COVID-19 Data Dispatch

Tag: Testing

  • The federal public health emergency ends next week: What you should know

    The federal public health emergency ends next week: What you should know

    A chart from the CDC’s recent report on surveillance changes tied to the end of the federal public health emergency.

    We’re now less than one week out from May 11, when the federal public health emergency (or PHE) for COVID-19 will end. While this change doesn’t actually signify that COVID-19 is no longer worth worrying about, it marks a major shift in how U.S. governments will respond to the ongoing pandemic, including how the disease is tracked and what public services are available.

    I’ve been writing about this a lot in the last couple of months, cataloging different aspects of the federal emergency’s end. But I thought it might be helpful for readers if I compiled all the key information in one place. This post also includes a few new insights about how COVID-19 surveillance will change after May 11, citing the latest CDC reports.

    What will change overall when the PHE ends?

    The ending of the PHE will lead to COVID-19 tests, treatments, vaccines, and data becoming less widely available across the U.S. It may also have broader implications for healthcare, with telehealth policies shifting, people getting kicked off of Medicaid, and other changes.

    Last week, I attended a webinar about these changes hosted by the New York City Pandemic Response Institute. The webinar’s moderator, City University of New York professor Bruce Y. Lee, kicked it off with a succinct list of direct and indirect impacts of the PHE’s end. These were his main points:

    • Free COVID-19 vaccines, tests, and treatments will run out after the federal government’s supplies are exhausted. (Health experts project that this will likely happen sometime in fall 2023.) At that point, these services will get more expensive and harder to access as they transition to private healthcare markets.
    • We will have fewer COVID-19 metrics (and less complete data) to rely on as the CDC and other public health agencies change their surveillance practices. More on this below.
    • Many vaccination requirements are being lifted. This applies to federal government mandates as well as many from state/local governments and individual businesses.
    • The FDA will phase out its Emergency Use Authorizations (EUAs) for COVID-19 products, encouraging manufacturers to apply for full approval. (This doesn’t mean we’ll suddenly stop being able to buy at-home tests—there’s going to be a long transition process.)
    • Healthcare worker shortages may get worse. During the pandemic emergency, some shifts to work requirements allowed facilities to hire more people, more easily; as these policies are phased out, some places may lose those workers.
    • Millions of people will lose access to Medicaid. A federal rule tied to the PHE forbade states from kicking people off this public insurance program during the pandemic, leading to record coverage. Now, states are reevaluating who is eligible. (This process actually started in April, before the official PHE end.)
    • Telehealth options may become less available. As with healthcare hiring, policies during the PHE made it easier for doctors to provide virtual care options, like video-call appointments and remote prescriptions. Some of these COVID-era rules will be rolled back, while others may become permanent.
    • People with Long COVID will be further left behind, as the PHE’s end leads many people to distance themselves even more from the pandemic—even though long-haulers desperately need support. This will also affect people who are at high risk for COVID-19 and continue to take safety precautions.
    • Pandemic research and response efforts may be neglected. Lee referenced the “panic and neglect” cycle for public health funding: a pattern in which governments provide resources when a crisis happens, but then fail to follow through during less dire periods. The PHE’s end will likely lead us (further) into the “neglect” part of this cycle.

    How will COVID-19 data reporting change?

    The CDC published two reports this week that summarize how national COVID-19 data reporting will change after May 11. One goes over the surveillance systems that the CDC will use after the PHE ends, while the other discusses how different COVID-19 metrics correlate with each other.

    A lot of the information isn’t new, such as the phasing out of Community Level metrics for counties (which I covered last week). But it’s helpful to have all the details in one place. Here are a few things that stuck out to me:

    • Hospital admissions will be the CDC’s primary metric for tracking trends in COVID-19 spread rather than cases. While more reliable than case counts, hospitalizations are a lagging metric—it takes typically days (or weeks) after infections go up for the increase to show up at hospitals, since people don’t seek medical care immediately. The CDC will recieve reports from hospitals at a weekly cadence, rather than daily, after May 11, likely increasing this lag and making it harder for health officials to spot new surges.
    • National case counts will no longer be available as PCR labs will no longer be required to report their data to the CDC. PCR test totals and test positivity rates will also disappear for the same reason, as will the Community Levels that were determined partially by cases. The CDC will also stop reporting real(ish)-time counts of COVID-associated deaths, relying instead on death certificates.
    • Deaths will be the primary metric for tracking how hard COVID-19 is hitting the U.S. The CDC will get this information from death certificates via the National Vital Statistics System. While deaths are reported with a significant lag (at least two weeks), the agency has made a lot of progress on modernizing this reporting system during the pandemic. (See this December 2021 post for more details.)
    • The CDC will utilize sentinel networks and electronic health records to gain more information about COVID-19 spread. This includes the National Respiratory and Enteric Virus Surveillance System, a network of about 450 laboratories that submit testing data to the CDC (previously established for other endemic diseases like RSV and norovirus). It also includes the National Syndromic Surveillance Program, a network of 6,300 hospitals that submit patient data to the agency.
    • Variant surveillance will continue, using a combination of PCR samples and wastewater data. The CDC’s access to PCR swab samples will be seriously diminished after May 11, so it will have to work with public health labs to develop national estimates from the available samples. Wastewater will help fill in these gaps; a few wastewater testing sites already send the CDC variant data. And the CDC will continue offering tests to international travelers entering the country, for a window into global variant patterns.
    • The CDC will continue tracking vaccinations, vaccine effectiveness, and vaccine safety. Vaccinations are generally tracked at the state level (every state health agency, and several large cities, have their own immunization data systems), but state agencies have established data sharing agreements with the CDC that are set to continue past May 11. The CDC will keep using its established systems for evaluating how well the vaccines work and tracking potential safety issues as well.
    • Long COVID notably is not mentioned in the CDC’s reports. The agency hasn’t put much focus on tracking long-term symptoms during the first three years of the pandemic, and it appears this will continue—even though Long COVID is a severe outcome of COVID-19, just like hospitalization or death. A lack of focus on tracking Long COVID will make it easier for the CDC and other institutions to keep minimizing this condition.

