plant lover, cookie monster, shoe fiend
20413 stories
·
20 followers

Newborn dies after mother drinks raw milk during pregnancy

1 Share

A newborn baby has died in New Mexico from a Listeria infection that state health officials say was likely contracted from raw (unpasteurized) milk that the baby’s mother drank during pregnancy.

In a news release Tuesday, officials warned people not to consume any raw dairy, highlighting that it can be teeming with a variety of pathogens. Those germs are especially dangerous to pregnant women, as well as young children, the elderly, and people with weakened immune systems.

“Raw milk can contain numerous disease-causing germs, including Listeria, which is bacteria that can cause miscarriage, stillbirth, preterm birth, or fatal infection in newborns, even if the mother is only mildly ill,” the New Mexico Department of Health said in the press release.

The health department noted that it could not definitively link the baby’s death to the raw milk the mother drank. But raw milk is notorious for transmitting Listeria monocytogenes bacterium. The Food and Drug Administration has a “Food Safety for Moms-to-Be” webpage about Listeria, in which it poses the question and answer: “How could I get listeriosis? You can get listeriosis by eating raw, unpasteurized milk and unpasteurized milk products… .”

Listeria is a particular danger during pregnancy. When exposed, pregnant people are 10 times more likely to develop a Listeria infection than other healthy adults because altered immune responses during pregnancy make it harder to fight off infections. Further, Listeria is one of a few pathogens that are able to cross the placental barrier and infect a developing fetus.

Read the whole story
sarcozona
51 minutes ago
reply
Epiphyte City
Share this story
Delete

‘Absolute hell’: Irishman with valid US work permit held by Ice since September – The Irish Times

1 Share

An Irishman living in the United States for more than 20 years has been held by US Immigration and Customs Enforcement (Ice) officials since being arrested last September.

Originally from Glenmore, Co Kilkenny, Seamus Culleton is married to a US citizen and owns a plastering business in the Boston area. He was arrested on September 9th, 2025, and has been in an Ice detention facility in Texas for nearly five months, despite having no criminal record, “not even a parking ticket”. In a phone interview from the facility, he said conditions there are “like a concentration camp, absolute hell”.

Culleton said he was carrying a Massachusetts driving licence and a valid work permit issued by the US government when he was pulled over by Ice on the way home from work in September. His work permit was issued as part of an application for a green card which he initiated in April 2025. He has a final interview remaining.

After his arrest, Culleton was allowed a brief phone call to his American wife Tiffany Smyth. She said she “broke down and cried. To know he was just taken, and he or I had no idea where they were taking him, was traumatising”.

For five days, Culleton was held in a small cell overflowing with other detainees, then flown to a Buffalo, New York, Ice facility.

In Buffalo he was interviewed by an Ice agent, who asked if he would sign a form agreeing to his deportation. Culleton said he refused, and instead ticked a box where detainees can state they wish to contest their arrest. He wrote down that his grounds for contesting were that he was married to a US citizen and had a valid work permit.

He was then flown to the Ice facility in El Paso, Texas.

He said he has been locked in the same large, cold and damp room for 4½ months with more than 70 men. He said detainees are constantly hungry because meals served at tables in the centre of the room offer only child-sized portions. Fights often break out over food, “even over those little child-sized juice containers”. Toilet areas are “filthy”.

He said there is little to do but lie on a bed all day. Most detainees do not speak any English. He said he has been allowed outside for air and exercise fewer than a dozen times in nearly five months. The atmosphere is full of “anxiety and depression”, he said.

Are you a fearful Irish immigrant in the US due to Ice raids? Tell us your storyOpens in new window ]

At a November bond hearing, a judge approved his release on a $4,000 bond, which his wife paid. When nothing further happened towards his release, they learned the US government had denied the bond, initially without explanation.

This is unusual, as is the length of time Culleton has been held. According to a recent New York Times article, US courts are being “deluged” with Ice detainee bond hearings, as “federal judges have found that the Trump administration has been ignoring longstanding legal interpretations that mandate the release of many people who are taken into immigration custody if they post a bond”. Most applicants are now being released on bond.

Culleton’s attorney, Ogor Winnie Okoye of BOS Legal Group in Massachusetts, then appealed the case to a federal court, where two Ice agents claimed that in Buffalo, Culleton had signed several documents agreeing to be deported.

Seamus Culleton with his wife Tiffany Smyth

However, he is adamant he did not and says the signatures are not his. “My whole life is here [in the US]. I worked so hard to build my business. My wife is here,” he said.

Although the judge noted numerous irregularities on Ice’s court documents, she ultimately sided with the agency.

Under US law, Culleton cannot appeal, though he would like the signatures to be examined by handwriting experts and believes a video of his interview with Ice in Buffalo would prove he refused to sign any deportation documents. He has no idea what will happen now and said the waiting is “psychological torture”. He says facility officials tried to get him to sign a deportation order last week, but he refused.

Use of Shannon Airport to deport Palestinians from US ‘reprehensible’Opens in new window ]

“You have one section of the government trying to deport me, and another trying to give me a green card,” he said.

Okoye said Culleton’s case is very unusual and that before the current Trump administration, a person in his situation – with a valid green card application made on the basis of marriage to a US citizen – would not have been detained and would almost certainly be granted the residency and employment permissions applied for.

She said Culleton was picked up on a random sweep for immigrants and continues to be held without charging documents, adding that the US government has acted in an “inept” and “capricious” manner.

In a case such as Culleton’s, the government has a “discretionary” option to simply release him but inexplicably has not done so, she said.

“Here’s a gentleman who is a model immigrant. He owned a successful business, he’s married to a US citizen,” and is properly going through the green card legal process, she said. It makes little sense that he would have agreed to be deported, she said.

Culleton’s wife Tiffany said she has endured “five months of heartbreak, stress, anxiety and anger”.

“I would never wish this on anyone or their family. I am still praying for a miracle every day.”

His sister Caroline Culleton said what concerns the family most “is Seamus and how he is coping, his physical and emotional state; the conditions that he is forced to bear are beyond comprehension.

“We are totally devastated by this situation, it’s a torment on a daily basis. We can only hope that this whole nightmare will come to an end very, very soon.”

A spokesperson for the Department of Foreign Affairs said the department is “aware of this case and is providing consular assistance. As with all consular cases, the department does not comment on the details of individual cases”.

Ice has been contacted for comment.

Are you an Irish person in the US concerned about Ice raids? Share your story below.

Read the whole story
sarcozona
3 hours ago
reply
Epiphyte City
Share this story
Delete

The Roles We Play

1 Share
Part of an Epigenetic Landscape from Waddington, C. H., The Strategy of the Genes (1957).

I had not felt any change, but to the people around me it was as if I had been profoundly transformed. Strangers held the door open. Old people stopped to chat. Women smiled at me, a new and novel experience.

I had transitioned, and rather suddenly at that. I was still getting used to it. One moment I was yet another tall, scruffy, bearded man – and in the next I had become a new-dad. Moving through the world with a sleeping baby strapped to my chest, I realized I was no longer being seen as a potential threat.

I was harmless now, benign even.

My role in society had changed. Before, I was cannon fodder. Privileged, taken seriously, of course, but also… kind of disposable? Young-ish men, prone to violence and goofy stunts, are meant to wander, to boldly explore – and should the ship start to sink be last in line for the lifeboats.

Now, I was a family man, a father. Fathers are expected to be stoic, strong, islands of stability. They’re meant to be providers, and for this reason they’re also given some slack. Your life gains meaning when you have a kid. Before a judge, you can beg for mercy: please be lenient, your honour, I have a wife and child to support!

In truth, no man can ever be an island: every man is a piece of society, and we all have a part to play in it. Living has always been a team sport. After I had a kid, I found myself thinking about how, as we change, as we move through our lifecycle, we move through different roles.

The expectations society places on us shifts, and our roles shift with them. Maiden, mother, crone is a classic archetype. At different points in your life you may be called upon to be a helpful son, a fun uncle, or a friendly grandfather. The roles to be filled are dictated by these societal expectations – by our culture, by our bodies, and by how we make or obtain the resources we need. Someone has to raise the children, and someone has to hunt for food.

These roles are a reflection of our environment. They’re a reflection of the jobs that have to be done to assure our collective survival. There’s work to be done, and that need exists independently of who, exactly, is around to fill it. In our culture, tasks are often perceived as gendered: men are warriors, labourers, or good at math, and women are healers, cooks, or good with feelings. But given a shortage of mothers or fathers, men will nurture and women will hunt.

These roles came to mind as I read through The Anthropology of Childhood by David F. Lancy. The book is a broad survey of what the ethnographic record has to say about children and childhood across different cultures, and I first read it during the pandemic. I felt a deep love for my spawn but, haggard and overwhelmed with childcare, I also felt certain that we weren’t meant to spend quite so much time directly supervising them.

In his book, Lancy confirmed my biases: in non-industrialized societies, and by extension for most of our history, children spend most of their time playing with other kids, loosely supervised by their older siblings or nearby kin. As I expected, tightly choreographed playdates and enrichment activities are a malaise of our modern era (and indeed that is Lancy’s explicit thesis). However, I was surprised to also discover how our attitudes and expectations towards children, and by extension the roles adults play, are downstream of our material circumstances, of our modes of production, and vary accordingly. This feels almost trite to type out, but it wasn’t obvious to me at the time.

