COVID-19 Data Dispatch

Tag: Federal data

  • Seroprevalence, incomplete data in the wake of the Omicron wave

    Seroprevalence, incomplete data in the wake of the Omicron wave

    Almost 60% of Americans had antibodies from a prior COVID-19 case in February 2022, a CDC study found. This rate was even higher among young children and teenagers.

    More than half of Americans have some antibodies from a recent coronavirus infection, according to a new CDC report. The study was published Tuesday in the CDC’s Morbidity and Mortality Weekly Report (MMWR), accompanied by a press conference and other fanfare. To me, this report (and its publicity) reflects the CDC’s current lack of urgency around addressing the pandemic and its continued impacts.

    The CDC regularly surveys COVID-19 antibody levels among the U.S. population, a metric that scientists call seroprevalence. For these surveys, the agency works with commercial laboratories to measure antibody rates from a nationally representative sample of Americans, with updates provided about once a month. The survey specifically looks at a type of antibody that develops in response to infection, not vaccination.

    This most recent iteration of the survey, providing data from February 2022, is particularly notable: the CDC estimates that 58% of Americans had this immune system indicator of a recent COVID-19 infection, immediately after the nation’s massive Omicron wave. Not all of these people got COVID-19 during the Omicron wave, though, since some of these antibodies stem from earlier infections.

    Other notable findings include:

    • National seroprevalence increased from 34% in December 2021 to 58% in February 2022—the largest jump recorded in this survey—reflecting the Omicron wave’s impact.
    • Children and teenagers had the highest antibody levels. For the 12 to 17 age group, seroprevalence went from 46% in December 2021 to 74% in February 2022.

    There are some major caveats to this study, though, including:

    • The imprecise nature of this antibody measurement. The type of antibody measured in this seroprevalence survey “stays positive for at least two years after infection,” CDC scientist Dr. Kristie Clarke said on the agency’s press call.
    • Antibodies wane at different rates and levels for different people, so it’s unclear to what extent this 58% finding actually reflects the share of Americans who have gotten COVID-19 since spring 2020.
    • Plus, some people infected by the coronavirus never seroconvert, meaning that they don’t develop antibodies at all (and thus wouldn’t show up in this study).
    • While we know that the COVID-19 antibodies identified in this study confer some protection against new infections, it’s unclear how long that protection lasts or how it might hold up against new variants.

    To me, this study (and the CDC’s choice to promote it with one of the agency’s infrequent press calls) exemplifies the Biden administration’s COVID-19 response right now.

    As I listened to the press call, the CDC’s interpretation of this study was clear: more than half of Americans have some protection against COVID-19 from a prior infection, and many of those people also have protection against vaccination. Much of that protection applies specifically to Omicron and will likely help us avoid a crisis from BA.2, so it gives the U.S. additional reason to relax safety measures, the CDC suggested.

    (Worth noting: the CDC still recommends vaccination and booster shots for anyone who had a previous coronavirus infection, including children. But that message is not getting across right now, as evidenced by our low booster shot uptake.)

    When you ask for more specifics on that “protection” from prior infections, though, the CDC isn’t able to provide much information. Again, we don’t know how long the protection lasts or how it holds up against other variants. And we have no idea how many people had mild or asymptomatic COVID-19 cases, then did not seroconvert.

    The CDC’s press call also failed to mention Long COVID, which is a risk from any COVID-19 case—no matter how mild. Some Long COVID researchers have also suggested that lack of seroconversion, or even a prior infection in general, may increase a patient’s future risk for prolonged symptoms the next time they get infected.

    And, of course, the CDC report also exemplifies our current lack of surveillance. How many of those Omicron infections between December and February were actually caught by PCR testing and reported to the CDC? A small fraction. At the press call. Dr. Clarke mentioned an upcoming CDC study that estimates how many infections go uncounted for every one reported case:

    In the Omicron period, we found that over that time period, the infection to case ratio was the highest that it’s been, at over three estimated infections per reported case. And that varied by region, so depending on which US census region the estimates were, you know, the ratios were higher or lower.

    Surely that ratio is getting even higher now. To me, this forthcoming study, combined with the seroprevalence report, is a reminder that the cases we see in our datasets and dashboards are a very incomplete picture of actual coronavirus transmission in the U.S. And yet the CDC is using this incomplete picture to suggest we all relax, take our masks off, and forget about the pandemic.

    More federal data

  • CDC launches new pandemic forecasting center

    CDC launches new pandemic forecasting center

    The CDC’s new Center for Forecasting and Outbreak Analytics (CFA) intends to modernize the country’s ability to predict disease outbreaks. Image via the CDC.

    This week, the CDC introduced a new team focused on modeling infectious diseases, called the Center for Forecasting and Outbreak Analytics (or CFA). The agency aims to hire about 100 scientists and communicators for the center; they’ll currently focus on COVID-19, but will expand to other diseases in the future.

    “We think of ourselves like the National Weather Service, but for infectious diseases,” Caitlin Rivers, the new center’s associate director for science, told the Washington Post.

    This idea of forecasting infectious diseases like the weather was a major theme of an event that the White House hosted last Tuesday, timed with the introduction of the CDC’s new center. This event, a three-hour summit, featured speeches from the administration’s COVID-19 response leaders (Dr. Ashish Jha, Dr. Rochelle Walensky, etc.), as well as panels bringing together the scientists who have joined CFA so far, healthcare leaders, and public health workers from around the country.

    I watched the event on a livestream, and kept a running Twitter thread of key points:

    As discussed at the summit and on CFA’s new website, this center has three main functions:

    • Predict: A team of disease modelers, epidemiologists, and data scientists will establish methods for forecasting disease spread and severity, in collaboration with state and local leaders.
    • Inform: A team of science communicators will share information from the Predict team’s modeling efforts with public health officials and with the public, ensuring that this information is actionable.
    • Innovate: In addition to its in-house analysis and communication, CFA will fund research and development to drive better data collection and forecasting strategies.

    According to the CDC, CFA has already awarded $26 million in funding to “academic institutions and federal partners” working on forecasting methods, as part of this “innovate” priority. Neither CFA’s website nor the summit provided any indication of what these institutions are or what they’re working on; I wrote to the CDC’s media team asking for more information, and have yet to hear back from them.

    At last Tuesday’s summit, it was nice to hear health officials from the local to the federal levels describe COVID-19 data issues that I’ve been writing about for two years. These included: the need for more timely data on issues like new variants and vaccine effectiveness; the need for more demographic data that can inform health equity priorities; the need for more coordination (and standardization) between different state and local health agencies; and the need for actionable data that are communicated in a way people outside science and health settings can understand.

