Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2010 November 1.
Published in final edited form as:
PMCID: PMC2810193

Decision Making About Cancer Screening: An Assessment of the State of the Science and a Suggested Research Agenda


Broad participation in screening is key to cancer prevention and early detection. Unfortunately, screening rates are low for many modalities. At its core, successful screening involves an individual deciding to take action (e.g., completing self-exams, scheduling appointments). Therefore, the science of decision making is central to understanding and increasing screening adherence. In this commentary, we (members of ASPO's Behavioral Oncology Interest Group) consider the state of the science on screening decision making and recommend research directions to advance the field. We address three key areas: implications of the nature of screening behavior for understanding decision making, expanding current decision-making theories to consider other influences on behavior, and using decision science findings to develop effective interventions.

Considering Features of Screening Behavior

Choices Among Multiple Screening Options

For some cancers, particularly colorectal cancer, multiple screening options exist. Current guidelines recommend that clinicians offer multiple options and invite people to choose (1-3). Given that preferences differ, offering patients the option to choose might enhance uptake (4). However, multiple options may cause confusion (5, 6). This confusion may contribute to low screening rates (7). Given the issues related to multiple screening options, issues of how to present choices and prevent confusion about screening options are of great import. This importance will likely grow as new screening tests are developed.

In terms of decision science, multiple options and the resulting complexity of screening decisions creates a need to enhance informed decision making and create better matches between patient preferences and screening options (8, 9). Such opportunities might translate into greater adherence. Recent research has examined innovative approaches to identifying patient preferences. For example, conjoint analysis approaches have been used to elicit preferences (8, 9) and to assist individuals in identifying a screening strategy consistent with those preferences. Conjoint analysis works by presenting competing alternatives or outcome scenarios and asking respondents to rank or rate them. The approach has been used to assess the perceived value of genetic testing (10), the personal utility of genomic information (11), and processes in shared decision making (12-15). Conjoint analysis allows assessment of the absolute and relative importance of choice attributes as well as how people utilize attribute judgments to make decisions. Combining screening information with such preference elicitation strategies may be an elegant decision aid strategy (16, 17) to promote informed screening decisions and more effective communication with providers (18) about screening options.

A second area to consider is comparative effectiveness research, a domain which has gained prominence in recent years as a tool for controlling medical expenditures. Such work has a potential impact on policy, choice, and personalized medicine (19). Given this fact and the aim of assessing effectiveness of treatment and prevention alternatives, knowledge of how individuals decide among multiple options may prove to be a valuable contribution to comparative effectiveness efforts (20). This may be especially relevant given that offering a smaller number of screening options may create missed opportunities for screening given individuals' preferences, barriers to screening, or non-availability of certain procedures in some areas.

Screening as a Population-Level Strategy

By definition, screening is a population-level enterprise. As such, the benefits and risks of screening tests are measured as properties of population groups (21). The screening goal of reducing cancer morbidity and mortality leads to an objective of testing large numbers of individuals in order to distinguish the small number of people who may have the disease from the much larger group who likely do not (22). Consequently, few individuals derive direct, individual benefit from screening; benefits accrue to the group, not the individual. However, most people fail to view screening in these population-based terms because epidemiologic risk is not intuitive to most individuals. People are therefore likely to define screening benefits in terms of their personal chances of averting disease/death rather than at the population level (23). Added to this conceptual complexity, all screening tests have performance limitations (e.g., false positives, false negatives) which influence effectiveness and affect willingness to participate.

The cognitive challenges associated with conceptualizing screening as a population-level strategy have not been well explored; decision research could aid understanding of how individuals consider population-level risks and benefits and how to best create interventions targeting such perceptions. Understanding how patients, clinicians, policy makers and insurers make decisions for screening in light of these parameters is critical if we are to develop a normative model for screening decisions.

