About one in five adults who have COVID-19 will face a health condition potentially related to long-term symptoms, a new CDC study found.
Two new studies on Long COVID, published this week, provide an important reminder of the continued dangers this condition poses to people infected with the coronavirus—even after vaccination. Neither study provides wholly new information, but both are more comprehensive than many other U.S. papers on this condition as they’re based on large databases of electronic health records.
In the new paper, published this week in Nature Medicine, the researchers put together a cohort of about 34,000 people who had breakthrough COVID-19 infections. They compared this group to larger control groups of people who hadn’t been infected and people who had been infected prior to vaccination, along with comparisons to the seasonal flu.
Vaccination does reduce the risk of Long COVID, the researchers found: people with breakthrough cases were 15% less likely to report Long COVID symptoms than those who were infected prior to vaccination. Breakthrough Long COVID patients were notably less likely to have blood clots and respiratory symptoms than non-breakthrough patients.
But a risk reduction of 15% is pretty minimal, compared to the protection that vaccination offers against COVID-related hospitalization and death. Moreover, for most Long COVID symptoms, patients who had breakthrough infections showed relatively little difference to those who had non-breakthroughs, the researchers found.
“Overall, the burden of death and disease experienced by people with breakthrough SARS-CoV-2 infection is not trivial,” lead researcher Dr. Ziyad Al-Aly wrote in a Twitter thread summarizing the study. That’s scientist speak for, “A breakthrough COVID-19 case can really fuck you up in the long term!” Later in his thread, Dr. Al-Aly advocated for additional public health measures—beyond simply vaccines—to reduce Long COVID risks.
Now that we know that vaccines are not sufficient as a sole line of defense, we need to urgently develop and deploy additional layers of protection to reduce risk of Long Covid https://t.co/UEexmL8svS
And second: a paper from the CDC’s COVID-19 Emergency Response Team, published in the CDC’s Morbidity and Mortality Weekly Report (MMWR) last week, used electronic health records to examine overall Long COVID risk after an infection. These health records came from Cerner Real-World Data, a dataset including about 63.4 million records from over 100 health providers.
The CDC researchers identified about 353,000 adults who had received either a COVID-19 diagnosis or a positive test result between March 2020 and November 2021. They matched this group of COVID-19 patients with a larger cohort of people who hadn’t tested positive, then looked at the COVID-19 patients’ risks of developing further symptoms more than a month after they were diagnosed.
The findings are striking: About one in five COVID-19 survivors between the ages of 18 and 64 developed at least one “incident condition” (or, prolonged symptoms) that could be connected to their coronavirus infection. For COVID-19 survivors over age 65, that risk is one in four.
Among the patients who potentially developed Long COVID, common symptoms were blockages in the lungs and other respiratory issues. Seniors were also likely to develop neurological and mental health symptoms, and the CDC researchers warned that Long COVID in this older age group could be linked to an increased risk of strokes and neurocognitive conditions, such as Alzheimers.
In their paper, the CDC authors noted that patients represented in this health records database may not represent the U.S. overall, and that the methods used to identify possible Long COVID symptoms might be “biased toward a population that is seeking care.” Similar caveats apply to the VA study.
Still, both studies clearly show the risk of just “letting COVID-19 rip” through the U.S. population, even after widespread vaccination. Studies like these should be headlines in every news publication, warning people that COVID-19 is not as mild as many of our leaders would like us to believe.
every time the MTA does one of their announcements about how masks are required on NYC public transit, they should also provide one fact or statistic about Long COVID
— betsy ladyzhets 📊 (@betsyladyzhets) May 24, 2022
Also, for journalists covering the pandemic: I highly recommend listening to this interview with Long COVID journalist and advocate Fiona Lowenstein, which aired on the WNYC show On the Media this weekend. (And I’m not just saying that because they plugged my recent story on the RECOVER study!) The Long COVID source list that Fiona and I collaborated on also continues to be a great resource for reporters covering this topic.
HHS begins releasing Paxlovid data: This past week, the Department of Health and Human Services (HHS) published data on how many courses of Paxlovid and other major COVID-19 therapeutics have been ordered and administered nationwide. As KHN reporter Hannah Recht pointed out on Twitter: “This is still not the detailed, complete data that county health officials need to do their jobs but it’s a start.” (See Recht’s article on this data gap for more context.)
COVID-19 Local Action Tracker: Since early 2020, the National League of Cities has tracked how cities and other local municipalities have responded to COVID-19. The tracker includes 800 cities and almost 5,000 policies, impacting over 100 million people; it links out to policy documents for each item. (H/t Data Is Plural.)
FEMA funeral assistance: The Federal Emergency Management Agency (FEMA) has distributed over $2 billion in aid for COVID-19 funerals since the beginning of the pandemic, supporting more than 300,000 families, the agency announced in a press release this spring. The press release also includes data providing the number of applicants and total funds awarded in each state.
