Why Neglected Tropical Diseases Should Matter to Americans
Daisy Hernández was five years old when one of her favorite aunts was struck with a mysterious illness. Tía Dora had stayed behind in Colombia when Daisy's mother immigrated to Union City, New Jersey. A schoolteacher in her late 20s, she began suffering from fevers and abdominal pain, and her belly grew so big that people thought she was pregnant. Exploratory surgery revealed that her large intestine had swollen to ten times its normal size, and she was fitted with a colostomy bag. Doctors couldn't identify the underlying problem—but whatever it was, they said, it would likely kill her within a year or two.
Tía Dora's sisters in New Jersey—Hernández's mother and two other aunts—weren't about to let that happen. They pooled their savings and flew her to New York City, where a doctor at Columbia-Presbyterian Medical Center with a penchant for obscure ailments provided a diagnosis: Chagas disease. Transmitted by the bite of triatomine insects, commonly known as kissing bugs, Chagas is endemic in many parts of Latin America. It's caused by the parasite Trypanoma cruzi, which usually settles in the heart, where it feeds on muscle tissue. In some cases, however, it attacks the intestines or esophagus. Tía Dora belonged to that minority.
In 1980, U.S. immigration laws were more forgiving than they are today. Tía Dora was able to have surgery to remove a part of her colon, despite not being a citizen or having a green card. She eventually married a legal resident and began teaching Spanish at an elementary school. Over the next three decades, she earned a graduate degree, built a career, and was widowed. Meanwhile, Chagas continued its slow devastation. "Every couple of years, we were back in the hospital with her," Hernández recalls. "When I was in high school, she started feeling like she couldn't swallow anything. It was the parasite, destroying the muscles of her esophagus."
When Tía Dora died in 2010, at 59, her niece was among the family members at her bedside. By then, Hernández had become a journalist and fiction writer. Researching a short story about Chagas disease, she discovered that it affected an estimated 6 million people in South America, Central America, and Mexico—as well as 300,000 in the United States, most of whom were immigrants from those places. "I was shocked to learn it wasn't rare," she says. "That made me hungry to know more about this disease, and about the families grappling with it."
Hernández's curiosity led her to write The Kissing Bug, a lyrical hybrid of memoir and science reporting that was published in June. It also led her to another revelation: Chagas is not unique. It's among the many maladies that global health experts refer to as neglected tropical diseases—often-disabling illnesses that afflict 1.7 billion people worldwide, while getting notably less attention than the "big three" of HIV/AIDs, malaria, and tuberculosis. NTDs cause fewer deaths than those plagues, but they wreak untold suffering and economic loss.
Shortly before Hernández's book hit the shelves, the World Health Organization released its 2021-2030 roadmap for fighting NTDs. The plan sets targets for controlling, eliminating, or eradicating all the diseases on the WHO's list, through measures ranging from developing vaccines to improving healthcare infrastructure, sanitation, and access to clean water. Experts agree that for the campaign to succeed, leadership from wealthy nations—particularly the United States—is essential. But given the inward turn of many such countries in recent years (evidenced in movements ranging from America First to Brexit), and the continuing urgency of the COVID-19 crisis, public support is far from guaranteed.
As Hernández writes: "It is easier to forget a disease that cannot be seen." NTDs primarily affect residents of distant lands. They kill only 80,000 people a year, down from 204,000 in 1990. So why should Americans to bother to look?
Breaking the circle of poverty and disease
The World Health Organization counts 20 diseases as NTDs. Along with Chagas, they include dengue and chikungunya, which cause high fevers and agonizing pain; elephantiasis, which deforms victims' limbs and genitals; onchocerciasis, which causes blindness; schistosomiasis, which can damage the heart, lungs, brain, and genitourinary system; helminths such as roundworm and whipworm, which cause anemia, stunted growth, and cognitive disabilities; and a dozen more. Such ailments often co-occur in the same patient, exacerbating each other's effects and those of illnesses such as malaria.
NTDs may be spread by insects, animals, soil, or tainted water; they may be parasitic, bacterial, viral, or—in the case of snakebite envenoming—non-infectious. What they have in common is their longtime neglect by public health agencies and philanthropies. In part, this reflects their typically low mortality rates. But the biggest factor is undoubtedly their disempowered patient populations.
"These diseases occur in the setting of poverty, and they cause poverty, because of their chronic and debilitating effects," observes Peter Hotez, dean of the National School of Tropical Medicine at Baylor University and co-director of the Texas Children's Hospital for Vaccine Development. And historically, the everyday miseries of impoverished people have seldom been a priority for those who set the global health agenda.
That began to change about 20 years ago, when Hotez and others developed the conceptual framework for NTDs and early proposals for combating them. The WHO released its first roadmap in 2012, targeting 17 NTDs for control, elimination, or eradication by 2020. (Rabies, snakebite, and dengue were added later.) Since then, the number of people at risk for NTDs has fallen by 600 million, and 42 countries have eliminated at least one such disease. Cases of dracunculiasis—known as Guinea worm disease, for the parasite that creates painful blisters in a patient's skin—have dropped from the millions to just 27 in 2020.
Yet the battle is not over, and the COVID-19 pandemic has disrupted prevention and treatment programs around the globe.
