Is Finding Out Your Baby’s Genetics A New Responsibility of Parenting?
Hours after a baby is born, its heel is pricked with a lancet. Drops of the infant's blood are collected on a porous card, which is then mailed to a state laboratory. The dried blood spots are screened for around thirty conditions, including phenylketonuria (PKU), the metabolic disorder that kick-started this kind of newborn screening over 60 years ago. In the U.S., parents are not asked for permission to screen their child. Newborn screening programs are public health programs, and the assumption is that no good parent would refuse a screening test that could identify a serious yet treatable condition in their baby.
Learning as much as you can about your child's health might seem like a natural obligation of parenting. But it's an assumption that I think needs to be much more closely examined.
Today, with the introduction of genome sequencing into clinical medicine, some are asking whether newborn screening goes far enough. As the cost of sequencing falls, should parents take a more expansive look at their children's health, learning not just whether they have a rare but treatable childhood condition, but also whether they are at risk for untreatable conditions or for diseases that, if they occur at all, will strike only in adulthood? Should genome sequencing be a part of every newborn's care?
It's an idea that appeals to Anne Wojcicki, the founder and CEO of the direct-to-consumer genetic testing company 23andMe, who in a 2016 interview with The Guardian newspaper predicted that having newborns tested would soon be considered standard practice—"as critical as testing your cholesterol"—and a new responsibility of parenting. Wojcicki isn't the only one excited to see everyone's genes examined at birth. Francis Collins, director of the National Institutes of Health and perhaps the most prominent advocate of genomics in the United States, has written that he is "almost certain … that whole-genome sequencing will become part of new-born screening in the next few years." Whether that would happen through state-mandated screening programs, or as part of routine pediatric care—or perhaps as a direct-to-consumer service that parents purchase at birth or receive as a baby-shower gift—is not clear.
Learning as much as you can about your child's health might seem like a natural obligation of parenting. But it's an assumption that I think needs to be much more closely examined, both because the results that genome sequencing can return are more complex and more uncertain than one might expect, and because parents are not actually responsible for their child's lifelong health and well-being.
What is a parent supposed to do about such a risk except worry?
Existing newborn screening tests look for the presence of rare conditions that, if identified early in life, before the child shows any symptoms, can be effectively treated. Sequencing could identify many of these same kinds of conditions (and it might be a good tool if it could be targeted to those conditions alone), but it would also identify gene variants that confer an increased risk rather than a certainty of disease. Occasionally that increased risk will be significant. About 12 percent of women in the general population will develop breast cancer during their lives, while those who have a harmful BRCA1 or BRCA2 gene variant have around a 70 percent chance of developing the disease. But for many—perhaps most—conditions, the increased risk associated with a particular gene variant will be very small. Researchers have identified over 600 genes that appear to be associated with schizophrenia, for example, but any one of those confers only a tiny increase in risk for the disorder. What is a parent supposed to do about such a risk except worry?
Sequencing results are uncertain in other important ways as well. While we now have the ability to map the genome—to create a read-out of the pairs of genetic letters that make up a person's DNA—we are still learning what most of it means for a person's health and well-being. Researchers even have a name for gene variants they think might be associated with a disease or disorder, but for which they don't have enough evidence to be sure. They are called "variants of unknown (or uncertain) significance (VUS), and they pop up in most people's sequencing results. In cancer genetics, where much research has been done, about 1 in 5 gene variants are reclassified over time. Most are downgraded, which means that a good number of VUS are eventually designated benign.
While one parent might reasonably decide to learn about their child's risk for a condition about which nothing can be done medically, a different, yet still thoroughly reasonable, parent might prefer to remain ignorant so that they can enjoy the time before their child is afflicted.
Then there's the puzzle of what to do about results that show increased risk or even certainty for a condition that we have no idea how to prevent. Some genomics advocates argue that even if a result is not "medically actionable," it might have "personal utility" because it allows parents to plan for their child's future needs, to enroll them in research, or to connect with other families whose children carry the same genetic marker.
Finding a certain gene variant in one child might inform parents' decisions about whether to have another—and if they do, about whether to use reproductive technologies or prenatal testing to select against that variant in a future child. I have no doubt that for some parents these personal utility arguments are persuasive, but notice how far we've now strayed from the serious yet treatable conditions that motivated governments to set up newborn screening programs, and to mandate such testing for all.
Which brings me to the other problem with the call for sequencing newborn babies: the idea that even if it's not what the law requires, it's what good parents should do. That idea is very compelling when we're talking about sequencing results that show a serious threat to the child's health, especially when interventions are available to prevent or treat that condition. But as I have shown, many sequencing results are not of this type.
