COVID-19 Data Dispatch

Category: Federal data

  • Booster shots: What we’ve learned—and what we still don’t know

    Booster shots: What we’ve learned—and what we still don’t know

    This week, the FDA’s vaccine advisory committee had a two-day meeting to discuss booster shots for Moderna’s and Johnson & Johnson’s COVID-19 vaccines. From the outside, these meetings may have appeared fairly straightforward: the committee voted unanimously to recommend booster shots for both vaccines.

    But in fact, the discussions on both days were wide-reaching and full of questions, touching on the many continued gaps in our knowledge about the need for additional vaccine doses. The FDA committee continues to make decisions based on rather limited data, as do other top U.S. officials. Case in point: on Thursday, the committee was asked to consider data from Israel’s booster shot campaign—which is utilizing Pfizer vaccines—as evidence for Moderna boosters in the U.S.

    In the Moderna vote on Thursday afternoon, committee member Dr. Patrick Moore, a virologist at the University of Pittsburgh, said that he voted “on gut feeling rather than really truly serious data.” The comment exemplified how much we still don’t know regarding the need for boosters, thanks in large part to the CDC’s failure to comprehensively track breakthrough cases in the U.S.

    Still, there are a few major facts that we have learned since the FDA and CDC discussions on Pfizer boosters that took place a couple of weeks ago. Here’s my summary of what we’ve learned—and what we still don’t know.

    What we’ve learned since the Pfizer discussion:

    Israel’s booster rollout continues to align with falling case numbers. On Thursday, representatives from the Israeli national health agency presented data on their booster shot rollout—which, again, is using Pfizer vaccines. The vast majority of seniors in Israel have now received a third dose, and over 50% of other age groups have as well. According to the Israeli scientists, this booster rollout both decreased the risk of severe COVID-19 disease for older adults and helped to curb the country’s Delta-induced case wave, causing even unvaccinated adults to have a decreased risk of COVID-19.

    In Israel, severe cases among both vaccinated and unvaccinated adults decreased after the country provided third Pfizer doses to its residents. Screenshot taken from Thursday’s VRBPAC meeting.

    You can read more about Israel’s booster campaign in this paper, published in the New England Journal of Medicine in early October. It’s worth noting, however, that Delta is known to spur both case increases and decreases in cycles that can be somewhat unpredictable—and may not be exactly linked to vaccination. So, I personally take the Israeli claims that boosters stopped their case wave with a grain of salt.

    Decreased vaccine effectiveness against infection may be tied more to Delta and behavioral factors than “waning antibodies.” This week, the New York State Department of Health (DOH) announced results from a large study of vaccine effectiveness which is, from what I’ve seen, the first of its kind in the U.S. The New York DOH used state databases on COVID-19 vaccinations, tests, and hospitalizations to examine vaccine effectiveness against both infection and hospitalization in summer 2021, when Delta spread rapidly through the state.

    They found that vaccine effectiveness against infection did decline over the summer. But the declines occurred similarly for all age groups, vaccine types, and vaccine timing (i.e. which month the New Yorkers in the study received their vaccines)—suggesting that the decline in effectiveness was not tied to waning immune system protection. Rather, the effectiveness decline correlated well with Delta’s rise in the state. It also correlated with reduced safety behaviors, like the lifting of New York’s indoor mask mandate and the reopening of various businesses.

    Vaccine effectiveness against hospitalization declined for older adults, but remained at very high levels for New Yorkers under age 65, the study found. Here’s what lead author Dr. Eli Rosenberg said in a statement:

    The findings of our study support the need for boosters in older people in particular, and we encourage them to seek out a booster shot from their health care provider, pharmacy or mass vaccination site. We saw limited evidence of decline in effectiveness against severe disease for people ages 18 to 64 years old. While we did observe early declines in effectiveness against infections for this age group, this appears to have leveled off when the Delta variant became the predominant strain in New York. Together, this suggests that ongoing waning protection may be less of a current concern for adults younger than 65 years.

    I was surprised that this study didn’t come up in the FDA advisory committee meetings this week, and will be curious to see if it’s cited in future booster shot discussions. The study does align, however, with the committee’s decision against recommending booster shots for all adults over age 18 who received Moderna vaccines.

    Johnson & Johnson vaccine recipients appear to need boosters more than mRNA vaccine recipients. On Friday, presentations from both J&J representatives and FDA scientists made a clear case for giving J&J vaccine recipients a second dose of this adenovirus vaccine. In one 30,000-patient study, patients who received a second J&J shot two months after their first shot saw their vaccine efficacy (against symptomatic infection) rise from 74% to 94%.

    Interestingly, unlike the Pfizer and Moderna vaccines, a J&J shot’s ability to protect against coronavirus infection appears relatively stable over time. However, a booster shot can make this vaccine more effective—especially against variants. Despite arguments from J&J representatives that their vaccine’s second dose should come six months after the first dose, the FDA advisory committee voted to recommend second J&J shots just two months after the first dose, for all adults over age 18.

    It’s worth noting that this vaccine regimen might effectively change J&J’s product from a one-shot vaccine to a two-shot vaccine. STAT’s Helen Branswell and Matthew Herper go into the situation more in their liveblog.

    Mixing and matching vaccines is a strong strategy for boosting immunity, especially if one of the vaccines involved uses mRNA technology. This week, the National Institutes of Health (NIH) released a highly anticipated study (posted as a preprint) on mix-and-match vaccine regimens. The NIH researchers essentially tested every possible booster combination among the three vaccines that have been authorized in the U.S. Before and after vaccination, the researchers took blood samples and tested for antibodies that would protect against the coronavirus.

    In short, the NIH study found that all three vaccines—Pfizer, Moderna, and J&J—will provide a clear antibody boost to people who have received any other vaccine. But the mRNA vaccines (Pfizer and Moderna) provide bigger benefits, both in the form of higher baseline antibody levels (after two shots) and a higher boost. The best combination was a J&J vaccine initially, followed by a Moderna booster, Dr. Katelyn Jetelina notes in a Your Local Epidemiologist summary of the study.

    Every vaccine provided a “boost” of protective antibodies to recipients of every other vaccine. Figure from the NIH preprint. mrna-1273 refers to the Moderna vaccine, Ad26.COV2.S refers to the J&J vaccine, and BNT162b2 refers to the Pfizer vaccine.

    The booster regimens also appeared to be safe, with limited side effects. But this was a relatively small study, including about 450 people. In their discussion on Friday afternoon, the FDA advisory committee members said that they would be very likely to authorize mix-and-match vaccine regimens after seeing more safety data.

    Moderna and J&J boosters appear to be safe, with similar side effects to second shots. Safety data from Moderna’s and J&J’s clinical trials of their booster shots, along with data from the NIH mix-and-match study, indicate that the additional doses cause similar side effects to first and second doses. After a booster, most recipients had a sore arm, fatigue, and other relatively minor side effects.

    And here’s what we still don’t know:

    Which medical conditions, occupations, and other settings confer higher breakthrough case risk? I wrote about this issue in detail in September. The U.S. continues to have little-to-no data on breakthrough case risk by specific population group, whether that’s groups of people with a specific medical condition or occupation. This data gap persists, even though U.S. researchers have some avenues for breakthrough risk analysis at their disposal (see: this post from last week).

    This lack of data came up in FDA advisory committee discussions on Thursday. An FDA representative was unable to cite any evidence that people in specific occupational settings are at a higher risk for breakthrough cases.

    Are there any rare vaccine side effects that may occur after breakthrough doses? When I covered the FDA advisory committee meeting on Pfizer boosters, I noted that Pfizer’s clinical trial of these shots included just 306 participants—providing the committee members with very limited data on rare adverse events, like myocarditis. Well, Moderna’s clinical trial of its booster shots was even smaller: just 171 people. J&J had a larger clinical trial, including over 9,000 people.

    These trials and the NIH mix-and-match study indicated that booster shots cause similar side effects to first and second shots, as I noted above. But few clinical trials are large enough to catch very rare (yet more serious) side effects like myocarditis and blood clots. (In J&J’s case, blood clots occur roughly twice for every million doses administered.) Federal officials will carefully watch for any side effects that show up when the U.S.’s booster rollout begins for Moderna and J&J.

