COVID-19 Data Dispatch

Tag: vaccination data

  • COVID-19 vaccine issues: Stories from COVID-19 Data Dispatch readers across the U.S.

    COVID-19 vaccine issues: Stories from COVID-19 Data Dispatch readers across the U.S.

    Last year, just 17% of the U.S. population received a bivalent booster. Will this year’s uptake be better?

    Last week, I asked you, COVID-19 Data Dispatch readers, to send me your stories of challenges you experienced when trying to get this fall’s COVID-19 vaccines. I received 35 responses from readers across the country, demonstrating issues with insurance coverage, pharmacy logistics, and more.

    I’ve published the full responses in the table below. Here are a few common themes that I saw in these stories:

    • Pharmacies aren’t receiving enough vaccines. Several readers shared that their pharmacies had inadequate vaccine supply to accommodate all the people who made vaccination appointments, or who wanted appointments. Vaccine supply may also be unpredictable—a pharmacy may think they’re getting more shots, but in fact not receive them—leading to appointment cancellations.
    • Insurance providers weren’t prepared for this vaccine rollout. Despite months of advance notice that a fall COVID-19 vaccine was coming, many insurance companies apparently failed to prepare billing codes or other system updates that would allow them to cover the shots. A couple of people who shared insurance issue stories are on Medicare—representing a population (i.e. seniors) who should be at the front of the vaccine line.
    • Very limited, confusing vaccine availability for young kids. Several readers shared that they were able to get vaccinated, but their children under 12 have not received a vaccine yet. While the FDA and CDC have authorized this fall’s COVID-19 vaccines for all Americans ages six months and older, younger children require a different vaccine formulation from adults. And this formulation appears either entirely unavailable or very difficult to access, depending on where you live.
    • People living in less dense areas may need to travel. A few readers shared that, as they searched for vaccine appointments in their areas, the closest pharmacies with doses available were miles away—over 10 miles, in one case. This is a significant barrier for people fitting vaccine appointments into their work schedules.
    • Information may be inconsistent. Vaccine availability listed in one place (such as a pharmacy chain’s website or the federal vaccines.gov website) may be inaccurate in another. Some readers shared that they spent extra time on the phone with pharmacies or health providers to get accurate information—another barrier.
    • Pharmacies don’t have enough staff for this. Even readers who were able to receive COVID-19 vaccines often had to wait a long time at their pharmacies. Several shared that their pharmacies appeared to be understaffed, dealing with the COVID-19 shots along with routine prescriptions and other duties. The days of mass vaccination sites, efficiently run by public health departments, are long over.
    • Kaiser Permanente members face delays. One company that appears to be causing outsized problems is Kaiser Permanente, one of the biggest insurers and health providers on the West Coast. Several readers shared that Kaiser was not providing new COVID-19 vaccines until early October, and would not cover the shots if their members went to another location. That’s a big delay, and it may be further impacted by a coming strike at the company.
    • These vaccines are expensive. If you decide to pay for a COVID-19 shot out-of-pocket (as some readers did), it costs almost $200. Even the federal government is paying about triple the cost of last year’s COVID-19 vaccines per shot, for the doses it is covering, STAT News reports. The U.S. may have received a “bad deal” here, STAT suggests, considering all of the federal funding that’s supported vaccine research and development.

    As I wrote last week, some news outlets have covered these challenges, but this issue really deserves more attention. The updated COVID-19 vaccines are basically the U.S. government’s only strategy to curb a surge this winter, and they should be easily, universally accessible. Instead, many people eager to get vaccinated are going through multiple rounds of appointments, phone calls, pharmacy lines, and more.

    For every one of these readers who has persisted in getting their shot, there are likely many other people who tried once and then gave up. And those people who don’t receive the vaccine will be at higher risk of severe illness, death, and long-term symptoms from COVID-19 this fall and winter. This is a public health failure, plain and simple.

    And it’s important to emphasize that this failure is not surprising. Many health commentators predicted that these challenges would arise as the federal public health emergency ended and COVID-19 tools transitioned from government-funded to covered-by-insurance. For more context on why this is happening, I recommend the Death Panel podcast’s latest episode, “Scenes from the Class Struggle at CVS.”

