HSS4303B Intro to Epidemiology

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

27. Sensitivity/Specificity Sensitivity = a/(a+c)

28. Sensitivity/Specificity Specificity = d/(b+d) Sensitivity = a/(a+c) How do you compute prevalence from these data? All cases / total pop =(a+c) / (a+b+c+d)

29. Can you fill in the blanks?

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 Sensitivity = a/(a+c) Specificity = d/(b+d) Which are the false positives? Which are the false negatives?

34. Sensitivity/Specificity 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 Sensitivity = a/(a+c) Specificity = d/(b+d)

41. Sensitivity/Specificity Sensitivity = a/(a+c) PV+ = a/(a+b) Specificity = d/(b+d)

42. Sensitivity/Specificity Sensitivity = a/(a+c) PV+ = a/(a+b) Specificity = d/(b+d) PV- = d/(c+d)

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

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

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

47. Performance Yield True Disease Status - + Results of Screening Test 400 995 + - 98905 100 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+ 0.1 90% 95% 1.8% 1.0 90% 95% 15.4% 5.0 90% 95% 48.6% 50.0 90% 95% 94.7%

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