New Long COVID studies with electronic records: Two recent papers on Long COVID caught my attention this week. First, researchers at data nonprofit FAIR Health analyzed a cohort of 78,000 Long COVID patients using a new diagnostic code for the condition, leading to useful findings about potential demographics and risk factors. Second, researchers at the University of North Carolina, the University of Colorado, and other collaborators used machine learning techniques on health records to identify potential Long COVID patients. Both studies used electronic health records to include wider patient pools than typical U.S. Long COVID research.
Limited immunity after Omicron infection: Another notable paper, published this week in Nature: researchers at Gladstone Institutes, a research organization in San Francisco, examined immunity after Omicron infections by testing out potential immune responses to different variants in mice and analyzing human serological samples. Their results suggest people infected with Omicron after vaccination have more protection against variants other than Omicron compared to unvaccinated people, which may only be protected against future Omicron infections.
FDA authorizes combined at-home test for COVID-19, flu, RSV: And a piece of diagnostic news for this week: for the first time, the FDA has provided emergency use authorization to an at-home test that can distinguish between COVID-19, the flu, and RSV. Processing the test does involve mailing results of a nasal swab to the testing company, Labcorp, so it’s not as simple as the at-home COVID-only tests we’ve all grown used to. Still, this authorization is an important step for future testing.
While the U.S. federal government has, so far, failed to produce meaningful research on Long COVID, other organizations are working to fill this gap. One such organization is Solve M.E., an advocacy and research initiative focused on myalgic encephalomyelitis (ME), Long COVID, and other related post-viral illnesses.
A friend who works on Solve M.E.’s You + ME Registry has asked me to share this research opportunity, on behalf of her organization. This program is providing funding for scientists to organize smaller-scale studies on Long COVID and ME, potentially leading to more substantial grants for larger studies. Solve M.E. hopes to invite new researchers who haven’t previously focused on post-viral illnesses into this space.
Here’s the full info:
Solve M.E. is accepting applications to study ME/CFS and Long COVID through May 23, 2022.
Through the Ramsay Program, Solve M.E. invests in research studies in ME/CFS or Long COVID with a particular emphasis on engaging young investigators and researchers new to the field. This year, we are accepting applications for projects that aim to explore the mechanisms at play in people who go on to develop Long COVID. Collaborative proposals (involving two or more research groups) are encouraged.
Ramsay 2022 marks the sixth cycle of the program, and we are excited to continue offering our rich longitudinal data set in the You + ME Registry.
In December 2020, Congress provided the National Institutes of Health (NIH) with over $1 billion to study Long COVID. A couple of months later, the agency announced it would use this funding for an initiative called RECOVER: a large clinical trial aiming to enroll 40,000 patients, designed to answer long-standing questions about Long COVID and, eventually, identify potential treatments.
At the time, Long COVID patients and researchers were thrilled to see this massive investment. Long COVID patients may suffer from hundreds of possible symptoms, many of them debilitating; reports estimate that millions of people are out of work as a result of the condition. To anyone who has experienced Long COVID or talked to patients, as I have in my reporting, it’s clear that we need treatment options, and we need them yesterday.
But that promising NIH study is floundering: it’s moving incredibly slowly (with treatment trials potentially years off); it’s enrolled a tiny fraction of the 40,000 patients originally planned; it’s failing to meet the needs of patients from the communities most vulnerable to COVID-19; and it has been critiqued by patient advocates on concerns of trial setup, transparency, engagement, inclusion of other post-viral illnesses, and more.
I explored the concerns around RECOVER for a story in Grid, published last Monday. My piece highlights critiques from patient advocates and Long COVID researchers outside of RECOVER, while also discussing some of the broader problems that make it difficult for an initiative like this to succeed in the first place.
in December 2020, Congress gave the NIH $1.15 billion to study #LongCovid.
over a year later, the agency's flagship study is moving slowly as patient advocates worry that it will fail to answer key questions.
In the COVID-19 Data Dispatch today, I’d like to dig deeper into those broader problems and share some material from my reporting for the Grid story that didn’t make it into the final piece. Here are five reasons why the U.S. is not set up for success when it comes to Long COVID research, based on my interviews and research for the piece.
The NIH is designed for stepwise research, not “disruptive innovation.”
One of my favorite quotes in the story comes from David Putrino, who directs a lab at Mount Sinai focused on health innovations and was one of the first scientists in the U.S. to begin focusing on Long COVID. Putrino described how the NIH’s usual mode of operation does not work when it comes to novel conditions like Long COVID:
“What the NIH does very well, better than most national research organizations around the world, is supporting research that slowly develops small innovations in scientific knowledge,” Putrino said. The agency normally supports series of stepwise trials, climbing from one tiny aspect of research into a condition or treatment to the next.
This method is good for “long-term innovations that take 20 years,” Putrino said, but not for “disruptive innovation.” Treatments for long covid fall into the latter category: higher-risk, higher-reward science that may be viewed as a waste of government funding if it doesn’t pay off.
The same day as my Grid story was published, last Monday, STAT News published a story by Lev Facher discussing an oversight board at the NIH that was supposed to improve efficiency at the agency… and has not met for seven years. While this story doesn’t discuss Long COVID specifically, it provides some pretty clear context for why a study like RECOVER—which is different from anything the agency has done before—may be hard to get off the ground.