From a policy perceptive, when there is strong evidence of a population benefit (e.g., cervical cancer screening), a public health approach to promote test uptake is appropriate. In such cases, participation in screening should still be voluntarily decided by the individual; however, this informed but voluntary decision ideal may be difficult to achieve when there is overwhelming public acceptance and support for tests (24). When evidence for population benefits is insufficient (e.g., prostate screening), public health approaches may be harder to justify. It is under these circumstances that the science of decision making may have its greatest application. This is especially relevant in countries where decisions to offer screening tests are not made by a central authority (e.g., the U.S.) and consequently scientific evidence of population benefit is not necessarily a prerequisite for test availability. While clinicians decide which tests to recommend and patients decide which tests to have, screening decisions are often driven by reimbursement policies and availability may be driven by market forces. Examination of how such factors influence screening decisions might suggest new routes to encouraging screening.

The tension between policy to promote uptake of effective cancer screening tests and individual decisions to participate in screening is unresolved (25). Future research that elucidates a conceptual model to integrate behavioral constructs at each step of the population screening algorithm with decision-making science that illuminates the choice points for the individual patient and provider would advance this field.

Expanding Screening Decision-Making Models

Affective Influences on Screening Decisions

Much of the focus in informed decision making about cancer screening has been on cognitive processes, especially using expected-utility weighing of the strengths and drawbacks of various options to decide whether to undergo screening or to decide among multiple screening options (26, 27). Affect – both negative (e.g., fear, embarrassment) and positive (e.g., satisfaction) – likely plays a key role in decisions about uptake and maintenance. To date, however, its role is understudied.

Affect may influence decisions through a variety of mechanisms. Importantly, fear, worry, and other aspects of negative affect have long been recognized as an influence on behavior (28, 29). Such negative affect is a key component of cancer risk perception, an important determinant of behavior (30-33). In some models relating risk perception to behavior, affect and cognition are seen as two parallel processes in decision making, with behavior change resulting when increased negative affect (e.g., fear) is coupled with a cognitively-based plan for reducing health threats (34-36). These models have been successfully applied to cancer screening (37-39). Another influence of negative affect is as an inhibitor or barrier of screening behavior in the Health Belief Model (e.g., 40, for a review see 41). Finally, newer work has examined a variety of other affective influences including embarrassment about screening tests (42-44), anticipated regret for not engaging in preventive behavior (32), and body image concerns (45). Positive affect (e.g., satisfaction with behavior change) influences preventive behavior maintenance (46-48), but its role has not been examined for cancer screening.

In summary, there is evidence that affect plays an important role in decisions about screening, is separate and distinct from cognitions, and can have unique effects on behavior (32, 49-51). The role of affect may be especially critical when screening tests have suboptimal sensitivity and specificity (e.g., ovarian cancer), may lead to unclear treatment options (e.g., prostate cancer), or require choice among multiple screening options (e.g., colorectal cancer). For example, elevated worry has been associated with risk overestimation and subsequent inappropriate utilization of ovarian cancer screening (52). Important research needs include clarifying the mechanisms by which affect drives screening decisions, delineation of whether constructs such as anticipated regret are best conceptualized as affect versus cognition, whether the association between affect and screening uptake differs from that with maintenance, and whether models of affective influences on decision making developed in other health-related domains generalize to screening behaviors. Accordingly, addressing the diverse roles of affect in screening decision making is an understudied, fruitful area for further exploration.

Role of System and Policy Factors

Facilitating screening and subsequent care requires a “fit” among patient-level (e.g., beliefs, knowledge), policy-level (e.g., guidelines, third-party payer eligibility), and system-level factors (e.g., health care delivery). Although recent policy changes have begun to address system-level barriers (e.g., mandated Medicare/Medicaid screening coverage), the complexity of policy and systems factors significantly impacts screening.

In particular, five system-level factors related to “access to care” (53) influence screening behavior: 1) Availability: Are there sufficient facilities, specialized services, and personnel to meet the community's needs? 2) Accessibility: How easily can available resources be accessed given transportation systems, distance, etc.? 3) Accommodation: How do providers structure services (e.g., hours, transportation assistance)? Is that structure seen as appropriate by patients? 4) Affordability: How are services priced? How is payment method taken into account? How do patients balance costs and benefits of services with ability to pay? 5) Acceptability: What are the reactions of patients to provider (e.g., demographics, beliefs), facility (e.g., type, location), and screening procedure (e.g., preparation) characeristics? What are providers' reactions to patient characteristics (e.g., SES, beliefs about willingness to screen)?

These dimensions are intertwined and reveal the inherent complexity of system-level factors. Such complexity raises multiple barriers to screening compliance. For example, trust in the provider (acceptability) may crucially influence whether provider recommendations lead to learning about and considering screening, whereas lack of accessibility or affordability create downstream barriers.

Overall, system-level barriers call for system-level changes (e.g., health care reform; culturally appropriate navigational programs 54). Greater sensitivity and research is needed to achieve “fit” between patient-level factors and the dimensions of access. For example, what policies can address financial barriers and are such policies effective? What communication channels are best for education addressing systems factors? How do we tailor or target messages to address these factors? How do we address low trust in the medical establishment, especially among certain racial groups (e.g., African-Americans)? How do low trust and limited service availability combine to create barriers in disadvantaged communities? What are the limits of accommodations that providers and patients are willing to make to facilitate screening? Addressing these and other questions are paramount to increase screening, especially in underserved and special populations.

Integrating Decision Making in Screening Programs and Intervention Development

Issues Related to Genetic Screening

Recent advances in genetic tests for cancer risk raise important issues. Such genetic risk testing is distinct from most cancer screening tests in that it is a prevention technique and, although done at the level of the individual patient, has direct health implications for the broader family unit. Two issues are of particular interest. The first concerns provider-centered versus patient-centered decision-making approaches. A key focus in research in this area is provider-centered work related to competencies for how to prepare patients and debrief them regarding results (55, 56). On the other hand, much of the practice in this area is increasingly patient-centered, focusing on informed decision making, education to allow for appropriate risk perception, patient-centered coping with risk information, and use of patient-reported outcomes to assess decision-making outcomes (57-59). The integration of these two areas is an area of needed further research. In particular, this consideration of integration of patient and provider approaches should consider the important issue of health literacy (60, 61). Many patients have difficulty understanding complex medical, numerical, or genetic information, so presenting information in a way that is understandable and facilitates decision making is a critical need (62). The rapid pace of advances in cancer genetics and genomics mean that attention to health literacy issues and their role in decision making is critically important.

A second key area concerns how patients interpret and respond to genetic risk tests and how those tests impact subsequent screening decisions. A majority of individuals receiving results indicating increased risk engage in subsequent screening, although the follow-up rate is far below 100% for some cancer genetic tests (63-65). However, individuals who have undergone genetic counseling and testing do not always follow screening recommendations (66). Some individuals who receive a favorable result (i.e., no increased risk) express relief and perceive their risk for the cancer to be almost non-existent. Based on this, they decide not to follow through with standard screening recommendations. On the other hand, those whose tests results do indicate increased risk sometimes face difficulty making decisions about further screening and/or risk-reducing surgery (67). Research is needed to understand decisions arising from genetic test results, regardless of mutation carrier status (68, 69), and further research into best practices for presenting genetic/genomic information so as to facilitate informed decisions is needed.

Decision Science and Intervention Development

Screening decisions shape important outcomes for individuals. Errors in making health care decisions related to screening can be costly, resulting in unnecessary screening or treatment, or leading to delayed diagnosis and subsequent increased morbidity and/or mortality. Interventions must be geared toward helping individuals make optimal decisions. Such interventions should increase knowledge, decrease decisional conflict over time, be sensitive to issues of numeracy and literacy, allow for decisions that are stable in the face of minor changes in context (e.g., framing effects), and incorporate the role of affect and of both deliberative and intuitive decisional processes in decision making.

Two areas of particular interest and research attention over the past decade have involved the formal use of decision aids and theory-based intervention tailoring, including culturally sensitive and systems approaches for underserved populations (e.g., access to care) (70). The latter are critically important given the growing trend of population-specific screening guidelines (e.g., those considered for colon cancer screening in African-Americans 71, 72).

