Risk Communication in Health

  • Nicolai Bodemer
  • Wolfgang Gaissmaier


Policy makers, health professionals, and patients have to understand health statistics to make informed medical decisions. However, health messages often follow a persuasive rather than an informative approach and undermine the idea of informed decision making. The current practice of health risk communication is often biased: Risks are communicated one sided and in nontransparent formats. Thereby, patients are misinformed and misled. Despite the fact that the public is often described as lacking basic statistical literacy skills, statistics can be presented in a way that facilitates understanding. In this chapter, we discuss how transparent risk communication can contribute to informed patients and how transparency can be achieved. Transparency requires formats that are easy to understand and present the facts objectively. For instance, using statistical evidence instead of narrative evidence helps patients to better assess and evaluate risks. Similarly, verbal probability estimates (e.g., “probable,” “rare”) usually result in incorrect interpretations of the underlying risk in contrast to numerical probability estimates (e.g., “20%,” “0.1”). Furthermore, we will explain and discuss four formats – relative risks, conditional probabilities, 5-year survival rates, and single-event probabilities – that often confuse people, and propose alternative formats – absolute risks, natural frequencies, annual mortality rates, and frequency statements – that increase transparency. Although research about graphs is still in its infancy, we discuss graphical visualizations as a promising tool to overcome low statistical literacy. A further challenge in risk communication is the communication of uncertainty. Evidence about medical treatments is often limited and conflicting, and the question arises how health professionals and laypeople deal with uncertainty. Finally, we propose further research to implement the concepts of transparency in risk communication.


Mammography Screening Risk Communication Absolute Risk Reduction Numerical Information Bayesian Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Allen M, Preiss R (1997) Comparing the persuasiveness of narrative and statistical evidence using meta-analysis. Commun Res Rep 14:125–131CrossRefGoogle Scholar
  2. Ancker JS, Kaufman D (2007) Rethinking health numeracy: a multidisciplinary literature review. J Am Med Inform Assoc 14:713–721CrossRefGoogle Scholar
  3. Ancker JS, Senathirajah Y, Kukafka R, Starren JB (2006) Design features of graphs in health risk communication: a systematic review. J Am Med Inform Assoc 3:608–618CrossRefGoogle Scholar
  4. Baesler JE (1997) Persuasive effects of story and statistical evidence. Argument Advocacy 33:170–175Google Scholar
  5. Baesler JE, Burgoon JK (1994) The temporal effects of story and statistical evidence on belief change. Commun Res 21:582–602CrossRefGoogle Scholar
  6. Barbey AK, Sloman SA (2007) Base-rate respect: from ecological rationality to dual processes. Behav Brain Sci 30:241–297Google Scholar
  7. Barton A, Mousavi S, Stevens JR (2007) A statistical taxonomy and another “chance” for natural frequencies. Behav Brain Sci 30:255–256CrossRefGoogle Scholar
  8. Berry D, Raynor T, Knapp P, Bersellini E (2004) Over the counter medicines and the need for immediate action: a further evaluation of European commission recommended wordings for communicating risk. Patient Educ Couns 53:129–134CrossRefGoogle Scholar