Consider hunting. We perceive hunting to be very male-coded but actual hunter-gatherers don’t tend to have strictly gendered divisions of labour. Hunter-gatherer societies tended to be pretty egalitarian, and at any rate hunting only provided about half of their calories. Everyone did all sorts of jobs. Of course, only some people can bear children and breastfeed them, but beyond that there simply wasn’t a lot of room for specialization. Nomadic lifestyles can’t support high population densities. After all the gathering and hunting was done, someone had to cook, and clean, and cuddle, and carry things – and there just weren’t that many “someones” around.

After a few days, once your band or tribe has exhausted all of the local foraging, and scared off the herds, it’s time to pack up, and move on to the next site. When you move around all the time, there is only so much stuff you can carry with you. That includes, well, babies. Babies are high maintenance. They spend most of their first two years of life strapped to their mothers’ bodies, which is inconvenient. For this reason, hunter-gatherers tended to have long intervals between births, and therefore lower fertility rates overall. Those who could bear children were disinclined to have more than a few.

This changed with the invention of agriculture and later, and more importantly, the invention of property rights. As a household’s survival became tied to successfully farming a plot of land, everyone’s incentives shifted. Society shifted. Farming shackled both men and women. As we began to dominate the land, we began to dominate each other.

Now, it made sense to have more children. A lot more children. The more children you can boss around, the more free labour you can extract before they become adults; even a four year old can fetch water, or watch a goat. As children became economic assets, large families became desirable. Be fruitful, and multiply.

That much child-rearing places a sharp constraint on women. Babies are high maintenance! To have ten or twelve live births is to spend twenty years or more pregnant and breastfeeding. If not stuck at home, women are forced to at least be relatively near their nursing infants. As women’s primary economic contribution declined so did their independence and relative status, and in many traditional pastoralist or farming societies women and their children are treated harshly, like chattel.

That’s the patriarchy for you.

The more tightly the lives of women are prescribed, the more rigidly gender divisions are enforced. Girls and boys are often treated differently from birth. They’re dressed differently, and they perform different chores. In many cultures, upon puberty boys are sequestered from girls, and forced to undergo painful initiation rituals whose aim is to suppress any feminine traits they may have picked up from their mothers. The work we do, the business of living, is so central to our lives that it is an important component of our identities. From an early age, boys learn to say no to “women’s work” – and yet, the gender inflection of any given task changes from society to society.

Lancy tells us that, in the Philippines, female Agta foragers hunt with bows; that on Java, in Indonesia, everyone works on the rice crop but boys and girls are responsible for different steps in its lifecycle; that for the Akwete Igbo, in Nigeria, weaving is the responsibility of women – but that among the Baulè in Cote d’Ivoire it is the exclusive domain of men.

Isn’t that interesting? Lancy doesn’t dwell on this, but I see it as evidence that the roles we perform are, to a large extent, arbitrary. They depend on your context, your culture, your modes of production. They’re fluid, they change over time, as our culture evolves, as the climate changes, as our technology improves or our access to it is degraded. For most of human history, hunter-gathering was the only option and then, a few thousand years ago, most humans became subsistence farmers or shepherds. We’re in the midst of another shift now: sociologists call it the “great demographic transition”.

This transition is over-determined, but a stylized account goes like this. The industrial revolution created a huge demand for labour in or near cities, and the invention of fertilizers and machinery greatly reduced the demand for labour on farms. At first, children were employed in factories alongside adults but improvements in automation eventually eliminated the kinds of menial tasks they were best suited for. A century ago most people lived and worked in rural areas; in rich countries today agriculture employs less than 2% of the population.

Our incentives have shifted: you can’t exploit your children like we used to. There’s no longer an upside to having large families, and at any rate we no longer live enmeshed in large kin networks with easy, free access to childcare. These days, survival in our complex service economy is understood to require long periods of education and specialization. This is an over-simplification but as women came to have smaller families, they gained greater independence, social status, and legal and political rights.

There’s still work to be done, but the material conditions that enabled this pattern of domination, and that forced certain people into certain roles have disappeared. This shift happened so rapidly that our culture is still catching up. In my family, it’s a living memory: my grandparents were born to large families, and put to work at an early age. Back then, no one thought it necessary to teach my grandmother how to read.

Life changes. Life is always changing.

A couple years after I became a father, I underwent another change.

I had experienced a profound interior shift, but to the people around me it was as if nothing had changed at all. I walked through the world with a new perspective, a different point of view. At night, I would lie awake, and think about how I could act on this revelation.

It would soon reshuffle my life.

It happened like this: one day, I took a good look at myself in the mirror. On my way out the door, I glanced at my reflection. It was the late pandemic, so I wore a mask, neatly covering my beard, and my hair, which, after two years without hair cuts draped past my shoulders, hang loose. I didn’t quite recognize the person staring back at me. Whoa, I thought, what’s her deal?, and this feels cool.

This feeling came to haunt me. It felt good. It felt really cool. The more I thought about it, the more enticing it felt. I didn’t fully understand it; I wasn’t supposed to feel this way, but I couldn’t ignore it. Before long I found myself staring, fascinated – jealous, even – at the transition timelines people posted online, blown away by how dramatically some of them had changed. The implication began to sink in.

I knew about trans people. I had queer friends in high school, and I had taken the time to question my sexuality. In my twenties, after one of my best friends transitioned, I had spent some time interrogating gender as well. The “classical” medicalized transgender narrative – feeling trapped in the wrong body – did not resonate with me, and so that was easy to rule out. But I also remember thinking that the way cis people were supposed to feel about their gender, how I was supposed to feel as a man, didn’t really make sense either.

Talking with my friends, I reasoned, if you could take a pill, and temporarily wake up as a woman, I’d try that out, who wouldn’t? Haha, given the choice I might not switch back! (I literally said this once). But those pills don’t exist, so, whatever. No big deal. Being trans was something other people did, and good for them! I did not understand their struggle, but I supported their right to exist. I concluded that, since I liked women, and since I liked my body, that was that: I was just another cishet man.

I was obviously a man. What else could I be?

I’m tall, taller than most. I have a somewhat aggressive, conflict-oriented personality. I have male-coded interests, I work in a male-dominated industry, most of my friends are men. Of course, looking back – I would not have described it this way at the time – I felt vaguely alienated from men, as a class. As a boy, I was rather bad at performing masculinity. Occasionally, my family worried that I might be gay. I was supposed to like sports, to climb trees, to rustle and tussle, but instead I was a sensitive, bookish, homebody.

It had never occurred to me that being trans was something I could do. That this was something that I could get away with, that this opportunity existed for me, too. Now that opportunity stared at me, blinking in the mirror, and suddenly my heart filled with yearning. It was possible, and that felt exciting, that felt good. I didn’t want to be trans; for months, I was scared to admit it. But whatever was going on, I had to admit that I was not cis.

I moved carefully. I took things one step at a time. I followed that feeling of joy, what felt right. I came out to my partner as non-binary, which she took in stride. I shaved my beards, and changed my pronouns. I started wearing feminine clothing, and began painting my nails. I would later go on hormones, burn off my facial hair, and change my name, but long before I took those steps, long before anyone would ever confuse me for a woman, I crossed an invisible threshold: waiting in the checkout line at the thrift store, women began to smile, and give me compliments.

I recognized that moment: I was no longer being seen as a potential threat. I was harmless now, benign even.

To transition is to take a leap of faith. In that sense, it’s not unlike having a kid: both experiences are one way doors. You can’t truly know what it’s like until after you’ve stepped through, but by then it’s too late. Once you’ve crossed over, at least for those of us pursuing medical transition, you can’t really go back to how things were before.

To transition is also to become illegible. We exist, almost by definition, at least for a while, at the intersection of what are supposedly discrete categories. We’re hard to see, we’re lurking in the background (to say nothing of how we might hide to avoid being marginalized and persecuted). Our haters use this to portray us as a recent phenomena, a malaise of our modern era, but the truth is we have always been here.

If you look carefully at the historical record you’ll find us. Which is exactly what Kit Heyam sought to do in their book Before We Were Trans, a sweeping account of gender nonconforming people across history. Certainly, the slang is new. Words like “transgender” did not enter our lexicon until the late twentieth century. But that’s also because how we think of gender and sexuality changes and shifts over time, and across cultures. The parts we play in society, the roles that are available for us, are not static. They change as our environment changes, as we continue to change.

Heyam tells us of rulers in ancient Egypt and pre-colonial Angola who were assigned female at birth but ruled as male kings; that in Queen Elizabeth’s reign gender nonconforming dress became fashionable, as male courtiers vying for the her favour adopted feminine-coded clothes and accessories; that the Japanese, during the isolationist Edo period, developed a third gender called the Wakashū – who were later suppressed during the Meiji restoration.

There is great diversity in how people experience gender, so as a historian Heyam is very careful to avoid privileging any one of the many different motivations – personal, spiritual, social, economic – that can lead someone to queer their gender, and go against the expectations that were assigned to them at birth. But reading the book I was struck by two historical examples, both rooted in military conflicts, which I thought highlighted how our environment can create these roles, and how our internal sense of self, our identities, are a negotiation with our external context, and the opportunities that are available to us.

The first is the American Civil War, for which we have evidence that at least four hundred people who were assigned female at birth enlisted as soldiers. Heyam takes great pains to point out that performing a differently-gendered task is not the same as changing one’s gender (today we accept that women can be soldiers, too), and that we will never know what was going through these people’s minds. But we do know that they really had to commit to the bit.