While one parent might reasonably decide to learn about their child's risk for a condition about which nothing can be done medically, a different, yet still thoroughly reasonable, parent might prefer to remain ignorant so that they can enjoy the time before their child is afflicted. This parent might decide that the worry—and the hypervigilence it could inspire in them—is not in their child's best interest, or indeed in their own. This parent might also think that it should be up to the child, when he or she is older, to decide whether to learn about his or her risk for adult-onset conditions, especially given that many adults at high familial risk for conditions like Alzheimer's or Huntington's disease choose never to be tested. This parent will value the child's future autonomy and right not to know more than they value the chance to prepare for a health risk that won't strike the child until 40 or 50 years in the future.
Parents are not obligated to learn about their children's risk for a condition that cannot be prevented, has a small risk of occurring, or that would appear only in adulthood.
Contemporary understandings of parenting are famously demanding. We are asked to do everything within our power to advance our children's health and well-being—to act always in our children's best interests. Against that backdrop, the need to sequence every newborn baby's genome might seem obvious. But we should be skeptical. Many sequencing results are complex and uncertain. Parents are not obligated to learn about their children's risk for a condition that cannot be prevented, has a small risk of occurring, or that would appear only in adulthood. To suggest otherwise is to stretch parental responsibilities beyond the realm of childhood and beyond factors that parents can control.
Ethan Lindenberger, the Ohio teenager who sought out vaccinations after he was denied them as a child, recently testified before Congress about why his parents became anti-vaxxers. The trouble, he believes, stems from the pervasiveness of misinformation online.
There is evidence that 'educating' people with facts about the benefits of vaccination may not be effective.
"For my mother, her love and affection and care as a parent was used to push an agenda to create a false distress," he told the Senate Committee. His mother read posts on social media saying vaccines are dangerous, and that was enough to persuade her against them.
His story is an example of how widespread and harmful the current discourse on vaccinations is—and more importantly—how traditional strategies to convince people about the merits of vaccination have largely failed.
As responsible members of society, all of us have implicitly signed on to what ethicists call the "Social Contract" -- we agree to abide by certain moral and political rules of behavior. This is what our societal values, norms, and often governments are based upon. However, with the unprecedented rise of social media, alternative facts, and fake news, it is evident that our understanding—and application—of the social contract must also evolve.
Nowhere is this breakdown of societal norms more visible than in the failure to contain the spread of vaccine-preventable diseases like measles. What started off as unexplained episodes in New York City last October, mostly in communities that are under-vaccinated, has exploded into a national epidemic: 880 cases of measles across 24 states in 2019, according to the CDC (as of May 17, 2019). In fact, the Unites States is only eight months away from losing its "measles free" status, joining Venezuela as the second country out of North and South America with that status.
The U.S. is not the only country facing this growing problem. Such constant and perilous reemergence of measles and other vaccine-preventable diseases in various parts of the world raises doubts about the efficacy of current vaccination policies. In addition to the loss of valuable life, these outbreaks lead to loss of millions of dollars in unnecessary expenditure of scarce healthcare resources. While we may be living through an age of information, we are also navigating an era whose hallmark is a massive onslaught on truth.
There is ample evidence on how these outbreaks start: low-vaccination rates. At the same time, there is evidence that 'educating' people with facts about the benefits of vaccination may not be effective. Indeed, human reasoning has a limit, and facts alone rarely change a person's opinion. In a fascinating report by researchers from the University of Pennsylvania, a small experiment revealed how "behavioral nudges" could inform policy decisions around vaccination.
In the reported experiment, the vaccination rate for employees of a company increased by 1.5 percent when they were prompted to name the date when they planned to get their flu shot. In the same experiment, when employees were prompted to name both a date and a time for their planned flu shot, vaccination rate increased by 4 percent.
A randomized trial revealed the subtle power of "announcements" – direct, brief, assertive statements by physicians that assumed parents were ready to vaccinate their children.
This experiment is a part of an emerging field of behavioral economics—a scientific undertaking that uses insights from psychology to understand human decision-making. The field was born from a humbling realization that humans probably do not possess an unlimited capacity for processing information. Work in this field could inform how we can formulate vaccination policy that is effective, conserves healthcare resources, and is applicable to current societal norms.
Take, for instance, the case of Human Papilloma Virus (HPV) that can cause several types of cancers in both men and women. Research into the quality of physician communication has repeatedly revealed how lukewarm recommendations for HPV vaccination by primary care physicians likely contributes to under-immunization of eligible adolescents and can cause confusion for parents.
A randomized trial revealed the subtle power of "announcements" – direct, brief, assertive statements by physicians that assumed parents were ready to vaccinate their children. These announcements increased vaccination rates by 5.4 percent. Lengthy, open-ended dialogues demonstrated no benefit in vaccination rates. It seems that uncertainty from the physician translates to unwillingness from a parent.
Choice architecture is another compelling concept. The premise is simple: We hardly make any of our decisions in vacuum; the environment in which these decisions are made has an influence. If health systems were designed with these insights in mind, people would be more likely to make better choices—without being forced.