    But for all this discussion of the problems with America’s current health data systems, the event included very little indication of potential solutions. For instance, as Bloomberg health editor Drew Armstrong pointed out, nobody mentioned that many of our problems would be solved with a national healthcare system, following the lead of the U.K.—whose data we’ve relied on throughout the pandemic.

    Moreover, Tuesday’s event was very rushed: each panel was just half an hour long, with only a few minutes for each expert panelist to make their points and barely any time for questions. I would’ve loved to hear entire keynote speeches from people like Dr. Anne Zink, director of Alaska’s public health agency, and Dr. Loretta Christensen, chief medical officer for the Indian Health Service. But they were relegated to brief comments.

    It almost felt like the Biden administration had taken a couple of hours in their schedule to appease the science and health experts who wanted to see some acknowledgment of the COVID-19 data issues—and then went right back to downplaying the pandemic. (Also not lost on me: this same day, administration officials were “weighing the political risks” of appealing the blocked travel mask mandate.)

    I would love to be proven wrong, and to see this new CDC center usher in an era of standardized, actionable infectious disease data and modeling across the country. But right now, I’m not very optimistic.

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  • New CDC mortality data release from the Documenting COVID-19 project

    New CDC mortality data release from the Documenting COVID-19 project

    Many readers may know that, since last fall, I’ve been working part-time at the Documenting COVID-19 project: a public records, data, and investigative project at Columbia University’s Brown Institute for Media Innovation and the public records site MuckRock.

    One major focus at Documenting COVID-19 is our Uncounted investigation, an effort to understand how COVID-19 deaths—and other deaths indirectly caused by the pandemic—have gone under-reported in the last two years. The CDC has reported nearly one million official COVID-19 deaths; but that figure doesn’t include over 300,000 deaths of natural causes that occurred over what researchers expected in 2020 and 2021.

    These natural causes logged on Americans’ death certificates—such as diabetes, heart disease, and respiratory conditions—may have been linked to COVID-19. In fact, about 158,000 deaths during the pandemic were specifically linked to natural causes that the CDC considers potentially COVID-related. But the official records make it hard to say for sure.

    In a story with USA TODAY published late last year, Documenting COVID-19 found massive gaps and inconsistencies in the U.S.’s death system, which likely contributed to these undercounts. These include: a lack of standardization for medical examiners and coroners’ offices, workers in these positions becoming overwhelmed during the pandemic, and failures in some cases to order COVID-19 tests for patients or push back when families insisted a death wasn’t COVID-related.

    Documenting COVID-19 is working on further follow-up stories in this investigation. But we also want to empower other reporters—especially local reporters—and researchers to investigate pandemic deaths. To that end, our team recently released a GitHub data repository that provides county-level CDC mortality data from 2020 and 2021.

    The data come from the CDC’s provisional mortality database; our team signed a data-use agreement with the agency so that we can use their API to gather data more quickly and efficiently than what’s possible with the CDC’s WONDER portal.

    !function(){“use strict”;window.addEventListener(“message”,(function(e){if(void 0!==e.data[“datawrapper-height”]){var t=document.querySelectorAll(“iframe”);for(var a in e.data[“datawrapper-height”])for(var r=0;r<t.length;r++){if(t[r].contentWindow===e.source)t[r].style.height=e.data["datawrapper-height"][a]+"px"}}}))}();

    Here’s a brief summary of what’s in the repository, taken from a write-up by my colleague Dillon Bergin:

    • Leading external causes of death in the 113 CDC code list, by underlying cause of death;
    • Natural causes of death associated with COVID-19, using the CDC’s categories for excess deaths associated with COVID-19, by underlying cause of death;
    • All deaths by race and ethnicity, with age-adjusted rate, regardless of underlying cause of death;
    • Information to help contextualize the CDC data, including excess mortality numbers modeled by demographers at Boston University, vaccination rates, and a Department of Justice survey released in December of all medical examiner and coroner offices in the country.

    And here are some other links related to Uncounted and the CDC’s mortality data:

    If you’re a journalist who wants to use these data, the Documenting COVID-19 team is happy to help! If you have questions or want support, feel free to reach out to the team at covid@muckrock.com, or to me specifically at betsy@muckrock.com.

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  • The US still doesn’t have the data we need to make informed decisions on booster shots

    The US still doesn’t have the data we need to make informed decisions on booster shots

    How often will we see new variants like Omicron, that are incredibly different from other lineages that came before them? According to Trevor Bedford, it could be between 1.5 and 10.5 years.

    Last fall, I wrote—both in the COVID-19 Data Dispatch and for FiveThirtyEight—that the U.S. did not have the data we needed to make informed decisions about booster shots. Several months later, we still don’t have the data we need, as questions about a potential BA.2 wave and other future variants abound. Discussions at a recent FDA advisory committee meeting made these data gaps clear.

    Our country has a fractured public health system: every state health department has its own data systems for COVID-19 cases, vaccinations, and other metrics, and these data systems are often very difficult to link up with each other. This can make it difficult to answer questions about vaccine effectiveness, especially when you want to get specific about different age groups, preexisting conditions, or variants.

    To quote from my November FiveThirtyEight story:

    In the U.S., vaccine research is far more complicated. Rather than one singular, standardized system housing health care data, 50 different states have their own systems, along with hundreds of local health departments and thousands of hospitals. “In the U.S., everything is incredibly fragmented,” said Zoë McLaren, a health economist at the University of Maryland Baltimore County. “And so you get a very fragmented view of what’s going on in the country.”

    For example, a database on who’s tested positive in a particular city might not be connected to a database that would reveal which of those patients was vaccinated. And that database, in turn, is probably not connected to health records showing which patients have a history of diabetes, heart disease or other conditions that make people more vulnerable to COVID-19.

    Each database has its own data fields and definitions, making it difficult for researchers to integrate records from different sources. Even basic demographics such as age, sex, race and ethnicity may be logged differently from one database to the next, or they may simply be missing. The Centers for Disease Control and Prevention, for instance, is missing race and ethnicity information for 35 percent of COVID-19 cases as of Nov. 7.*

    *As of April 9, the CDC is still missing race and ethnicity information for 35% of COVID-19 cases.

    This past Wednesday, the FDA’s Vaccines and Related Biological Products Advisory Committee (VRBPAC) met to discuss the future of COVID-19 booster shots. Notably, this committee didn’t actually need to vote on anything, since the FDA and CDC had already authorized a second round of boosters for Americans over age 50 and immunocompromised people the week before. 

