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. Author manuscript; available in PMC: 2017 Jun 1.
Published in final edited form as: Health Psychol. 2016 Feb 11;35(6):594–603. doi: 10.1037/hea0000324

Affective Forecasting and Medication Decision Making in Breast Cancer Prevention

Michael Hoerger 1, Laura D Scherer 2, Angela Fagerlin 3
PMCID: PMC4868645  NIHMSID: NIHMS757708  PMID: 26867042

Abstract

Objectives

Over two million American women at elevated risk of breast cancer are eligible to take chemoprevention medications such as Tamoxifen and Raloxifene, which can cut in half the risk of developing breast cancer but also have a number of side effects. Historically, very few at-risk women have opted to use chemoprevention medications. Affective forecasting theory suggests that people may avoid these medications if they expect taking them to increase their health-related stress.

Methods

After receiving an individually tailored decision aid that provided personalized information about the risks and benefits of these medications, 661 women at elevated risk of breast cancer were asked to make three affective forecasts, predicting what their level of health-related stress would be if taking Tamoxifen, Raloxifene, or neither medication. They also completed measures of decisional preferences and intentions, and at a three-month follow-up reported on whether or not they had decided to use either medication.

Results

On the affective forecasting items, very few women (< 10%) expected the medications to reduce their health-related stress, relative to no medication at all. Participants with more negative affective forecasts about taking a chemoprevention medication expressed lower preferences and intentions for using the medications (Cohen’s ds from 0.74 to 0.79) and were more likely to have opted against using medication at follow-up (odds ratios from 1.34 to 2.66).

Conclusions

These findings suggest that affective forecasting may explain avoidance of breast cancer chemoprevention medications. They also highlight the need for more research aimed at integrating emotional content into decision aids.

Keywords: Tamoxifen, Raloxifene, Affective forecasting, Emotional beliefs, Breast cancer, Behavioral decision making, Decision aids

Each year, over 1.5 million women worldwide are diagnosed with breast cancer, and over 500,000 die from the disease (Stewart & Wild, 2014). Women at elevated risk have the option of taking a chemoprevention medication, such as Tamoxifen or Raloxifene, which can reduce the 5-year risk of developing breast cancer by about 50% (Nelson, Smith, Griffin, & Fu, 2013). Based on a systematic literature review of randomized controlled trials and meta-analyses, the American Society for Clinical Oncology (Visvanathan et al., 2013) began advocating in 2013 that chemoprevention medications “should be discussed” by physicians and at-risk women. Also recognizing this evidence, since 2013 the U.S. Preventive Services Task Force has given Tamoxifen and Raloxifene “Grade B” recommendations, meaning there is moderate-to-high certainty of at least moderate benefit of these medications for at-risk women, and eligible patients should be engaged in a shared decision making process with their physicians (Moyer, 2013). Although over two million American women with elevated risk are eligible to take chemoprevention medications (Freedman et al., 2003), few choose to do so even when provided with information of the risks and benefits (Fagerlin et al., 2011). Theories of decision making (Halpern & Arnold, 2008; Rhodes & Strain, 2008) suggest that healthcare decisions are often driven by emotional processes such as “affective forecasting,” which refers to people’s expectations about the impact of their life choices on emotional well-being. For example, an individual might opt for a chemoprevention medication if she expects it to reduce her stress about developing cancer, but opt against medication if she believes it will increase her overall stress. In this study, we investigated at-risk women’s affective forecasts to examine the extent to which they believe initiating a chemoprevention medication would increase or decrease their health-related stress, and whether these affective forecasts explained decision making about these medications.

In making healthcare decisions about chemoprevention, emotional concerns are the proverbial elephant in the room, as the term “cancer” is inherently emotionally-laden. Decision making about chemoprevention medications can be particularly emotionally burdensome given tradeoffs between the benefits and risks of medication. Women with an elevated risk of developing breast cancer can be identified through self-report screening procedures (Amir et al., 2010; Gail et al., 1989) that take no more than a few minutes and have evidence of high reliability and validity. For women identified as at-risk (e.g., ≥1.66% 5-year risk; Amir et al., 2010; Gail et al., 1989), the decision to initiate a chemoprevention medication can be challenging because these medications reduce the risk of developing breast cancer (and bone fractures) but also increase the risk of side effects, such as cataracts, hormonal symptoms, sexual problems, deep vein thrombosis, pulmonary embolism, stroke, and endometrial cancer (Visvanathan et al., 2013). Severe side effects are rare (e.g., among women ages 50 and over on Tamoxifen, the 5-year absolute risk of stroke increases by 0.5% and risk of invasive endometrial cancer increases by 0.2%; Fisher et al., 1998), though less-serious side effects are more common (e.g., 12% absolute increase in the presence of hot flashes). These concerns about medication-specific physical side effects may compound more general concerns that taking any new medication would be stressful due to the hassle of taking a daily pill, in this case for a minimum of 5 years, as well as increased attention to health and mortality and other factors (Goldenberg & Arndt, 2008; Ropka et al., 2010; Waters et al., 2009, 2010; Zeber et al., 2013).

