Behavioral Economics: “Nudging” Underserved Populations to Be Screened for Cancer

Jason Q. Purnell, PhD, MPH; Tess Thompson, MPH, MPhil; Matthew W. Kreuter, PhD; Timothy D. McBride, PhD

Suggested citation for this article: Purnell JQ, Thompson T, Kreuter MW, McBride TD. Behavioral Economics: “Nudging” Underserved Populations to Be Screened for Cancer. Prev Chronic Dis 2015;12:140346. DOI: http://dx.doi.org/10.5888/pcd12.140346.

PEER REVIEWED

PEER REVIEWED

Abstract

Persistent disparities in cancer screening by race/ethnicity and socioeconomic status require innovative prevention tools and techniques. Behavioral economics provides tools to potentially reduce disparities by informing strategies and systems to increase prevention of breast, cervical, and colorectal cancers. With an emphasis on the predictable, but sometimes flawed, mental shortcuts (heuristics) people use to make decisions, behavioral economics offers insights that practitioners can use to enhance evidence-based cancer screening interventions that rely on judgments about the probability of developing and detecting cancer, decisions about competing screening options, and the optimal presentation of complex choices (choice architecture). In the area of judgment, we describe ways practitioners can use the availability and representativeness of heuristics and the tendency toward unrealistic optimism to increase perceptions of risk and highlight benefits of screening. We describe how several behavioral economic principles involved in decision-making can influence screening attitudes, including how framing and context effects can be manipulated to highlight personally salient features of cancer screening tests. Finally, we offer suggestions about ways practitioners can apply principles related to choice architecture to health care systems in which cancer screening takes place. These recommendations include the use of incentives to increase screening, introduction of default options, appropriate feedback throughout the decision-making and behavior completion process, and clear presentation of complex choices, particularly in the context of colorectal cancer screening. We conclude by noting gaps in knowledge and propose future research questions to guide this promising area of research and practice.

Top

Introduction

Low-income, minority, and uninsured people in the United States are disproportionately affected by illness and death from cancer and have lower rates of participation in recommended screenings (1,2). This article explores how insights from behavioral economics may reduce these disparities by providing tools to increase screening for breast, colorectal, and cervical cancer.

Breast, colorectal, and cervical cancer affect hundreds of thousands of Americans each year, and there is an evidence base for both supporting screening behavior and administering screening for all 3 of these cancer sites (3). The US Preventive Services Task Force (USPSTF) recommends that women receive screening mammography every 2 years from age 50 through 74 (3). For colorectal cancer, the USPSTF recommends screening for adults aged 50 to 75 using colonoscopy, sigmoidoscopy, or fecal occult blood testing (FOBT) (4). The availability of several screening methods with different recommended intervals adds to the complexity of a patient’s decision to be screened for colorectal cancer. For cervical cancer, the USPSTF recommends screening for women aged 21 to 65 by Papanicolaou (Pap) test every 3 years; women aged 30 to 65 may now choose a longer screening interval by combining cytology and testing for human papilloma virus (HPV) every 5 years (5). As part of Healthy People 2020, the US Department of Health and Human Services has set ambitious screening targets (compliance of 70.5% for colorectal cancer screening recommendations, 93.0% for cervical cancer, and 81.1% for breast cancer). However, underserved populations continue to be screened at lower rates (1,6).

Reaching the Healthy People 2020 goals in the face of persistent disparities requires new strategies for promoting screening tests. Three concepts from behavioral economics — judgment, decision-making, and choice architecture — can enhance existing evidence-based screening strategies by providing new insights into the behavior of patients and clinicians and suggesting new tools and techniques to promote screening.