    How do antibody levels correlate to protection against COVID-19, and what other aspects of the immune system are involved? The NIH mix-and-match study focused on measuring antibody levels in vaccine recipients’ blood, as did other booster shot trials. While it may sound impressive to say, for example, “J&J recipients had a 76-fold increase in neutralizing antibodies after receiving a Moderna booster,” we don’t actually know how this corresponds to protection against COVID-19 infection, severe disease, and death.

    Some experts—including a couple of those on the FDA advisory committee—have said that discussions focusing on antibodies distract from other types of immunity, like the memory cells that retain information about a virus long after antibody levels have fallen. More research is needed to tie various immune system measurements to real-world protection against the coronavirus.

    What needs to happen at the FDA for mix-and-match vaccination to be authorized? One challenge now facing the FDA is, the federal agency has clear evidence that mix-and-match vaccine regimens are effective—but it does not have a traditional regulatory pathway to follow in authorizing these regimens. Typically, a company applies for FDA authorization of its specific product. And right now, no vaccine company wants to apply for authorization of a regimen that would involve people getting a different product from the one that brings this company profit.

    So, how will the FDA move forward? There are a couple of options, like the CDC approving mix-and-match boosters directly. See this article for more info.

    Finally: I can’t end this post without acknowledging that, as we discuss booster shots in the U.S., millions of people in low-income countries have yet to even receive their first doses. Many countries in Africa have under 1% of their populations vaccinated, according to the Bloomberg tracker. While the Biden administration has pledged to donate doses abroad, boosters take up airtime in expert discussions and in the media—including in this publication. Boosters distract from discussions of what it will take to vaccinate the world, which is our true way out of the pandemic.

    More vaccine reporting

  • 12 Long COVID stats that demonstrate the importance of vaccination

    12 Long COVID stats that demonstrate the importance of vaccination

    Long COVID patients may suffer from about 100 different possible symptoms. Chart via Patient-Led Research Collaborative.

    Last week, one of the reader questions I answered addressed Long COVID, the condition in which people have COVID-related symptoms for weeks or months after their initial coronavirus infection. One reader had asked about monitoring for Long COVID patients (also called long-haulers); I later received another question about the risks of Long COVID after vaccination.

    These questions made me realize that I’ve devoted very little space to Long COVID in the COVID-19 Data Dispatch—even though I consider it one of the biggest COVID-19 data gaps in the U.S. Though it’s now been well over a year since the first Long COVID patients were infected, there is still so much we don’t know about the condition.

    For example, we don’t know a very rudimentary number: how many people in the U.S. are struggling with Long COVID. We also don’t have a clear, detailed picture of Long COVID symptoms, or how these symptoms arise from a coronavirus infection, or how they impact the daily lives of Long COVID patients.

    Why does this massive data gap exist? Long COVID studies are challenged by the lack of standardized patient data in the U.S., making it difficult to identify symptom patterns across large groups of people. We face a similar problem in tracking breakthrough cases, demographic information, and other COVID-19 trends.

    Plus, thanks to limited COVID-19 testing in the U.S. throughout the pandemic (and restrictions on who could get tested, back in spring 2020), a lot of Long COVID patients never had a positive test result—making it difficult for them to get a formal diagnosis. And many of the Long COVID studies that have been conducted focus on patients who had a positive COVID-19 test or were hospitalized for the disease, thus narrowing much of our clinical data to a small subset of the actual Long COVID population. 

    As I noted last week, the National Institutes of Health (NIH) has set up a major research initiative to study Long COVID. This initiative, called RECOVER, is poised to become our best source for Long COVID data in the future. But it’s in early stages right now, beginning to distribute funding to different research groups and recruit Long COVID patients for study. It could be years before we get results.

    All of that said, there are still a few things we know about Long COVID based on research thus far. Here’s a roundup of twelve key statistics.

    • Between 10% and 30% of coronavirus infections lead to Long COVID. This statistic comes from the NIH’s RECOVER Initiative website; it summarizes findings from past studies. Consider: 30% of the 44.2 million Americans with a documented COVID-19 case amounts to 13.3 million people with Long COVID. Even 10% of those 44.2 million would amount to 4.4 million people.
    • Some studies suggest that as many as one-third of COVID-19 patients may have persistent or returning symptoms. A recent study of electronic health records in the U.S. and U.K., run by scientists at the University of Oxford, suggests that the true share of COVID-19 patients who contract Long COVID is on the higher end of that 10%-30% estimate that the NIH provides. This new study found that 36% of COVID-19 patients (among a sample size of 270,000) had symptoms three and six months after their diagnosis.
    • Long COVID may manifest with over 100 different potential symptoms. There is a Long COVID paper that I personally come back to, whenever I want to see a clear picture of the many ways that this condition can impact patients. The paper, published in The Lancet in July, reports results from a survey of over 3,000 Long COVID patients conducted by the Patient-Led Research Collaborative. According to this survey, Long COVID patients may suffer from about 100 possible symptoms, including systemic, reproductive, cardiovascular, musculoskeletal, immunologic/autoimmune, head/eyes/ears/nose/throat, pulmonary, gastrointestinal, and dermatologic symptoms.
    • Long COVID symptoms may change over time. The Patient-Led Research Collaborative survey found that some patients may have changing symptoms, or relapses brought on by different activities. One very common Long COVID symptom is Post-Exertional Malaise, a condition in which patients experience a relapse after physical or mental exertion, even if that exertion is relatively minor.
    • Some Long COVID patients have been sick for over 18 months. The Patient-Led Research Collaborative survey covers symptoms over a course of seven months, but some Long COVID patients have been suffering for far longer. Some patients who initially contracted the coronavirus in spring 2020, during the first wave in the U.S., have now been sick for 18 months or more.
    • Many Long COVID patients are unable to work. According to the Patient-Led Research Collaborative survey, almost half of the Long COVID patients who responded (45%) “required a reduced work schedule, compared to pre-illness.” Another 22% were not working at the time of the survey because of Long COVID. Other studies have backed up the findings from this survey. At this point in the pandemic, some Long COVID patients are struggling to receive accommodations from their employers, even though the condition is recognized as a disability at the federal level.
    • Long COVID can occur at all age ranges, but is documented most in younger and middle-age adults. Among respondents to the Patient-Led Research Collaborative survey, about 24% were in their thirties, 31% in their forties, and 25% in their fifties—though patients ranged in ages from 18 to over 80. This survey and others have also found that Long COVID seems to be more common for women; this pattern aligns with other post-infectious conditions, like chronic fatigue syndrome and chronic Lyme disease.
    • Long COVID may lead to long-term neurological issues. This past summer at the Alzheimer’s Association International Conference, a few researchers presented findings on Long COVID and Alzheimer’s. Brain scans of COVID-19 patients, along with observations of patients’ prolonged symptoms, suggest that adults who suffer from Long COVID may have an increased risk of Alzheimer’s later in life. Severe COVID-19 patients in their sixties and seventies are already starting to see symptoms matching early-onset Alzheimer’s, one researcher told NPR.
    • Autoimmune response may be one cause for Long COVID symptoms. While scientists are still working to determine exactly how a coronavirus infection may lead to numerous symptoms, research thus far suggests that overreaction of the immune system could be a major player. Some clinicians who work with Long COVID patients have developed treatments based on dysautonomia, medical conditions caused by immune and autonomic nervous system issues.
    • About 5,200 children in the U.S. have been diagnosed with MIS-C, and 46 have died. MIS-C stands for Multisystem Inflammatory Syndrome in Children. The condition follows a COVID-19 infection in rare cases, leading to inflammation of different parts of the body. While this condition is not directly comparable to Long COVID, scientists think it may have similar causes. The condition has disproportionately impacted children of color in the U.S.: out of 5,200 cases, 61% are Black or Hispanic/Latino.
    • The risk of Long COVID is dramatically lower after a breakthrough infection, even if you contract the coronavirus. A recent study published in The Lancet found that vaccinated patients who later had a breakthrough COVID-19 case were about half as likely to report symptoms after four weeks, compared to unvaccinated patients who had a non-breakthrough COVID-19 case. Plus, vaccinated people are already far less likely to contract the coronavirus in the first place, because vaccination reduces risk of infection. Commenting on the study, NIH Director Dr. Francis Collins called it “encouraging news,” though he cautioned that more research is needed on this topic.
    • Vaccination may help alleviate COVID-19 symptoms for Long COVID patients. In addition to reducing one’s risk of developing Long COVID, vaccination can alleviate symptoms for Long COVID patients. A recent preprint, posted online at the end of September, found that Long COVID patients who got vaccinated were about twice as likely to completely recover, compared to unvaccinated patients. “Overall, this study adds to growing evidence that vaccines can improve symptoms and lessen the disease impact in Long COVID,” wrote Long COVID researcher Dr. Akiko Iwasaki, sharing the study on Twitter.

