics of infectious disease in making these new risk categories.
People infected with SARS-CoV-2 can transmit for days before they show symptoms, and for a week or more before they develop “severe disease”.
Another issue with relying heavily on this lagging indicator is, new COVID-19 safety measures may come too late to protect essential workers, children in schools, and others who face high risk of coronavirus exposure. “These high exposure populations get COVID first and most,” writes health policy expert Julia Raifman.
In other words, by the time case and hospitalization rates are high enough for a community to institute new safety measures under this new CDC guidance, those high-risk people are likely to be the ones already in the hospital. Raifman points to data from the U.S. Census’ Household Pulse Survey, showing that low-income workers were most likely to miss work for COVID-19 throughout last year.
Beyond this lagging indicator issue, another challenge with relying on hospitalizations is that, for many Americans, the hospitals that they might go to if they come down with severe COVID-19 are not located in their county. Plenty of counties, particularly in rural areas, don’t have hospitals! To deal with this, the CDC is actually using regional hospitalization figures, compiling statistics from multiple counties that rely on the same healthcare facilities.
University of South Florida epidemiologist Jason Salemi lays out this calculation in an excellent Twitter thread, linked below. While it makes sense that the CDC would need to use regional instead of local figures here, the agency is being pretty misleading by labeling this new guidance as county-level metrics when really, the metrics are not that localized.
The numerator is based on WHERE PEOPLE ARE ADMITTED.
The denominator is based on WHERE PEOPLE LIVE.
If you just look at FL, there are many counties that either do not report hospitalizations (NO HOSPITAL) or reported 0 in the past 7d.
There are more equity concerns embedded in the new CDC guidance as well. For counties with “low” or “medium” community COVID-19 levels, the CDC recommends that most Americans do not need to wear masks in public. But people who are immunocompromised or at high risk for severe disease should “talk to a healthcare provider” about the potential need to wear a mask indoors, stock up on rapid tests, or consider COVID-19 treatments.
For one thing, telling people, “talk to your doctor” is not a great public health strategy when one in four Americans do not have a primary care physician, and one in ten do not even have health insurance! For another thing, one-way masking among immunocompromised and otherwise high-risk people is also not a great strategy, because masks protect the people around a mask-wearer more than they protect the mask-wearer themselves. (I recommend this recent Slate piece on one-way masking for more on this topic.)
Many of my colleagues on here have eloquently explained their concerns with the new CDC guidelines, so I’ll just add that any guidelines that include “Talk with your provider.” when 25% of Americans don’t have a PCP and 10% are uninsured is inherently problematic.
It is also pretty unclear how the CDC landed on a case threshold for “low transmission” that is much higher in this new guidance than in the old guidance, as Dr. Katelyn Jetelina points out in a recent Your Local Epidemiologist post. If anything, honestly, I would expect that the CDC needs to lower its case threshold, given that current case numbers are not accounting for millions of rapid tests done across the country.
Finally, the new CDC guidance completely fails to account for Long COVID. Of course, it would be very difficult for the CDC to do this, since the U.S. basically isn’t tracking Long COVID in any comprehensive way. Still, overly focusing this new guidance on hospitalizations essentially ignores the fact that a “mild” COVID-19 case which does not lead to hospitalization can still cause major, long-term damage.
Which metrics you should follow right now
Here are my recommendations of COVID-19 metrics to watch in your area as you navigate risk in this confusing pandemic era.
Both the old and new CDC thresholds. While the CDC pushes its new guidance with a brand-new page on CDC.gov, community transmission metrics calculated under the old guidance are still available on the CDC’s COVID-19 dashboard. If you’re not feeling comfortable taking off your mask in public and want to wait until transmission is seriously low in your area, you can look at the old thresholds; though keep in mind that case data are seriously unreliable these days, for the reasons I explained above.
Remember that masks are useful beyond COVID-19. Not a metric, but an additional note about thinking through risk: masks reduce risk of infection for a lot of respiratory diseases! We had a record-low flu season last winter and many Americans have avoided colds for much of the pandemic, thanks in part to masking. Helen Branswell has a great article in STAT News that unpacks this further.
Wastewater data, if available to you. As I mentioned above, wastewater surveillance data are not available in much of the country. But if you live somewhere that this surveillance is happening, I highly recommend keeping an eye on those trends to watch for early warnings of future surges. You can look at the CDC dashboard or Biobot’s dashboard to see if your county is reporting wastewater data.
Vaccination levels. It makes sense that vaccination was not included in the CDC guidance, because vaccinated people can still spread highly contagious variants like Omicron. Still, more highly-vaccinated counties—particularly those with high vaccination rates for seniors—are likely to have less burden on their healthcare systems when a surge arises, so knowing the vaccination rate in your county can still be useful when thinking about your risk tolerance.
