Seven ways a dementia statistic can mislead you — and how to check

Short answer: Almost every misleading dementia statistic goes wrong in one of seven ways — it ignores how rare the condition is, it ignores that people die of other things, it compares two different kinds of life, it quotes a population number as if it were your number, it counts who is alive rather than who newly gets ill, it mistakes an early symptom for a cause, or it uses the wrong comparison group. None of these are lies. They are mostly accurate numbers being asked to mean something they don't. Below is each one, with a real example from real research, and the question that catches it.

This page exists because we kept needing it. Every post we write runs into one of these, and re-explaining them each time is worse than putting them in one place.

1. The base rate: a test's accuracy is not your answer

The trap. A screening test advertises "94% accurate." You score badly. It feels like a 94% chance something is wrong. It isn't — and it usually isn't close.

The real example. A 2024 Cochrane review of self-administered cognitive tests examined eleven studies and 2,303 participants. One widely used test showed pooled sensitivity of 94% — it catches most people who do have dementia — alongside specificity of 66%, meaning it wrongly flags about a third of people who don't.

Now put that into a population where about 5 in 100 people have dementia. Out of 1,000 people: roughly 52 have it, and the test catches about 49 of them. Of the 948 who don't, about a third — some 322 people — get flagged anyway. So around 371 people "fail," and fewer than 50 of them actually have the condition.

Roughly seven out of every eight people who fail that test do not have dementia.

The check to run: How common is this condition in people like me? Sensitivity is what tests advertise. Specificity multiplied by rarity is what decides what your bad result means. The rarer the condition, the more of the alarms are false.

More on this in are online dementia tests accurate?

2. Competing mortality: you can lower your "lifetime risk" by dying sooner

The trap. A study reports that a high-risk group has a lower lifetime chance of dementia than a low-risk group, and someone concludes the risk factors are protective.

The real example. The ARIC study followed 12,409 adults for around 26 years and produced a genuinely useful headline: people with none of three midlife vascular risk factors stayed dementia-free for about 30.1 years from age 55, against 17.5 years for those with all three — a gap of roughly 12.6 years.

The part that almost no coverage carried: the people with all three risk factors also had about 5.6 times the hazard of dying without dementia. Because dementia is largely a disease of late life, dying earlier removes the years in which you would have been most likely to develop it. Their cumulative lifetime incidence by age 95 came out lower than that of the healthiest group.

They did not avoid dementia. They developed it earlier when they developed it, and often did not live long enough to.

The check to run: What else was happening to this group over the same period? Any "lifetime risk of a late-life disease" number is silently competing with death from everything else. This is the single strongest reason we prefer dementia-free years to lifetime risk percentages — see what midlife blood pressure, diabetes and smoking cost you in dementia-free years.

3. Healthy-user bias: comparing two different kinds of life

The trap. People who take a supplement, a medicine or a habit are compared with people who don't — as if the only difference between the groups were the thing being studied.

The real example. Hormone therapy is the cleanest case on record. Pooled observational studies suggested women who took it had around 22% lower Alzheimer's risk (RR 0.78). Pooled randomised trials of the same therapy pointed the other way (RR 1.38). Same treatment, opposite directions.

The explanation is not that one set of researchers was incompetent. Women prescribed hormone therapy were historically more affluent, better educated, more engaged with healthcare, less likely to smoke and more physically active — every one of which independently lowers dementia risk. Comparing users with non-users in the real world partly compares two different kinds of life. Randomised trials exist precisely to break that link, and when the link is broken, the benefit goes with it.

The check to run: Was this randomised — and if not, who chooses to do this thing? If the people doing it are systematically healthier, wealthier or more health-engaged, expect the observational estimate to flatter it. More in HRT and dementia risk.

4. A population attributable fraction is not your personal risk

The trap. You read that a risk factor accounts for "2% of dementia cases" and conclude it barely matters. Or that fourteen factors account for "45% of cases" and conclude you personally can cut your risk by 45%.

The real example. Smoking carries a population attributable fraction of roughly 2% in the 2024 Lancet Commission's accounting. That number is small largely because smoking has become much less common — not because smoking does little to an individual smoker's brain. A PAF multiplies how much harm a factor does by how many people are exposed to it. Change the prevalence and the PAF changes, while the personal risk stays exactly where it was.

It runs the other way too. "Up to 45% of cases could be prevented or delayed" is a statement about what would happen to a whole population if fourteen exposures were eliminated everywhere, over a lifetime. It is not a personal discount code, and nobody can promise you a percentage.