This theory, proposed by Richard Thaler, who won the 2017 Nobel Prize in Economics, was put to the test by physicians at the University of Pennsylvania. In their study, flu vaccination rates at primary care practices increased by 9.5 percent all because the staff implemented "active choice intervention" in their electronic health records—a prompt that nudged doctors and nurses to ask patients if they'd gotten the vaccine yet. This study illustrated how an intervention as simple as a reminder can save lives.
To be sure, some bioethicists do worry about implementing these policies. Are behavioral nudges akin to increased scrutiny or a burden for the disadvantaged? For example, would incentives to quit smoking unfairly target the poor, who are more likely to receive criticism for bad choices?
The measles outbreak is a sober reminder of how devastating it can be when the social contract breaks down.
While this is a valid concern, behavioral economics offers one of the only ethical solutions to increasing vaccination rates by addressing the most critical—and often legal—challenge to universal vaccinations: mandates. Choice architecture and other interventions encourage and inform a choice, allowing an individual to retain his or her right to refuse unwanted treatment. This distinction is especially important, as evidence suggests that people who refuse vaccinations often do so as a result of cognitive biases – systematic errors in thinking resulting from emotional attachment or a lack of information.
For instance, people are prone to "confirmation bias," or a tendency to selectively believe in information that confirms their preexisting theories, rather than the available evidence. At the same time, people do not like mandates. In such situations, choice architecture provides a useful option: people are nudged to make the right choice via the design of health delivery systems, without needing policies that rely on force.
The measles outbreak is a sober reminder of how devastating it can be when the social contract breaks down and people fall prey to misinformation. But all is not lost. As we fight a larger societal battle against alternative facts, we now have another option in the trenches to subtly encourage people to make better choices.
Using insights from research in decision-making, we can all contribute meaningfully in controversial conversations with family, friends, neighbors, colleagues, and our representatives — and push for policies that protect those we care about. A little more than a hundred years ago, thousands of lives were routinely lost to preventive illnesses. We've come too far to let ignorance destroy us now.
New Tech Can Predict Breast Cancer Years in Advance
Every two minutes, a woman is diagnosed with breast cancer. The question is, can those at high risk be identified early enough to survive?
New AI software has predicted risk equally well in both white and black women for the first time.
The current standard practice in medicine is not exactly precise. It relies on age, family history of cancer, and breast density, among other factors, to determine risk. But these factors do not always tell the whole story, leaving many women to slip through the cracks. In addition, a racial gap persists in breast cancer treatment and survival. African-American women are 42 percent more likely to die from the disease despite relatively equal rates of diagnosis.
But now those grim statistics could be changing. A team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory have developed a deep learning model that can more accurately predict a patient's breast cancer risk compared to established clinical guidelines – and it has predicted risk equally well in both white and black women for the first time.
The Lowdown
Study results published in Radiology described how the AI software read mammogram images from more than 60,000 patients at Massachusetts General Hospital to identify subtle differences in breast tissue that pointed to potential risk factors, even in their earliest stages. The team accessed the patients' actual diagnoses and determined that the AI model was able to correctly place 31 percent of all cancer patients in the highest-risk category of developing breast cancer within five years of the examination, compared to just 18 percent for existing models.
"Each image has hundreds of thousands of pixels identifying something that may not necessarily be detected by the human eye," said MIT professor Regina Barzilay, one of the study's lead authors. "We all have limited visual capacities so it seems some machines trained on hundreds of thousands of images with a known outcome can capture correlations the human eye might not notice."
Barzilay, a breast cancer survivor herself, had abnormal tissue patterns on mammograms in 2012 and 2013, but wasn't diagnosed until after a 2014 image reading, illustrating the limitations of human processing alone.
MIT professor Regina Barzilay, a lead author on the new study and a breast cancer survivor herself.
(Courtesy MIT)
Next up: The MIT team is looking at training the model to detect other cancers and health risks. Barzilay recalls how a cardiologist told her during a conference that women with heart diseases had a different pattern of calcification on their mammograms, demonstrating how already existing images can be used to extract other pieces of information about a person's health status.
Integration of the AI model in standard care could help doctors better tailor screening and prevention programs based on actual instead of perceived risk. Patients who might register as higher risk by current guidelines could be identified as lower risk, helping resolve conflicting opinions about how early and how often women should receive mammograms.
Open Questions: While the results were promising, it's unknown how well the model will work on a larger scale, as the study looked at data from just one institution and used mammograms supplied by just one hospital. Some risk factor information was also unavailable for certain patients during the study, leaving researchers unable to fully compare the AI model's performance to that of the traditional standard.
One incentive to wider implementation and study, however, is the bonus that no new hardware is required to use the AI model. With other institutions now showing interest, this software could lead to earlier routine detection and treatment of breast cancer — resulting in more lives saved.