    On May 11, the CDC plans to relaunch its COVID-19 tracker to incorporate all of these changes. The MMWR on surveillance changes includes a list of major pages that will shift or be discontinued at this time.

    Overall, the CDC will start tracking COVID-19 similar to the way it tracks other endemic diseases. Rather than attempting to count every case, it will focus on certain severe outcomes (i.e., hospitalizations and deaths) and extrapolate national patterns from a subset of healthcare facilities with easier-to-manage data practices. The main exception, I think, will be a focus on tracking potential new variants, since the coronavirus is mutating faster and more aggressively than other viruses like the flu.

    What should I do to prepare for May 11?

    If you’ve read this far, you’re probably concerned about how all these shifts will impact your ability to stay safe from COVID-19. Unfortunately, the CDC, like many other public agencies, is basically leaving Americans to fend for themselves with relatively little information or guidance.

    But a lot of information sources (like this publication) are going to continue. Here are a few things I recommend doing this week as the PHE ends:

    • Look at your state and local public health agencies to see how they’re responding to the federal shift. Some COVID-19 dashboards are getting discontinued, but many are sticking around; your local agency will likely have information that’s more tailored to you than what the CDC can offer.
    • Find your nearest wastewater data source. With case counts basically going away, wastewater surveillance will be our best source for early warnings about surges. You can check the COVID-19 Data Dispatch list of wastewater dashboards and/or the COVIDPoops dashboard for sources near you.
    • Stock up on at-home tests and masks. This is your last week to order free at-home/rapid tests from your insurance company if you have private insurance. It’s also a good time to buy tests and masks; many distributors are having sales right now.
    • Figure out where you might get a PCR test and/or Paxlovid if needed. These services will be harder to access after May 11; if you do some logistical legwork now, you may be more prepared for when you or someone close to you gets sick. The People’s CDC has some information and links about this.
    • Contact your insurance company to find out how their COVID-19 coverage policies are changing, if you have private insurance. Folks on Medicare and Medicaid: this Kaiser Family Foundation article has more details about changes for you.
    • Ask people in your community how you can help. This is a confusing and isolating time for many Americans, especially people at higher risk for COVID-19. Reaching out to others and offering some info or resources (maybe even sharing this post!) could potentially go a long way.

    That was a lot of information packed into one post. If you have questions about the ending PHE (or if I missed any important details), please email me or leave a comment below—and I’ll try to answer in next week’s issue.

    More about federal data

  • National numbers, April 30

    National numbers, April 30

    The number of COVID-19 tests reported to the CDC has declined precipitously since peak COVID-19 surges, even though COVID-19 spread has not. Chart from the CDC.

    In the past week (April 20 through 26), the U.S. officially reported about 88,000 new COVID-19 cases, according to the CDC. This amounts to:

    • An average of 13,000 new cases each day
    • 10% fewer new cases than last week (April 13-19)

    In the past week, the U.S. also reported about 11,000 new COVID-19 patients admitted to hospitals. This amounts to:

    • An average of 1,500 new admissions each day
    • 3.2 total admissions for every 100,000 Americans
    • 16% fewer new admissions than last week

    Additionally, the U.S. reported:

    • 1,100 new COVID-19 deaths (150 per day)
    • 69% of new cases are caused by Omicron XBB.1.5; 13% by XBB.1.9; 12% by XBB.1.16 (as of April 29)
    • An average of 40,000 vaccinations per day

    Major COVID-19 metrics continue to suggest an ongoing (though slight) decline in the virus’ spread nationally, despite the rise of newer and more contagious variants. The moderate plateau persists.

    Officially-reported cases and new hospital admissions declined by 10% and 16% respectively last week, compared to the week prior. According to the CDC’s data notes, three states (Florida, Iowa, and Pennsylvania) did not report cases last week, while two states (Louisiana and Indiana) reported extra cases from their historical backlogs.

    In addition to the ongoing reporting issues from state health departments, it’s important to remember that PCR testing continues to decline across the country. About one million PCR and similar lab test results were reported to the CDC last week, compared to peaks over 10 million per week during major surges.

    Still, the hospitalization numbers and wastewater surveillance data lead me to suggest that we really are in a transmission plateau. Wastewater data from Biobot show a slight decline in national coronavirus concentrations over the last month.