    To me, these Long COVID statistics—along with everything we still don’t know about the condition—provide a strong argument for vaccination. Long COVID can impact people who were young and healthy before they were infected, completely messing up their lives for months or even years. It surprises me that public health and political leaders don’t discuss this condition more when they tell people to get vaccinated.

    As for continued research: the NIH’s RECOVER Initiative has received over $1 billion in funding from Congress, and it’s just getting started on setting up studies. If you’re interested in learning more about the research—or signing up to participate in a RECOVER study—you can sign up for email alerts on the NIH website. 

  • The data problem underlying booster shot confusion

    The data problem underlying booster shot confusion

    This is all the breakthrough case data that the CDC gives us. Screenshot taken on September 26.

    This past Thursday, an advisory committee to the CDC recommended that booster doses of the Pfizer vaccine be authorized for seniors and individuals with high-risk health conditions. The committee’s recommendation, notably, did not include individuals who worked in high-risk settings, such as healthcare workers—whom the FDA had included in its own Emergency Use Authorization, following an FDA advisory committee meeting last week.

    Then, very early on Friday morning, CDC Director Rochelle Walensky announced that she was overruling the advisory committee—but agreeing with the FDA. Americans who work in high-risk settings can get booster shots. (At least, they can get booster shots if they previously received two doses of Pfizer’s vaccine.)

    This week’s developments have been just the latest in a rather confusing booster shot timeline:

    Why has this process been so confusing? Why don’t the experts agree on whether booster shots are necessary, or on who should get these extra shots? Part of the problem, of course, is that the Biden administration announced booster shots were coming in August, before the scientific agencies had a chance to review all the relevant evidence.

    But from my (data journalist’s) perspective, the booster shot confusion largely stems from a lack of data on breakthrough cases.

    Let’s go back in time—back four months, or about four years in pandemic time. In May, the CDC announced a major change in its tracking of breakthrough cases. The agency had previously investigated and published data on all breakthrough cases, including those that were mild. But starting in May, the CDC was only investigating and publishing data on those severe breakthrough cases, i.e. those which led to hospitalization or death.

    At the time, I called this a lazy choice that would hinder the U.S.’s ability to track how well the vaccines are working. I continued to criticize this move, when researchers and journalists attempted to do the CDC’s job—but were unable to provide data as comprehensive as what the CDC might make available. 

    Think about what might have been possible if the CDC had continued tracking all breakthrough cases, or had even stepped up its investigation of these cases through increased testing and genomic sequencing. Imagine if we had data showing breakthrough cases by age group, by high-risk health condition, or by occupational setting—all broken out by their severity. What if we could compare the risk of someone with diabetes getting a breakthrough case, to the risk of someone who works in an elementary school?

    If we had this kind of data, the FDA and CDC advisory committees would have information that they could use to determine the potential benefits of booster shots for specific subsets of the U.S. population. Instead, these committees had to make guesses. Their guesses didn’t come out of nowhere; they had scientific studies to review, data from Pfizer, and information from Israel and the U.K., two countries with better public health data systems than the U.S. But still, these guesses were much less informed than they might have been if the CDC had tracked breakthrough cases and outbreaks in a more comprehensive manner.

    From that perspective, I can’t really fault the CDC and the FDA for casting their guesses with a fairly wide net—including the majority of Americans who received Pfizer shots in their authorization. There’s also a logistical component here; the U.S. has a lot of doses that are currently going unused (thanks to vaccine hesitancy), and may be wasted if they aren’t used as boosters.

    But it is worth emphasizing how a lack of data on breakthrough cases has driven a booster shot decision based on fear of who might be at risk, rather than on hard evidence about who is actually at risk. Other than seniors; the risk for that group is fairly clear.

    The booster shot decision casts a wide net. But at the same time, it creates a narrow band of booster eligibility: only people who got two doses of Pfizer earlier in 2021 are now eligible for a Pfizer booster. Recipients of the Moderna and Johnson & Johnson vaccines are still left in the dark, even though some of those people may need a booster more than many people who are now eligible for additional Pfizer shots. (Compare, say, a 25-year-old teacher who got Pfizer to a 80-year-old, living in a nursing home, with multiple health conditions who got Moderna.)

    That Pfizer-only restriction also stems from a data issue. The federal government’s current model for approving vaccines is very specific: first a pharmaceutical company submits its data to the FDA, then the FDA reviews these data, then the FDA makes a decision, then the CDC reviews the data, then the CDC makes a decision.

    By starting with the pharmaceutical company, the decision-making process is restricted to options presented by that company. As a result, we aren’t seeing much data on mixing-and-matching different vaccines, which likely wouldn’t be profitable for vaccine manufacturers. (Even though immunological evidence suggests that this could be a useful strategy, especially for Johnson & Johnson recipients.)

    In short, the FDA and CDC’s booster shot decision is essentially both ahead of evidence on who may benefit most from a booster, but behind evidence for non-Pfizer vaccine recipients. It’s kind-of a mess.

    I also can’t end this post without acknowledging that we need to vaccinate the whole world, not just the U.S. Global vaccination went largely undiscussed at the FDA and CDC meetings, even though it is a top concern for many public health experts outside these agencies.

    At an international summit this week, President Biden announced more U.S. donations to the global vaccine effort. His administration seems convinced that the U.S. can manage both boosters at home and donations abroad. But the White House only has so much political capital to spend. And right now, it’s pretty clearly getting spent on boosters, rather than, say, incentivizing the vaccine manufacturers to share their technology with the Global South.

    I can only imagine this situation getting messier in the months to come.

    More vaccine reporting

  • Biden’s new COVID-19 plan excludes data

    Biden’s new COVID-19 plan excludes data

    No mention of data reporting or infrastructure here. Screenshot taken from whitehouse.gov on September 12.

    On Thursday, President Joe Biden unveiled a major new plan to bring the U.S. out of the pandemic. If you missed the speech, you can read through the plan’s details online.

    Key points include vaccination requirements for large employers, federal workers, and federal contractors; booster shots (if the FDA and CDC approve them); and making rapid tests more accessible for the average American. Much of the plan aligns with safety strategies that COVID-19 experts have been recommending for months—or, in the case of rapid testing access, over a year.

    But I and other data nerd friends were quick to notice that one major topic is missing: data collection. Numerous reports and investigations have demonstrated how the U.S.’s underfunded state and local public health agencies were completely unprepared to collect and report COVID-19 metrics, hindering our response to the pandemic. (This POLITICO investigation is one recent example of such a story.) Local data collection has gotten even worse during the latest surge, as many states cut back on their COVID-19 reporting and the federal government has failed to comprehensively track breakthrough cases.

    As a result, one might expect Biden’s plan to take steps towards improving COVID-19 data collection in the U.S. Perhaps the plan could have provided funding to local public health agencies, tied to a requirement that they report certain COVID-19 metrics on a daily basis. Perhaps it could have included increased tracking for breakthrough cases, or increased genomic sequencing to identify the next variant that inevitably becomes a concern after Delta.

    Instead, the plan’s only mention of “data” is a line about how well the vaccines work: “recent data indicates there is only 1 confirmed positive case per 5,000 fully vaccinated Americans per week.”

    Without prioritizing data, the Biden administration is failing to prepare the U.S.—both for future phases of this pandemic and for future public health crises.

    more federal data
  • U.S. moves to approve booster shots despite minimal evidence

    U.S. moves to approve booster shots despite minimal evidence

    Timeline of the scientific results and policy moves leading up to Wednesday’s announcement. Chart via Your Local Epidemiologist.

    This week, the federal government announced that the U.S. intends to provide third vaccine doses to all Americans who received the Pfizer or Moderna vaccines. This booster shot distribution will start in September, with adults becoming eligible once they hit eight months after their second shot.

    While the booster shot regimen still must be approved by the FDA and CDC, federal officials are making it sound like a pretty sure thing—President Biden himself announced the decision at a press conference on Wednesday. However, many epidemiologists, vaccine experts, global health experts, and other scientists have criticized the decision.

    Here are three main criticisms I’ve seen in the past few days.