Top

Behavioral Economics

We focus on 3 concepts from behavioral economics: judgment, decision-making, and choice architecture. The Table includes concepts in each area, with examples of how each can be applied to increase cancer screening in low-income and minority populations. Many of these ideas can be easily incorporated into strategies suggested by The Guide to Community Preventive Services (Community Guide) (7) (an evidence-based, accessible resource for health promotion and disease prevention, with specific coverage of breast, cervical, and colorectal cancer, compiled by an expert task force using systematic reviews as the basis for its recommendations), including evidence-based client- and provider-directed interventions to promote cancer screening, client reminders (eg, letters, post cards), small media (eg, brochures, newsletters), reducing structural barriers, provider assessment and feedback, one-on-one education, group education, and reducing client out-of-pocket costs (8).

Judgment

Judgment refers to the subjective assessment of the probability of outcomes or events. Decades of psychological and behavioral economics research has shown that judgments, particularly those in which people have limited experience or information, can be greatly influenced by seemingly irrelevant factors (9,10). Three aspects of judgment are relevant to cancer screening: availability, representativeness, and unrealistic optimism.

Availability refers to a tendency to judge the likelihood of future events, such as a cancer diagnosis, based on how easy it is to imagine them or call up similar events in memory (9,10). For example, women may be more likely to focus on their risk of breast cancer after the diagnosis of a family member or close friend because this experience is highly “available” in memory.

Representativeness refers to judgments about the probability or frequency of an event based on its resemblance to one’s past experiences or assumptions (9,10). If clinicians and health educators can provide memorable messages about cancer screening and show that people similar to the target audience undergo screening, then both availability and representativeness can be used to increase the perception of risk and highlight the benefits of screening.

Unrealistic optimism occurs when individuals have unreasonably low estimates of their own susceptibility to harm or overly high estimates of their chances of success or benefit (11). For example, people tend to underestimate their chances of receiving a diagnosis of cancer (12). Personally relevant messages that provide accurate information about cancer risk and the true prevalence of screening should increase uptake of screening tests, in part, by counteracting a tendency for unrealistic optimism.

Decision-making

Decision-making examines the choices people make when faced with multiple options. The manner in which information is presented can influence decisions. Framing effects occur when the way choices are presented influences the options individuals prefer (13). In the classic example from early work in behavioral economics, people were presented with a scenario in which a disease is expected to kill 600 people and then given the choice of taking a course of action that would save 200 people or a course of action that had a one-third probability of saving 600 people and a two-thirds probability that no one would be saved. A majority chose the certain option of saving 200 people even though the probabilities are identical (13). Message framing promoting cancer screening also appears to affect the decision to be screened, particularly when messages are tailored with personal information (14). For example, women from underserved populations responded more favorably to letters from their primary care physicians that addressed their specific mammography beliefs, stage of change, perceived barriers, and risk factors than to a generic letter encouraging breast cancer screening. Several studies also suggest that framing the choice of colorectal screening tests should focus on the relative mortality risk reduction, sensitivity and specificity, preparation, and pain associated with each type of test (15,16).

Fairly consistent context effects also have been observed wherein people are frequently drawn to the one option in a set that dominates the others — or, if no single option dominates, the middle option between two extremes (17). For instance, colonoscopy often dominates the options for colorectal cancer screening among recommending physicians because of its effectiveness in detecting and preventing cancer, but compared with FOBT it may be seen as an extreme option among patients concerned about inconvenient preparation and invasiveness. This is an instance in which the context of the choices may differ depending on the perspective of an expert compared with that of a patient, which argues for shared decision-making and informed patient choice.

Health decisions also can be influenced by loss aversion, or a stronger preference for avoiding losses than acquiring gains (9,10). A consistent aversion to loss is a central finding in behavioral economics. Framing breast cancer screening messages in terms of loss aversion (eg, if a woman is not screened appropriately, she may risk loss of her health, her breasts, or her life) may have a minimal advantage over gain-framed messages (18). Loss-framed messages for diagnostic follow-up of abnormal cervical cancer screening are associated with heightened distress, particularly among women with attentional styles highly attuned to medical threats, but no difference in adherence compared with neutral or positive frames (19).