    If you’re a reporter who would like to connect with one of the COVID-19 Data Dispatch readers who shared a story, please email me at betsy@coviddatadispatch.com. Most of the people in the database below shared an email or other contact info.

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    more vaccine coverage

  • Sources and updates, September 17

    • Public comments to the CDC about infection control measures: The People’s CDC, a public health communication and advocacy organization that seeks to fill gaps left by the federal CDC, has published a database of comments about the importance of infection control measures in healthcare settings. These comments were sent to the CDC’s Healthcare Infection Control Practices Advisory Committee (HICPAC), as the committee considers changing the agency’s guidance to be more lenient about preventing infections in healthcare settings. As the CDC has not published comments publicly itself, the People’s CDC “asked people to forward us their comments to HICPAC, and created the People’s Register.” For more details about HICPAC, see this post.
    • Recommendations for masks, nasal sprays, other tools: In response to last week’s post discussing how nasal sprays may be used to reduce COVID-19 risk, a reader shared this video from RTHM Health, a telehealth clinic focused on Long COVID and related complex chronic diseases. “This video has a section with a good overview of different sprays and the strength of evidence for each one,” the reader wrote. The video also includes recommendations for high-quality reusable masks and respirators, along with other COVID-19 safety tools.
    • Wastewater surveillance for flu, RSV: A new study, published this week in the CDC’s Morbidity and Mortality Weekly Report, discusses how wastewater surveillance can complement other methods of monitoring the flu and respiratory syncytial virus (RSV). Researchers at Wisconsin’s state health department, the CDC, and other collaborators tracked flu and RSV in three Wisconsin cities’ sewage during last winter’s respiratory virus season. They found that wastewater trends “often preceded a rise” in emergency department visits for these viruses. This study follows other research that has shown wastewater surveillance can be a predictive tool for many diseases, not just COVID-19.
    • Better understanding coronavirus interactions with human cells: Another recent study, published in the journal Viruses, discusses how SARS-CoV-2 interacts with the proteins in human cells as it replicates. The research team (based at the University of California Riverside) identified a specific cellular process that the virus’ N protein hijacks and uses to copy its genetic material, leading to more coronavirus in the body. These findings could be used to develop new antiviral treatments that target this cellular process, both for COVID-19 and other similar diseases, the researchers said in a press release.
    • Limitations of prior immunity to COVID-19: One more recent paper that caught my attention: researchers at the University of Geneva in Switzerland studied how prior infection and/or vaccination can impact COVID-19 risk, based on about 50,000 cases and associated contact tracing data from the city of Geneva. The researchers found that both a recent infection and vaccination reduced the risk of getting infected from a close contact sick with COVID-19. But both types of immunity faded within a few months, leading people to remain vulnerable in the long-term. This study suggests that vaccines alone are not sufficient to control the spread of COVID-19; masks, ventilation improvements, and other interventions are needed, the authors argue.
    • NIH tests universal flu vaccine: Speaking of vaccines: the National Institute of Allergy and Infectious Diseases (or NIAID, one of the National Institutes of Health) announced this week that it’s starting a new trial for a universal flu vaccine. This vaccine, developed by NIAID researchers, can prompt the body to make antibodies against a wide variety of flu strains rather than focusing on one variant. The vaccine has done well in animal studies and is now ready for a phase one clinical trial. NIAID plans to test the vaccine in 24 volunteers, and will follow them closely through immune system testing to see how the vaccine performs.

  • Sources and updates, March 26

    • Paxlovid may lower risk of Long COVID: Taking paxlovid in the acute phase of a COVID-19 case may lower a patient’s risk of long-term symptoms by about 25%, according to a paper published this week in JAMA Internal Medicine. The paper, which summarizes an analysis of health records in the Veterans Affairs database, was originally posted as a preprint in the fall; lead author Ziyad Al-Aly and his colleagues at the St. Louis VA did more number-crunching during the peer review process. Several clinical trials (including one just announced at Yale this week) will test paxlovid as a potential treatment for Long COVID, with a longer course than people typically take during the acute disease.
    • Estimating true vaccination rates in the U.S.: A new report from the COVID States Project, a group of academic researchers focused on connections between social behaviors and COVID-19 spread, provides estimates of vaccination rates by state in the U.S. The researchers compared vaccination data from the CDC to polling sources, including the Kaiser Family Foundation and original polling conducted by the COVID States Project. They found that CDC data often diverged from survey data, suggesting that the public health agency’s information has errors due to the CDC’s inability to connect disparate immunization records from different states. (In other words, if someone got their primary series in one state and a booster in another, they might show up twice in the CDC’s data.)
    • Comparing COVID-19 outcomes by state: Another report looking at state-by-state data: researchers at the University of Washington’s Institute for Health Metrics and Evaluation compared COVID-19 death rates to state actions on COVID-19. The researchers found that states with higher poverty, more income inequality, higher Black and Hispanic/Latino populations, and less access to healthcare faced higher COVID-19 rates. States where more people voted for Trump in 2020 also saw more COVID-19. These patterns “seem to reflect the release of public health mandates” in more Republican states, journalist Amy Maxmen wrote in a Twitter thread summarizing the study.
    • COVID-19 origins docs, raccoon dog analysis: Federal intelligence documents about investigations into the coronavirus’ origins will be declassified in the coming months, as required by a new law that President Biden signed this week. The law specifically requires that the Director of National Intelligence release “all information relating to potential links between China’s Wuhan Institute of Virology and COVID-19.” This information will first go to Congress, and then may become public. Meanwhile, there’s been some controversy about a recent analysis of viral samples at the Huanan Seafood Wholesale Market in Wuhan: news about this analysis was shared in the media before a scientific report was completed, and some researchers who worked on the analysis had their access to sequence repository GISAID revoked. This article in Science Magazine has more details on the situation.
    • Increased Candida auris spread during the pandemic: C. auris is a pathogenic fungus that has developed resistance to multiple common drugs, and that can pose a serious threat to human health. (Yes, a fungus similar to the one that causes a pandemic in “The Last of Us”—though C. auris doesn’t turn people into zombies.) The fungus has spread more widely during the pandemic according to a recent CDC report, with a 44% increase in cases from 2019 to 2021. Other types of anti-microbial resistance have been on the rise as well, as COVID-19 led to less rigorous monitoring and heightened antibiotic use in many hospitals. More recent CDC data on the fungus are available here.

    All featured sources
  • The future of COVID-19 vaccines needs more data

    The future of COVID-19 vaccines needs more data

    The FDA recommends that the U.S. shifts to annual COVID-19 vaccines, with a variety of data sources feeding into decision-making. Screenshot from the VRBPAC meeting on January 26, 2023.

    On Thursday, the FDA’s Vaccines and Related Biological Products Advisory Committee (or VRBPAC) met to discuss the future of COVID-19 vaccines. While the committee readily agreed that our current, Omicron-specific shots are working well and should be used more broadly, it had a hard time answering other questions about future vaccine regimens—largely due to a lack of good data.

    Now, the lack of good U.S. data on vaccine effectiveness is not a new problem. I personally have been writing about this since fall 2021, to the point that I feel like a broken record for bringing it up again. To summarize: the U.S. has a fractured health system in which every state tracks vaccinations differently, with a lot of local public health departments and private companies in the mix, too. As a result, it’s challenging for researchers to determine exactly who is getting COVID-19 after vaccination and how the virus is impacting them.

    This lack of detailed vaccine effectiveness data was a problem in fall 2021, when federal officials decided on an initial round of booster shots. And it’s still a problem in winter 2023, as the same officials attempt to plot out a future in which COVID-19 is another disease that we deal with on an annual basis.

    But this week’s VRBPAC meeting revealed some other areas of data that are also lacking as we try to answer questions about future vaccines. Here’s my summary of five primary data gaps that came up at the meeting, and some suggestions for potential solutions.

    Detailed vaccine effectiveness data

    The biggest data gap, of course, is our lack of answers to the question: Who is getting sick with COVID-19 after vaccination? And related questions: How sick did they get? Which variants did they get sick with? What preexisting conditions or comorbidities did they have?

    Our lack of standardized medical data in the U.S. makes it tough to answer these questions at the population level. Analyzing variants is particularly tricky, given that variant surveillance in the U.S. tends to be entirely anonymized—with no connections between the genomic sequencing of random PCR tests and the health outcomes (or vaccination statuses) of those patients. And analyzing preexisting conditions can be crucial as officials try to decide which groups of people need extra boosters, but these conditions often are not collected in standard databases or linked to COVID-19 records.