Consider the reality of being a soldier in 1861. These people had to endure unspeakable privations and live, eat, sleep and defecate, with no privacy, among men. If discovered, at any point, these people would be summarily discharged and prevented from serving; and so they had to look like men, behave like men, and pass as men – pass flawlessly – for the entire duration of their tour of duty. For most people, this takes conscious effort. In a very real sense, for years on end, these people became men.

Consider also the social impact the war had on their communities. The American Civil War was a totalizing conflict, and would come to involve up to a third of military-aged men in the North, and a majority of military-aged men in the South. Their society had created a great demand for men and so, in a very real sense, men stepped up to fill it.

The other example happened during the First World War. At the beginning of the war, the British government rounded up every military-aged man living in the United Kingdom who was also a citizen of an enemy country, and imprisoned them. Most of them – around 20,000 – were sent to the Isle of Man (yes, really), and forced to live in a shambolic interment camp built for this purpose.

Overnight, this created a very weird place. The camp was a crowded and stressful environment, with little privacy, and to avoid going crazy from inactivity the camp internees became highly organized. Before long, the camp featured sports leagues, educational classes, newspapers and orchestras. But the centre of life in the camp, the activity that most allowed them to escape their harsh circumstance, was the theatre. At its peak, the Knockaloe Interment Camp had a bustling scene, with twenty theatres. By the time it closed, internees had performed over 1,500 shows, and hundreds had worked as actors, stagehands, technicians, and costume-makers.

A lot of plays had female parts, so they had to make do. In theatre, this is not unusual; historically, women were often banned from being performers. The men sewed dresses, and improvised wigs, and every night stepped on the stage and played women. They played women not for comedic effect, but seriously, intentionally: they sought to play women convincingly.

After a while, a funny thing started to happen: at the end of the night, when the show was over, some of the actors kept wearing dresses, and wigs, and female names. They started living full-time as women – and the camp accepted them as such. They were referred to by female names, they attracted performance reviews that treated them as women, and received fan mail and devoted followers that saw them as women.

In doing so, the camp stopped being an “all-male” environment. Which, judging by the letters and diaries that have survived, seems to have been important for everybody. With all the isolation and confinement of their interment, having women around provided a sense of normalcy. The camp’s theatres needed someone to play female roles, and to do so convincingly, and so did the camp as a whole, to help safeguard everyone’s mental health.

Their society, as it were, had a great demand for women – and so women stepped up to play the part.

These stories resonated with me. Growing up, I had spent a lot of time trying to be tougher, to show the world my sharp spiky quills. It was only after I saw that I had the opportunity, that I understood that I too could play this part, that I realized that I might want to live differently. That I wanted to care more, to show the world my soft underbelly instead.

I just could not conceive of it. It kind of… wasn’t an option, back when I was a teenager. How we think of gender and sexuality are always shifting; words and concepts like “non-binary” were not available to me twenty years ago. You either had a deep-seated conviction that something was terribly wrong, or you didn’t. There was no in between. There was no room for the idea that I might not hate being masculine, but experience far more joy presenting feminine.

My mental model of the world did not include this as an option.

Our mental models are cognitive tools. We need them. They’re necessary simplifications of the real world. All models are wrong, but some are useful. A good model can be used to improve our understanding, and make accurate predictions. A bad model, though, can prevent you from seeing the world as it really is. Our ideas about the world can be so deeply rooted that they can blind us from seeing what is right in front of our nose.

This can sound abstract, or loftily high-minded, but excessively wrong models can have disastrous consequences. When we encounter something in the real world that doesn’t fit mental our model, humans often find it easier to to change reality than the ideas in our heads.

Take sex, for example. We’re taught that sex, biological sex, is a binary with two clear and unequivocal categories. Some people produce big gametes, others produce little gametes, and that’s it, the end. Except… that’s not the whole story.

In reality, sex is more like a spectrum with a bimodal distribution. The closer we look, the fuzzier the boundary between categories gets. Some people really do have a combination of sexual characteristics, and today we use the label “intersex” to describe them. There are dozens of different developmental pathways that can lead to someone having intersex traits.

Sometimes this manifests as ambiguous genitalia, and is immediately obvious. Sometimes, this only becomes apparent at puberty, when a person begins developing secondary sexual characteristics that don’t match an assumption made at birth. Some people produce children and live most of their lives before accidentally discovering, in late middle age, that they have an atypical complement of gonads. If you’ve never been karyotyped, you might never know for sure.

Unfortunately, the history of intersex people is one of intense stigma, discrimination, and violence. Medical professionals have a long and sordid history of mutilating intersex children that did not match their mental model of the world. Sometimes in the name of curiosity, but more often as part of an effort to force their bodies to conform, intersex children have been and continue to be subjected to medically unnecessary surgeries, without their consent, that are intended to remove “excess” tissue, or to correct a “disorder”.

These days, people are often encouraged to imagine gender as a spectrum. Cartoonishly, you could picture an axis with two arrows pointing in opposite directions. At one end, there is Yosemite Sam, and at the other Jessica Rabbit, resplendent in high femme glamour. In this model, you could imagine a steady progression of genders, as femme leads to butch, butch leads to twink, twink leads to bear, and so on. However, the butchiest butch is more masculine than the twinkiest twink; the term “high femme” implies the existence of “low femme”. We can improve this model.

By contrast, I like to think of gender as a vector in high dimensional space. Imagine that you could encode the totality of someone’s presentation – the shape and abilities of their body, how they dress and behave, how they are seen by others, how they would like to be seen – as an n-tuple, and that we could plot everyone’s presentation. These points would cluster in particular places. If we drew a line around these clusters, and folded it into something we could perceive, the manifolds might look like mountainous ranges with peaks and valleys.

Gender consists of this landscape. As we grow up, and we age, and we change, we traverse it. Think of it like hiking down a mountain. It’s not a linear process. You can get trapped in local minima; sometimes you have to gain altitude in order to keep descending. The journey between two peaks may seem distant, and far apart, but they’re actually only separated by a narrow gap. All it takes is a leap of faith.

I hope that makes sense; I know it’s a bad model.

The way I see it, everyone transitions. Everyone is transitioning. Everyone experiences different genders, or forms or stages of gender. We’re all moving through different roles. We are all on a journey. Some of us roll downhill, and some of us don’t mind going for a long hike.

The only constant is change, and all of us are always changing, all of the time. This is a cliché, but it’s true. To live is to change, for only death is immutable. You are not the man your wife married all those years ago (that guy had more hair). Your closet is full of clothes you don’t (or can’t) wear anymore. You used to stay up all night, and these days you like to be in bed by ten. You’re a soccer mom now. You got a haircut. You got swole. You changed your name.

Yesterday you were a baby, and tomorrow you will be a babushka.

# 2026-02-08

Read the whole story
sarcozona
8 hours ago
reply
Epiphyte City
Share this story
Delete

Washington imposes 'terrorist-grade sanctions' on Francesca Albanese, ICC judges

1 Share
Read the whole story
sarcozona
1 day ago
reply
Epiphyte City
Share this story
Delete

Fighting Cancer, Facing Deportation and Denied Health Care | The Tyee

1 Comment

Francisco Barahona is recovering at home with a shattered arm after Surrey Memorial Hospital, he says, refused to treat him on the weekend because of unpaid past bills.

The 53-year-old’s health has been deteriorating over the past three years as a cancer hollowed out his bones and he couldn’t afford health care.

Now the government is deporting him.

Barahona has lived in Canada for 15 years but has not yet found a way to get immigration status that would grant him lasting health-care coverage.

When he was healthy he could get temporary Medical Services Plan or MSP coverage under a work permit.

But once he got sick he could no longer work.

Now Barahona is trapped. He is too sick to survive a deportation flight to El Salvador and has no way of quickly getting approved for health-care coverage, said Yanni Nicolidakis-Mustafa, an immigration lawyer with Edelmann & Co. Law Offices.

Nicolidakis-Mustafa is working with Barahona to help delay his deportation and apply for immigration status in the hopes of getting him covered by B.C.’s MSP and starting cancer treatment.

Nicolidakis-Mustafa said Barahona went to Surrey Memorial Hospital on Saturday because the cancer had eaten a hole through the skin and bone of his arm.

But the hospital refused to treat Barahona unless he paid his previous bills, Nicolidakis-Mustafa said.

The Canada Border Services Agency, or CBSA, has said Barahona will be deported next Wednesday, he said, and the agency is looking into whether a team of doctors with him would reduce the health risks of the flight.

“We have been fighting his removal from Canada and just trying to convey to CBSA that this is somebody who is quite literally falling apart and needs palliative treatment. He is not safe to fly. Putting him on a plane will very likely result in his death,” Nicolidakis-Mustafa said.

“Even now he can’t really walk because his legs will break,” he added. “Coughing could break bones.”

Barahona’s situation is not unique.

Dr. Kelly Lau is medical director of the urgent and primary care centre at the Reach Community Health Centre in East Vancouver, which is known for providing culturally sensitive care for Indigenous people, immigrants and refugees.

She said patients regularly come in suffering from cancer or other diseases and are not able to access the care they need because they don’t have MSP coverage.

