NNT, NNH, and NNS: benefit and harm in whole people

Written and reviewed by a practicing emergency physician · Updated August 2026

"This drug cuts heart-attack risk by a third" and "104 people must take this drug for five years for one of them to avoid a heart attack" can describe the same trial. The first phrasing sells; the second informs. The number needed to treat — NNT — is medicine's most effective honesty device: it converts a treatment effect from a percentage change, which minds inflate, into a count of whole people, which minds can actually picture. This guide covers the NNT, its shadow the NNH, and the screening cousin NNS that gets confused with both.

From risk difference to a count of people

The machinery is one division. If untreated people suffer some outcome at rate x and treated people at a lower rate y, the absolute risk reduction is ARR = x − y, and

NNT = 1 ÷ ARR.

Treat a group whose five-year heart-attack risk is 2% with something that lowers it to 1%: the ARR is one percentage point, so NNT = 1 ÷ 0.01 = 100 people treated for five years per heart attack prevented. The measure was introduced by Laupacis, Sackett, and Roberts in 1988 precisely because the alternative framing misleads: that same treatment boasts a "50% relative risk reduction," and a 50% reduction sounds like it helps every other person who takes it. It helps one in a hundred. Relative risk reduction describes what happens to the risk; the NNT describes what happens to people.

The same division explains why one drug can carry wildly different NNTs. A treatment's relative effect is often roughly stable across populations, but the baseline risk it multiplies is not — halve a 20% risk and NNT is 10; halve a 0.2% risk and NNT is 1,000. Same pill, hundredfold difference, and neither number is "the" NNT of the drug. An NNT is always a statement about a treatment in a population with a particular baseline risk, over a particular time. Quoted without the population and the horizon, it is decoration.

Real NNTs, from the trials

Worked from published meta-analyses (each figure's source is in the references):

• Statins after a heart attack or stroke (secondary prevention): about 1 death prevented per 83 people treated for five years — from the Cholesterol Treatment Trialists' pooled analysis of fourteen randomized trials.
• Statins in people without heart disease (primary prevention): about 1 heart attack prevented per 104 treated for five years — from the Cochrane review. Same drug class as above; the lower-risk population stretches the NNT.
• Antibiotics for a child's ear infection: about 1 child in 7 to 20 with less pain at days two to twelve — a reminder that NNTs apply to symptom relief, not just death.

Notice what these numbers do to intuition. Statins are among the most prescribed drugs on earth, backed by unusually strong evidence — and even in the highest-risk group, 82 of 83 people take them for five years without their own death being the one prevented. That is not an argument against statins; it is what effective preventive medicine actually looks like when counted in whole people. If that surprises you, the number is doing its job.

NNH: the same division, run on harms

Treatments also cause things, and the identical arithmetic on an absolute risk increase yields the number needed to harm. Anticoagulants for atrial fibrillation prevent about one stroke per 25 people per year — and cause about one major bleed per 25 people over the same span. Antibiotics for ear infections: benefit in 1 per 7–20, vomiting, diarrhea, or rash in about 1 per 14.

An NNT next to an NNH looks like a ready-made verdict: helped-per-harmed. Resist the reflex, for two reasons. First, the events differ in weight — a death prevented and a transient rash are not exchangeable units, and for statins the commonly quoted harm (muscle symptoms in roughly 1 in 10, an estimate from theNNT.com that runs well above the myopathy rates seen in blinded trials — the figure itself is contested) is mostly mild and reversible, while the benefit counted is death. Second, both numbers inherit all the population- and horizon-dependence above. The honest use of an NNT/NNH pair is not a ratio but a sentence: out of every hundred people like this who take this for this long, about one avoids X and about ten experience Y — and then you weigh X against Y with your own values. The calculator's outcome view renders exactly that sentence as a picture, splitting the treated into helped, harmed, and — always the largest group — unchanged either way.

NNS: the screening cousin, and why it dwarfs both

Screening programs quote a third number: the number needed to screen — how many people must be screened (not treated) for one to avoid the outcome. For low-dose CT lung screening, about 320 screened per lung-cancer death prevented; for mammography, the Cochrane review's contested estimate runs around 2,000 screened per breast-cancer death avoided (other bodies, counting differently, put it several-fold lower — the disagreement is itself instructive and is taken up on the screening-harms page).

Why is an NNS of 320 impressive when an NNT of 83 is sobering? Because screening pays a gauntlet of attrition that treatment never faces. Of 1,000 people screened, most don't have the disease — prevalence takes the first and largest cut. The test misses some who do. Some cancers found were never destined to kill (overdiagnosis), and some people die despite early detection. The NNS compresses that whole cascade — prevalence, sensitivity, follow-through, treatment efficacy — into one number, which is exactly what the calculator's full population → test → treatment → outcome pipeline lets you decompose slider by slider. Comparing an NNS to an NNT is comparing a whole assembly line to one station; the lung-CT worked example runs a real NNS of 320 back through every stage that produced it.

Using the numbers without being used by them

Three habits keep these numbers honest. Ask per how long — an NNT without a time horizon is meaningless, and doubling the horizon roughly halves a small ARR's NNT. Ask in whom — find the baseline risk the figure assumes and check it resembles the person in front of you. And ask NNT of what, against NNH of what — insist on knowing both events before weighing them. None of this requires more math than the one division this page opened with; it requires only refusing to let a single glossy number stand in for the four or five unglamorous ones underneath it.