Screening: Validity, Reliability, and Predictive Value | MyMerci
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Screening: Validity, Reliability, and Predictive Value

Unit 2 · Topic 7Screening: Validity, Reliability, and Predictive Value
1.Key Concepts

Screening is the systematic application of a simple test to apparently healthy people to identify those who probably have an unrecognized disease or risk factor, so they can be referred for diagnostic confirmation and early treatment. Screening is secondary prevention. A screening test does not make a diagnosis; a positive result means "needs further testing."

Examples in Philippine community practice: blood pressure measurement of adults, blood glucose testing, visual inspection and Pap testing for cervical cancer, sputum or chest X-ray screening of TB contacts, and newborn screening, which is mandated by the Newborn Screening Act of 2004 (RA 9288).

Types of screening

  • Mass screening — the whole population or a large group (e.g., all adults in a barangay)
  • Selective (targeted, high-risk) screening — people with risk factors (e.g., household contacts of TB patients)
  • Multiphasic screening — several tests in one session (e.g., BP, glucose, BMI, and visual acuity at a health fair)
  • Opportunistic (case finding) — testing people who come for other reasons

Validity and reliability

ConceptMeaningComponents / how measured
Validity (accuracy)Does the test measure what it is supposed to measure — does it separate those with disease from those without?Sensitivity and specificity
Reliability (precision, repeatability)Does the test give the same result when repeated under the same conditions?Intra-observer (same examiner twice), inter-observer (different examiners), test–retest agreement; often summarized with the kappa statistic

A reliable test can still be invalid (consistently wrong, like a scale that always reads 2 kg too high). A test must be reliable to be valid.

2.Principles & Frameworks

The 2 × 2 table

Disease presentDisease absentTotal
Test positivea = true positive (TP)b = false positive (FP)a + b
Test negativec = false negative (FN)d = true negative (TN)c + d
Totala + cb + dN
MeasureFormulaQuestion it answers
Sensitivitya ÷ (a + c) × 100Of those WITH disease, what % test positive?
Specificityd ÷ (b + d) × 100Of those WITHOUT disease, what % test negative?
Positive predictive value (PPV)a ÷ (a + b) × 100Of those who test positive, what % truly have disease?
Negative predictive value (NPV)d ÷ (c + d) × 100Of those who test negative, what % are truly free of disease?
Accuracy (overall agreement)(a + d) ÷ N × 100What % of all results are correct?
False-positive rateb ÷ (b + d) = 1 − specificity
False-negative ratec ÷ (a + c) = 1 − sensitivity
Prevalence (in the screened group)(a + c) ÷ N × 100

Rules to remember

  • Sensitivity and specificity are properties of the test and do not change with prevalence.
  • PPV and NPV depend on prevalence. As prevalence rises, PPV rises and NPV falls; in a low-prevalence population, even a good test produces many false positives.
  • A highly sensitive test is used when missing a case is dangerous (serious, treatable, or infectious conditions); a negative result helps rule the disease out ("SnNout").
  • A highly specific test is used when a false positive is harmful or costly, and to confirm disease; a positive result helps rule it in ("SpPin").
  • Moving the cut-off trades one for the other: lowering the fasting glucose cut-off raises sensitivity and lowers specificity.
  • Series (sequential) testing — a positive first test is followed by a second test; raises specificity and PPV. Parallel (simultaneous) testing — positive if either is positive; raises sensitivity.

Criteria for a good screening program (Wilson and Jungner, WHO)

  • The condition is an important health problem.
  • There is an accepted, effective treatment and facilities for diagnosis and treatment.
  • There is a recognizable latent or early symptomatic stage.
  • The test is suitable, acceptable to the population, safe, and inexpensive.
  • The natural history is adequately understood.
  • There is an agreed policy on whom to treat.
  • Case finding is a continuing process, and its cost is balanced against overall health spending.

Biases in evaluating screening

  • Lead-time bias — earlier detection makes survival look longer even if death is not delayed.
  • Length-time bias — screening preferentially detects slow-growing disease with a better prognosis.
  • Volunteer (selection) bias — people who attend screening are often healthier.
3.Application in Practice

Worked example 1. A new rapid test was given to 400 adults and checked against the confirmatory test.

Disease presentDisease absentTotal
Test positive7236108
Test negative8284292
Total80320400
  • Sensitivity = 72 ÷ 80 × 100 = 90% (check: 72 ÷ 80 = 0.90 ✓)
  • Specificity = 284 ÷ 320 × 100 = 88.75% ≈ 88.8% (check: 284 ÷ 320 = 0.8875 ✓)
  • PPV = 72 ÷ 108 × 100 = 66.7% (check: 72 ÷ 108 = 0.6667 ✓)
  • NPV = 284 ÷ 292 × 100 = 97.3% (check: 284 ÷ 292 = 0.9726 ✓)
  • Accuracy = (72 + 284) ÷ 400 × 100 = 356 ÷ 400 = 89% ✓
  • Prevalence = 80 ÷ 400 × 100 = 20% ✓

Worked example 2 — same test, two populations (sensitivity 90%, specificity 95%)

Population A: 1,000 people, prevalence 10%.

