Pregnant & Breastfeeding Women Who Get the COVID-19 Vaccine Are Protecting Their Infants, Research Suggests
Becky Cummings had multiple reasons to get vaccinated against COVID-19 while tending to her firstborn, Clark, who arrived in September 2020 at 27 weeks.
The 29-year-old intensive care unit nurse in Greensboro, North Carolina, had witnessed the devastation day in and day out as the virus took its toll on the young and old. But when she was offered the vaccine, she hesitated, skeptical of its rapid emergency use authorization.
Exclusion of pregnant and lactating mothers from clinical trials fueled her concerns. Ultimately, though, she concluded the benefits of vaccination outweighed the risks of contracting the potentially deadly virus.
"Long story short," Cummings says, in December "I got vaccinated to protect myself, my family, my patients, and the general public."
At the time, Cummings remained on the fence about breastfeeding, citing a lack of evidence to support its safety after vaccination, so she pumped and stashed breast milk in the freezer. Her son is adjusting to life as a preemie, requiring mother's milk to be thickened with formula, but she's becoming comfortable with the idea of breastfeeding as more research suggests it's safe.
"If I could pop him on the boob," she says, "I would do it in a heartbeat."
Now, a study recently published in the Journal of the American Medical Association found "robust secretion" of specific antibodies in the breast milk of mothers who received a COVID-19 vaccine, indicating a potentially protective effect against infection in their infants.
The presence of antibodies in the breast milk, detectable as early as two weeks after vaccination, lasted for six weeks after the second dose of the Pfizer-BioNTech vaccine.
"We believe antibody secretion into breast milk will persist for much longer than six weeks, but we first wanted to prove any secretion at all after vaccination," says Ilan Youngster, the study's corresponding author and head of pediatric infectious diseases at Shamir Medical Center in Zerifin, Israel.
That's why the research team performed a preliminary analysis at six weeks. "We are still collecting samples from participants and hope to soon be able to comment about the duration of secretion."
As with other respiratory illnesses, such as influenza and pertussis, secretion of antibodies in breast milk confers protection from infection in infants. The researchers expect a similar immune response from the COVID-19 vaccine and are expecting the findings to spur an increase in vaccine acceptance among pregnant and lactating women.
A COVID-19 outbreak struck three families the research team followed in the study, resulting in at least one non-breastfed sibling developing symptomatic infection; however, none of the breastfed babies became ill. "This is obviously not empirical proof," Youngster acknowledges, "but still a nice anecdote."
Leaps.org inquired whether infants who derive antibodies only through breast milk are likely to have a lower immunity than infants whose mothers were vaccinated while they were in utero. In other words, is maternal transmission of antibodies stronger during pregnancy than during breastfeeding, or about the same?
"This is a different kind of transmission," Youngster explains. "When a woman is infected or vaccinated during pregnancy, some antibodies will be transferred through the placenta to the baby's bloodstream and be present for several months." But in the nursing mother, that protection occurs through local action. "We always recommend breastfeeding whenever possible, and, in this case, it might have added benefits."
A study published online in March found COVID-19 vaccination provided pregnant and lactating women with robust immune responses comparable to those experienced by their nonpregnant counterparts. The study, appearing in the American Journal of Obstetrics and Gynecology, documented the presence of vaccine-generated antibodies in umbilical cord blood and breast milk after mothers had been vaccinated.
Natali Aziz, a maternal-fetal medicine specialist at Stanford University School of Medicine, notes that it's too early to draw firm conclusions about the reduction in COVID-19 infection rates among newborns of vaccinated mothers. Citing the two aforementioned research studies, she says it's biologically plausible that antibodies passed through the placenta and breast milk impart protective benefits. While thousands of pregnant and lactating women have been vaccinated against COVID-19, without incurring adverse outcomes, many are still wondering whether it's safe to breastfeed afterward.
It's important to bear in mind that pregnant women may develop more severe COVID-19 complications, which could lead to intubation or admittance to the intensive care unit. "We, in our practice, are supporting pregnant and breastfeeding patients to be vaccinated," says Aziz, who is also director of perinatal infectious diseases at Stanford Children's Health, which has been vaccinating new mothers and other hospitalized patients at discharge since late April.
Earlier in April, Huntington Hospital in Long Island, New York, began offering the COVID-19 vaccine to women after they gave birth. The hospital chose the one-shot Johnson & Johnson vaccine for postpartum patients, so they wouldn't need to return for a second shot while acclimating to life with a newborn, says Mitchell Kramer, chairman of obstetrics and gynecology.
The hospital suspended the program when the Food and Drug Administration and the Centers for Disease Control and Prevention paused use of the J&J vaccine starting April 13, while investigating several reports of dangerous blood clots and low platelet counts among more than 7 million people in the United States who had received that vaccine.