    All four regions of the country are also experiencing COVID-19 plateaus, according to Biobot’s data. The West Coast and Midwest have slightly higher coronavirus levels than the Northeast and South, but there aren’t huge differences between the regions.

    The West and Midwest are also hotspots for XBB.1.16 and XBB.1.9, the two Omicron subvariants that have started competing with XBB.1.5 over the last few weeks. This competition is happening slowly; XBB.1.5 declined from an estimated 84% of new cases during the last week of March to 69% of new cases this past week, according to the CDC’s estimates.

    At this point, it’s hard to tell how much of an impact the latest variants will have on overall COVID-19 spread. And these connections likely will only get more difficult to parse out, as PCR testing continues to decline and reporting gets less reliable. The CDC itself is currently evaluating how to adjust its data-sharing practices when the federal public health emergency ends on May 11.

    more national numbers
  • At-home tests, wastewater: COVID-19 testing after the public health emergency ends

    At-home tests, wastewater: COVID-19 testing after the public health emergency ends

    Nationwide, fewer people are getting lab-based COVID-19 tests now than at any time since the start of the pandemic. Chart via the CDC.

    When the public health emergency ends this spring, COVID-19 testing is going to move further in two separate directions: rapid, at-home tests at the individual level, and wastewater testing at the community level.

    That was my main takeaway from an online event last Tuesday, hosted by Arizona State University and the State and Territory Alliance for Testing. This event discussed the future of COVID-19 testing following the public health emergency, with speakers including regulatory experts, health officials from state agencies, and executives from diagnostic companies. 

    “The purpose of testing has shifted” from earlier in the pandemic, said Dr. Thomas Tsai, the White House’s COVID-19 testing coordinator, in opening remarks at the event. Public health agencies previously used tests to monitor COVID-19 in their communities and direct contact-tracing efforts; now, individual tests are mostly used for diagnosing people, and the resulting data are widely considered to be a major undercount of true cases.

    While the speakers largely agreed about the continued value of rapid, at-home tests (for diagnosing people) and wastewater surveillance (for tracking COVID-19), they saw a lot of challenges ahead for both technologies. Here are some challenges that stuck out to me.

    Challenges for rapid, at-home tests:

    The public health emergency’s end won’t have an immediate impact on which COVID-19 tests are available, health policy researcher Christina Silcox from Duke University explained at the event. But, in the coming months, the FDA is likely to also end its emergency use authorization for COVID-19 diagnostics. As a result, companies that currently have tests authorized under this emergency will need to apply for full approval. Relatively few rapid tests are currently approved in this way, so the change could lead to fewer choices for people buying tests.

    At the same time, it will become harder for many Americans to access rapid tests. After the federal emergency ends, private insurance companies will no longer be required to cover rapid tests. Some insurance providers might still do this (especially if large employers encourage it), said Amy Kelbik from McDermott+Consulting, but it will no longer be a universal option. At the same time, Medicare will stop covering rapid tests; Medicaid coverage will continue through fall 2024.

    In light of these federal changes, state health officials at the ASU event talked about a need for continued funding to support rapid test distribution from state and local agencies. “Testing will continue to inform behavior, but will become drastically less available,” said Heather Drummond, testing and vaccine program leader at the Washington State Department of Health. Washington has led a free test distribution program, but it’s slated to end with the conclusion of the federal health emergency, Drummond said; she’d like to see services like this continue for the people who most need free tests.

    Drummond and other health officials also discussed the challenges of educating people about how to interpret their test results, as COVID-19 guidance becomes less widely available. The vast majority of rapid, at-home test results are not reported to public health agencies—and, based on the event’s speakers, this isn’t a problem health agencies are particularly interested in devoting resources to solving right now. But as rapid tests become the default for diagnosing COVID-19, continued outreach will be needed on how to use them.

    Also, as I’ve written before, some PCR testing infrastructure should still be maintained, for cases when someone needs a more definitive test result or wants documentation in case of long-term symptoms. PCR test access will likely get even worse after the federal health emergency ends, though, as insurance plans will also stop covering (or cover fewer costs for) these tests.

    Challenges for wastewater surveillance:

    Overall, wastewater surveillance is the best source for community-level COVID-19 data, speakers at the ASU event agreed. Official case numbers represent significant undercounts of true infections, and hospitalizations (while more reliable) are a delayed indicator. Wastewater data are unbiased, real-time, population-level—and the technology can be expanded to other common viruses and health threats, health officials pointed out at the event.

    But wastewater surveillance is still very uneven across the U.S. It’s clear just from looking at the CDC’s map that some states have devoted resources to detailed wastewater testing infrastructure, with a testing site in every county—while others just have a handful of sites. Funding uncertainty likely plays a role here; speakers at the event expressed some confusion about the availability of CDC funds for long-term wastewater programs.

    The CDC’s wastewater surveillance system has also faced challenges with standardizing data from different testing programs. And, at state and local agencies, health officials are still figuring out how to act on wastewater data. Agencies with more robust surveillance programs (such as Massachusetts, which had two officials speak at the ASU summit) may be able to provide success stories for other agencies that aren’t as far along.

    Broader testing challenges:

    For diagnostic company leaders who spoke at the event, one major topic was regulatory challenges. Andrew Kobylinski, CEO and co-founder of Primary.Health, said that the FDA’s test requirements prioritize highly accurate tests, even though less sensitive (but easier to use) tests might be more useful in a public health context.

    Future COVID-19 tests—and tests for other common diseases—may need a new paradigm of regulatory requirements that focus more on public health use. At the same time, health agencies and diagnostic companies could do more to collect data on how well different test options are actually working. While it’s hard to track at-home tests on a large scale, more targeted studies could help show which tests work best in specific scenarios (such as testing after an exposure to COVID-19, or testing to leave isolation).

    Company representatives also talked about financial challenges for developing new tests, particularly as interest in COVID-19 dies down and as recession worries grow this year. While a lot of biotech companies dove into COVID-19 over the last three years, they haven’t always received significant returns on their investments. For example, Lucira, the company behind the first flu-and-COVID-19 at-home test to receive authorization, recently filed for bankruptcy and blamed the long FDA authorization process.

    Mara Aspinall, the ASU event’s moderator and a diagnostic expert herself, ended the event by asking speakers whether COVID-19 has led to lasting changes in this industry. The answer was a resounding, “yes!” But bringing lessons from COVID-19 to other diseases and health threats will require a lot of changes—to regulatory processes, funding sources, data collection practices, and more.

    More testing data

  • National numbers, March 12

    National numbers, March 12

    In New York City, where I live, COVID-19 test positivity is the lowest it’s been since early spring 2022. Chart from the NYC health department.

    In the past week (March 2 through 8), the U.S. officially reported about 170,000 new COVID-19 cases, according to the CDC. This amounts to:

    • An average of 24,000 new cases each day
    • 52 total new cases for every 100,000 Americans
    • 25% fewer new cases than last week (February 23-March 1)

    In the past week, the U.S. also reported about 20,000 new COVID-19 patients admitted to hospitals. This amounts to:

    • An average of 2,800 new admissions each day
    • 6.1 total admissions for every 100,000 Americans
    • 13% fewer new admissions than last week

    Additionally, the U.S. reported:

    • 1,900 new COVID-19 deaths (270 per day)
    • 90% of new cases are caused by Omicron XBB.1.5; 2% by XBB.1.5.1; 1% by CH.1.1 (as of March 11)
    • An average of 50,000 vaccinations per day

    Following the same pattern we’ve seen for the last few weeks, COVID-19 spread is still on the decline nationally. Official case counts, hospital admissions, and wastewater surveillance data all continue to point in this direction.

    This week, the decline in CDC-reported cases was sharper than it’s been in a couple of months (with 25% fewer cases reported than the prior week). But this may be due to reporting issues, rather than an actual change in transmission patterns: the CDC’s case trends page explains that Florida, Washington State, and Utah all did not report cases in the week ending March 8.

    Still, I’m heartened by the fact that hospital admissions—which are reported more reliably—dropped by 13% this week, compared to smaller week-over-week changes over the last month. Wastewater surveillance data from Biobot also continue to show steady declines, though we’re still not close to the national lows observed during this time in 2021 and 2022.

    Biobot’s data suggest declining surveillance in all four major regions of the country, with coronavirus levels in the Northeast now dropping below the Midwest, South, and West coast. Some individual counties in the Midwest are still reporting increased viral concentrations in their wastewater; I specifically noted Sheridan County and Teton County, Wyoming in Biobot’s data.

    Omicron XBB.1.5 has been the dominant variant in the U.S. since mid-January, and we have yet to see a new subvariant rise to meaningfully compete with it. CH.1.1, which has driven increased transmission in other parts of the world, has remained under 2% of new cases nationally, per the CDC’s estimates.

    The CDC’s latest variant update also breaks out XBB.1.5.1, an offshoot of XBB.1.5, at about 2% of new cases nationally. I have yet to see much discussion of this offshoot or how it differs from XBB.1.5; I’ll cover it more in future issues as we learn more. In addition, variant experts are keeping an eye on XBB.1.9, XBB.1.16, and other subvariants that have further mutated from the XBB lineage.

    In his latest Substack newsletter, long-time COVID-19 commentator Eric Topol suggests that the U.S. might be in a welcome “break from COVID-19 waves.” He points to XBB.1.5’s dominance and the fact that its rise “was not associated with a surge of COVID-19 hospitalizations or deaths in the United States or elsewhere in the world” despite the subvariant’s increased capacity to spread.

    At the same time, Topol explains the problem with our current “high baseline” of continued COVID-19 spread, which leads to continued severe cases among vulnerable people and the ongoing risk of Long COVID. He also explores the potential for another Omicron-like event, which would potentially cause another major surge. His article is helpful for understanding our current COVID-19 moment.