  9. Betsch C, Renkewitz F, Betsch T, Ulshöfer C (2010) The influence of vaccine-critical Internet pages on perception of vaccination risks. J Health Psychol 15:446–455CrossRefGoogle Scholar
  10. Bodemer N, Müller SM, Okan Y, Garcia-Retamero R, Neumeyer-Gromen A (2011) Do the media provide transparent health information? A cross-cultural comparison of public information about the HPV vaccine (Submitted)Google Scholar
  11. Brase GL (2009) Pictorial representations in statistical reasoning. Appl Cogn Psychol 23:369–381CrossRefGoogle Scholar
  12. Brun W, Teigen KH (1988) Verbal probabilities: ambiguous, context-dependent, or both? Organ Behav Hum Decis Process 41:390–404CrossRefGoogle Scholar
  13. Bucher HC, Weinbacher M, Gyr K (1994) Influence of method of reporting study results on decision of physicians to prescribe drugs to lower cholesterol concentration. Br Med J 309:761–764CrossRefGoogle Scholar
  14. Budescu DV, Wallsten TS (1985) Consistency in interpretation of probabilistic phrases. Organ Behav Hum Decis Process 36:391–405CrossRefGoogle Scholar
  15. Casscells W, Schoenberger A, Grayboys T (1978) Interpretation by physicians of clinical laboratory results. N Engl J Med 299:999–1000CrossRefGoogle Scholar
  16. Consort (2009) Consolidated standards of reporting trials. Accessed April 2011
  17. Cosmides L, Tooby J (1996) Are humans good intuitive statisticians after all? Rethinking some conclusions of the literature on judgment under uncertainty. Cognition 58:1–73Google Scholar
  18. Covey J (2007) A meta-analysis of the effects of presenting treatment benefits in different formats. Med Decis Making 27:638–654CrossRefGoogle Scholar
  19. Davids SL, Schapira MM, McAuliffe TL, Nattinger AB (2004) Predictors of pessimistic breast cancer risk perception in a primary care population. J Gen Intern Med 19:310–315CrossRefGoogle Scholar
  20. deWit JBF, Das E, Vet R (2008) What works best: objective statistics or a personal testimonial? An assessment of the persuasive effects of different types of message evidence on risk perception. Health Psychol 27:110–115CrossRefGoogle Scholar
  21. Diaz JA, Griffith RA, Ng JJ, Reinert SE, Friedmann PD, Moulton AW (2002) Patients’ use of the Internet for medical information. J Gen Intern Med 17:180–185CrossRefGoogle Scholar
  22. Dieckmann NF, Slovic P, Peters E (2009) The use of narrative evidence and explicit likelihood by decisionmakers varying in numeracy. Risk Anal 29:1473–1488CrossRefGoogle Scholar
  23. Djulbegovic M, Beyth RJ, Neuberger MM, Stoffs TL, Vieweg J, Djulbegovic B et al (2010) Screening for prostate cancer: systematic review and meta-analysis of randomised controlled trials. Br Med J 341:c4543CrossRefGoogle Scholar
  24. Dören M, Gerhardus A, Gerlach FM, Hornberg C, Kochen MM, Kolip P et al. (2008) Wissenschaftler/innen fordern Neubewertung der HPV-Impfung und ein Ende der irreführenden Informationen. Accessed April 2011
  25. Durand M-A, Stiel M, Boivin J, Elwyn G (2008) Where is the theory? Evaluating the theoretical frameworks described in decision support technologies. Patient Educ Couns 71:125–135CrossRefGoogle Scholar
  26. Eddy DM (1982) Probabilistic reasoning in clinical medicine: problems and opportunities. In: Kahneman D, Slovic P, Tversky A (eds) Judgment under uncertainty: heuristics and biases. Cambridge University Press, Cambridge, pp 246–267Google Scholar
  27. Edwards A, Elwyn G (2009) Shared decision making in health care: achieving evidence-based patient choice. Oxford University Press, OxfordGoogle Scholar
  28. Edwards A, Elwyn G, Covey J, Matthews E, Pill R (2001) Presenting risk information – A review of the effects of “framing” and other manipulations on patient outcomes. J Health Commun 6:61–82CrossRefGoogle Scholar