In lifting the pause April 23, the agencies announced the vaccine's fact sheets will bear a warning of the heightened risk for a rare but serious blood clot disorder among women under age 50. As a result, Kramer says, "we will likely not be using the J&J vaccine for our postpartum population."
So, would it make sense to vaccinate infants when one for them eventually becomes available, not just their mothers? "In general, most of the time, infants do not have as good of an immune response to vaccines," says Jonathan Temte, associate dean for public health and community engagement at the University of Wisconsin School of Medicine and Public Health in Madison.
"Many of our vaccines are held until children are six months of age. For example, the influenza vaccine starts at age six months, the measles vaccine typically starts one year of age, as do rubella and mumps. Immune response is typically not very good for viral illnesses in young infants under the age of six months."
So far, the FDA has granted emergency use authorization of the Pfizer-BioNTech vaccine for children as young as 16 years old. The agency is considering data from Pfizer to lower that age limit to 12. Studies are also underway in children under age 12. Meanwhile, data from Moderna on 12-to 17-year-olds and from Pfizer on 12- to 15-year-olds have not been made public. (Pfizer announced at the end of March that its vaccine is 100 percent effective in preventing COVID-19 in the latter age group, and FDA authorization for this population is expected soon.)
"There will be step-wise progression to younger children, with infants and toddlers being the last ones tested," says James Campbell, a pediatric infectious diseases physician and head of maternal and child clinical studies at the University of Maryland School of Medicine Center for Vaccine Development.
"Once the data are analyzed for safety, tolerability, optimal dose and regimen, and immune responses," he adds, "they could be authorized and recommended and made available to American children." The data on younger children are not expected until the end of this year, with regulatory authorization possible in early 2022.
For now, Vonnie Cesar, a family nurse practitioner in Smyrna, Georgia, is aiming to persuade expectant and new mothers to get vaccinated. She has observed that patients in metro Atlanta seem more inclined than their rural counterparts.
To quell some of their skepticism and fears, Cesar, who also teaches nursing students, conceived a visual way to demonstrate the novel mechanism behind the COVID-19 vaccine technology. Holding a palm-size physical therapy ball outfitted with clear-colored push pins, she simulates the spiked protein of the coronavirus. Slime slathered at the gaps permeates areas around the spikes—a process similar to how our antibodies build immunity to the virus.
These conversations often lead hesitant patients to discuss vaccination with their husbands or partners. "The majority of people I'm speaking with," she says, "are coming to the conclusion that this is the right thing for me, this is the common good, and they want to make sure that they're here for their children."
CORRECTION: An earlier version of this article mistakenly stated that the COVID-19 vaccines were granted emergency "approval." They have been granted emergency use authorization, not full FDA approval. We regret the error.
The Case for an Outright Ban on Facial Recognition Technology
[Editor's Note: This essay is in response to our current Big Question, which we posed to experts with different perspectives: "Do you think the use of facial recognition technology by the police or government should be banned? If so, why? If not, what limits, if any, should be placed on its use?"]
In a surprise appearance at the tail end of Amazon's much-hyped annual product event last month, CEO Jeff Bezos casually told reporters that his company is writing its own facial recognition legislation.
The use of computer algorithms to analyze massive databases of footage and photographs could render human privacy extinct.
It seems that when you're the wealthiest human alive, there's nothing strange about your company––the largest in the world profiting from the spread of face surveillance technology––writing the rules that govern it.
But if lawmakers and advocates fall into Silicon Valley's trap of "regulating" facial recognition and other forms of invasive biometric surveillance, that's exactly what will happen.
Industry-friendly regulations won't fix the dangers inherent in widespread use of face scanning software, whether it's deployed by governments or for commercial purposes. The use of this technology in public places and for surveillance purposes should be banned outright, and its use by private companies and individuals should be severely restricted. As artificial intelligence expert Luke Stark wrote, it's dangerous enough that it should be outlawed for "almost all practical purposes."
Like biological or nuclear weapons, facial recognition poses such a profound threat to the future of humanity and our basic rights that any potential benefits are far outweighed by the inevitable harms.
We live in cities and towns with an exponentially growing number of always-on cameras, installed in everything from cars to children's toys to Amazon's police-friendly doorbells. The use of computer algorithms to analyze massive databases of footage and photographs could render human privacy extinct. It's a world where nearly everything we do, everywhere we go, everyone we associate with, and everything we buy — or look at and even think of buying — is recorded and can be tracked and analyzed at a mass scale for unimaginably awful purposes.
Biometric tracking enables the automated and pervasive monitoring of an entire population. There's ample evidence that this type of dragnet mass data collection and analysis is not useful for public safety, but it's perfect for oppression and social control.