These two areas of decision-making intervention research have already shown great promise. Further research needs include addressing the following questions: How might the role of deliberative and intuitive decision making be determined and integrated within the context of decision aid interventions? What are the best ways to integrate these systems by providing information in a way that may maximize optimal decision making? Based upon the needs for culturally sensitive and tailored interventions, how and in what situations might interventions be geared to moving individuals to more deliberative or intuitive thinking in order to enhance optimal decision making?


The complexity of screening behavior and the numerous influences on adherence with recommendations raise a number of issues for understanding screening decisions and using that knowledge to inform interventions and public policy. Research to advance such understanding, such as that recommended in this commentary, is critical if we are to advance the public health goal of using screening to reduce cancer mortality.


Marc T. Kiviniemi was supported by NIH grant K07CA106225; Karen Kaiser was supported by NIH grant R25TCA57699-14. Thanks to Rich Hoffman for comments on a draft of this manuscript and to Kaitlin Smith for assistance with manuscript preparation.


1. Smith RA, Cokkinides V, Brawley OW. Cancer screening in the United States, 2009: a review of current American Cancer Society guidelines and issues in cancer screening. CA Cancer J Clin. 2009;59:27–41. [PubMed]
2. Rex DK, Johnson DA, Anderson JC, Schoenfeld PS, Burke CA, Inadomi JM. American College of Gastroenterology guidelines for colorectal cancer screening 2009 [corrected] Am J Gastroenterol. 2009;104:739–50. [PubMed]
3. U. S. Preventive Services Task Force. Screening for colorectal cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2008;149:627–37. [PubMed]
4. Woolf SH. The best screening test for colorectal cancer--a personal choice. N Engl J Med. 2000;343:1641–3. [PubMed]
5. Sarfaty M, Wender R. How to Increase Colorectal Cancer Screening Rates in Practice. 2007:354–66. [PubMed]
6. Vernon SW, Meissner HI. Evaluating approaches to increase uptake of colorectal cancer screening: lessons learned from pilot studies in diverse primary care settings. Med Care. 2008;46:S97–102. [PubMed]
7. Schwartz B. Information overload. Journal of Life Sciences. 2007;1:56–61.
8. Hawley ST, Volk RJ, Krishnamurthy P, Jibaja-Weiss M, Vernon SW, Kneuper S. Preferences for colorectal cancer screening among racially/ethnically diverse primary care patients. Med Care. 2008;46:S10–6. [PubMed]
9. Marshall DA, Johnson FR, Phillips KA, Marshall JK, Thabane L, Kulin NA. Measuring patient preferences for colorectal cancer screening using a choice-format survey. Value Health. 2007;10:415–30. [PubMed]
10. Grosse SD, Wordsworth S, Payne K. Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis. Genet Med. 2008;10:648–54. [PubMed]
11. Grosse SD, McBride CM, Evans JP, Khoury MJ. Personal utility and genomic information: look before you leap. Genet Med. 2009;11:575–6. [PMC free article] [PubMed]
12. Bridges JFP, Kinter ET, Kidane L, Heinzen RR, McCormick C. Things are looking up since we started listening to patients: trends in the application of conjoint analysis in health 1982-2007.(Conference Paper)(Report) The Patient: Patient-Centered Outcomes Research. 2008;1:273(10). [PubMed]
13. Lee JT, Bridges JFP, Shockney L. Can pharmacoeconomics and outcomes research contribute to the empowerment of women affected by breast cancer? Expert Review of Pharmacoeconomics and Outcomes Research. 2008;8:73–9. [PubMed]