Ultimately, few women choose to take a breast cancer prevention medication (Visvanathan et al., 2013; Waters et al., 2010). Only 0.08% of American women ages 40 and over have used one of these medications for chemoprevention, and in studies where at-risk women are informed of the risks and benefits, as little as 1% opt for medication (Fagerlin et al., 2011; Waters et al., 2010). There have been calls for more research aimed at understanding the emotional processes that drive healthcare decision making (see Nelson, Stefanek, Peters, & McCaul, 2005, and other articles in that special issue; Elwyn et al., 2011; Reyna, Nelson, Han, & Pignone, 2015; Sweeny, 2008) and it has been suggested that the side effect profile of these medications, coupled with more general concerns about the emotional impact of taking a medication, might shape affective forecasts that drive medication avoidance. Specifically, people might avoid chemoprevention medications due to expectations that medication will increase, rather than decrease, their overall level of health-related stress (Waters et al., 2009, 2010).

Outside of the breast cancer prevention literature, a rapidly growing body of research on affective forecasting indicates that people routinely make life decisions based on their expectations about how their choices will impact later emotional well-being (e.g., work, personal finance, relationships, education, and leisure; see Wilson & Gilbert, 2013). While healthcare decision making in older populations is a significant and growing public health concern (Zikmund-Fisher et al., 2010), a central limitation of the affective forecasting literature is that the vast majority of the 100+ studies conducted in this area have involved student samples of young adults who were not facing health decisions (see Mathieu & Gosling, 2012; Wilson & Gilbert, 2013). A few exceptions stand out. For example, affective forecasting has been linked to decisions about diet (Walsh & Kiviniemi, 2014), exercise (Ruby et al., 2011), vaccines (Chapman & Coups, 2006), and colorectal cancer screening (Dillard et al., 2010), such that people engage in proactive health behaviors if they believe doing so will reduce stress but avoid health behaviors if they believe engaging in them will increase stress. Affective forecasting might similarly influence decisions about chemoprevention medications.

The problem is that affective forecasts provide only a rudimentary window into the future, so they can leave people vulnerable to making imperfect decisions. Scores of studies have evaluated the accuracy of affective forecasting by systematically comparing people’s predicted emotional reactions to events with their actual emotional reactions (see Mathieu & Gosling, 2012; Wilson & Gilbert, 2013). That research has shown that affective forecasts are often prone to a number of errors and biases that can lead people to make decisions that are suboptimal, contrary to personal preferences, or later evoke regret (Dunn et al., 2011; Gilbert & Ebert, 2002; Kermer et al., 2006; Nisbet & Zelenski, 2011; though see also, Hoerger, Chapman, & Duberstein, in press). That body of research suggests that when imagining stressful scenarios – perhaps including initiating a medication with the potential for side effects – people’s affective forecasts tend to be negatively biased (Wilson & Gilbert, 2013) due to underlying cognitive biases that lead people to focus on the most stressful aspect of the situation (Hoerger, Quirk, Lucas, & Carr, 2010; Kahneman et al., 2006; Wilson et al., 2000), underestimate their coping skills (Gilbert et al., 1998; Hoerger, 2012; Hoerger, Quirk, Lucas, & Carr, 2009), and let the stress of the moment color perceptions of the future (Hoerger et al., 2012; Wenze et al., 2012).

In a longitudinal study of women at high risk for breast cancer, we examined whether decision making about chemoprevention medications was explained by affective forecasting about health-related stress. The study involved primary analyses of existing data from the Guide to Decide study (Fagerlin et al., 2011), which provided women with a tailored web-based decision aid characterizing the medical risks and benefits of Tamoxifen and Raloxifene. The primary goal of the parent study was to examine the impact of risk communication strategies on chemoprevention decision making. The current investigation is unique from others (e.g., Dillard et al., 2013; Zikmund-Fisher et al., 2008) that focused solely on Tamoxifen and did not examine affective forecasting. In the present investigation our first goal was to test whether participants’ affective forecasts reflected a belief that chemoprevention medication would increase, decrease, or have no effect on health-related stress, relative to taking no medication. Second, we examined whether affective forecasts of increased stress accounted for decisional preferences and intentions to avoid medication. Third, we examined whether affective forecasts predicted decisions to opt-out against using a medication at three month follow-up and whether that relationship was explained by decisional preferences and intentions. To our knowledge, this is the first study seeking to apply basic research on affective forecasting toward understanding medication decision making in breast cancer prevention.