Lau said Barahona has accessed Reach several times, but an urgent and primary care clinic is limited in the care it can provide. For example, it cannot do surgeries or offer cancer care.

Lau said Barahona’s case is “not a one-off.”

“There’s a lot of ways in which people fall through the cracks of our medical system,” she said.

Immigrants with work visas or student visas can easily lose their coverage if, for example, they get injured and are no longer able to work or study, Lau said.

“We see a lot of people with work-related injuries who come in, we put in a WorkSafeBC claim and then they get fired from their job,” she said.

Lau said undocumented people want to work and contribute, but there are barriers to being able to apply for more secure status.

Nicolidakis-Mustafa echoed this. People who are applying for immigration from within Canada can apply for humanitarian and compassionate consideration, but that process is currently estimated to take more than 10 years, he said.

When people aren’t covered by MSP they have to choose between paying out of pocket, returning to their home country for treatment or not getting treatment.

Nicolidakis-Mustafa said that when undocumented people go to the hospital they are being identified by CBSA, which has the right to deport undocumented people even if they are accessing health care.

The Canada Health Act says health care is supposed to “protect, promote and restore the physical and mental well-being of residents of Canada and to facilitate reasonable access to health services without financial or other barriers.”

Lau said she’s seen patients suffering from breast cancer, prostate cancer, lung cancer and appendicitis who haven’t been able to access or afford the care they need.

‘I am very afraid’

Barahona spoke with The Tyee on Saturday, shortly before he broke his arm.

The interview was translated with the help of Byron Cruz, a longtime advocate for migrant workers and a member of the Sanctuary Health collective, a grassroots organization that helps people access health care, regardless of immigration status or documentation.

A sick-looking man stands in a sparsely furnished apartment. He is supported by a walker and wears a blue hoodie and pyjama bottoms. Francisco Barahona says the advancing cancer and imminent deportation to El Salvador have left him ‘very afraid.’ Photo supplied.

Cruz has been advocating on behalf of Barahona and helping him get admitted to hospitals and access surgeries.

Sanctuary Health is also helping Barahona by organizing a crowdfunding campaign to help cover medical and legal bills, as well as his rent.

In 2023 Barahona was diagnosed with multiple myeloma, a cancer that can weaken bones.

He said he is afraid of being deported and flown back to El Salvador because he is at high risk of developing blood clots after several surgeries to repair broken bones. He said his femur (thigh bone), his tibia (shin bone), his coccyx (tailbone), a vertebra in his mid-back, and his arm have broken since his cancer diagnosis.

He is also in “constant pain.” He has a prescription but it only reduces the pain level.

Barahona said he was first diagnosed with cancer in 2023 while he was covered by MSP through a work permit.

At that time, he said, he started treatment with BC Cancer, which withdrew his stem cells. But his MSP coverage ran out days before he was going to have stem cells injected back into him as part of the treatment, Nicolidakis-Mustafa said.

The treatment was stopped and he was discharged home with a prescription to manage the pain, he said, adding Barahona has not received cancer treatment since.

Barahona also wouldn’t be able to access the stem cell treatment in El Salvador, he said.

“So yes, I am afraid. I am very afraid,” he said.

Barahona told The Tyee BC Cancer has contacted him several times over the last two years. But he has been told his treatment will be approved only once he has MSP.

The Tyee contacted BC Cancer to ask what its policies are when people don’t have MSP. We were referred to the Health Ministry, which said in an email that “BC Cancer does not refuse treatment if someone is unable to pay” and that BC Cancer will “work directly with the patient to find a solution if they are unable to make their payments.”

If a patient is not covered by MSP, BC Cancer may ask them to pay $5,000 before starting treatment and bill the rest of the treatment cost incrementally, the ministry added.

However, Lau said that’s not her experience.

She said it’s really hard to get specialists to work with patients without status, even if the patent has lived in B.C. for decades and, for example, recently lost their status because they are sick and can’t work.

“We’ve called and asked BC Cancer for support and to see these patients, and they will not provide care if they don’t have insurance and cannot pay,” she said.

‘Hospitals are not supposed to call immigration’

On Sept. 23, 2025, Barahona went to Langley Memorial Hospital’s emergency department. His leg was extremely painful and he later learned it was because a bone leg had shattered, likely due to the cancer.

While he was waiting in the emergency department he was approached by a CBSA officer. Barahona had been flagged as a person without immigration status, which meant the officer had the right to start the deportation process — even while Barahona waited to be admitted to hospital.

“There’s no other way for CBSA to find out he was there unless the hospital called them,” Nicolidakis-Mustafa said.

It used to be common for Fraser Health to call immigration services when undocumented people sought care; it referred patients to the CBSA about 500 times from 2014 to 2015.

After Fraser Health faced public criticism for its actions, it revised its policy in 2016 to not contact CBSA without patient permission.

In an emailed statement to The Tyee, Fraser Health said it was still not proactively sharing information with the CBSA. When The Tyee asked how a CBSA officer knew where to find Barahona, or if Fraser Health would be reviewing what happened, the spokesperson said Fraser Health would not comment further.

Lau said she knows of three other recent cases where Fraser Health called immigration when people sought health care.

CBSA officers are allowed to arrest anyone who is in violation of the Immigration and Refugee Protection Act, the agency told The Tyee in an emailed statement.

Essentially, anyone who is in Canada without status can be arrested and deported as quickly as possible. However, CBSA officers are not supposed to get in the way of people receiving health care.

“The CBSA will not remove an individual from the hospital until they have been medically discharged,” the emailed statement continued.

Cruz said he’s frustrated that Fraser Health is once again reporting patients to immigration services.

“Hospitals are not supposed to call immigration,” he said. “They’re supposed to keep a patient’s privacy and confidentiality.”

Barahona said he wanted to move to Canada to be with family and, literally, build a better future. He works in construction and has helped build hospitals and donated his time to help people repair their roofs, even if they couldn’t pay him.

“I came here to work and show we are good people — not to damage anyone,” he told The Tyee.

He said a CBSA officer said he was a “nightmare for this country.”

“It hurt a lot when he said that,” Barahona said.

He also feels abandoned by the health-care system.

By withholding cancer treatment, “they are damaging a person, a family,” he said. “Canada is characterized as a humanitarian place. But I feel alone, like there’s cold water on my back.”

Cruz said Canada needs to revise its health-care policies to improve access for residents and payment options for people not covered by provincial health plans.

If someone is working in B.C. they deserve to be given MSP coverage on arrival, he said.

For everyone else there should be a “humane way of paying,” with a sliding scale of fees based on a person’s income.

image atom
How Medical Bills Can Slam a Newcomer to BC
read more

“We’re not speaking about tourists,” he said. “We’re talking about people who are working here, whose refugee claim was denied and now they’re trying to get status another way. Or a woman whose immigration was sponsored by her partner, but the partner gets abusive and when she leaves him he cancels her insurance. What then?”

Barahona said his health-care bills currently add up to about $10,000.

That bill could increase sharply if hospitals decide to charge him a per-night fee, which can be up to $6,000 per night, Cruz said. Barahona has recently spent months in hospitals.

Cruz said he has worked with undocumented people who were charged $75,000 and $300,000 in health-care fees.

Barahona said he is paying what he can and getting small donations from his community.

“The medical system failed me,” Barahona said. “When I went into hospital, when things started, I had MSP. They started the process but didn’t finish it. What has happened since is a consequence of them not finishing the procedure.”  [Tyee]

Read the whole story
sarcozona
4 days ago
reply
We killed this man
Epiphyte City
Share this story
Delete

Global Assessment of COVID-19 Mortality Displacement From 2020 to 2024

1 Share

For most of the last century, high-income countries experienced annual declines in age-specific mortality rates.1 However, the COVID-19 pandemic created upturns in mortality rates between 2020 and 2021 in many high-income countries.2-4 In the wake of the pandemic, some mortality rates have resumed their pre–COVID-19 pattern of annual declines. However, a portion of the mortality decline observed immediately after a pandemic can be explained by mortality displacement (also referred to as the mortality harvesting effect, harvesting, and the harvesting effect). This occurs when the pandemic causes frail or high-risk individuals, who would have died in the near future, to die earlier than they otherwise would have. As a result, the months or years following the crisis may show fewer deaths than expected because many of the most at-risk individuals had already died prematurely.5 This demographic phenomenon has been documented after previous health crises, including epidemics, pandemics, severe influenza seasons, and heat waves, and could therefore be plausibly observed following COVID-19 as well.6-8

In parallel, other factors may drive postpandemic mortality decline. Rapid scale-up of vaccination campaigns reduced COVID-19–related mortality and helped prevent future waves.9,10 On average, easing COVID-19 mobility and cross-border restrictions coincided with a rebound in trade and travel, contributing to a partial economic recovery and some improvements in employment and poverty, which are key social determinants of health, although these gains were highly unequal between and within countries.11-17 Together, these multiple processes shape observed mortality trends after a pandemic and underscore the need to distinguish the temporary mortality displacement from longer-term recovery. Disentangling the contribution of mortality displacement from other population health drivers is crucial to accurately assess the pandemic’s true burden and to guide public health strategies during system recovery.18

There are various methods to calculate excess mortality.19-23 Overall, these studies affirm the general approach to estimate excess deaths relative to a projected baseline of expected deaths before the pandemic. While commonly used during periods of elevated mortality, the method also applies when observed deaths fall below baseline, often termed negative excess or deficit mortality. In both cases, estimates are benchmarked against the counterfactual of uninterrupted prepandemic trends. However, a critical gap involves quantifying the extent of the mortality displacement on mortality across different age groups immediately following the COVID-19 pandemic. Prior studies focusing on excess mortality have a limited scope, concentrating on the pandemic period and not covering the postpandemic recovery phase.

