How long can humans live? We simply don’t know

Morbid: Debunking Modern Longevity Science Saul Justin Newman MIT Press (2026)

How old can a human live to be — and for how long can they be expected to live in good health? Average lifespans have increased over the past century thanks to improved nutrition and advances in medical science. As a result, these questions have increasingly exercised demographers and researchers studying ageing. The answers matter: not just to individuals, but to societies seeking to build sustainable social systems around ageing populations.

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Yet there is little agreement about what the available data show. Some researchers say that gains in overall life expectancy are mostly or entirely due to reductions in mortality earlier in life, rather than more people living to grand old ages, and that there could be a limit to longevity hard-wired into human genetics. Others see exactly the opposite pattern in the data. Meanwhile, claims about the prevalence of ‘supercentenarians’ — people living to an age of 110 or more — have come under the spotlight, with many cases of extremely long-lived individuals being questioned or debunked.

Saul Newman, a longevity researcher at the Oxford Institute of Population Ageing, UK, attracted attention with work¹ that sought to debunk the concept of ‘blue zones’ — regions in nations such as Greece, Italy and Japan that supposedly have unusually high numbers of extremely old people. Newman’s claims prompted a furious response from some demographers.

In his book Morbid, he doubles down on the topic, arguing that all of the claims and counterclaims about maximum human lifespan rest on fragile foundations. Many celebrated cases of extreme longevity arise simply from faulty records, he says, raising broader questions about how ageing is measured and interpreted.

What first led you to question the reliability of supercentenarian records?

Curiosity, at first, sparked by a clearly flawed Nature paper sent to me by a colleague. The authors had argued that there was a hard limit to human survival², but I found that the analysis rested on basic mathematical errors.

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What struck me was the response from researchers. Many people debated the paper’s findings, yet the scale of the errors was not recognized. That drew me into the broader question of how long humans can live.

Soon afterwards, a Science paper based on Italian data from people aged 105 and above was published. It claimed to show a late-life mortality plateau³: a pattern in which the increasing risk of mortality seen during middle age slows down at greater ages, and eventually stops getting worse altogether. This result depended entirely on how middle age was defined. Of more than 850 possible model specifications, only one produced a plateau. Any small change to the modelling made the statistical significance evaporate.

By then I had been captured by the combination of bad science and interesting questions, so I began solving problems.

What kinds of error appear most often in extreme-age data sets?

Many errors are undetectable and, therefore, we do not know their underlying frequency. This has prompted a rather absurd response from demographers, who say that, sure, some errors occasionally escape detection, but these errors must be rare. I usually ask them: if you cannot detect particular errors, how do you know that they are rare?

The core problem is that age relies on one measurement system: paperwork. If a person’s paperwork is consistent but wrong, there is no reproducible way of knowing. You often see a famous case discussed, the details exhaustively validated and all of the paperwork examined. But after decades, the case turns out to be false. It has passed every test that demography has, and it is still wrong.

I did not just observe this in individual cases. I found it in entire populations. In Greece, for example, at least 72% of centenarian records were cases of pension fraud. The person was left alive on paper while their younger relatives collected the pension cheques. That was the secret to longevity in Greece, and nobody in demography saw it for decades.

Why are records at extreme ages particularly vulnerable to these problems?

There are several overlapping error processes. Pension fraud is one. Clerical error is another, and that can be undetectable. People who have paperwork with incorrect details often do not know, because literacy rates a century ago were low. Some people purposefully increase their age to escape military service, others to marry or work earlier when they are young, and some just inherited paperwork from older relatives because it was easier than travelling or paying to register a new birth.

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Then there are identity substitutions. Imagine a room with 100 people over 100, all holding valid paperwork. Replace one of them with a younger sibling. How do you detect the swap? The paperwork is real. The person knows enough about their sibling to answer questions.

Even if you understand the social and administrative context, there is still no reproducible method to test whether the age on a person’s paperwork is correct. That is the central issue.

There are also broader patterns. Extreme longevity often appears in places with weak record systems, low incomes and low historical levels of birth certification. That pattern runs against expectations if the signal were biological.

You argue that small errors can dominate at very old ages. How does that happen?

The mathematical process is counter-intuitive but simple. Normally, rare errors can be ignored. But in this case, they grow non-linearly.

Take a large population at age 50. Introduce a small number of people whose true age is younger than this recorded age. These individuals are biologically younger than the rest, so they die at lower rates as the cohort ages. Each year, the proportion of people with an error in their records increases because people with an inflated age are more likely to survive than are people with accurate data. Even with tiny starting error rates, you can end up with a population that has a 100% rate of errors at very old ages.

Moreover, the process is asymmetrical. If the error runs in the other direction, individuals who are biologically older than their reported ages die out faster. The result is that, at the oldest ages, the data become dominated by these invisible age-coding errors.

Do you trust some national data sets more than others?

No. This is a universal problem. Five to ten per cent of people in the United States misstate their age in the census. Often, they simply do not know. Nearly one-quarter of the world’s children still do not receive a birth certificate. Add that to the slow historical roll-out of birth registration and you get widespread uncertainty.

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During the Second World War, more than half of the working-age population in the United States had no paperwork. Birth certificates were often issued mostly on the basis of whatever age people reported. By the 1960s, one-tenth of the white population and one-quarter of the non-white population had discrepancies of ten years or more between their census age and documented age.

Unreliable records became such a problem in Puerto Rico that birth records there were reset entirely in 2010. Similar data anomalies appear all over the world. These are structural issues that cannot be corrected retrospectively.

If records are unreliable, where does that leave the debate about a maximum human lifespan?

It leaves it in an absolute shambles. There has been a 40-year debate about whether there is a limit to human lifespan. Both sides seem to be wrong, and the data seem to be junk. Demographers have been drawing shaky inferences from bad data for decades.

Could biomarkers of ageing bypass some of these problems?

Only if they are calibrated against a physical measure. At the moment, biomarkers are calibrated against paperwork. If the paperwork is wrong, the biomarker cannot correct that error. It is as if we have two rulers — paperwork and biomarkers — and when the measurements do not match, we are assuming that the cause is biological.

But if someone’s epigenetic age is younger than their age on paper, how can you tell whether that is caused by their biology or by incorrect paperwork? Nobody can discount the second possibility, because so many paperwork errors are undetectable. Yet people have bought the ‘super-ageing’ hype anyway.

The solution is to introduce a third ruler: a physics-based measure. Something anchored in a physical process such as amino-acid racemization or radiocarbon dating, which can be measured in teeth and tissue from the eye. Only then can you calibrate biomarkers properly.

What can longevity research do to reduce false signals in extreme-age data sets?

We need to calibrate biomarker estimates of age against physical dating methods. And we need to understand the distribution of age-coding errors in the population.