    As a result, U.S. officials tend to rely on other countries with more comprehensive, standardized data systems for information on how well the vaccines work. We also have to rely on the pharmaceutical companies producing these vaccines, which often don’t openly share their data—they tend to present clinical trial results in press releases, over peer-reviewed studies. Companies also tend to do trials that align better with their own financial interests, rather than looking at the full scope of vaccine effectiveness.

    Even in this week’s VRBPAC meeting, scientists from Moderna presented results from a clinical trial—conducted in the U.K.—that tested the company’s bivalent boosters against the original (non-Omicron) boosters.

    Better tracking of variants

    The primary reason why our COVID-19 vaccines require updates in the first place is the coronavirus’ continued evolution. Every new lineage of Omicron that rises to prevalence is either a bit better at spreading quickly, a bit better at evading immunity from prior infection or vaccination, or both. To successfully tweak our vaccines in the future, scientists will need to know which variants are out there and how dangerous they are.

    Right now, variant tracking largely relies on PCR testing, as researchers randomly select some swab samples to sequence. But with fewer and fewer people getting PCR tests, the sample pool is dwindling. As a result, to stay ahead of new variants, the U.S. needs to diversify its surveillance options. That will likely include more variant sequencing from wastewater (as a population-level COVID-19 sample), more sequencing at hospitals and healthcare centers, and more travel surveillance focused on international variant patterns.

    Variant surveillance will also need to inform how suited U.S.-developed COVID-19 vaccines are for the rest of the world. Right now, the pharmaceutical companies that have produced the most effective vaccines (i.e. Pfizer and Moderna) are American—so American regulators are essentially dictating vaccine policy for the world, even though their priority is the U.S. FDA official Jerry Weir said as much at the meeting. U.S. hegemony over COVID-19 vaccines will continue to be a complex, fraught topic going forward.

    Tracking different types of immunity

    At the VRBPAC meeting, Moderna, Pfizer, and Novavax all presented data on how well their vaccines work against currently-dominant coronavirus variants. While they included some clinical data (case rates, hospitalization rates), the presentations mostly focused on one metric: antibody titers. To calculate if a vaccine works against a certain variant, the easiest strategy is measuring the antibodies produced after a vaccinated blood sample is exposed to that variant.

    While this is the easiest strategy, it’s far from the only way to examine how well a vaccine works. Members of the VRBPAC committee frequently asked the pharmaceutical companies for those other metrics: T cells, B cells, and more ways of measuring the immune system’s response to COVID-19. But the companies had little response to these questions. Even FDA and NIH officials at the meeting admitted that they still didn’t have a good understanding of how, exactly, our current vaccines impact our immune systems, beyond generating antibodies.

    To better evaluate future vaccines, scientists will need to get better at measuring other aspects of our immune responses. That includes future mRNA vaccines as well as next-generation vaccines in the works right now, such as nasal vaccines (recently authorized in China and India) and vaccines designed to protect against all variants (currently in development at Duke University and other institutions).

    I also think it’s worth noting that, as Katelyn Jetelina writes in her coverage of the VRBPAC meeting at Your Local Epidemiologist, the FDA could require pharmaceutical companies to study the immune system more holistically when they submit further vaccine updates for authorization. “The FDA could require sponsors to do detailed investigations, e.g. assessing lymph nodes, bone marrow, and breakthroughs,” she writes. “This would help us understand immunity better, so we can make better recommendations. It’s not clear why they aren’t pushing for this.”

    Improving vaccine safety tracking

    Two years after the first COVID-19 vaccines were authorized, we now know that the vaccines are overwhelmingly safe and effective. Most people have mild side effects following their shots, like sore arms and fatigue, but the benefits of getting vaccinated far outweigh the risks. However, some discussion at the VRBPAC meeting indicated that federal agencies could do a better job of tracking rare (yet important) serious side effects.

    For example, a safety presentation from the Kaiser Permanente Vaccine Study Center suggested that there might be a small increase in stroke risk for older adults who get vaccinated. The risk has only appeared in one vaccine safety database so far and appears to be minimal, per the FDA, but it’s still worth closer examination.