Method

Procedures

Participants were women at elevated risk of developing breast cancer who were recruited from two U.S. health organizations: Group Health in Seattle, Washington and the Henry Ford Health System in Detroit, Michigan. Inclusion criteria included having an elevated risk of breast cancer, being ages 40–74 (ages approved for breast cancer chemoprevention medications), and being post-menopausal (Raloxifene has not been studied in premenopausal women). An elevated risk of breast cancer was defined as ≥1.66% chance of developing breast cancer during the next 5 years (the recommended minimum) as determined by the National Cancer Institute’s Breast Cancer Risk Assessment Tool (BCRAT), which calculates risk based on the participant’s age, race/ethnicity, breast cancer history, age of first live birth, age of first menses, history of breast biopsies, and the number of first-degree relatives with histories of breast cancer, using the Gail model (Amir et al., 2010; Gail et al., 1989). Women were excluded if they were premenopausal (as they would not be eligible to take Raloxifene), had a history of breast cancer or breast cancer chemoprevention medication use, were pregnant or nursing, had contraindications to either medication, or had a terminal illness. Participants were not required to be aware of their elevated risk of breast cancer prior to enrolling in the study. IRB approval for this research was obtained from the Group Health Research Institute, Henry Ford Health System, and University of Michigan. An automated electronic health record review was used to identify potentially eligible women (N = 14,048), namely those whose health records suggested they were at elevated risk. These women were then mailed letters inviting them to participate in an Internet study described as examining how health information can help women to make decisions about breast cancer prevention. Of those contacted, 2,340 accessed the study site, where they completed an online consent form, followed by an eligibility screener assessing each of the inclusion and exclusion criteria.

Of the potential participants accessing the study web site, 1,012 were eligible, consented, and participated in the study. The analyses reported here involved participants randomized to the intervention arm (n = 661) who completed a battery of survey measures following receipt of an Internet decision aid that provided personalized information about the risks and benefits of breast cancer chemoprevention medications (322 controls did not receive tailored risk information, and 29 intervention participants (3%) were missing survey data). Analyses also examined responses from a subset of participants who completed a brief 3-month follow-up survey enquiring about their medication decision making. Participants received $10 gift cards for participating in the study.

The decision aid provided a combination of general and tailored information about breast cancer. General information included background information on breast cancer, a description of how breast cancer chemoprevention medications work, an overview of the research used to study each drug, and information on potential benefits and risks of medication. The tailored information included providing each participant with individualized information about their 5-year risk of developing breast cancer based on their BCRAT score from the screener, as well as tailored information about the impact of Raloxifene and Tamoxifen on their risk of breast cancer, as well as their risk of side effects (tailored on age and race). Side effects described were hormonal symptoms, sexual problems, blood clots, cataracts, and endometrial cancer. The tailored risk information was repeated throughout the decision aid. All materials were written at an 8th-grade reading level.

Participants

Participant characteristics of the 661 women completing baseline measures are shown in Table 1. The sample ranged in age from 46 to 74 and had BCRAT scores (representing risk of developing breast cancer in the next five years), ranging from 1.70–10.40. Three-month follow-up data were available from 368 participants. In comparison to those completing baseline measures only, those providing follow-up data were more likely to have at least a Bachelor’s degree (73% vs. 59%), χ2 (1, N = 661) = 14.25, p < .001, and had higher numeracy, d = 0.16, t(659) = 2.01, p = .04; however, they did not differ on age, race, BCRAT score, perceived cancer risk, or breast cancer anxiety.

Table 1.

Descriptive Statistics

Variable M (SD) or N (%)
Age, years 61.72 (5.09)
Race, non-Latino white 642 (97.1%)
Education
 Less than Bachelor’s degree 219 (33.1%)
 Bachelor’s degree 203 (30.7%)
 Graduate degree 239 (36.2%)
BCRAT score 2.68 (1.21)
Perceived risk, 1 to 7 3.23 (1.52)
Numeracy, 1 to 6 4.25 (0.92)
Breast cancer anxiety, 1 to 5 2.27 (0.79)
Concerned about side effects, present 293 (44.3%)
Affective forecast of change in health-related stress, −5 to 5a 0.80 (1.12)
Behavioral preferences, 1 to 5 1.96 (1.01)
Behavioral intentions, 1 to 5 2.16 (1.05)
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Note. N = 661 women at elevated risk of breast cancer. BCRAT score refers to 5-year risk of breast cancer according to the National Cancer Institute’s Breast Cancer Risk Assessment Tool.

a

The affective forecasting score was coded such that positive scores reflected predicted increases in health-related stress on a breast cancer prevention medication, relative to no medication.

