Screening harms and biases

Worth the Test · Written by an emergency physician · Last reviewed September 2026

A screening program produces two columns of numbers. One counts the people it helps — cancers caught early, deaths deferred. The other counts the people it burdens — healthy people told they might be sick, disease found that never needed finding, procedures that changed nothing. A fair reading puts both on the same page; this guide walks the harm column and how the calculator quantifies it.

The false positives add up

Sensitivity and specificity describe a single test. But screening repeats that test on the same person year after year, and each round is a fresh chance for a false alarm — so the probability of at least one false positive grows with every round.

If the rounds were independent, the arithmetic compounds the pass rate. A test with 90% specificity clears a healthy person 9 times out of 10, so the chance of clearing them every time across k rounds is 0.9^k, and the chance of at least one false alarm is 1 − 0.9^k. After 5 rounds that is 1 − 0.9⁵ = 1 − 0.590 = 0.41, about 41%; after 10 rounds it is 1 − 0.9¹⁰ = 1 − 0.349 = 0.65, roughly 65%. In that model, a healthy person screened every year for a decade is more likely than not to be recalled at least once.

That formula assumes equal specificity, independent errors, and people who remain disease-free. Repeated errors can depend on one another, making the cumulative rate lower or higher than this model. With per-round false-positive risk q, the general bounds are q and min(1, kq), not the independence curve. The repeat-testing view shows the independence result alongside these bounds, and the guide to repeated screening works through both extremes.

Measured rates in real programs are of a similar size. From ten years of screening records for 2,400 women, Elmore and colleagues estimated a 49.1% cumulative risk of at least one false-positive result after 10 screening mammograms. In the PLCO cancer screening trial, Croswell and colleagues found that after 14 screening tests the cumulative risk of at least one false positive was 60.4% for men and 48.8% for women.

Overdiagnosis: disease that would never have surfaced

A false positive is a scare that resolves — the follow-up comes back clean. Overdiagnosis is the opposite problem: the disease is real, but it would never have caused symptoms or death in the person's lifetime. Welch and Black define it as the detection of a cancer "that would otherwise not go on to cause symptoms or death" — which requires both a reservoir of silent, indolent disease and a test sensitive enough to find it.

Overdiagnosis is not a testing error; it is a correct diagnosis of something that did not need diagnosing. That is what makes it both hard to see and self-reinforcing: the patient is treated, survives, and is counted as a screening success — inflating the apparent benefit — though the treatment carried only risk and no upside. Drawing on randomized trials, Welch and Black estimate that about 25% of mammographically detected breast cancers, 50% of lung cancers detected by chest x-ray and/or sputum testing, and 60% of PSA-detected prostate cancers are overdiagnosed. The lung figure comes from those older chest x-ray and sputum screening trials, not from today's low-dose CT; for CT-detected lung cancer, they point to observational evidence of overdiagnosis rather than a trial estimate. They also call for better estimates of how large overdiagnosis really is.

Why five-year survival can mislead: lead-time and length-time bias

Two biases make screening look more effective than a mortality count would.

Lead-time bias. Screening moves the moment of diagnosis earlier without necessarily moving death. If a cancer would have surfaced with symptoms at 67 and caused death at 70, catching it at 64 turns a "3-year survivor" into a "6-year survivor" even if the person still dies at 70. Survival measured from diagnosis stretches; the lifespan does not. Welch, Schwartz, and Woloshin showed this directly across tumor types: over four decades the change in 5-year survival had essentially no correlation with the change in mortality (Pearson r = .00), while it did track rising incidence — a sign of broader detection, not better treatment. Five-year survival is a sound way to compare treatments inside a trial, but a poor way to judge whether screening postpones death.

Length-time bias. Screening at fixed intervals preferentially catches slow-growing disease. A fast tumor tends to surface with symptoms between screens; a slow, indolent one waits quietly and is far likelier to still be there at the next scan. So the cancers a screen finds are, on average, the least dangerous — flattering survival statistics before any treatment. Overdiagnosis is length-time bias taken to its limit: disease so slow it never would have mattered.

What a false positive sets in motion

A positive screen is rarely the end of it. It triggers a cascade: a recall for repeat imaging or testing, then sometimes a biopsy or other invasive procedure, each with its own rate of complications and cost. Those procedures accumulate across rounds too. Elmore and colleagues estimated that 18.6% of women without breast cancer would undergo a biopsy after 10 screening mammograms. In Croswell's PLCO analysis, the cumulative risk of an invasive diagnostic procedure prompted by a false positive was 28.5% for men and 22.1% for women after 14 tests — measured across all participants, not only those who had a false alarm. Between the positive result and its resolution sits a stretch of anxiety, real even when the answer is benign.

Whole-body and multi-organ screens add a distinct harm: the incidental finding — an unrelated nodule, cyst, or shadow the scan was not looking for. Each one reopens the same follow-up cascade, and most turn out to be nothing, which is precisely the problem.

Reading the ledger: number needed to screen, treat, and harm

The honest way to present a screen puts its benefits and harms on the same denominator. The calculator's outcome panel does this for a whole screened population. It shows the expected number of people helped and the expected number harmed by treatment — separate effects that can overlap, since one person can be both — along with the treated people who have neither modeled effect and the cases the test missed. Three numbers frame the panel:

• NNT (number needed to treat, an input): how many people with the disease must be treated for one to benefit.
• NNH (number needed to harm, an input): how many people must be treated for one to be harmed. Anyone treated can be harmed, including people treated after a false positive.
• Screens per modeled benefit (an output): everyone screened divided by the expected number helped — the whole modeled chain, from test to treatment, in one number.

Screens per modeled benefit describes one modeled round of testing and treatment. It is not the number needed to screen (NNS) that screening trials report, which counts people invited or screened across a whole program — repeated rounds, follow-up care and years of outcomes — against a comparison group. A screening program's number needed to screen is not a treatment NNT; the NNT guide explains why.

Mammography is the worked case where these numbers are most openly contested. The Cochrane review by Gøtzsche and Jørgensen reads the trials conservatively: for roughly every 2,000 women invited to screening over 10 years, on its reading about 1 avoids dying of breast cancer while about 10 are overdiagnosed and treated for a cancer that would never have harmed them. The U.S. Preventive Services Task Force reaches a more favorable net-benefit judgment, giving biennial screening at ages 40–74 a grade B recommendation; its modeling puts overdiagnosis near 14 per 1,000 women screened over a lifetime (a range of 4 to 37 across models). The two rest on different trials, follow-up windows, and definitions of overdiagnosis — much of why they diverge. Experts genuinely disagree here, and this tool takes no side — read each estimate with its own population, comparator and follow-up.

The test column gives about 4.4% PPV at this approximate prevalence.

The harm column is only half the picture, and it is not this site's claim that harms outweigh benefits — that verdict depends on the numbers you enter, and reasonable numbers point different ways for different tests and ages. For the intuitions this page rests on, see why a rare condition makes most positives false in the base-rate fallacy, and how one result revises risk in pre-test to post-test probability. Both screens argued over most are worked end to end in mammography by the numbers and the PSA test by the numbers; the methodology lays out exactly how the calculator turns these inputs into a helped-and-harmed ledger, and which figures are well-sourced versus weak.

Correction, September 2026. Elmore's 49.1% is an estimated cumulative risk after 10 mammograms, not an observed share of women, and the 18.6% biopsy estimate applies to all women without breast cancer. The 50% lung-cancer overdiagnosis estimate comes from older chest x-ray and sputum screening trials, not low-dose CT. The calculator section now describes helped and harmed as effects that can overlap. The corrections log lists every change.