    In addition, as Helen Branswell and Matthew Herper discuss in the STAT News liveblog, the VRBPAC meeting didn’t present much new data about vaccine safety risks for children, such as myocarditis among boys and young men. Plus, we have limited data so far on whether vaccination may contribute to autoimmune conditions or Long COVID-like symptoms, a problem that has shown up in some studies and anecdotal reports.

    If public health officials are going to continue encouraging Americans to get COVID-19 shots once a year (or more), they will need to thoroughly address concerns about these potential side effects. This is particularly true for young children, a group that’s been vaccinated at fairly low numbers so far.

    Navigating COVID-19’s interactions with other vaccines

    At the VRBPAC meeting, FDA officials suggested a potential future in which most Americans get one COVID-19 vaccine per year, on a similar timeline to the annual flu shot. Variant strains might be selected in the spring or summer, with vaccines developed and produced in time for a fall vaccination campaign. Some at-risk groups (older adults, people with compromised immune systems, etc.) might get two doses each year.

    To make this possible, the VRBPAC committee members suggested that we’ll need to track how COVID-19 vaccines intersect with other vaccines. For example, if an older adult receives their flu shot and COVID-19 shot in the same doctor’s visit, does that dampen how well one or the other vaccine works? Does it increase the risks of severe side effects? We don’t know, at this point.

    Another major area of future study will be how COVID-19 vaccines may fit into regular, childhood immunization schedules for young kids. Similarly to the COVID-19 plus flu question, scientists will need to track any potential interactions between COVID-19 shots and other regular shots—along with answering questions about how many shots are needed, timing between shots, and more.

    One day, I’m sure, we will have a regular COVID-19 vaccination schedule in the U.S. that runs parallel to our flu vaccination schedule. But it will take time, discussions, and a lot more data to get there.

    More vaccination data

  • National numbers, October 16

    National numbers, October 16

    After a small uptick in vaccinations thanks to the new boosters, vaccinations are already slowing again. Chart via the CDC, data as of October 12.

    In the past week (October 8 through 14), the U.S. reported about 270,000 new COVID-19 cases, according to the CDC. This amounts to:

    • An average of 39,000 new cases each day
    • 83 total new cases for every 100,000 Americans
    • 12% fewer new cases than last week (October 1-7)

    In the past week, the U.S. also reported about 23,000 new COVID-19 patients admitted to hospitals. This amounts to:

    • An average of 3,300 new admissions each day
    • 7.0 total admissions for every 100,000 Americans
    • 4% fewer new admissions than last week

    Additionally, the U.S. reported:

    • 2,300 new COVID-19 deaths (330 per day)
    • 12% of new cases are caused by Omicron BA.4.6; 11% by BQ.1 and BQ.1.1; 5% by BF.7;  3% by BA.2.75 and BA.2.75.2 (as of October 15)
    • An average of 400,000 vaccinations per day

    While official case numbers remain low compared to past fall seasons—both national cases and hospital admissions dropped again this week—signals of a coming fall surge are accumulating from wastewater and local data.

    According to Biobot’s dashboard, the coronavirus continues to spread in the Northeast at higher levels than the rest of the country with a new uptick this week. In places like Franklin County, Massachusetts, Fairfield County, Connecticut, and Middlesex County, New Jersey, coronavirus levels are higher now than they have been at any point in the last six months.

    Similar patterns are starting to show up in clinical data: Northeast states including Vermont, Maine, Connecticut, New Hampshire, Massachusetts, New York, and New Jersey reported increased COVID-19 patients this past week, according to the October 13 Community Profile Report.

    Along with colder weather and behavior patterns, new Omicron lineages could contribute to the increased transmission—if they aren’t contributing already. BQ.1 and BQ.1.1, two sublineages from BA.5, are now causing about 11% of new cases nationwide, according to the CDC’s most recent variant prevalence update. In the northeast, their prevalence is approaching to 20%. (More on the new subvariants in the next post.)

    As many of the sublineages now circulating are descended from BA.5 or BA.4, the bivalent booster shots designed to protect against these variants should still help protect against newer strains. In fact, the FDA and CDC recently expanded eligibility for these new shots to younger age groups, going down to kids ages five to eleven.

