Americans Fell for a Theranos-Style Scam 100 Years Ago. Will We Ever Learn?
The huckster understands what people want – an easy route to good health -- and figures out just how to provide it as long as no one asks too many questions.
"Americans are very much prone to this sort of thinking: Give me a pill or give me a magical bean that can make me lose weight!"
The keys to success: Hoopla, fancy technology, and gullibility. And oh yes, one more thing: a blood sample. Well, lots and lots of blood samples. Every testing fee counts.
Sound familiar? It could be the story of the preternaturally persuasive Elizabeth Holmes, the disgraced founder of Theranos who stands accused of perpetrating a massive blood-testing fraud. But this is a different story from a different time, one that dates back 100 years but sounds almost like it could unfold on the front page of The Wall Street Journal today.
The main difference: Back then, watchdogs thought they'd be able to vanquish fake medicine and scam science. Fat chance, it turned out. It seems like we're more likely to lose-weight-quick than make much of a dent into quackery and health fraud.
Why? Have we learned anything at all over the past century? As we sweep into a new decade, experts says we're not as advanced as we'd like to think. But the fight against fraud and fakery continues.
Quackery: As American As America Itself
In the 17th century, British healers of questionable reputation got a new name -- "quack," from the Dutch word "quacksalver," which originally referred to someone who treats others with home remedies but developed a new meaning along the lines of "charlatan." And these quacks got a new place to sell their wares: the American colonies.
By 1692, a Boston newspaper advertised a patent medicine that promised to cure "the Griping of the Guts, and the Wind Cholick" and – for good measure – "preventeth that woeful Distemper of the Dry Belly Ach." A couple centuries later, the most famous woman in the United States wasn't a first lady or feminist but a hawker of nostrums named Lydia Estes Pinkham whose "vegetable compound" promised to banish "female complaints." One advertisement suggested that the "sure cure" would have saved the life of a Connecticut clergyman whose wife killed him after suffering from feminine maladies for 16 years.
By the early 20th century, Americans were fascinated by electricity and radiation, and both healers and hucksters embraced the new high-tech era. Men with flagging libidos, for example, could irradiate their private parts with the radioactive Radiendocrinator or buy battery-powered electric belts equipped with dangling bits to supercharge their, um, dangling bits.
The Rise of the Radio Wave 'Cure'
Enter radionics, the (supposed) science of better health via radio waves. The idea was that "healthy people radiate healthy energy," and sickness could be reversed through diagnosis and re-tuning, write Dr. Lydia Kang and Nate Pedersen in their 2017 book "Quackery: A Brief History of the Worst Ways to Cure Everything."
Detecting illness and fixing it required machinery -- Dynamizers, Radioclasts and Oscillocasts – that could cost hundreds of dollars each. Thousands of physicians bought them. Fortunately, they could work remotely, for a fee. The worried-and-potentially-unwell just needed to send a blood sample and, of course, a personal check.
Sting operations revealed radionics to be bogus. A skeptic sent a blood sample to one radionics practitioner in Albuquerque who reported back with news of an infected fallopian tube. In fact, the blood sample came from a male guinea pig. As an American Medical Association leader reported, the guinea pig "had shown no female characteristics up to that time, and a postmortem examination yielded no evidence of ladylike attributes."
When Quackery Refused to Yield
The rise of bogus medical technology in the early 20th century spawned a watchdog industry as organizations like the American Medical Association swept into action, said medical historian Eric Boyle, author of 2012's "Quack Medicine: A History of Combating Health Fraud in Twentieth-Century America."
"When quackery was recognized as a major problem, the people who campaigned for its demise were confident that they could get rid of it," he said. "A lot of people believed that increased education, the truths of science, and laws designed to protect consumers would ultimately drive quackery from the marketplace. And then throughout the century, as modern medicine developed, and more effectively treated one disease after another, many observers remained confident in that prediction."
There's a bid to "flood the information highway with truth to turn the storm of fake promotional stuff into a trickle."
But fake medicine persisted as Americans continued their quest to get- healthy-quick… or get-rich-quick by promising to help others to get- healthy-quick. Even radionics refused to die. It's still around in various forms. And, as the Theranos scandal reveals, we're still hoping our blood can offer the keys to longevity and good health.
Why Do We Still Fall for Scams?
In our own era, the Theranos company rose to prominence when founder and CEO Elizabeth Holmes convinced journalists and investors that she'd found a way to cheaply test drops of blood for hundreds of conditions. Then it all fell apart, famously, when the world learned that the technology didn't work. The company has folded, and Holmes faces a federal trial on fraud charges this year.
"There were a lot of prominent, very smart people who bought into the myth of Elizabeth Holmes," a former employee told "60 Minutes," even though the blood tests never actually worked as advertised.
Shouldn't "prominent, very smart people" know better? "People are gullible," said Dr. Stephen Barrett, a psychiatrist and leading quack-buster who runs the QuackWatch website. But there's more to the story. According to him, we're uniquely vulnerable as individuals to bogus medicine.
Scam artists specifically pinpoint their target audiences, such as "smart people," desperate people and alienated people, he said.
Smart people, for example, might be overconfident about their ability to detect fraud and fall for bogus medicine. Alienated people may distrust the establishment, whether it's the medical field or government watchdogs, and be more receptive to alternative sources of information.
Dr. Barrett also points a finger at magical thinking, which comes in different forms. It could mean a New Age-style belief that our minds can control the world around us. Or, as professional quack-buster Alex Berezow said, it could refer to "our cultural obsession with quick fixes."
"Americans are very much prone to this sort of thinking: Give me a pill or give me a magical bean that can make me lose weight! But complex problems need complex solutions," said Berezow, a microbiologist who debunks junk science in his job as a spokesman for the American Council on Science & Health.
American mistrust of expertise makes matters worse, he said. "When I tell people they need to get vaccinated, I'm called a shill for the pharmaceutical industry," he said. "If I say dietary supplements generally don't work, I'm a shill for doctors who want to keep people sick."
What can ordinary citizens do to protect themselves from fake medicine? "You have to have a healthy skepticism of everything," Berezow said. "When you come across something new, is someone trying to take advantage of you? It's a horrible way to think about the world, but there's some truth to it."
"Like any chronic disease, we will have to live with it while we do our best to fight it."
The government and experts have their own roles to play via regulation and education, respectively. For all the criticism it gets, the Food & Drug Administration does serve as a bulwark against fakery in prescription medicine. And while celebrities like Gwyneth "Goop" Paltrow hawk countless questionable medical products on the Internet, scientists and physicians are fighting back by using social media as a tool to promote the truth. There's a bid to "flood the information highway with truth to turn the storm of fake promotional stuff into a trickle," said Dr. Randi Hutter Epstein, a writer in residence at Yale School of Medicine and author of 2018's "Aroused: The History of Hormones and How They Control Just About Everything."
What's next? Like death, taxes and Cher, charlatans are likely to always be with us. Boyle quoted the late William Jarvis, a pioneering quack-buster in the late 20th century who believed health fraud would never be eradicated: "Like any chronic disease, we will have to live with it while we do our best to fight it."
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.