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1. Introduction

Screening is the application of a test to detect a potential disease or condition in a person who has no known signs of that disease or condition. So notes Dr. David Eddy in his chapter “How to think About Screening” in Screening for Diseases, Prevention in Primary Care. “There are two main purposes for screening,” he continues. “One is to detect a disease early in its natural history when treatment might be more effective, less expensive, or both. The other purpose is to detect risk factors that put a person at a higher than average risk for developing a disease, with the goal of modifying the risk factor or factors to prevent the disease.”

Patients who have symptoms of a disease, or have signs of a disease on physical examination, have a “work-up” for the disease to detect its presence or absence. This is not screening. An example of a work-up would be a 55 year-old male complaining of substernal chest pain, or a woman presenting to the physician with a new breast lump.

2. Screening Levels of Prevention

  • Primary: To prevent the disease from occurring; Example: cholesterol screening to prevent heart disease.
  • Secondary: To reduce the impact of a disease; Example: mammogram screening to identify patients at an early stage of breast cancer that may be favorably altered.
  • Tertiary: To improve the quality of life associated with a disease; Example: Metastatic bone screen in certain cancers to prevent pathologic fractures.

3. Some Goals of Screening Programs

3.1. Exclusionary

Examples: Military; sport physicals; pilot license

3.2. Societal Protection

Examples: TB; eye test for driver's license; pilot license

3.3. Prevention and Control of Disease

Examples: Diabetes Mellitus; Hemophilia; Sickle-Cell Disease

3.4. Surveillance and Monitoring

Examples: HIV status in high risk groups; Hepatitis B status in IV drug abusers

4. Diseases Appropriate for Screening

  • Serious Diseases
  • Treatment begun before symptoms develop should be more beneficial than treatment begun after symptoms develop.
    Natural History of Disease

    _____a_______b___________c__________d____

    a: disease begins b: disease detectable by screening c. symptoms develop d: outcome

  • Prevalence of disease should warrant testing.

5. Examples of screening programs in primary care medicine

  • Pap smears re cervical cancer (Primary and Secondary)
  • Blood sugars re diabetes mellitus (Secondary)
  • Mammograms re breast cancer (Secondary)
  • Sigmoidoscopies re colo-rectal cancer (Secondary)
  • Cholesterol re vascular disease (Primary and Secondary)
  • PKU blood testing in newborns (Secondary)
  • Blood pressure re vascular disease (Primary and Secondary)

6. Diseases not routinely screened in primary care medicine

  • Coronary artery disease in young adults
  • Degenerative joint disease in the elderly

(See November 30, 2000 article appearing in the New England Journal of Medicine titled "Screening for Lung Cancer".)

7. Criteria for Test Selection

  • Available
  • Inexpensive
    • Cost Benefit Analysis: Total costs saved as a result of the screening process divided by the total costs of the screening program
    • Cost Effective Analysis: a.)Total cost of screening program per diagnosis, or b.) Total cost of the screening program per life year saved, or c.) Total cost of the screening program divided by the quality adjusted life year saved.
  • Low Risk
  • Easily performed
  • Reliable: results reproducible
  • Accurate: results are correct

Various groups, such as the American Cancer Society or the American College of Cardiology, publish screening recommendations. Sometimes there are different recommendations from different groups.Ideally, such decisions are based on evidence supported by studies. Dr. Eddy points out that RCTs to determine the benefits of a screening program can be subject to dilution bias, patients offered the screening test don’t receive it, and contamination bias, patients not offered the screening test receive it anyway.In addition to these potential internal validity biases, trials done in an experimental setting might not reflect what would happen in actual practice, an external validity concern.

Another issue to consider is lead time bias. Because the screening process has presumably identified a disease earlier in its natural history, it might appear that patients live longer than they would have had they not had a screening test. “Because of this,” Dr. Eddy cautions, “a comparison of survival rates in screened or unscreened populations can be misleading.”

8. Measures of Test Performance

8.1. Sensitivity:

Given the disease is present, the likelihood of testing positive.

Example: 100 people are known to have AIDS. A new HIV test done on these people says 90 are positive and 10 are negative. The sensitivity is 90/100 = 90%. The 90 patients are true positives because they really have the disease. The other 10 patients who test negative are false negatives because they were incorrectly called negative for the disease.

