1. Screening and Diagnostic Testing Sue Lindsay, Ph.D., MSW, MPH Division of Epidemiology and Biostatistics Institute for Public Health San Diego State University

2. Early Diagnosis of Disease Prompt attention to the earliest symptoms Detection of disease in asymptomatic individuals

3. Early Diagnosis of Disease Screening and diagnostic tests improve the ability to estimate the probability of the presence or absence of a disease

4. Screening vs. Diagnostic Tests Screening Tests Tests performed on asymptomatic individuals with the goal of detecting pre-clinical cases of disease Diagnostic Tests Tests performed to increase probability of disease identification and confirmation in cases of suspected disease How good is your test?

5. The Progress of Disease ExposureDeath Disease begins Disease or precursor detectable by screening Screening Test + Symptoms begin Disease confirmed by diagnostic testing “Gold standard” pre-clinical lead time

6. Considerations for Screening Programs 1.The disease should be a significant public health problem 2.There should be a recognizable latent or early symptomatic stage 3.There should be a suitable screening test acceptable to the population 4.There should be well-established and available diagnostic tests 5.There should be an accepted treatment for the disease 6.Facilities for diagnosis and treatment should be available 7.The cost of case-finding, diagnosis, and treatment should be anticipated 8.The process should be regular and on-going

7. Participation in Screening Programs 1.The disease must be known to the individual. 2.It must be regarded as a serious threat to health 3.Each individual must feel vulnerable to the disease 4.There must be a firm belief that action will have meaningful results

8. The Screening 2X2 Table a true-positives DiseaseNo Disease Test Positive Test Negative b false-positives c false-negatives d true-negatives a + c a+b+c+d Prevalence of disease =

9. Sensitivity and Specificity a true-positives DiseaseNo Disease Test Positive Test Negative b false-positives c false-negatives d true-negatives a a+c Sensitivity = True positives All with disease = Specificity = d b+d = True negatives All without disease

10. Important! Determination of the sensitivity and specificity of a test requires that a diagnosis of disease be established or ruled out for every person tested by the screening procedure, regardless of whether he screens negative or positive The diagnosis must be established by techniques independent of the screening test

11. Sensitivity The greater the sensitivity, the more likely the tests will detect persons with the disease. A negative result on a test with excellent sensitivity can virtually rule out disease Specificity The greater the specificity, the more likely it is that persons without the disease will be excluded A positive result on a test with excellent specificity will strongly suggest the presence of disease. Sensitivity and Specificity are descriptors of the accuracy of a test

12. Sensitivity and Specificity a true-positives DiseaseNo Disease Test Positive Test Negative b false-positives c false-negatives d true-negatives a a+c Sensitivity = True positives All with disease = Specificity = d b+d = True negatives All without disease

13. Sensitivity and Specificity 34 DiabetesNo Diabetes Glucose Tolerance Positive Glucose Tolerence Negative 20 1169,830 34 34 +116 Sensitivity = 22.6% = Specificity = 9,830 20 + 9,830 =99.7%

14. Predictive Value a true-positives DiseaseNo Disease Test Positive Test Negative b false-positives c false-negatives d true-negatives a a+b Positive Predictive Value = PV+ True positives All who test positive = Negative Predictive Value = PV- d c+d = True negatives All who test negative

15. Positive Predictive Value The percentage of persons with positive test results who actually have the disease How likely is it that the disease of interest is present if the test is positive? Negative Predictive Value The percentage of persons with negative test results who do not have the disease of interest How likely is it that the disease of interest is not present if the test is negative? Predictive Values are estimates of the probability of the presence or absence of disease based on the test result

16. Predictive Value a true-positives DiseaseNo Disease Test Positive Test Negative b false-positives c false-negatives d true-negatives a a+b Positive Predictive Value = PV+ True positives All who test positive = Negative Predictive Value = PV- d c+d = True negatives All who test negative expensive !

17. Predictive Value 140 GlaucomaNo glaucoma Intraocular pressure + Intraocular pressure - 80 10910 140 140 + 80 Positive Predictive Value = PV+ Negative Predictive Value = PV- 910 10+910 = 64%= 99%

18. Screening and Diagnostic Tests Breast Cancer Clinical Breast Exam Screening Mammogram Diagnostic Mammogram Fine Needle Aspiration Biopsy Core Biopsy Excisional Biopsy (gold standard)

19. Predictive Values are Influenced by Prevalence of Disease 36 Disease No disease Test positive Test negative Sensitivity = 36/40 = 90% 4912 48 Test positive Test negative 940 50 1 9 DiseaseNo disease Specificity = 912/960 = 95% PV+ = 36/84 = 43% PV- = 912/916 = 99.5% Prevalence = 40/1,000 = 4% Prevalence = 10/1,000 = 1% Sensitivity = 9/10 = 90% Specificity = 940/990 = 95% PV+ = 9/59 = 15.3% PV- = 940/941 = 99.8% 1,000