    But uptake of the new boosters remains low—in part because public communication has been so limited, many Americans don’t know they qualify for these shots. Only 15 million people have received the boosters as of October 12, a tiny fraction of the eligible population.

    more national numbers

  • Sources and updates, May 1

    • Nursing Home Inspect (ProPublica): ProPublica recently published a major investigation into medical exemptions to COVID-19 vaccines among nursing home workers, finding that high numbers of workers are claiming these exemptions even though the actual, medical reasons causing someone to be ineligible for vaccination are fairly limiting. Along with the investigation, the newsroom added staff COVID-19 vaccination data to its Nursing Home Inspect database, which allows users to compare nursing homes based on negative inspection reports and other deficiencies.
    • Neighborhood Atlas: One source I learned about at the health journalists’ conference this weekend is the Neighborhood Atlas tool from researchers at the University of Wisconsin School of Medicine and Public Health. The atlas maps out metrics that put neighborhoods—i.e. Census block groups, a geographical level much more granular than counties—at a health disadvantage, including income, education, employment, and housing.
    • Access to hospital services for minority groups: Another source from the AHCJ conference: this February 2022 paper and corresponding dataset, measuring how far different minority communities across the country have to go to access hospital services. Over half of rural Native American communities are more than 30 miles from the closest intensive care unit, said Dr. Mary-Katherine McNatt in a talk introducing this source.
    • KFF’s State Health Facts: Also at the conference, Juliette Cubanski from the Kaiser Family Foundation (KFF) gave a presentation on the organization’s data tools and resources for journalists, focusing on Medicare data. One broadly useful KFF tool is the State Health Facts dashboard, which enables journalists and researchers to search through over 800 health indicators at the state level. These indicators are frequently updated with the most recent data.
    • Nursing home staffing reports: COVID-19 revealed how unprepared America’s nursing homes were for a health crisis. In a panel discussing this issue, Richard Mollot from the Long Term Care Community Coalition (a nonprofit that advocates for better long-term care) shared some data from his organization, highlighting drops in staffing during the pandemic that have not yet been recovered.

    All featured sources
  • All the U.S.’s COVID-19 metrics are flawed

    All the U.S.’s COVID-19 metrics are flawed

    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.

    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.

    Again, you can read the full FiveThirtyEight story here!

    More federal data

  • COVID source callout: Still no state-by-state data on vaccinations by race/ethnicity

    COVID source callout: Still no state-by-state data on vaccinations by race/ethnicity

    This week, the CDC added a new feature to the vaccination section of its COVID-19 dashboard: you can now look at demographic vaccination trends at the state level, not just nationally and regionally.

    But there’s a catch: the state-by-state demographic trends only include age and sex data. Vaccination trends by race and ethnicity are still only available at the national level; in fact, when you click on “Race/Ethnicity” on the booster shots section of this dashboard, the CDC directs you to “please visit the relevant health department website” for more local data.

    For state-level race and ethnicity data, the CDC directs users to state public health agencies. Screenshot taken on March 20.

    It is now over a year into the U.S.’s vaccine rollout, and the CDC is still failing to publicly share data on vaccinations by state and race/ethnicity. I actually wrote a callout post about this in March 2021, and nothing has changed since then!

    This is a major issue because such data are needed to examine equity in the vaccine rollout. While it’s possible to compile data from the states that report vaccinations by race and ethnicity themselves, major inconsistencies in state reporting practices make these data hard to standardize. Why isn’t the CDC doing this? Or, if the CDC is doing this, why aren’t the data public?

    more source spotlights

  • Sources and updates, March 13

    A couple of data sources, and a few data-related news items:

