1. Thomas B. Newman, MD, MPH Andi Marmor, MD, MSEd

2. Outline  Overview and definitions  Observational studies of screening  Randomized trials of screening  Conclusion – ecologic view

3. What is screening?  Common definition: “Testing to detect asymptomatic disease”  Better definition*: “Application of a test to detect a potential disease or condition in people with no known signs or symptoms of that disease or condition” *Common screening tests. David M. Eddy, editor. Philadelphia, PA: American College of Physicians, 1991

4. What is screening?  Common definition: “Testing to detect asymptomatic disease”  Better definition*: “Application of a test to detect a potential disease or condition in people with no known signs or symptoms of that disease or condition” *Common screening tests. David M. Eddy, editor. Philadelphia, PA: American College of Physicians, 1991

5. What is screening?  Common definition: “Testing to detect asymptomatic disease”  Better definition*: “Application of a test to detect a potential disease or condition in people with no known signs or symptoms of that disease or condition”  “ Condition” includes a risk factor for a disease… *Common screening tests. David M. Eddy, editor. Philadelphia, PA: American College of Physicians, 1991

6. Screening Spectrum Risk factor Recognized symptomatic disease Presymptomatic disease Unrecognized symptomatic disease è Fewer people recognized and treated è Easier to demonstrate benefit è Less potential for harm

7. Examples of Screening Along the Spectrum  Risk factor for disease: Hypercholesterolemia, hypertension  Presymptomatic disease: Neonatal hypothyroidism, syphilis, HIV  Unrecognized symptomatic disease: Vision and hearing problems in young children; iron deficiency anemia, depression  Somewhere in between?: Prostate cancer, breast carcinoma in situ, more severe hypertension

8. Screening for risk factors  Relationship between risk factor, disease and treatment difficult to establish Does test predict disease? Does treatment of risk factor reduce disease? Does treatment reduce risk factor? (eg: CAST)  Measures of test accuracy apply to disease that is prevalent at the time the test is done  With risk factors, trying to measure incidence of disease over time  Potential for harm greatest when screening for risk factors!

9. Goals of Screening for Presymptomatic Disease  Detect disease in earlier stage than would be detected by symptoms Only possible if an early detectable phase is present Only beneficial if earlier treatment is more effective than later treatment  Do this without incurring harm to the patient Net benefit must exceed net harm Long follow up and randomized trial may be needed to prove this

10. Screening for Cancer  Natural history heterogeneous Screening test may pick up slower growing or less aggressive cancers Not all patients diagnosed with cancer will become symptomatic  Diagnosis is subjective There is no gold standard

11. “It’s just a simple blood test.” How can screening be bad???

12. Possible harms from screening  To all  To those with negative results  To those with positive results  To those not tested

13. Public Health Threats from Excessive Screening  “When your only tool is a hammer, you tend to see every problem as a nail.” Abraham Maslow  Interventions aimed at individuals are overemphasized  Biggest threats are public health threats  Biggest gains in longevity have been PUBLIC HEALTH interventions

14. Top Ten Countries’ Per Capita Healthcare Spending, 1997 ($) 010002000300040005000 Norway Netherlands Denmark Iceland France Canada Germany Luxembourg Switzerland United States Anderson GF and Poullier JP Health Affairs 18;178-88 May/June 1999

15. Potential Years of Life Lost*/100,000 population, top 10 spending Countries, 1995 0200040006000800010000 Norway Netherlands Denmark Iceland France Canada Germany Luxembourg Switzerland United States Male Female Before age 70. From Anderson GF and Poullier JP Health Affairs 18;178-88 May/June 1999

16. Economic and Political Forces behind excessive screening  Companies selling machines to do the test  Companies selling the test itself  Companies selling products to treat the condition  Managed care organizations  Politicians who are (or want to appear) sympathetic

17. Ad by company that makes the machines

22. Ad for: Frosted flakes! ( no cholesterol)

23. Ad sponsored by the company that makes interferon.

26. Copyright restrictions may apply. Schwartz, L. M. et al. JAMA 2004;291:71-78. Screening as an Obligation

27. Cultural characteristics  "We live in a wasteful, technology driven, individualistic and death- denying culture.“ George Annas, New Engl J Med, 1995

28. E-mail Excerpt PLEASE, PLEASE, PLEASE TELL ALL YOUR FEMALE FRIENDS AND RELATIVES TO INSIST ON A CA-125 BLOOD TEST EVERY YEAR AS PART OF THEIR ANNUAL PHYSICAL EXAMS. Be forewarned that their doctors might try to talk them out of it, saying, "IT ISN'T NECESSARY." …Insist on the CA-125 BLOOD TEST; DO NOT take "NO" for an answer!

