Concept Architecture
Screening Economics
Screening economics evaluates the resources, benefits, harms and distributional consequences of offering tests to people who do not yet have recognized symptoms of a target condition. Its unit of analysis is the whole programme: invitation, uptake, testing, diagnostic confirmation, treatment, follow-up and downstream outcomes. This distinct economic question complements the concept of screening itself. A technically accurate test can still be a poor use of resources if the programme yields little health gain, causes substantial harm, or diverts capacity from more valuable care.
Define the programme and comparator
Start with the eligible population, age or risk criteria, test, interval, delivery setting and actions after positive, negative and indeterminate results. Compare with a feasible alternative such as no organized screening, an existing programme, a different interval or targeted screening. State the analytic perspective, time horizon and whether costs and effects are discounted when they occur across years.
| Programme component | Resource or consequence to include | Common omission |
|---|---|---|
| Invitation and participation | Identification, outreach, administration, travel and time where relevant to perspective. | Treating every invited person as tested. |
| Initial test | Equipment, staff, consumables, repeat testing and quality assurance. | Applying unit test cost only to true cases. |
| Diagnostic resolution | Confirmatory procedures, false-positive work-up, anxiety and procedure harms. | Treating a positive screen as a diagnosis. |
| Treatment and follow-up | Earlier treatment, surveillance, adverse effects and later care. | Assuming every earlier diagnosis yields benefit. |
| Long-term outcomes | Condition-specific morbidity, mortality, quality of life and opportunity cost. | Counting additional survival time created only by earlier diagnosis dates. |
The population invited and the population actually tested are different denominators. Programme effectiveness depends on attendance, valid specimens, timely confirmation, access to beneficial treatment and sustained follow-up, not just laboratory sensitivity or specificity.
Trace a transparent screening cohort
Suppose a fictional one-time programme tests 10,000 people. For illustration, disease prevalence at testing is 2%, sensitivity is 80%, and specificity is 95%. There are 200 people with the condition and 9,800 without it. Expected true positives are $200\times0.80=160$; false negatives are $200-160=40$; false positives are $9{,}800\times(1-0.95)=490$; and true negatives are $9{,}800-490=9{,}310$.
| Test result | Disease present | Disease absent | Total |
|---|---|---|---|
| Positive | 160 true positives | 490 false positives | 650 |
| Negative | 40 false negatives | 9,310 true negatives | 9,350 |
| Total | 200 | 9,800 | 10,000 |
Among positive tests, the illustrative positive predictive value is $160/650\approx24.6%$. The lower figure than sensitivity is expected because the condition is uncommon: most people tested do not have it. Predictive value will change if prevalence changes, even with the same test sensitivity and specificity. The four cells are expected counts under simplifying assumptions, not guaranteed observed outcomes.
Suppose the initial test costs £10 per person and every positive result receives a £200 confirmatory assessment. Initial testing costs $10{,}000\times£10=£100{,}000$ and confirmation costs $650\times£200=£130{,}000$, for £230,000 across these two components. This excludes invitations, treatment, adverse events, follow-up and later cost offsets; £230,000 divided by 160 true positives is a descriptive cost per detected case for these two components, not a cost-effectiveness ratio or a sufficient basis to recommend screening.
In a spreadsheet, calculate disease count with =Population*Prevalence, true positives with =DiseaseCount*Sensitivity, false positives with =(Population-DiseaseCount)*(1-Specificity), and diagnostic work-up cost with =(TruePositives+FalsePositives)*CostPerConfirmation. Check that all four test-result cells sum to the tested population and that the test and work-up costs use the same currency and price year. Fractional expected counts can occur in smaller modeled cohorts; avoid silently rounding intermediate calculations.
Value health effects, not just detection
Earlier diagnosis matters when it allows an intervention that changes outcomes important to patients. Lead-time bias can make survival since diagnosis look longer merely because diagnosis occurred earlier, without changing death dates. Length bias can favor detection of slower-progressing disease. Overdiagnosis finds conditions that would never cause harm within a person's lifetime, potentially leading to unnecessary treatment. These effects must be separated from genuine reductions in morbidity or mortality.
Estimate the full pathway with credible evidence on natural history, uptake, stage or risk shifts, treatment effectiveness and adverse effects. Some programmes can cause harm from false reassurance after a false negative, false-positive work-up, overdiagnosis or repeated invasive procedures. Where QALYs are used, include relevant disutility and duration, but do not assume that every detected case yields the same QALY gain. A model may need to represent competing death risks and repeated screening rounds.
Compare economic value and feasibility
For an incremental cost-utility analysis, compare programme and comparator costs and QALYs in the same eligible population, perspective and time horizon. If a policy uses an incremental cost-effectiveness ratio, its numerator is the difference in total expected costs and its denominator is the difference in total expected QALYs; the £230,000 testing calculation above is only part of one arm. Alternative thresholds, budget constraints and displaced services may affect the decision. Programme budget impact is related but distinct: it asks what spending changes for a particular payer and period, including implementation scale and uptake.
Targeting high-risk groups or changing the interval can alter prevalence, predictive value, workload, harms and equity. A targeted programme may improve yield while missing people at risk outside its criteria. Analyze uptake and downstream access by relevant groups, not just average performance among attendees. Capacity for diagnostic resolution and treatment must be adequate before a large invitation campaign; otherwise apparent early detection may create queues without health gain.