    First: Scientific evidence is lacking. As the booster shot decision was announced on Wednesday, the CDC published three new studies that appear to show a decline in the Pfizer and Moderna vaccines’ ability to stave off symptomatic COVID-19 infection after several months. One of these reports, from a network of U.S. nursing homes, suggests that efficacy among nursing home residents fell to just 53% by June and July 2021, many months after this vulnerable population was vaccinated. The other two reports show similar declines, though the CDC found that vaccination remains effective against severe disease, hospitalization, and death.

    The federal government—and others arguing in favor of booster shots—have also pointed to data from Israel, which appear to similarly demonstrate that the vaccines lose their effectiveness after several months. In Israel, where almost 80% of residents over age 12 are vaccinated, the majority of those hospitalized with COVID-19 are now fully vaccinated individuals.

    But the act of interpreting these data is more complicated than it first appears. In a blog post at COVID-19 Data Science, biostatistics professor Jeffrey Morris explains that, when the majority of a population is vaccinated, vaccination numbers will go up in this population simply because they are the majority. But the risk remains far higher for the unvaccinated. Plus, Morris explains, stratifying hospitalization numbers by age reveals that older adults are more likely to have a severe COVID-19 case regardless of vaccination status, while younger adults are less likely to be vaccinated (and thus have a non-breakthrough case).

    Simply put, the vaccines do still work well against severe COVID-19—you just need to be precise in calculating effectiveness. And yet, the U.S. government is saying that vaccine efficacy wanes so much, everyone’s going to need a third shot in the fall or early next year. This suggests that the federal government has more data that it is not sharing publicly, which leads us to the second criticism.

    Second: Transparency is also lacking. Typically, when the government makes a decision about approving a new medical product, this decision follows a series of prescribed steps: data submission from the company behind the product, review by FDA scientists, FDA approval, followed by more review by other agencies (such as the CDC or the Centers for Medicare & Medicaid Services) as needed. Review meetings are typically open to the public, with data shared in advance of a decision. In the case of these booster shots, however, the president has announced a specific rollout plan before full scientific review has taken place.

    As STAT’s Helen Branswell explains:

    To many experts, including Baylor, the sequencing of the decisions being made is also out of whack. While U.S. health officials said booster shots could start being offered the week of Sept. 20, the Food and Drug Administration has not even ruled yet on Pfizer’s application for approval of a third shot; it was filed only Monday. Moderna hasn’t yet asked the agency to authorize a third shot at all.

    Plus, remember that the CDC has not publicly shared any comprehensive data on breakthrough cases since the spring, before Delta became dominant.

    The FDA and CDC will certainly still be reviewing the need for booster shots, but the experts cited in Branswell’s piece are skeptical that any decision other than, “Yes, go ahead” will be considered. I, for one, will be very curious to see how the discussions proceed—and what data get cited—at the FDA and CDC committee meetings.  

    Third: We need to vaccinate the world. As I’ve explained in the CDD before, getting vaccines to the low-income nations that have yet to start their rollouts is not just a humanitarian priority. It also protects us, here in the U.S., because the longer the coronavirus circulates, the more opportunities it has to mutate into increasingly-dangerous variants.

    By moving to provide booster shots to everyone—not just the immunocompromised, the elderly, or the otherwise extra-vulnerable—the U.S. is likely delaying shots to other countries, prolonging the pandemic overall.

    As Dr. Michael Ryan, emergencies chief at the World Health Organization, told reporters last week: “We’re planning to hand out extra life jackets to people who already have life jackets, while we’re leaving other people to drown without a single life jacket.”

    More vaccine news

    • Sources and updates, November 12
      Sources and updates for the week of November 12 include new vaccination data, a rapid test receiving FDA approval, treatment guidelines, and more.
    • How is the CDC tracking the latest round of COVID-19 vaccines?
      Following the end of the federal public health emergency in May, the CDC has lost its authority to collect vaccination data from all state and local health agencies that keep immunization records. As a result, the CDC is no longer providing comprehensive vaccination numbers on its COVID-19 dashboards. But we still have some information about this year’s vaccination campaign, thanks to continued CDC efforts as well as reporting by other health agencies and research organizations.
    • Sources and updates, October 8
      Sources and updates for the week of October 8 include new papers about booster shot uptake, at-home tests, and Long COVID symptoms.
    • COVID source shout-out: Novavax’s booster is now available
      This week, the FDA authorized Novavax’s updated COVID-19 vaccine. Here’s why some people are excited to get Novavax’s vaccine this fall, as opposed to Pfizer’s or Moderna’s.
    • COVID-19 vaccine issues: Stories from COVID-19 Data Dispatch readers across the U.S.
      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.
  • Three more COVID-19 data points, August 15

    Three more COVID-19 data points, August 15

    The number of children hospitalized with COVID-19 has shot up in recent weeks. Chart from the CDC COVID Data Tracker.

    A couple of additional items from this week’s COVID-19 headlines:

    • 1,900 children now hospitalized with COVID-19 in the U.S.: More kids are now seriously ill with COVID-19 than at any other time in the pandemic. The national total hit 1,902 on Saturday, according to HHS data. Asked about this trend at a press briefing on Thursday, Dr. Anthony Fauci explained that, thanks to Delta’s highly contagious properties, we’re now seeing more children get sick with COVID-19 just as we are seeing more adults get it. The vast majority of kids who contract the virus have mild cases, but this is still a worrying trend as schools reopen with, in many cases, limited safety measures. For more on this issue, I recommend Katherine J. Wu’s recent article in The Atlantic.
    • 2.7% of Americans now eligible for a third vaccine dose: Both the FDA and the CDC have now given the go-ahead for cancer patients, organ transplant recipients, and other immunocompromised Americans to get additional vaccine doses. There are about 7 million Americans eligible, comprising 2.7% of the population. Studies have shown that two Pfizer or Moderna doses do not provide these patients with sufficient COVID-19 antibodies to protect against the virus, while three doses bring the patients up to the same immune system readiness that a non-immunocompromised person would get out of two dioses. Still, this move goes against the World Health Organization’s push for wealthy nations to stop giving out boosters until the rest of the world has received more shots.
    • 203 cases so far linked to Lollapalooza, out of 385,000 attendees: Chicago residents and public health experts worried that Lollapalooza, a massive music festival held in the city in late July, would become a superspreader event. Two weeks out from the festival, however, local public health officials are seeing no evidence of superspreading, with a low number of cases identified in attendees. Lollapalooza may thus be an indicator that large events can still be held safely during the Delta surge—if events are held outdoors and the vast majority of attendees are vaccinated. (Officials estimated that 90% of the Lollapalooza crowd had gotten their shots.)

  • Yes, we still need better data on COVID-19 and race: An interview with Dr. Debra Furr-Holden

    Yes, we still need better data on COVID-19 and race: An interview with Dr. Debra Furr-Holden

    Dr. Debra Furr-Holden, photo via Michigan State University

    I recently had the opportunity to discuss data equity with Dr. Debra Furr-Holden, a public health expert at Michigan State University. Dr. Furr-Holden is the university’s Associate Dean for Public Health Integration and Director of the Flint Center for Health Equity Solutions, a health research center focused on Flint, Michigan, where she is based.

    At one of my National Science-Health-Environment Reporting Fellowship training sessions, Dr. Furr-Holden spoke about the Flint water crisis and other health equity issues. Her comments made me think about continued issues in COVID-19 data collection and reporting, so I asked her to discuss COVID-19 data further in an interview for the CDD.

    We talked about the ongoing challenges of collecting and reporting COVID-19 race data, how data gaps fuel vaccine hesitancy, the equity challenges inherent in vaccine mandates, and more.

    The interview below has been lightly edited and condensed for clarity.

    Betsy Ladyzhets: First, I’m curious about your backstory, how you got involved in doing this kind of [health equity] work.

    Dr. Debra Furr-Holden: I think it probably was born out of my own lived experience. My dad died at 37, of a complication from hypertension. My mom died at 56 of an asthma attack.

    It wasn’t until I went to college that I realized that my peers had very different experiences. I went to college with no living grandparents and one living parent, and I just assumed everybody had relatives with, you know, amputated limbs and with diabetes and heart disease. And I realized that’s not the case.

    As I networked with the very small cohort of African-American students in my class, I noticed despite our socioeconomic backgrounds—because I came from sort of more humble beginnings than some of my Black and brown peers—I was like, Oh, [these health conditions are] over-represented in black and brown people.

    BL: How has that informed the work that you’ve been doing with COVID? I saw that you’ve been advocating for better vaccine access and stuff like that?.