  29. Einhorn HJ, Hogarth RM (1985) Ambiguity and uncertainty in probabilistic inference. Psychol Rev 92:433–461CrossRefGoogle Scholar
  30. Epstein LG (1999) A definition of uncertainty aversion. Rev Econ Stud 66:579–608CrossRefGoogle Scholar
  31. Erev I, Cohen BL (1990) Verbal versus numerical probabilities: efficiency, biases, and the preference paradox. Organ Behav Hum Decis Process 45:1–18CrossRefGoogle Scholar
  32. Estrada CA, Martin-Hryniewicz M, Peek BT, Collins C, Byrd JC (2004) Literacy and numeracy skills and anticoagulation control. Am J Med Sci 328:88–93CrossRefGoogle Scholar
  33. European Commission (1998) A guideline on the readability of the label and package leaflet of medicinal products for human use. EC Pharmaceuticals Committee, BrusselsGoogle Scholar
  34. Fagerlin A, Wang C, Ubel PA (2005) Reducing the influence of anecdotal reasoning on people’s health care decisions: is a picture worth a thousand statistics? Med Decis Making 25:398–405CrossRefGoogle Scholar
  35. Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM (2007) Measuring numeracy without a math test: development of the subjective numeracy scale. Med Decis Making 27:672–680CrossRefGoogle Scholar
  36. Feufel M, Antes G, Gigerenzer G (2010) Competence in dealing with uncertainty lessons to learn from the influenza pandemic (H1N1) 2009. Bundesgesundheitsblatt 53:1283–1289CrossRefGoogle Scholar
  37. Frewer LJ (1999) Risk perception, social trust, and public participation in strategic decision making: implications for emerging technologies. Ambio 28:569–574Google Scholar
  38. Frewer LJ, Hunt S, Brennan M, Kuznesof S, Ness M, Ritson C (2003) The views of scientific experts on how the public conceptualize uncertainty. J Risk Res 6:75–85CrossRefGoogle Scholar
  39. Frosch DL, Krueger PM, Hornik RC, Cronholm PF, Barg FK (2007) Creating demand for prescription drugs: a content analysis of television direct-to-consumer advertising. Ann Fam Med 5:6–13CrossRefGoogle Scholar
  40. Gaissmaier W, Straubinger N, Funder DC (2007) Ecologically structured information: the power of pictures and other effective data presentations. Behav Brain Sci 30:263–264CrossRefGoogle Scholar
  41. Gal I (1995) Big picture: what does “numeracy” mean? Accessed April 2011
  42. Galesic M, Garcia-Retamero R (2010) Statistical numeracy for health: a cross-cultural comparison with probabilistic national samples. Arch Intern Med 170:462–468CrossRefGoogle Scholar
  43. Galesic M, Garcia-Retamero R (2011) Graph literacy: a cross-cultural comparison. Med Decis Making 31:444–457CrossRefGoogle Scholar
  44. Galesic M, Garcia-Retamero R, Gigerenzer G (2009) Using icon arrays to communicate medical risks: overcoming low numeracy. Health Psychol 28:210–216CrossRefGoogle Scholar
  45. Garcia-Retamero R, Galesic M (2009) Communicating treatment risk reduction to people with low numeracy skills: a cross-cultural comparison. Am J Public Health 99:2196–2202CrossRefGoogle Scholar
  46. Garcia-Retamero R, Galesic M (2010) Who profits from visual aids: overcoming challenges in people’s understanding of risks. Soc Sci Med 70:1019–1025CrossRefGoogle Scholar
  47. Garcia-Retamero R, Galesic M, Gigerenzer G (2010) Do icon arrays help reduce denominator neglect? Med Decis Making 30:672–684CrossRefGoogle Scholar
  48. Gigerenzer G (2002) Calculated risks: how to know when numbers deceive you. Simon & Schuster, New YorkGoogle Scholar
  49. Gigerenzer G (2007) Gut feeling: the intelligence of the unconscious. Viking, New YorkGoogle Scholar
