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SCREENING FOR DISEASES

Source: K. Park's Textbook of Preventive and Social Medicine

1. SCREENING

Definition (K. Park): Screening is defined as "the search for unrecognized disease or defect by means of rapidly applied tests, examinations or other procedures in apparently healthy individuals."

Screening is based on the concept of the iceberg phenomenon of disease - the hidden submerged portion of the iceberg represents undiagnosed disease in the community. Screening aims to detect this hidden mass of disease at an early stage.

Key features of screening:

It is applied to apparently healthy (asymptomatic) individuals
It is a preventive care function
It uses simple, rapid, inexpensive tests
The initiative comes from the investigator or health agency
A positive screening test requires further diagnostic workup - it is NOT a diagnosis

Differences from periodic health examination:

Capable of wide application
Relatively inexpensive
Requires little physician-time (technicians can administer the test; physician only interprets)

Uses of screening:

To find presymptomatic cases for early treatment
To estimate the prevalence of disease
To understand the natural history of disease
To provide health education opportunities

2. DIFFERENCE BETWEEN SCREENING TEST AND DIAGNOSTIC TEST

As per K. Park (Table 1):

Screening Test	Diagnostic Test
Done on apparently healthy individuals	Done on those with indications or sick
Applied to groups	Applied to single patients
Test result is arbitrary and based on a cut-off point	Diagnosis is the sum of all evidence (not final; modified by new evidence)
Based on one criterion or cut-off point	Based on evaluation of many symptoms, signs, and lab findings
Less accurate	More accurate
Less expensive	More expensive
Not a basis for treatment	Used as a basis for treatment
Initiative comes from investigator/health agency	Initiative comes from the patient with a complaint

3. LEAD TIME

Lead time is defined as the "period between the diagnosis of disease by screening and the time it would have presented clinically in the absence of screening."

Lead time = difference between the time of screen-detected diagnosis and the time clinical symptoms would have appeared
It represents the extra time gained by screening (i.e., how far ahead of clinical presentation the disease was caught)
The goal of screening is to use the lead time to begin treatment early and improve prognosis

Importance: Lead time bias can make a screening programme appear more effective than it really is. If screening merely advances the time of diagnosis without truly prolonging survival, the patient lives "longer" with the knowledge of disease but does not actually live longer.

Lead time bias is a major bias in evaluating screening programmes - it can make survival appear better simply because the clock started earlier, not because of any true benefit.

4. CRITERIA FOR DISEASES TO BE SCREENED (Wilson & Jungner Criteria)

K. Park lists the classic criteria (Wilson & Jungner, WHO 1968):

Criteria related to the disease:

The condition should be an important health problem (common or serious)
There should be a recognizable latent or early symptomatic stage
The natural history of the condition should be adequately understood

Criteria related to the test: 4. There should be a suitable screening test 5. The test should be acceptable to the population 6. The test should be reliable (repeatable) and valid (accurate)

Criteria related to treatment/management: 7. Accepted and effective treatment for the condition should be available 8. Facilities for diagnosis and treatment should be available 9. There should be an agreed policy on whom to treat as patients

Criteria related to the programme: 10. The cost of case-finding (including diagnosis and treatment) should be economically balanced in relation to possible expenditure on medical care as a whole 11. Case-finding should be a continuing process and not a once-and-for-all project

5. TWO-BY-TWO TABLE IN SCREENING TEST

The 2x2 table is used to evaluate screening test performance against a "gold standard" diagnostic test:

	Disease Present	Disease Absent	Total
Test Positive	a (True Positive)	b (False Positive)	a+b
Test Negative	c (False Negative)	d (True Negative)	c+d
Total	a+c	b+d	a+b+c+d

a = True Positives: diseased persons correctly identified as positive
b = False Positives: non-diseased persons incorrectly labelled as positive
c = False Negatives: diseased persons incorrectly labelled as negative
d = True Negatives: non-diseased persons correctly identified as negative

Derived measures:

Sensitivity = a/(a+c) × 100
Specificity = d/(b+d) × 100
Predictive value of positive test = a/(a+b) × 100
Predictive value of negative test = d/(c+d) × 100

6. SENSITIVITY

Definition: Sensitivity is the ability of a test to identify correctly all those who have the disease (i.e., true positive rate).

Formula:

Sensitivity = a/(a+c) × 100 = True Positives / (True Positives + False Negatives) × 100

A sensitive test has few false negatives
A highly sensitive test means: if a person has the disease, the test will (almost always) be positive
A 100% sensitive test would miss no cases of disease
Sensitivity is important when: missing a case is dangerous (e.g., HIV, TB, cancer screening)

Mnemonic - SNOUT: A highly SeNsitive test, when Negative, rules OUT the disease.