Here’s the final quote in Facher’s story, from Robert Cook-Deegan, founding director of the Duke Center for Genome Ethics, Law and Policy:
“About every 10 years, the National Academies [of Sciences, Engineering, and Medicine] are asked to review NIH, and they make recommendations, most of which are ignored,” he said. The agency’s “large, inertial, and ponderous bureaucracy,” he added, is “not terribly open to criticism as a whole.”
Clinical trials are difficult and time-consuming to set up, especially when they involve new drugs.
My story also discusses the red tape that U.S. researchers face when they attempt to test potential treatments on human subjects. For such a clinical trial, researchers need to get approval from an Institutional Review Board (or IRB), an oversight board that ensures a study’s design protects the rights and welfare of people who participate in the trial.
In the U.S., this approval can take months, and may have extra steps for government-funded research. Researchers in other countries often have much shorter processes, Lauren Stiles, president of the research and advocacy organization Dysautonomia International, told me. She gave the example of a researcher in Sweden studying a potential Long COVID treatment with funding from her organization: for this researcher, the equivalent of IRB approval took a few hours rather than a few months.
Clinical trials in the U.S. also face extra hurdles when they involve studying new drugs, as our research system makes it easier for companies that develop these drugs to do new clinical trials than for outside academics to undertake similar studies. For example, Putrino told me that he would love to study the potential for Paxlovid, the antiviral drug for acute COVID-19, to treat Long COVID patients. But, he said, “I physically don’t have the bandwidth to fill out the hundreds of pages of documents” that would be required for such a trial.
The company is “considering how we would potentially study it,” Kit Longley, a spokesperson for Pfizer, wrote in an email, but declined to clarify why the company has no study under way. That frustrates Putrino, of Mount Sinai, who thinks Pfizer will need to spearhead many of these efforts; it’s Pfizer’s drug, after all, and the company has the best data on it, and the means to move it forward… When asked to elaborate on Paxlovid’s experimental status, the NIH said only that the agency “is very interested in long term viral activity as a potential cause of PASC (long COVID), and antivirals such as Paxlovid are in the class of treatments being considered for the clinical trials.”
The NIH has historically underfunded and undervalued research into other post-viral conditions.
When I shared my Grid story on Twitter this week, a lot of patients with myalgic encephalomyelitis (ME), dysautonomia, and other post-viral illnesses said that the issues outlined in my piece felt very familiar.
After all, the NIH has been failing to fund research into their conditions for decades. Pots, one type of dysautonomia, received less than $2 million a year in NIH funding before the pandemic, Stiles told me. As a result, scientists and clinicians in the U.S. have fairly limited information on these other chronic conditions—in turn, limiting the sources that Long COVID researchers may use as starting points for their own work.
Long COVID patients share a lot of symptoms with ME, dysautonomia, and other chronic post-viral illness patients; in fact, many Long COVID patients have been diagnosed with these other conditions. According to one study by the Patient-Led Research Collaborative, almost 90% of Long COVID patients experience post-exertional malaise, the most common symptom of ME.
Despite the historical underfunding, post-viral illness researchers have still made major strides in studying this condition that could provide springboards for RECOVER. But the NIH trial isn’t using them, say experts I talked to. Here are a few paragraphs from an early draft of the story:
“NIH is approaching Long COVID as a brand-new phenomenon,” said Emily Taylor, an advocate at Solve ME, even though it has extensive overlaps with these other conditions. “We’re starting at square one, instead of starting at square 100.”
Long COVID patients and those ME have already come together organically to share tips and resources, she said. For example, Long COVID patients versed in medical research have educated ME patients on potential biological mechanisms for their chronic illness, while ME patients have shared methods for resting, pacing, and managing their conditions.
Experts in conditions like ME were not included in the trial’s leadership early on, and are now outnumbered in committees by cardiologists, respiratory experts, and others who have limited existing knowledge about post-viral illness. “Right now, there are three people with [dysautonomia] expertise on these committees,” Stiles said.
With the other two experts, Stiles has advocated for autonomic testing—a series of tests measuring the autonomic nervous system, believed to be a key driver of Long COVID symptoms—to be conducted on all RECOVER patients. A few of these tests have been added to the protocol, she said, but not the full list needed to get a comprehensive reading of patients’ nervous systems.
America’s fractured medical system and lack of broad knowledge on Long COVID have contributed to data gaps, access issues.
How does a Long COVID patient know that they have Long COVID? Ideally, more than two years into the pandemic, the U.S. medical system would have developed a consistent way of diagnosing the condition. Instead, patients are still getting diagnoses in a variety of ways, including (but not limited to):
A positive PCR test, followed by prolonged symptoms.
A positive rapid/at-home test, followed by prolonged symptoms.
Prolonged symptoms, perhaps later associated with COVID-19 via a positive antibody test.
Self-diagnosis based on prolonged symptoms.
An official diagnosis of Long COVID from a doctor.
An official diagnosis of ME, pots, mass cell activation syndrome, and/or other conditions from a doctor.
Patients also continue to face numerous barriers to formal Long COVID diagnoses, compounded by the fractured nature of the medical system. A lot of doctors and other medical providers—especially at the primary care level—still don’t know about the condition, and may make it hard for patients to learn that their prolonged fatigue is actually Long COVID. PCR or lab-based COVID-19 testing is also getting harder to access across the country, and many doctors won’t take a positive antigen test as proof of infection.
All of this means that the U.S. does not have a good estimate of how many Americans are actually suffering from Long COVID. There’s no central registry of patients who can be contacted for potential trials; there aren’t even basic demographic estimates of how many Long COVID patients are Black, Hispanic, or otherwise from marginalized communities. These data gaps make it hard for researchers studying Long COVID to set goals for patient recruitment.
And then, beyond receiving a diagnosis, actually getting care for Long COVID may require patients to wait weeks for appointments with specialists, contact many different doctors, and generally advocate for themselves in the medical system—while dealing with chronic, debilitating symptoms. As a result, as I wrote in the story:
The long covid patients who are believed by their doctors, who garner media attention, who serve on RECOVER committees — they’re more likely to be white and financially better-off, said Netia McCray, a Black STEM entrepreneur and long covid patient who has enrolled in the trial.
So far, RECOVER has not been doing much to combat this inherent bias in the patients who know about the trial (and about their own condition) and are able to sign up for participation.
Clinical trials in the U.S. are not typically set up in a way that prioritizes patient engagement, especially chronically ill patient engagement.
One major concern from Long COVID patient advocates involved with RECOVER is that the trial has not prioritized patient engagement—which should be a priority, considering all the medical bias that patients have faced while they’ve become experts in their own condition over the last two years.
Here’s a bit more detail on this issue, taken from an early draft of my Grid story:
Patients serving on the committees are dramatically outnumbered by scientists, creating an “intimidating” environment that makes it hard to speak up about their needs, said Karyn Bishof, founder of the COVID-19 Longhauler Advocacy Project. This feeling is exacerbated when scientists on the committees are misinformed about Long COVID and dismiss patients’ experiences, she said.
Some scientists on the committees are receptive to patient input, representatives told me. Still, the structure is not in their favor: not only are patents outnumbered, it’s also a challenge for them to simply show up to committee meetings. Many Long COVID patients are, by definition, dealing with chronic symptoms that are not conducive to regular meeting attendance. Some are managing a barrage of doctors appointments, jobs, caregiving responsibilities, and more.
For instance, a second patient representative on a committee with Lauren Stiles—who serves as a representative because she has suffered from Long COVID in addition to other forms of dysautonomia—once missed a meeting because she had to go to the hospital. “If I wasn’t there, no patient would have been represented at all,” Stiles said.
Patients are compensated for their time in meetings, but not for hours spent doing other research outside those calls. And there’s no structure for patient representatives to coordinate more broadly; patients are operating in silos, with limited information about what representatives on other committees may be doing.
The NIH has potential models for improving this structure; it could draw from past HIV/AIDS clinical trials that had oversight from that patient community, advocate JD Davids told me. And leaders of RECOVER have acknowledged that they need to improve: as I highlighted in the story, trial leadership met with patient advocates earlier this month to discuss potential changes:
[Lisa McCorkell, advocate and researcher from the Patient-Led Research Collaborative] said that the meeting made it clear that the NIH and RECOVER leadership understand that improving patient engagement is key to the study’s success. “We agreed to work together to strengthen trust, improve representation of patients, and ensure greater accountability and transparency,” she said in an emailed statement.
The pressure is on for the NIH and RECOVER leadership to follow up on their promises. I, for one, intend to continue reporting on the trial (and on Long COVID research more broadly) as much as possible.
I spent a lot of time this weekend talking about the U.S.’s lack of Long COVID data, particularly our inability to answer such questions as, “How many Americans have Long COVID?” and, “What is the condition’s demographic breakdown?”
In comparison, I pointed other journalists to reports from the U.K.’s Office for National Statistics. This office regularly surveys Brits on their Long COVID experiences, asking a representative sample of the population whether they are experiencing any long-term COVID-19 symptoms.
The most recent report, published in early April, found that about 1.7 million people—or 2.7% of the U.K.—was “experiencing self-reported long COVID” (defined as symptoms persisting for four weeks or more after an initial infection). Imagine what the scale of Long COVID might be in the U.S.
Hospitalizations of young children during Omicron: A major study released in the CDC’s Morbidity and Mortality Weekly Report (MMWR) this week describes hospitalization rates among children ages five to 11, focusing on the Omicron wave in December through February. Findings include: about nine in ten of the children hospitalized during this period were unvaccinated, and hospitalization rates were twice in high in unvaccinated children compared to vaccinated children, demonstrating the importance of vaccination in the five to 11 age group.