    When asked why the FDA hadn’t waited to hear from its advisory committee before making this authorization decision, vaccine regulator Peter Marks said that the agency had relied on data from the U.K. and Israel to demonstrate the need for more boosters—combined with concerns about a potential BA.2 wave. The FDA relied on data from the U.K. and Israel when making its booster decision in the fall, too; these countries, with centralized health systems and better-organized data, are much more equipped to track vaccine effectiveness than we are.

    With that authorization of second boosters for certain groups already a done deal, the VRBPAC meeting this past Wednesday focused more on the information we need to make future booster decisions. Should we expect annual COVID-19 shots, like we do for the flu? What about shots that are designed to combat specific variants? A lot of this is up in the air right now, the meeting discussion indicated.

    Also up in the air: will the FDA ever host a virtual VRBPAC meeting without intensive technical difficulties? The meeting had to pause for more than half an hour to sort out a livestream issue.

    Here are some vaccine data questions that came up on Wednesday, drawing from my own notes on the meeting and the STAT News liveblog:

    • How much does protection from a booster shot wane over time? We know that booster shots increase an individual’s protection from a coronavirus infection, symptoms, hospitalization, and other severe outcomes; CDC data presented during the VRBPAC meeting showed that, during the Omicron surge, Americans who were boosted were much more protected than those with fewer doses. But we don’t have a great sense of how long these different types of protection last.
    • How much does booster shot protection wane for different age groups? Waning immunity has been a bigger problem among seniors and immunocompromised people, leading to the FDA’s decision on fourth doses for these groups. But what about other age groups? What about people with other conditions that make them vulnerable to COVID-19, like diabetes or kidney disease? This is less clear.
    • To what degree is waning immunity caused by new variants as opposed to fewer antibodies over time? This has been a big question during the Delta and Omicron surges, and it can be hard to answer because of all the confounding variables involved. In the U.S., it’s difficult to link up vaccine data and case data; tacking on metrics like which variant someone was infected with or how long ago they were vaccinated often isn’t possible—or if it is possible, it’s very complicated. (The U.K. does a better job of this.)
    • Where will the next variant of concern come from, and how much will it differ from past variants? Computational biologist Trevor Bedford gave a presentation to VRBPAC that attempted to answer this question. The short answer is, it’s hard to predict how often we’ll see new events like Omicron’s emergence, in which a new variant comes in that is extremely different from the variants that preceded it. Bedford’s analysis suggests that we could see “Omicron-like” events anywhere from every 1.5 years to every 10.5 years, and we should be prepared for anything on that spectrum. The coronavirus has evolved quite quickly in the last two years, Bedford said, and will likely continue to do so; though he expects some version of Omicron will be the main variant we’re dealing with for a while.
    • What will the seasonality of COVID-19 be? The global public health system has a well-established process for developing new flu vaccines, based on monitoring circulating flu strains in the lead-up to flu seasons in different parts of the world. Eventually, we will likely get to a similar place with COVID-19 (if annual vaccines become necessary! also an open question at the moment). But right now, the waxing and waning of surges caused by new variants and human behavior makes it difficult to identify the actual seasonality of COVID-19.
    • At what point do we say the vaccine isn’t working well enough? This question was asked by VRBPAC committee member Cody Meissner of Tufts University, during the discussion portion of the meeting. So far, the most common way to measure COVID-19 vaccine effectiveness in the lab is by testing antibodies generated by a vaccine against different forms of the coronavirus. But these studies don’t account for other parts of the immune system, like T cells, that garner more long-term protection than antibodies. We need a unified method for measuring vaccine effectiveness that takes different parts of the immune system into account, along with real-world data.
    • How might vaccine safety change over time? This question was brought up by Hayley Ganz of Stanford, another VRBPAC committee member. The CDC does have an extensive system for monitoring vaccine safety; data from that system should be readily available to the experts making booster shot decisions.

    Another thing I’m wondering about right now, personally, is how the U.S.’s shifting focus away from case data might make all of this more complicated. As public health agencies scale down case investigations and contact tracing—and more people test positive on at-home, rapid tests that are never reported to these agencies—we’re losing track of how many Americans are actually getting COVID-19. And breakthrough cases, which are more likely to be mild or asymptomatic, might also be more likely to go unreported.

    So, how does the U.S. public health system study vaccine effectiveness in a comprehensive way if we simply aren’t logging many of our cases? Programs such as randomized surveillance testing and cohort studies might help, but outside of a few articles and Twitter conversations, I’m not seeing much discussion of these solutions.

    Finally: a few friends and relatives over age 50 have asked me about when (or whether) to get another booster shot, given all of the uncertainties I laid out above. If you’re in the same position, here are a couple of resources that might help:

    More vaccine data

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

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

    This week, I had a big retrospective story published at FiveThirtyEight: I looked back at the major metrics that the U.S. has used to track COVID-19 over the past two years—and how our country’s fractured public health system hindered our use of each one.

    The story is split into seven sections, which I will briefly summarize here:

    • Case counts, January to March 2020: Early on in the pandemic, the U.S. had a very limited picture of COVID-19 cases due to our very limited testing: after rejecting a test made by the WHO, the CDC made its own test—which turned out to have contamination issues, further slowing down U.S. testing. In early March 2020, for example, the majority of cases in NYC were identified in hospitals, suggesting that official counts greatly underestimated the actual numbers of people infected.
    • Tests administered, March to September 2020: Test availability improved after the first wave of cases, with organizations like the COVID Tracking Project keeping a close eye on the numbers. But there were a lot of challenges with the testing data (like different units across different states) and access issues for Americans with lower socioeconomic status.
    • Hospitalizations, October to December 2020: By late 2020, many researchers and journalists were considering hospitalizations to be a more reliable COVID-19 metric than cases. But it took a long time for hospitalization data to become reliable on a national scale, as the HHS launched a new tracking system in the summer and then took months to work out kinks in this system.
    • Vaccinations, January to June 2021: When the vaccination campaign started in late 2020, it was “tempting to forget about all other COVID-19 metrics,” I wrote in the story. But the U.S.’s fractured system for tracking vaccinations made it difficult to analyze how close different parts of the country were to prospective “herd immunity,” and distracted from other public health interventions that we still needed even as people got vaccinated.
    • Breakthrough cases, July to November 2021: The Delta surge caused widespread infections in people who had been vaccinated, but the CDC—along with many state public health agencies—was not properly equipped to track these breakthrough cases. This challenge contributed to a lack of good U.S. data on vaccine effectiveness, which in turn contributed to confusion around the need for booster shots.
    • Hospitalizations (again), December to January 2022: The Omicron surge introduced a need for more nuance in hospitalization data, as many experts asked whether COVID-19 patients admitted with Omicron were actually hospitalized for their COVID-19 symptoms or for other reasons. Nuanced data can be useful in analyzing a variant’s severity; but all COVID-related hospitalizations cause strain on the healthcare system regardless of their cause.
    • New kinds of data going forward: In our post-Omicron world, a lot of public health agencies are shifting their data strategies to treat COVID-19 more like the flu: less tracking of individual cases, and more reliance on hospitalization data, along with newer sources like wastewater. At this point in the pandemic, we should be fortifying data systems “for future preparedness,” I wrote, rather than letting the systems we built up during the pandemic fall to the wayside.