    DFH: What I’ve realized is, a lot of what we do around disparities, we do to people, and for people, and on their behalf. But the populations most affected very rarely have a voice, and the solutions that get created and implemented and employed—and we saw it with COVID, we’re seeing it now.

    The President has made a national declaration, give everybody $100 for the newly vaccinated. And that doesn’t make sense to a lot of people. People who are having trouble paying for their hypertension medication or their other things are now being told, we’ll give you $100 to get this COVID vaccine. When earlier in the pandemic, those same people couldn’t get access to a COVID test.

    BL: And in some cases, probably still can’t get access to a COVID test.

    DFH: Yeah. And I’ve just realized, like, my own lived experience that is ongoing still informs my work, but it elevated my authentic and deep appreciation for how important the voice of community and affected populations is in the work. It’s not just about the data. It’s not just about the science… You can only glean but so much from a data table. You need more wind underneath that. And that wind is the voice of community, and the voice of the people that you’re trying to impact and serve.

    So, the big gap to me in our work around how to bridge this gap among the unvaccinated is: we are quantifying who is unvaccinated, but we’re not asking the question of, what is needed to bridge that gap for you to get the vaccine? Instead, I think we’ve got a lot of well-meaning people who are coming up with solutions, but those solutions are not mapping onto people’s concerns. And it’s not moving the needle.

    In Ohio, they offered this big lottery, it did not cause a big boom in vaccination. Same thing is happening in Michigan right now. It did not rapidly accelerate the pace of newly vaccinated people. And because my work is so community engaged, when I talk to people and they tell me the reasons underneath [their vaccination choice], it’s not about the money.

    I call the money the carrot. We’ve tried to dangle the carrot in front of people. That didn’t produce much. Now we’re using the stick.

    BL: The mandates.

    DFH: The mandates, yeah. That will likely produce more [vaccination] than the carrot did, because people will have their hands forced. But that will likely elevate resentment and give way to—any negative consequences or outcomes that come from people being forced into vaccination will likely only further fuel their mistrust of the healthcare system, and our government overall. I just feel like the solutions are not being informed by the people that we’re trying to get on board.

    BL: Yeah. What kind of information do we need to actually inform better solutions, do you think? 

    DFH: We need to hear from the very large and diverse pool of unvaccinated people. Because there’s no one solution here.

    Now, I do believe fundamentally, as a public health professional, I think of public health big population-level interventions that make health choices easy. So things like fluoride in drinking water. We don’t [remember] the time when the cavities and dental cavities were contributing to all of this excess death and morbidity. Why, because we got fluoride in drinking water. So it’s just a non-starter for us now. Same thing for standardized childhood immunizations, which were transformative for eradicating diseases that took millions of lives before we not only developed those vaccines, but made them a part of the standard immunization protocol for children.

    We’ve now got to do the work to figure out how to implement and integrate these COVID protections into our system of care, and have them be more normative. I think all of the mistakes around how the whole pandemic has been handled in the US—how the resources, not just the vaccine, but other resources, like payroll protection, enhanced unemployment, support for essential workers.

    You know, we weren’t providing PPE to essential workers in the beginning. We had national leaders saying you don’t have to wear a mask. All of these things now conflict with, “Oh, we care so much, and everybody has to get vaccinated. Everybody needs to take one for the team.” People just aren’t buying into that.

    BL: They think there’s something else going on, I guess. So, I know, when we were closer to the start of the vaccine rollout, like earlier in 2021, I saw a lot of press attention on the lack of demographic data on vaccinations. A couple of my colleagues at the COVID Tracking Project wrote an article in The Atlantic and there was other kind-of big name publication stuff. But now we still don’t have good data. And it seems like no one is really drawing attention to that. I’m wondering if you have any thoughts on this, and if there’s anything we can do to continue that pressure, because we still do need this information.

    DFH: Yeah, it’s unfortunate, because I always say a lack of data continues to fuel the debate. And the lack of quality data around COVID resources is only fueling the problem. It is an unnecessary and unacceptable omission for providers to administer COVID tests and not collect basic demographic data on the people that they’re testing. It dampens our ability to quantify who is most impacted and what should be the targets of our outreach, engagement, and intervention efforts. And it’s unnecessary and unacceptable.

    In Michigan, the system that we use is called MICR… It would take a programmer about eight seconds to make race, and ZIP code, and gender, and age category a required field to be entered. And we just simply haven’t done it. And so as a result, it’s hard for us to quantify the extent of a problem.

    Because, remember, COVID cases are only a function of COVID testing. You can only get identified as a COVID case as a function of having a COVID test. If you’re in a household, and there’s a known case in the household, and all of the other [household] members display classic COVID symptoms, if they don’t get a test, they don’t get counted anywhere. So we know that we’ve greatly underestimated the extent of the problem.

    BL: When I asked you about this at the SHERF session, you mentioned that there’s a provision in the CARES Act that requires providers to do this [data collection]. Can you talk more about that? And what we can do to actually have some accountability there?

    DFH: Yes. There is a provision in the CARES Act that all COVID testing providers have to collect these core demographic variables. And then there was follow up guidance that was issued. And when the new administration took office, they haven’t enforced that [guidance].

    So COVID testing providers continue to receive these resources to provide COVID testing, with no quality assurance or quality control, to ensure that they’re actually collecting and entering that demographic data. It then shifts the responsibility to backfill that information to local health departments and other providers, to try to link insurance records or electronic health records. Or even worse to do outreach and contact tracing and actually contact cases, by phone or by email to try to backfill that information. When there are so many other competing demands, it’s an unfair and undue burden to place on an already overstressed segment of our healthcare system. 

    What it’s akin to is gums without teeth. We have the law, but there’s no enforcement or compliance checks to ensure that that law is being honored. And I think a simple solution is compliance checks. We need compliance checks, and we need enforcement.

    BL: Do you have any thoughts on other stories that we should be telling? Like, what should I tell my journalist friends to cover around COVID and health equity?

    DFH: One thing is probably already on your radar, which is the fact that we’re not doing systematic genetic sequencing on current strains of COVID. So it’s hard to estimate, you know—people keep talking about the Delta variant, but we have thousands of variants of SARS-CoV-2 now. And we just don’t have a good system for genomic surveillance to understand them.

    And the CDC a few weeks ago said, we’re just going to stop doing the genomic sequencing on any kind of systematic level and reporting. It’s a problem, because with breakthrough cases, and

    the vaccinated now showing up in hospitals and emerging data saying that even if you’re vaccinated, you can still spread and transmit… I just had a conversation with somebody who works in our building who said, I don’t want to get vaccinated, because if I get COVID, I want to have symptoms, so I’ll know, so I can protect my nine-year-old who’s got asthma. Like, I want to know. A lot of people now feel like the vaccine increases the chances of them being an asymptomatic carrier.

    We just really have to collect data. Instead of mandating shots in arms, we should be mandating the data so that we have better information and can do more credible and transparent information dissemination to communities.

    BL: Yeah, so that we can actually answer people’s questions on these things.

    DFH: Yeah.

    BL: I was also wondering if you had any recommendations, either of good stories that do a good job of covering these issues we’ve been talking about, or data sources or resources that myself and other journalists in this space should be paying attention to.

    DFH: We should be putting the press on the CDC to collect and compile the data. Like, the data on cases, all of that data should be disaggregated by race. And the percentage of cases with unknown race or unknown gender or unknown geography should also be reported. Because I don’t know if people notice this, but a lot of times [the CDC is] presenting data only on cases with complete information. But the missing information points to something important as well.

    BL: I think it’s something like they have maybe 50% or 60% of cases with known race. But where’s that other share of cases? [Editor’s note: It’s 63%, as of August 14.]

    DFH: The assumption is that the distribution of these variables in the unknown is similar to that of the known. But it is a major assumption. And it’s not an assumption that we should be making.

    BL: I see. Yeah. Anything else [you’d recommend as a story idea]?

    DFH: I do like this carrot stick analogy. The carrot is not working, the dangling the big incentive is not working. The stick will likely work. If you tell people, “You can’t get on a plane, if you’re unvaccinated,” there will be a lot of people who are unvaccinated right now who will get vaccinated because they’ll not want to lose the opportunity to travel.

    Think about the media. If you are chasing a story, or if you’ve got to be on site for something… If you’re in New York and you’ve got a story in California, you’re not going to drive to California, you will likely get off the fence and get vaccinated.