Rapid test availability. This is a bit more anecdotal rather than an actual data source, but: looking at rapid test availability in your local pharmacies may be another way to get a sense of community transmission in your area. Right now, these tests are easy to find in many places as case numbers drop; if finding these tests becomes more competitive again, it could be a signal that more people are getting sick or having exposures.
As always, if you have any questions or topics that you’d like me to tackle in this area, please reach out.
Biden administration is reportedly shifting hospital reporting on COVID-19 patients: During the Omicron surge, there’s been a push among some COVID-19 experts (and in the media) to separately report patients who are admitted to hospitals because of their COVID-19 symptoms from patients who are admitted to hospitals for some other reason, but then test positive later. This push, also called the “with” versus “for” issue, has reached the White House, according to a recent report from POLITICO. The Biden administration now wants all hospitals to separate out their COVID-19 numbers in this way, to get a better picture of severe disease caused by the virus. Such a shift may be tricky for hospitals to follow, however, in part because a lot of people who appear to be incidental, “with COVID-19” patients actually had rare symptoms or chronic conditions exacerbated by the virus. “You need a panel of experts to review the cases” and judge this issue, expert Eric Topol told POLITICO.
Long-term cardiovascular outcomes of COVID-19: A new paper from researchers at the Department of Veterans Affairs (VA), published this week in Nature Medicine, sheds light on potential long-term COVID-19 impacts for the heart. The researchers used national health records databases from the VA to study over 150,000 COVID-19 patients—a much larger study size than most Long COVID research in the U.S. The paper found that, after their first month of infection, COVID-19 patients are at increased risk for a variety of cardiovascular issues, including heart inflammation and heart failure. Outside scientists commenting on the paper in Sciencemagazinesaid that the findings clearly demonstrate that COVID-19 has grave long-term risks for heart health.
A new CDC analysis found that Omicron led to record hospitalizations, but lower levels of ICU admissions and other indicators of the most severe disease compared to past surges. Chart via CDC MMWR.
While BA.2 has dominated the news cycle this week, we’re still learning more about—and seeing policy shifts due to—the original Omicron strain, BA.1. Here are a few major updates:
Omicron is causing 100% of new COVID-19 cases in the U.S. According to the CDC’s latest update of its variant prevalence estimates, Omicron caused more than 99% of new COVID-19 cases in the country for the second week in a row: there was a slight increase from 99.4% of new cases in the week ending January 15 to 99.9% in the week ending January 22. The remaining 0.1% of cases are caused by Delta. The CDC is not yet distinguishing between BA.1 and BA.2 in its estimates, but will likely start doing so in the coming weeks.
ICU admissions and other indicators of severe COVID-19 symptoms were lower during the Omicron surge than during previous surges in the U.S. The CDC released another round of studies presenting Omicron’s impact on the healthcare system last week, including this report focusing on disease severity. CDC researchers analyzed data from three hospital surveillance systems and an additional large database; while the peak of new COVID-19 cases was five times higher during the Omicron surge than the Delta surge, they found, the peak of hospital admissions was only 1.8 times higher. And ICU admissions, the average stay length for hospitalized COVID-19 patients, and death rates were all lower during the Omicron surge than in the Delta and winter 2020-2021 surges.
Pfizer and BioNTech have started a clinical trial of an Omicron-specific vaccine. Quickly after Omicron was identified, both Pfizer and Moderna started updating their vaccines for this variant and investigating whether such Omicron-specific shots would be necessary. Pfizer is now entering a new clinical trial with its Omicron-specific shot, along with its vaccine development partner BioNTech, according to Reuters. Trial participants will include about 1,400 people who received third doses from Pfizer’s current COVID-19 vaccine regimen three to six months earlier. Pfizer intends to study the Omicron-specific shot’s safety and effectiveness against this variant.
The FDA recommends that U.S. facilities stop using monoclonal antibody treatments that don’t work well against Omicron. Speaking of Omicron-specific drugs: the Food and Drug Administration (FDA) announced this week that the agency is limiting use of two monoclonal antibody treatments, made by Regeneron and Eli Lilly. Both treatments, which boost patients’ immune systems by providing them with lab-made antibodies, worked well at reducing severe symptoms for past variants but have less of an impact on Omicron infections. As a result, healthcare providers should “ limit their use to only when the patient is likely to have been infected with or exposed to a variant that is susceptible to these treatments,” the FDA says. A third monoclonal antibody treatment, along with other drugs (including Pfizer’s hard-to-find pill), do work well against Omicron.