    I did a lot of reporting for this piece, including interviews with some of the U.S.’s foremost COVID-19 data experts and communicators. As long as the piece is, there were a lot of metrics (and issues with these metrics) that came up in these interviews that I wasn’t able to include in the final story—so I wanted to share some bonus material from my reporting here.

    Long COVID:

    As I’ve discussed in previous issues, the U.S. has done a terrible job of collecting data on Long COVID. The NIH estimates that this condition follows a significant share of coronavirus infections (between 10% and 30%), but we have limited information on its true prevalence, risk factors, and strategies for recovery.

    Here’s Dr. Eric Topol, the prolific COVID-19 commentator and director of the Scripps Research Translational Institute, discussing this data problem:

    [Long COVID has] been given very low priority, very little awareness and recognition. And we have very little data to show for it, because it hasn’t been taken seriously. But it’s a very serious matter.

    We should have, early on, gotten at least a registry of people —a large sample, hundreds of thousands of people prospectively assessed, like is being done elsewhere [in the U.K. and other countries]. So that we could learn from them: how long the symptoms lasted, what are the symptoms, what are the triggers, what can be done to avoid it, the role of vaccines, the role of boosters, all this stuff. But we have nothing like that.

    The NIH’s RECOVER initiative may answer some of these questions, but it will take months—if not years—for the U.S. to actually collect the comprehensive data on Long COVID that we should have started gathering when the condition first began gaining attention in 2020.

    Demographic data:

    In the testing section of the story, I mention that the U.S. doesn’t provide much demographic data describing who’s getting tested for COVID-19. There is actually a little-known provision in the CARES Act that requires COVID-19 testing providers to collect certain demographic data from all people who seek tests. But the provision is not enforced, and any data that are collected on this subject aren’t making it to most state COVID-19 dashboards, much less to the CDC’s public data dashboard.

    Here’s Dr. Ellie Murray, an epidemiologist at the Boston University School of Public Health, discussing why this is an issue:

    We don’t collect reason for seeking a test. We don’t collect age, race, ethnicity, occupation of people who seek a test. Those kinds of things could provide us with some really valuable information about who is getting tested, when, and why—that could help us figure out, what are the essential occupations where people are having a lot of exposures and therefore needing to get a lot of tests? Or are there occupations where we’re seeing a lot of people end up in hospital, who have those occupations, but they’re not getting tests, because actually, the test sites are nowhere near where they need to work, or they don’t have the time to get there before they close.

    And so we don’t really know who is getting tested, and that, I think, is a bigger problem, than whether the numbers that are being tested tell us anything about the trajectory of COVID. Because we have case data, and hospitalization data, and death data to tell us about the trajectory. And the testing could really tell us more about exposure, and concern, and access—if we collected some more of this data around who is getting tested and why.

    Test positivity:

    Speaking of testing: another metric that I didn’t get into much in the story was test positivity. Test positivity—or, the share of COVID-19 tests that return a positive result—has been used from the CDC to local school districts as a key metric to determine safety levels. (For more on this metric, check out my FAQ post from this past January.)

    But even when it’s calculated correctly, test positivity faces the same challenges as case data: namely, bias in who’s getting tested. Here’s Lauren Ancel Meyers, director of the University of Texas at Austin’s COVID-19 Modeling Consortium, explaining this:

    Test positivity is just as fraught [as cases]. It’s just as difficult, because you need to know the numerator and the denominator—what’s influencing the numerator and the denominator? Who is going to get tested, who has access to tests? … It used to be, at the very beginning [of the pandemic], nobody could get a test who wanted a test. And now, today, everybody has a test in their medicine cabinet, and they don’t get reported when they test. It’s different issues that have ebbed and flowed throughout this period.

    Often, if you’re a good data analyst or a modeler, and you have all the information, you can handle those kinds of biases. But the problem is, we don’t know the biases from day to day. And so even though there are statistical tools to deal with incomplete bias, without knowing what those biases are, it’s very hard to do reliable inference, and really hard to understand what’s actually going on.

    Genetic surveillance:

    Also related to testing: genetic surveillance for coronavirus variants of concern. Genetic surveillance is important because it can help identify new variants that may be more transmissible or more likely to evade protection from vaccines. It can additionally help track the qualities of concerning variants once they are identified (if variant data is linked to hospitalization data, vaccination data, and other metrics—which is not really happening in the U.S. right now.)

    Our current genetic surveillance systems have a lot of gaps. Here’s Leo Wolansky, from the Rockefeller Foundation’s Pandemic Prevention Institute (PPI), discussing how his organization seeks to address these challenges:

    [We’re trying to understand] where our blind spots are, and the bias that we might experience with a lot of health system reporting. One of the things that PPI has been doing is identifying centers of excellence in different parts of the world that can improve the sequencing of new cases in underrepresented countries. And so for example, we’ve provided quite a bit of support to the folks in South Africa that ultimately rang the alarm on Omicron.

    We’re also doing this by actually trying to systematically assess countries’ capacity for this type of genomic surveillance. So thinking about, how many tests have been recorded? What’s that test positivity rate? Do we have confidence in the basic surveillance system of the country? And then, do we also see enough sequences, as well as sequencing facility data, to demonstrate that this country can sequence and just isn’t doing enough—or cannot sequence because it needs foundational investment in things like laboratories and devices. We’ve been mapping this capacity just to make sure that we understand where we should be investing as a global community.

    The Pandemic Prevention Institute is taking a global perspective in thinking about data gaps. But these gaps also exist within the U.S., as is clear when one looks at the differences in published coronavirus sequences from state to state. Some states, like Wyoming, Vermont, and Colorado, have sequenced more than 10% of their cumulative cases, according to the CDC. Others, like Oklahoma, Iowa, and South Dakota, have sequenced fewer than 3%. These states need additional investment in order to thoroughly monitor coronavirus transmission among their residents.

    Cohort studies:

    In a cohort study, researchers follow a group of patients over time in order to collect long-term data on specific health conditions and/or the outside factors that influence them. The U.S. has set up a few cohort studies for COVID-19, but they haven’t been designed or utilized in a way that has actually provided much useful data—unlike cohort studies in some other countries. (The U.K., for example, has several ongoing cohort studies collecting information on COVID-19 symptoms, infections in schools, seroprevalence, and more.)