Bor et al22 provided insights into postpandemic mortality declines in the US, and Riou et al24 provided findings of those in Switzerland; however, a multicountry analysis of the mortality displacement across age groups, to our knowledge, is largely absent. Thus, this study systematically measured the mortality displacement using high-quality data available by country, year, age, and sex following the COVID-19 pandemic.

In this cross-sectional study, we used the Short-Term Mortality Fluctuations dataset, which compiles harmonized weekly all-cause mortality statistics from national vital registration systems of multiple countries. We extracted weekly mortality data covering the period from January 2015 through December 2024 for 34 selected countries. Most included countries were classified as high-income economies by the World Bank; Bulgaria (newly reclassified to high-income in fiscal year 2025) was included because of its reliable and harmonized mortality surveillance system.25 This study represents a secondary analysis of publicly available aggregate data from the Human Mortality Database, without patient or public involvement; as such, this study did not require institutional review board approval or informed patient consent, in accordance with 45 CFR §46. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Mortality data were stratified by country, year, week, sex, and age groups (0-14 years, 15-64 years, 65-74 years, 75-84 years, and ≥85 years). The Short-Term Mortality Fluctuations dataset provided both weekly death counts and weekly death rates, with death rates calculated as the number of deaths occurring each week divided by the annual population exposure, normalized to weekly units. Data for each year typically included 52 weeks; week 53 was excluded to ensure comparability across years. Countries included in the analysis were categorized into 5 quintiles based on the severity of their cumulative excess mortality from January 2020 to December 2022. Mortality displacement was hypothesized to have been highest in countries with the highest January 2020 to December 2022 excess mortality and negligible in countries with the lowest January 2020 to December 2022 mortality. Quintiles were defined from the lowest excess mortality (quintile 1) to the highest excess mortality (quintile 5), providing a structured comparison of pandemic outcomes across countries.

We defined 2020 to 2022 (or 2020 to 2023) as the pandemic period, and 2023 to 2024 (or only 2024) as the postpandemic period; this definition was applied consistently in all analyses. To illustrate the analytic concept, a stylized mortality index for 2015 to 2025 under 2 counterfactual scenarios, 1 with mortality displacement (Figure, A) and 1 without (Figure, B), is shown. In both panels, the prepandemic trend from 2015 to 2019 is extrapolated and represents the number of deaths expected had the pandemic not occurred. A temporary spike in deaths from 2020 to 2022 is shown in the Figure. In the scenario without mortality displacement, deaths returned to the projected trend from 2023 onward; in the scenario with mortality displacement, deaths fell below the projected trend in 2023 and 2024, creating a visible gap in the prepandemic trend.

Figure.  Stylized Mortality Index Under 2 Counterfactual Scenarios, 2015 to 2025

The black lines extrapolate the prepandemic trend from 2015 to 2019 and represent the number of deaths expected had the pandemic not occurred. In the mortality displacement scenario, the deficit below the line indicates mortality displacement.

In our empirical analysis, we treated this gap as an association with mortality displacement and quantified it in 3 steps. First, for each country and year, we project the expected number of deaths in 2020 to 2024 by extending the 2015-to-2019 trend. Second, we summed the excess deaths from 2020 to 2022 (or from 2020 to 2023), defined as deaths above this expected level. Third, we summed the mortality deficit during 2023 to 2024 (or only 2024), defined as deaths falling below the expected level. The mortality displacement fraction was then interpreted as the share of the initial excess deaths in 2020 to 2022 (or 2020 to 2023) that was paid back by the mortality deficit in 2023 to 2024 or only 2024 (ie, the ratio of the postpandemic deficit to the pandemic-period excess). The methodologic details for each step are described.

The expected weekly deaths were estimated with an overdispersed Poisson generalized linear model using historical mortality data from the prepandemic period (2015-2019). For each country (c), sex (s), and age group (a), we fitted prepandemic data (week 1, 2015, to week 52, 2019) as follows in Equation 119,21,26:

Image description not available. (opens in new tab)

in which μc,s,a(t) is the expected number of deaths at week t; Ec,s,a(t) is the weekly person-weeks exposure used as an offset; and β5,c,s,a(year) adjusted for temporal trend. Two harmonic pairs captured the dominant annual and semiannual seasonal components. Robust SEs were used to accommodate overdispersion. Based on each country’s parameters for age and sex, we projected death counts for week 1, 2020, to week 52, 2024. Afterward, we compared the observed weekly number of deaths in 2020 to 2024 with the expected number of weekly deaths for each country, stratified by age and sex, to estimate weekly excess mortality. Specifically, weekly excess deaths for 2020 to 2024 were calculated in Equation 2: excessc,s,a(t) = dc,s,a(t) − μ̂c,s,a(t), in which μ̂c,s,a(t) is the fitted value from Equation 1. Annual excess mortality rates per 100 000 Rc,s,a,y were obtained by Equation 3:

Image description not available. (opens in new tab)

Given the linear trend in expected mortality derived from 2015 to 2019, having an excess mortality rate of 0 in 2023 or 2024 would imply exactly resuming the established pre–COVID-19 mortality decline. If a country did better than achieving 0 excess mortality in 2023 or 2024, then it signals the potential presence of mortality displacement. It is also possible that a country superseded its pre–COVID-19 population health performance by improving the overall health system. However, if the country was in a high quintile for COVID-19–era excess deaths, then mortality displacement became a more plausible explanation. To isolate mortality displacement from chance, we defined mortality displacement as a sustained cumulative postpandemic mortality deficit in 2023 and 2024 or in 2024, conditional on a positive excess in 2020 to 2022 (or 2020 to 2023). Deficits were assessed against the bootstrapped 95% prediction interval of the prepandemic baseline. For each qualifying stratum, we then computed the cumulative excess mortality during the initial surge (2020-2022 or 2020-2023) and the subsequent deficit (2023-2024 or 2024). The mortality displacement fraction20 was finally expressed in Equation 4:

Image description not available. (opens in new tab)

or Equation 5:

Image description not available. (opens in new tab)

To obtain 95% CIs for both Rc,s,a,y, we performed nonparametric bootstrapping with 1000 resamples. For each bootstrap replicate, we resampled the weekly mortality data with replacement within each country-sex-age stratum. For each resample, we reestimated the expected deaths by refitting Equation 1, recomputing excess deaths and the excess death rate in Equations 2 and 3, and recalculating the mortality displacement ratio in Equations 4 and 5. The 2.5 percentile of the resulting bootstrap distributions was taken as the lower 95% CI bound and the 97.5 percentile as the upper 95% CI bound.

We conducted 3 prespecified sensitivity analyses to evaluate robustness: (1) refitting the baseline model using a negative binomial specification, (2) fitting a Poisson generalized estimating equation with an autoregressive (AR[1]) working correlation structure to account for serial correlation in weekly deaths, and (3) repeating all models using broader age bands (0-64 years, 65-74 years, 75-84 years, and ≥85 years) to reduce sparsity in age-specific strata. To address the risk of an inflated type I error from multiple hypothesis testing, we used the Benjamini-Hochberg adjustment to control the false discovery rate (q ≤ 0.05). We compared the main results with the unadjusted P values to assess the outcome of the adjustment. A 2-sided P < .05 was considered statistically significant. All calculations were performed using Stata, version 18.5 (StataCorp LLC).

Our analysis included 352 182 284 deaths in 34 countries from 2015 to 2024; the median (IQR) population composition in 2020 was 50.75% (50.33%-51.44%) females, 49.25% (48.56%-49.67%) males, and 19.64% (17.74%-20.64%) aged 65 years or older. Based on Equation 1, we found that in the prepandemic period (2015-2019), 13 countries (Australia, Iceland, Israel, Luxembourg, Norway, New Zealand, Canada, Switzerland, Sweden, Belgium, Spain, England and Wales, and Austria) exhibited significant negative mortality trends (eg, Australia: rate, −0.0212 [95% CI, −0.0234 to −0.0190]; P <.001), 8 (Portugal, Slovenia, Czechia, Greece, Bulgaria, Croatia, Latvia, and Poland) showed significant positive trends (eg, Portugal: rate, 0.0096 [95% CI, 0.0033 to 0.0160]; P = .003), and the remaining 13 were fairly similar over time (eTable 3 in Supplement 1). We defined baselines as well-estimated if they were significant.