29. Source: Funny Times. (1-888-Funnytimes x 476)

30. Evaluating Studies of Screening Screening test Detect disease early Treat disease Patient outcome

31. Screening test Detect disease early Treat disease Patient outcome Evaluating Studies of Screening

32. Screening test Detect disease early Treat disease Patient outcome Evaluating Studies of Screening

33.  Ideal Study: Randomized to screen/control Compares outcomes in ENTIRE screened group to ENTIRE unscreened group  Observational studies Compare outcomes in screened patients vs unscreened (not randomized) Among patients with disease, compare outcomes among those dx by screening vs those dx by symptoms

34. Screened Not screened Survival from Randomization R Diagnosed by symptoms Diagnosed by screening Not screened Screened Survival after Diagnosis D+ D- D+ Patients with Disease D+ R Survival after Diagnosis Survival from Randomization Survival from Enrollment Patients with Disease Not screened Screened Survival after Diagnosis

35. Biases in Observational Studies of Screening Tests  Volunteer bias  Lead time bias  Length bias  Stage migration bias  Pseudodisease

36. Volunteer Bias  People who volunteer for studies differ from those who do not  Examples HIP Mammography study: ○ Women who volunteered for mammography had lower heart disease death rates Coronary drug project: ○ RCT of medications for secondary prevention of CAD ○ Men who took their medicine (drug or placebo!) had half the mortality of men who didn't  Can occur in any non-randomized trial of screening

37. Avoiding Volunteer Bias  Randomize patients to screened and unscreened groups  Control for factors which might be associated with both receiving screening AND the outcome eg: family history, level of health concern, other health behaviors

38. Lead Time Bias (zero-time bias)  Screening identifies disease during a latent period before it becomes symptomatic  If survival is measured from time of diagnosis, screening will always improve survival even if treatment is ineffective

39. Latent Phase Onset of symptoms Death Detectable by screening Detected by screening Biological Onset Survival After Diagnosis Lead Time Lead Time Bias Contribution of lead time to survival measured from diagnosis

40. Avoiding Lead Time Bias  Only present when survival from diagnosis is compared between diseased persons Screened vs not screened Diagnosed by screening vs by symptoms  Avoiding lead time bias Measure survival from time of randomization

41. How Much Lead Time is Present?  Depends on relative lengths of latent phase (LP) and screening interval (S)  Screening interval shorter than LP: Maximum false increase in survival = LP Minimum = LP – S  Screening interval longer than LP: Max = LP Proportion of disease dx by screening = LP/S

42. Figure 1: Maximum and minimum lead time bias possible when screening interval is shorter than latent phase Max = LP Min =LP – S S LP Max Min Screen Onset of symptomsDeath Detectable by screening Detected by screening Screen

43. Figure 2: Maximum lead time bias possible when screening interval is longer than latent phase Max = LP Proportion of disease diagnosed by screening: P = LP/S S LP Max Screen

44. Length Bias (Different Natural History Bias)  If disease is heterogeneous: Slowly progressive : more time in presymptomatic phase Cases picked up by screening disproportionately those that are slowly developing  Higher proportion of less aggressive disease in group detected by screening creates appearance of reduced mortality even if treatment is ineffective

45. Screen 1Screen 2 TIME

46. Mortality when cancer detected by screening Mortality when cancer detected by symptoms

47. Avoiding Length Bias  Only present when survival from diagnosis is compared between diseased persons  AND disease is heterogeneous  Lead time bias usually present as well  Avoiding length bias: Compare mortality in the ENTIRE screened group to the ENTIRE unscreened group

48. Stage Migration Bias  Also called the "Will Rogers Phenomenon" "When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states."  Described by Feinstein and colleagues (1985) as an explanation for lower stage- specific survival in a 1954 cohort of patients with lung cancer in comparison to a 1977 cohort  New technologies resulted in the 1977 group diagnosed with more advanced lung cancer

49. Stage Migration Bias Stage 1 Stage 2 Stage 3 Stage 4 Stage 0 Stage 2 Stage 3 Stage 4 Stage 1 Old testNew test

50. A Non-Cancer Example  “Infants in each of 3 birthweight strata (VLBW, LBW and NBW) who are exposed to Factor X have decreased mortality compared with unexposed weight-matched infants”  Is factor X beneficial?  Maybe not! Factor X could be cigarette smoking! Smoking moves otherwise healthy babies to lower birthweight group, improving mortality in each group

51. Other Examples Abound…  The more you look for disease, and the more advanced the technology the higher the prevalence, the higher the stage, and the better the (apparent) outcome for the stage  Beware of stage migration in any stratified analysis Check OVERALL survival in screened vs unscreened group