14. Constantinescu F, Goucher S, Weinstein A, Smith W, Fraenkel L. Understanding why rheumatoid arthritis patient treatment preferences differ by race. Arthritis Rheum. 2009;61:413–8. [PubMed]
15. Fraenkel L. Conjoint analysis at the individual patient level: issues to consider as we move from a research to a clinical tool.(Editorial)(Editorial) The Patient: Patient-Centered Outcomes Research. 2008;1:251(3). [PMC free article] [PubMed]
16. Elwyn G, O'Connor A, Stacey D, et al. Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. Bmj. 2006;333:417. [PMC free article] [PubMed]
17. O'Connor AM, Bennett C, Stacey D, et al. Do patient decision aids meet effectiveness criteria of the international patient decision aid standards collaboration? A systematic review and meta-analysis. Med Decis Making. 2007;27:554–74. [PubMed]
18. Street RL., Jr Aiding medical decision making: a communication perspective. Med Decis Making. 2007;27:550–3. [PubMed]
19. Garber AM, Tunis SR. Does comparative-effectiveness research threaten personalized medicine? N Engl J Med. 2009;360:1925–7. [PubMed]
20. Bridges JFP. Stated preference methods in health care evaluation: an emerging methodological paradigm in health economics. Appl Health Econ Health Policy. 2003;2:213–24. [PubMed]
21. Gifford S. The meaning of lumps: A case study of the ambiguities of risk. In: Janes CR, Stall R, Gifford SM, editors. Anthropology and Epidemiology. Dordrecht: Reidel; 1986. pp. 213–46.
22. Meissner HI, Smith RA, Rimer BK, et al. Promoting cancer screening: Learning from experience. Cancer. 2004;101:1107–17. [PubMed]
23. Han PKJ, Lehman TC, Massett H, Lee SJC, Klein WMP, Freedman AN. Conceptual problems in laypersons' understanding of individualized cancer risk: a qualitative study. Health Expect. 2009;12:4–17. [PubMed]
24. Irwig L, McCaffery K, Salkeld G, Bossuyt P. Informed choice for screening: implications for evaluation. Bmj. 2006;332:1148–50. [PMC free article] [PubMed]
25. Jepson RG, Hewison J, Thompson AGH, Weller D. How should we measure informed choice? The case of cancer screening. J Med Ethics. 2005;31:192–6. [PMC free article] [PubMed]
26. Rimer BK, Briss PA, Zeller PK, Chan EC, Woolf SH. Informed decision making: what is its role in cancer screening? Cancer. 2004;101:1214–28. [PubMed]
27. Myers RE. Decision counseling in cancer prevention and control. Health Psychol. 2005;24:S71–7. [PubMed]
28. Rosenberg MJ, Hovland CI, McGuire WJ, Abelson RP, Brehm JW. Yales studies in attitude and communication. III. Oxford England: Yale Univer. Press; 1960. Attitude organization and change: An analysis of consistency among attitude components.
29. Janis IL, Feshbach S. Effects of fear-arousing communications. The Journal of Abnormal and Social Psychology. 1953;48:78–92. [PubMed]
30. Leventhal H, Kelly K, Leventhal EA. Population risk, actual risk, perceived risk, and cancer control: a discussion. J Natl Cancer Inst Monogr. 1999:81–5. [PubMed]
31. Slovic P, Peters E, Finucane ML, MacGregor DG. Affect, Risk, and Decision Making. Health Psychology. 2005;24:S35–s40. [PubMed]
32. Chapman GB, Coups EJ. Emotions and Preventive Health Behavior: Worry, Regret, and Influenza Vaccination. Health Psychology. 2006;25:82–90. [PubMed]
33. Loewenstein GF, Weber EU, Hsee CK, Welch N. Risk as feelings. Psychological Bulletin. 2001;127:267–86. [PubMed]
34. Leventhal H. Findings and theory in the study of fear communications. In: Berkowitz L, editor. Advances in Experimental Social Psychology. New York: Academic Press; 1970. pp. 119–86.
35. Leventhal H, Brissette I, Leventhal EA, Cameron LD. The common-sense model of self-regulation of health and illness The self-regulation of health and illness behaviour. New York, NY US: Routledge; 2003. pp. 42–65.
