1. 1 EPI-820 Evidence-Based Medicine LECTURE 5: SCREENING Mat Reeves BVSc, PhD

2. 2 Objectives Two components of screening (Dx & Tx). Determinants of pre-clinical phase Concept of lead time Characteristics of screening tests (Se, Sp, yield) Biases - lead time, selection, length-biased The only valid measures of screening efficacy Evaluations and criteria for screening

3. 3 I. Introduction Objective: to reduce mortality and/or morbidity by early detection and treatment. Secondary prevention. Asymptomatic individuals are classified as either unlikely or possibly having disease.

4. 4 Introduction Screening involves both diagnostic and treatment components Screening differs from diagnostic testing:

5. 5 Screening - Introduction There are two forms of screening which involve fundamentally different formats, organization and intent: Mass or population-based screening Case finding This lecture covers mass screening for non- infectious (chronic) diseases only.

6. 6 II. Characteristics of Disease The Pre-Clinical Phase (PCP) the period between when early detection by screening is possible and when the clinical diagnosis would usually be made. Pathology begins Disease detectable Normal Clinical Presentation Pre-Clinical Phase

7. 7 Prevalence of Pre-clinical disease A critical determinant of the potential utility of screening. Prevalence is affected by: disease incidence average duration of the PCP previous screening sensitivity of the test Example long PCP= Colorectal cancer short PCP = childhood diabetes

8. 8 Lead Time Lead time = amount of time by which diagnosis is advanced or made earlier Pathology begins Disease detectable Normal Clinical Presentation Lead Time Screen

9. 9 Relationship between screening, pre-clinical phase, clinical phase and lead time

10. 10 Lead Time Equals the amount of time by which treatment is advanced or made “early” Not a theory or statistical artifact but what is expected and must occur with early detection Does not imply improved outcome Necessary but not sufficient condition for effective screening.

11. 11 Lead Time Impossible to determine lead time for any individual, can only compare distribution between screened and non-screened populations (RCT) Knowledge of expected lead time useful to: Indicates amount of time diagnosis and treatment must be advanced Determine frequency of screening Example lead times: Mam screening women 40-49 = 1-2 years Mam screening women 50-69 = 3-4 years Invasive Colo-rect cancer= 7-10 years

12. 12 Example of Estimation of Lead Time Distributions within an Screening RCT Screened Group Non-screened Group Cumulative Number of Cases Years after Screening Total lead time = 5 + 4 + 5 + 2.5 + 2 = 18.5 years Average lead time = 18.5/ 9 = 2.05 years

13. 13 IV. Characteristics of screening tests a) Sensitivity (Se) Definition: the proportion of cases with a positive screening test among all individuals with pre-clinical disease Influences: – the prevalence of pre-clinical disease – the distribution of lead times Concept of the Sensitivity Function –Average probability of detection for cases a certain time away from clinical diagnosis

14. 14 Issues Related to Determining Sensitivity Determining the denominator who are the false negatives? (FNs) Cannot justify full work-up of negative test results Verification bias FN’s estimated by counting number of interval cases Spectrum bias Se varies with spectrum of disease Screening intensity No previous screening = more advanced PCP = higher Se Se decreases with repeat screening

15. 15 IV. Characteristics of screening tests b) Specificity (Sp) Definition: the proportion of individuals with a negative screening test result among all individuals with no pre- clinical disease Imperfect Sp affects many (the healthy), imperfect Se affects few (the sick) Screening PVP usually low because prevalence of PCP is low Influences: – the number of false-positive test results – the PVP and thereby the feasibility and efficiency of the screening program

16. 16 IV. Characteristics of screening tests c) Yield Definition: the amount of previously unrecognized disease that is diagnosed and treated as a result of screening. Another measure of screening efficiency. Influenced by: – Se – Prevalence of PCP

17. 17 V. Effects of screening on disease incidence

18. 18 V. Effects of screening on disease mortality

19. 19 VI. Evaluation of Screening Outcomes a)Study Designs RCT Compare disease-specific cumulative mortality rate between those randomized (or not) to screening Eliminates confounding and lead time bias But, problems of: –Expense, time consuming, logistically difficult, ethical concerns, changing technology.

20. 20 VI. Evaluation of Screening Outcomes a)Study Designs Observational Studies Cohort: –Compare disease-specific cumulative mortality rate between those who choose (or not) to be screened Case-control: –Compare screening history between those with advanced disease (or death) and healthy. Ecological: –Compare screening patterns and disease experience (both incidence and mortality) between populations

21. 21 Problems with Observational Studies Confounding due to health awareness - screenees are more healthy (selection bias) More susceptible to effects of lead-time bias and length-biased sampling Poor quality, often retrospective data CCS: Difficult to determine appropriate control group Difficult to distinguish screening from diagnostic examinations

22. 22 b) Measures of Effect of Screening 1)Disease-specific Mortality Rate (MR) the number of deaths due to disease Total person-years experience The only gold-standard outcome measure for screening NOT affected by lead time, when calculated from a RCT - not affected by selection bias or length-biased sampling.

23. 23 Effects of screening on disease survival - I

24. 24 Effects of screening on disease survival - II

25. 25 Effects of screening on disease survival - III

26. 26 Biases that effect survival duration The efficacy of screening cannot be assessed by comparing survival rates (or CFR) because: Selection bias: –Volunteers are more healthy Lead-time bias –Introduced into survival experience of screen detected cases Length-biased sampling –Screening preferentially identified slower growing or less progressive cases with a better prognosis

27. 27 Length-biased Sampling SCREENING DX X X X X Better Prognosis Cases Worse Prognosis Cases

28. 28 VII. Pseudo-disease and Over-diagnosis Over-diagnosis Limited malignant potential Extreme form of length-biased sampling Examp: Ductal-carcinoma in-situ Competing risks Cases detected that would have been interrupted by an unrelated death Examp: Prostate CA and CVD death Serendipity Chance detection due to diagnostic testing for another reason Examp: PSA and prostate CA, FOBT and CR CA

29. 29 Over-diagnosis – Effect of Mass Pap Screening in Connecticut (Laskey 1976) Age-adj. Incidence Rate (per 100,000) YearIn-situInvasiveTotal% In-situ 1950-543.818.121.917 1955-599.717.126.836 1960-6418.813.632.458 1965-6928.611.640.271 1970-7332.810.943.775

30. 30 VIII. Assessing the feasibility of screening Acceptability convenience, comfort, efficiency, economical Efficiency Low PVP Potential to reduce mortality Effectiveness of treatment without screening Effect of competing mortality Cost-effectiveness Mam screening = $30 – 50K /YLS

31. 31 IX. Summary Efficacy Effectiveness Cost-effectiveness Should we screen? (scientific) Can we screen? (practical) Is it worth it? (scientific, practical, policy, political) Three questions to ask before advocating screening:

32. 32 Need and Feasibility of Screening? All cases of disease can be classified into three groups relevant to screening: 1. Cure is necessary but not possible (Group I) 2. Cure is possible but not necessary (Group II) 3. Cure is necessary and maybe possible (Group III = only group that could benefit!)

33. 33 Lung Cancer?

34. 34 Colorectal Cancer?

35. 35 Prostate Cancer?