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Conveying Genetic Risk to Teenagers

  • Isaac M. Lipkus
Chapter
Part of the Issues in Clinical Child Psychology book series (ICCP)

Abstract

With the mapping of the human genome and the rapid discovery and application of new technologies, recent years have brought about unprecedented advances in genetics and genomics, the latter being defined as “the study not just of single genes, but of the functions and interactions of all the genes in the genome” (Guttmacher & Collins, 2002, p. 1512). In the foreseeable future, it is expected that predictive genetic tests will be available for as many as a dozen common conditions (Collins & McKusick, 2001). For example, strides have been made in the discovery of genetic and genomic markers for such diseases as asthma, diabetes, certain cancers, and heart disease (Altshuler et al., 2000; Bell, 2004; Bottini, Musumeci, Alonso, Rahmouni, Nika et al., 2004; Malerba & Pignatti, 2005; Ober & Hoffjan, 2006; Palma, Ristori, Ricevuto, Giannini, & Gulino, 2006; Sogaard, Kjaer, & Gayther, 2006; Wooster et al., 1994). Results of genetic testing for these common disorders will be used to inform, often in individuals with family histories of the disorder, their chance of developing the disease and as a consequence what steps can be taken, if any, to minimize or eliminate future harm.

Keywords

Genetic Testing Risk Communication Risk Information Nicotine Addiction Optimistic Bias 
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.

Notes

Acknowledgments

Writing of this chapter was supported by NIH grants R01CA114389 and R01CA121922. I thank Dr. Rochelle Schwartz-Bloom for her comments on science education and ideas as to how to use a science education approach to help adolescents understand processes of nicotine addiction. I thank Dr. Valerie Reyna for her discussions with me concerning conveying risk to adolescents.

References

  1. Alonso, D., & Fernandez-Berrocal, P. (2003). Irrational decisions: Attending to numbers rather than ratios. Personality and Individual Differences, 35, 1537–1547.Google Scholar
  2. Altshuler, D., Hirschhorn, J., Klannemark, M., Lindgren, C., Vohl, M., & Minesh, J. (2000). The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nature and Genetics, 26, 76–80.Google Scholar
  3. Ancker, J., & Kaufman, D. (2007). Rethinking health numeracy: A multidisciplinary literature review. Journal of the American Medical Informatics Association, 14, 713–721.PubMedGoogle Scholar
  4. Ancker, J., Senathirajah, Y., Kukafka, R., & Starren, J. (2006). Design features of graphs in health risk communication: A systematic review. Journal of the American Medical Informatics Association, 13, 608–618.PubMedGoogle Scholar
  5. Bell, J. (2004). Predicting diseases using genomics. Nature, 429, 453–456.PubMedGoogle Scholar
  6. Beyth-Marom, R., Austin, L., Fischhoff, B., Palmgren, C., & Jacobs-Quadrel, M. (1993). Perceived consequences of risky behaviors: Adults and adolescents. Developmental Psychology, 29, 549–563.Google Scholar
  7. Bibace, R., & Walsh, M. E. (1980). Development of children’s concepts of illness. Pediatrics, 66, 912–917.PubMedGoogle Scholar
  8. Bierut, L. J., Madden, P. A., Breslau, N., Johnson, E. O., Hatsukami, D., Pomerleau, O. F., et al. (2007). Novel genes identified in a high-density genome wide association study for nicotine dependence. Human Molecular Genetics, 16, 24–35.PubMedGoogle Scholar
  9. Blascovich, J., Loomis, J., Beall, A. C., Swinth, K. R., Hoyt, C. L., & Bailenson, J. N. (2002). Immersive virtual environment technology as a methodological tool for social psychology. Psychological Inquiry, 13, 103–124.Google Scholar
  10. Bogardus, S. J., Holmboe, E., & Jekel, J. (1999). Perils, pitfalls, and possibilities in talking about medical risk. Journal of the American Medical Association, 281, 1037–1041.PubMedGoogle Scholar
  11. Bottini, N., Musumeci, L., Alonso, A., Rahmouni, S., Nika, K., & Rostamkhani, M. (2004). A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nature Genetics, 36, 337–338.PubMedGoogle Scholar
  12. Brase, G., Cosmides, L., & Tooby, J. (1998). Individuation, counting, and statistical inference: The role of frequency and whole-object representations in judgment under uncertainty. Journal of Experimental Psychology, 127, 3–21.Google Scholar
  13. Brock, T. C. (1967). Communication discrepancy and intent to persuade as determinants of counterargument production. Journal of Experimental Social Psychology, 3, 296–309.Google Scholar
  14. Burbach, D. J., & Peterson, L. (1986). Children’s concepts of physical illness: A review and critique of the cognitive-developmental literature. Health Psychology, 5, 307–325.PubMedGoogle Scholar
  15. Chaiken, S., Giner-Sorolla, R., & Chen, S. (1996). Beyond accuracy: Defense and impression motives in heuristic and systematic information processing. In P. M. Gollwitzer & J. A. Bargh (Eds.), The psychology of action: Linking cognition and motivation to behavior (pp. 553–578). New York: The Guilford Press.Google Scholar
  16. Chaiken, S., & Trope, Y. (1999). Dual-process theories in social psychology. New York: Guilford Press.Google Scholar
  17. Chen, S., & Chaiken, S. (1999). The heuristic-systematic model in its broader context. In S. Chaiken & Y. Trope (Eds.), Dual process theories in social psychology (pp. 73–96). New York: Guilford Press.Google Scholar
  18. Collins, F., & McKusick, V. (2001). Implications of the Human Genome Project for medical science. Journal of the American Medical Association, 285, 540–544.PubMedGoogle Scholar
  19. Covello, V., Sandman, P., & Slovic, P. (1988). Risk communication, risk statistics, and risk comparison. Washington, DC: Chemical Manufacturers Association.Google Scholar
  20. Covey, J. (2007). A meta-analysis of the effects of presenting treatment benefits in different formats. Medical Decision Making, 27, 638–654.PubMedGoogle Scholar
  21. Croyle, R. T., Sun, Y. C., & Hart, M. (1997). Processing risk factor information: Defensive biases in health-related judgments and memory. In K. J. Petrie & J. A. Weinman (Eds.), Perceptions of health and illness, current research and applications (pp. 267–290). Amsterdam, The Netherlands: Hardwood Academic Publishers.Google Scholar
  22. de Bruin, W. B., Parker, A. M., & Fischhoff, B. (2007). Can adolescents predict significant life events? Journal of Adolescent Health, 41, 208–210.PubMedGoogle Scholar
  23. Dede, C., Salzman, M., Loftin, R. B., & Ash, K. (1997). Using virtual reality technology to convey abstract scientific concepts. In M. J. Jacobson & R. B. Kozma (Eds.), Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments. Upper Saddle River, NJ: Lawrence Erlbaum.Google Scholar
  24. Denes-Raj, V., & Epstein, S. (1994). Conflict between intuitive and rational processing: When people behave against their better judgment. Journal of Personality and Social Psychology, 66, 819–829.PubMedGoogle Scholar
  25. Denes-Raj, V., Epstein, S., & Cole, J. (1995). The generality of the ratio-bias phenomenon. Personality and Social Psychology Bulletin, 21, 1083–1092.Google Scholar
  26. Diefenback, M. A., Weinstein, N. D., & O’Reilly, J. (1993). Scales for assessing perceptions of health hazard susceptibility. Health Education Research, 8, 181–192.Google Scholar
  27. Ditto, P. H., Munro, G. D., Apanovich, A. M., Scepansky, J. A., & Lockhart, L. K. (2003). Spontaneous skepticism: The interplay of motivation and expectation in responses to favorable and unfavorable medical diagnoses. Personality and Social Psychology Bulletin, 29, 1120–1132.PubMedGoogle Scholar
  28. Edwards, A., & Elwyn, G. (1999). How should effectiveness of risk communication to aid patients’ decisions be judged? A review of the literature. Medical Decision Making, 19, 428–434.PubMedGoogle Scholar
  29. 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. Journal of Health Communication, 6, 61–82.PubMedGoogle Scholar
  30. Edwards, A., Elwyn, G., & Stott, N. (1999). Communicating risk reductions. Researchers should present results with both relative and absolute risks. British Medical Association, 318, 603.Google Scholar
  31. Fagerlin, A., Zikmund-Fisher, B. J., Ubel, P. A., Jankovic, A., Derry, H. A., & Smith, D. M. (2007). Measuring numeracy without a math test: Development of the Subjective Numeracy Scale (SNS). Medical Decision Making, 27, 672–680.PubMedGoogle Scholar
  32. Fischhoff, B. (1995). Risk perception and communication unplugged: Twenty years of process. Risk Analysis, 15, 137–145.PubMedGoogle Scholar
  33. Fischhoff, B. (1999). Why (cancer) risk communication can be hard. Journal of the National Cancer Institute Monographs, 25, 7–13.PubMedGoogle Scholar
  34. Fisher, A., McClelland, G., & Schulze, W. (1989). Communicating risk under Title III of SARA: Strategies for explaining very small risks in a community context. Journal of the Air Pollution Control Association, 39, 271–276.Google Scholar
  35. Garfield, J., & Ben-Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and statistics. International Statistical Review, 75, 372–396.Google Scholar
  36. Gibbons, F. X., & Gerrard, M. (1995). Predicting young adults’ health risk behavior. Journal of Personality and Social Psychology, 69, 505–517.PubMedGoogle Scholar
  37. Golbeck, A. L., Ahlers-Schmidt, C. R., Paschal, A. M., & Dismuke, S. E. (2005). A definition and operational framework for health numeracy. American Journal of Preventive Medicine, 29, 375–376.PubMedGoogle Scholar
  38. Guttmacher, A., & Collins, F. (2002). Genomic medicine–a primer. New England Journal of Medicine, 347, 1512–1520.PubMedGoogle Scholar
  39. Halpern, D., Blackman, S., & Salzman, B. (1989). Using statistical risk information of assess oral contraceptive safety. Applied Cognitive Psychology, 3, 251–260.Google Scholar
  40. Hibbard, J., Peters, E., Slovic, P., & Tusler, M. (2005). Can patients be part of the solution? Views on their role in preventing medical errors. Medical Care Research, 62, 601–616.Google Scholar
  41. Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescents. New York: Basic Books.Google Scholar
  42. Janis, I. L. (1967). Effects of fear arousal on attitude change: Recent developments in theory and experimental research. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 3, pp. 166–225). New York: Academic Press.Google Scholar
  43. Kalyuga, S. (2007). Enhancing instructional efficiency of interactive e-learning environments: A cognitive load perspective. Educational Psychological Review, 19, 387–399.Google Scholar
  44. Kaphingst, K. A., Persky, S., McCall, C., Lachance, C., Beall, A. C., & Blascovich, J. (2009). Testing communication strategies to convey genomic concepts using virtual reality technology. Journal of Health Communication, 14(4), 384–399.Google Scholar
  45. Klein, C. T. F., & Helweg-Larsen, M. (2002). Perceived control and the optimistic bias: A meta-analytic review. Psychology and Health, 17, 437–446.Google Scholar
  46. Koehler, J. (1996). The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges. Personality and Social Psychology Bulletin, 3, 1511–1523.Google Scholar
  47. Kruglanski, A. W. (1996). Motivated social cognition: Principles of the interface. In T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp. 493–522). New York: Guilford Press.Google Scholar
  48. Kunda, Z. (1987). Motivated inference: Self-serving generation and evaluation of causal theories. Journal of Personality and Social Psychology, 53, 636–647.Google Scholar
  49. Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108, 480–498.PubMedGoogle Scholar
  50. Kwiek, N. C., Halpin, M. J., Reiter, J. P., Hoeffler, L. A., & Schwartz-Bloom, R. D. (2007). Pharmacology in the high-school classroom. Science, 317, 1871–1872.PubMedGoogle Scholar
  51. Lee, D. H., & Mehta, M. D. (2003). Evaluation of a visual risk communication tool: Effects on knowledge and perception of blood transfusion risk. Transfusion, 43, 779–787.PubMedGoogle Scholar
  52. Lerman, C., Shields, P. G., Wileyto, E. P., Audrain, J., Hawk, L. H., Jr., Pinto, A., et al. (2003). Effects of dopamine transporter and receptor polymorphisms on smoking cessation in a bupropion clinical trial. Health Psychology, 22, 541–548.PubMedGoogle Scholar
  53. Leventhal, H. (1971). Fear appeals and persuasion: The differentiation of a motivational construct. American Journal of Public Health, 61, 1208–1224.PubMedGoogle Scholar
  54. Leventhal, H., Brissette, I., & Leventhal, E. A. (2003). The common-sense model of self-regulation of health and illness. In L. D. Cameron & H. Leventhal (Eds.), The self-regulation of health and illness behaviour (pp. 42–65). London: Routledge.Google Scholar
  55. Leventhal, H., Leventhal, E., & Cameron, L. D. (2001). Representations, procedures, and affect in illness self regulation: A perceptual-cognitive approach. In A. Baum, T. Revenson, & J. Singer (Eds.), Handbook of health psychology (pp. 19–48). New York: Erlbaum.Google Scholar
  56. Liberman, A., & Chaiken, S. (1992). Defensive processing of personally relevant health messages. Personality and Social Psychology Bulletin, 18, 669–679.Google Scholar
  57. Lipkus, I. M. (2007). Numeric, verbal, and visual formats of conveying health risks: Suggested best practices and future recommendations. Medical Decision Making, 27, 696–713.PubMedGoogle Scholar
  58. Lipkus, I. M., & Hollands, J. G. (1999). The visual communication of risk. Journal of the National Cancer Institute Monographs, 25, 149–163.PubMedGoogle Scholar
  59. Lipkus, I. M., & Peters, E. (2009). Understanding the role of numeracy in health: proposed theoretical framework and practical insights. Health Education & Behavior, 36(6), 1065–1081.Google Scholar
  60. Lipkus, I. M. (in press). Tidbits about risk communication: It is more than communicating and understanding probabilities. In The international encyclopedia of communication.Google Scholar
  61. Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127, 267–286.PubMedGoogle Scholar
  62. Malerba, G., & Pignatti, P. (2005). A review of asthma genetics: Gene expression studies and recent candidates. Journal of Applied Genetics, 46, 93–104.PubMedGoogle Scholar
  63. Mazur, D. J., & Hickam, D. H. (1994). The effect of physician’s explanations on patients’ treatment preferences: Five-year survival data. Medical Decision Making, 14, 255–258.PubMedGoogle Scholar
  64. McGuire, W. J. (1964). Inducing resistance to persuasion: Some contemporary approaches. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 1, pp. 191–229). New York: Academic Press.Google Scholar
  65. Morgan, M. G., Fischhoff, B., Bostrom, A., & Atman, C. J. (2002). Risk communication: A mental models approach. New York: Cambridge University Press.Google Scholar
  66. Moxey, A., O’Connell, D., McGettigan, P., & Henry, D. (2003). Describing treatment effects to patients. Journal of General Internal Medicine, 18, 948–959.PubMedGoogle Scholar
  67. National Center for Educational Statistics. National assessment of educational process (NAEP). (2006) The nation’s report card. Retrieved May 1, 2006, from http://nces.ed.gov/nationsreportcard/science/results/natachieve-g12.asp
  68. Natter, H., & Berry, D. (2005). The effects of presenting baseline risk when communicating absolute and relative risk reduction. Psychology, Health, and Medicine, 10, 326–334.Google Scholar
  69. Nelson, W., Reyna, V. F., Fagerlin, A., Lipkus, I., & Peters, E. (2008). Clinical implications of numeracy: Theory and practice. Annals of Behavioral Medicine, 35, 261–274.PubMedGoogle Scholar
  70. Ober, C., & Hoffjan, S. (2006). Asthma genetics 2006: The long and winding road to gene discovery. Genes and Immunity, 7, 95–100.PubMedGoogle Scholar
  71. Olson, J., & Zanna, M. (1996). Expectancies. In T. Higgins & A. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp. 211–238). New York: Guilford Press.Google Scholar
  72. Pacini, R., & Epstein, S. (1999). The relation of rational and experiential information processing styles to personality, basic beliefs, and the ratio-bias phenomenon. Journal of Personality and Social Psychology, 76, 972–987.PubMedGoogle Scholar
  73. Paling, J. (1997). Up to your armpits in alligators: How to sort out what risks are worth worrying about. Gainesville, FL: Risk Communication and Environmental Institute.Google Scholar
  74. Paling, J. (2003). Strategies to help patients understand risks. British Medical Journal, 327, 745–748.PubMedGoogle Scholar
  75. Palma, M., Ristori, E., Ricevuto, E., Giannini, G., & Gulino, A. (2006). BRCA1 and BRCA2: The genetic testing and the current management options for mutation carriers. Critical Reviews in Oncology/Hematology, 57, 1–23.PubMedGoogle Scholar
  76. Perrin, E. C., & Gerrity, P. S. (1981). There’s a demon in your belly: Children’s understanding of illness. Pediatrics, 67, 841–849.PubMedGoogle Scholar
  77. Persky, S., & McBride, C. M. (in press). Virtual reality in the genomic era: Immersive virtual environment technology as a tool for social and behavioral genomics research and practice. Health Communication.Google Scholar
  78. Petty, R. E., Tormala, Z. L., & Rucker, D. (2004). Resisting persuasion by counterarguing: An attitude strength perspective. In J. T. Jost & M. R., Banaji (Eds.), Perspectivism in social psychology: The yin and yang of progress. Washington, DC: American Psychological Association.Google Scholar
  79. Pidgeon, V. (1985). Children’s concepts of illness: Implications for health teaching. Maternal Child Nursing, 14, 23–35.Google Scholar
  80. Quadrel, M., Fischhoff, B., & Davis, W. (1993). Adolescent (in)vulnerability. American Psychologist, 48, 102–116.PubMedGoogle Scholar
  81. Reyna, V. F. (2008). A theory of medical decision making and health: Fuzzy trace theory. Medical Decision Making, 28, 850–865.PubMedGoogle Scholar
  82. Reyna, V. F., & Brainerd, C. J. (1994). The origins of probability judgment: A review of data and theories. In G. Wright & P. Ayton (Eds.), Subjective probability (pp. 239–272). New York: Wiley.Google Scholar
  83. Reyna, V. F., & Brainerd, C. J. (2007). The importance of mathematics in health and human judgment: Numeracy, risk communication, and medical decision making. Learning and Individual Differences, 17, 147–159.Google Scholar
  84. Reyna, V. F., & Brainerd, C. J. (2008). Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learning and Individual Differences, 18, 89–107.Google Scholar
  85. Reyna, V. F., & Farley, F. (2006). Risk and rationality in adolescent decision-making: Implications for theory, practice and public policy. Psychological Science in the Public Interest, 7, 1–44.Google Scholar
  86. Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. T. Cacioppo & R. E. Petty (Eds.), Social psychophysiology: A sourcebook (pp. 153–176). New York: Guilford Press.Google Scholar
  87. Rohrmann, B. (1992). The evaluation of risk communication effectiveness. Acta Psychologica, 81, 169–192.Google Scholar
  88. Rothman, A. J., & Kiviniemi, M. T. (1999). Treating people with information: An analysis and review of approaches to communicating health risk information. Journal of the National Cancer Institute Monographs, 25, 44–51.PubMedGoogle Scholar
  89. Saccone, S. F., Pergadia, M. L., Loukola, A., Broms, U., Montgomery, G. W., Wang, J. C., et al. (2007). Genetic linkage to chromosome 22q12 for a heavy-smoking quantitative trait in two independent samples. American Journal of Human Genetics, 80, 856–866.PubMedGoogle Scholar
  90. Sandman, P., & Weinstein, N. (1994). Communicating effectively about risk magnitudes. Bottom line conclusions and recommendations for practitioners (Report No. 230). Washington, DC: Environmental Protection Agency.Google Scholar
  91. Sandman, P., Weinstein, N., & Miller, P. (1994). High risk or low: How location on a “risk ladder” affected perceived risk. Risk Analysis, 14, 35–45.PubMedGoogle Scholar
  92. Schapira, M. M., Davids, S. L., McAuliffe, T. L., & Nattinger, A. B. (2004). Agreement between scales in the measurement of breast cancer risk perceptions. Risk Analysis, 24, 665–673.PubMedGoogle Scholar
  93. Schwartz-Bloom, R. D., & Halpin, M. J. (2003). Integration of pharmacology topics into high school biology and chemistry classes improves student performance. Journal of Research in Science Teaching, 40, 922–938.Google Scholar
  94. Sedlmeier, P. (1999). Improving statistical reasoning: Theoretical models and practical implications. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  95. Slovic, P., Peters, E., Finucane, M. L., & MacGregor, D. G. (2005). Affect, risk, and decision making. Health Psychology, 24(4 Suppl), S35–S40.PubMedGoogle Scholar
  96. Sogaard, M., Kjaer, S., & Gayther, S. A. (2006). Ovarian cancer and genetic susceptibility in relation to the BRCA1 and BRCA2 genes. Occurrence, clinical importance and intervention. Acta Ostetricia et Gynecologica Scandinavica, 85, 93–105.Google Scholar
  97. Stallings, S. P., & Paling, J. E. (2001). New tool for presenting risk in obstetrics and gynecology. Obstetrics and Gynecology, 98, 345–349.PubMedGoogle Scholar
  98. Stapleton, J. A., Sutherland, G., & O’Gara, C. (2007). Association between dopamine transporter genotypes and smoking cessation: A meta-analysis. Addiction Biology, 12, 221–226.PubMedGoogle Scholar
  99. Stone, E., Sieck, W., Bull, B., Yates, J., Parsk, S., & Rush, C. (2003). Foreground: Background salience: Explaining the effects of graphical displays on risk avoidance. Organizational Behavior and Human Decision Processes, 90, 19–36.Google Scholar
  100. Stone, E., Yates, J., & Parker, A. (1997). Effects of numerical and graphical displays on professed risk-taking behavior. Journal of Experimental Psychology, Applied, 3, 243–256.Google Scholar
  101. Swan, G. E., Valdes, A. M., Ring, H. Z., Khroyan, T. V., Jack, L. M., & Ton, C. C. (2005). Dopamine receptor DRD2 genotype and smoking cessation outcome following treatment with bupropion SR. Journal of Pharmacogenomics, 5, 21–29.Google Scholar
  102. Takahira, S. (1998). National Center for Educational Statistics, Third international mathematics and science study. Pursuing excellence: A study of US twelfth-grade mathematics and science achievement in international context. Washington, DC: National Center for Education Statistics, Office of Educational Research and Improvement, US Department of Education.Google Scholar
  103. Tercyak, K. P., Peshkin, B. N., Wine, L. A., & Walker, L. R. (2006). Interest of adolescents in genetic testing for nicotine addiction susceptibility. Preventive Medicine, 42, 60–65.PubMedGoogle Scholar
  104. Tormala, Z. L., & Petty, R. E. (2002). What doesn’t kill me makes me stronger: The effects of resisting persuasion on attitude certainty. Journal of Personality and Social Psychology, 83, 298–1313.Google Scholar
  105. Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological Review, 110, 403–421.PubMedGoogle Scholar
  106. Verplanken, B. (1997). The effect of catastrophe potential on the interpretation of numerical probabilities of the occurrence of hazards. Journal of Applied Social Psychology, 27, 1453–1467.Google Scholar
  107. Walter, F. M., Emery, J., Braithwaite, D., & Marteau, T. M. (2004). Lay understanding of familial risk of common chronic illnesses: A systematic review and synthesis of qualitative research. Annals of Family Medicine, 2, 583–594.PubMedGoogle Scholar
  108. Waters, E. A., Weinstein, N. D., Colditz, G. A., & Emmons, K. (2006). Formats for improving risk communication in medical tradeoff decisions. Journal of Health Communication, 11, 167–182.PubMedGoogle Scholar
  109. Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39, 806–820.Google Scholar
  110. Weinstein, N. D. (1989a). Effects of personal experience on self-protective behavior. Psychological Bulletin, 105, 31–50.PubMedGoogle Scholar
  111. Weinstein, N. D. (1989b). Perceptions of personal susceptibility to harm. In V. M. Mays, G. W. Albee, & S. F. Schneider (Eds.), Primary prevention of AIDS: Psychological approaches (pp. 142–167). Thousand Oaks, CA: Sage Publications.Google Scholar
  112. Weinstein, N. D. (1999). What does it mean to understand a risk? Evaluating risk comprehension. Journal of the National Cancer Institute Monographs, 25, 15–20.PubMedGoogle Scholar
  113. Weinstein, N. D., & Klein, W. M. (1995). Resistance of personal risk perceptions to debiasing interventions. Health Psychology, 14, 132–140.PubMedGoogle Scholar
  114. Weinstein, N. D., & Lachendro, E. (1982). Egocentrism as a source of unrealistic optimism. Personality and Social Psychology Bulletin, 8, 195–200.Google Scholar
  115. Weinstein, N., & Sandman, P. (1993). Some criteria for evaluating risk messages. Risk Analysis, 13, 103–114.Google Scholar
  116. Witte, K. (1998). Fear as motivator, fear as inhibitor: Using the extended parallel process model to explain fear appeal successes and failures. In P. A. Anderson & L. K. Guerrero (Eds.), Handbook of communication and emotion: Research, theory, applications and contexts (pp. 424–451). New York: Academic Press.Google Scholar
  117. Wooster, R., Neuhausen, S. L., Mangion, J., Quirk, Y., Ford, D., & Collins, N. (1994). Localization of a breast cancer susceptibility gene, BRCA2, to chromosome 13q12-13. Science, 265, 2088–2091.PubMedGoogle Scholar
  118. Yamagishi, K. (1997). When a 12.86% mortality rate is more dangerous than 24.14%: Implications for risk communication. Applied Cognitive Psychology, 11, 495–506.Google Scholar
  119. Zikmund-Fisher, B. J., Fagerlin, A., & Ubel, P. A. (2007). Mortality versus survival graphs: Improving temporal consistency in perceptions of treatment effectiveness. Patient Education and Counseling, 66, 100–107.PubMedGoogle Scholar
  120. Zuwerink, J., & Devine, P. (1996). Attitude importance and resistance to persuasion: It’s just not the thought that counts. Journal of Personality and Social Psychology, 70, 931–944.Google Scholar

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Isaac M. Lipkus
    • 1
  1. 1.Duke University Medical CenterDurhamUSA

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