COVID-19 death rates by race and ethnicity: Another notable study published in MMWR this week: CDC researchers used provisional mortality data (based on death certificates) to study COVID-19 death rates among different racial and ethnic groups, comparing 2020 and 2021. Death rates for Hispanic, Black, and Native Americans were closer to the rates for white Americans in 2021 than they had been in 2020, the report found; this is likely tied to lower vaccination rates and, consequently, higher death rates in conservative and rural areas. For any reporters seeking to investigate these patterns in their regions, the Documenting COVID-19 project’s CDC mortality data repository includes county-level death data from the same source as this MMWR report.
New CMS data on hospital and nursing home ownership: Nursing homes and other long-term care facilities have been under increased scrutiny during the pandemic, as COVID-19 revealed major flaws in facilities’ ability to care for vulnerable seniors, A series of new datasets from the Centers of Medicare & Medicaid Services (CMS) aims to enable more scrutiny: the datasets include changes of ownership for skilled nursing facilitiesand for hospitals. CMS plans to update these datasets on a quarterly basis, according to a press release about the new data.
New funding for patient-led Long COVID research: The Patient-Led Research Collaborative (PLRC), a group of Long COVID patients that have produced leading research on their condition, announced this week that they’ve received $3 million in funding. This funding comes from Balvi, a fund for high-impact COVID-19 projects established by Ethereum co-creator Vitalik Buterin. PLRC announced that $2 million will go to start a pool of Long COVID research grants—to be awarded directly by patients—while the remaining $1 million will fund a series of PLRC-led studies. I look forward to reporting on the results of this research! (Also, related: this week, I updated the source list of Long COVID patients and experts willing to talk to reporters, which I compiled with Fiona Lowenstein.)
FDA authorizes breathalyzer for COVID-19: The latest new COVID-19 test is a breathalyzer: this machine, developed by Texas-based diagnostics company InspectIR, analyzes chemicals in a person’s breath to quickly detect compounds signifying a coronavirus infection. This test can deliver results in just three minutes—even faster than an antigen test—but it needs to be performed in a medical setting; InspectIR is working on a version that could be hand-held, like breathalyzers for alcohol. Impressive as the technology is, this data reporter is asking: how will those test results get reported to public health agencies?
Lessons learned from the non-superspreader Anime NYC convention: Last fall, one of the first Omicron cases detected in the U.S. was linked to the Anime NYC convention, a gathering of more than 50,000 fans. Many worried that the event had been a superspreader for this highly contagious variant, but an investigation from the CDC later found that, in fact, Omicron spread at the convention was minimal. My latest feature story for Science News unpacks what we can learn from this event about preventing infectious disease spread—not just COVID-19—at future large events. I am a big anime fan (and have actually attended previous iterations of Anime NYC!), so this was a very fun story for me; I hope you give it a read!
my latest @ScienceNews feature: despite Omicron's presence at the Anime NYC convention last fall, it turned out not to be a COVID-19 superspreader. what can we learn from Anime NYC about preventing disease spread at future large events? (1/5) https://t.co/MMzlyInTiypic.twitter.com/ZMOJ7EEvt9
States keep reducing their data reporting frequency: Last Sunday, I noted that Florida—one of the first states to shift from daily to weekly COVID-19 data updates—has now gone down to updating its data every other week. This is part of an increasing trend, writes Beth Blauer from the Johns Hopkins COVID-19 data team in a recent blog post. “As of March 30, only eight states and territories (AR, DE, MD, NJ, NY, PA, PR, and TX) report case data every day of the week,” Blauer says. And it seems unlikely that states will increase reporting frequencies again without a major change in public health funding or the state of the pandemic.
Biden administration announces Long COVID task force: This week, the Biden administration issued a memo addressing the millions of Americans living with Long COVID. The administration is creating a new, interagency task force, with the goal of developing a “national research action plan” on Long COVID, as well as a report laying out services and resources that can be directed to people experiencing this condition. It’s worth noting that recent estimates from the U.K. indicate 1.7 million people in that country (or one in every 37 residents) are living with Long COVID; current numbers in the U.S. are unknown due to data gaps, but are likely on a similar scale, if not higher.
Long COVID data just out from the ONS TL;DR -1.7 million people now living with long COVID (28 day definition) – that's 1 in 37 people in the commiunity -780,000 have had this for *more than a yr* -at least 334000 got this during the omicron wave (impact since Feb not felt yet)🧵
Study indicates continued utility for COVID-19 testing in schools: During the Omicron surge, testing programs in a lot of schools collapsed, simply because institutions didn’t have enough resources to handle all of the students and staff getting sick. The surge led some schools to consider whether school testing programs are worth continuing at all. But a new study, released last week in The Lancet, suggests that yes, surveillance testing can still reduce transmission—even when schools are dealing with highly contagious variants. (Note that this was a modeling study, not a real-world trial.)
Preprint shows interest in self-reporting antigen test results: Another interesting study released recently: researchers at the University of Massachusetts distributed three million free rapid, at-home antigen tests between April and October 2021, then studied how test recipients interacted with a digital app for ordering tests and logging results. About 8% of test recipients used the app, the researchers found; but more than 75% of those who used it did report their antigen test results to their state health agency. The results (which haven’t yet been peer-reviewed) suggest that, if institutions make it easy and accessible for people to self-report their test results, the reporting will happen.