  50. Gigerenzer G, Gray M (2011) Launching the century of the patient. In: Gigerenzer G, Gray M (eds) Better doctors, better patients, better decisions: envisioning health care 2020. MIT Press, Cambridge, pp 3–28Google Scholar
  51. Gigerenzer G, Hoffrage U (1995) How to improve Bayesian reasoning without instruction: frequency formats. Psychol Rev 102:684–704CrossRefGoogle Scholar
  52. Gigerenzer G, Hoffrage U, Ebert A (1998) AIDS counselling for low-risk clients. AIDS Care 10:197–211CrossRefGoogle Scholar
  53. Gigerenzer G, Hertwig R, van den Broek E, Fasolo B, Katsikopoulos KV (2005) “A 30% chance of rain tomorrow”: how does the public understand probabilistic weather forecasts? Risk Anal 25:623–629CrossRefGoogle Scholar
  54. Gigerenzer G, Gaissmaier W, Kurz-Milcke E, Schwartz LM, Woloshin S (2007) Helping doctors and patients to make sense of health statistics. Psychol Sci Public Interest 8:53–96Google Scholar
  55. Gigerenzer G, Mata J, Frank R (2009) Public knowledge of benefits of breast and prostate cancer screening in Europe. J Natl Cancer Inst 101:1216–1220CrossRefGoogle Scholar
  56. Gigerenzer G, Wegwarth O, Feufel M (2010) Misleading communication of risk: editors should enforce transparent reporting in abstracts. Br Med J 341:c4830CrossRefGoogle Scholar
  57. Golbeck AL, Ahlers-Schmidt CR, Paschal AM, Dismuke S (2005) A definition and operational framework for health numeracy. Am J Prev Med 29:375–376CrossRefGoogle Scholar
  58. Grigg W, Donahue P, Dion G (2007) The nation’s report card:12th-grade reading and mathematics 2005 (NCES Report No. 2007–468). U.S. Department of Education, National Center for Education Statistics, Washington, DCGoogle Scholar
  59. Grilli R, Ramsey C, Minozzi S (2009) Mass media interventions: effects on health care utilization. Cochrane Database Syst Rev 1:CD000389Google Scholar
  60. Gurmankin AD, Baron J, Armstrong K (2004) The effect of numerical statements of risk on trust and comfort with hypothetical physician risk communication. Med Decis Making 24:265–271CrossRefGoogle Scholar
  61. Hacking I (1975) The emergence of probability. Cambridge University Press, CambridgeGoogle Scholar
  62. Hargittai E (2005) Survey measures of web-oriented digital literacy. Soc Sci Comput Rev 23:371–379CrossRefGoogle Scholar
  63. Hargittai E (2009) An update on survey measures of web-oriented digital literacy. Soc Sci Comput Rev 27:130–137CrossRefGoogle Scholar
  64. Hargittai E, Fullerton F, Menchen-Trevino E, Thomas K (2010) Trust online: young adults’ evaluation of web content. Int J Commun 4:468–494Google Scholar
  65. Heesen C, Köpke S, Kasper J, Richter T, Beier M, Mühlhauser I (2008) Immuntherapien der Multiplen Sklerose. Accessed April 2011
  66. Heesen C, Kleiter I, Nguyen F, Schäffler N, Kasper J, Köpke S et al (2010) Risk perceptions in natalizumab-treated multiple sclerosis patients and their neurologists. Mult Scler 16:1507–1512CrossRefGoogle Scholar
  67. Hembroff LA, Holmes-Rovner M, Wills CE (2004) Treatment decision-making and the form of risk communication: results of a factorial survey. BMC Med Inform Decis Mak 4:1184–1120CrossRefGoogle Scholar
  68. Hoffrage U, Gigerenzer G, Krauss S, Martignon L (2002) Representation facilitates reasoning: what natural frequencies are and what they are not. Cognition 84:343–352CrossRefGoogle Scholar
  69. Holmes BJ, Henrich N, Hancock S, Lestou V (2009) Communicating with the public during health crises: experts’ experiences and opinions. J Risk Res 12:793–807CrossRefGoogle Scholar
  70. Ibrekk H, Morgan MG (1987) Graphical communication of uncertain quantities to nontechnical people. Risk Anal 7:519–529CrossRefGoogle Scholar
