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How to navigate covid news without spiraling

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How to navigate covid news without spiraling


But because this is a novel disease, scientists and public health authorities are learning in real time—and more than a year and a half in, knowledge around key topics like immunity and long covid is still evolving. Scientists are often looking for answers at the same time the public is, but that’s not always clear to ordinary people, who may expect immediate and authoritative information.

“One of the things [public health authorities] weren’t necessarily doing that we need to see moving forward is actually communicating about the uncertainty,” says Renée DiResta, technical research manager at the Stanford Internet Observatory. 

This lack of clarity—and sometimes the conflict—in public health messages can filter down to the press and create a vacuum where misleading or unverified information can fester and spread, DiResta says.

“That void can be filled by anyone with an opinion,” she adds.

All those conflicting messages, combined with the reality of slow scientific timelines, can exacerbate distrust. Instead of seeing changes in official guidance as signs that health authorities are responding to new data responsibly, it‘s easy for the public to believe that those authorities and the media had it wrong again—for example, when the CDC changed its mask guidelines. Politically motivated actors exploit that distrust. Sloppy headlines and misleading tweets by reputable news outlets, or journalists’ predictions that age poorly, can be repurposed into ”gotcha” memes that hyperpartisan influencers use to continue chipping away at trust in the media. 

“Entities like Newsmax will take any opportunity to find a misreported or changed fact from a CNN broadcast,” DiResta says. 

Public health officials (and the reporters covering what officials say and do) need a better system of communicating what we don’t yet know and explaining that guidance could change on the basis of new information. DiResta has argued for a Wikipedia-like approach to public health, where the evolution of scientific knowledge and debate is public and transparent, and a wide range of experts can contribute what they know. “It is never going to go back to the old way, where they make some determination in some back room and present a unified consensus to a trusting public,” she says. “That model is over.”

“If journalists spent less time on this day-to-day, blow-by-blow, and more time developing these complex and nuanced stories, we will be doing a much greater public service.”

Erika Check Hayden, UC Santa Cruz

We already see that kind of scientific back-and-forth play out on social media between researchers, public health experts, and doctors. Erika Check Hayden, a science journalist and director of the science communication program at the University of California, Santa Cruz, says that journalists need to remember to do their due diligence with this increased access to scientific deliberation. 

“It can be informative, from a journalist’s perspective, if you understand [how experts] are working out what is going on,” she says. “What’s unhelpful is if you latch on to that at any given moment and portray it as some sort of conclusion.”

That’s good advice for the average reader, too.

Focus on what’s most useful

So how can you find trustworthy news that feels relevant to your life? One option is to keep an eye out for sources, especially local ones, that don’t exclusively focus on blow-by-blow coverage. Reporting that contextualizes the daily numbers you see is likely more helpful than an endless series of stories that simply rattle off the top-line data.

South Side Weekly—a nonprofit newspaper based in Chicago—offers a model for something different. The Weekly covers the South Side of Chicago, a majority nonwhite area. The largely volunteer newspaper produced the ChiVaxBot, an automated Twitter account that shares two maps side by side each day: covid-19 vaccination rates by zip code and covid-19 death rates by zip code. Instead of showing a snapshot of the data on one day, the daily updates demonstrated a pattern over time. Because of this consistent, slow tracking, the bot sounded the alarm on vaccine disparities: Black and Latino areas showed high deaths but low rates of vaccinations, a situation that continues to this day.



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The quest to show that biological sex matters in the immune system

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Sabra Klein and Janna Shapiro look at a specimen on a lightbox.


She ultimately found a postdoctoral position in the lab of one of her thesis committee members. And in the years since, as she has established a lab of her own at the university’s Bloomberg School of Public Health, she has painstakingly made the case that sex—defined by biological attributes such as our sex chromosomes, sex hormones, and reproductive tissues—really does influence immune responses. 

Through research in animal models and humans, Klein and others have shown how and why male and female immune systems respond differently to the flu virus, HIV, and certain cancer therapies, and why most women receive greater protection from vaccines but are also more likely to get severe asthma and autoimmune disorders (something that had been known but not attributed specifically to immune differences). “Work from her laboratory has been instrumental in advancing our understanding of vaccine responses and immune function on males and females,” says immunologist Dawn Newcomb of the Vanderbilt University Medical Center in Nashville, Tennessee. (When referring to people in this article, “male” is used as a shorthand for people with XY chromosomes, a penis, and testicles, and who go through a testosterone-dominated puberty, and “female” is used as a shorthand for people with XX chromosomes and a vulva, and who go through an estrogen-dominated puberty.)

Through her research, as well as the unglamorous labor of arranging symposia and meetings, Klein has helped spearhead a shift in immunology, a field that long thought sex differences didn’t matter. Historically, most trials enrolled only males, resulting in uncounted—and likely uncountable—consequences for public health and medicine. The practice has, for example, caused women to be denied a potentially lifesaving HIV therapy and left them likely to endure worse side effects from drugs and vaccines when given the same dose as men.


Men and women don’t experience infectious or autoimmune diseases in the same way. Women are nine times more likely to get lupus than men, and they have been hospitalized at higher rates for some flu strains. Meanwhile, men are significantly more likely to get tuberculosis and to die of covid-19 than women. 

In the 1990s, scientists often attributed such differences to gender rather than sex—to norms, roles, relationships, behaviors, and other sociocultural factors as opposed to biological differences in the immune system.

For example, even though three times as many women have multiple sclerosis as men, immunologists in the 1990s ignored the idea that this difference could have a biological basis, says Rhonda Voskuhl, a neuroimmunologist at the University of California, Los Angeles. “People would say, ‘Oh, the women just complain more—they’re kind of hysterical,’” Voskuhl says. “You had to convince people that it wasn’t just all subjective or environmental, that it was basic biology. So it was an uphill battle.” 

Sabra Klein (left) and Janna Shapiro in Klein’s laboratory at Johns Hopkins University in Baltimore, Maryland.

ROSEM MORTON

Despite a historical practice of “bikini medicine”—the notion that there are no major differences between the sexes outside the parts that fit under a bikini—we now know that whether you’re looking at your metabolism, heart, or immune system, both biological sex differences and sociocultural gender differences exist. And they both play a role in susceptibility to diseases. For instance, men’s greater propensity to tuberculosis—they are almost twice as likely to get it as women—may be attributed partly to differences in their immune responses and partly to the fact that men are more likely to smoke and to work in mining or construction jobs that expose them to toxic substances, which can impair the lungs’ immune defenses. 

How to tease apart the effects of sex and gender? That’s where animal models come in. “Gender is a social construct that we associate with humans, so animals do not have a gender,” says Chyren Hunter, associate director for basic and translational research at the US National Institutes of Health Office of Research on Women’s Health. Seeing the same effect in both animal models and humans is a good starting point for finding out whether an immune response is modulated by sex. 

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Why can’t tech fix its gender problem?

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From left to right: Gordon MOORE, C. Sheldon ROBERTS, Eugene KLEINER, Robert NOYCE, Victor GRINICH, Julius BLANK, Jean HOERNI and Jay LAST.


Not competing in this Olympics, but still contributing to the industry’s success, were the thousands of women who worked in the Valley’s microchip fabrication plants and other manufacturing facilities from the 1960s to the early 1980s. Some were working-class Asian- and Mexican-Americans whose mothers and grandmothers had worked in the orchards and fruit can­neries of the prewar Valley. Others were recent migrants from the East and Midwest, white and often college educated, needing income and interested in technical work. 

With few other technical jobs available to them in the Valley, women would work for less. The preponderance of women on the lines helped keep the region’s factory wages among the lowest in the country. Women continue to dominate high-tech assembly lines, though now most of the factories are located thousands of miles away. In 1970, one early American-owned Mexican production line employed 600 workers, nearly 90% of whom were female. Half a century later the pattern continued: in 2019, women made up 90% of the workforce in one enormous iPhone assembly plant in India. Female production workers make up 80% of the entire tech workforce of Vietnam. 

Venture: “The Boys Club”

Chipmaking’s fiercely competitive and unusually demanding managerial culture proved to be highly influential, filtering down through the millionaires of the first semiconductor generation as they deployed their wealth and managerial experience in other companies. But venture capital was where semiconductor culture cast its longest shadow. 

The Valley’s original venture capitalists were a tight-knit bunch, mostly young men managing older, much richer men’s money. At first there were so few of them that they’d book a table at a San Francisco restaurant, summoning founders to pitch everyone at once. So many opportunities were flowing it didn’t much matter if a deal went to someone else. Charter members like Silicon Valley venture capitalist Reid Dennis called it “The Group.” Other observers, like journalist John W. Wilson, called it “The Boys Club.”

The men who left the Valley’s first silicon chipmaker, Shockley Semiconductor, to start Fairchild Semiconductor in 1957 were called “the Traitorous Eight.”

WAYNE MILLER/MAGNUM PHOTOS

The venture business was expanding by the early 1970s, even though down markets made it a terrible time to raise money. But the firms founded and led by semiconductor veterans during this period became industry-defining ones. Gene Kleiner left Fairchild Semiconductor to cofound Kleiner Perkins, whose long list of hits included Genentech, Sun Microsystems, AOL, Google, and Amazon. Master intimidator Don Valentine founded Sequoia Capital, making early-stage investments in Atari and Apple, and later in Cisco, Google, Instagram, Airbnb, and many others.

Generations: “Pattern recognition”

Silicon Valley venture capitalists left their mark not only by choosing whom to invest in, but by advising and shaping the business sensibility of those they funded. They were more than bankers. They were mentors, professors, and father figures to young, inexperienced men who often knew a lot about technology and nothing about how to start and grow a business. 

“This model of one generation succeeding and then turning around to offer the next generation of entrepreneurs financial support and managerial expertise,” Silicon Valley historian Leslie Berlin writes, “is one of the most important and under-recognized secrets to Silicon Valley’s ongoing success.” Tech leaders agree with Berlin’s assessment. Apple cofounder Steve Jobs—who learned most of what he knew about business from the men of the semiconductor industry—likened it to passing a baton in a relay race.

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Predicting the climate bill’s effects is harder than you might think

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Predicting the climate bill’s effects is harder than you might think


Human decision-making can also cause models and reality to misalign. “People don’t necessarily always do what is, on paper, the most economic,” says Robbie Orvis, who leads the energy policy solutions program at Energy Innovation.

This is a common issue for consumer tax credits, like those for electric vehicles or home energy efficiency upgrades. Often people don’t have the information or funds needed to take advantage of tax credits.

Likewise, there are no assurances that credits in the power sectors will have the impact that modelers expect. Finding sites for new power projects and getting permits for them can be challenging, potentially derailing progress. Some of this friction is factored into the models, Orvis says. But there’s still potential for more challenges than modelers expect.

Not enough

Putting too much stock in results from models can be problematic, says James Bushnell, an economist at the University of California, Davis. For one thing, models could overestimate how much behavior change is because of tax credits. Some of the projects that are claiming tax credits would probably have been built anyway, Bushnell says, especially solar and wind installations, which are already becoming more widespread and cheaper to build.

Still, whether or not the bill meets the expectations of the modelers, it’s a step forward in providing climate-friendly incentives, since it replaces solar- and wind-specific credits with broader clean-energy credits that will be more flexible for developers in choosing which technologies to deploy.

Another positive of the legislation is all its long-term investments, whose potential impacts aren’t fully captured in the economic models. The bill includes money for research and development of new technologies like direct air capture and clean hydrogen, which are still unproven but could have major impacts on emissions in the coming decades if they prove to be efficient and practical. 

Whatever the effectiveness of the Inflation Reduction Act, however, it’s clear that more climate action is still needed to meet emissions goals in 2030 and beyond. Indeed, even if the predictions of the modelers are correct, the bill is still not sufficient for the US to meet its stated goals under the Paris agreement of cutting emissions to half of 2005 levels by 2030.

The path ahead for US climate action isn’t as certain as some might wish it were. But with the Inflation Reduction Act, the country has taken a big step. Exactly how big is still an open question. 

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