This week, I had a big retrospective story published at FiveThirtyEight: I looked back at the major metrics that the U.S. has used to track COVID-19 over the past two years—and how our country’s fractured public health system hindered our use of each one.
very excited to share this one: for @FiveThirtyEight, I looked back at the metrics that the U.S. has used to track COVID-19 over the last two years — and how our fractured public health system doomed each one. (1/5) https://t.co/wgzTYL8Dh9pic.twitter.com/sqxwIrufQT
The story is split into seven sections, which I will briefly summarize here:
Case counts, January to March 2020: Early on in the pandemic, the U.S. had a very limited picture of COVID-19 cases due to our very limited testing: after rejecting a test made by the WHO, the CDC made its own test—which turned out to have contamination issues, further slowing down U.S. testing. In early March 2020, for example, the majority of cases in NYC were identified in hospitals, suggesting that official counts greatly underestimated the actual numbers of people infected.
Tests administered, March to September 2020: Test availability improved after the first wave of cases, with organizations like the COVID Tracking Project keeping a close eye on the numbers. But there were a lot of challenges with the testing data (like different units across different states) and access issues for Americans with lower socioeconomic status.
Hospitalizations, October to December 2020: By late 2020, many researchers and journalists were considering hospitalizations to be a more reliable COVID-19 metric than cases. But it took a long time for hospitalization data to become reliable on a national scale, as the HHS launched a new tracking system in the summer and then took months to work out kinks in this system.
Vaccinations, January to June 2021: When the vaccination campaign started in late 2020, it was “tempting to forget about all other COVID-19 metrics,” I wrote in the story. But the U.S.’s fractured system for tracking vaccinations made it difficult to analyze how close different parts of the country were to prospective “herd immunity,” and distracted from other public health interventions that we still needed even as people got vaccinated.
Breakthrough cases, July to November 2021: The Delta surge caused widespread infections in people who had been vaccinated, but the CDC—along with many state public health agencies—was not properly equipped to track these breakthrough cases. This challenge contributed to a lack of good U.S. data on vaccine effectiveness, which in turn contributed to confusion around the need for booster shots.
Hospitalizations (again), December to January 2022: The Omicron surge introduced a need for more nuance in hospitalization data, as many experts asked whether COVID-19 patients admitted with Omicron were actually hospitalized for their COVID-19 symptoms or for other reasons. Nuanced data can be useful in analyzing a variant’s severity; but all COVID-related hospitalizations cause strain on the healthcare system regardless of their cause.
New kinds of data going forward: In our post-Omicron world, a lot of public health agencies are shifting their data strategies to treat COVID-19 more like the flu: less tracking of individual cases, and more reliance on hospitalization data, along with newer sources like wastewater. At this point in the pandemic, we should be fortifying data systems “for future preparedness,” I wrote, rather than letting the systems we built up during the pandemic fall to the wayside.
I did a lot of reporting for this piece, including interviews with some of the U.S.’s foremost COVID-19 data experts and communicators. As long as the piece is, there were a lot of metrics (and issues with these metrics) that came up in these interviews that I wasn’t able to include in the final story—so I wanted to share some bonus material from my reporting here.
Long COVID:
As I’ve discussed in previous issues, the U.S. has done a terrible job of collecting data on Long COVID. The NIH estimates that this condition follows a significant share of coronavirus infections (between 10% and 30%), but we have limited information on its true prevalence, risk factors, and strategies for recovery.
Here’s Dr. Eric Topol, the prolific COVID-19 commentator and director of the Scripps Research Translational Institute, discussing this data problem:
[Long COVID has] been given very low priority, very little awareness and recognition. And we have very little data to show for it, because it hasn’t been taken seriously. But it’s a very serious matter.
We should have, early on, gotten at least a registry of people —a large sample, hundreds of thousands of people prospectively assessed, like is being done elsewhere [in the U.K. and other countries]. So that we could learn from them: how long the symptoms lasted, what are the symptoms, what are the triggers, what can be done to avoid it, the role of vaccines, the role of boosters, all this stuff. But we have nothing like that.
The NIH’s RECOVER initiative may answer some of these questions, but it will take months—if not years—for the U.S. to actually collect the comprehensive data on Long COVID that we should have started gathering when the condition first began gaining attention in 2020.
Demographic data:
In the testing section of the story, I mention that the U.S. doesn’t provide much demographic data describing who’s getting tested for COVID-19. There is actually a little-known provision in the CARES Act that requires COVID-19 testing providers to collect certain demographic data from all people who seek tests. But the provision is not enforced, and any data that are collected on this subject aren’t making it to most state COVID-19 dashboards, much less to the CDC’s public data dashboard.