A new direction — and longstanding obstacles
The WHO's new roadmap sets even more ambitious goals for 2030. Among them: reducing by 90 percent the number of people requiring treatment for NTDs; eliminating at least one NTD in another 100 countries; and fully eradicating dracunculiasis and yaws, a disfiguring skin infection.
The plan also places an increased focus on "country ownership," relying on nations with high incidence of NTDs to design their own plans based on local expertise. "I was so excited to see that," says Kristina Talbert-Slagle, director of the Yale College Global Health Studies program. "No one is a better expert on how to address these situations than the people who deal with it day by day."
Another fresh approach is what the roadmap calls "cross-cutting" targets. "One of the really cool things about the plan is how much it emphasizes coordination among different sectors of the health system," says Claire Standley, a faculty member at Georgetown University's Center for Global Health Science and Security. "For example, it explicitly takes into account the zoonotic nature of many neglected tropical diseases—the fact that we have to think about animal health as well as human health when we tackle NTDs."
Whether this grand vision can be realized, however, will depend largely on funding—and that, in turn, is a question of political will in the countries most able to provide it. On the upside, the U.S. has ended its Trump-era feud with the WHO. "One thing that's been really encouraging," says Standley, "has been the strong commitment toward global cooperation from the current administration." Even under the previous president, the U.S. remained the single largest contributor to the global health kitty, spending over $100 million annually on NTDs—six times the figure in 2006, when such financing started.
On the downside, America's outlay has remained flat for several years, and the Biden administration has so far not moved to increase it. A "back-of-the-envelope calculation," says Hotez, suggests that the current level of aid could buy medications for the most common NTDs for about 200 million people a year. But the number of people who need treatment, he notes, is at least 750 million.
Up to now, the United Kingdom—long the world's second-most generous health aid donor—has taken up a large portion of the slack. But the UK last month announced deep cuts in its portfolio, eliminating 102 previously supported countries and leaving only 34. "That really concerns me," Hotez says.
The struggle for funds, he notes, is always harder for projects involving NTDs than for those aimed at higher-profile diseases. His lab, which he co-directs with microbiologist Maria Elena Bottazzi, started developing a COVID-19 vaccine soon after the pandemic struck, for example, and is now in Phase 3 trials. The team has been working on vaccines for Chagas, hookworm, and schistosomiasis for much longer, but trials for those potential game-changers lag behind. "We struggle to get the level of resources needed to move quickly," Hotez explains.
Two million reasons to care
One way to prompt a government to open its pocketbook is for voters to clamor for action. A longtime challenge with NTDs, however, has been getting people outside the hardest-hit countries to pay attention.
The reasons to care, global health experts argue, go beyond compassion. "When we have high NTD burden," says Talbert-Slagle, "it can prevent economic growth, prevent innovation, lead to more political instability." That, in turn, can lead to wars and mass migration, affecting economic and political events far beyond an affected country's borders.
Like Hernández's aunt Dora, many people driven out of NTD-wracked regions wind up living elsewhere. And that points to another reason to care about these diseases: Some of your neighbors might have them. In the U.S., up to 14 million people suffer from neglected parasitic infections—including 70,000 with Chagas in California alone.
When Hernández was researching The Kissing Bug, she worried that such statistics would provide ammunition to racists and xenophobes who claim that immigrants "bring disease" or exploit overburdened healthcare systems. (This may help explain some of the stigma around NTDs, which led Tía Dora to hide her condition from most people outside her family.) But as the book makes clear, these infections know no borders; they flourish wherever large numbers of people lack access to resources that most residents of rich countries take for granted.
Indeed, far from gaming U.S. healthcare systems, millions of low-income immigrants can't access them—or must wait until they're sick enough to go to an emergency room. Since Congress changed the rules in 1996, green card holders have to wait five years before they can enroll in Medicaid. Undocumented immigrants can never qualify.
Closing the great divide
Hernández uses a phrase borrowed from global health crusader Paul Farmer to describe this access gap: "the great epi divide." On one side, she explains, "people will die from cancer, from diabetes, from chronic illnesses later in life. On the other side of the epidemiological divide, people are dying because they can't get to the doctor, or they can't get medication. They don't have a hospital anywhere near them. When I read Dr. Farmer's work, I realized how much that applied to neglected diseases as well."
When it comes to Chagas disease, she says, the epi divide is embodied in the lack of a federal mandate for prenatal or newborn screening. Each year, according to the Centers for Disease Control and Prevention, up to 300 babies in the U.S. are born with Chagas, which can be passed from the mother in utero. The disease can be cured with medication if treated in infancy. (It can also be cured in adults in the acute stage, but is seldom detected in time.) Yet the CDC does not require screening for Chagas—even though newborns are tested for 15 diseases that are less common. According to one study, it would be 10 times cheaper to screen and treat babies and their mothers than to cover the costs related to the illness in later years. Few states make the effort.
The gap that enables NTDs to persist, Hernández argues, is the same one that has led to COVID-19 death rates in Black and Latinx communities that are double those elsewhere in America. To close it, she suggests, caring is not enough.
"When I was working on my book," she says, "I thought about HIV in the '80s, when it had so much stigma that no one wanted to talk about it. Then activists stepped up and changed the conversation. I thought a lot about breast cancer, which was stigmatized for years, until people stepped forward and started speaking out. I thought about Lyme disease. And it wasn't only patients—it was also allies, right? The same thing needs to happen with neglected diseases around the world. Allies need to step up and make demands on policymakers. We need to make some noise."
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.