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Screening

Screening. Principles of Epidemiology Lecture 12 Dona Schneider , PhD, MPH, FACE. Principles Underlying Screening Programs. Validity – the ability to predict who has the disease and who does not Sensitivity – the ability of a test to correctly identify those who have the disease

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Screening

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  1. Screening Principles of Epidemiology Lecture 12 Dona Schneider, PhD, MPH, FACE

  2. Principles Underlying Screening Programs • Validity – the ability to predict who has the disease and who does not • Sensitivity – the ability of a test to correctly identify those who have the disease • A test with high sensitivity will have few false negatives • Specificity – the ability of a test to correctly identify those who do not have the disease • A test that has high specificity will have few false positives

  3. Principles Underlying Screening Programs (cont.) • An ideal screening test would be 100% sensitive and 100% specific – that is there would be no false positives and no false negatives • In practice these are usually inversely related • It is possible to vary the sensitivity and specificity by varying the level at which the test is considered positive

  4. Calculating Measures of Validity True Diagnosis Disease Test Result No Disease Total a b a+b Positive c c+d Negative d Total a+c b+d a+b+c+d

  5. Note the Following Screening Relationships • Specificity + false positive rate = 1 d/(b+d) + b/(b+d) = 1 • If the specificity is increased, the false positive rate is decreased • If the specificity is decreased, the false positive rate is increased • Sensitivity + false negative rate = 1 a/(a+c) + c/(a+c) = 1 • If the sensitivity is increased, the false negative rate is decreased • If the sensitivity is decreased, the false negative rate is increased

  6. Probability of Disease • Pre-test probability of disease = disease prevalence • Post-test probability of disease = • If normal, c/(c+d) • If negative, a/(a+b)

  7. Interrelationship Between Sensitivity and Specificity

  8. Sensitivity and Specificity of a Blood Glucose Level Sensitivity and Specificity of a Blood Glucose Level of 110 mg/100 ml for Presumptive Determination of Diabetes Status Blood Glucose Level (mg/100 ml) Diabetics (Percent) Nondiabetics (Percent) All those with level over 110 mg/100 ml are classified as diabetics 92.9 (true positives) 51.6 (false positives) All those with level under 110 mg/100 ml are classified as nondiabetics 7.1 (false negatives) 48.4 (true negatives) 100.0 100.0

  9. Adjusting Sensitivity and Specificity by Adjusting Cut Points

  10. Which is Preferred: High Sensitivity orHigh Specificity? • If you have a fatal disease with no treatment (such as for early cases of AIDS), optimize specificity • If you are screening to prevent transmission of a preventable disease (such as screening for HIV in blood donors), optimize sensitivity

  11. Remember…. • Sensitivity and specificity are functions of the screening test • If you use a given screening test on a low prevalence population, you will have a low positive predictive value and potentially many false positives

  12. Elisa is about 90% sensitive and 99% specific Translated into Real Life….. Population PV+ PV- Prevalence of HIV 1.5% 58% 99.8% NJ (7 million) Disease Yes Disease No Total Test + 94,500 68,950 163,450 Test- 10,500 6,826,050 6,836,550 Total 105,000 6,895,000 7 million Efficiency of test = (TP + TN)/Total tested = 98.9% But, 10,500 people who are HIV+ think they are disease free Another 68,950 are frightened into believing they have the disease and require more testing

  13. If You Change To a High Risk Population, You Get Better Results…. Population PV+ PV- Prevalence of HIV IV Drug User 50% 98.9% 90.8% Total Disease Yes Disease No Test + 3,150 3,185 35 Test - 350 3,465 3,185 3,500 3,500 7,000 Total Efficiency of test = (TP + TN)/Total tested = 94.5% Now 350 people who are HIV+ think they are disease free But only 35 are frightened into believing they have the disease and require more testing

  14. Suppose You Have a Very High Prevalence? • HIV seropositivity is 90% among IV drug users in Newark PV+ = 99.9% PV- = 52% • But, why bother to screen?