  • With disease = 100 → TP = 0.90 × 100 = 90; FN = 10
  • Without disease = 900 → TN = 0.95 × 900 = 855; FP = 45
  • PPV = 90 ÷ (90 + 45) = 90 ÷ 135 = 66.7% (check: 0.6667 ✓)
  • NPV = 855 ÷ (855 + 10) = 855 ÷ 865 = 98.8% (check: 0.9884 ✓)

Population B: 10,000 people, prevalence 1%.

  • With disease = 100 → TP = 90; FN = 10
  • Without disease = 9,900 → TN = 0.95 × 9,900 = 9,405; FP = 495
  • PPV = 90 ÷ (90 + 495) = 90 ÷ 585 = 15.4% (check: 0.1538 ✓)
  • NPV = 9,405 ÷ (9,405 + 10) = 9,405 ÷ 9,415 = 99.9% (check: 0.9989 ✓)

Interpretation: sensitivity and specificity were identical, but when prevalence fell from 10% to 1%, PPV fell from 66.7% to 15.4%. In the low-prevalence group, about 85 of every 100 positives are false positives. This is why targeted screening of high-risk groups gives a higher yield.

Running a community screening activity

  1. Define the target population and the condition (with the RHU physician and program coordinator).
  2. Use a standardized, validated tool and calibrated equipment (e.g., validated BP devices, correct cuff sizes).
  3. Train BHWs and staff and check inter-observer agreement.
  4. Obtain consent and explain that a positive screen is not a diagnosis.
  5. Screen, record in the ITR/TCL, and give results privately.
  6. Refer all positives for confirmation and track that they arrived — screening without follow-up is wasted effort.
  7. Evaluate: yield (new cases found), referral completion, cost per case detected.
4.Nurse's Role & Responsibilities
  • Select appropriate target groups based on local prevalence and risk
  • Perform tests correctly and consistently (a major source of poor reliability is technique)
  • Counsel clients before and after screening; manage anxiety from false positives
  • Ensure referral, confirmation, and linkage to treatment; follow up defaulters
  • Maintain records and compute performance measures for program evaluation
5.Legal & Ethical Considerations
  • Informed consent and the right to refuse. Under RA 9288, parents must be informed about newborn screening before delivery; refusal on religious grounds must be acknowledged in writing and documented.
  • Do no harm: false positives cause anxiety and unnecessary procedures; false negatives give false reassurance. Screening should be offered only when there is a pathway to diagnosis and treatment.
  • Confidentiality: results are sensitive personal information under RA 10173; announce no results publicly.
  • Justice: prioritize under-served groups, not only those who are easy to reach.
6.Case Examples

Case 1 — Choosing a test for a rare condition. The RHU plans to screen the general population for a rare disease using one of two tests. Which property matters most to avoid overwhelming the referral hospital with false alarms?

  • Correct answer: high specificity (and use a second confirmatory test in series).
  • Why: in low prevalence, false positives dominate; high specificity keeps PPV acceptable.

Case 2 — Missed cases are dangerous. For screening blood donors for an infection, which property is most important?

  • Correct answer: high sensitivity.
  • Why: a false negative lets infected blood reach a recipient.

Case 3 — Two nurses disagree. Two nurses measuring the same adults' BP get different readings in many clients. Which attribute is poor?

  • Correct answer: inter-observer reliability.
  • Action: retrain on technique, use the same device and cuff size, and recheck agreement.
7.Common Pitfalls
  • Mixing up sensitivity (read down the "disease present" column) and PPV (read across the "test positive" row).
  • Saying sensitivity or specificity changes with prevalence — only predictive values do.
  • Treating a positive screen as a diagnosis.
  • Believing a reliable test must be valid.
  • Choosing high sensitivity for a rare disease when the question stresses false positives, or high specificity when it stresses missing cases.
  • Forgetting that screening is secondary, not primary, prevention.
8.High-Yield Points
  • Screening = secondary prevention; positives need diagnostic confirmation.
  • Sensitivity = TP ÷ all with disease; specificity = TN ÷ all without disease.
  • PPV = TP ÷ all test positives; NPV = TN ÷ all test negatives.
  • Validity = sensitivity + specificity; reliability = consistency (inter- and intra-observer).
  • Sensitivity and specificity are fixed by the test; PPV rises and NPV falls as prevalence rises.
  • High sensitivity: rule out, dangerous to miss (SnNout). High specificity: rule in, rare disease, costly false positives (SpPin).
  • Series testing ↑ specificity; parallel testing ↑ sensitivity.
  • Lowering a cut-off ↑ sensitivity and ↓ specificity.
  • Accuracy = (TP + TN) ÷ total.
  • Newborn Screening Act of 2004 = RA 9288 (informed parents; written refusal on religious grounds).

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