    In NYC, where I live, COVID-19 case rates and test positivity are lower than they’ve been since early 2022—while still much higher than we saw last spring post-Omicron BA.1, or in spring 2021 as vaccines were widely rolled out. And the numbers are likely going to get more unreliable soon, as the city begins to wind down public testing sites.

    more national numbers

  • Sources and updates, March 5

    • FDA authorizes joint COVID/flu rapid test, but there’s a catch: Late last week, the FDA issued emergency use authorization to the U.S.’s first at-home, rapid test capable of detecting both COVID-19 and the flu. This could be a really useful tool for people experiencing respiratory symptoms, since COVID-19 and flu can appear so similar. But you might not be seeing this test on pharmacy shelves anytime soon: Lucira Health, the test’s manufacturer, just declared bankruptcy. And the company actually blamed FDA authorization delays for contributing to its financial situation, as it had produced supplies anticipating a fall/winter sale of tests. Brittany Trang at STAT News reported on the situation; read her story for more details.
    • COVID-19 surveillance stressed out essential workers: For a new report, the nonprofit Data & Society interviewed 50 essential workers from meatpacking and food processing, warehousing, manufacturing, and grocery retail industries about their experiences with COVID-19 surveillance efforts, like temperature checks and proximity monitoring. Overall, workers found that these surveillance measures added time and stress to the job but did not actually provide information about COVID-19 spread in their workplaces. (Companies often cited privacy concerns as a reason not to share when someone got sick, according to the report.) The report shows how health data often doesn’t make it back to the people most impacted by its collection.
    • Vaccinations vs. Long COVID meta-analysis: A new paper published this week in the BMJ examines how COVID-19 vaccination impacts Long COVID risk. The researchers (at Bond University in Australia) performed a meta-analysis, compiling results from 16 prior studies. While the studies overall showed that vaccination can decrease risk of getting Long COVID after an infection (and may reduce symptoms for patients already sick with Long COVID), the studies were too different in their methodologies to actually allow for “any meaningful meta-analysis,” the authors noted. To better study this question, more rigorous clinical trials are needed, the researchers wrote.
    • Tracking Long COVID with insurance data: Another notable Long COVID paper, published this week in JAMA Health Forum: researchers at the insurance company Elevance Health compared health outcomes for about 13,000 people with post-COVID symptoms compared to 27,000 who did not have symptoms. The researchers found that, in the one year following acute COVID-19, Long COVID patients had higher risks for several health outcomes, including strokes, heart failure, asthma, and COPD; people in the post-COVID cohort were also more likely to die in that year-long period. I expect insurance databases like the one used in this paper may become more common Long COVID data sources. Also, see Eric Topol’s Substack for commentary.
    • FDA committee recommends RSV vaccine applications: Finally, a bit of good news on the “other respiratory viruses” front: the FDA’s vaccine advisory committee has recommended the agency move forward with two applications for RSV vaccines. Major pharmaceutical companies Pfizer and GlaxoSmithKline (GSK) have been working on RSV vaccine options; while early data appear promising, clinical trials on both vaccines have found potentially concerning safety signals. The trial populations have been relatively small, making these signals difficult to interpret right now but worthy of additional study. As usual, Katelyn Jetelina at Your Local Epidemiologist has provided a great summary of the FDA advisory committee meeting.

    All featured sources

  • China’s not the only country with unreliable COVID-19 data

    China is currently facing a massive COVID-19 surge, after ending many of its stringent “zero COVID” policies in December. Some estimates suggest that the country is experiencing over a million new cases each day, and widespread travel over the Lunar New Year later this month will likely prolong the surge.

    Among U.S. media outlets covering the situation, a common topic is China’s lack of reliable COVID-19 data. For example: “The country no longer tallies asymptomatic infections or reliably reports COVID deaths—employing not the distortion of statistics but their omission,” writes Dhruv Khullar in The New Yorker.

    Articles like Khullar’s accurately describe how difficult it is to understand the scale of COVID-19’s impact on a country without accurate data. But they fail to explain that this is far from a uniquely Chinese problem. In fact, many of the same claims that writers and health experts have made about China could also apply to the U.S., albeit on a different scale.

    Some examples:

    • Without widespread PCR testing, officially-reported case counts are likely significant underestimates of true infections.
    • Public health agencies are no longer doing widespread contact tracing or attempting to track asymptomatic cases.
    • Official death statistics are also likely underestimates, due to errors and omissions on death certificates.
    • Unchecked spread of the virus could contribute to the development of new variants that evade prior infections and/or vaccinations, but such variants will be hard to quickly identify due to low testing rates.

    This Twitter thread, from the writer and podcast host Artie Vierkant, shows the similarities pretty clearly:

    Don’t get me wrong—the current surge in China is an immense tragedy. But we can’t talk about it in a vacuum, or ignore the very similar problems plaguing the U.S. and many other countries. Poor COVID-19 data is, unfortunately, a global issue right now.

    More international data

  • Data implications of China ending its zero-COVID policies

    Data implications of China ending its zero-COVID policies

    As China rolls back on COVID-19 safety measures, its rising case load is likely to shoot up further. Chart from Our World in Data.

    China has rolled back some of its most rigorous COVID-19 safety policies, essentially moving away from its “zero COVID” strategy, following recent protests. I am no expert on China’s political or health policies here, but I did want to share some reflections on what this rollback could mean for global COVID-19 data, citing from Katherine Wu’s recent story in The Atlantic.

    First of all, it’s important to note that we don’t have much information about coronavirus variants circulating in China. According to the global database GISAID, China has submitted a total of just 667 Omicron sequences—compared to nearly two million from the U.S. The country’s most recent sample was submitted on November 29, almost two weeks ago. Some reports, like this one in the Global Times, suggest that Omicron BF.7 is the dominant variant in Beijing, but the pattern could be different in other parts of the country.