Omicron has a short incubation period, so test timing is key. As I’ve written in the past, Omicron infections tend to move more quickly than infections with past variants: people can go from an exposure to symptoms within three days, rather than four or five. As a result, there’s less time to catch an infection before becoming contagious; but at the same time, rapid tests may give negative results in those early days of an infection, before patients have built up enough of a viral load for an antigen test to identify the infection. A recent New York Times article summarizes the science on how Omicron infections compare to past variants and provides recommendations on testing. For instance: “many experts now recommend taking a rapid test two to four days after a potential exposure,” and ideally testing twice about a day apart.
Some experts are optimistic that we could see a COVID-19 lull after Omicron surges.Last week, I shared a STAT News article explaining that, thanks to high levels of population immunity, the U.S. might have “a bit of a break from the Covid roller coaster” after our Omicron surge ends in the coming weeks. Despite BA.2 concerns, Dr. Hans Henri Kluge, WHO’s regional director for Europe, shared a similarly optimistic view in a statement last week. “The pandemic is far from over, but I am hopeful we can end the emergency phase in 2022 and address other health threats that urgently require our attention,” he said.
Although deadly new Covid variants could emerge, I'm more optimistic today than at any point since the pandemic began. Here’s why. 1/thread
Post-Acute Sequelae of SARS-CoV-2 infections estimates and insights: Continuing with the Long COVID theme of this issue: I recently learned about this dashboard from the American Academy of Physical Medicine and Rehabilitation. It provides estimates of Long COVID cases in the U.S. based on case numbers from Johns Hopkins University and a model assuming that 30% of surviving COVID-19 cases will lead to long-term symptoms. The dashboard includes estimates of total Long COVID cases, cases over time, and cases by state.
Disease severity among hospitalized patients (CDC): The CDC added a new page to its COVID-19 dashboard this week, providing data on the shares of COVID-19 patients in U.S. hospitals who require intensive care and ventilation, and who die while at the hospital. The data come from the CDC’s hospitalization surveillance network and other federal hospital sources.
Vaccination dashboard annotations: This weekend, I updated my annotations page detailing how every U.S. state and several national sources track vaccinations. 39 states are now reporting some data on booster shots or third doses, I found, though most of them still aren’t providing demographic data reflecting the recipients of these additional doses.
A record number of COVID-19 patients are now receiving care in U.S. hospitals, according to data from the Department of Health and Human Services (HHS). As of January 16, the agency reports that about 157,000 COVID-19 patients are currently hospitalized nationwide, and one in every five hospitalized Americans has been diagnosed with this disease.
The HHS also reports that about 78% of staffed hospital beds and 82% of ICU beds are currently occupied. These numbers, like the total COVID-19 patient figure, are higher than they have been at any other point during the pandemic.
Even so, reports from the doctors and other staff working in these hospitals—conveyed in the news and on social media—suggest that the HHS data don’t capture the current crisis. The federal data may be reported with delays and fail to capture the impact of staffing shortages, obscuring the fact that many regions and individual hospitals are currently operating at 100% capacity.
Dr. Jeremy Faust, an emergency physician at Brigham and Women’s Hospital and professor at Harvard Medical School, recently made this argument in Inside Medicine, his Bulletin newsletter. Last week, I shared Faust and colleagues’ circuit breaker dashboard, which extrapolates from both federal hospitalization figures and current case data to model hospital capacity in close-to-real-time. This week, Faust used that dashboard to show that the crisis inside hospitals is more dire than HHS numbers suggest.
He writes:
There seems to be a disconnect between the official data made available to the public and what’s happening on the ground. The reason for this is unacceptable delays in reporting. HHS and other agencies have always acknowledged that public reports on hospital capacity—for Covid-19 and all other conditions—actually reflect data that are 1-2 weeks old. But until now, such lags rarely mattered because most hospitals haven’t had to operate near or above 100% capacity routinely, even during the pandemic. Under normal circumstances, whether a hospital was 65% or 75% full does not make much of a difference, though as the numbers creep up, care can be compromised. And even in past moments when capacity was closer to 100%, a wave of Omicron-driven Covid-19 was not headed towards hospitals.
For example: on Monday, Faust wrote, his team’s circuit breaker dashboard showed that “every single county in Maryland appears to be over 100% capacity,” even though the HHS said that 87% of hospital beds were occupied in the state. Healthcare workers in Maryland backed up the claim that all counties were over 100% capacity, with personal accounts of higher-than-ever cases and hospitals going into crisis standards.
On Thursday, Faust shared an update: the circuit breaker dashboard, at that point, projected that hospitals in Arizona, California, Washington, and Wisconsin were approaching 100% capacity, if they weren’t at that point already. As of Saturday, California and Arizona are still projected to be at “at capacity,” according to the dashboard, while 14 other states ranging from Montana to South Carolina are “forecasted to exceed capacity” in coming days.