    I feel like a larger problem is, we have to engage experts in the work to make sure that we’re not furthering inequity [with mandates]. Because if we use, now, the stick, and start to mandate it…. [Michigan State University] has now mandated vaccination for all faculty, staff and students who want to return to campus by September 7. I know that that will likely produce greater increases in vaccination than did the incentives of cash payments, or lotteries or other things.

    But we have to keep an eye toward equity, and make sure—what if there’s disproportionality and then who does that impact? Are we going to see an increase in Black and brown people, or people with disabilities, or people with chronic health conditions, losing their jobs, or dropping out of school, or some of these other things? There just needs to be more thoughtfulness to how we apply these policy interventions to make sure that it’s not furthering inequity.

    BL: Have you seen any examples of where that’s been done successfully?

    DFH: No, because it’s all just coming out now.

    BL: I know there are some places, like in New York, they’re giving you an option, saying, “You can get vaccinated or you have to be tested once a week.” Is that effective? Or does that still fit into what you’re talking about?

    DFH: I think we’re gonna figure that out. And if that’s the case, then again, we gotta deal with the access issue, and people need to have fair and equitable access—and affordable access—to COVID testing.

    BL: Yeah, totally. And the last kind of big question I had for you: one thing I think a lot about as a journalist who is still rather early-career and has been covering COVID very intensely is that this is probably just the beginning of us dealing with major public health crises. You know, continued climate disasters and all that stuff.

    And I’m wondering how you think about preparing for the next COVID, or the next whatever it’s going to be. What lessons do we take from these past couple of years?

    DFH: Well, I think we’ve learned there is a business case for preparedness, and a business case for equity. Our lack of preparation for this pandemic will have cost our country tremendously. There’s going to be tremendous financial toll. So, there’s a business case to be made for preparedness.

    We learned that with the Flint water crisis. Not having the million-dollar investment in the water treatment system, not spending the 150 bucks a day on anticorrosives, those things will have cost us hundreds of millions of dollars to now replace and repair the whole water infrastructure system and pay settlements from the Flint water crisis.

    And then there’s also a business case for equity. Not doing a better job of equitably rolling out the vaccine early on caused a lot of people who were a “yes” to sort of say, “why bother?” And now many of them are a “no.” These are people who earlier on [were amenable], but then all these reports come out and get sensationalized by the media of side effects and blood clots and heart inflammation. And so a lot of people who were in line, trying to move through the line to get vaccinated are now an absolute “no.”

    That’s going to cost us as well, because we have fallen well short of that 70% goal. And new vaccinations are moving at a snail’s pace. So I think what we’ve learned—and we’ll really know, the impact of it in the next few years—is not being prepared and not practicing equity will have a tremendous financial toll on the country.

  • Lessons for COVID-19 from the HIV/AIDS pandemic

    Lessons for COVID-19 from the HIV/AIDS pandemic

    In the U.S., southern states have the highest numbers of HIV-positive residents. Chart via the CDC.

    This is the last week of Pride Month for 2021, and it’s also officially Pride weekend in NYC, where I live. (As the newsletter goes out, I’ll likely be marching with the Stonewall Protests, a group that advocates for Black trans women.)

    So, it felt appropriate for me to take this issue to highlight a couple of lessons that the U.S. response to COVID-19 has taken from our response to another pandemic—one that is still ongoing.

    HIV, the virus that causes AIDS, infects over one million new people every year. The HHS estimates that there were about 38 million people living with this virus around the world in 2020, including 1.2 million in the U.S. While many of us might associate HIV/AIDS with American outbreaks in the 1980s and 90s, it continues to disproportionately impact people of color and queer people in the U.S. and globally.

    In the U.S., the South has higher HIV infection rates than any other part of the country. Black Americans are diagnosed with the virus at rates almost ten times higher than white Americans, according to CDC data from 2014 to 2018. At a global scale, the virus disproportionately impacts African nations; Swaziland has the highest infection rate, at 27%.

    Treatments do exist for HIV, a virus that attacks the body’s immune system, and AIDS, the immunodeficiency condition that this virus causes. The most common treatment is antiretroviral therapy (or ART), which allows people with HIV infections to live long, healthy lives and avoid transmitting the virus to sexual partners. The HHS estimates that about 16% of the global population with HIV (or about 6 million people) does not know they’re infected, and still needs access to tests and treatment.

    This is a pandemic that demands continued focus even after urgency around COVID-19 wanes. But the responses to HIV/AIDS—both scientific and political—can show us how an understanding of intersectionality and local community focus may contribute to pandemic response.

    Understanding disparities and comorbidities

    People living with HIV are more vulnerable to severe COVID-19. One study of HIV patients in New York state found that, if diagnosed with COVID-19, these patients were more likely to go to the hospital and more likely to die from the disease compared to non-HIV patients.

    An HIV diagnosis, like a case of diabetes or asthma, is a disease that hits people of color harder and may contribute to their worse COVID-19 rates. Public health efforts around COVID-19 can learn from clinicians focused on HIV/AIDS, who are already used to connecting with vulnerable communities and understanding the intersectional socioeconomic factors that contribute to their health.

    It takes a long time to learn disease origins

    This page on the evolution of HIV may give you an idea of the many steps that typically go into finding a disease’s source. When the page was first written, in 2008, scientists had found ties between the virus and chimpanzees in west-central Africa, but they didn’t know all the details of its first jump to humans. News updates in 2010, 2015, and 2020 provide more information, reflecting updates in scientific knowledge: newer research suggests that the virus spread to humans in the early 20th century and went undetected for decades.

    These updates remind us that scientists cannot pinpoint biological disease origins overnight. Scientists are still working to understand the evolution of HIV, decades after we first became aware of the disease. There are other outbreaks, not as old as HIV but older than COVID-19, that we still don’t understand:

    Regulatory pathways need to prioritize patients

    In the 1980s, AIDS activists led by the AIDS Coalition to Unleash Power (or ACTUP) protested the FDA and other public health officials. They saw the agency’s drug approval process as a barrier, keeping them from accessing potentially life-saving treatments; while small numbers of patients received new drugs in clinical trials, the vast majority of HIV-positive Americans had to wait for data to come out. Even Dr. Anthony Fauci was involved: AIDS activist Larry Kramer called him a killer and an idiot in a 1988 letter. Fauci later credited Kramer with pushing for change in the medical establishment.

    As a result, we can thank those AIDS activists who advocated for processes that allow faster drug development and patient treatment in times of crisis. This includes faster vaccine trials and the hundreds of Emergency Use Authorizations provided to COVID-19 tests and treatments over the past year.

    Neighborhood-level healthcare provides critical services

    People living with HIV in the U.S. often were not able to access support from the government or healthcare insurance, especially earlier in the 1980s. As a result, many queer communities organized locally to provide their own support. Neighborhoods like the Castro district in San Francisco and Greenwich Village in New York saw healthcare clinics, free testing, information-sharing about virus prevention, and more. These local institutions built trust in their communities.

    Such trust was also key in the COVID-19 pandemic, when government agencies from the federal to the county level weren’t ready to serve their residents. In an article for The Conversation, Daniel Baldwin Hess and Alex Bitterman describe how some of the same community groups that started to provide HIV testing decades ago added COVID-19 testing to their repertoire this year:

    For example, in New York, the Erie County Department of Health requested that Evergreen Health – an LGBTQ community group originally established in the 1980s as a volunteer effort to fight HIV – assume responsibility for HIV testing during the COVID-19 pandemic so that the county government could focus on COVID-19 testing. Evergreen also opened a drive-though COVID-19 testing center in the spring of 2020 – four decades after it had introduced HIV testing to the Buffalo region.

    These local institutions have also helped build vaccine trust and administer doses.

    Finally, there’s one lesson we may take from COVID-19 back to the continued fight against HIV/AIDS: mRNA vaccines! Moderna is currently partnering with International AIDS Vaccine Initiative to develop a potential mRNA vaccine for HIV.

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  • A new tracker highlights the racial disparities—and the missing data—in America’s COVID-19 outbreaks

    A new tracker highlights the racial disparities—and the missing data—in America’s COVID-19 outbreaks

    Screenshot of the Health Equity Tracker showing which states are missing race and ethnicity data for COVID-19 cases.

    Two weeks ago, a major new COVID-19 data source came on the scene: the Health Equity Tracker, developed by the Satcher Health Leadership Institute at Morehouse School of Medicine.