Test uncertainty and report the decision
Vary uncertain prevalence, test performance, uptake, follow-up completion, natural history, treatment benefit, quality-of-life effects and costs. Structural uncertainty matters when the model assumes a particular progression pathway or lasting treatment effect. Validate model predictions against independent observations where feasible and show which assumptions change the preferred policy.
Report population, comparator, test and interval, pathway, horizon, perspective, prices, harms, outcomes, subgroup effects and evidence gaps. The UK National Screening Committee evaluates complete screening programmes, not isolated tests; its modeling guidance illustrates why a decision model may be needed when evidence comes from different parts of the pathway. Screening economics is useful when it makes those trade-offs explicit rather than equating more testing or more diagnoses with better health.
Sources and further reading
- World Health Organization Regional Office for Europe, Screening programmes: a short guide (2020), on programme design, benefits and harms: https://www.who.int/europe/publications/i/item/9789289054782
- UK National Screening Committee, disease, clinical effectiveness and cost-effectiveness modelling guidance: https://www.gov.uk/government/publications/uk-nsc-disease-clinical-effectiveness-and-cost-effectiveness-modelling/uk-nsc-disease-clinical-effectiveness-and-cost-effectiveness-modelling
- UK National Screening Committee, evidence review criteria for national screening programmes: https://www.gov.uk/government/publications/evidence-review-criteria-national-screening-programmes
- NICE, Health technology evaluations: the manual, economic evaluation methods applicable to comparing health technologies: https://www.nice.org.uk/process/pmg36/chapter/economic-evaluation-2
Related Concepts (2)
Library
Publications
1
Cost-Effectiveness of Interventions to Prevent and Control Diabetes Mellitus: A Systematic Review — Li, Zhang, Barker, Chowdhury & Zhang, Vol. 33, No. 8 ed., 2010 (Diabetes Care)
A comprehensive systematic review grading the cost-effectiveness of diabetes prevention and control interventions (lifestyle modification, screening, glycaemic control, statin therapy, retinopathy screening), a benchmark reference for diabetes economics.
Journal ArticleView source →
Frequently Asked Questions (6)
What is screening economics?
The application of health economic evaluation to programmes testing asymptomatic individuals for early disease signs, from cancer to cardiovascular risk factors.
Source: Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. 4th ed. Oxford University Press; 2015.
What programmes does screening economics evaluate?
Screening economics applies economic evaluation to programmes that test people without symptoms for early signs of disease, from cancer screening to checks for cardiovascular risk factors. Because most of those tested are well, it must weigh the disease caught early against the harms of testing the healthy, false alarms, unnecessary follow-up, and overdiagnosis, all set against cost. Whether a programme is worthwhile turns on this whole balance, not on the cases it finds alone. Costing the testing of the symptomless is its scope. Drummond and colleagues (2015) describe such evaluations.
Source: Drummond et al. 2015
What does screening economics evaluate?
Screening economics evaluates the costs of screening programmes; the benefits from detecting disease or risk early and enabling beneficial treatment; and the harms and costs of false positives, false negatives, and overdetection, weighing these to assess the overall value of screening. So screening economics evaluates both the benefits and the harms of screening, which is why it considers the consequences of true and false results, since screening asymptomatic populations inevitably produces some false results and may detect conditions that would not have caused harm, and the value of screening depends on the balance of its benefits against these costs and harms.
Source: Drummond et al. 2015
Why must screening balance benefits and harms?
Screening must balance benefits and harms because it tests apparently healthy people, most of whom do not have the condition, so while it can benefit those with disease through early detection, it also causes harms such as false positives leading to unnecessary testing and anxiety, false negatives giving false reassurance, and overdetection of harmless conditions. So screening balances benefits against harms because its population-wide testing produces both, which is why its value is judged on this balance, since a screening programme is worthwhile only when the benefits of early detection outweigh the costs and harms across the whole population screened, not just for those with disease.
Source: Drummond et al. 2015
What determines whether screening is worthwhile?
Whether screening is worthwhile is determined by the prevalence of the condition, the accuracy of the test, the effectiveness of early treatment, and the costs and harms of screening and false results, which together decide whether the benefits justify the costs and harms. So screening is worthwhile when these factors combine favourably, with sufficient prevalence, an accurate test, effective early treatment, and acceptable costs and harms, which is why screening economics assesses them together, since screening for a rare condition with an inaccurate test or without effective early treatment may cause more harm and cost than benefit, whereas well-designed screening for suitable conditions can be cost-effective.
Source: Drummond et al. 2015
How are screening programmes evaluated economically?
Screening programmes are evaluated economically by comparing the costs and outcomes of screening with those of not screening, typically through cost-effectiveness analysis capturing the screening costs, the consequences of true and false results, the benefits of early detection and treatment, and outcomes such as disease prevented and quality of life. So screening programmes are evaluated by weighing the full costs and harms against the benefits of early detection, which allows their value to be assessed, and because screening tests asymptomatic populations, the evaluation carefully accounts for the harms and the balance of benefits and costs, informing whether screening for a condition is worthwhile.
Source: Drummond et al. 2015
Trust Record
Verified by Dr Darrin Baines
British health economist
Professional identity: darrinbaines.org
Verification date: 24 Sep 2026
Content version: 1.0.0
Canonical Identity
- Persistent URI
- https://healtheconomics.wiki/concept/screening-economics
- Term code
- HE-PE-CD-056
Stable URI · Machine-readable · Resolvable · CC BY 4.0