Here’s Dr. Ellie Murray, an epidemiologist at the Boston University School of Public Health, discussing why this is an issue:
We don’t collect reason for seeking a test. We don’t collect age, race, ethnicity, occupation of people who seek a test. Those kinds of things could provide us with some really valuable information about who is getting tested, when, and why—that could help us figure out, what are the essential occupations where people are having a lot of exposures and therefore needing to get a lot of tests? Or are there occupations where we’re seeing a lot of people end up in hospital, who have those occupations, but they’re not getting tests, because actually, the test sites are nowhere near where they need to work, or they don’t have the time to get there before they close.
And so we don’t really know who is getting tested, and that, I think, is a bigger problem, than whether the numbers that are being tested tell us anything about the trajectory of COVID. Because we have case data, and hospitalization data, and death data to tell us about the trajectory. And the testing could really tell us more about exposure, and concern, and access—if we collected some more of this data around who is getting tested and why.
Test positivity:
Speaking of testing: another metric that I didn’t get into much in the story was test positivity. Test positivity—or, the share of COVID-19 tests that return a positive result—has been used from the CDC to local school districts as a key metric to determine safety levels. (For more on this metric, check out my FAQ post from this past January.)
But even when it’s calculated correctly, test positivity faces the same challenges as case data: namely, bias in who’s getting tested. Here’s Lauren Ancel Meyers, director of the University of Texas at Austin’s COVID-19 Modeling Consortium, explaining this:
Test positivity is just as fraught [as cases]. It’s just as difficult, because you need to know the numerator and the denominator—what’s influencing the numerator and the denominator? Who is going to get tested, who has access to tests? … It used to be, at the very beginning [of the pandemic], nobody could get a test who wanted a test. And now, today, everybody has a test in their medicine cabinet, and they don’t get reported when they test. It’s different issues that have ebbed and flowed throughout this period.
Often, if you’re a good data analyst or a modeler, and you have all the information, you can handle those kinds of biases. But the problem is, we don’t know the biases from day to day. And so even though there are statistical tools to deal with incomplete bias, without knowing what those biases are, it’s very hard to do reliable inference, and really hard to understand what’s actually going on.
Genetic surveillance:
Also related to testing: genetic surveillance for coronavirus variants of concern. Genetic surveillance is important because it can help identify new variants that may be more transmissible or more likely to evade protection from vaccines. It can additionally help track the qualities of concerning variants once they are identified (if variant data is linked to hospitalization data, vaccination data, and other metrics—which is not really happening in the U.S. right now.)
Our current genetic surveillance systems have a lot of gaps. Here’s Leo Wolansky, from the Rockefeller Foundation’s Pandemic Prevention Institute (PPI), discussing how his organization seeks to address these challenges:
[We’re trying to understand] where our blind spots are, and the bias that we might experience with a lot of health system reporting. One of the things that PPI has been doing is identifying centers of excellence in different parts of the world that can improve the sequencing of new cases in underrepresented countries. And so for example, we’ve provided quite a bit of support to the folks in South Africa that ultimately rang the alarm on Omicron.
We’re also doing this by actually trying to systematically assess countries’ capacity for this type of genomic surveillance. So thinking about, how many tests have been recorded? What’s that test positivity rate? Do we have confidence in the basic surveillance system of the country? And then, do we also see enough sequences, as well as sequencing facility data, to demonstrate that this country can sequence and just isn’t doing enough—or cannot sequence because it needs foundational investment in things like laboratories and devices. We’ve been mapping this capacity just to make sure that we understand where we should be investing as a global community.
The Pandemic Prevention Institute is taking a global perspective in thinking about data gaps. But these gaps also exist within the U.S., as is clear when one looks at the differences in published coronavirus sequences from state to state. Some states, like Wyoming, Vermont, and Colorado, have sequenced more than 10% of their cumulative cases, according to the CDC. Others, like Oklahoma, Iowa, and South Dakota, have sequenced fewer than 3%. These states need additional investment in order to thoroughly monitor coronavirus transmission among their residents.
Cohort studies:
In a cohort study, researchers follow a group of patients over time in order to collect long-term data on specific health conditions and/or the outside factors that influence them. The U.S. has set up a few cohort studies for COVID-19, but they haven’t been designed or utilized in a way that has actually provided much useful data—unlike cohort studies in some other countries. (The U.K., for example, has several ongoing cohort studies collecting information on COVID-19 symptoms, infections in schools, seroprevalence, and more.)
Here’s Dr. Ellie Murray explaining the lost potential of these studies in the U.S.:
There are a number of existing cohort studies that have been asked or who asked to pivot to collecting COVID information and therefore collecting long-term COVID information on their cohorts. But there doesn’t seem to be any kind of system to [determine], what are the questions we need answered about COVID from these kinds of studies? And how do we link up people who can answer those questions with the data that we’re collecting here, and making sure we’re collecting the right data? And if this study is going to answer these questions, and this one is going to answer those questions—or, here’s how we standardize those two cohorts so that we can pull them together into one big COVID cohort.
And so, we end up in this situation where, we don’t know what percent of people get Long COVID, even though we’ve been doing this for over two years. We don’t even really know, what are all the different symptoms that you can get from COVID? … There are all these questions that we could be sort-of systematically working our way through, getting answers and using them to inform our planning and our response. [In addition to having] standardized questions, you also need a centralized question, instead of just whatever question occurs to someone who happens to have the funding to do it.