Law enforcement defenders of facial recognition often state that the technology simply lets them do what they would be doing anyway: compare footage or photos against mug shots, drivers licenses, or other databases, but faster. And they're not wrong. But the speed and automation enabled by artificial intelligence-powered surveillance fundamentally changes the impact of that surveillance on our society. Being able to do something exponentially faster, and using significantly less human and financial resources, alters the nature of that thing. The Fourth Amendment becomes meaningless in a world where private companies record everything we do and provide governments with easy tools to request and analyze footage from a growing, privately owned, panopticon.
Tech giants like Microsoft and Amazon insist that facial recognition will be a lucrative boon for humanity, as long as there are proper safeguards in place. This disingenuous call for regulation is straight out of the same lobbying playbook that telecom companies have used to attack net neutrality and Silicon Valley has used to scuttle meaningful data privacy legislation. Companies are calling for regulation because they want their corporate lawyers and lobbyists to help write the rules of the road, to ensure those rules are friendly to their business models. They're trying to skip the debate about what role, if any, technology this uniquely dangerous should play in a free and open society. They want to rush ahead to the discussion about how we roll it out.
We need spaces that are free from government and societal intrusion in order to advance as a civilization.
Facial recognition is spreading very quickly. But backlash is growing too. Several cities have already banned government entities, including police and schools, from using biometric surveillance. Others have local ordinances in the works, and there's state legislation brewing in Michigan, Massachusetts, Utah, and California. Meanwhile, there is growing bipartisan agreement in U.S. Congress to rein in government use of facial recognition. We've also seen significant backlash to facial recognition growing in the U.K., within the European Parliament, and in Sweden, which recently banned its use in schools following a fine under the General Data Protection Regulation (GDPR).
At least two frontrunners in the 2020 presidential campaign have backed a ban on law enforcement use of facial recognition. Many of the largest music festivals in the world responded to Fight for the Future's campaign and committed to not use facial recognition technology on music fans.
There has been widespread reporting on the fact that existing facial recognition algorithms exhibit systemic racial and gender bias, and are more likely to misidentify people with darker skin, or who are not perceived by a computer to be a white man. Critics are right to highlight this algorithmic bias. Facial recognition is being used by law enforcement in cities like Detroit right now, and the racial bias baked into that software is doing harm. It's exacerbating existing forms of racial profiling and discrimination in everything from public housing to the criminal justice system.
But the companies that make facial recognition assure us this bias is a bug, not a feature, and that they can fix it. And they might be right. Face scanning algorithms for many purposes will improve over time. But facial recognition becoming more accurate doesn't make it less of a threat to human rights. This technology is dangerous when it's broken, but at a mass scale, it's even more dangerous when it works. And it will still disproportionately harm our society's most vulnerable members.
Persistent monitoring and policing of our behavior breeds conformity, benefits tyrants, and enriches elites.
We need spaces that are free from government and societal intrusion in order to advance as a civilization. If technology makes it so that laws can be enforced 100 percent of the time, there is no room to test whether those laws are just. If the U.S. government had ubiquitous facial recognition surveillance 50 years ago when homosexuality was still criminalized, would the LGBTQ rights movement ever have formed? In a world where private spaces don't exist, would people have felt safe enough to leave the closet and gather, build community, and form a movement? Freedom from surveillance is necessary for deviation from social norms as well as to dissent from authority, without which societal progress halts.
Persistent monitoring and policing of our behavior breeds conformity, benefits tyrants, and enriches elites. Drawing a line in the sand around tech-enhanced surveillance is the fundamental fight of this generation. Lining up to get our faces scanned to participate in society doesn't just threaten our privacy, it threatens our humanity, and our ability to be ourselves.
[Editor's Note: Read the opposite perspective here.]
Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu
Applied mathematician Sara del Valle works at the U.S.'s foremost nuclear weapons lab: Los Alamos. Once colloquially called Atomic City, it's a hidden place 45 minutes into the mountains northwest of Santa Fe. Here, engineers developed the first atomic bomb.
Like AccuWeather, an app for disease prediction could help people alter their behavior to live better lives.
Today, Los Alamos still a small science town, though no longer a secret, nor in the business of building new bombs. Instead, it's tasked with, among other things, keeping the stockpile of nuclear weapons safe and stable: not exploding when they're not supposed to (yes, please) and exploding if someone presses that red button (please, no).
Del Valle, though, doesn't work on any of that. Los Alamos is also interested in other kinds of booms—like the explosion of a contagious disease that could take down a city. Predicting (and, ideally, preventing) such epidemics is del Valle's passion. She hopes to develop an app that's like AccuWeather for germs: It would tell you your chance of getting the flu, or dengue or Zika, in your city on a given day. And like AccuWeather, it could help people alter their behavior to live better lives, whether that means staying home on a snowy morning or washing their hands on a sickness-heavy commute.
Sara del Valle of Los Alamos is working to predict and prevent epidemics using data and machine learning.