36. Leventhal H, Benyamini Y, Brownlee S, et al. Perceptions of health and illness: Current research and applications. Amsterdam Netherlands: Harwood Academic Publishers; 1997. Illness representations: Theoretical foundations; pp. 19–45.
37. Bowen DJ, Helmes A, Powers D, et al. Predicting breast cancer screening intentions and behavior with emotion and cognition. Journal of Social and Clinical Psychology. 2003;22:213–32.
38. Cameron LD, Leventhal H, Love RR. Trait anxiety, symptom perceptions, and illness-related responses among women with breast cancer in remission during a tamoxifen clinical trial. Health Psychology. 1998;17:459–69. [PubMed]
39. Diefenbach MA, Miller SM, Daly MB. Specific worry about breast cancer predicts mammography use in women at risk for breast and ovarian cancer. Health Psychology. 1999;18:532–6. [PubMed]
40. Miller AM, Champion VL. Mammography in women > or = 50 years of age. Predisposing and enabling characteristics. Cancer Nurs. 1993;16:260–9. [PubMed]
41. Hay JL, Buckley TR, Ostroff JS. The Role of Cancer Worry in Cancer Screening: A Theoretical and Empirical Review of the Literature. Psycho-Oncology. 2005;14:517–34. [PubMed]
42. Codori AM, Petersen GM, Miglioretti DL, Boyd P. Health beliefs and endoscopic screening for colorectal cancer: potential for cancer prevention. Preventive Medicine. 2001;33:128–36. [PubMed]
43. Janz NK, Lakhani I, Vijan S, Hawley ST, Chung LK, Katz SJ. Determinants of colorectal cancer screening use, attempts, and non-use. Preventive Medicine. 2007;44:452–8. [PubMed]
44. Consedine NS, Christie MA, Neugut AI. Physician, affective, and cognitive variables differentially predict ‘initiation’ versus ‘maintenance’ PSA screening profiles in diverse groups of men. Br J Health Psychol. 2009;14:303–22. [PubMed]
45. Thomas E, Usher L. One More Hurdle to Increasing Mammography Screening Pubescent, Adolescent, and Prior Mammography Screening Experiences. Womens Health Issues. 2009 [PMC free article] [PubMed]
46. Baldwin AS, Rothman AJ, Hertel AW, et al. Specifying the Determinants of the Initiation and Maintenance of Behavior Change: An Examination of Self-Efficacy, Satisfaction, and Smoking Cessation. Health Psychology. 2006;25:626–34. [PubMed]
47. Rothman AJ. Toward a theory-based analysis of behavioral maintenance. Health Psychol. 2000;19:64–9. [PubMed]
48. Hertel AW, Finch EA, Kelly KM, et al. The impact of expectations and satisfaction on the initiation and maintenance of smoking cessation: an experimental test. Health Psychol. 2008;27:S197–206. [PubMed]
49. Kiviniemi MT, Voss-Humke AM, Seifert AL. How Do I Feel About the Behavior? The Interplay of Affective Associations With Behaviors and Cognitive Beliefs as Influences on Physical Activity Behavior. Health Psychology. 2007;26:152–8. [PubMed]
50. Kiviniemi MT, Duangdao KM. Affective associations mediate the influence of cost-benefit beliefs on fruit and vegetable consumption. Appetite. 2009;52:771–5. [PMC free article] [PubMed]
51. Lawton R, Conner M, McEachan R. Desire or reason: Predicting health behaviors from affective and cognitive attitudes. Health Psychology. 2009;28:56–65. [PubMed]
52. Andersen MR, Peacock S, Nelson J, et al. Worry about ovarian cancer risk and use of ovarian cancer screening by women at risk for ovarian cancer. Gynecol Oncol. 2002;85:3–8. [PubMed]
53. Penchansky R, Thomas JW. The concept of access: definition and relationship to consumer satisfaction. Med Care. 1981;19:127–40. [PubMed]
54. Chen LA, Santos S, Jandorf L, et al. A program to enhance completion of screening colonoscopy among urban minorities. Clin Gastroenterol Hepatol. 2008;6:443–50. [PubMed]