Effect on sensitivity and specificity: Sensitivity and specificity are inversely related. Lowering the cut-off point increases sensitivity but decreases specificity.

7. SPECIFICITY

Definition: Specificity is the ability of a test to identify correctly those who do NOT have the disease (i.e., true negative rate).

Formula:

Specificity = d/(b+d) × 100 = True Negatives / (True Negatives + False Positives) × 100

A specific test has few false positives
A highly specific test means: if a person does NOT have the disease, the test will (almost always) be negative
A 100% specific test would have no false positives
Specificity is important when: false positive labelling causes harm (e.g., unnecessary invasive treatment or psychological harm)

Mnemonic - SPIN: A highly SPecIfic test, when Positive, rules IN the disease.

Example from K. Park (EEG vs CAT scan for brain tumour):

CAT scan is both more sensitive AND more specific than EEG for brain tumour diagnosis

8. PREDICTIVITY (PREDICTIVE VALUE) OF SCREENING TEST

Definition: Predictive value reflects the diagnostic power of a test - the probability that a person with a positive test truly has the disease, or that a person with a negative test truly does not have the disease.

Two types:

A. Predictive Value of a Positive Test (PVP):

PVP = a/(a+b) × 100 = True Positives / (True Positives + False Positives) × 100

Answers: "If the test is positive, what is the probability that the person actually has the disease?"

B. Predictive Value of a Negative Test (PVN):

PVN = d/(c+d) × 100 = True Negatives / (True Negatives + False Negatives) × 100

Answers: "If the test is negative, what is the probability that the person is truly disease-free?"

Factors affecting predictive value:

Sensitivity of the test
Specificity of the test
Prevalence of the disease in the population - this is the most important factor
- As prevalence increases, PVP increases and PVN decreases
- As prevalence decreases, PVP decreases even with high sensitivity/specificity

Key principle: Even a highly sensitive and specific test has low positive predictive value when applied to a low-prevalence population.

9. IDEAL SCREENING TEST

According to K. Park, an ideal screening test should have the following characteristics:

Properties of the test:

Simple - easy to perform and interpret
Rapid - results available quickly
Inexpensive - economically viable for mass application
Safe - no risk or harm to the subject
Acceptable - accepted willingly by the population
Sensitive - 100% sensitive (no false negatives)
Specific - 100% specific (no false positives)
Repeatable (Reliable) - consistent results when repeated

Additional criteria:

High positive predictive value
Should be able to detect disease at a latent/pre-symptomatic stage
Should be followed by effective treatment that alters the prognosis

Note: In practice, no test is 100% sensitive AND 100% specific simultaneously, since these two properties are inversely related.

10. SCREENING TESTS IN SERIES AND PARALLEL

When no single test is both 100% sensitive and specific, two or more tests are combined:

A. Tests in Series (Sequential Testing)

The second test is applied only to those who were positive on the first test
A person is labelled positive only if ALL tests in the series are positive
Effect:
- Increases Specificity (reduces false positives)
- Decreases Sensitivity (increases false negatives)
Used when: High specificity is required (e.g., confirmatory testing before invasive treatment)
Example: RPR screening for syphilis followed by TPHA confirmation

B. Tests in Parallel (Simultaneous Testing)

Both tests are applied simultaneously to all subjects
A person is labelled positive if ANY ONE test is positive
Effect:
- Increases Sensitivity (reduces false negatives)
- Decreases Specificity (increases false positives)
Used when: High sensitivity is required (e.g., blood bank screening, emergency situations)

Summary:

Feature	Series	Parallel
Sensitivity	Decreases	Increases
Specificity	Increases	Decreases
False Positives	Decreases	Increases
False Negatives	Increases	Decreases

11. INTERPRETATION OF DISTRIBUTION CURVE IN TERMS OF SENSITIVITY AND SPECIFICITY

K. Park describes two distribution patterns:

A. Bimodal Distribution

When diseased and non-diseased populations show two separate bell-shaped curves with little or no overlap
A cut-off point (discriminant value) can be placed between the two curves
Result: Both sensitivity AND specificity can be high
The cut-off is set where the two distributions intersect
Example: Normal vs. clearly elevated blood glucose (overt diabetes)

B. Unimodal Distribution

When there is a continuous distribution and the two populations (diseased and non-diseased) substantially overlap
Setting the cut-off point involves a trade-off:
- Lowering the cut-off (e.g., lower blood glucose threshold for diabetes): increases sensitivity, decreases specificity
- Raising the cut-off: increases specificity, decreases sensitivity
Example: Blood pressure - there is no clear bimodal separation between "hypertensive" and "normotensive"

Which is more important - sensitivity or specificity? K. Park states: "No categorical answer can be given." It depends on:

If missing a case is dangerous - prioritize sensitivity
If false positive labelling causes harm - prioritize specificity

12. LIKELIHOOD RATIO

The Likelihood Ratio (LR) indicates how much a test result changes the probability of disease. It combines sensitivity and specificity into a single measure.

