Concept Architecture
Concept
Theoretically, Number Needed to Screen (NNS) is an epidemiological measure that quantifies the number of individuals who must undergo a screening programme to prevent one adverse health outcome or detect one additional clinically important case over a specified period. It represents the efficiency of a screening intervention by relating the size of the screened population to the resulting health benefit. The concept is founded on absolute risk reduction and population-based preventive medicine and exists to evaluate the effectiveness of screening programmes.
Mathematically, the Number Needed to Screen is commonly represented as the reciprocal of the absolute reduction in the probability of the target outcome attributable to screening. Depending on the objective of the screening programme, the absolute risk reduction may refer to disease incidence, disease-specific mortality or another clinically relevant endpoint. Smaller NNS values indicate more efficient screening programmes because fewer individuals must be screened to achieve one beneficial outcome.
In practice, Number Needed to Screen is estimated using data from randomised screening trials, observational studies and population screening programmes. It is widely applied in health technology assessment, public health and health economic evaluation to compare screening strategies, estimate programme efficiency and inform resource allocation decisions.
Purpose
Used to quantify the efficiency of screening programmes, estimate the number of individuals requiring screening to achieve one additional beneficial outcome, compare screening strategies and support public health and health economic decision-making.
Mathematical Formulae
Primary Formula
NNS = 1 / ARR
where:
- NNS = number needed to screen
- ARR = absolute risk reduction attributable to screening
Supporting Formulae
ARR = Risk?? ? Risk?
Risk = Events / Population
NNS = 1 / (Risk?? ? Risk?)
where:
- Risk?? = risk in the unscreened or control population
- Risk? = risk in the screened population
Related Mathematical Methods
- Absolute Risk Reduction
- Number Needed to Treat
- Number Needed to Harm
- Risk Difference
- Relative Risk
- Cost-Effectiveness Analysis
- Screening Programme Evaluation
Example
A colorectal cancer screening programme reduces disease-specific mortality from 0.50% to 0.30% over ten years.
Absolute Risk Reduction:
ARR = 0.0050 ? 0.0030 = 0.0020
Number Needed to Screen:
NNS = 1 / 0.0020 = 500
Five hundred individuals must be screened to prevent one colorectal cancer death during the study period.
Excel Implementation
| Function | Example Formula | Health Economics Application |
|---|---|---|
| Subtraction | =B2-C2 | Calculates the absolute risk reduction between unscreened and screened populations. |
| Division | =1/(B2-C2) | Calculates the Number Needed to Screen. |
| IF | =IF(B2>C2,1/(B2-C2),"No screening benefit") | Calculates NNS only when screening reduces risk. |
| ROUND | =ROUND(1/(B2-C2),0) | Rounds the Number Needed to Screen to the nearest whole individual for reporting. |
VBA (Optional)
A VBA macro can automatically calculate Number Needed to Screen values for multiple screening strategies and generate comparative programme efficiency reports.
Sources
- Rembold CM. Number needed to screen: development of a statistic for disease screening. BMJ. 1998;317:307?312.
- Wald NJ, Hackshaw AK, Frost CD. When can a risk factor be used as a worthwhile screening test? BMJ. 1999;319:1562?1565.
- Drummond MF, et al. Methods for the Economic Evaluation of Health Care Programmes. 4th ed.
- Wilson JMG, Jungner G. Principles and Practice of Screening for Disease. World Health Organization.
- NICE. Health Technology Evaluation Manual.
Related Concepts (2)
Library
Publications
1
Statistical Analysis of Cost-Effectiveness Data — Willan & Briggs, 1st Edition ed., 2006 (John Wiley & Sons)
A synthesis of statistical methods for analysing cost-effectiveness data, including net-benefit regression, confidence intervals for the ICER, cost-effectiveness acceptability curves, and covariate adjustment. Part of the Wiley Statistics in Practice series.
BookView source →
Frequently Asked Questions (6)
What is the number needed to screen?
The number of individuals who would need screening for a condition to prevent one adverse outcome, based on prevalence and programme effectiveness.
Source: Rembold 1998
What does the number needed to screen tell a screening programme?
The number needed to screen is how many people must be screened for a condition to prevent one adverse outcome, given how common the condition is and how effective the programme is. It tells a screening programme the scale of effort required for each benefit gained, which helps judge whether the resources, and the harms of screening the many who will not benefit, are justified. A large number warns that great effort buys little, a small one that screening is efficient. Weighing effort against benefit is its use. Gordis (2014) describes this measure.
Source: Gordis 2014
How is the number needed to screen calculated?
The number needed to screen is calculated from the absolute risk reduction in the adverse outcome achieved by the screening programme, taking its reciprocal, where that risk reduction reflects the prevalence of the condition, the effectiveness of the screening test in detecting it, and the effectiveness of treatment for those detected. So the number needed to screen is calculated as one divided by the absolute reduction in the adverse outcome attributable to screening the population, which incorporates how common the condition is and how well screening and treatment work, translating these into the number who must be screened for one prevented outcome.
Source: Rembold 1998
How does the number needed to screen relate to the number needed to treat?
The number needed to screen relates to the number needed to treat as its extension to whole-population screening: the number needed to treat concerns patients receiving a treatment, whereas the number needed to screen concerns people undergoing screening, and it is typically much larger because it includes the many screened who do not have the condition. So the number needed to screen builds on the same reciprocal-of-risk-reduction logic as the number needed to treat but applies it to the screening of a population, reflecting that screening involves testing many to benefit few, which makes the numbers involved considerably larger.
Source: Rembold 1998
Why is the number needed to screen useful?
The number needed to screen is useful because it expresses the yield of a screening programme in an interpretable way, showing how many people must be screened to prevent one adverse outcome, which aids judgements about a programme's value and the balance of benefits against harms and costs. So the number needed to screen is useful for evaluating and communicating the efficiency of screening, since a very large number needed to screen may indicate that a programme prevents few outcomes relative to the effort and potential harms of screening many people, informing decisions about whether and how to screen.
Source: Rembold 1998
What are the limitations of the number needed to screen?
The limitations of the number needed to screen include its dependence on prevalence, screening and treatment effectiveness, and the time horizon, so it varies between populations and periods; that it captures only the prevented adverse outcome and not the harms of screening, such as false positives and overdiagnosis; and that it requires reliable estimates of the inputs. So the number needed to screen is interpreted alongside the harms and costs of screening and with attention to the population and assumptions, since it summarises only the benefit side and depends on estimates that vary by setting, meaning it informs but does not by itself determine the value of a screening programme.
Source: Rembold 1998
Trust Record
Verified by Dr Darrin Baines
British health economist
Professional identity: darrinbaines.org
Verification date: 9 Dec 2025
Content version: 1.0.0
Canonical Identity
- Term code
- HE-ES-RM-017
Stable URI · Machine-readable · Resolvable · CC BY 4.0