Messages about screening may also be framed to appeal to one’s sense of fairness (20). People who perceive the health care system as unfair (eg, have high levels of perceived discrimination or medical mistrust) are generally less trusting and less adherent to recommendations for preventive health behaviors such as cancer screening (21,22), though some exceptions to this finding have been noted (23). Addressing issues of trust and other barriers through improved communication, cultural competence, engagement in medical decision-making, and by providing better information regarding screening options may improve screening in underserved populations (24). Research suggests that people will often prefer to take nothing than to take an offer they believe is unfair (20). In the case of colorectal cancer screening, for example, there is a perception among some patients that a colonoscopy is a higher-quality procedure than an FOBT (25), which suggests that low-income and minority adults, especially those who mistrust medical providers or have experienced discrimination in past health care encounters, could reject screening altogether if the only test they were offered was one they perceived to be inferior to other options. A similar concern about fairness might be attached to mammography and prostate-specific antigen screening in light of recent USPSTF recommendations (26) and the burden of mortality among African Americans (27). Understanding individuals’ perceptions about the fairness of requiring (or recommending against) a particular test and their preferences for screening could help insurers communicate with specific subgroups about the appropriateness of various types of cancer screening.

Screening messages can also be framed in terms of social and cultural norms (28). Such messaging may describe the majority of a peer group engaging in behaviors such as screening. One study in men found that descriptive norms (ie, “what others like me do”) were predictive of cancer screening attitudes and intention, particularly when subjective norms (ie, “what significant others expect me to do”) were low. Those who received information that screening was common reported greater intention to be screened, rated their probability of being screened as higher, and were more likely to leave their names and addresses to get more information about screening (29). This finding has particular relevance for underserved populations, who may be unlikely to encounter frequent messages from family and friends regarding cancer screening.

Affect, or emotional response, can have a significant influence on decisions as well (30). Fear of cancer diagnosis, pain, and embarrassment have been recurring themes in the literature on barriers to cancer screening, particularly among the underserved (31). It may be effective to frame health messages to elicit positive affect about screening or counter negative affect regarding screening expectations. For example, describing the relief of knowing more about one’s health status or the sense of pride that comes with taking care of one’s health may be more effective than addressing the fear of a cancer diagnosis.

Finally, people tend to discount future rewards, or focus on immediate gratification rather than longer-term benefits (32). Therefore, it may be effective to reframe the costs of screening as minimal and identify immediate benefits, such as peace of mind or the ability to prevent cancer. Another strategy from behavioral economics that may offset future discounting is providing incentives to be screened. This allows screening to have an immediate and tangible benefit that may offset the inconvenience or discomfort of screening.

Choice architecture

Synthesizing these principles, scholars in behavioral economics have proposed a set of strategies to counter some of the flaws in human judgment and decision-making in the face of uncertainty and risk. They suggest that optimal systems for high-stakes decisions will have 1) proper alignment of incentives, 2) an appreciation of how individuals understand the consequences of their decisions, 3) sensible default options, 4) appropriate feedback, 5) allowance for expected errors, and 6) a clear presentation of information for making complex choices (33,34).

Choice architecture, defined as “organizing the context in which people make decisions” (33,34), provides a promising means for encouraging screening. As competing choices grow in number, structuring complex choices can increase the quality of decision-making. As noted above, emphasizing aspects of colorectal cancer screening such as mortality risk reduction, specificity, and pain may help to organize options for patients. Default options exploit the tendency to accept the status quo when an option is presented as standard or prescribed. Defaults are powerful tools in promoting behaviors while respecting patient self-determination (35). Studies of interventions (ie, vaccination, HIV testing, organ donation) that use default options suggest that they may be a viable strategy to increase cancer screening (36). Substantial increases in participation across several behavioral domains are often observed by simply making the desired behavior the default option and requiring people to opt out (35), though the use of an opt-in strategy may be preferable for more controversial procedures such as HPV vaccination, where parents are more likely to consent when they can opt in (37). Providers could select 1 test and screening interval as the default and automatically schedule appointments for procedures while allowing patients to opt out.

Choice architecture also emphasizes feedback on performance. Information technologies that allow medical practices to track, remind, and provide feedback to both providers and patients are especially promising strategies for increasing cancer screening (24,38). Ideally, positive feedback should be provided when screening is completed, but prompts should also be given when screening opportunities are missed or when screening is overdue. Financial incentives to patients can also be effective in promoting screening. Incentives are among the most effective methods for increasing colorectal cancer screening in primary care (39). On the basis of promising findings in colorectal cancer screening and recommendations from the Community Guide regarding removal of structural barriers, incentives may be more appealing to low-income people if they could partially or fully cover costs incurred before screening (eg, transportation, time off of work, childcare) as opposed to being given in cash or noncash forms after screening. This may counteract the tendency to discount future rewards by adding proximal, immediate benefits of screening. Patients should also be assisted in understanding the consequences of not being screened and the consequences of various screening intervals and screening tests through informed decision-making that relies on both traditional presentations of risk and the principles of behavioral economics outlined above. Finally, a well-designed system should include an allowance for errors, making it easy for individuals to correct the “error” of not being screened with multiple reminders or on-demand scheduling and testing.

Future directions for “nudging” underserved populations toward cancer screening

Research. Although we have noted some evidence supporting the principles of behavioral economics in cancer screening interventions, many research questions remain. There is some evidence that tailoring information to be consistent with patient characteristics and beliefs improves responsiveness to cancer screening recommendations, but fine-tuned experimental manipulations of behavioral economic factors such as availability, representativeness, affect, and social and cultural norms are needed as well. To test whether framing effects significantly alter attitudes and behaviors, randomized experiments should examine emotional reactions (eg, affect), information processing (eg, understanding), judgments and beliefs (eg, about cancer, about screening tests, about barriers), intentions (eg, to be screened), and actual screening behavior in response to various presentations of information. Research is also needed to test the speculation regarding fairness and the potential rejection of screening methods perceived as inferior.

Systems-level research should evaluate the effectiveness of organizing clinical practice to include elements of choice architecture, such as default options, feedback, incentives, and allowance for errors. In the area of incentives in particular, future research might also explore whether more invasive forms of screening require greater incentives among low-income adults, as might be expected based on research in other populations (15,16).

Policy and practice. In light of the promising evidence of effectiveness, changes in policy and practice may not need to wait on additional evidence to be implemented. For example, leading cancer organizations and federal agencies should consider advocating a “default option” approach to scheduling cancer screening appointments (with an opportunity to opt out) that is integrated into electronic medical records, particularly for providers and systems who serve underserved populations. Incentives delivered in ways that both facilitate (eg, transportation and child care) and reward (eg, gift cards) screening behavior should also be considered. At the very least, systems and providers should incorporate the recommendations made in this article and by others to include appropriate framing in their communications with patients regarding cancer screening decisions.

Top

Conclusion

Colorectal cancer, breast cancer, and cervical cancer are major causes of illness and death in the United States that disproportionately affect low-income and racial and ethnic minority populations. We have suggested a set of enhancements to existing strategies and related research questions to advance the use of behavioral economics in efforts to increase cancer screening in underserved populations. Many of our suggestions with respect to the framing of choices and message delivery could be added and evaluated within existing systems with little cost. Default options and provision of feedback are also consistent with the increasing use of clinical treatment guidelines and electronic medical records. The Affordable Care Act has also significantly lowered the cost of preventive treatments for underserved populations, but additional, creatively administered incentives may be necessary to translate increased access into completed screenings. The challenge of reducing, and ultimately eliminating, cancer-related health disparities is formidable, but tools from behavioral economics may move our efforts forward in meaningful ways by taking a realistic approach to human behavior and decision-making.

Top

Acknowledgments

The authors received no financial support for the work described in this article.

Top

Author Information

Corresponding Author: Jason Q. Purnell, PhD, MPH, Assistant Professor The Brown School, Washington University in St. Louis, One Brookings Drive, Campus Box 1196, St. Louis, MO 63130. Telephone: 314-935-3738. E-mail: jpurnell@wustl.edu.

