1. HSS4303B Intro to Epidemiology Feb 4, 2010 – Screening Tests

2. Student Obesity Conference www.studentobesitymeeting.ca June 9-12 at uOttawa Abstract deadline is Feb 12 Registration fee is $95 (includes meals, etc) 200 students + 25 mentors

3. CSEB Student Conference May 27-28, 2010 Kingston Details will be posted on www.cseb.ca

4. Your Abstracts Marks are now posted 3 people did not submit Min = 5.7/10 Max=9.5/10 Mean = 7.6/10 No one failed (except the above 3)

5. Your abstracts – Erin’s comments The students who got >85% were clear about their research topics and provided intros and conclusions. Students who got between 70 and 85% followed the instructions, but there is some variance in marks due to style/grammar, and the quality of their references. Students who received a grade of <70% did not follow the instructions – ie. all of their references were web-based (PHAC, StatsCan, etc.) – they went way over the word count (some over 400 or 500 words) – and/or there was nothing at all related to epidemiology in their abstract.

6. Erin’s Office Hours Erin can be available during reading week. Does anyone intend to come by? She will not be available March 4 Always available by appointment

7. Tuberculosis What is it? We apply tuberculin skin test (also called PPD – purified protein derivative) test Positive response is an “induration” – a hard, raised area with clearly defined margins at and around the injection site

8. What type of curve is this?

9. Bimodal curve ________________ identifies two types of traits in a population _________________ separates individuals ho had not prior experience with tuberculosis from those who had prior experience Bimodal distribution allows to separate people on the basis of the trait, characteristic or disease However, for many of the conditions and diseases people fall under uni-modal distribution What’s it called when there’s only one hump?

10. Distribution of systolic blood pressure for men (unimodal distribution)

11. Unimodal curve _________________ has a single peak with normal distribution or tailed distribution Since it does not categorize people an arbitrary cutoff has to be used to separate people as hypertensive or normotensive Cutoff is usually based on statistical evidence, however, biological, genetic and other information also need to be considered Which men are at a higher risk of stroke, myocardial infraction Unimodal or bimodal there will still be people in the grey zone and there is uncertainty about these cases

12. So…. We are concerned about TB and High BP in the population, and we have screening tests for both But you can see that the challenges are different for both types of screening tests

13. What is a Screening Test? A test given to persons who do not show clinical signs of a disease to nonetheless test for that disease Validity Reliability Sensitivity Specificity

14. Examples of screening tests? PSA CT scans Pregnancy tests DRE Phenylketonuria (PKU) Test

15. Validity ability to distinguish between those who have the disease and those who do not have the disease i.e., is it detecting what it says it’s detecting?

16. e.g. PPD Test The PPD test purports to test for TB infection However, it is possible to get a reaction from the BCG TB vaccine (which is not available in North America) With respect to distinguishing between actual TB exposure and vaccine exposure, the PPD test has poor validity

17. There are many types of validity Internal vs External – Refers to the validity of a study – Not relevant for screening tests – We’ll revisit this dude

18. There are many types of validity Construct validity – The extent to which the measurement corresponds to theoretical concepts – "Are we actually measuring (are these means a valid form for measuring) what (the construct) we think we are measuring?" – IQ test

19. Validity Content validity – Also known as “logical validity” – The extent to which the test incorporates all that is known about the disease – Eg. If test purports to measure “functional health” then it should include measurements of social happiness, etc, and not just biological markers

20. Validity Criterion validity – The extent to which the test correlates with an external criterion of the thing you’re studying Concurrent validity – The measurement and the criterion refer to the same point in time – If visually looking at a wound is your test for injuries in a battle, how do you know if the would was inflicted during the battle? Predictive validity – The measurement can predict the criterion – SAT scores are a good predictor of freshman marks

21. Reliability Can you repeat the test and get the same result? – Let’s say you’re measuring nose length to determine cancer risk --will you get different results everytime you measure the same nose? – Blood pressure has poor reliability because it changes every few minutes

22. Reliability of Screening Tests RELIABILITY: The extent to which the screening test will produce the same or very similar results each time it is administered. --- A test must be reliable before it can be valid. --- However, an invalid test can demonstrate high reliability.

23. Reliability of Screening Tests Sources of variability that can affect the reproducibility of results of a screening test: 1. Biological variation (e.g. blood pressure) 2. Reliability of the instrument itself 3. Intra-observer variability (differences in repeated measurement by the same screener) 4.Inter-observer variability (inconsistency in the way different screeners apply or interpret test results)

24. Measures of Validity Sensitivity Specificity

25. Measure of Validity Sensitivity – The probability of correctly diagnosing a case (case= person with the disease) – i.e. the proportion of truly diseased people who are identified as diseased by the test Specificity – The probability of correctly rejecting a case – i.e., “true negative rate”

26. Sensitivity/Specificity Screening test results Truly diseases (cases) Truly non- diseases Totals Positive (thinks it’s a case) aba+b Negative (thinks it’s not a case) cdc+d totalsa+cb +da+b+c+d

27. Sensitivity/Specificity Screening test results Truly diseases (cases) Truly non- diseases Totals Positive (thinks it’s a case) aba+b Negative (thinks it’s not a case) cdc+d totalsa+cb +da+b+c+d Sensitivity = a/(a+c)

28. Sensitivity/Specificity Screening test results Truly diseases (cases) Truly non- diseases Totals Positive (thinks it’s a case) aba+b Negative (thinks it’s not a case) cdc+d totalsa+cb +da+b+c+d Sensitivity = a/(a+c) Specificity = d/(b+d) How do you compute prevalence from these data? All cases / total pop=(a+c) / (a+b+c+d)

29. Example: Assume a population of 1,000 people, of whom 100 have a disease and 900 do not have the disease Screening Test to Identify the 100 People with the Disease True Characteristics in the Population Results of ScreeningDiseaseNo Disease Total Positive 180 Negative 820 Total100900 1,000 Can you fill in the blanks?