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From Faust’s descriptions and the accounts of healthcare workers he quotes, it’s also evident that determining between hospitalizations “with” COVID-19 and hospitalizations “from” COVID-19 is not a useful way to spend time and resources right now. Even if some of the COVID-19 patients currently in U.S. hospitals “happened to test positive” while seeking treatment for some other condition, these patients are still contributing to the intense pressure our healthcare system is under right now.
Plus, as Ed Yong explains in a recent article in The Atlantic describing this false patient divide, COVID-19 can worsen other conditions that at first seem unrelated:
The problem with splitting people into these two rough categories is that a lot of patients, including those with chronic illnesses, don’t fit neatly into either. COVID isn’t just a respiratory disease; it also affects other organ systems. It can make a weak heart beat erratically, turn a manageable case of diabetes into a severe one, or weaken a frail person to the point where they fall and break something. “If you’re on the margin of coming into the hospital, COVID tips you over,” Vineet Arora, a hospitalist at the University of Chicago Medicine, told me. In such cases, COVID might not be listed as a reason for admission, but the patient wouldn’t have been admitted were it not for COVID.
In short: Omicron might be a milder variant at the individual level—thanks to a combination of the variant’s inherent biology and protection from vaccines and prior infections—but at a systemic level, it’s devastating. And rather than asking hospitals to split their patients into “with” versus “from” numbers, we should be giving them the staff, supplies, and other support they need to get through this crisis.
COVID-19 Hospital Capacity Circuit Breaker Dashboard: This dashboard from emergency physician Dr. Jeremy Faust and colleagues shows which U.S. states and counties are operating at unsustainable levels, or are likely to get there in coming days. Faust further explains circuit breakers in this post: these are “short-term restrictions, regardless of vaccination status, designed to slow the spread of COVID-19” and help prevent hospitals from becoming overwhelmed. Dashboard data come from the CDC, HHS, and Johns Hopkins University.
CDC Cruise Ship Color Status: Throughout the pandemic, cruise ships have been hotbeds for coronavirus spread. This is especially true right now, thanks to Omicron, and the CDC is investigating a number of outbreaks. The agency reports on all cruise ships that it’s monitoring for COVID-19, classifying them based on the number of cases reported among passengers and crew; as of January 7, the vast majority of ships have reported enough cases to meet the threshold for CDC investigation.
Deaths and hospitalizations averted by vaccines: This December report from philanthropy foundation the Commonwealth Fund provides estimates on the severe COVID-19 cases prevented by the U.S. vaccination effort. Without vaccination, the report estimates, “there would have been approximately 1.1 million additional COVID-19 deaths and more than 10.3 million additional COVID-19 hospitalizations in the U.S. by November 2021.” (H/t Your Local Epidemiologist.)
COVID Behaviors Dashboard from Johns Hopkins: John Hopkins University maintains one of the oldest and best-known COVID-19 dashboards of the pandemic. The team recently expanded its data offerings with a new dashboard focused on pandemic attitudes and practices around the world. This dashboard draws from surveys conducted in over 100 countries, in collaboration with the WHO; read more about it here.
COVID-19 K-12 School Testing Impact Estimator: What COVID-19 testing strategy would make the most sense for your local K-12 school? This dashboard, by the Rockefeller Foundation and Mathematica (the data research organization), is designed to help stakeholders find out. Simply plug in the school’s characteristics and COVID-19 safety goals, and the dashboard will tell you how different testing strategies may measure up.
Vaccine hesitancy roundup from the Journalist’s Resource: This resource page includes a wealth of data and insights on vaccine hesitancy in the U.S., drawing from a variety of surveys and research papers on the topic. As of early September, author Naseem Miller writes, the PubMed research database included over 750 studies on COVID-19 vaccine hesitancy, signifying growing academic interest in this topic.
Hospital challenges to public health reporting: A new report from the National Coordinator for Health Information Technology explores the challenges that non-government hospitals have faced in electronically exchanging information with public health agencies. One major finding: in both 2018 and 2019, half of all hospitals lacked the capacity for this data exchange. No wonder electronic reporting has been such a challenge during the pandemic.
NIH Long COVID initiative revs up: This isn’t an actual data source, more of an update: the National Institutes of Health (NIH)’s RECOVER Initiative to study Long COVID awarded a major research grant this week. About $470 million goes to New York University’s Langone Medical Center, which will serve as a national hub for Long COVID research and award sub-grants to other institutions. The NIH’s RECOVER website currently reports that between 10% and 30% of people infected with the coronavirus will go on to develop Long COVID; hopefully research at NYU and elsewhere will lead to some more precise numbers.
COVID-19 Data Dispatch 