Disease Positive
Test Positive 90 (True Positives)
Test Negative 10 (False Negatives)
Total 100

Sensitivity = 90 / [ 90 + 10 ] = 90%

= TP/ [ TP + FN ]

8.2. Specificity:

Given the disease is not present, the likelihood of testing negative.

Example: 200 people are known to be fully healthy. A screening VDRL indicates 190 are negative while 10 have latent syphilis. The specificity is 190 / 200 = 95%

Disease Negative
Test Positive 10 (False Positives)
Test Negative 190 (True Negatives)
Total 200

Specificity = 190 / [ 190 + 10 ] = 95%

= TN / [ TN + FP ]

Further Examples: 80 people are known to have alcoholic hepatitis and 60 people are known not to have alcoholic hepatitis. The sensitivity of serum SGOT for alcoholic hepatitis is 90% and its specificity is 70%. How many true positives and false positives were identified?

Disease Positive Disease Negative
SGOT Positive (80) (0.9) = 72 (60) - (42) = 18
SGOT Negative (80) - (72) = 8 (60) (0.7) = 42

TP = 72; FN = 8; TN = 42; FP = 18

9. Interpreting Test Results

9.1. Predictive Value Positive:

Given a test is positive, the likelihood disease is present.

Example: 60 patients with a positive exercise stress test are referred for coronary artery angiography (cardiac catheterization) that showed 10 of them had coronary artery disease (CAD) and 50 did not. What was the predictive value positive of the stress test for CAD?

CAD Positive CAD Negative
Stress Test Positive 10 (True Positives) 50 (False Positives)

PV+ = 10 / [ 10 + 50 ] = 10/60 = 16.7%

= TP / [ TP + FP ]

9.2. Predictive Value Negative:

Given a test is negative, the likelihood disease is not present.

Example: 250 people have a negative lung scan for pulmonary embolus (PE). After undergoing a pulmonary artery angiography, 25 of them are shown to have a pulmonary embolus (PE). What was the predictive value negative of the lung scan for PE? (Note: A lung scan is not a screening test as defined in this lecture. Rather, it is part of a work-up when a concern has been raised. However, the concept as it pertains to predictive values is the same as it is for screening tests.)

PE Positive PE Negative
Lung Scan Negative 25 (False Negatives) 225 (True Negatives)

PV (-) = 225 / [ 225 + 25 ] = 225/250 = 90%

= TN / [ TN + FN ]

10. Baye's Theorem:

Combines the sensitivity and specificity of a given test with the disease prevalence of a tested group to determine predictive values.

Example: An HIV test has 90% sensitivity and 95% specificity. Researchers test 1000 high-school students with an HIV+ prevalence of 1%. What are the predictive values?

HIV Positive HIV Negative
Test Positive (10) (0.90) = 9 (990) - (941) = 49
Test Negative (10) - (9) = 1 (990) (0.95) = 941
Total (1000) (0.01) = 10 (1000) - (10) = 990

PV(+) = 9/58 = 15.5%
PV(-) = 941/942 = 99.9%

The researchers then test 1200 IV drug abusers with an HIV+ prevalence of 40%. What are the predictive values with this group

HIV Positive HIV Negative
Test Positive (480) (0.90) = 432 (720) - (684) = 36
Test Negative (480) - (432) = 48 (720) (0.95) = 684
Total (1200) (0.40) = 480 (1200) - (480) = 720

PV(+) = 432/468 = 92.3%
PV(-) = 684/732 = 93.4%

As the prevalence of HIV(+) increased from the high-school students to the IV drug abusers, the PV(+) increased from 15.5% to 92.3% while the PV(-) decreased from 99.9% to 93.4%. This change had nothing to do with the test's sensitivity and/or specificity that did not change. These results demonstrate why it may not be beneficial to test low prevalence groups for a disease, no matter how well the screening test itself performs.

Note: As the prevalence increases, the predictive value positive increases and the predictive value negative decreases. Conversely, as the prevalence decreases, the predictive value negative increases and the predictive value positive decreases.

Following is a diagram that may help you to remember how to calculate sensitivity, specificity and the predictive values.

Predictive Value

11. Ancillary Material

11.1. Readings

11.1.1. Required

  • Read Chapter 4, Assessing the Validity and Reliability of Diagnostic and Screening Tests (Skip: Use of Multiple Tests, Simultaneous Testing and Kappa Statistic), Gordis Text
  • Read Chapter 17, The Epidemiologic Approach to Evaluation of Screening Programs, Gordis Text