  71. Johnson BB, Slovic P (1995) Presenting uncertainty in health risk assessment: initial studies of its effects on risk perception and trust. Risk Anal 15:485–494CrossRefGoogle Scholar
  72. Kahneman D, Tversky A (1972) Subjective probability: a judgment of representativeness. Cognit Psychol 3:430–454CrossRefGoogle Scholar
  73. Kirsch IS, Jungeblut A, Jenkins L, Kolstad A (2007) Adult literacy in America: a first look at the findings of the National Adult Literacy Survey (NCES Report No. 1993–275; 3rd edn, U.S. Department of Education, National Center for Education Statistics. Washington, DCGoogle Scholar
  74. Knapp P, Raynor DK, Berry DC (2004) Comparison of two methods of presenting risk information to patients about the side effects of medicines. Qual Saf Health Care 13:176–180CrossRefGoogle Scholar
  75. Knight FH (1921) Risk, uncertainty and profit. Harper, New YorkGoogle Scholar
  76. Kotler P, Lee NR (2007) Social marketing: influencing behaviors for good. Sage, LondonGoogle Scholar
  77. Kuhn KM (2000) Message format and audience values: interactive effects of uncertainty information and environmental attitudes on perceived risk. J Environ Psychol 20:41–51CrossRefGoogle Scholar
  78. Kurzenhäuser S (2003) Welche Informationen vermitteln deutsche Gesundheitsbroschüren über die Screening-Mammographie? Z Arztl Fortbild Qualitatssich 97:53–57Google Scholar
  79. Kurz-Milcke E, Martignon L (2007) Stochastische Urnen und Modelle in der Grundschule (Stochastic urns and models in elementary school). In: Kaiser G (ed) Tagungsband der Jahrestagung der Gesellschaft für Didaktik der Mathematik. Verlag Franzbecker, BerlinGoogle Scholar
  80. Kurz-Milcke E, Gigerenzer G, Martignon L (2008) Transparency in risk communication: graphical and analog tools. In: Tucker WT, Ferson S, Finkel A, Long TF, Slavin D, Wright P (eds) Strategies for risk communication: evolution, evidence, experience, vol 1128, Annals of the New York Academy of Sciences. Blackwell, New York, pp 18–28Google Scholar
  81. Kutner M, Greenberg E, Jin Y, Paulsen C (2006) The health literacy of America’s adults: results from the 2003 national assessment of adult literacy (NCES Report No. 2006–483). Government Printing Office, Washington, DCGoogle Scholar
  82. Lafata JE, Simpkins J, Lamerato L, Poisson L, Divine G, Johnson CC (2004) The economic impact of false-positive cancer screens. Cancer Epidemiol Biomarkers Prev 13:2126–2132Google Scholar
  83. Lewis C, Keren G (1999) On the difficulties underlying Bayesian reasoning: comment on Gigerenzer and Hoffrage. Psychol Rev 106:411–416CrossRefGoogle Scholar
  84. Lipkus IM (2007) Numeric, verbal, and visual formats of conveying health risks: suggested best practices and future recommendations. Med Decis Making 27:696–713CrossRefGoogle Scholar
  85. Lipkus IM, Hollands JG (1999) The visual communication of risks. J Natl Cancer Inst Monogr 25:149–162Google Scholar
  86. Lipkus IM, Peters E (2009) Understanding the role of numeracy in health: proposed theoretical framework and practical insight. Health Educ Behav 36:1065–1081CrossRefGoogle Scholar
  87. Lipkus IM, Samsa G, Rimer BK (2001) General performance on a numeracy scale among highly educated samples. Med Decis Making 21:37–44CrossRefGoogle Scholar
  88. Macchi L, Mosconi G (1998) Computational features vs frequentist phrasing in the base-rate fallacy. Swiss J Psychol 57:79–85Google Scholar
  89. Malenka DJ, Baron JA, Johansen S, Wahrenberger JW, Ross JM (1993) The framing effect of relative versus absolute risk. J Gen Intern Med 8:543–548CrossRefGoogle Scholar