20. Yield The yield of a screening test is the amount of previously unrecognized disease that is diagnosed with screening 1.Yield is influenced by: 1.The sensitivity of the test 2.The prevalence of unrecognized disease in the population 2.In screening tests, a high positive predictive value is desirable. 3.However, if the prevalence of a disease is low, even a highly sensitive test will yield a low positive predictive value 4.For the most yield, screening should be aimed at populations with a high prevalence of disease

21. An Example A manufacturer would like to sell you a new rapid screening test developed to screen for strep throat. You know the prevalence of strep throat in your pediatric population in the high peak season is 27%. The manufacturer of the new test describes the sensitivity as 70% and the specificity as 73%. Assuming that you will use this test with 1,000 children, what are the positive and negative predictive values of this test in your population? Would you buy this product?

22. Strep Throat Example 189 Strep ThroatNo Strep Throat Test positive Test negative 197 81533 1,000 Prevalence is 27% 270 Sensitivity is 70% Specificity is 73% 730 Positive predictive value = 189/386 = 49% Negative predictive value = 533/614 = 87% 386 614

23. The probability of a particular test result for a person with the disease Likelihood Ratios The probability of a particular test result for a person without the disease Likelihood ratios do not vary with prevalence

24. Likelihood Ratio for a Positive Test The probability of a positive test result for a person with the disease The probability of a positive test result for a person without the disease The larger the size of the LR+, the better the diagnostic value of the test An LR+ value of 10 or greater is considered a good test Likelihood Ratio for a Negative Test The probability of a negative test result for a person with the disease The probability of a negative test result for a person without the disease The smaller the size of the LR-, the better diagnostic value of the test An LR- value of 0.10 or less is considered a good test Likelihood Ratios

25. Likelihood Ratio a true-positives DiseaseNo Disease Test Positive Test Negative b false-positives c false-negatives d true-negatives a/a+c b/b+d Likelihood ratio for positive test = Sensitivity (1-Specificity) Likelihood ratio for neg test = c/a+c d/b+d = (1-Sensitivity) = LR+ LR- Specificity

26. Likelihood Ratio is Not Influenced by Prevalence 36 Disease No disease Test positive Test negative Sensitivity = 36/40 = 90% 4912 48 Test positive Test negative 940 50 1 9 DiseaseNo disease Specificity = 912/960 = 95% LR+ = 36/40= 0.90 = 18 48/960 0.05 LR- = 4/40 = 0.10 = 0.10 912/960 0.95 Prevalence = 40/1,000 = 4% Prevalence = 10/1,000 = 1% Sensitivity = 9/10 = 90% Specificity = 940/990 = 95% LR+ = 9/10 = 0.90 = 18 50/990 0.05 LR- = 1/10 = 0.10 = 0.10 940/990 0.95 1,000

27. Screening Tests with Categorical Results: Mammography: BIRADS 1: negative BIRADS 2: benign BIRADS 3: probably benign BIRADS 4: suspicious for cancer BIRADS 5: highly suggestive for malignancy What is Abnormal? The decision about what results to call “abnormal” will effect sensitivity, specificity, and predictive values of your screening tests. Cut-Points for Screening Tests

28. Screening Tests with Continuous Results: Blood Pressure Cholesterol Levels Blood sugar What is Abnormal? There are many options concerning where to set the cut-off point Along a continuous scale, different cut-off points will result in differing levels of sensitivity and specificity As sensitivity increases, specificity decreases Low cut-points are very sensitive, but not specific Those with disease are correctly classified, but those without disease are not High cut-points are very specific, but not sensitive Those without disease are correctly classified, but those with disease are not How to you decide the cut-off point? Cut-Points for Screening Tests

29. Blood Glucose and Diabetes Sensitivity and Specificity at Different Cut-Off Points Blood Glucose Level Sensitivity Specificity 200 180 160 140 120 100 80 37 50 56 74 89 97 100 99 98 91 68 25 2 Percent diabetics correctly identified Percent non-diabetics correctly identified

30. ROC Curves (Receiver Operating Characteristics) Sensitivity (signal) (1-Specificity) (noise)

31. The Evaluation of Screening Programs Does early detection of disease: 1.Reduce morbidity? 2.Reduce mortality? 3.Improve quality of life? 4.Reduce cost of disease?

32. Lead-Time Bias Survival time is increased in those screened because of earlier detection May be no actual improvement in disease progression or mortality Length-Biased Sampling Disease detected by screening is less aggressive than disease detected without screening. Cases detected with a screening program tend to have longer pre-clinical stages than those missed by screening Patient Self-Selection Bias Individuals who participate in screening programs may differ from those who do not on characteristics that may be related to survival Bias in the Evaluation of Screening Programs