Excess deaths:
Excess deaths measure the deaths that occur in a certain region, over a certain period of time, above the number of deaths that researchers expect to see in that region and time period based on modeling from past years’ data. Excess deaths are the COVID-19 metric with the longest lag time: it takes weeks from initial infection for someone to die of the disease, and can take weeks further for a death certificate to be incorporated into the public health system.
Once that death information is available, however, it can be used to show the true toll of the pandemic—analyzing not just direct COVID-19 deaths, but also those related to isolation, financial burden, and other indirect issues—as well as who has been hit the hardest.
Here’s Cecile Viboud, a staff scientist at the NIH who studies infectious disease mortality, discussing this metric:
We’ve been using the excess death approach for a long time. It comes from flu research, basically starting in 1875 in the U.K. And it was used quite a lot during the 1918 pandemic. It can be especially good in examining historical records where you don’t have lab confirmation—there was no testing ability back in those days…
So, I think it’s kind of natural to use it for a pandemic like COVID-19. Very early on, you could see how useful this method was, because there was so little testing done. In March and April 2020, you see substantial excess, even when you don’t see lab-confirmed deaths. There’s a disconnect there between the official stats, and then the excess mortality… [We can also study] the direct effect of COVID-19 versus the indirect effect of the pandemic, like how much interventions affected suicide, opioids, death, accidents, etc. The excess approach is also a good method to look at that.
Viboud also noted that excess deaths can be useful to compare different parts of the U.S. based on their COVID-19 safety measures. For example, one can analyze excess deaths in counties with low vaccination rates compared to those with high vaccination rates. This approach can identify the pandemic’s impact even when official death counts are low—an issue that the Documenting COVID-19 project has covered in-depth.
A couple of data sources, a couple of data-related updates:
State plans for utilizing COVID-19 relief funding: The federal Office of Elementary and Secondary Education has posted every state’s plan for utilizing ESSER funding, a $13-billion fund set aside to help schools address the impact of COVID-19. Money can be utilized for academic assistance, improving ventilation in schools, testing, and more. State plans were due to the federal government last June, though some materials are still pending on the website.
New GAO report on Long COVID: Between 8 and 23 million Americans may have developed Long COVID in the last two years—and an estimated one million are out of work because of this condition—according to a new report from the U.S. Government Accountability Office. The report discusses medical and economic impacts of Long COVID, including current efforts by the federal government to study the condition.
KFF COVID-19 Vaccine Monitor update: This week, the Kaiser Family Foundation published a new report detailing America’s sentiments on COVID-19 vaccines and other pandemic issues. Key findings include: COVID-19 vaccine uptake “remains relatively unchanged since January” for both adults and children; a majority of parents with children under five say they “don’t have enough information” about vaccines for that age group; and “most adults believe that the worst of the COVID-19 pandemic is over but there are disagreements about what returning to normal means and when it should happen.”
Vaccination disparities between urban and rural counties: Here’s a CDC MMWR study that caught my eye this week: researchers compared vaccination rates in urban and rural U.S. counties, finding that the rate of people in urban counties who have received at least one dose (75.4%) is much higher than the rate in rural counties (58.5%). Moreover, the gap between urban and rural counties has more than doubled between April 2021 and January 2022, the researchers found.
CDC updates seroprevalence data: The CDC recently updated a dashboard showing data from seroprevalence surveys, which use information from labs across the country to estimate how many Americans have resolving or recent coronavirus infections. (This does not include vaccinations, unlike other seroprevalence estimates.) According to this new update, about 43% of the country had antibodies from a recent infection as of late January. In some parts of the country that were harder-hit by Omicron, the esimate is over 50%.
COVID-19 drug order and inventory info: Rob Relyea has produced three interactive maps that show state-by-state availability for COVID-19 drugs: one for Sotrovimab, the one widely used monoclonal antibody treatment (out of three available in the U.S.) that is still effective against Omicron; one for Paxlovid, the highly effective Pfizer pill; and one for Evusheld, a monoclonal antibody drug that works preemptively for COVID-19, reducing COVID-19 risk for immunocompromised people. Click on a state to see drug provider locations and drug supplies.
(Updated) Vaccine Breakthrough Reporting Scorecard: A couple of months ago, I shared a scorecard from the Rockefeller Foundation and former COVID Tracking Project researchers that grades state reporting of breakthrough COVID-19 cases. This scorecard was recently updated. According to the Pandemic Prevention Institute’s twitter, as of this update: “46 states are now regularly reporting some breakthrough data. Currently, 5 states get an A, up from 4 states in January.”
Updated Long COVID source list: Journalists covering Long COVID can use this public database, compiled by myself and Fiona Lowenstein, to find patients, scientists, and advocates who are interested in talking to reporters for their stories. The database was published in January, but I recently updated it by adding a few new sources to the list. Read more about the resource here!
COVID-19 Data Dispatch 