Pregnant & Breastfeeding Women Who Get the COVID-19 Vaccine Are Protecting Their Infants, Research Suggests
Becky Cummings, who got vaccinated in December, snuggles her newborn, Clark, while he takes a nap.
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 Death Predictor: A Helpful New Tool or an Ethical Morass?
A senior in hospice care.
Whenever Eric Karl Oermann has to tell a patient about a terrible prognosis, their first question is always: "how long do I have?" Oermann would like to offer a precise answer, to provide some certainty and help guide treatment. But although he's one of the country's foremost experts in medical artificial intelligence, Oermann is still dependent on a computer algorithm that's often wrong.
Doctors are notoriously terrible at guessing how long their patients will live.
Artificial intelligence, now often called deep learning or neural networks, has radically transformed language and image processing. It's allowed computers to play chess better than the world's grand masters and outwit the best Jeopardy players. But it still can't precisely tell a doctor how long a patient has left – or how to help that person live longer.
Someday, researchers predict, computers will be able to watch a video of a patient to determine their health status. Doctors will no longer have to spend hours inputting data into medical records. And computers will do a better job than specialists at identifying tiny tumors, impending crises, and, yes, figuring out how long the patient has to live. Oermann, a neurosurgeon at Mount Sinai, says all that technology will allow doctors to spend more time doing what they do best: talking with their patients. "I want to see more deep learning and computers in a clinical setting," he says, "so there can be more human interaction." But those days are still at least three to five years off, Oermann and other researchers say.
Doctors are notoriously terrible at guessing how long their patients will live, says Nigam Shah, an associate professor at Stanford University and assistant director of the school's Center for Biomedical Informatics Research. Doctors don't want to believe that their patient – whom they've come to like – will die. "Doctors over-estimate survival many-fold," Shah says. "How do you go into work, in say, oncology, and not be delusionally optimistic? You have to be."
But patients near the end of life will get better treatment – and even live longer – if they are overseen by hospice or palliative care, research shows. So, instead of relying on human bias to select those whose lives are nearing their end, Shah and his colleagues showed that they could use a deep learning algorithm based on medical records to flag incoming patients with a life expectancy of three months to a year. They use that data to indicate who might need palliative care. Then, the palliative care team can reach out to treating physicians proactively, instead of relying on their referrals or taking the time to read extensive medical charts.
But, although the system works well, Shah isn't yet sure if such indicators actually get the appropriate patients into palliative care. He's recently partnered with a palliative care doctor to run a gold-standard clinical trial to test whether patients who are flagged by this algorithm are indeed a better match for palliative care.
"What is effective from a health system perspective might not be effective from a treating physician's perspective and might not be effective from the patient's perspective," Shah notes. "I don't have a good way to guess everybody's reaction without actually studying it." Whether palliative care is appropriate, for instance, depends on more than just the patient's health status. "If the patient's not ready, the family's not ready and the doctor's not ready, then you're just banging your head against the wall," Shah says. "Given limited capacity, it's a waste of resources" to put that person in palliative care.
The algorithm isn't perfect, but "on balance, it leads to better decisions more often."
Alexander Smith and Sei Lee, both palliative care doctors, work together at the University of California, San Francisco, to develop predictions for patients who come to the hospital with a complicated prognosis or a history of decline. Their algorithm, they say, helps decide if this patient's problems – which might include diabetes, heart disease, a slow-growing cancer, and memory issues – make them eligible for hospice. The algorithm isn't perfect, they both agree, but "on balance, it leads to better decisions more often," Smith says.
Bethany Percha, an assistant professor at Mount Sinai, says that an algorithm may tell doctors that their patient is trending downward, but it doesn't do anything to change that trajectory. "Even if you can predict something, what can you do about it?" Algorithms may be able to offer treatment suggestions – but not what specific actions will alter a patient's future, says Percha, also the chief technology officer of Precise Health Enterprise, a product development group within Mount Sinai. And the algorithms remain challenging to develop. Electronic medical records may be great at her hospital, but if the patient dies at a different one, her system won't know. If she wants to be certain a patient has died, she has to merge social security records of death with her system's medical records – a time-consuming and cumbersome process.
An algorithm that learns from biased data will be biased, Shah says. Patients who are poor or African American historically have had worse health outcomes. If researchers train an algorithm on data that includes those biases, they get baked into the algorithms, which can then lead to a self-fulfilling prophesy. Smith and Lee say they've taken race out of their algorithms to avoid this bias.
Age is even trickier. There's no question that someone's risk of illness and death goes up with age. But an 85-year-old who breaks a hip running a marathon should probably be treated very differently than an 85-year-old who breaks a hip trying to get out of a chair in a dementia care unit. That's why the doctor can never be taken out of the equation, Shah says. Human judgment will always be required in medical care and an algorithm should never be followed blindly, he says.
Experts say that the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully.
Researchers are also concerned that their algorithms will be used to ration care, or that insurance companies will use their data to justify a rate increase. If an algorithm predicts a patient is going to end up back in the hospital soon, "who's benefitting from knowing a patient is going to be readmitted? Probably the insurance company," Percha says.
Still, Percha and others say, the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully. "These are new and exciting tools that have a lot of potential uses. We need to be conscious about how to use them going forward, but it doesn't mean we shouldn't go down this road," she says. "I think the potential benefits outweigh the risks, especially because we've barely scratched the surface of what big data can do right now."
“Young Blood” Transfusions Are Not Ready For Primetime – Yet
A young woman donates blood.
The world of dementia research erupted into cheers when news of the first real victory in a clinical trial against Alzheimer's Disease in over a decade was revealed last October.
By connecting the circulatory systems of a young and an old mouse, the regenerative potential of the young mouse decreased, and the old mouse became healthier.
Alzheimer's treatments have been famously difficult to develop; 99 percent of the 200-plus such clinical trials since 2000 have utterly failed. Even the few slight successes have failed to produce what is called 'disease modifying' agents that really help people with the disease. This makes the success, by the midsize Spanish pharma company Grifols, worthy of special attention.
However, the specifics of the Grifols treatment, a process called plasmapheresis, are atypical for another reason - they did not give patients a small molecule or an elaborate gene therapy, but rather simply the most common component of normal human blood plasma, a protein called albumin. A large portion of the patients' normal plasma was removed, and then a sterile solution of albumin was infused back into them to keep their overall blood volume relatively constant.
So why does replacing Alzheimer's patients' plasma with albumin seem to help their brains? One theory is that the action is direct. Alzheimer's patients have low levels of serum albumin, which is needed to clear out the plaques of amyloid that slowly build up in the brain. Supplementing those patients with extra albumin boosts their ability to clear the plaques and improves brain health. However, there is also evidence suggesting that the problem may be something present in the plasma of the sick person and pulling their plasma out and replacing it with a filler, like an albumin solution, may be what creates the purported benefit.
This scientific question is the tip of an iceberg that goes far beyond Alzheimer's Disease and albumin, to a debate that has been waged on the pages of scientific journals about the secrets of using young, healthy blood to extend youth and health.
This debate started long before the Grifols data was released, in 2014 when a group of researchers at Stanford found that by connecting the circulatory systems of a young and an old mouse, the regenerative potential of the young mouse decreased, and the old mouse became healthier. There was something either present in young blood that allowed tissues to regenerate, or something present in old blood that prevented regeneration. Whatever the biological reason, the effects in the experiment were extraordinary, providing a startling boost in health in the older mouse.
After the initial findings, multiple research groups got to work trying to identify the "active factor" of regeneration (or the inhibitor of that regeneration). They soon uncovered a variety of compounds such as insulin-like growth factor 1 (IGF1), CCL11, and GDF11, but none seemed to provide all the answers researchers were hoping for, with a number of high-profile retractions based on unsound experimental practices, or inconclusive data.
Years of research later, the simplest conclusion is that the story of plasma regeneration is not simple - there isn't a switch in our blood we can flip to turn back our biological clocks. That said, these hypotheses are far from dead, and many researchers continue to explore the possibility of using the rejuvenating ability of youthful plasma to treat a variety of diseases of aging.
But the bold claims of improved vigor thanks to young blood are so far unsupported by clinical evidence.
The data remain intriguing because of the astounding results from the conjoined circulatory system experiments. The current surge in interest in studying the biology of aging is likely to produce a new crop of interesting results in the next few years. Both CCL11 and GDF11 are being researched as potential drug targets by two startups, Alkahest and Elevian, respectively.
Without clarity on a single active factor driving rejuvenation, it's tempting to try a simpler approach: taking actual blood plasma provided by young people and infusing it into elderly subjects. This is what at least one startup company, Ambrosia, is now offering in five commercial clinics across the U.S. -- for $8,000 a liter.
By using whole plasma, the idea is to sidestep our ignorance, reaping the benefits of young plasma transfusion without knowing exactly what the active factors are that make the treatment work in mice. This space has attracted both established players in the plasmapheresis field – Alkahest and Grifols have teamed up to test fractions of whole plasma in Alzheimer's and Parkinson's – but also direct-to-consumer operations like Ambrosia that just want to offer patients access to treatments without regulatory oversight.
But the bold claims of improved vigor thanks to young blood are so far unsupported by clinical evidence. We simply haven't performed trials to test whether dosing a mostly healthy person with plasma can slow down aging, at least not yet. There is some evidence that plasma replacement works in mice, yes, but those experiments are all done in very different systems than what a human receiving young plasma might experience. To date, I have not seen any plasma transfusion clinic doing young blood plasmapheresis propose a clinical trial that is anything more than a shallow advertisement for their procedures.
The efforts I have seen to perform prophylactic plasmapheresis will fail to impact societal health. Without clearly defined endpoints and proper clinical trials, we won't know whether the procedure really lowers the risk of disease or helps with conditions of aging. So even if their hypothesis is correct, the lack of strong evidence to fall back on means that the procedure will never spread beyond the fringe groups willing to take the risk. If their hypothesis is wrong, then people are paying a huge amount of money for false hope, just as they do, sadly, at the phony stem cell clinics that started popping up all through the 2000s when stem cell hype was at its peak.
Until then, prophylactic plasma transfusions will be the domain of the optimistic and the gullible.
The real progress in the field will be made slowly, using carefully defined products either directly isolated from blood or targeting a bloodborne factor, just as the serious pharma and biotech players are doing already.
The field will progress in stages, first creating and carefully testing treatments for well-defined diseases, and only then will it progress to large-scale clinical trials in relatively healthy people to look for the prevention of disease. Most of us will choose to wait for this second stage of trials before undergoing any new treatments. Until then, prophylactic plasma transfusions will be the domain of the optimistic and the gullible.