    Here’s Dr. Ellie Murray explaining the lost potential of these studies in the U.S.:

    There are a number of existing cohort studies that have been asked or who asked to pivot to collecting COVID information and therefore collecting long-term COVID information on their cohorts. But there doesn’t seem to be any kind of system to [determine], what are the questions we need answered about COVID from these kinds of studies? And how do we link up people who can answer those questions with the data that we’re collecting here, and making sure we’re collecting the right data? And if this study is going to answer these questions, and this one is going to answer those questions—or, here’s how we standardize those two cohorts so that we can pull them together into one big COVID cohort.

    And so, we end up in this situation where, we don’t know what percent of people get Long COVID, even though we’ve been doing this for over two years. We don’t even really know, what are all the different symptoms that you can get from COVID? … There are all these questions that we could be sort-of systematically working our way through, getting answers and using them to inform our planning and our response. [In addition to having] standardized questions, you also need a centralized question, instead of just whatever question occurs to someone who happens to have the funding to do it.

    Excess deaths:

    Excess deaths measure the deaths that occur in a certain region, over a certain period of time, above the number of deaths that researchers expect to see in that region and time period based on modeling from past years’ data. Excess deaths are the COVID-19 metric with the longest lag time: it takes weeks from initial infection for someone to die of the disease, and can take weeks further for a death certificate to be incorporated into the public health system.

    Once that death information is available, however, it can be used to show the true toll of the pandemic—analyzing not just direct COVID-19 deaths, but also those related to isolation, financial burden, and other indirect issues—as well as who has been hit the hardest.

    Here’s Cecile Viboud, a staff scientist at the NIH who studies infectious disease mortality, discussing this metric:

    We’ve been using the excess death approach for a long time. It comes from flu research, basically starting in 1875 in the U.K. And it was used quite a lot during the 1918 pandemic. It can be especially good in examining historical records where you don’t have lab confirmation—there was no testing ability back in those days…

    So, I think it’s kind of natural to use it for a pandemic like COVID-19. Very early on, you could see how useful this method was, because there was so little testing done. In March and April 2020, you see substantial excess, even when you don’t see lab-confirmed deaths. There’s a disconnect there between the official stats, and then the excess mortality… [We can also study] the direct effect of COVID-19 versus the indirect effect of the pandemic, like how much interventions affected suicide, opioids, death, accidents, etc. The excess approach is also a good method to look at that.

    Viboud also noted that excess deaths can be useful to compare different parts of the U.S. based on their COVID-19 safety measures. For example, one can analyze excess deaths in counties with low vaccination rates compared to those with high vaccination rates. This approach can identify the pandemic’s impact even when official death counts are low—an issue that the Documenting COVID-19 project has covered in-depth.

    Again, you can read the full FiveThirtyEight story here!

    More federal data

  • Pandemic preparedness: Improving our data surveillance and communication

    Pandemic preparedness: Improving our data surveillance and communication

    Screenshot of the new Biden COVID-19 plan.

    As COVID-19 safety measures are lifted and agencies move to an endemic view of the virus, I’m thinking about my shifting role as a COVID-19 reporter. To me, this beat is becoming less about reporting on specific hotspots or control measures and more about preparedness: what the U.S. learned from the last two years, and what lessons we can take forward—not just for the future COVID-19 surges that are almost certainly coming, but also for future infectious disease outbreaks.

    To that end, I was glad to see the Biden administration release a new COVID-19 plan focused on exactly this topic: preparedness for new surges, new variants, and new infectious diseases beyond this current pandemic.

    From the plan’s executive summary:

    Make no mistake, President Biden will not accept just “living with COVID” any more than we accept “living with” cancer, Alzheimer’s, or AIDS. We will continue our work to stop the spread of the virus, blunt its impact on those who get infected, and deploy new treatments to dramatically reduce the occurrence of severe COVID-19 disease and deaths.

    The Biden plan was released last week, in time with the president’s State of the Union address. I read through it this morning, looking for goals and actions connected to data collection and reporting.

    Here are a few items that stuck out to me, either things that the Biden administration is already doing or should be doing: 