    • COVID-19 vaccine data annotations: Yesterday, I updated my annotations page on U.S. vaccination data sources for the first time in a few weeks. The page lists both national dashboards and vaccine data pages from all 50 state public health agencies, including notes on what each source offers. Going through the dashboards yesterday, I was struck by how many states are now offering data on booster shots (43, by my count), as well as how counts of doses distributed in a state, once a major feature of these dashboards, have become less useful now that the U.S. has ample vaccine supplies.
    • Order more free rapid tests from the federal government: The COVIDtests.gov site is now open for additional orders of free rapid at-home tests, as part of the federal program that launched in mid-January. Each household can now order two sets of four tests. I ordered a set of tests last Monday, and received them on Thursday—much faster than the initial round of this program!
    • Scientists are investigating combinations of Delta and Omicron: You might have seen some recent headlines about “Deltacron,” a portmanteau of the two variants of concern. When a very unlucky person gets infected with both Delta and Omicron at the same time, the variants can combine and form a new strain with genetic elements of both lineages. Scientists have recently identified a small number of “Deltacron” cases in France, Denmark, the Netherlands, and the U.S.; it’s not cause for major concern at this time, but is under study to determine if this combined strain might have any transmission or severity advantages. The Guardian has a good explainer on the subject.
    https://twitter.com/PeacockFlu/status/1502726359096909825
    • New studies on masks, vaccines for kids: This week, the CDC MMWR published a new study on masking in K-12 schools; the researchers found that Arkansas school districts with a universal mask requirement in the fall 2021 semester had 23% lower cases than schools that did not have a requirement. The journal also published a new study on vaccinations in children ages 5 to 11; this study found that, within three months of COVID-19 vaccines becoming available for this age group, 92% of kids ages 5 to 11 lived within 5 miles of a vaccine provider. However, vaccination coverage in this age group is low, suggesting the need for more targeted communication to families with young kids.
    • NIH starts new trial on allergic reactions to vaccines: The National Institutes of Health (NIH) recently announced a new clinical trial to understand “rare but potentially serious systemic allergic reactions” to the COVID-19 vaccines. The trial will include up to 100 people between the ages of 16 and 69 who had allergic reactions to their first vaccine doses; the NIH will provide second doses under heavily monitored conditions and study how these patients respond.
    • How to better recruit for COVID-19 trials: Speaking of clinical trials, a new preprint posted this week to medRxiv outlines a potential strategy for better studying effectiveness and potential rare side effects of COVID-19 treatments. The preprint authors propose targeting recruitment to people who are high-risk for coronavirus infection, so that studies may collect data on a statistically significant number of cases more quickly.
    • COVID-19 at the Tokyo Olympics: Another study that caught my eye this week: researchers from Tokyo described the results of intensive surveillance testing for athletes who competed in the 2021 Tokyo Olympics and Paralympics. In total, among over one million PCR tests conducted before and during the Olympic games, just 299 returned positive results—a positivity rate of 0.03%.
    • COVID-19 on Capitol Hill: Reporters at The Hill analyzed data on COVID-19 test results among House and Senate lawmakers, finding that more than one-quarter have tested positive since the pandemic began. The highest case numbers occurred in January 2022 during the Omicron wave, aligning with the U.S. overall. (Though I imagine many legislators travel and socialize indoors more than the average American.)

    All featured sources

  • Sources and updates, February 6

    • Vaccination data from dialysis facilities: A recent addition to the CDC’s COVID Data Tracker, this dataset reports vaccination coverage among patients and staff working in dedicated dialysis facilities, which offer treatment to patients with chronic kidney diseases—a group at high risk for severe COVID-19. The vaccine coverage rates for dialysis staff are new as of this week. Overall, about 74% of dialysis patients and 79% of staff are fully vaccinated, and smaller percentages are boosted, as of late January.
    • CDC report provides vaccination data by sexual orientation and gender identity: As health equity advocates have pushed for more demographic data describing who’s been vaccinated in the U.S., the focus is often on race and ethnicity data. But it’s also important to track vaccinations among the LGBTQ+ community, as these Americans are at higher risk for severe COVID-19 due to HIV, mental health issues, and other conditions common in this group. This new CDC report provides a snapshot of these important data, sourced from the National Immunization Survey. Notably, the report found that vaccine coverage was higher overall among gay and lesbian adults compared to straight adults—but lower among Black LGBTQ+ people across all identities.
    • Association of child masking with COVID-related childcare closures: A new paper published in JAMA Network Open this week provides additional evidence showing that mask requirements can help keep schools and childcare centers open. The paper found that childcare programs where children were masked were 14% less likely to close over the course of a year than programs without child masking. For more commentary on the paper, see Inside Medicine.

    All featured sources