Positive Likelihood Ratio (LR+):

LR+ = Sensitivity / (1 - Specificity) = True Positive Rate / False Positive Rate

Indicates how much more likely a positive test result is in a person with disease compared to someone without disease
LR+ > 10 is considered very useful; LR+ = 1 means the test has no value

Negative Likelihood Ratio (LR-):

LR- = (1 - Sensitivity) / Specificity = False Negative Rate / True Negative Rate

LR- < 0.1 is considered very useful for ruling out disease

Advantages of LR:

Can be applied to tests with multiple cut-off levels
Can be combined with pre-test probability (Bayes' theorem) to calculate post-test probability
Not affected by disease prevalence (unlike predictive values)

13. RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE

The ROC curve is a graphical method used to evaluate the overall performance of a diagnostic/screening test and to choose the best cut-off point.

Construction:

X-axis: False Positive Rate (1 - Specificity)
Y-axis: True Positive Rate (Sensitivity)
The curve is plotted by calculating sensitivity and specificity at multiple cut-off values

Interpretation:

A perfect test would have a curve passing through the top-left corner (100% sensitivity, 0% false positive rate) - area under curve (AUC) = 1.0
A useless test (no discrimination) would fall along the diagonal line from bottom-left to top-right - AUC = 0.5
The AUC (Area Under the Curve) is the overall measure of test accuracy:
- AUC = 0.5: No discrimination
- AUC = 0.7-0.8: Acceptable
- AUC = 0.8-0.9: Excellent
- AUC = >0.9: Outstanding

Uses of ROC curve:

To select the optimal cut-off point (best balance of sensitivity and specificity)
To compare two or more diagnostic tests - the test with the larger AUC is superior
To evaluate overall test performance

14. BAYES' THEOREM

Bayes' theorem provides a mathematical framework to revise the probability of disease (post-test probability) based on:

Pre-test probability (prior probability = disease prevalence)
Test result (positive or negative)
Test characteristics (sensitivity and specificity)

Formula:

Post-test probability (PVP) = (Prevalence × Sensitivity) / [(Prevalence × Sensitivity) + (1-Prevalence) × (1-Specificity)]

Key concept:

Pre-test probability = prevalence of the disease in the population
Post-test probability = probability that a person with a positive test actually has the disease (= Positive Predictive Value)

Clinical relevance:

Even with a highly sensitive and specific test, if the pre-test probability (prevalence) is very low, the post-test probability (PVP) will remain low
This is why screening low-prevalence populations can lead to many false positives

Bayes' theorem using Likelihood Ratio:

Post-test odds = Pre-test odds × Likelihood Ratio Post-test probability = Post-test odds / (1 + Post-test odds)

15. ACTUAL BAYES' THEOREM (Application with 2x2 Table)

The actual (applied) Bayes' theorem can be demonstrated using a worked example with the 2x2 table. K. Park illustrates this with a numerical example:

Example:

Disease prevalence = 10% (pre-test probability = 0.10)
Test Sensitivity = 90%
Test Specificity = 90%
Population screened = 1000

Constructing the 2x2 table:

True diseased = 100 (10% of 1000)
True non-diseased = 900

	Disease +	Disease -	Total
Test +	90 (TP)	90 (FP)	180
Test -	10 (FN)	810 (TN)	820
Total	100	900	1000

Calculations:

Sensitivity = 90/100 = 90%
Specificity = 810/900 = 90%
PVP = 90/180 = 50% - only 50% of those testing positive actually have disease!
PVN = 810/820 = 98.8%

Key lesson: Even with 90% sensitivity and 90% specificity, when prevalence is only 10%, only half of all positive test results are true positives. This demonstrates why Bayes' theorem and disease prevalence are fundamental to interpreting screening results.

Source: Park's Textbook of Preventive and Social Medicine (K. Park), Chapter on Epidemiology - Screening for Diseases (pp. 154-162)

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