    Without more data, it’s hard to say for sure. And this is concerning because, if a new variant evolves in China as the virus spreads more widely there in the coming weeks, it could take more time for the rest of the world to learn about it than if a new variant emerged in other countries. Quick responses and international collaboration have been crucial in responses to new variants over the last two years; the global scientific community needs to be prepared to study and adapt to any new variant that might come out of China.

    At the same time, China’s case data are going to become less reliable as the country reduces its clinical testing. Daily case numbers have already appeared to drop, per Our World in Data, but this could be a product of less testing for asymptomatic people (and/or data delays) rather than a surge actually turning around. I also noted that Our World in Data does not have any testing numbers for China more recent than April 2022.

    China is already more limited at sharing COVID-19 data than other countries. But if case numbers become less reliable, it will get harder for international health experts to keep tabs on how bad China’s surge is getting. And it could get very bad: one modeling analysis, published in Nature in May, found that an unchecked Omicron wave in the country could lead to demand for intensive care units at 15.6 times the country’s current capacity—and 1.55 million deaths.

    Based on its current healthcare system, China is not prepared for a massive national surge of severe COVID-19 cases. It’s probably even less prepared for the massive surge of Long COVID cases that could follow. This has implications for global health, economics, and more.

    From the last paragraph of Wu’s great article:

    Even without a spike in severe disease, a wide-ranging outbreak is likely to put immense strain on China—which may weigh heavily on its economy and residents for years to come. After the SARS outbreak that began in 2002, rates of burnout and post-traumatic stress among health-care workers in affected countries swelled. Chinese citizens have not experienced an epidemic of this scale in recent memory, Chen told me. “A lot of people think it is over, that they can go back to their normal lives.” But once SARS-CoV-2 embeds itself in the country, it won’t be apt to leave. There will not be any going back to normal, not after this.

    More international data

  • Answering reader questions about data interpretation, good masking

    Answering reader questions about data interpretation, good masking

    As this chart from Biobot shows, trends in wastewater and case data often look a bit different. But how do you compare wastewater numbers to true infection numbers?

    This week, I’m sharing answers to three questions from readers that came in recently, through emails and the COVID-19 Data Dispatch Google form. The questions discuss interpreting wastewater and case data, and an interesting masking conundrum.

    Q1: Comparing wastewater trends to case trends

    I would love to know if there is any data on what levels of COVID in wastewater equals what risk level—are there any guidelines that could be used to turn masking policies on or off, for example? We know going up is bad and that the data is noisy but, if there’s any information on what concentrations in sewage corresponds to what level of cases I would love to know.

    I would love to be able to point you to specific guidelines about matching wastewater levels to cases, but unfortunately this isn’t really available right now. And if it were available, you would likely need to tailor the analysis pretty closely to where you live.

    An ongoing challenge with using wastewater surveillance data, as I wrote about for FiveThirtyEight and MuckRock in the spring, is that this type of environmental information is categorically pretty different from traditional case data. When a public health agency provides case numbers, they are adding up results from tests done in hospitals, doctors’ offices, and other healthcare settings. Each test result generally represents one person and can be interpreted with that framework.

    But with wastewater data, figuring out exactly what your test results represent can be more complicated. The data generally include people sick with COVID-19 who shed the coronavirus in their waste, but different people might shed different amounts of virus depending on what stage of illness they’re at, the severity of their symptoms, and possibly other factors that scientists are still working to figure out. Environmental factors like a big rainstorm or runoff from nearby agriculture could also interfere with the data. Population shifts, like college students returning to their campus after a break, can cause noise, too.

    As a result, public health experts who interpret wastewater data generally need a lot of data—like, a year or more of testing’s worth of data—from a specific location in order to analyze how wastewater trends correlate with case trends. And the data has to be consistent; if your wastewater collection team switches their sample processing methods halfway through the year, that might interrupt the analysis.

    A few institutions have figured out the wastewater-to-cases correlation for their communities. For examples, see the section on San Diego in this story and this paper by researchers in Gainesville, Florida. But for most research groups and health departments, it’s still a work in progress.

    All of that said, I don’t think this complexity should stop individuals or organizations from using wastewater data to recommend turning mask policies (or other policies) on or off. This surveillance might be less precise, but a sustained increase in coronavirus concentrations in the sewer is still certainly cause for concern and can be used to inform public health guidance.

    Q2: Estimating case underreporting

    How do you estimate how undercounted COVID testing is? Asking because I work for Whentotest.org—our COVID Risk Quiz assumes that COVID testing is undercounted by 7x, but I believe I’ve seen you estimate that it could be undercounted by as much as 20x. Wondering how you get to that number—we want to keep our Quiz as up to date as possible, and that number is a moving target.

    It is definitely a moving target, since COVID-19 testing (especially the lab-based PCR testing that generally contributes to official case numbers) can go up or down depending on people’s access to tests, perceptions of how much transmission is going on, and so many other factors.

    That said, I would personally put undercounting in the 10 times to 20 times range for this fall, likely with different levels of undercounting for different locations. I have two sources for the 20 times number: the first is an estimate from the Institute for Health Metrics and Evaluation made in September, suggesting that 4% to 5% of infections in the U.S. were reported at that time. (If 5% of infections are reported, case counts are 20 times higher than reported cases.)

