Aus dem CharitéCentrum für Human- und Gesundheitswissenschaften Institut für Medizinische Soziologie und Rehabilitationswissenschaften Direktorin: Prof. Dr. Adelheid Kuhlmey Habilitationsschrift “Statistical literacy in medicine: Physicians’ and patients’ understanding of health statistics in cancer screening and prevention” zur Erlangung der Lehrbefähigung für das Fach Medizinische Soziologie und Rehabilitationswissenschaften vorgelegt dem Fakultätsrat der Medizinischen Fakultät Charité-Universitätsmedizin Berlin von Dr. rer. nat. Odette Wegwarth geboren in Salzwedel Eingereicht: 11/2014 Dekanin: Prof. Dr. med. Annette Grüters-Kieslich 1. Gutachter: Prof. Dr. Ulrich Mansmann, Ludwig-Maximilians-Universität München 2. Gutachter: Prof. Dr. Karl Wegscheider, Universitätsklinikum Hamburg-Eppendorf To all the fabulous, open-minded, and inquiring physicians who believed in the importance of my research and were willing to divulge their weak spot, health statistics. Content table 1. Introduction: What is statistical literacy in medicine and why does it matter? …………………p. 1 1.1. Benefit and harms can be express in different statistical formats …………………p. 2 1.2. Not all health statistic formats are appropriate for all setting …………………p. 5 1.3. Statistical literacy and transparency: The fundament of informed decisions ………p. 9 2. Five studies on physicians’ and patients’ understanding of health statistics and facts in cancer screening and prevention …………………p. 12 2.1. "There is nothing to worry about": Gynecologists' counseling on mammography …………………p. 12 2.2. Deceiving numbers: Survival rates and their impact on doctors' risk communication …………………p. 20 2.3. Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States …………………p. 31 2.4. Overdiagnosis and overtreatment: Evaluation of what physicians tell patients about screening harms …………………p. 46 2.5. Overcoming the knowledge-behavior gap: The effect of evidence-based HPV vaccination leaflets on understanding, intention, and actual vaccination decision …………………p. 50 3. Discussion: Many physicians lack understanding of health statistics and leave their patients uniformed …………………p. 58 4. Conclusion: The need to overcome the educational blind spot on statistical literacy …………………p. 65 5. References …………………p. 68 6. Appendices …………………p. 73 6.1. There is nothing to worry about": Gynecologists' counseling on mammography …………………p. 74 6.2. Deceiving numbers: Survival rates and their impact on doctors' risk communication …………………p. 77 6.3. Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States …………………p. 81 6.4. Overdiagnosis and overtreatment: Evaluation of what physicians tell patients about screening harms …………………p. 90 6.5. Overcoming the knowledge-behavior gap: The effect of evidence-based HPV vaccination leaflets on understanding, intention, and actual vaccination decision …………………p. 95 Acknowledgments …………………p. 99 Erklärung …………………p. 100 Figures Figure 1: The public mammography screening advertisement during the breast cancer awareness month 2011 of the charity Susan G. Komen ..................p. 2 Figure 2. Lead-time bias. Even if the time of death has not changed due to screening, advancing the time of diagnosis already leads to a dramatic but meaningless increase in the 5-year survival rate ..................p. 7 Figure 3. Overdiagnosis bias. Even if the number of people who died did not change with screening, the propensity of screening to detect nonprogressive cancer inflates the 5-year survival rate ..................p. 8 Figure 4. Screening fact box on mammography. Fact boxes can help structure the conversation about screening’s benefit and harms as it gives data side by side and on equal weights ..................p. 66 1 1. Introduction: What is statistical literacy in medicine and why does it matter? No other month has become so prominent in disease awareness than October. For one, October is breast cancer awareness month, during which many Western countries such as the US or Germany turn “pink.” No other organization has done more to promote this observance than the world’s largest breast cancer charity Susan G. Komen, creator of the ubiquitous “pink ribbon”1. While laudably working hard to empower people with breast cancer and ensure good quality of care for all, Komen is best known for promoting mammography screening1. During the 2011 breast cancer awareness month, the charity promoted mammography screening with a public advertising campaign (Figure 1) telling women that the key to surviving breast cancer is to get screened. To accentuate this claim, the ad presented two impressive figures: a 5-year survival rate for those who regularly attend screening of 98% and a 5-year survival rate for those who do not