    • Improving surveillance to identify new variants: The U.S. significantly improved its variant sequencing capacity in 2021, multiplying the number of cases sequenced by more than tenfold from the beginning to the end of the year. But the new Biden plan promises to take these improvements further, by adding more capacity for sequencing at state and local levels—and, crucially, “strengthening data infrastructure and interoperability so that more jurisdictions can link case surveillance and hospital data to vaccine data.” In plain language, that means: making it easier to track breakthrough cases (which I have argued is a key data problem in the U.S.).
    • Expanding wastewater surveillance: As I’ve written before, in the current national wastewater surveillance network, some states are very well-represented with over 50 collection sites; while other states are not included in the data at all. The Biden administration is committed to bring more local health agencies and research institutions into the surveillance network, thus expanding our national capacity to get early warnings about surges.
    • Standardizing state and local data systems: I’ve written numerous times that the U.S. suffers from a lack of standardization among its 50 different states and hundreds of local health agencies. According to the new plan, the Biden administration plans to facilitate data sharing, aggregating, and analyzing data across state and local agencies—including wastewater monitoring and other potential methods of surveillance that would provide early warnings of new surges. This would be huge if it actually happens.
    • Modernize the public health data infrastructure: One thing that could help health agencies better coordinate and share data: modernizing their data systems. That means phasing out fax machines and mail-in reports (which, yes, some health departments still use) and investing in new electronic health record technologies, while hiring public health workers who can manage such systems.
    • Use a new variant playbook to evaluate new virus strains: Also in the realm of variant preparedness, the Biden administration has developed a new “COVID-19 Variant Playbook” that may be used to quickly determine how a new variant impacts disease severity, transmissibility, vaccine effectiveness, and other factors. The new playbook may be used to quickly update vaccines, tests, and treatments if needed, by working in partnership with health systems and research institutions.
    • Collecting demographic data on vaccinations and treatments: The Biden plan boasts that, “Hispanic, Black, and Asian adults are now vaccinated at the same rates as White adults.” However, CDC data shows that this trend does not hold true for booster shots: eligible white Americans are more likely to be boosted than those in other racial and ethnic groups. The administration will need to continue collecting demographic data to identify and address gaps among vaccinations and treatments; indeed, the Biden plan discusses continued efforts to improve health equity data.
    • Tracking health outcomes for people in high-risk settings: Along with its health equity focus, the Biden plan discusses a need to better track and report on health outcomes in nursing homes, other long-term care facilities, and other congregate settings like correctional facilities and homeless shelters. Congregate facilities continue to be major COVID-19 hotspots whenever there’s a new outbreak, so improving health standards in these settings should be a major priority.
    • Studying and combatting vaccine misinformation, vaccine safety: The new plan acknowledges the impact of misinformation on vaccine uptake in the U.S., and commits the Biden administration to addressing this trend. This includes a Request for Information that will be issued by the Surgeon General’s office, asking researchers to share their work on misinformation. Meanwhile, the administration will also continue monitoring vaccine safety and reporting these data to the public.
    • Test to Treat: One widely publicized aspect of the Biden plan is an initiative called “Test to Treat,” which would allow people to get tested for COVID-19 at pharmacies, health clinics, long-term care facilities, and other locations—then, if they test positive, immediately receive treatment in the form of antiviral pills. If this initiative is widely funded and adopted, the Biden administration should require all participating health providers to share testing and treatment data. This would allow researchers to evaluate whether this testing and treatment rollout has been equitable across different parts of the country and minority groups.
    • Website for community risk levels and public health guidance: The Biden plan includes the launch of a government website “that allows Americans to easily find public health guidance based on the COVID-19 risk in their local area and access tools to protect themselves.” The CDC COVID-19 dashboard was recently redesigned to highlight the agency’s new Community Level guidance, which is likely connected to this goal. Still, the CDC dashboard leaves much to be desired when it comes to comprehensive information and accessibility, compared to other trackers.
    • A new logistics and operational hub at HHS: In the last two years, the Department of Health and Human Services (HHS) built up an office for coordinating the development, production, and delivery of COVID-19 vaccines and treatments. The new Biden plan announced that this office will become a permanent part of the agency, and may be used for future disease outbreaks. At the same time, the Biden administration has added at-home tests, antiviral pills, and masks to America’s national stockpile for future surges; and it is supporting investments in laboratory capacity for PCR testing.
    • Tracking Long COVID: Biden’s plan also highlights Long COVID, promoting the need for government efforts to “detect, prevent, and treat” this prolonged condition. The plan mentions NIH’s RECOVER initiative to study Long COVID, discusses funding new care centers for patients, and proposes a new National Research Action Plan on Long COVID that will bring together the HHS, VA, Department of Defense, and other agencies. Still, the plan doesn’t discuss actual, financial support for patients who have been out of work for up to two years.
    • Supporting health and well-being among healthcare workers: The new Biden plan acknowledges major burnout among healthcare workers, and proposes a new grant program to fund mental health resources, support groups, and other systems of combatting this issue. Surveying healthcare workers and developing systematic solutions to the challenges they face could be a major aspect of preparing for future disease outbreaks. The Biden plan also mentions investing in recruitment and pipeline programs to support diversity, equity, and inclusion among health workers.
    • More international collaboration: The new Biden plan also focuses on international aid—delivering vaccine donations to low-income nations—and collaboration—improving communication with the WHO and other global organizations that conduct disease surveillance. This improved communication may be especially key for identifying and studying new variants in a global pandemic surveillance system.

    This week, a group of experts—including some who have advised the Biden administration— followed up on the Biden plan with their own plan, called “A Roadmap for Living with COVID.” The Roadmap plan also emphasizes data collection and reporting, with a whole section on health data infrastructure; here, the authors emphasize establishing centralized public health data platforms, linking disparate data types, designing data infrastructure with a focus on health equity, and improving public access to data.

    Both the Biden administration’s plan and the Roadmap plan give me hope that U.S. experts and leaders are thinking seriously about preparedness. However, simply releasing a plan is only the first step to making meaningful changes in the U.S. healthcare system. Many aspects of the Biden plan involve funding from Congress… and Congress is pretty unwilling to invest in COVID-19 preparedness right now. Just this week, a $15 billion funding plan collapsed in the legislature after the Biden administration already made major concessions.

    Readers, I recommend calling your Congressional representatives and urging them to support COVID-19 preparedness funding. You can also look into similar measures in your state, city, or other locality. We need to improve our data in order to be prepared for future disease outbreaks, COVID-19 and beyond.

    More national data

  • Five more things, February 27

    Five additional news items from this week:

    • The CDC is not publicly releasing a lot of its COVID-19 data. Last weekend, New York Times reporter Apoorva Mandavilli broke the news that the CDC has withheld a lot of its COVID-19 data from the public, including information on breakthrough cases, demographic data, and wastewater data. This news was honestly not surprising to me because it follows a pattern: the CDC doesn’t like to share information unless it can control the interpretations. But I appreciated the conversation brought on by this article, with public health experts saying they’d rather have imperfect data than a complete data void. (I agree!)
    • BA.2 is definitely more transmissible than the original Omicron strain, but it does not appear to be significantly more severe or more capable of evading vaccines. Two recent posts, one in the New York Times COVID-19 updates page and one from Your Local Epidemiologist, share some updates on what scientists have learned about BA.2 in the past couple of weeks. In the U.S. and other countries with BA.2, this sublineage doesn’t seem to be causing a major rise in cases—at least so far.
    • New CDC study shows the utility of rapid testing out of isolation. More than half of patients infected with the coronavirus tested positive on rapid antigen tests between five and nine days after their initial diagnosis or symptom onset, a new CDC report found. The report includes over 700 patients at a rural healthcare network in Alaska. These findings suggest that rapid testing out of isolation is a good way to avoid transmitting the virus to others, if one has the tests available.
    • January saw record-high coronavirus infections in hospitals. POLITICO reporters analyzed hospitalization data from the Department of Health and Human Services (HHS), finding that: “More than 3,000 hospitalized patients each week in January had caught Covid sometime during their stay, more than any point of the pandemic.” This high number demonstrates Omicron’s high capacity to infect other people.
    • Hong Kong’s surge shows the value of vaccinations. Hong Kong has been a global leader in keeping COVID-19 cases low throughout the pandemic, yet Omicron has tested this territory’s strategy—causing record cases and overwhelming hospitals. One major issue for Hong Kong has been low vaccination rates, particularly among the elderly, as people did not see the need to get vaccinated when cases in the territory were practically nonexistent.
  • The CDC is finally publishing wastewater data—but only ten states are well-represented

    The CDC is finally publishing wastewater data—but only ten states are well-represented

    This week, the CDC added wastewater tracking to its COVID-19 data dashboard. Wastewater has been an important COVID-tracking tool throughout the pandemic, but it gained more public interest in recent months as Omicron’s rapid spread showed the utility of this early warning system. While the CDC’s new wastewater tracker offers a decent picture of national COVID-19 trends, it’s basically useless for local data in the majority of states.