The cumulative excess death rate per 100 000 (based on mean population) for 2020 to 2022 compared with expected deaths based on 2015-to-2019 mortality is provided in Table 1 and for 2020 to 2024 in Table 2. Positive values indicate more deaths than expected; negative values indicate fewer deaths than expected. The 34 countries were grouped into 5 quintiles according to their cumulative excess death rate in 2020 to 2022 (CEDR20-22). In the lowest quintile, the CEDR20-22 values were −21 (95% CI, −35 to −6) per 100 000 for New Zealand, 3 (95% CI, −37 to 42) per 100 000 for Luxembourg, 26 (95% CI, 6 to 46) per 100 000 for Denmark, 81 (95% CI, 68 to 94) per 100 000 for Australia, 82 (95% CI, 62 to 103) per 100 000 for Norway, 108 (95% CI, 93 to 124) for per 100 000 Israel, and 113 (95% CI, 62 to 165) per 100 000 for Iceland, with all values except Luxembourg achieving statistical significance. New Zealand’s value of −21 indicates that it was able to sustain better-than-expected mortality declines even during the 2020-to-2022 period. New Zealand’s reduction persisted through 2020 to 2024 (−35), and Denmark (−21) and Luxembourg (−100) also recorded reductions. New Zealand pulled off statistically significantly negative cumulative excess mortality from 2020 to 2024 in all age groups except ages 75 to 84 years. Notably, Denmark and Luxembourg, with the lowest COVID-19–era mortality, exhibited significant cumulative negative excess mortality among individuals aged 85 years or older from 2020 to 2024. The remaining quintile 1 countries, namely Denmark, Australia, Norway, Israel, and Iceland, failed to sustain negative values and showed statistically significant excess mortality from 2020 to 2024. Conversely, CEDR20-22 values for Poland were 520 (95% CI, 457 to 584) per 100 000; Latvia, 540 (95% CI, 474 to 606) per 100 000; Slovakia, 549 (95% CI, 487 to 611) per 100 000; Croatia, 600 (95% CI, 532 to 668) per 100 000; Lithuania, 814 (95% CI, 740 to 887) per 100 000; and Bulgaria, 1070 (95% CI, 955 to 1186) per 100 000.

Table 1.  Cumulative Excess Death Rate Per 100 000 Population From 2020 to 2022 by Country Compared With Expected Deaths Based on 2015 to 2019 Mortality

Excess mortality quintileaCountryCumulative excess deaths per 100 000 (95% CI)b
1New Zealand−21 (−35 to −6)
1Luxembourg3 (−37 to 42)
1Denmark26 (6 to 46)
1Australia81 (68 to 94)
1Norway82 (62 to 103)
1Israel108 (93 to 124)
1Iceland113 (62 to 165)
2Sweden131 (105 to 156)
2Canada142 (128 to 157)
2Finland167 (140 to 194)
2France201 (171 to 231)
2Germany212 (180 to 244)
2Switzerland213 (178 to 249)
2Netherlands230 (199 to 261)
3Northern Ireland235 (196 to 273)
3Belgium258 (212 to 304)
3England and Wales268 (226 to 311)
3Portugal271 (225 to 318)
3Scotland280 (239 to 321)
3Spain301 (251 to 350)
3Slovenia319 (265 to 373)
4Austria321 (283 to 359)
4Greece355 (315 to 395)
4Estonia358 (313 to 403)
4United States405 (375 to 434)
4Italy443 (393 to 493)
4Hungary456 (398 to 514)
4Czechia514 (451 to 577)
5Poland520 (457 to 584)
5Latvia540 (474 to 606)
5Slovakia549 (487 to 611)
5Croatia600 (532 to 668)
5Lithuania814 (740 to 887)
5Bulgaria1070 (955 to 1186)

Table 2.  Cumulative Excess Death Rate Per 100 000 Population From 2020 to 2024 by Country Compared With Expected Deaths Based on 2015 to 2019 Mortality

Excess mortality quintileaCountryCumulative excess deaths per 100 000 (95% CI)b
1Luxembourg−100 (−150 to −49)
1New Zealand−35 (−53 to −16)
1Denmark−21 (−47 to 6)
1Israel126 (105 to 146)
1Norway152 (128 to 177)
1Australia158 (142 to 175)
2Sweden169 (140 to 199)
2Canada172 (155 to 189)
2France225 (194 to 257)
2Iceland233 (168 to 299)
2Finland259 (225 to 293)
2Germany261 (224 to 298)
2Portugal297 (247 to 348)
2Switzerland301 (262 to 340)
3Belgium306 (257 to 354)
3Greece323 (278 to 367)
3Slovenia330 (273 to 388)
3Netherlands341 (307 to 375)
3Estonia364 (310 to 419)
3New England and Wales366 (319 to 412)
3Spain386 (332 to 439)
4Northern Ireland394 (344 to 443)
4Scotland404 (353 to 455)
4Poland413 (351 to 475)
4Latvia429 (358 to 501)
4United States431 (401 to 462)
4Austria446 (401 to 490)
4Hungary474 (412 to 535)
5Italy529 (477 to 581)
5Slovakia549 (487 to 610)
5Czechia578 (513 to 642)
5Croatia703 (630 to 776)
5Lithuania1028 (951 to 1105)
5Bulgaria1127 (1009 to 1245)

The cumulative excess deaths per 100 000 population from 2020 to 2024 by country and age group, relative to expected deaths based on 2015-to-2019 mortality is shown in Table 3. Notably, 22 of 34 countries recorded negative or insignificantly positive cumulative excess mortality from 2020 to 2024 for those aged 0 to 14 years. However, only 6 countries recorded reduced excess mortality for individuals aged older than 14 years, among which 4 of 6 countries were in quintile 1, and the remaining 2 countries had statistically insignificant reductions. In all countries, older age groups recorded much greater cumulative excess mortality compared with younger age groups across the entire 2020-to-2024 period.

Table 3.  Cumulative Excess Deaths Per 100 000 Population From 2020 to 2024 by Country and Age Group Relative to Expected Deaths Based on 2015 to 2019 Mortality

Excess mortality quintileaCountryCumulative excess deaths per 100 000 (95% CI)b
Ages 0-14 yAges 15-64 yAges 65-74 yAges 75-84 yAges ≥85 y
1Australia0 (−1 to 2)31 (26 to 37)217 (179 to 254)1040 (923 to 1157)2813 (2320 to 3306)
1Denmark9 (0 to 17)10 (−0 to 21)−155 (−233 to −77)563 (372 to 754)−2388 (−3173 to −1604)
1Iceland90 (61 to 119)−39 (−75 to −3)1247 (969 to 1525)326 (−381 to 1033)6541 (4121 to 8962)
1Israel4 (−3 to 11)55 (30 to 80)470 (397 to 544)343 (142 to 543)2991 (2290 to 3692)
1Luxembourg7 (−22 to 36)−108 (−136 to −80)71 (−166 to 309)−147 (−670 to 376)−1268 (−2859 to 323)
1Norway−4 (−10 to 3)32 (23 to 42)98 (21 to 175)840 (636 to 1043)3214 (2425 to 4002)
1New Zealand−20 (−23 to −18)−26 (−38 to −15)−5 (−68 to 58)404 (247 to 560)−1907 (−2653 to −1161)
2Canada10 (8 to 12)79 (70 to 87)345 (304 to 385)1216 (1107 to 1324)533 (3 to 1064)
2Switzerland15 (8 to 22)37 (30 to 45)156 (86 to 225)1468 (1254 to 1682)5900 (4845 to 6955)
2Germany−0 (−2 to 2)82 (74 to 91)680 (613 to 748)1763 (1581 to 1946)−273 (−1149 to 602)
2Finland−5 (−11 to 1)39 (26 to 51)340 (264 to 416)991 (801 to 1180)4081 (3370 to 4792)
2France−2 (−4 to 1)2 (−5 to 8)175 (124 to 226)658 (488 to 827)4726 (4072 to 5380)
2Netherlands−3 (−7 to 2)67 (60 to 74)506 (441 to 571)1945 (1736 to 2154)4814 (3826 to 5803)
2Sweden17 (12 to 22)47 (39 to 54)50 (−18 to 118)1291 (1116 to 1467)1342 (545 to 2139)
3Belgium−36 (−41 to −31)65 (54 to 76)293 (196 to 390)1398 (1091 to 1705)5170 (3918 to 6422)
3Spain4 (1 to 7)23 (15 to 31)394 (308 to 480)1518 (1213 to 1823)6900 (5708 to 8092)
3England and Wales9 (6 to 12)96 (83 to 110)491 (378 to 605)1611 (1286 to 1936)5946 (4648 to 7243)
3Northern Ireland3 (−10 to 15)88 (59 to 118)1164 (997 to 1331)2190 (1796 to 2584)4459 (3081 to 5836)
3Scotland11 (3 to 19)31 (11 to 52)1012 (886 to 1139)1177 (844 to 1510)7916 (6797 to 9035)
3Portugal−20 (−26 to −13)27 (15 to 40)441 (357 to 524)659 (388 to 931)5032 (4039 to 6025)
3Slovenia−5 (−16 to 5)65 (43 to 88)528 (373 to 683)2295 (1870 to 2719)2573 (1111 to 4035)
4Austria−4 (−10 to 2)82 (71 to 93)506 (421 to 591)3869 (3624 to 4114)2734 (1769 to 3698)
4Czechia−22 (−27 to −17)92 (71 to 114)903 (690 to 1117)3852 (3392 to 4312)8083 (6531 to 9634)
4Estonia23 (8 to 38)271 (241 to 302)553 (344 to 762)1615 (1228 to 2001)631 (−561 to 1823)
4Greece0 (−6 to 6)71 (55 to 87)530 (432 to 628)1494 (1263 to 1725)2605 (1713 to 3497)
4Hungary15 (8 to 22)97 (62 to 131)1301 (1075 to 1526)2828 (2308 to 3348)3235 (1913 to 4556)
4Italy−4 (−6 to −1)72 (64 to 81)679 (588 to 770)2095 (1849 to 2342)5944 (5107 to 6781)
4United States17 (15 to 19)195 (172 to 218)981 (882 to 1079)2537 (2317 to 2757)3738 (3059 to 4418)
5Bulgaria−8 (−18 to 2)238 (176 to 299)2384 (1940 to 2829)5497 (4658 to 6335)14 405 (12 421 to 16 388)
5Croatia4 (−6 to 15)58 (33 to 83)1699 (1509 to 1889)3462 (2954 to 3969)8573 (7207 to 9940)
5Lithuania15 (3 to 26)442 (405 to 480)2070 (1858 to 2282)4281 (3807 to 4755)8077 (6652 to 9502)
5Latvia2 (−11 to 15)204 (156 to 251)1246 (1001 to 1490)2454 (1929 to 2979)−647 (−2191 to 896)
5Poland−15 (−18 to −12)34 (5 to 63)996 (766 to 1226)2124 (1567 to 2680)7695 (6287 to 9104)
5Slovakia−4 (−14 to 7)142 (109 to 176)1380 (1123 to 1637)4258 (3603 to 4914)5038 (3551 to 6525)