Since the beginning of del Valle's career, she's been driven by one thing: using data and predictions to help people behave practically around pathogens. As a kid, she'd always been good at math, but when she found out she could use it to capture the tentacular spread of disease, and not just manipulate abstractions, she was hooked.
When she made her way to Los Alamos, she started looking at what people were doing during outbreaks. Using social media like Twitter, Google search data, and Wikipedia, the team started to sift for trends. Were people talking about hygiene, like hand-washing? Or about being sick? Were they Googling information about mosquitoes? Searching Wikipedia for symptoms? And how did those things correlate with the spread of disease?
It was a new, faster way to think about how pathogens propagate in the real world. Usually, there's a 10- to 14-day lag in the U.S. between when doctors tap numbers into spreadsheets and when that information becomes public. By then, the world has moved on, and so has the disease—to other villages, other victims.
"We found there was a correlation between actual flu incidents in a community and the number of searches online and the number of tweets online," says del Valle. That was when she first let herself dream about a real-time forecast, not a 10-days-later backcast. Del Valle's group—computer scientists, mathematicians, statisticians, economists, public health professionals, epidemiologists, satellite analysis experts—has continued to work on the problem ever since their first Twitter parsing, in 2011.
They've had their share of outbreaks to track. Looking back at the 2009 swine flu pandemic, they saw people buying face masks and paying attention to the cleanliness of their hands. "People were talking about whether or not they needed to cancel their vacation," she says, and also whether pork products—which have nothing to do with swine flu—were safe to buy.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place.
They watched internet conversations during the measles outbreak in California. "There's a lot of online discussion about anti-vax sentiment, and people trying to convince people to vaccinate children and vice versa," she says.
Today, they work on predicting the spread of Zika, Chikungunya, and dengue fever, as well as the plain old flu. And according to the CDC, that latter effort is going well.
Since 2015, the CDC has run the Epidemic Prediction Initiative, a competition in which teams like de Valle's submit weekly predictions of how raging the flu will be in particular locations, along with other ailments occasionally. Michael Johannson is co-founder and leader of the program, which began with the Dengue Forecasting Project. Its goal, he says, was to predict when dengue cases would blow up, when previously an area just had a low-level baseline of sick people. "You'll get this massive epidemic where all of a sudden, instead of 3,000 to 4,000 cases, you have 20,000 cases," he says. "They kind of come out of nowhere."
But the "kind of" is key: The outbreaks surely come out of somewhere and, if scientists applied research and data the right way, they could forecast the upswing and perhaps dodge a bomb before it hit big-time. Questions about how big, when, and where are also key to the flu.
A big part of these projects is the CDC giving the right researchers access to the right information, and the structure to both forecast useful public-health outcomes and to compare how well the models are doing. The extra information has been great for the Los Alamos effort. "We don't have to call departments and beg for data," says del Valle.
When data isn't available, "proxies"—things like symptom searches, tweets about empty offices, satellite images showing a green, wet, mosquito-friendly landscape—are helpful: You don't have to rely on anyone's health department.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place. But del Valle wants more than weekly numbers on a government website; she wants that weather-app-inspired fortune-teller, incorporating the many diseases you could get today, standing right where you are. "That's our dream," she says.
This plot shows the the correlations between the online data stream, from Wikipedia, and various infectious diseases in different countries. The results of del Valle's predictive models are shown in brown, while the actual number of cases or illness rates are shown in blue.
(Courtesy del Valle)
The goal isn't to turn you into a germophobic agoraphobe. It's to make you more aware when you do go out. "If you know it's going to rain today, you're more likely to bring an umbrella," del Valle says. "When you go on vacation, you always look at the weather and make sure you bring the appropriate clothing. If you do the same thing for diseases, you think, 'There's Zika spreading in Sao Paulo, so maybe I should bring even more mosquito repellent and bring more long sleeves and pants.'"
They're not there yet (don't hold your breath, but do stop touching your mouth). She estimates it's at least a decade away, but advances in machine learning could accelerate that hypothetical timeline. "We're doing baby steps," says del Valle, starting with the flu in the U.S., dengue in Brazil, and other efforts in Colombia, Ecuador, and Canada. "Going from there to forecasting all diseases around the globe is a long way," she says.
But even AccuWeather started small: One man began predicting weather for a utility company, then helping ski resorts optimize their snowmaking. His influence snowballed, and now private forecasting apps, including AccuWeather's, populate phones across the planet. The company's progression hasn't been without controversy—privacy incursions, inaccuracy of long-term forecasts, fights with the government—but it has continued, for better and for worse.
Disease apps, perhaps spun out of a small, unlikely team at a nuclear-weapons lab, could grow and breed in a similar way. And both the controversies and public-health benefits that may someday spin out of them lie in the future, impossible to predict with certainty.