Author Affiliations: Tess Thompson, Matthew W. Kreuter, Timothy D. McBride, Washington University in St. Louis, St. Louis, Missouri.

Top

References

1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin 2013;63(1):11–30. CrossRef PubMed
2. Smith RA, Brooks D, Cokkinides V, Saslow D, Brawley OW. Cancer screening in the United States, 2013: a review of current American Cancer Society guidelines, current issues in cancer screening, and new guidance on cervical cancer screening and lung cancer screening. CA Cancer J Clin 2013;63(2):88–105. CrossRef PubMed
3. United States Preventive Services Task Force. Screening for breast cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 2009;151(10):716–26. CrossRef PubMed
4. United States Preventive Services Task Force. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 2008;149(9):627–37. CrossRef PubMed
5. Moyer VA, US Preventive Services Task Force. Screening for cervical cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 2012;156(12):880–91. Erratum in: Ann Intern Med 2013;158(11):852. CrossRef PubMed
6. American Cancer Society. Cancer facts and figures 2013. Atlanta (GA): American Cancer Society; 2013.
7. Centers for Disease Control and Prevention. The guide to community preventive services. Atlanta (GA): Centers for Disease Control and Prevention; 2013.
8. Sabatino SA, Lawrence B, Elder R, Mercer SL, Wilson KM, DeVinney B, et al. Effectiveness of interventions to increase screening for breast, cervical, and colorectal cancers: nine updated systematic reviews for the guide to community preventive services. Am J Prev Med 2012;43(1):97–118. CrossRef PubMed
9. Kahneman D. A perspective on judgment and choice: mapping bounded rationality. Am Psychol 2003;58(9):697–720. CrossRef PubMed
10. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science 1974;185(4157):1124–31. CrossRef PubMed
11. Weinstein ND, Marcus SE, Moser RP. Smokers’ unrealistic optimism about their risk. Tob Control 2005;14(1):55–9. CrossRef PubMed
12. Katapodi MC, Lee KA, Facione NC, Dodd MJ. Predictors of perceived breast cancer risk and the relation between perceived risk and breast cancer screening: a meta-analytic review. Prev Med 2004;38(4):388–402. CrossRef PubMed
13. Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science 1981;211(4481):453–8. CrossRef PubMed
14. Skinner CS, Strecher VJ, Hospers H. Physicians’ recommendations for mammography: do tailored messages make a difference? Am J Public Health 1994;84(1):43–9. CrossRef PubMed
15. 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(5):415–30. CrossRef PubMed
16. van Dam L, Hol L, de Bekker-Grob EW, Steyerberg EW, Kuipers EJ, Habbema JDF, et al. What determines individuals’ preferences for colorectal cancer screening programmes? A discrete choice experiment. Eur J Cancer 2010;46(1):150–9. CrossRef PubMed
17. Huber J, Payne J, Puto C. Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypotheses. J Consum Res 1982;9:90–8. CrossRef
18. O’Keefe DJ, Jensen JD. The relative persuasiveness of gain-framed and loss-framed messages for encouraging disease detection behaviors: a meta-analytic review. J Commun 2009;59(7):296–316. CrossRef
19. Miller SM, Buzaglo JS, Simms SL, Green V, Bales C, Mangan CE, et al. Monitoring styles in women at risk for cervical cancer: implications for the framing of health-relevant messages. Ann Behav Med 1999;21(1):27–34. CrossRef PubMed
20. Rabin M. Incorporating fairness into game theory and economics. Am Econ Rev 1993;83(5):1281–302.
21. Born W, Engelman K, Greiner KA, Bhattacharya SB, Hall S, Hou Q, et al. Colorectal cancer screening, perceived discrimination, and low-income and trust in doctors: a survey of minority patients. BMC Public Health 2009;9(1):363. CrossRef PubMed