The annual excess mortality per 100 000 population and the estimated mortality displacement by country from 2020 to 2024 are provided in Table 4. Even though in quintile 1, the highest observed mortality displacement ratios were found in Denmark at 180% and in Luxembourg at 2770%, these results were statistically insignificant and likely reflect their relatively low early excess mortality combined with substantial subsequent mortality deficits, rather than a large absolute displacement of deaths. However, 3 countries exhibited significant mortality displacement. In particular, Greece at 10% (95% CI, 4%-15%), Latvia at 21% (95% CI, 14%-28%), and Poland at 21% (95% CI, 17%-25%) showed mortality displacement ratios with statistical significance. By 2024, the US had returned to its prepandemic stable trend (3 [95% CI, −2 to 7] per 100 000). In contrast, most European countries (including Norway, France, Switzerland, the Netherlands, Belgium, Spain, the UK, Austria, Italy, and Lithuania) had not yet resumed their prepandemic trajectories, exhibiting excess mortality rates ranging from 11 (95% CI, 3 to 18) per 100 000 in France to 115 (95% CI, 94 to 135) per 100 000 in Lithuania.

Table 4.  Annual Excess Death Rate Per 100 000 Population and Estimated Mortality Displacement by Country From 2020 to 2024 Relative to Expected Deaths Based on 2015 to 2019 Mortality

Excess mortality quintileaCountryAnnual excess deaths per 100 000 (95% CI)Mortality displacement ratio (95% CI), %b
20202021202220232024
1Australia−16 (−21 to −11)15 (10 to 20)81 (67 to 94)38 (31 to 46)39 (32 to 46)NA
1Denmark−21 (−31 to −10)12 (−2 to 25)35 (23 to 46)−2 (−13 to 9)−44 (−57 to −30)180 (−217 to 577)
1Iceland3 (−25 to 31)9 (−18 to 37)99 (64 to 133)58 (29 to 87)60 (28 to 92)NA
1Israel28 (19 to 36)42 (34 to 50)39 (29 to 48)5 (−10 to 20)14 (9 to 19)NA
1Luxembourg25 (1 to 49)1 (−20 to 22)−22 (−43 to −2)−45 (−67 to −24)−54 (−75 to −33)2770 (−41 884 to 47 424)
1Norway−9 (−18 to −0)14 (1 to 27)77 (62 to 92)35 (24 to 45)35 (24 to 46)NA
1New Zealand−45 (−56 to −35)−16 (−23 to −10)40 (30 to 50)8 (−0 to 17)−22 (−29 to −15)NA
2Canada33 (25 to 42)32 (26 to 38)76 (65 to 87)33 (27 to 39)−3 (−11 to 5)2 (−2 to 5)
2Switzerland94 (62 to 125)41 (28 to 54)79 (62 to 95)46 (34 to 58)43 (32 to 53)NA
2Germany28 (12 to 43)71 (53 to 89)113 (91 to 136)49 (36 to 62)−0 (−14 to 14)0 (−3 to 3)
2Finland6 (−4 to 16)36 (23 to 49)125 (104 to 146)80 (61 to 100)12 (1 to 23)NA
2France73 (51 to 96)57 (44 to 69)71 (55 to 88)14 (6 to 22)11 (3 to 18)NA
2Netherlands76 (54 to 98)87 (67 to 106)68 (54 to 81)55 (45 to 65)57 (48 to 66)NA
2Sweden73 (54 to 92)18 (7 to 29)40 (28 to 51)35 (23 to 46)4 (−5 to 14)NA
3Belgium153 (109 to 196)36 (23 to 49)70 (54 to 86)24 (14 to 34)25 (15 to 34)NA
3Spain147 (99 to 194)63 (49 to 77)91 (71 to 111)37 (28 to 46)49 (34 to 64)NA
3England and Wales119 (85 to 154)85 (64 to 107)64 (48 to 79)66 (51 to 81)32 (22 to 42)NA
3Northern Ireland85 (61 to 109)86 (62 to 111)64 (44 to 83)66 (42 to 89)93 (72 to 115)NA
3Scotland103 (73 to 133)99 (80 to 118)78 (59 to 97)81 (60 to 101)44 (28 to 59)NA
3Portugal87 (65 to 108)96 (62 to 131)89 (67 to 110)20 (8 to 32)8 (−8 to 23)NA
3Slovenia144 (96 to 192)107 (81 to 134)68 (48 to 88)14 (−4 to 31)−2 (−19 to 14)1 (−3 to 4)
4Austria91 (67 to 116)103 (84 to 123)126 (104 to 148)79 (62 to 95)47 (31 to 64)NA
4Czechia151 (113 to 189)269 (226 to 312)94 (75 to 113)30 (18 to 43)32 (20 to 45)NA
4Estonia18 (−3 to 40)221 (187 to 255)119 (95 to 143)11 (−13 to 35)−5 (−28 to 18)1 (−4 to 7)
4Greece56 (40 to 73)171 (139 to 203)129 (105 to 152)−1 (−12 to 11)−34 (−51 to −17)10 (4 to 15)
4Hungary100 (65 to 135)271 (228 to 313)85 (69 to 102)10 (−2 to 22)5 (−10 to 21)NA
4Italy174 (135 to 213)127 (108 to 147)141 (115 to 167)54 (43 to 66)30 (18 to 42)NA
4United States141 (120 to 162)166 (148 to 185)98 (84 to 111)25 (21 to 30)3 (−2 to 7)NA
5Bulgaria220 (158 to 281)624 (536 to 711)227 (182 to 271)9 (−5 to 22)37 (18 to 55)NA
5Croatia128 (90 to 166)300 (249 to 350)173 (142 to 203)45 (29 to 61)56 (37 to 75)NA
5Lithuania193 (152 to 234)386 (337 to 436)235 (200 to 270)97 (78 to 116)115 (94 to 135)NA
5Latvia38 (12 to 64)353 (302 to 405)150 (122 to 178)−5 (−30 to 20)−109 (−139 to −79)21 (14 to 28)
5Poland159 (117 to 201)278 (234 to 322)83 (67 to 99)−39 (−48 to −30)−71 (−81 to −62)21 (17 to 25)
5Slovakia86 (63 to 109)356 (303 to 410)106 (89 to 124)5 (−7 to 17)−5 (−16 to 7)1 (−1 to 3)

The annual excess death rate and mortality displacement by country and age group are presented in eFigures 1-5 and eTable 1 in Supplement 1. Significant mortality displacement were observed in 0 of 3 countries in the 0-to-14–year age group, in 6 of 15 countries in the 15-to-64–year age group, in 5 of 15 countries in the 65-to-74–year age group, and in 4 of 8 countries in the 75-to-84–year age group, with effect magnitudes ranging from 7% to 68% in the 15-to-64–year age group, 8% to 62% in the 65-to-74–year age group, and 9% to 42% in the 75-to-84–year age group. Notably, in the 85 years or older age group, the mortality displacement was more pronounced, with 10 of 13 countries showing significant mortality displacement, ranging from 6% to 106%. Across all 25 significant age-group findings, 40% were from countries in mortality-burdened quintile 5, and 32% were from countries in mortality-burdened quintile 4, indicating that the highest burdened countries were disproportionately associated with the observed significant mortality displacement. The mortality displacement did not differ significantly between females and males in the younger age groups, as shown in eTable 2 in Supplement 1. However, in the 85 years or older age group, the difference was more pronounced, with 12 of 34 countries exhibiting insignificantly higher mortality displacement in females compared with only 3 countries where mortality displacement was higher in males.

The sensitivity analyses generally supported the robustness of our findings (eTables 4-6 in Supplement 1). For Greece, Latvia, and Poland, mortality displacement ratios and 95% CIs were very similar across the negative binomial, the AR(1) generalized estimating equation, and alternative age-band specifications, and all 3 countries remained classified as having significant mortality displacement. In the US, however, the 2024 excess mortality estimate was close to 0 and not statistically different from the baseline in the primary model (3 [95% CI, −2 to 7] per 100 000) but became a modest deficit under the AR(1) generalized estimating equation specification (−9 [95% CI, −14 to −3] per 100 000), consistent with a possible mortality displacement. Because these estimates are small in magnitude and lie near the null, we interpreted the US mortality displacement signal as sensitive to modeling assumptions about serial correlation. After applying the false discovery rate correction, the pattern of statistical significance generally remained consistent with the unadjusted P values (eTable 7 in Supplement 1). This suggests that controlling for multiple testing was not associated with the interpretation of our results.