    My second source is a paper from epidemiologist Denis Nash and his team at the City University of New York, released as a preprint earlier this fall. The researchers surveyed a representative sample of 3,000 U.S. adults, finding that about 17% of the respondents had Omicron during a two-week period in the summer BA.5 surge. Extrapolating from the survey findings, the researchers estimated that about 44 million people across the country had COVID-19 in this timeframe—compared to 1.8 million reported cases. This estimate suggests reported cases were undercounted by a factor of 24.

    Unfortunately, I have to use months-old estimates here because the U.S. does not have a regular data source comparing cases to true infections. The Census and CDC’s Household Pulse Survey comes close to this, as it includes questions about whether survey respondents have recently received a COVID-19 diagnosis; but it doesn’t ask about rapid tests, recent exposure, or other factors needed to determine the true infection rate, so the numbers here are also underestimates.

    Personally, I keep a close eye out for survey studies like those done by Nash and his team at CUNY and use those results to inform how I interpret national case data. I’ll make sure to flag any future studies like this for readers.

    Q3: Nose-only masking

    I follow some masking subs on Reddit and folks periodically suggest to others or refer to hacking masks that only cover their nose (KN95, N95s, etc.) for dental appointments or unavoidable indoor eating scenarios. Assuming they’re successful in creating a proper seal for these “half masks,” would there actually be any scientific backing this is helpful in minimizing risk?

    I wasn’t sure how to answer this question, so I shared it on Twitter, tagging a couple of masking and ventilation experts I know.

    Overall, the consensus that emerged from my replies is that it could be helpful to wear a mask over one’s nose for short periods of time, but it’s hard to say for sure due to a lack of rigorous research in this area. Behavior also plays a big role in how effective such a mask might be in alleviating risk.

    One expert, Devabhaktuni Srikrishna, pointed out that having a sealed filter over one’s nose could reduce the amount of virus that gets inhaled, if the coronavirus is present in the space. (This “inhalation dose” might correlate with one’s chances of infection and/or severity of symptoms if infected, though research is still ongoing on these questions.)

    Achieving a sealed filter over the nose is easier said than done, though. You can’t just use a standard mask, since that’s designed for the nose and mouth. One commenter shared a system that he uses, an elastomeric nose mask held in place with a headband. Another suggested using nasal filters designed to block allergens. As far as I know, there hasn’t been any research showing what might be most successful—unlike the extensive research that has gone into showing the value of high-quality face-masks and respirators.

    In addition to the discussion of designing a nose-only mask, this reader’s question led to some discussion about the careful behavior needed to use it successfully. One commenter pointed out that, if you’re eating alone, it’s easier to stay focused on breathing patterns than if you’re eating in a group and engaged in conversation. I also appreciated this reply from a Louisiana-based behavioral scientist:

    So, to summarize, I’d say that a nose filter could be helpful for situations like a dentist appointment and could be helpful (but trickier) for indoor dining—but it’s hard to say for sure. A much easier conclusion: avoid indoor dining as much as possible during COVID-19 surges like the one we’re in right now.

    More reader responses

  • COVID source shout-out: New NIH tool to report at-home test results

    COVID source shout-out: New NIH tool to report at-home test results

    Make My Test Count is a new NIH website for people to report at-home COVID-19 test results.

    This week, the National Institutes of Health launched a new website that allows people to anonymously report their at-home test results. While I’m skeptical about how much useful data will actually result from the site, it could be a helpful tool to gauge how willing Americans are to self-report test results.

    The website, MakeMyTestCount.org, puts users through a series of basic questions about their at-home test experience: your test result, the test brand you used, when you tested, and whether you have COVID-19 symptoms. The site also asks for basic demographic information, including your age, ZIP code, race, and ethnicity. After you report your test result, the website provides additional context on interpreting that result, such as suggesting a repeat test in the next two days if you have symptoms.

    These survey questions mimic the information that typically gets collected when someone receives a PCR test, and the resulting data could potentially be used to examine who is using at-home tests and what their results are. The NIH’s Rapid Acceleration of Diagnostics (or RADx) initiative, a program to speed up development and use of COVID-19 testing technologies, designed the website.

    Of course, there are a lot of potential issues here. This website was launched more than two years after the first COVID-19 rapid tests were authorized and almost one year after they gained widespread popularity during the first Omicron surge. No matter how many people report their results now, the NIH will miss a lot of data and a lot of opportunities to advertise the site.

    And how many people will report their results now? Pandemic safety measures like at-home testing are less popular than they were a year ago, and the launch of this website doesn’t seem to be paired with a public outreach campaign about using and reporting at-home tests. Basically, the results shared with the NIH are likely to be biased towards people who still care about taking precautions (and those who pay attention to federal COVID-19 resources). It’s also very easy to submit false results, as the website doesn’t ask for a photo of your test or anything similar.

    Still, I’m excited to see this website launched—collecting some at-home test results is better than no test results! I hope lots of people use it, and I look forward to seeing any data the NIH eventually releases from the tool.

    more source spotlights
  • The U.S.’s flu and RSV surveillance is insufficient for tracking this fall’s outbreaks

    The U.S.’s flu and RSV surveillance is insufficient for tracking this fall’s outbreaks

    The CDC’s FluView dashboard does not provide precise case numbers, only an approximation of “activity level.”