    This tracker incorporates data from the CDC, the Census, and other sources to provide comprehensive information on which communities have been hit hardest by COVID-19—and why they are more vulnerable. Notably, it is currently the only place where you can find COVID-19 race/ethnicity case data at the county level.

    I featured this tracker in the CDD the week it launched, but I wanted to dig more into this unique, highly valuable resource. A couple of days ago, I got to do that by talking to Josh Zarrabi, senior software engineer at the Satcher Health Leadership Institute—and a fellow former volunteer with yours truly at the COVID Tracking Project.

    Zarrabi has only been working on the Health Equity Tracker for a couple of months, but he was able to share many insights into how the tracker was designed and how journalists and researchers might use it to look for stories. We talked about the challenges of obtaining good health data broken out by race/ethnicity, communicating data gaps, and more.

    The interview below has been lightly edited and condensed for clarity.

    Betsy Ladyzhets: Give me the backstory on the Health Equity Tracker, like how it got started, how the different stakeholders got involved.

    Josh Zarrabi: At the beginning of the pandemic, the Satcher Health Leadership Institute at Morehouse School of Medicine saw the lack of good COVID data in the country, and especially the lack of racial data. The COVID Tracking Project kind-of tried to solve that as well with the Racial Data Tracker. 

    Morehouse wanted to do something similar. And so they applied for a Google.org grant… After about nine months, the tracker just got released. It went through a couple of different iterations, but what it is now is, it’s a general health equity tracker, so it tracks a couple of different determinants of health. And it really has a focus on equity between races and amplifying marginalized races as much as possible.

    Probably the most innovative thing it does is, it shows COVID rates by race down to the county level. We think that’s relatively hard to find anywhere else. (Editor’s note: It is basically impossible to find anywhere else.)  So that’s probably like the main feature that it has that people care about, but it does track other health metrics. We also have poverty, health insurance, and we try to track diabetes and COPD, but there’s not great data on that, unfortunately, in the United States. We’re planning to add more metrics in the future.

    BL: How does this project build on the COVID Racial Data Tracker? And I know, like APM has a tracker for COVID deaths by race. And there are a couple other similar projects. So what is this one doing that is taking it to the next level?

    JZ: A couple of things. We’re using the CDC restricted dataset. Basically what the dataset looks like is, it’s like a very large CSV file where every single line is an individual COVID case. So we’re able to break it down basically however we want. So we were able to break that down to the county level, state level and national level.

    And what we do is we allow you to compare that [COVID rates] to rates of poverty, and rates of health insurance in different counties. We think that’s pretty innovative, and we’re gonna allow you to compare it to other things in the future. So that’s one thing that we do. And I mean, the second thing that I would say is like, probably makes us stand out the most I would say is our real focus on racial equity, and showing where the data gaps are and how that affects health equity. So what you’ll notice, if you go to our website, we very prominently display the amount of unknown… 

    BL: Yeah, I was gonna ask you about that, because I know the COVID Racial Data Project had similar unknown displays. Why is it so important to be highlighting those unknowns? And what do you want people to really be taking away from those red flag notes?

    JZ: We really try to do our best to display the data in context as much as possible. First of all, the most important thing, I think, is just showing the high percentage of unknown race and ethnicity of COVID cases in the United States. For something like 40% of cases, we don’t know the race and ethnicity of the person who had COVID.

    We want people to really think about that when they look at, for example, you’ll notice that it looks like Black Americans are affected to the exact level of their population. Black Americans look like 12% of the population and 11% of cases. But we don’t know the race of 40% of people who have COVID. And so we really wanted people to think about that when they look at these numbers. And it’s the same for American Indian/Alaskan Native populations. It doesn’t look like they’re that heavily affected in the United States. But that’s why we allow you to break down into the county level, where race is not being reported. And so we really want people to look and say, like, oh, wow, like in Atlanta, 60% of cases are not being counted for race and ethnicity.

    We’re not doing any extrapolation. We’re not multiplying, we’re not like trying to guess the races of unknowns, or anything like that. We really want people to think about that, when they’re saying like, oh, wow, it looks like Native American people are not really heavily affected by COVID. It’s like, no, we just don’t know. We don’t know their races, or those people are just not being reported properly by the health agencies.

    And if you look at places that have high percentages of Black Americans and high percentages of American Indian/Alaskan Natives, you’ll see that those places are the same places that are not reporting the race and ethnicity of the people who had COVID.

    We had a team of about 20 health equity experts advising us throughout the entire project. That’s where those red flags that you see come from. It’s explaining, for example, if you look into deaths for Native American and Alaska Natives, there’s an article about how a lot of American Indian/Alaskan Native people who died are not, are improperly categorized racially, and they’re often categorized as white. And so we have that kind of stuff to really try to put the numbers in context.

    We were only able to do that, because we had this large team of racial equity experts and health equity experts advising us throughout the entire time. And so we really had diverse representation on the project as we were building it, and people who really knew what they were talking about.

    BL: What can public health agencies and also researchers and journalists do to push for better data in this area?

    JZ: The good thing is we are seeing [data completion] get better over time. And so we’ve seen, for example, the percentage of race and ethnicity for cases improved from about 50% to about 60% over the last couple of months.

    And, I mean, really, all you can do is—it’s really a thing that goes down to the county level. So, everybody’s just got to call their county representatives. I’d be like, hey, could you please report the race and ethnicity of the county’s COVID cases to the CDC? Unfortunately, a lot of that work might be too late, because [data were submitted months ago]. But we have seen it get better. And so we’re hoping that, you know, these health agencies are able to do the work and really, like, properly report these cases to the CDC… 

    BL: ‘Cause a lot of it comes from the case identification point, where if you’re not asking on your testing form, what race are you, then you just might not have that information. Or you might be, like, guessing and getting it wrong or something, right?

    JZ:  Yeah, there’s guessing. There’s two different categories of unknown cases—there’s unknown and there’s missing. The vast majority of these cases have filled out unknown [in the line file], which means that the person who’s filling out the data form literally puts “unknown” as the race. We don’t really know exactly what that means in every case. But it could be they didn’t ask, it could be the person didn’t feel comfortable saying it, just said, “I don’t want to tell you my race.” Or it could just be that they just didn’t make an effort to figure out what their race is.

    (Editor’s note: For more on the difficulties of collecting COVID-19 race data, I recommend this article by Caroline Chen at ProPublica.)

    BL: Do you have a sense of how that 60% known cases compares to what the COVID Racial Data Tracker had in compiling from the states?

    JZ: Yeah, I think the COVID Racial Data Tracker was a bit higher [in how many cases had known race/ethnicity]. But the thing is, as far as I understand, the COVID Racial Data Tracker was using aggregate numbers.

    BL: We were looking at the states and then kind-of like, synthesizing their data to the best of our ability, which was pretty challenging because every state had slightly different race and ethnicity categories. There were some states that had almost no unknown cases, but there were some where almost all cases or almost all deaths were unknown. New York, I don’t know if they ever started reporting COVID cases by race.

    JZ: They do to the CDC, I don’t think they report—

    BL: They don’t report it on their own, state public health site.

    JZ: Let me actually check that… Yeah, so New York is not great. They have a 60% unknown rate. [Race and ethnicity is only reported to the CDC for 40% of cases.] Not great. Actually, New York City is pretty good. But the rest of New York State is not doing a good job reporting the race and ethnicity of cases.

    BL: Because I’ve gotten tested here, I know that New York City is good about collecting that [race and ethnicity] from everybody.

    JZ: I was one of those cases in New York City, actually. When [I got called by a contact tracer], I was kind of chatting with them about this. They asked me about my race—I actually became a probable case for COVID, like, the day after I started this job. And [NYC Health] called me, they were like, “What’s your race?” I was like, “Oh, that’s kind of funny, I just started working on this racial data project.” And—this is totally anecdotal. But she told me, most people just refuse to report their race. 

    And then for deaths… 40% of COVID deaths in New York state, they don’t know the race, which is not great. New York is not good compared to the rest of the states. It’s one of the worst states for unknowns.

    BL: Could you tell me more about the process of getting the [restricted] case surveillance data from the CDC and how you’ve been using that?

    JZ: The process of getting it’s not that hard. You just apply, and then they give you access to a GitHub repo, and then you can just use it. Using the data itself is pretty hard because the data files are so large. We were lucky enough to have a team of Google engineers working on this project, they wrote a bunch of Python scripts that analyze the data and aggregate it in a way that the CDC isn’t doing.