    Wastewater, as you might guess from the name, is water that returns to the public utility system after it’s been used for some everyday purpose: flushing a toilet, bathing, washing dishes, and so forth. In wastewater surveillance, scientists identify a collection point in the sewer system—either beneath a specific building or at a water treatment plant that handles sewage from a number of buildings. The scientists regularly collect wastewater samples from that designated point and test these samples for COVID-19 levels.

    When someone is infected with the coronavirus, they are likely to shed its genetic material in their waste. This genetic signal shows up in wastewater regardless of people’s symptoms, so a wastewater sample may return a positive result for the coronavirus earlier than other screening tools like rapid antigen tests. And, because wastewater samples are typically collected from public sewer networks, this type of surveillance provides information for an entire community—there’s no bias based on who’s able to get a PCR or rapid test.

    Scientists and organizations who utilize wastewater testing consider it an early warning system: trends in wastewater often precede trends in reported COVID-19 cases. For example, the coronavirus RNA levels identified in Boston’s wastewater shot up rapidly before Boston’s actual Omicron case numbers did, then also went down before case numbers did. Similarly, Missouri’s wastewater surveillance system—which includes genetic sequencing for variants—identified Delta cases last summer weeks before PCR testing did.

    Wastewater surveillance is also a popular strategy for colleges and universities, which can set up collecting sites directly underneath student dormitories. Barnard College, where I went to undergrad, is one school that’s employed this strategy. At one point in the fall 2021 semester, the college instructed students living in the Plimpton residence hall (where I lived as a sophomore!) to get individual PCR COVID-19 tests because the wastewater surveillance program had found signals of the virus under their dorm.

    Screenshot of the CDC’s new wastewater dashboard, retrieved on February 6.

    The CDC has been coordinating wastewater surveillance efforts since September 2020, Dr. Amy Kirby, team lead for the National Wastewater Surveillance System, said during a CDC media briefing on Friday. “What started as a grassroots effort by academic researchers and wastewater utilities has quickly become a nationwide surveillance system with more than 34,000 samples collected representing approximately 53 million Americans,” Kirby said.

    It’s a little unclear why it took the CDC so long to set up a dashboard with this wastewater data when surveillance efforts have been underway for a year and a half. Still, many researchers and reporters are glad to see the agency finally publishing this useful information. The dashboard represents wastewater collection sites as colored dots: blue dots indicate that coronavirus RNA levels have dropped at this site in the last two weeks; yellow, orange, and red dots indicate RNA levels have risen; and gray dots indicate no recent data. You can download data from a dropdown beneath the dashboard and on the CDC’s data portal site.

    “More than 400 testing sites around the country have already begun their wastewater surveillance efforts,” Kirby said at the media briefing. But she failed to mention that, out of these sites—the actual total is 471, according to the CDC dashboard—more than 200 are located in just three states: Missouri, Ohio, and Wisconsin. Missouri, with 80 sites, has a long-established system to monitor wastewater, through a collaboration between state agencies and the University of Missouri. Ohio has 71 sites of its own, while Wisconsin has 61.

    After these Midwest wastewater powerhouses, other states with a relatively high number of collection sites include North Carolina with 38, Texas with 35, New York with 32, Utah with 31, Virginia with 29, Colorado with 21, and California with 17. No other state has more than 10 wastewater collection sites, and 18 states do not have any wastewater collection sites at all.

    So, the CDC dashboard is pretty useful if you live in one of these ten states with a high number of collection sites. Otherwise, you just have to… wait for more sites in your area to get added to the dashboard, I guess? (Kirby did say during the media briefing that several hundred more collection sites are in development.) Even within the states that are doing a lot of wastewater surveillance, though, reporting is uneven at more local levels; for instance, many New York sites are concentrated in New York City and surrounding suburbs.

    In this way, biased wastewater surveillance coverage in the U.S. echoes biased genetic sequencing coverage, an issue I’ve written about many times before. (See the genetic surveillance section of this post, for example.) Some states, like California, New York, and others with high-tech laboratories set up for sequencing, have identified variants for a much higher share of their COVID-19 cases than states with fewer resources.

    The CDC gives wastewater treatment plants, local health departments, and research laboratories the ability to join its national surveillance network. But again, this is much easier for institutions in some places than others. Consider the resources available for wastewater sampling in New York City compared to in rural parts of the Midwest and South.

    In addition, for places that do have robust wastewater surveillance systems, there are some caveats to the data, the CDC expert told reporters. Data may be hard to interpret “in communities with minimal or no sewer infrastructure and in communities with transient populations, such as areas with high tourism,” she said. “Additionally, wastewater surveillance cannot be used to determine whether a community is free from infections.”

    If you’re looking for more wastewater data beyond the CDC tracker, here are two sources to check out:

    • Biobot’s Nationwide Wastewater Monitoring Network, which I included in last week’s Featured Sources: This wastewater epidemiology company collects samples from water treatment facilities across the country; their dashboard includes both estimates of coronavirus levels in the U.S. overall and estimates for specific counties in which data are collected. Biobot’s data are available for download on Github. (Interestingly, it seems that some of the counties included in Biobot’s dashboard are not currently included in the CDC’s dashboard; I’ll be curious to see if that changes in the coming weeks.)
    • COVIDPoops19 dashboard: This dashboard, run by researchers at the University of California Merced, provides a global summary of wastewater surveillance efforts. It includes over 3,300 wastewater collection sites tied to universities, public health agencies, and other institutions; click on individual sites to see links to dashboards, align with related news articles and scientific papers.

    More federal data

  • CovidTests.gov early rollout raises equity concerns; where’s the data?

    CovidTests.gov early rollout raises equity concerns; where’s the data?

    The federal government’s policies aimed at helping Americans get free rapid tests are insufficient for many households including people of color. Graphic via KHN.

    This week, the U.S. government unveiled a new website where Americans can get free at-home COVID-19 tests. The site is hosted by the U.S. Postal Service (USPS)—which will also distribute the tests—and it’s been lauded for its straightforward navigation and ability to handle a high level of traffic, both of which are unusual with government sites.

    On Tuesday, the site went live early in “beta test” form before its formal launch on Wednesday. Within hours of it going live, public health experts were already raising equity concerns about the free test distribution program. To address these concerns, the federal government should release data on where the free tests go—including breakdowns by state, county, ZIP code, race and ethnicity, the tests’ delivery dates, and more.

    As the link to the testing order site was shared widely on social media, one thing quickly became clear: people who lived in high-density settings were at a disadvantage. Americans in traditional apartment buildings, houses split into multiple living spaces, dormitories, and other multi-unit dwellings attempted to order tests—only to get an error message stating someone at their address had ordered tests already.