    I recently received a question from a reader, asking how to follow both COVID-19 and the flu in the county where she lives. For COVID-19, county-level data sources aren’t too hard to find: the CDC still provides some clinical data—though case numbers are now updated weekly, instead of daily—and many counties have wastewater surveillance available. (See last week’s post for more details.)

    But following flu transmission is much harder: there’s no county-level tracking of this virus. The same thing goes for respiratory syncytial virus (RSV), a virus currently sending record cases to children’s hospitals across the country. There are a few data sources available, which I’ll list later in this post, though nothing as comprehensive as what we’ve come to expect for COVID-19.

    As I’ve previously written, the COVID-19 pandemic inspired nationwide disease surveillance at a level the U.S. has never seen before. The healthcare and public health systems had not previously attempted to count up every case of a widely-spreading virus and share that information back to the public in close-to-real-time.

    It’s unlikely that flu, RSV, and other diseases will get the same resources as COVID-19 did for intensive tracking—at least not in the near future. But the scale of data we’ve had during the pandemic reveals that our current surveillance for these diseases is pretty inadequate, even for such basic purposes as giving hospitals advanced warning about new surges. 

    Insufficient RSV data

    A recent CNN story by Deidre McPhillips and Jacqueline Howard explains how data gaps have hindered preparation for the current RSV surge. The reporters explain that the CDC’s RSV data are “based on voluntary reporting from a few dozen labs that represent about a tenth of the population.” The CDC uses these reports to provide weekly estimates about RSV cases, though recent data tend to be incomplete due to reporting delays.

    Here’s a helpful quote from the story (though I recommend reading the whole piece):

    “For hospitals [using CDC data], it’s a little like looking through the rearview mirror. They’ve already begun to experience that uptick in cases themselves before it’s noticeable in the federal data,” said Nancy Foster, vice president for quality and patient safety with the American Hospital Association.

    “We’re talking about data that are collected inside hospitals, transmitted through a data trail to get to the federal government, analyzed there and then fed back to hospitals.”

    In other words, it’s not surprising that we saw plenty of stories about higher-than-normal RSV cases in children’s hospitals before national data actually picked up the surge. For more details on why RSV is spreading this fall and how it’s impacting children’s hospitals, I recommend this piece by Jonathan Lambert at Grid.

    Insufficient flu data

    Meanwhile, this year’s flu season is clearly starting earlier than normal; but current data aren’t able to tell us how severe the season might get or who, exactly, is being hit the hardest. According to the CDC’s flu surveillance report for this week, the agency estimates that the U.S. has seen “at least 880,000 flu illnesses, 6,900 hospitalizations, and 360 deaths from flu” so far this fall.

    The CDC’s estimates come from networks of testing labs, hospitals, and outpatient healthcare providers that participate in the agency’s flu surveillance networks. National flu data tend to be imprecise estimates, clearly labeled as “preliminary” by the CDC, while state-by-state data are estimates reported with delays. Note, for example, that the CDC’s map of “influenza-like-activity” by state and by metro area provides only general categories of activity (ranging from “minimal” to “very high”) rather than actual case numbers.

    The flu data we have so far aren’t sufficient for making predictions about how the rest of this fall and winter will go, explains STAT’s Helen Branswell in a recent story. “The virus is maddeningly unpredictable,” she writes. U.S. experts often look to the flu season in the Southern Hemisphere, which precedes ours, for clues, but this can be unreliable (just as the U.S. shouldn’t rely on other countries for all its vaccine effectiveness data).

    For both flu and RSV, one major problem with our surveillance methods is that our systems overly rely on healthcare centers. When public health agencies have to wait for hospitals and clinics to report cases of these viruses before starting to analyze data, they miss the opportunity to warn healthcare providers at the very beginning of a surge—and give them time to prepare.

    In the future, expanding non-clinical surveillance methods like wastewater and population surveys to these diseases would provide more data, more quickly; both for healthcare providers and for the general public. (I provided some more specific ideas here.)

    Existing sources

    With all the above caveats in mind, here are a few sources you can look at to track flu and RSV:

    • CDC’s weekly flu surveillance report: This page is updated once a week with national estimates of flu activity, hospitalizations, flu virus variants, and more. Data tend to be preliminary.
    • CDC’s FluView dashboard: Information from the CDC’s flu surveillance system also appears on this dashboard in a more interactive format; for example, you can see how flu activity by state has changed over time.
    • CDC’s RSV trends report: Similar to its flu reports, the CDC provides weekly updates of estimated RSV tests and cases, including national, regional, and state-by-state trends.
    • Walgreens flu index: Walgreens tracks prescriptions for antiviral medications at its pharmacies as a proxy for flu activity, by state and for select metro areas. For more information on the index, see this press release.
    • WastewaterSCAN: The SCAN network, run by researchers at Stanford University and Emory University, tests wastewater for flu, RSV, and monkeypox in addition to COVID-19 in select counties across the U.S. So far, this network is the first I know of to publicly share flu and RSV wastewater data, though other researchers are working in this area.

    Please let me know if I missed any data sources! (You can email me or comment below.)

    More federal data