52. Pseudodisease  A condition that looks just like the disease, but never would have bothered the patient Type I: Indolent forms of disease which would never cause symptoms Type II: Preclinical disease in people who will die from another cause before disease presents  The Problem: Treating pseudodisease can only cause harm

53. Analogy to Double Gold Standard Bias  Screening (test) result negative Clinical FU (first gold standard)  Screening (test) result positive Biopsy (2 nd gold standard)  If pseudodisease exists Sensitivity (true positive rate) of screening falsely increased Screening will also prolong survival among diseased individuals

54. Example: Mayo Lung Project  RCT of lung cancer screening  9,211 male smokers randomized to two study arms Intervention: CXR and sputum cytology every 4 months for 6 years (75% compliance) Usual care: recommendation to receive same tests annually *Marcus et al., JNCI 2000;92:1308-16

55. MLP Extended Follow-up Results  Among those with lung cancer, intervention group had more cancers diagnosed at early stage and better survival Marcus et al., JNCI 2000;92:1308-16

56. MLP Extended Follow-up Results  Intervention group: slight increase in lung-cancer mortality (P=0.09 by 1996) Marcus et al., JNCI 2000;92:1308-16

57. What happened?  After 20 years of follow up, there was a significant increase (29%) in the total number of lung cancers in the screened group Excess of tumors in early stage No decrease in late stage tumors  Overdiagnosis (pseudodisease) Black, cause of confusion and harm in cancer screening. JNCI 2000;92:1280-1

58. Looking for Pseudodisease  Impossible to distinguish from successfully treated asymptomatic disease in individual patient Very few compelling stories describe patients or physician’s victories over pseudodisease…  Appreciate the varying natural history of disease, and limits of diagnosis  Clues to pseudodisease: Higher cumulative incidence of disease in screened group No difference in overall mortality between screened and unscreened groups

59. Screened GroupProlonged survival Better health behaviors Volunteer Bias

60. Early detectionProlonged survival Earlier “zero time” Lead Time Bias

61. Early detectionHigher cure rate Slower growing tumor with better prognosis Length Bias

62. Early detectionHigher cure rate Lower stage assignment Stage Migration Bias

63. Early detectionHigher cure rate Pseudodisease Overdiagnosis

64. Diagnosed by symptoms Diagnosed by screening Not screened Screened Survival after Diagnosis D- Patients with Disease D+ R Survival after Diagnosis Survival from Enrollment

65. Screened Not screened Survival from Randomization R D+ D- D+Survival from Randomization Issues with RCTs of Cancer Screening Quality of randomization Cause-specific vs total mortality

66. Poor Quality Randomization  Edinburgh mammography trial  Randomization by healthcare practice  7 practices changed allocation status  Highest SES 26% of women in control group 53% of women in screening group  26% reduction in cardiovascular mortality in mammography group

67. Cause-Specific Mortality  Problems: Assignment of cause of death is subjective Screening or treatment may have important effects on other causes of death  Bias introduced can make screening appear better or worse!

68. Example  Meta-analysis of 40 RCT’s of radiation therapy for early breast cancer (N = 20,000)* Breast cancer mortality reduced (20-yr ARR 4.8%; P =.0001) BUT mortality from “other causes” increased (20-yr ARR -4.3%; P = 0.003)  Were these additional deaths actually due to screening *Early Breast Cancer Trialists Collaborative Group. Lancet 2000;355:1757

69. Biases in Cause-Specific Mortality  “Sticky diagnosis” bias: If cancer diagnosis made, deaths of unclear cause more often attributed to cancer Effect: overestimates cancer mortality in screened group  “Slippery linkage” bias: Linkage lost between death and cancer diagnosis (eg: due to screening or treatment) Death less likely counted in cause specific mortality Effect: underestimates cancer mortality in screened group

70. The truth about total mortality  Mortality from other causes generally exceeds screening or cancer-related mortality  Effect on condition of interest more difficult to detect  Total mortality more important for some screening tests than others…

71. Conclusions -1  Promotion of screening by entities with a vested interest and public enthusiasm for screening are challenges to EBM  High-quality RCT’s are needed  Attention to study design, size of effect and unmeasured costs

72. Conclusions - 2  Dysfunctional metaphors for health care * Military metaphor – battle disease, no cost too high for victory, no room for uncertainty Market metaphor -- medicine as a business; health care as a product; success measured economically  Reframing of priorities is needed *Annas G. Reframing the debate on health care reform by replacing our metaphors. NEJM 1995;332:744-7

73. Reframing Priorities: Ecology Metaphor  Sustainability  Limited resources  Interconnectedness  More critical of technology  Move away from domination, buying, selling, exploiting  Focus on the big picture Populations rather than individuals Causes rather than symptoms