  90. Marcus PM, Bergstrahl EJ, Zweig MH, Harris A, Offord KP, Fontana RS (2006) Extended lung cancer incidence follow-up in the Mayo lung project and overdiagnosis. J Natl Cancer Inst 98:748–756CrossRefGoogle Scholar
  91. Marteau TM, Saidi G, Goodburn S, Lawton J, Michie S, Bobrow M (2000) Numbers or words? A randomized controlled trial of presenting screen negative results to pregnant women. Prenat Diagn 20:714–718CrossRefGoogle Scholar
  92. Mathematics and medicine (1937, January 2). Lancet i:31Google Scholar
  93. Mazur DJ, Hickham DH, Mazur MD (1999) How patients’ preferences for risk communication influence treatment choice in a case of high risk and high therapeutic uncertainty: asymptotic localized prostate cancer. Med Decis Making 19:394–398CrossRefGoogle Scholar
  94. McMullan M (2006) Patients using the Internet to obtain health information: how this affects the patient–health professional relationship. Patient Educ Couns 63:24–28CrossRefGoogle Scholar
  95. Moxey A, O’Connell D, McGettigan P, Henry D (2003) Describing treatment effects to patients: how they are expressed makes a difference. J Gen Intern Med 18:948–959CrossRefGoogle Scholar
  96. Moynihan R, Bero L, Ross-Degnan D, Henry D, Lee K, Watkins J et al (2000) Coverage by the news media of the benefits and risks of medications. New Engl J Med 342:1645–1650CrossRefGoogle Scholar
  97. Mühlhauser I, Kasper J, Meyer G (2006) FEND: understanding of diabetes prevention studies: questionnaire survey of professionals in diabetes care. Diabetologia 49:1742–1746CrossRefGoogle Scholar
  98. Nadav-Greenberg L, Joslyn S (2009) Uncertainty forecasts improve decision making among nonexperts. J Cogn Eng Decis Making 3:209–227CrossRefGoogle Scholar
  99. Naylor CD, Chen E, Strauss B (1992) Measured enthusiasm: does the method of reporting trial results alter perceptions of therapeutic effectiveness? Ann Intern Med 171:916–921Google Scholar
  100. Neumeyer-Gromen A, Bodemer N, Müller SM, Gigerenzer G (in press) Ermöglichen Medienberichte und Informationsbroschüren zur Gebärmutterhalskrebsprävention informierte Entscheidungen? Eine Medienanalyse in Deutschland (Submitted)Google Scholar
  101. Nielsen Company (2009) U.S. ad spending fell 2.6% in 2008, Nielson reports. Nielson Company, New YorkGoogle Scholar
  102. Nisbett RE, Ross LD (1980) Human inference: strategies and shortcomings of social judgment. Prentice-Hall, Englewood CliffsGoogle Scholar
  103. Paulos JA (1988) Innumeracy: mathematical illiteracy and its consequences. Hill & Wang, New YorkGoogle Scholar
  104. Pepper S, Prytulak LS (1974) Sometimes frequently means seldom: context effects in the interpretation of quantitative expressions. J Res Pers 8:95–101CrossRefGoogle Scholar
  105. Peters E, Västfjäll D, Slovic P, Mertz CK, Mazzocco K, Dickert S (2006) Numeracy and decision making. Psychol Sci Public Interest 17:407–413Google Scholar
  106. Politi MC, Han PKJ, Col NF (2007) Communicating the uncertainty of harms and benefits of medical interventions. Med Decis Making 27:681–695CrossRefGoogle Scholar
  107. Politi MC, Clark MA, Ombao H, Dizon D, Elwyn G (2010) Communicating uncertainty can lead to less decision satisfaction: a necessary cost of involving patients in shared decision making? Health Expect 14:1–8Google Scholar
  108. Reinard JC (1988) The empirical study of the persuasive effects of evidence: the status after fifty years of research. Hum Commun Res 15:3–59CrossRefGoogle Scholar
  109. Reyna V, Nelson WL, Han PK, Dieckmann NF (2009) How numeracy influences risk comprehension and medical decision making. Psychol Bull 135:943–973CrossRefGoogle Scholar
