Abortions Before Fetal Viability Are Legal: Might Science and the Change on the Supreme Court Undermine That?
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
Viability—the potential for a fetus to survive outside the womb—is a core dividing line in American law. For almost 50 years, the Supreme Court of the United States has struck down laws that ban all or most abortions, ruling that women's constitutional rights include choosing to end pregnancies before the point of viability. Once viability is reached, however, states have a "compelling interest" in protecting fetal life. At that point, states can choose to ban or significantly restrict later-term abortions provided states allow an exception to preserve the life or health of the mother.
This distinction between a fetus that could survive outside its mother's body, albeit with significant medical intervention, and one that could not, is at the heart of the court's landmark 1973 decision in Roe v. Wade. The framework of viability remains central to the country's abortion law today, even as some states have passed laws in the name of protecting women's health that significantly undermine Roe. Over the last 30 years, the Supreme Court has upheld these laws, which have the effect of restricting pre-viability abortion access, imposing mandatory waiting periods, requiring parental consent for minors, and placing restrictions on abortion providers.
Viability has always been a slippery notion on which to pin legal rights.
Today, the Guttmacher Institute reports that more than half of American women live in states whose laws are considered hostile to abortion, largely as a result of these intrusions on pre-viability abortion access. Nevertheless, the viability framework stands: while states can pass pre-viability abortion restrictions that (ostensibly) protect the health of the woman or that strike some kind a balance between women's rights and fetal life, it is only after viability that they can completely favor fetal life over the rights of the woman (with limited exceptions when the woman's life is threatened). As a result, judges have struck down certain states' so-called heartbeat laws, which tried to prohibit abortions after detection of a fetal heartbeat (as early as six weeks of pregnancy). Bans on abortion after 12 or 15 weeks' gestation have also been reversed.
Now, with a new Supreme Court Justice expected to be hostile to abortion rights, advances in the care of preterm babies and ongoing research on artificial wombs suggest that the point of viability is already sooner than many assume and could soon be moved radically earlier in gestation, potentially providing a legal basis for earlier and earlier abortion bans.
Viability has always been a slippery notion on which to pin legal rights. It represents an inherently variable and medically shifting moment in the pregnancy timeline that the Roe majority opinion declined to firmly define, noting instead that "[v]iability is usually placed at about seven months (28 weeks) but may occur earlier, even at 24 weeks." Even in 1977, this definition was an optimistic generalization. Every baby is different, and while some 28-week infants born the year Roe was decided did indeed live into adulthood, most died at or shortly after birth. The prognosis for infants born at 24 weeks was much worse.
Today, a baby born at 28 weeks' gestation can be expected to do much better, largely due to the development of surfactant treatment in the early 1990s to help ease the air into babies' lungs. Now, the majority of 24-week-old babies can survive, and several very premature babies, born just shy of 22 weeks' gestation, have lived into childhood. All this variability raises the question: Should the law take a very optimistic, if largely unrealistic, approach to defining viability and place it at 22 weeks, even though the overall survival rate for those preemies remains less than 10% today? Or should the law recognize that keeping a premature infant alive requires specialist care, meaning that actual viability differs not just pregnancy-to-pregnancy but also by healthcare facility and from country to country? A 24-week premature infant born in a rural area or in a developing nation may not be viable as a practical matter, while one born in a major U.S. city with access to state-of-the-art care has a greater than 70% chance of survival. Just as some extremely premature newborns survive, some full-term babies die before, during, or soon after birth, regardless of whether they have access to advanced medical care.
To be accurate, viability should be understood as pregnancy-specific and should take into account the healthcare resources available to that woman. But state laws can't capture this degree of variability by including gestation limits in their abortion laws. Instead, many draw a somewhat arbitrary line at 22, 24, or 28 weeks' gestation, regardless of the particulars of the pregnancy or the medical resources available in that state.
As variable and resource-dependent as viability is today, science may soon move that point even earlier. Ectogenesis is a term coined in 1923 for the growth of an organism outside the body. Long considered science fiction, this technology has made several key advances in the past few years, with scientists announcing in 2017 that they had successfully gestated premature lamb fetuses in an artificial womb for four weeks. Currently in development for use in human fetuses between 22 and 23 weeks' gestation, this technology will almost certainly seek to push viability earlier in pregnancy.
Ectogenesis and other improvements in managing preterm birth deserve to be celebrated, offering new hope to the parents of very premature infants. But in the U.S., and in other nations whose abortion laws are fixed to viability, these same advances also pose a threat to abortion access. Abortion opponents have long sought to move the cutoff for legal abortions, and it is not hard to imagine a state prohibiting all abortions after 18 or 20 weeks by arguing that medical advances render this stage "the new viability," regardless of whether that level of advanced care is available to women in that state. If ectogenesis advances further, the limit could be moved to keep pace.
The Centers for Disease Control and Prevention reports that over 90% of abortions in America are performed at or before 13 weeks, meaning that in the short term, only a small number women would be affected by shifting viability standards. Yet these women are in difficult situations and deserve care and consideration. Research has shown that women seeking later terminations often did not recognize that they were pregnant or had their dates quite wrong, while others report that they had trouble accessing a termination earlier in pregnancy, were afraid to tell their partner or parents, or only recently received a diagnosis of health problems with the fetus.
Shifts in viability over the past few decades have already affected these women, many of whom report struggling to find a provider willing to perform a termination at 18 or 20 weeks out of concern that the woman may have her dates wrong. Ever-earlier gestational limits would continue this chilling effect, making doctors leery of terminating a pregnancy that might be within 2–4 weeks of each new ban. Some states' existing gestational limits on abortion are also inconsistent with prenatal care, which includes genetic testing between 12 and 20 weeks' gestation, as well as an anatomy scan to check the fetus's organ development performed at approximately 20 weeks. If viability moves earlier, prenatal care will be further undermined.
Perhaps most importantly, earlier and earlier abortion bans are inconsistent with the rights and freedoms on which abortion access is based, including recognition of each woman's individual right to bodily integrity and decision-making authority over her own medical care. Those rights and freedoms become meaningless if abortion bans encroach into the weeks that women need to recognize they are pregnant, assess their options, seek medical advice, and access appropriate care. Fetal viability, with its shifting goalposts, isn't the best framework for abortion protection in light of advancing medical science.
Ideally, whether to have an abortion would be a decision that women make in consultation with their doctors, free of state interference. The vast majority of women already make this decision early in pregnancy; the few who come to the decision later do so because something has gone seriously wrong in their lives or with their pregnancies. If states insist on drawing lines based on historical measures of viability, at 24 or 26 or 28 weeks, they should stick with those gestational limits and admit that they no longer represent actual viability but correspond instead to some form of common morality about when the fetus has a protected, if not absolute, right to life. Women need a reasonable amount of time to make careful and informed decisions about whether to continue their pregnancies precisely because these decisions have a lasting impact on their bodies and their lives. To preserve that time, legislators and the courts should decouple abortion rights from ectogenesis and other advances in the care of extremely premature infants that move the point of viability ever earlier.
[Editor's Note: This article was updated after publication to reflect Amy Coney Barrett's confirmation. To read other articles in this special magazine issue, visit the e-reader version.]
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