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.
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers