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Preparing for a pandemic that never came ended up setting off another − how an accidental virus release triggered 1977’s ‘Russian flu’

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theconversation.com – Donald S. Burke, Dean Emeritus and Distinguished University Professor Emeritus of Science and Policy, and of Epidemiology, at the School of Public Health, University of Pittsburgh – 2024-09-04 07:28:24

Vaccine research quickly picked up to try to prevent a possible flu pandemic in 1976.

AP Photo

Donald S. Burke, University of Pittsburgh

Nineteen-year-old U.S. Army Pvt. David Lewis set out from Fort Dix on a 50-mile hike with his unit on Feb. 5, 1976. On that bitter cold day, he collapsed and died. Autopsy specimens unexpectedly tested positive for an H1N1 swine influenza virus.

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Virus disease surveillance at Fort Dix found another 13 cases among recruits who had been hospitalized for respiratory illness. Additional serum antibody testing revealed that over 200 recruits had been infected but not hospitalized with the novel swine H1N1 strain.

masked nurse and military man stand above patient in bed

worried about a repeat of something like the 1918 flu pandemic, which took hold in soldiers and spread globally.

PhotoQuest/Getty Images

Alarm bells instantly went off within the epidemiology community: Could Pvt. Lewis’ from an H1N1 swine flu be a harbinger of another global pandemic like the terrible 1918 H1N1 swine flu pandemic that killed an estimated 50 million people worldwide?

The U.S. acted quickly. On March 24, 1976, President Gerald Ford announced a plan to “inoculate every man, woman, and child in the United States.” On Oct. 1, 1976, the mass immunization campaign began.

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Meanwhile, the initial small outbreak at Fort Dix had rapidly fizzled, with no new cases on the base after February. As Army Col. Frank Top, who headed the Fort Dix virus investigation, later told me, “We had shown pretty clearly that (the virus) didn’t go anywhere but Fort Dix … it disappeared.”

Nonetheless, concerned by that outbreak and witnessing the massive crash vaccine program in the U.S., biomedical scientists worldwide began H1N1 swine influenza vaccine research and development programs in their own countries. Going into the 1976-77 winter season, the world waited – and prepared – for an H1N1 swine influenza pandemic that never came.

piles of cardboard boxes and two men lifting them

By September 1976, New York Health Department workers were unloading cartons of swine flu vaccine for distribution.

AP Photo/Jim McKnight

But that wasn’t the end of the story. As an experienced infectious disease epidemiologist, I make the case that there were unintended consequences of those seemingly prudent but ultimately unnecessary preparations.

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What was odd about H1N1 Russian flu pandemic

In an epidemiological twist, a new pandemic influenza virus did emerge, but it was not the anticipated H1N1 swine virus.

In November 1977, health officials in Russia reported that a human – not swine – H1N1 influenza strain had been detected in Moscow. By month’s end, it was reported across the entire USSR and soon throughout the world.

Compared with other influenzas, this pandemic was peculiar. First, the mortality rate was low, about a third that of most influenza strains. Second, only those younger than 26 were regularly attacked. And finally, unlike other newly emerged pandemic influenza viruses in the past, it failed to displace the existing prevalent H3N2 subtype that was that year’s seasonal flu. Instead, the two flu strains – the new H1N1 and the long-standing H3N2 – circulated side by side.

Here the story takes yet another turn. Microbiologist Peter Palese applied what was then a novel technique called RNA oligonucleotide mapping to study the genetic makeup of the new H1N1 Russian flu virus. He and his colleagues grew the virus in the lab, then used RNA-cutting enzymes to chop the viral genome into hundreds of pieces. By spreading the chopped RNA in two dimensions based on size and electrical charge, the RNA fragments created a unique fingerprint-like map of spots.

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dark spots in a funnel shape on a lighter background

Researchers were surprised to see the ‘genetic fingerprint’ for the 1977 H1N1 Russian flu strain closely matched that of an extinct influenza virus.

Peter Palese

Much to Palese’s surprise, when they compared the spot pattern of the 1977 H1N1 Russian flu with a variety of other influenza viruses, this “new” virus was essentially identical to older human influenza H1N1 strains that had gone extinct in the early 1950s.

So, the 1977 Russian flu virus was actually a strain that had disappeared from the planet a quarter century early, then was somehow resurrected back into circulation. This explained why it attacked only younger people – older people had already been infected and become immune when the virus circulated decades ago in its earlier incarnation.

But how did the older strain back from extinction?

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black and white photo of people sitting on subway in Moscow, 1977

Though called the Russian flu, the virus appears to have been circulating elsewhere before being identified in the Soviet population.

Gilbert UZAN/Gamma-Rapho via Getty Images

Refining the timeline of a resurrected virus

Despite its name, the Russian flu probably didn’t really start in Russia. The first published reports of the virus were from Russia, but subsequent reports from China provided evidence that it had first been detected months earlier, in May and June of 1977, in the Chinese port city of Tientsin.

In 2010, scientists used detailed genetic studies of several samples of the 1977 virus to pinpoint the date of their earliest common ancestor. This “molecular clock” data suggested the virus initially infected people a full year earlier, in April or May of 1976.

So, the best evidence is that the 1977 Russian flu actually emerged – or more properly “re-emerged” – in or near Tientsin, China, in the spring of 1976.

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A frozen lab virus

Was it simply a coincidence that within months of Pvt. Lewis’ death from H1N1 swine flu, a heretofore extinct H1N1 influenza strain suddenly reentered the human population?

Influenza virologists around the world had for years been using freezers to store influenza virus strains, including some that had gone extinct in the wild. Fears of a new H1N1 swine flu pandemic in 1976 in the United States had prompted a worldwide surge in research on H1N1 viruses and vaccines. An accidental release of one of these stored viruses was certainly possible in any of the countries where H1N1 research was taking place, including China, Russia, the U.S., the U.K. and probably others.

Years after the reemergence, Palese, the microbiologist, reflected on personal conversations he had at the time with Chi-Ming Chu, the leading Chinese expert on influenza. Palese wrote in 2004 that “the introduction of the 1977 H1N1 virus is now thought to be the result of vaccine trials in the Far East involving the of several thousand military recruits with H1N1 virus.”

Although exactly how such an accidental release may have occurred during a vaccine trial is unknown, there are two leading possibilities. First, scientists could have used the resurrected H1N1 virus as their starting material for of a live, attenuated H1N1 vaccine. If the virus in the vaccine wasn’t adequately weakened, it could have become transmissible person to person. Another possibility is that researchers used the live, resurrected virus to test the immunity provided by conventional H1N1 vaccines, and it accidentally escaped from the research setting.

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Whatever the specific mechanism of the release, the combination of the detailed location and timing of the pandemic’s origins and the stature of Chu and Palese as highly credible sources combine to make a strong case for an accidental release in China as the source of the Russian flu pandemic virus.

black and grey bubbly blobs

The H1N1 influenza virus identified at Fort Dix wasn’t the one that ended up causing a pandemic.

CDC/Dr. E. Palmer, R.E. Bates, 1976 via Getty Images

A sobering history lesson

The resurrection of an extinct but dangerous human-adapted H1N1 virus came about as the world was scrambling to prevent what was perceived to be the imminent emergence of a swine H1N1 influenza pandemic. People were so concerned about the possibility of a new pandemic that they inadvertently caused one. It was a self-fulfilling-prophecy pandemic.

I have no intent to lay blame here; indeed, my main point is that in the epidemiological fog of the moment in 1976, with anxiety mounting worldwide about a looming pandemic, a research unit in any country could have accidentally released the resurrected virus that came to be called the Russian flu. In the global rush to head off a possible new pandemic of H1N1 swine flu from Fort Dix through research and vaccination, accidents could have happened anywhere.

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Of course, biocontainment facilities and policies have improved dramatically over the past half-century. But at the same time, there has been an equally dramatic proliferation of high-containment labs around the world.

woman fully contained in personal protective gear reaches across glass bottles

Across the globe, researchers work on dangerous pathogens in labs that are part of biocontainment facilities.

AP Photo/Michael Probst

Overreaction. Unintended consequences. Making matters worse. Self-fulfilling prophecy. There is a rich variety of terms to describe how the best intentions can go awry. Still reeling from , the world now faces new threats from cross-species jumps of avian flu viruses, mpox viruses and others. It’s critical that we be quick to respond to these emerging threats to prevent yet another global disease conflagration. Quick, but not too quick, history suggests.The Conversation

Donald S. Burke, Dean Emeritus and Distinguished University Professor Emeritus of Health Science and Policy, and of Epidemiology, at the School of Public Health, University of Pittsburgh

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The Conversation

Tiny robots and AI algorithms could help to craft material solutions for cleaner environments

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theconversation.com – Mahshid Ahmadi, Assistant Professor of Materials Science and Engineering, of Tennessee – 2024-09-17 07:31:57

Air pollution is a global problem, but scientists are investigating new materials that could clean it up.
AP Photo/Sergei Grits

Mahshid Ahmadi, University of Tennessee

Many human activities release pollutants into the air, water and soil. These harmful chemicals threaten the of both people and the ecosystem. According to the World Health Organization, air pollution causes an estimated 4.2 million deaths annually.

Scientists are looking into , and one potential avenue is a class of materials called photocatalysts. When triggered by light, these materials undergo chemical reactions that initial studies have shown can break down common toxic pollutants.

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I am a materials science and engineering researcher at the University of Tennessee. With the help of robots and artificial intelligence, my colleagues and I are making and testing new photocatalysts with the goal of mitigating air pollution.

Breaking down pollutants

The photocatalysts work by generating charged carriers in the presence of light. These charged carriers are tiny particles that can move around and cause chemical reactions. When they into contact with water and oxygen in the environment, they produce substances called reactive oxygen species. These highly active reactive oxygen species can bond to parts of the pollutants and then either decompose the pollutants or turn them into harmless – or even useful – products.

A cube-shaped metal machine with a chamber filled with bright light, and a plate of tubes shown going under the light.
To facilitate the photocatalytic reaction, researchers in the Ahmadi lab put plates of perovskite nanocrystals and pollutants under bright light to see whether the reaction breaks down the pollutants.
Astita Dubey

But some materials used in the photocatalytic have limitations. For example, they can’t start the reaction unless the light has enough energy – infrared rays with lower energy light, or visible light, won’t trigger the reaction.

Another problem is that the charged particles involved in the reaction can recombine too quickly, which means they join back together before finishing the job. In these cases, the pollutants either do not decompose completely or the process takes a long time to accomplish.

Additionally, the surface of these photocatalysts can sometimes change during or after the photocatalytic reaction, which affects how they work and how efficient they are.

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To overcome these limitations, scientists on my team are to develop new photocatalytic materials that work efficiently to break down pollutants. We also focus on making sure these materials are nontoxic so that our pollution-cleaning materials aren’t causing further pollution.

A plate of tiny tubes, with some colored dark blue, others light blue, and others transparent.
This plate from the Ahmadi lab is used while testing how perovskite nanocrystals and light break down pollutants, like the blue dye shown. The light blue color indicates partial degradation, while transparent water signifies complete degradation.
Astita Dubey

Teeny tiny crystals

Scientists on my team use automated experimentation and artificial intelligence to figure out which photocatalytic materials could be the best candidates to quickly break down pollutants. We’re making and testing materials called hybrid perovskites, which are tiny crystals – they’re about a 10th the thickness of a strand of hair.

These nanocrystals are made of a blend of organic (carbon-based) and inorganic (non-carbon-based) components.

They have a few unique qualities, like their excellent light-absorbing properties, which come from how they’re structured at the atomic level. They’re tiny, but mighty. Optically, they’re amazing too – they interact with light in fascinating ways to generate a large number of tiny charge carriers and trigger photocatalytic reactions.

These materials efficiently transport electrical charges, which allows them to transport light energy and the chemical reactions. They’re also used to make solar panels more efficient and in LED lights, which create the vibrant displays you see on TV screens.

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There are thousands of potential types of hybrid nanocrystals. So, my team wanted to figure out how to make and test as many as we can quickly, to see which are the best candidates for cleaning up toxic pollutants.

Bringing in robots

Instead of making and testing samples by hand – which takes weeks or months – we’re using smart robots, which can produce and test at least 100 different materials within an hour. These small liquid-handling robots can precisely move, mix and transfer tiny amounts of liquid from one place to another. They’re controlled by a computer that guides their acceleration and accuracy.

A researcher in a white lab coat smiling at the camera next to a fume hood, with plates of small tubes inside it.
The Opentrons pipetting robot helps Astita Dubey, a visiting scientist working with the Ahmadi lab, synthesize materials and treat them with organic pollutants to test whether they can break down the pollutants.
Jordan Marshall

We also use machine learning to guide this process. Machine learning algorithms can analyze test data quickly and then learn from that data for the next set of experiments executed by the robots. These machine learning algorithms can quickly identify patterns and insights in collected data that would normally take much longer for a human eye to catch.

Our approach aims to simplify and better understand complex photocatalytic , helping to create new strategies and materials. By using automated experimentation guided by machine learning, we can now make these systems easier to analyze and interpret, overcoming challenges that were difficult with traditional methods.The Conversation

Mahshid Ahmadi, Assistant Professor of Materials Science and Engineering, University of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The Conversation

A public health historian sizes up their records

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theconversation.com – Zachary W. Schulz, Lecturer of History, Auburn University – 2024-09-17 07:33:53

The presidential debate on Sept. 10, 2024, did not add much context to what the two candidates would do on beyond their own records.
Visual China Group/Getty Images

Zachary W. Schulz, Auburn University

care is a defining issue in the 2024 election – Democratic presidential nominee Kamala Harris and Republican contender Donald Trump have starkly different records on the issue. Rather than focusing on what they promise to do, let’s examine what their past actions reveal about their approaches to Medicare, the Affordable Care Act, public health , drug policy and child abuse and domestic violence prevention.

As a specialist in public health history and policy, I have carefully examined both candidates’ records on American health care. With years of experience in the health care field and being a cystic fibrosis patient myself, I have developed a deep understanding of our health care system and the political dynamics that shape it.

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For me, as for many other Americans, health care is more than just a political issue; it is a deeply personal one.

Medicare

During Harris’ time in the Senate, she co-sponsored the Medicare for All Act, which aimed to expand Medicare to all Americans, effectively eliminating private insurance.

At the presidential debate on Sept. 10, 2024, Harris clarified her former of “Medicare for All” by emphasizing her prior legislative efforts to preserve and expand protections for ‘ rights and access to affordable health care.

Harris’s legislative efforts, primarily around the 2017-2020 period, reflect a commitment to broadening access to Medicare and reducing costs for seniors. During that time, Harris advocated for the Medicare program to negotiate drug prices directly with pharmaceutical companies.

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Later, as vice president, Harris cast a tie-breaking vote on the 2022 Inflation Reduction Act, allowing the government to negotiate drug prices for Medicare with pharmaceutical companies.

In contrast, during Trump’s presidency, he made several attempts, some of which were successful, to cut funding for Medicare. The 2020 budget proposed by his administration included cuts to Medicare totaling more than US$800 billion over 10 years, primarily by reducing payments to providers and slowing the growth of the program.

The proposed cuts did not take effect because they required Congressional approval, which was not granted. The plan faced significant opposition due to concerns about potential negative impacts on beneficiaries.

Affordable Care Act

Harris has been a staunch defender of the Affordable Care Act, also known as the ACA or “Obamacare.” As a senator, Harris consistently voted against any efforts to repeal the ACA. She advocated for expanding its provisions, supporting legislation that aimed to strengthen protections for people with preexisting conditions and increase funding for Medicaid expansion.

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Harris’ record shows a clear commitment to ensuring broader health coverage under the ACA. And, in the recent debate, Harris noted this record and reasserted her commitment to the act.

During his presidency, Trump led multiple efforts to repeal the ACA, including the 2017 American Health Care Act, which would have significantly reduced the scope of expansion and individual mandates.

Although these efforts ultimately failed in the Senate, Trump succeeded in weakening the ACA by eliminating the individual mandate penalty through the 2017 Tax Cuts and Jobs Act. In the debate against Harris, Trump reiterated his position that the Affordable Care Act “was lousy health care,” though he did not ultimately offer a replacement plan, stating only that he has “concepts of a plan.”

Donald Trump claims that as president, he had an obligation to save Obamacare, otherwise known as the Affordable Care Act, but says it is too expensive. He says he has ‘concepts of a plan’ for something to replace the ACA.

Public health infrastructure

Harris’ tenure in the Senate, from January 2017 to January 2021, shows a consistent pattern of supporting public health infrastructure. She co-sponsored several bills aimed at increasing funding for community health centers and expanding access to preventive care.

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Harris also advocated for more federal to address public health emergencies, such as the opioid epidemic and the COVID-19 pandemic.

During Trump’s presidency, however, he made significant cuts to public health programs. The Trump administration proposed budget cuts to the Centers for Disease Control and Prevention and other public health agencies, arguing that they were necessary for fiscal responsibility. These proposals drew criticism for potentially undermining the nation’s ability to respond to public health emergencies, a concern that was underscored by the CDC’s struggles during the early days of the COVID-19 pandemic. Trump frequently has responded to these criticisms by asserting he “cut bureaucratic red tape” rather than essential services.

Drug pricing policy

Harris has also supported legislation to lower drug prices and increase transparency in the pharmaceutical industry. She co-sponsored the Drug Price Relief Act, which aimed to allow the federal government to negotiate drug prices for Medicare directly. She also supported efforts to import cheaper prescription drugs from Canada. Her record reflects a focus on reducing costs for consumers and increasing access to affordable medications.

Trump’s record on drug policy is mixed. While Trump took credit for some decreases in prescription drug prices during his presidency, his administration’s most significant regulatory changes favored pharmaceutical companies. The administration’s attempts to implement a rule allowing the importation of cheaper drugs from Canada faced significant hurdles and did not lead to immediate changes.

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Trump also ended a rule that would have required pharmaceutical companies to disclose drug prices in television ads, citing concerns over its legality.

Child abuse and domestic violence

Harris has a strong record of advocating for the prevention of child abuse and domestic violence. During her time as California’s attorney general and as a senator, Harris pushed for legislation that increased funding for domestic violence prevention programs and expanded legal protections for survivors. She has consistently supported measures to enhance child welfare services and improve coordination among agencies to protect children.

Trump’s record on these issues is less defined, but his administration did sign into law the Family First Prevention Services Act, which aimed to keep more children safely at home and out of foster care by providing new resources to families. However, critics argue that the Trump administration’s broader cuts to social services and health programs could indirectly undermine efforts to combat child abuse and domestic violence. In addition, some experts suggest that Trump’s family separation policies on the southern border contributed to an increase in child trauma during his administration.The Conversation

Zachary W. Schulz, Lecturer of History, Auburn University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The Conversation

How researchers measure wildfire smoke exposure doesn’t capture long-term health effects − and hides racial disparities

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theconversation.com – Joan Casey, Associate Professor of Environmental and Occupational Health Sciences, of Washington – 2024-09-16 07:26:33

Fine particulate matter from wildfires can cause long-term health harms.
Gary Hershorn/Getty Images

Joan Casey, University of Washington and Rachel Morello-Frosch, University of California, Berkeley

Kids born in 2020 worldwide will experience twice the number of wildfires during their lifetimes with those born in 1960. In California and other western states, frequent wildfires have become as much a part of summer and fall as popsicles and Halloween candy.

Wildfires produce fine particulate matter, or PM₂.₅, that chokes the air and penetrates deep into lungs. Researchers know that short-term exposure to wildfire PM₂.₅ increases acute care visits for cardiorespiratory problems such as asthma. However, the long-term effects of repeated exposure to wildfire PM₂.₅ on chronic health conditions are unclear.

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One reason is that scientists have not decided how best to measure this type of intermittent yet ongoing exposure. Environmental epidemiologists and health scientists like us usually summarize long-term exposure to total PM₂.₅ – which from power plants, industry and transportation – as average exposure over a year. This might not make sense when measuring exposure to wildfire. Unlike traffic-related air pollution, for example, levels of wildfire PM₂.₅ vary a lot throughout the year.

To improve health and equity research, our team has developed five metrics that better capture long-term exposure to wildfire PM₂.₅.

Measuring fluctuating wildfire PM₂.₅

To understand why current measurements of wildfire PM₂.₅ aren’t adequately capturing an individual’s long-term exposure, we need to delve into the concept of averages.

Say the mean level of PM₂.₅ over a year was 1 microgram per cubic meter. A person could experience that exposure as 1 microgram per cubic meter every day for 365 days, or as 365 micrograms per cubic meter on a single day.

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While these two scenarios result in the same average exposure over a year, they might have very different biological effects. The body might be able to fend off damage from exposure to 1 microgram per cubic meter each day, but be overwhelmed by a huge, single dose of 365 micrograms per cubic meter.

For perspective, in 2022, Americans experienced an average total PM₂.₅ exposure of 7.8 micrograms per cubic meter. Researchers estimated that in the 35 states that experience wildfires, these wildfires added on average just 0.69 micrograms per cubic meter to total PM₂.₅ each year from 2016 to 2020. This perspective misses the mark, however.

For example, a census tract close to the 2018 Camp Fire experienced an average wildfire PM₂.₅ concentration of 1.2 micrograms per cubic meter between 2006 to 2020. But the actual fire had a peak exposure of 310 micrograms per cubic meter – the world’s highest level that day.