This cross-sectional study found the magnitude and heterogeneity of the recovery and sustained decline in mortality across 34 countries following the COVID-19 pandemic, with results stratified by age, sex, and mortality quintile. The primary outcome of this research assesses how excess mortality during the pandemic was associated with subsequent mortality displacement. Statistically significant mortality displacement was seen in only 3 countries where pandemic-era excess mortality was high (Greece, Latvia, and Poland). They were predominantly observed among the oldest age group (≥85 years).

Our study also found the pace of recovery of pre–COVID-19 mortality trends. By 2024, the US had returned to its prepandemic pattern of relatively stable all-cause mortality. In contrast, as of 2024, most European countries, including Norway, France, Switzerland, the Netherlands, Belgium, Spain, the UK, Austria, Italy, and Lithuania, had still not resumed their prepandemic mortality trajectories. Among the 8 countries (Portugal, Slovenia, Czechia, Greece, Bulgaria, Croatia, Latvia, and Poland) with prepandemic positive mortality trends, 5 countries (Portugal, Slovenia, Czechia, Bulgaria, and Croatia) continued to experience significant excess mortality or sustained elevated baseline mortality. Of the 8 countries that had rising pre–COVID-19 mortality trends, only those achieving negative excess mortality would represent a return to conditions that were normal for high-income health systems. This situation prompts consideration of whether persistent outcomes from the pandemic, associated with sustained health system strain or depleted population health capital, may have continued to influence mortality patterns through 2024.

Our study also offers insight into the cumulative outcome of COVID-19 when including both the pandemic period of 2020 to 2022 and the recovery years of 2023 to 2024. Luxembourg uniquely demonstrated a statistically significantly faster-than-expected reduction in mortality, whereas New Zealand and Denmark maintained their expected rates of mortality decline. New Zealand had statistically significantly negative cumulative excess mortality from 2020 to 2024 in all age groups except ages 75 to 84 years. However, this was exceptional. For the 7 countries in the best quintile of low COVID-19–era mortality, most age groups experienced positive cumulative excess mortality when summing 2020 to 2024. Notably, Denmark and Luxembourg, with the lowest COVID-19–era mortality, exhibited significant cumulative negative excess mortality among individuals aged 85 years or older from 2020 to 2024. This is hard to explain and may be potentially due to factors such as smaller sample sizes, imprecise denominators, high mobility among older populations, and the suppression of influenza during 2020.

Most top-performing countries regarding overall excess mortality did not succeed in sustaining overall declines in mortality from 2020 to 2024. The worse the cumulative death rate was in 2020 to 2022, the worse the cumulative mortality was for 2020 to 2024. In other words, mortality displacement did not compensate for failing to protect the population during the pandemic. Although younger cohorts showed lower net mortality, associated with the 50-year trend toward better health of younger ages, the significant and persistent excess mortality among older adults, even in the presence of mortality displacement, demonstrates that targeted protection alone was insufficient. The scale of mortality displacement observed in the most affected countries may further indicate that any shielding strategies used in those countries failed to adequately protect high-risk groups.

Multiple factors contribute to the COVID-19 mortality displacement. Extensive studies have found that the pandemic disproportionately affected older adults and individuals with preexisting comorbidities, including type 2 diabetes,27-29 high blood pressure,30-32 and obesity.33-35 Additionally, in countries with high COVID-19 mortality rates, individuals at elevated risk might either succumb to or recover from the virus earlier, thus reducing subsequent expected mortality rates.24

Our findings have several policy implications. First, many European countries as of 2024 have yet to resume the typical pattern of mortality decline that characterized the last century of progress. The resumption of a postpandemic mortality decline in Greece, Latvia, and Poland plausibly may be driven primarily by the displaced timing of deaths of at-risk individuals, especially older adults in countries with high mortality, and may reflect a return to prepandemic trajectories rather than substantive improvements beyond the 2015 to 2019 downtrend. Pandemic management policies must prioritize protecting high-risk populations through comprehensive, population-wide measures, as targeted shielding alone was insufficient. In parallel, integrating retrospective analyses of age-specific excess mortality into routine surveillance frameworks may help detect potential mortality displacement. While real-time monitoring may be limited by data lags, periodic assessment of cumulative excess deaths across age groups, especially among older adults, may inform evaluations of recovery and health system resilience. Finally, future research should aim to disentangle temporary mortality displacement from genuine recovery, ensuring that public health strategies are based on an accurate understanding of a pandemic’s impact and ongoing vulnerabilities.

Strengths and Limitations

Few studies, to our knowledge, have explicitly quantified mortality displacement after COVID-19 across such a large set of countries and demographic groups, making this a unique contribution to understanding postpandemic dynamics. A key strength of this study is its comprehensive inclusion of 34 countries with high-quality data, stratified by age, sex, and mortality quintile, which enabled the detection of heterogeneous patterns that would be masked in aggregate analyses. The use of nationally reported data, subjected to rigorous quality checks, with standardized formats, longitudinal and cross-country comparability, and detailed survival data at the oldest ages further enhanced the study’s external validity and generalizability.

This study has several limitations. First, despite using harmonized and validated national data, reporting quality and completeness may vary across countries, potentially introducing bias. Second, our analysis was ecological and based on aggregated age-group and country-level data, which limits our ability to adjust for individual-level factors such as comorbidities, socioeconomic status, or vaccination status, and raises the possibility of an ecologic fallacy; that is, inferences made about individuals from group-level data may not hold at the individual level. Third, demographic dynamics during and after the pandemic, such as selective migration of older adults, shifts in the age distribution, or differential survival of frail individuals, may vary across countries and age groups and could generate patterns that resemble mortality displacement even in the absence of true mortality displacement. Fourthly, while our study quantifies the association of mortality displacement with postpandemic mortality declines and underscores the importance of considering the mortality displacement when interpreting mortality trends after 2020, it does not fully disentangle this temporary outcome from longer-term recovery associated with other factors such as vaccination, health care improvements, and socioeconomic recovery. In addition, the observational nature of the study precludes causal inference, and findings should be interpreted in light of these constraints.

The findings of this cross-sectional study suggest that 31 of 34 countries with high-quality mortality data exhibited no statistically significant evidence of mortality displacement. In contrast, for Greece, Latvia, and Poland, countries with above-average COVID-19 mortality, part of the postpandemic decline may reflect mortality displacement, particularly among the oldest age groups. However, the scale of early excess mortality far exceeded what displacement alone could make up for COVID-19. This underscores a widely seen failure of shielding strategies to adequately protect broadly defined individuals. Recognizing this distinction is essential for accurately interpreting postpandemic mortality trends and for designing policies that effectively safeguard broader populations without propagating the myth that older individuals who died in the COVID-19 era were already near death. Future research should further disentangle mortality displacement from genuine recovery to establish a clearer causal understanding of the factors driving postpandemic mortality trends, as well as unravel why so many European countries have failed to resume their pre–COVID-19 pattern of mortality decline.

Back to top

Article Information

Accepted for Publication: November 30, 2025.

Published: January 29, 2026. doi:10.1001/jamanetworkopen.2025.55442

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2026 Chen X et al. JAMA Network Open.

Corresponding Author: David Makram Bishai, MD, PhD, Director’s Office, G/F, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Patrick Manson Building (North Wing), 7 Sassoon Rd, Hong Kong SAR, China (dbishai@hku.hk).

Author Contributions: Prof Bishai had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Bishai.

Acquisition, analysis, or interpretation of data: Chen, Ye, Cowling.

Drafting of the manuscript: Chen, Ye.

Critical review of the manuscript for important intellectual content: Chen, Cowling, Bishai.

Statistical analysis: Chen, Bishai.

Obtained funding: Bishai.

Administrative, technical, or material support: Chen, Bishai.

Supervision: Bishai.

Conflict of Interest Disclosures: Prof Cowling reported being a consultant for AstraZeneca, Fosun Pharma, GlaxoSmithKline, Haleon, Moderna, Novavax, Pfizer, Roche, and Sanofi Pasteur. No other disclosures were reported.

Funding/Support: This study is partly supported by HKU Daniel and Mayce Yu Medical Development Fund (No. 200010837).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

3.

Aburto  JM, Schöley  J, Kashnitsky  I,  et al.  Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries.   Int J Epidemiol. 2022;51(1):63-74. doi:10.1093/ije/dyab207PubMedGoogle ScholarCrossref

19.

Islam  N, Shkolnikov  VM, Acosta  RJ,  et al.  Excess deaths associated with covid-19 pandemic in 2020: age and sex disaggregated time series analysis in 29 high income countries.   BMJ. 2021;373(1137):n1137. doi:10.1136/bmj.n1137PubMedGoogle ScholarCrossref

32.

Caillon  A, Zhao  K, Klein  KO,  et al.  High systolic blood pressure at hospital admission is an important risk factor in models predicting outcome of COVID-19 patients.   Am J Hypertens. 2021;34(3):282-290. doi:10.1093/ajh/hpaa225PubMedGoogle ScholarCrossref

Read the whole story
sarcozona
5 days ago
reply
Epiphyte City
Share this story
Delete
Next Page of Stories