22. Shariff-Marco S, Klassen AC, Bowie JV. Racial/ethnic differences in self-reported racism and its association with cancer-related health behaviors. Am J Public Health 2010;100(2):364–74. CrossRef PubMed
23. Dailey AB, Kasl SV, Holford TR, Jones BA. Perceived racial discrimination and nonadherence to screening mammography guidelines: results from the race differences in the screening mammography process study. Am J Epidemiol 2007;165(11):1287–95. CrossRef PubMed
24. Klabunde CN, Lanier D, Breslau ES, Zapka JG, Fletcher RH, Ransohoff DF, et al. Improving colorectal cancer screening in primary care practice: innovative strategies and future directions. J Gen Intern Med 2007;22(8):1195–205. CrossRef PubMed
25. Greiner KA, Born W, Nollen N, Ahluwalia JS. Knowledge and perceptions of colorectal cancer screening among urban African Americans. J Gen Intern Med 2005;20(11):977–83. CrossRef PubMed
26. Recommendations for primary care practice. Rockville (MD): US Preventive Services Task Force; 2014. http://www.uspreventiveservicestaskforce.org/Page/Name/recommendations. Accessed November 5, 2014.
27. DeSantis C, Naishadham D, Jemal A. Cancer statistics for African Americans, 2013. CA Cancer J Clin 2013;63(3):151–66. CrossRef PubMed
28. Gigerenzer G. Why heuristics work. Perspect Psychol Sci 2008;3(1):20–9. CrossRef
29. Sieverding M, Decker S, Zimmermann F. Information about low participation in cancer screening demotivates other people. Psychol Sci 2010;21(7):941–3. CrossRef PubMed
30. Slovic P, Finucane M, Peters E, MacGregor D. The affect heuristic. In: Gilovich T, Griffin D, Kahneman D, editors. Heuristics and biases: the psychology of intuitive thought. New York (NY): Cambridge University Press; 2002. p. 397–420.
31. Flynn PM, Betancourt H, Ormseth SR. Culture, emotion, and cancer screening: an integrative framework for investigating health behavior. Ann Behav Med 2011;42(1):79–90. CrossRef PubMed
32. Camerer C, Loewenstein G. Behavioral economics: past, present, future. In: Camerer C, Loewenstein G, Rabin M, editors. Advances in behavioral economics. Princeton (NJ): Princeton University Press; 2004. p. 3–51.
33. Sunstein C. Selective fatalism. J Legal Stud 1998;27(S2):799–823. CrossRef
34. Thaler RH, Sunstein CR. Nudge: improving decisions about health, wealth, and happiness. New Haven (CT): Yale University Press; 2008.
35. Madrian B, Shea D. The power of suggestion: inertia in 401(k) participation and savings behavior. Q J Econ 2001;116(4):1149–87. CrossRef
36. Halpern SD, Ubel PA, Asch DA. Harnessing the power of default options to improve health care. N Engl J Med 2007;357(13):1340–4. CrossRef PubMed
37. Reiter PL, McRee AL, Pepper JK, Brewer NT. Default policies and parents’ consent for school-located HPV vaccination. J Behav Med 2012;35(6):651–7. CrossRef PubMed
38. Sabatino SA, Habarta N, Baron RC, Coates RJ, Rimer BK, Kerner J, et al. Interventions to increase recommendation and delivery of screening for breast, cervical, and colorectal cancers by healthcare providers systematic reviews of provider assessment and feedback and provider incentives. Am J Prev Med 2008;35(1, Suppl):S67–74. CrossRef PubMed
39. Stone EG, Morton SC, Hulscher ME, Maglione MA, Roth EA, Grimshaw JM, et al. Interventions that increase use of adult immunization and cancer screening services: a meta-analysis. Ann Intern Med 2002;136(9):641–51. CrossRef PubMed
40. Thaler R. Toward a positive theory of consumer choice. J Econ Behav Organ 1980;1(1):39–60. CrossRef

Top

Table

Table. Selected Principles of Behavioral Economics and Examples of Approaches and Interventions for Breast, Cervical, and Colorectal Cancer Screening

Top