attend screening of 23%. The difference of 75% in 5-year survival between the screened and the unscreened group is so dramatic that it is hard to imagine why any woman would reject mammography screening1. Yet, the question is whether these numbers really tell what they seem to tell? Patients and asymptomatic people alike are bombarded with statements and advertisements concerning screening and medical treatments similar to the one above. Most of these messages deliberately use health statistics that are either intransparent or inappropriate for the context chosen2. In the classical view of informed decision making, physicians have sufficient knowledge about health statistics and the benefit and harms of medical interventions, which they are mandated to share with their patients. When a women confronted with the above numbers indicating a 75% increase 2 in 5-year survival asks her physician whether mammography screening (or any other medical procedure) is beneficial to her, risk communication comes into play. Good risk communication requires statistical literacy: the ability to know that different statistical formats for expressing benefit (risk reduction) and harm (risk increase) exist, to know that some formats are appropriate for expressing some risks but not others, and to know that there are transparent and intransparent ways of communicating benefit and harms to the patient. Statistical literacy does not require a degree in statistics. Rather, it means having basic competencies in health statistics, as the following paragraphs will illustrate. Figure 1: The public mammography screening advertisement during the breast cancer awareness month 2011 of the charity Susan G. Komen. 1.1 Benefit and harms can be expressed in different statistical formats Information on benefit (risk reduction) and harm (risk increase) can be communicated in different “currencies.” Ratio measures such as odds ratios 3 or relative risk information are the most commonly used formats in medicine. The problem with ratio measures, however, is that the underlying absolute risk information is concealed, making it impossible for most readers to judge the clinical significance of the effect correctly. Consider the following example. When data on Europe’s first randomized controlled trial on the effectiveness of prostate cancer screening (ERSPC trial)3 involving more than 182,000 men were published, the respective press release announced that prostate cancer screening was found to reduce the chances of dying from prostate cancer by 20%. What this relative risk statement suggests to most readers is that of 100 people who are screened, 20 less will die of prostate cancer. But this is not what the number is saying. Without being told the underlying absolute risks—the absolute numbers of prostate cancer deaths in the screening group as well as in the non-screening group—the information is incomplete7. A fact that often goes unnoticed by both lay people and fully licensed physicians4,5 alike. What lies behind the 20% risk reduction of prostate cancer death announced by the investigators of the PLCO trial? After an 11-year follow-up, 5 men of 1,000 in the screening group and 4 men of 1,000 in the non- screening group died of prostate cancer3. Hence, the 20% relative risk reduction of prostate cancer death corresponds with an absolute risk reduction of 0.1%. In terms of overall mortality, the screening and non- screening groups did not differ. Unmentioned in the press release were the harms of screening: overdiagnosis and overtreatment. For every man saved from prostate cancer death due to screening, 36 men are overdiagnosed and overtreated as a consequence of this screening3. The misleading reporting of the PLCO trial results is not an exception in the reporting of cancer screening results. Take the reporting of mammography screening. For more than two decades, the constantly updated findings of large-scale randomized 4 controlled trials on the effectiveness of mammography comprising approximately 600,000 women, reported in a Cochrane review, have been showing that regular mammography screening for more than 10 years reduces the chances of dying from breast cancer for no more than 1 in 1,000 women (from 5 to 4 in 1,000) at best6. The reduction is already the least conservative estimate, given that it also encompasses trials of poorer quality. If high-quality trials alone are considered, the absolute reduction is estimated to be about 8 to 7 in 2,0006. The Cochrane review further points out that for every woman saved from breast cancer death per 1,000, 5 women will be wrongly overdiagnosed and unnecessarily overtreated6. Are women informed about the delicate benefit-harm ratio? Unfortunately