    The USPS ordering page is set up to allow just one test order per address, to prevent people from abusing the free test program. But, despite having literally every address in the U.S. on file, the USPS apparently failed to account for many apartment buildings. Some apartment-dwellers were able to get around this issue by placing their apartment number on the first address line, removing “Apt” from the address, or otherwise adjusting how they filled out the form, but these tricks didn’t work for everyone.

    I myself ordered the free tests before I learned about these issues on Twitter; I later sheepishly texted the groupchat for my Brooklyn, seven-unit apartment building, preemptively apologizing in case I’d fucked up my neighbors’ chances of obtaining free tests. (Luckily, my building seemed to be unaffected by the USPS issue—one of my neighbors responded saying that she was able to order the tests without a problem.)

    This issue “stems from buildings not being registered as multi-unit complexes and affected only a ‘small percentage of orders,’” the USPS said in a statement to POLITICO. And people facing this issue as they order tests can file a service request with USPS or call the agency at 1-800-ASK-USPS, according to KHN.

    Still, a “small percentage of orders” could add up to millions of people living in multi-unit housing who were unable to obtain free tests, or would have to share just four tests among an apartment building’s worth of residents. Without more precise data, it’s hard to understand the scope of this problem.

    All the Twitter discourse about apartment buildings obscures another group that shouldn’t have to share a small number of tests among many people: large households. The USPS is sending just four tests in each order—not four testing kits, four individual tests. That’s not enough for a family of four to test themselves according to FDA recommendations (i.e. twice within two days) after a potential exposure; it’s certainly not enough for large families including five or more people.

    And minority communities are more likely to include such large households. According to a Kaiser Family Foundation analysis of Census data: “More than a third of Hispanic Americans plus about a quarter of Asian and Black Americans live in households with at least five residents…Only 17% of white Americans live in these larger groups.”

    Households in West coast states are also more likely to include five or more residents, according to a similar analysis from the University of North Carolina Chapel Hill’s Carolina Demography center. States with the highest shares of five or more resident households are: Utah (18.8%), California (13.7%), Hawaii (13.5%), Idaho (13.2%), and Alaska (12.9%). On the other hand, in some East coast states, under 7% of households include five or more residents.

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    The USPS test distribution system also gave an advantage to Americans with internet access. At one point on Tuesday afternoon, the USPS order site was drawing more than half of all government website traffic, demonstrating its popularity with internet users—while people without internet were not yet able to order tests.

    As of Friday, those without internet access can order the free tests over the phone, at 1-800-232-0233. This phone line is open daily from 8 AM to midnight Eastern Time, according to NPR, and Americans can order in over 150 languages. The USPS website itself is available in English, Spanish, and Chinese.

    While this phone line is very helpful now, the delay between the website’s release (on Tuesday) and the phone line’s release (on Friday) means that Americans without internet may be behind in the queue for actually receiving their tests. Already, the federal government has said that people who ordered their tests may need to wait for weeks to receive their tests.

    Of course, as analysis from KHN has shown, Americans of color are less likely to have internet access than their white neighbors. 27% of Native Americans, 20% of Black Americans, and 16% of Hispanic Americans have no internet subscription, compared to 12% of white Americans.

    Finally, the USPS test distribution system leaves out one major group of vulnerable Americans: those who don’t have an address at all. Homeless people are particularly vulnerable to COVID-19: many outbreaks have occurred in shelters, and many of these people have health conditions that increase their risk of severe symptoms. The impact of COVID-19 among homeless Americans is not well understood due to a lack of data collection; still, we know enough to indicate free tests should be a priority for this group.

    The White House has said that equity will be a priority for the free rapid test rollout: each day, 20% of test shipments will go to people who live in highly vulnerable communities, as determined by the CDC’s Social Vulnerability Index. This index ranks ZIP codes according to the communities’ ability to recover from adverse health events, based on a number of social, environmental, and economic factors.

    This priority is nice to hear. But without data on the test rollout, it’ll be difficult to evaluate how well the federal government is living up to its promise of equitable test distribution. I’d like to see data on the free test distribution that goes to the same level of detail as the data on our vaccine distribution, if not even more granular.

    The data could include: tests distributed by state, county, and ZIP code; tests distributed to ZIP codes that rank highly on the Social Vulnerability Index; tests distributed by race, ethnicity, age, gender, and household size; dates that tests were ordered and delivered; tests delivered to single- and multi-unit buildings; and more.

    Unlike other COVID-19 metrics that are difficult to collect and report at the federal level, the federal government literally has all of this information already—they’re collecting the address of every person that orders tests! There is no excuse for the government not to make these data public.

    In short: USPS, where is your free rapid test distribution dashboard? I’m waiting.

    https://twitter.com/JuliaRaifman/status/1483542002990002176

  • COVID source callout: COVID-19 deaths in U.S. hospitals

    Readers active on COVID-19 Data Twitter may have seen this alarmist Tweet going around earlier this weekend. In this post, a writer (notably, one with no science, health, or data background) posted a screenshot showing that the Department of Health and Human Services (HHS) is no longer requiring hospitals to include COVID-19 deaths that occur at their facilities in their daily reports to the agency.

    This is not the end of U.S. COVID-19 death reporting, as the Tweet’s author insinuated. Primarily because: hospitals are not the primary source of COVID-19 death numbers. These statistics come from death certificates, which are processed by local health departments, coroners, and medical examiners; death certificate statistics are sent to state health departments, which in turn send the numbers to the CDC. The CDC is still reporting COVID-19 deaths with no disruptions, and, in fact, released a highly detailed new dataset on these deaths last month.

    For more explanation, see this thread by Erin Kissane (COVID Tracking Project co-founder) and this one from epidemiologist Justin Feldman. It’s particularly important to note here that, as Feldman points out, plenty of COVID-19 deaths don’t occur in hospitals! About one-third of COVID-19 deaths occurred outside these facilities in 2020.

    https://twitter.com/kissane/status/1482140736636129282

    (Note: The Documenting COVID-19 project has written, in great detail, about how COVID-19 deaths are reported in our Uncounted series. See: this article at USA Today and this reporting recipe.)

    It is certainly worth asking why the HHS took in-hospital COVID-19 deaths off the list of required metrics for hospitals. This data field had some utility for researchers looking to identify COVID-19 mortality rates within these facilities—though, from what I could tell, nobody was looking at it very much before this weekend.

    But, again, this is not the end of COVID-19 death reporting! This is the HHS making one small change to a massive hospitalization dataset—which was primarily used for looking at other metrics—while the CDC’s death reporting continues as usual.

    more source spotlights