55. National Coalition for Health Professional Education in Genetics. Core competencies in genetics for health professional. 2007. [cited 2009 September 19]; 3rd:[Available from: <>.
56. Zon RT, Goss E, Vogel VG, et al. American Society of Clinical Oncology policy statement: the role of the oncologist in cancer prevention and risk assessment. J Clin Oncol. 2009;27:986–93. [PMC free article] [PubMed]
57. Croyle RT, Lerman C. Risk communication in genetic testing for cancer susceptibility. J Natl Cancer Inst Monogr. 1999:59–66. [PubMed]
58. Khoury MJ, MDP, Gwinn M, MDMPH, Yoon PW, PMPH, Dowling N, P, Moore CA, MDP, Bradley L., P The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention? [Review] 2007 [PubMed]
59. Gaff CL, Clarke AJ, Atkinson P, et al. Process and outcome in communication of genetic information within families: a systematic review. Eur J Hum Genet. 2007;15:999–1011. [PubMed]
60. Williams MV, Baker DW, Parker RM, Nurss JR. Relationship of functional health literacy to patients' knowledge of their chronic disease. A study of patients with hypertension and diabetes. Arch Intern Med. 1998;158:166–72. [PubMed]
61. Rudd RE. Health literacy skills of U.S. adults. Am J Health Behav. 2007;31 1:S8–18. [PubMed]
62. Rothman AJ, Kiviniemi MT. Treating people with information: an analysis and review of approaches to communicating health risk information. Journal of the National Cancer Institute. 1999:44–51. [PubMed]
63. Wainberg S, Husted J. Utilization of screening and preventive surgery among unaffected carriers of a BRCA1 or BRCA2 gene mutation. Cancer Epidemiol Biomarkers Prev. 2004;13:1989–95. [PubMed]
64. Claes E, Denayer L, Evers-Kiebooms G, et al. Predictive testing for hereditary nonpolyposis colorectal cancer: subjective perception regarding colorectal and endometrial cancer, distress, and health-related behavior at one year post-test. Genet Test. 2005;9:54–65. [PubMed]
65. Ersig AL, Hadley DW, Koehly LM. Colon cancer screening practices and disclosure after receipt of positive or inconclusive genetic test results for hereditary nonpolyposis colorectal cancer. Cancer. 2009;115:4071–9. [PMC free article] [PubMed]
66. Lerman C, Hughes C, Croyle RT, et al. Prophylactic surgery decisions and surveillance practices one year following BRCA1/2 testing. Prev Med. 2000;31:75–80. [PubMed]
67. Litton JK, Westin SN, Ready K, et al. Perception of screening and risk reduction surgeries in patients tested for a BRCA deleterious mutation. Cancer. 2009;115:1598–604. [PMC free article] [PubMed]
68. Loescher LJ, Lim KH, Leitner O, Ray J, D'Souza J, Armstrong CM. Cancer surveillance behaviors in women presenting for clinical BRCA genetic susceptibility testing. Oncol Nurs Forum. 2009;36:E57–67. [PubMed]
69. Morgan D, Sylvester H, Lucas F, Miesfeldt S. Cancer prevention and screening practices among women at risk for hereditary breast and ovarian cancer after genetic counseling in the community setting. Familial Cancer. in press. [PubMed]
70. Legler J, Meissner HI, Coyne C, Breen N, Chollette V, Rimer BK. The effectiveness of interventions to promote mammography among women with historically lower rates of screening. Cancer Epidemiol Biomarkers Prev. 2002;11:59–71. [PubMed]
71. Theuer CP, Wagner JL, Taylor TH, et al. Racial and ethnic colorectal cancer patterns affect the cost-effectiveness of colorectal cancer screening in the United States. Gastroenterology. 2001;120:848–56. [PubMed]
72. Agrawal S, Bhupinderjit A, Bhutani MS, et al. Colorectal cancer in African Americans. The American Journal Of Gastroenterology. 2005;100:515. [PubMed]