The check to run: Is this number about a population or about a person? If it is a share of cases, it is telling you where public-health effort pays off — not what your own odds are. See the 14 modifiable risk factors, in plain English.

5. Prevalence is not incidence: who is alive with it vs who newly gets it

The trap. A statistic counts the people currently living with a condition and gets read as a statement about who develops it.

The real example. Almost everyone has heard that two-thirds of people living with Alzheimer's are women. That figure is real, and it is a prevalence figure — it counts who is alive with the diagnosis at a point in time. Women live longer on average, and age is by far the strongest risk factor for dementia, so a longer-lived group accumulates more cases.

When researchers look at incidence instead — who newly develops dementia at a given age — the Alzheimer's Association's own 2026 report states that most US studies have found no meaningful difference between men and women at any given age.

The two-thirds number is, to a large degree, a statement about longevity. Which does not make women's dementia risk uninteresting: what genuinely differs is which modifiable risk factors each sex tends to carry, and those can be changed.

The check to run: Is this counting who has it, or who is getting it? See women and dementia risk.

6. The prodrome window: mistaking an early symptom for a cause

The trap. A study finds that people who take a drug — or have a symptom — go on to develop dementia more often, and it gets reported as the drug causing the dementia.

The real example. Disturbed sleep, anxiety, low mood and bladder trouble are all things dementia produces on its way in, years before anyone names it. So people prescribed sleeping tablets, sedatives and bladder drugs are disproportionately people already in the earliest phase of the illness.

There is a clean way to test for this: ask when the association appears. In a 2025 Canadian case-control study, chronic benzodiazepine use predicted dementia only within the four years before diagnosis. Go back further, before prodromal symptoms began, and the link faded. A genuine drug effect should not switch off the further back you look. Reverse causation should.

The same topic supplies a companion warning. The largest analysis of sleeping pills and Alzheimer's — 13 studies, 721,354 people — reported odds 29% higher (OR 1.29). The same paper, restricted to the studies that followed people forward in time, found no significant association (HR 1.17, 95% CI 0.87–1.58). Both numbers are in one abstract. Page one of the internet quotes the first.

The check to run: Could the outcome have caused the exposure? And: does this paper contain a second, quieter number? See do sleeping pills cause dementia?

7. The missing comparison group: compared with whom?

The trap. A drug's users are compared with the general population, when the honest comparison is with people who have the same condition.

The real example. Acid-reflux drugs. Pooled across nine prospective studies and 204,108 dementia cases, proton pump inhibitor users compared with non-users showed RR 1.16 (95% CI 1.00–1.35) — a small signal sitting right on the edge of significance.

The same researchers then compared PPI users with people taking a different acid-suppressing drug — people with broadly the same underlying complaint, the same reason to be medicated, the same tendency to visit doctors. The result: RR 1.03 (95% CI 0.66–1.62). Nothing.

Compare people with reflux to people with reflux and the signal disappears. Compare them to everyone else and it reappears. What was being measured was mostly the reason for the prescription, not the prescription.

The check to run: Compared with whom? An "active comparator" — people taking a different treatment for the same problem — is one of the most informative designs in this whole field, and one of the least reported. See does omeprazole cause dementia?

The short version, as a checklist

When you meet a frightening or exciting dementia number, ask:

1. How common is this condition in people like me? (base rate)
2. What else happened to this group — including dying? (competing mortality)
3. Was it randomised, and who chooses to do this? (healthy-user bias)
4. Is this about a population or about me? (attributable fractions)
5. Is it counting who has it or who is getting it? (prevalence vs incidence)
6. Could the outcome have caused the exposure? (the prodrome window)
7. Compared with whom? (the missing comparator)

And one more that catches a surprising amount: does the same paper contain a second number that points the other way? In our experience it often does, and it is usually the more honest one.

What this means for what we publish

We apply these to ourselves, because the same traps are available to anyone selling a health product — including us.

It is why we do not give anyone a personal "your dementia risk is X%" figure. A number like that would need to survive every trap on this page, and it cannot: it would mix population attributable fractions with individual odds, ignore competing mortality, and imply a precision that no risk model has for an individual.

What the evidence does support is more modest and more useful: showing which of the modifiable factors you currently carry, which ones the research supports acting on, and letting you work on them consistently. That is what our free profile does, and where the evidence is weak we would rather say so than round it up.

You can read how we research and review our content — and the independent physician audit of the app, with all nine findings published, including the ones that were critical.

How we research and review our content →

An independent physician audited the Solenna app. We published all nine findings →