30. Example: Assume a population of 1,000 people, of whom 100 have a disease and 900 do not have the disease Screening Test to Identify the 100 People with the Disease True Characteristics in the Population Results of ScreeningDiseaseNo Disease Total Positive80100 180 Negative20800 820 Total100900 1,000

31. So…. What’s a false positive? What’s a false negative?

32. So…. What’s a false positive? – Test says positive but in reality it’s a negative What’s a false negative? – Test says it’s negative but in reality it’s a positibe

33. Sensitivity/Specificity Screening test results Truly diseases (cases) Truly non- diseases Totals Positive (thinks it’s a case) aba+b Negative (thinks it’s not a case) cdc+d totalsa+cb +da+b+c+d Sensitivity = a/(a+c) Specificity = d/(b+d) Which are the false positives? Which are the false negatives?

34. Sensitivity/Specificity Screening test results Truly diseases (cases) Truly non- diseases Totals Positive (thinks it’s a case) aba+b Negative (thinks it’s not a case) cdc+d totalsa+cb +da+b+c+d Sensitivity = a/(a+c) Specificity = d/(b+d) Which are the false positives? Which are the false negatives?

35. False positives and false negatives False positives – Burden on health care for follow tests – Anxiety and worry for the people – Psychosocial aspects of the label False negatives – Missed being diagnosed and provided with the timely treatment has compromised prognosis – Shock and disbelief upon diagnosis in advanced stage of the disease

36. So…. We define two more concepts: – Positive Predictive Value (PV+ or PPV) – Negative Predictive Value (PV- or NPV) These are measures of “performance yield”

37. PV+ Also called “precision rate” Also called “post-test probability of disease” the proportion of patients with positive test results who are correctly diagnosed Sounds like sensitivity, right?

38. PV- the proportion of patients with negative test results who are correctly diagnosed

39. Performance Yield People with positive screening test results will also test positive on the diagnostic test: Predictive Value Positive (PV+) People with negative screening test results are actually free of disease Predictive Value Negative (PV-)

40. Sensitivity/Specificity Screening test results Truly diseases (cases) Truly non- diseases Totals Positive (thinks it’s a case) aba+b Negative (thinks it’s not a case) cdc+d totalsa+cb +da+b+c+d Sensitivity = a/(a+c) Specificity = d/(b+d)

41. Sensitivity/Specificity Screening test results Truly diseases (cases) Truly non- diseases Totals Positive (thinks it’s a case) aba+b Negative (thinks it’s not a case) cdc+d totalsa+cb +da+b+c+d Sensitivity = a/(a+c) Specificity = d/(b+d) PV+ = a/(a+b)

42. Sensitivity/Specificity Screening test results Truly diseases (cases) Truly non- diseases Totals Positive (thinks it’s a case) aba+b Negative (thinks it’s not a case) cdc+d totalsa+cb +da+b+c+d Sensitivity = a/(a+c) Specificity = d/(b+d) PV+ = a/(a+b) PV- = d/(c+d)

43. Relationship between Sens/Spec and PV-/PV+

44. Performance Yield 400 98905 100 995 True Disease Status + - Results of Screening Test + - Compute sensitivity, specificity, PV+ and PV-

45. Performance Yield 400 98905 100 995 True Disease Status + - Results of Screening Test + - Sensitivity:a / (a + c) = 400 / (400 + 100) = 80% Specificity:d / (b + d) = 98905 / (995 + 98905) = 99% PV+:a / (a + b) = 400 / (400 + 995) = 29% PV-:d / (c + d) = 98905 / (100 + 98905) = 99%

46. Performance Yield 400 98905 100 995 True Disease Status + - Results of Screening Test + - PV+:a / (a + b) = 400 / (400 + 995) = 29% Among persons who screen positive, 29% are found to have the disease.

47. Performance Yield 400 98905 100 995 True Disease Status + - Results of Screening Test + - PV-:d / (c + d) = 98905 / (100 + 98905) = 99.9% Among persons who screen negative, 99.9% are found to be disease free.

48. Performance Yield Factors that influence PV+ and PV- 1.The more specific the test, the higher the PV+ 2.The higher the prevalence of preclinical disease in the screened population, the higher the PV+ 3.The more sensitive the test, the higher the PV-

49. Performance Yield Prevalence (%) Sensitivity Specificity PV+ 0.190% 95% 1.8% 1.090% 95% 15.4% 5.090% 95% 48.6% 50.090% 95% 94.7%

50. Relationship between prevalence and positive predictive value of a test

51. Performance Yield Thus, the PV+ is maximized when used in “high risk” populations since the prevalence of pre- clinical disease is higher than in the general population…. screening a total population for a relatively infrequent disease can be very wasteful of resources and may yield few previously undetected cases.

52. Homework Geenberg p. 105, question 1-13: – 13786 Japanese patients underwent CT scans to detect first signs of cancer, then had pathology tests 2 years later to confirm whether or not they actually had cancer CT resultCancer present Cancer absent Positive56532 negative413194 Compute: Prevalence of cancer Sensitivity & specificity % of false positives % of false negatives PV+ and PV-