  15. Example: Breast Cancer Screening Breast Cancer Mammogram Results Disease No Disease Total Positive 132 983 1,115 Negative 45 63,650 63,695 Total 177 64,633 64,810

  16. Example: Disease X (prevalence = 2%) True Diagnosis of Disease X No Disease Total Disease Test Results 18 67 Positive 49 2 Negative 933 931 Total 1000 20 980

  17. Example: Disease X (prevalence = 1%) True Diagnosis of Disease X Disease No Disease Total Test Results 9 49.5 58.5 Positive Negative 940.5 941.5 1 980 1000 Total 10 To increase positive predictive value increase prevalence by screening high risk populations

  18. Importance of Prevalence in Screening Assume we have a test for AIDS which has a sensitivity of 100% and a specificity of 99.995%. We wish to apply it to female blood donors who have an HIV prevalence of 0.01% and we wish to apply it to male homosexuals in San Francisco, in whom the prevalence is 50%. For every 100,000 screened we find: Female Donors True Diagnosis of HIV Test Results Disease No Disease Total 10 5 15 Positive 99,985 0 99,985 Negative Total 10 99.990 100,000 PV+ = 0.66667 True Diagnosis of HIV Male Homosexuals Disease No Disease Total 50,003 3 Positive 50,000 49,997 Negative 0 49,997 Total 50,000 50,000 100,000 PV+ = 0.99994

  19. Disease Disease - + + - + 250 250 500 100 + 400 500 Test Test 500 - 250 250 500 - 400 100 200 1,000 500 500 1,000 800 Prev = 50%, Sens = 50%, Spec = 50%, PV = 250/500 = 50% Prev = 20%, Sens = 50%, Spec = 50%, PV = 100/500 = 20% Disease Disease - - + + 100 80 + 400 580 + 180 180 Test 420 820 720 Test 400 100 - - 20 1,000 1,000 200 800 200 800 Prev = 20%, Sens = 50%, Spec = 90%, PV = 100/180 = 56% Prev = 20%, Sens = 90%, Spec = 50%, PV = 180/520 = 31% Relationship of Specificity to Predictive Value

  20. Suppose You Are Faced With the Following Brain Teaser • In a given population of 1,000 persons, the prevalence of Disease X is 10%. You have a screening test that is 95% sensitive and 90% specific. • What is the positive predictive value? • What is the efficiency of the test?

  21. Suppose You Are Faced With the Following Brain Teaser (cont.) • Set up a 2x2 table True Diagnosis of Disease X Disease No Disease Test Results Total Positive True Positive False Positive Negative False Negative True Negative Total 100 900 1000

  22. Suppose You Are Faced With the Following Brain Teaser (cont.) True Diagnosis of Disease X Test Results Disease No Disease Total Positive 90 185 95 Negative 5 810 815 Total 100 900 1000

  23. Principles Underlying Screening Programs • Reliability – the ability of a test to give consistent results when performed more than once on the same individual under the same conditions • Variation in the method due to variability of test chemicals or fluctuation in the item measured (e.g., diurnal variation in body temperature or in relation to meals) • Standardize fluctuating variables • Use standards in laboratory tests, run multiple samples whenever possible • Observer variation • Train observers • Use more than one observer and have them check each other

  24. Principles Underlying Screening Programs • Yield – the amount of previously unrecognized disease that is diagnosed and brought to treatment as a result of the screening program • Sensitivity • You must detect a sufficient population of disease to be useful • Prevalence of unrecognized disease • Screen high risk populations • Frequency of screening • Screening on a one time basis does not allow for the natural history of the disease, differences in individual risk, or differences in onset • Diseases have lead time • Participation and follow-up • Tests unacceptable to those targeted for screening will not be utilized

  25. Conditions for Establishing Screening Programs • The condition should be an important health problem • There should be an accepted treatment for patients with recognized disease • If there is no treatment, it is premature to institute screening • Facilities for diagnosis and treatment should be available • It is unethical to screen without providing possibilities for follow-up • There should be a recognizable latent or early symptomatic stage • If early detection does not improve survival, there is no benefit from screening

  26. Conditions for Establishing Screening Programs (cont.) • There should be a suitable test for examination, with sufficient sensitivity and specificity to be of use in identifying new cases • The test should be acceptable to the population • The natural history of the condition, including development from latent to declared disease, should be adequately understood • There should be an agreed-upon policy concerning whom to treat as patients

  27. Conditions for Establishing Screening Programs (cont.) • The cost of case-finding should be economically balanced in relation to possible expenditure on medical care as a whole • Case-finding should be in a continuing process and not a one-time project

  28. Biases in Screening • Referral Bias (volunteer bias) • Length Bias • Screening selectively identifies those with a long preclinical and clinical phase (i.e., those who would have a better prognosis regardless of the screening program)

  29. Biases in Screening (cont.) • Lead Time Bias • The apparently better survival that is observed for those screened is not because these patients are actually living longer, but instead because diagnosis is being made at an earlier point in the natural history of the disease

  30. Biases in Screening (cont.) • Overdiagnosis Bias (a misclassification bias) • Enthusiasm for a new screening program may result in a higher rate of false positives and give false impression of increased rates of diagnosis and detection • Also, false positives would result in unrealistically favorable outcomes in persons thought to have the disease

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