Orange haze blanketing a city skyline, small silhouette of a person taking a photo by a streetlight
Classic estimates of average PM₂.₅ levels miss the peak exposure of wildfire .
Angela Weiss/AFP via Getty Images

Scientists want to better understand what such extreme exposures mean for long-term human health. Prior studies on long-term wildfire PM₂.₅ exposure focused mostly on people living close to a large fire, up years later to check on their health status. This misses any new exposures that took place between baseline and follow-up.

More recent studies have tracked long-term exposure to wildfire PM₂.₅ that changes over time. For example, researchers reported associations between wildfire PM₂.₅ exposure over two years and risk of death from cancer and any other cause in Brazil. This work again relied on long-term average exposure and did not directly capture extreme exposures from intermittent wildfire events. Because the study did not evaluate it, we do not know whether a specific pattern of long-term wildfire PM₂.₅ exposure was worse for health.

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Most days, people experience no wildfire PM₂.₅ exposure. Some days, wildfire exposure is intense. As of now, we do not know whether a few very bad days or many slightly bad days are riskier for health.

A new framework

How can we get more realistic estimates that capture the huge peaks in PM₂.₅ levels that people are exposed to during wildfires?

When thinking about the wildfire PM₂.₅ that people experience, exposure scientists – researchers who study contact between humans and harmful agents in the environment – consider frequency, duration and intensity. These interlocking factors help describe the body’s true exposure during a wildfire event.

In our recent study, our team proposed a framework for measuring long-term exposure to wildfire PM₂.₅ that incorporates the frequency, duration and intensity of wildfire events. We applied air quality models to California wildfire data from 2006 to 2020, deriving new metrics that capture a range of exposure types.

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Five heat maps of California paired with bar graphs of exposures over time
The researchers proposed five ways to measure long-term wildfire PM₂.₅ exposure.
Casey et al. 2024/PNAS, CC BY-NC-ND

One metric we devised is number of days with any wildfire PM₂.₅ exposure over a long-term period, which can identify even the smallest exposures. Another metric is average concentration of wildfire PM₂.₅ during the peak week of smoke levels over a long period, which highlights locations that experience the most extreme exposures. We also developed several other metrics that may be more useful, depending on what effects are being studied.

Interestingly, these metrics were quite correlated with one another, suggesting places with many days of at least some wildfire PM₂.₅ also had the highest levels overall. Although this can make it difficult to decide between different exposure patterns, the suitability of each metric depends in part on what health effects we are investigating.

Environmental injustice

We also assessed whether certain racial and ethnic groups experienced higher-than-average wildfire PM₂.₅ exposure and found that different groups faced the most exposure depending on the year.

Consider 2018 and 2020, two major wildfire years in California. The most exposed census tracts, by all metrics, were composed primarily of non-Hispanic white individuals in 2018 and Hispanic individuals in 2020. This makes sense, since non-Hispanic white people constitute about 41.6% and Hispanic people 36.4% of California’s population.

To understand whether other groups faced excess wildfire PM₂.₅ exposure, we used relative comparisons. This means we compared the true wildfire PM₂.₅ exposure experienced by each racial and ethnic group with what we would have expected if they were exposed to the average.

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We found that Indigenous communities had the most disproportionate exposure, experiencing 1.68 times more PM₂.₅ than expected. In comparison, non-Hispanic white Californians were 1.13 times more exposed to PM₂.₅ than expected, and multiracial Californians 1.09 times more exposed than expected.

Person holding child, sitting by two other people; in the foreground, a child approaches the camera
Better metrics for long-term PM2.5 exposure can help researchers better understand who’s most vulnerable to wildfire smoke.
Eric Thayer/Stringer via Getty Images News

Rural tribal lands had the highest mean wildfire PM₂.₅ concentrations – 0.83 micrograms per cubic meter – of any census tract in our study. A large portion of Native American people in California in rural areas, often with higher wildfire risk due to decades of poor forestry management, legal suppression of cultural burning practices that studies have shown to aid in reducing catastrophic wildfires. Recent state legislation has liability risks of cultural burning on Indigenous lands in California.

Understanding the drivers and health effects of high long-term exposure to wildfire PM₂.₅ among Native American and Alaska Native people can help address substantial health disparities between these groups and other Americans.The Conversation

Joan Casey, Associate Professor of Environmental and Occupational Health Sciences, University of Washington and Rachel Morello-Frosch, Professor of Environmental Science, Policy and Management and of Public Health, University of California, Berkeley

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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