  110. Rothman RL, Housam R, Weiss H, Davis D, Gregory R, Gebretsadik T, Shintani A, Elasy TA (2006) Patient understanding of food labels: the role of literacy and numeracy. Am J Prev Med 31:391–398CrossRefGoogle Scholar
  111. Sandblom G, Varenhorst E, Rosell J, Löfman O, Carlsson P (2011) Randomised prostate cancer screening trial: 20 year follow-up. Br Med J 342:d1539CrossRefGoogle Scholar
  112. Sarfati D, Howden-Chapman P, Woodward A, Salmond C (1998) Does the frame affect the picture? A study into how attitudes to screening for cancer are affected by the way benefits are expressed. J Med Screen 5:137–140Google Scholar
  113. Schapira MM, Nattinger AB, McHorney CA (2001) Frequency or probability? A qualitative study of risk communication formats used in health care. Med Decis Making 21:459–467Google Scholar
  114. Schröder FH, Hugosson J, Roobol MJ et al (2009) Screening and prostate-cancer mortality in a randomized European study. N Engl J Med 360:1320–1328CrossRefGoogle Scholar
  115. Schwartz LM, Woloshin S, Black WC, Welch HG (1997) The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med 127:966–972Google Scholar
  116. Schwartz LM, Woloshin S, Dvorin EL, Welch HG (2006) Ratio measures in leading medical journals: structured review of accessibility of underlying absolute risks. Br Med J 333:1248–1252CrossRefGoogle Scholar
  117. Schwartz LM, Woloshin S, Welch HG (2007) Using a drug facts box to communicate drug benefits and harms. Ann Intern Med 150:516–527Google Scholar
  118. Sedrakyan A, Shih C (2007) Improving depiction of benefits and harms: analyses of studies of well-known therapeutics and review of high-impact medical journals. Med Care 45:523–528CrossRefGoogle Scholar
  119. Shaw NJ, Dear PRF (1990) How do parents of babies interpret qualitative expressions of probability. Arch Dis Child 65:520–523CrossRefGoogle Scholar
  120. Sheridan S, Pignone MP, Lewis CL (2003) A randomized comparison of patients’ understanding of number needed to treat and other common risk reduction formats. J Gen Intern Med 18:884–892CrossRefGoogle Scholar
  121. Smeeth L, Haines A, Ebrahim S (1999) Numbers needed to treat derived from meta-analyses – sometimes informative, usually misleading. Br Med J 318:1548–1551CrossRefGoogle Scholar
  122. Steckelberg A, Balgenorth A, Mühlhauser I (2001) Analyse von deutschsprachigen Verbraucher-Informationsbroschüren zum Screening auf kolorektales Karzinom. Z Arztl Fortbild Qualitatssich 95:535–538Google Scholar
  123. Steckelberg A, Berger B, Köpke S, Heesen C, Mühlhauser I (2005) Criteria for evidence-based patient information. Z Arztl Fortbild Qualitatssich 99:343–351Google Scholar
  124. Steurer J, Held U, Schmidt M, Gigerenzer G, Tag B, Bachmann L (2009) Legal concerns trigger prostate-specific antigen testing. J Eval Clin Pract 15:390–392CrossRefGoogle Scholar
  125. Stone ER, Yates JF, Parker AM (1997) Effects of numerical and graphical displays on professed risk-taking behavior. J Exp Psychol Appl 3:243–256CrossRefGoogle Scholar
  126. Stone ER, Sieck WR, Bull BE, Yates JF, Parks SC, Rush CJ (2003) Foreground:background salience: explaining the effects of graphical displays on risk avoidance. Organ Behav Hum Decis Process 90:19–36CrossRefGoogle Scholar
  127. Strobe-Statement (2007) Strengthening the reporting of observational studies in epidemiology. Accessed April 2011
  128. Taylor SE, Thompson SC (1982) Stalking the elusive “vividness” effect. Psychol Rev 89:155–181CrossRefGoogle Scholar
  129. Thompson KM (2002) Variability and uncertainty meet risk management and risk communication. Risk Anal 22:647–654CrossRefGoogle Scholar