    The reason why they restrict the use is because it’s line-by-line data. [Each line is a case.] And the CDC does suppress some of the data because they think it would make those cases identifiable. Still, you’re not allowed to just, like, release the data into the wild, because they want to know who else has track of it. So, we wrote some Python to aggregate the data, in exactly the way you see on the website. We aggregate it to the amount of cases, deaths and hospitalizations per county, per race, essentially. 

    The CDC has been extremely helpful, like, we’ve had a couple of meetings with them. We think we were one of the heaviest users of the data at the beginning, because we pointed out a couple of problems with the data that they actually fixed. So, that’s cool.

    BL: That’s good to hear that they were responsive.

    JZ: Yeah, definitely. We meet with them every couple of weeks. They’re really good partners in this.

    BL: And they update that [case surveillance] dataset once a month?

    JZ: They started doing it every two weeks now. Every other Monday, they update the dataset.

    BL: Could you talk more about the feature of the tracker that lets you compare COVID to other health conditions and insurance rates? I thought that was really unique and worth highlighting.

    JZ: We wanted to really provide the [COVID] numbers in context. And so that’s one way that we thought that we could do that and really show how… These numbers don’t happen, like a high rate of COVID for race doesn’t happen in a vacuum. There are political determinants of health.

    For example, you’ll see everywhere that Hispanic Americans are just by far the most impacted by COVID case-wise. In California especially. And we provide those numbers in context—Hispanic Americans are also much less likely to be insured than white Americans, for example, and much more likely to be in poverty. And, you know, it’s not a crazy surprise that they would also be more likely to have contracted COVID at some point.

    [The comparison feature] was a way that we thought, we would just allow people to really view numbers in context and get a better understanding of what the political situation is on the ground with where these high numbers are happening.

    BL: What are the next conditions that you want to add to the tracker?

    JZ: I want to be careful, because we can’t make any promises. But we’re talking about adding smoking rates, maybe. [The challenge is] where we can find data that we can aggregate correctly.

    BL: Right. Are you looking specifically for data that’s county level as opposed to state level?

    JZ: Hopefully… It depends. I was pretty surprised by the lack of quality in, for example, COPD and diabetes data, where like, if you look at [the dataset], like it’s state level—but in most states, there’s not a statistical significance for most races.

    BL: Wow.

    JZ: For example, we use the BRFSS survey. [The Behavioral Risk Factor Surveillance System.] It’s a CDC survey. And as far as we can tell, it’s the gold standard for diabetes [data] in the country.

    And if you look at, say, diabetes, for most states… There’s only, like, four states where Asian people are statistically significant in the survey to make any sort of guess about how many people have diabetes, which is pretty atrocious. But that [data source] is the best we could do, you know. Ideally, we would like to find places that do go down to the county level, but it’s hard.

    For as paltry as the COVID data is, it’s much better than—as far as I’ve seen, like, the fact that there’s like a line-by-line database that the CDC provides, that you can really make all these breakdowns of, is a huge step ahead [compared to other health data]. I’m not like a data expert on this kind of stuff, I’ve just been working on this project for two and a half months. But as far as I’ve seen, that’s what the situation is.

    BL: Yeah, I mean, that kind of lines up with what I have seen as well. And I bet a lot of it is a case where, like, a journalist could FOIA [the data] from a county or from a state. But that’s not the same as getting something that is comprehensive, line-by-line, from the CDC.

    JZ: And we [the Satcher institute] don’t want to be a data collection agency, like the COVID Tracking Project or the New York Times is. I mean, we want this to be a sustainable project. And the COVID Tracking Project was not a sustainable project.

    BL: Yeah, totally. I was there doing the [data entry] shifts twice a week, that’s not something we could have done forever.

    JZ: Yeah, I was there, too. I always think, like, the COVID Tracking Project could only exist when there’s an army of unemployed people who are too afraid to leave their house.

    BL: And volunteers who were like, yeah, sure, I’ll do this on my evenings and weekends.

    JZ: Who, you know, you don’t want to leave, you’re too afraid to go, like talk to people. You want to stay home in front of your computer all day, and feel useful.

    I’m sure you could find all the diabetes data by going to county and state health department websites, but it’s too much work. So we really want everything to come from federal sources, basically, that’s our goal.

    BL: How are you finding that people have used the tracker so far? Like, do you know of any research projects that folks are doing?

    JZ: We released it a couple weeks ago, and we haven’t really heard of any yet… But we hope people are looking at it. And we have a couple of meetings lined up with some interesting research groups and stuff like that. So hopefully, they’ll like it.

    BL: Are there any specific statistics or comparisons or anything else you found in working on it that you would want to see explored further? Are there any stories that you want to see come out of it?

    JZ: The high rates of unknown data in a lot of places, that really needs to be looked into. Because it’s just hard to make any conclusions about what’s going on if—I mean, in some states like New York, over 50% of cases are unknown. That’s a huge problem. And that’s definitely something that needs to be looked into, like, why that’s happening. And if there’s anything that can be done to change that [unknown rate.] The reason why I do think that it can get better is because the COVID Tracking Project racial data had higher completeness rates. And so they [the states] probably do know the races of people who got sick, but they’re just not reporting it for whatever reason.

    And for me, something that’s really stuck out was the extremely high rates of COVID for Hispanic and Latino people, especially in California. If you look at them and compare them to white rates, it’s, like, the exact opposite pattern. So it kind of does look like Hispanic and Latino people were kind-of shielding white people from getting COVID, if you compare the numbers. That’s something I would look into, too, like, why that happened.

    (Editor’s note: This story from The Mercury News goes into how the Bay Area’s COVID-19 response heightened disparities for the region’s Hispanic/Latino population.)

    BL: And another question along the same lines, is there a specific function or aspect of the tracker that you would encourage people to check out?

    JZ: The unknowns. Just, like, look into your county and see what percentage of cases in your county have reported race and ethnicity at all. I think you can really see how good of a job your county has done at reporting that data. I know I was kind-of shocked by that rate for the county like I grew up in, like, I know that they have the resources to [report more data], but they’re just not doing a very good job.

    BL: How would you say this experience with tracking COVID cases might impact the world of public health data going forward, specifically health equity data, and how do you see the tracker project playing a role in that?

    JZ: We really want this project to show the importance of tracking racial health data down to the county level or even lower than that. County is the best we can do right now, but we’d love to see city level or something like that. And again, I kind-of said this before—as much as was missing for the COVID data, it’s still better than the data that there is for most other diseases and other determinants of health. So we would like to see, like, more things able to be filled out on the tracker. We would like to be able to get more granular on more different determinants of health, so that we can see, for example, how poverty impacts health, or a lack of health insurance, or how diabetes and COVID are related down to the county level. You can’t really do that right now… 

    We want people to see that, A, there’s a lot of data missing. But B, even with the data that we have, we can see that there’s like a huge problem. And so we would like to be able to fill out the data more to really get a better picture of what’s going on. If we can see there’s a problem, we can make better policy to help and make these disparities not as stark.

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  • Source updates, May 16

    Two more important CDC data updates for this week:

    • Vaccine demographics, for the past 14 days and over time: This week, the CDC added a new category to its Vaccine Demographics page. Previously, the page allowed users to compare overall first dose and fully vaccinated rates for different race/ethnicity, age, and sex groups; now, you can also make those comparisons specifically for vaccinations in the last two weeks. For a time series view, check out the Vaccine Demographic Trends page, which shows vaccination rates over time—now available for race/ethnicity, sex, and age. The race/ethnicity view clearly shows that White and Asian Americans are getting vaccinated at higher rates than other groups.
    • Variant tracker “Nowcast”: Loyal CDD readers will already know that I love to drag the CDC for reporting their variant data with an enormous lag; often the most recent figures on the agency’s Variant Proportions page are a month old. Well, maybe somebody on their team is reading, because this week, the CDC added a new option to its variant dashboard that addresses this issue. Selecting “Nowcast On” (below the variant color bars) allows you to view prevalence estimates for the current week, in addition to the agency’s most recent week of data collection. A note below the dashboard explains that the “Nowcast” figures are based on modeling estimates that extrapolate from known proportions. For example, B.1.1.7 is known to cause 66% of U.S. cases as of April 24, but the “Nowcast” estimate puts it at 72% of cases as of May 8. This is actually pretty useful, thanks CDC!

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