  130. Ubel PA, Jepson C, Baron J (2001) The inclusion of patient testimonials in decision aids: effects on treatment choices. Med Decis Making 21:60–68CrossRefGoogle Scholar
  131. van Dijk H, Houghton J, van Kleef E, van der Lans I, Rowe G, Frewer LJ (2008) Consumer responses to communication about food risk management. Appetite 50:340–352CrossRefGoogle Scholar
  132. Viscusi WK, Magat WA, Huber J (1991) Communication of ambiguous risk information. Theory Decis 31:159–173CrossRefGoogle Scholar
  133. Wager E, Mhaskar R, Warburton S, Djulbegovic B (2010) JAMA published fewer industry-funded studies after introducing a requirement for independent statistical analysis. PLoS One 5:e13591CrossRefGoogle Scholar
  134. Wainer H (1984) How to display data badly. Am Stat 38:137–147CrossRefGoogle Scholar
  135. Wallsten TS, Fillenbaum S, Cox JA (1986) Base rate effects on the interpretation of probability and frequency expressions. J Mem Lang 25:571–587CrossRefGoogle Scholar
  136. Wallsten TS, Budescu DV, Zwick R, Kemp SM (1993) Preferences and reasons for communicating probabilistic information in verbal or numerical terms. Bull Psychon Soc 31:135–138Google Scholar
  137. Weber EU, Hilton DJ (1990) Contextual effects in the interpretations of probability words: perceived base rate and severity of events. J Exp Psychol Hum Percept Perform 16:781–789CrossRefGoogle Scholar
  138. Wegwarth O, Gaissmaier W, Gigerenzer G (2011) Deceiving and informing: the risky business of risk perception. Med Decis Making 31:378–379CrossRefGoogle Scholar
  139. Weinfurt KP, Seils DM, Tzeng JP, Lin L, Schulman KA, Califf RM (2008) Consistency of financial interest disclosures in the biomedical literature: the case of coronary stents. PLoS One 3:e2128CrossRefGoogle Scholar
  140. Welch HG, Schwartz LM, Woloshin S (2000) Are increasing 5-year survival rates evidence of success against cancer? J Am Med Assoc 283:2975–2978CrossRefGoogle Scholar
  141. Wilde J (2009) PSA screening cuts deaths by 20%, says world’s largest prostate cancer study. ERSPC press office, carver wilde communications. Accessed April 2011
  142. Witte K, Allen M (2000) A meta-analysis of fear appeals: implications for effective public health campaigns. Health Educ Behav 27:591–615CrossRefGoogle Scholar
  143. Woloshin S, Schwartz LM, Black WC, Welch HG (1999) Women’s perceptions of breast cancer risk: how you ask matters. Med Decis Making 19:221–229CrossRefGoogle Scholar
  144. Zhu L, Gigerenzer G (2006) Children can solve Bayesian problems: the role of representation in mental computation. Cognition 98:287–308CrossRefGoogle Scholar
  145. Zikmund-Fisher BJ, Smith DM, Ubel PA, Fagerlin A (2007) Validation of the subjective numeracy scale (SNS): effects of low numeracy on comprehension of risk communications and utility elicitations. Med Decis Making 27:663–671CrossRefGoogle Scholar
  146. Zikmund-Fisher BJ, Fagerlin A, Ubel PAU (2008a) Improving understanding of adjuvant therapy options by using simpler risk graphics. Cancer 113:3382–3390CrossRefGoogle Scholar
  147. Zikmund-Fisher BJ, Ubel PA, Smith DM, Derry HA, McClure JB, Stark AT et al (2008b) Communicating side effect risks in a tamoxifen prophylaxis decision aid: the debiasing influence of pictographs. Patient Educ Couns 73:209–214CrossRefGoogle Scholar
  148. Zimmer AC (1983) Verbal vs. numerical processing of subjective probabilities. In: Scholz RW (ed) Decision making under uncertainty. Elsevier, Amsterdam, pp 159–182Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Harding Center for Risk LiteracyMax Planck Institute for Human DevelopmentBerlinGermany

Personalised recommendations