Measures

Affective forecasting

Affective forecasting was operationalized as the predicted change in health-related stress if taking a chemoprevention medication, relative to taking no medication. Following the decision aid, participants completed three items. These assessed forecasts for taking Tamoxifen (“Imagine that you do take Tamoxifen. How worried would you be about getting any of the above health conditions?”), Raloxifene (“Imagine that you do take Raloxifene. How worried would you be about getting any of the above health conditions?”), or no medication (“Imagine that you do not take a breast cancer prevention medication. How worried would you be about getting any of the above health conditions?”). The “above health conditions” referred to cancer and the medication side effects summarized above the questions. Each forecast was made on a scale from 1 (Not at all worried) to 6 (Extremely worried). Forecasts for Tamoxifen and Raloxifene were highly correlated (r = .76, p < .001) and showed the same pattern of findings, so they were averaged (Cronbach’s α = .86), and then we subtracted ratings for the no-medication scenario to provide a difference score reflecting expected change in health-related stress on medication relative to off medication: Affective Forecasting Score = (Tamoxifen Forecast + Raloxifene Forecast)/2 – No-medication Forecast. Positive scores indicated more forecasted worry on medication, and negative scores indicated less forecasted worry on medication. Comparable methods of assessment are used across the affective forecasting literature (e.g. Chapman & Coups, 2006; Mathieu & Gosling, 2012).

Decisional preferences and intentions

Participants rated their preference for taking chemoprevention by responding to the question, “How good of a choice is taking a breast cancer prevention drug as a way to reduce your chance of getting breast cancer?” using a 5-point rating scale, with higher values indicating greater preference. For behavioral intentions, participants responded to three items (e.g., “Given what you know right now, how likely do you think you are to… take a breast cancer prevention drug?”) assessing their intentions of talking to their doctor, looking up more information, and taking the medication; ratings were made on a 5-point scale, with higher scores indicating greater behavioral intentions (Cronbach’s α = .85).

Decision Making at Follow-up

To assess the status of participants’ decision making at the three-month follow-up, they were asked, “Have you made a decision about whether or not to take a breast cancer prevention drug?” Response options included (A) currently taking Tamoxifen, (B) currently taking Raloxifene, (C) still weighing whether to take a breast cancer prevention drug, and (D) decided against taking a breast cancer prevention drug.

Covariates

Age, numeracy, breast cancer risk (BCRAT score), perceived risk of breast cancer, breast cancer anxiety, and concerns about side effects were included as covariates. Numeracy was measured with the 8-item (Cronbach’s α = .83) Subjective Numeracy Scale (Fagerlin et al., 2007), which used a 6-point rating scale. For perceived risk of breast cancer, participants responded to the question, “Compared to the average woman your age, what are your chances of developing breast cancer in the next 5 years?” using a 7-point rating scale. Participants completed a 4-item (Cronbach’s α = .68) measure of breast cancer anxiety (e.g., “I am worried about developing breast cancer in the next 5 years”), which used a 5-point rating scale. Concerns about side effects were assessed with an open-ended question asking what their main reason would be for not taking a breast cancer prevention drug. Three raters, including a clinical psychologist (first author), a clinical psychology PhD student, and an undergraduate, independently coded each response (Mdn = 26 words) for the presence or absence of concerns about side effects; coding demonstrated excellent inter-rater reliability, namely a raw percent agreement of 94% and an intraclass correlation coefficient, ICC (analogous to a kappa for >2 raters) of .96, with any inconsistencies resolved by consensus.

Analyses

Descriptive analyses were used to summarize ratings on each of the three affective forecasting items, and related to our first goal, a within-subject t-test was used to compare whether participants’ affective forecasts for taking a chemoprevention medication differed from their affective forecasts for not taking a medication. The remaining analyses focused on the affective forecasting difference score (average predicted stress on medication – predicted stress not on medication).

Related to our second goal, we examined whether affective forecasting was associated with medication preferences and intentions using zero-order correlations as well as regression analyses that controlled for covariates (age, numeracy, breast cancer risk, perceived risk, breast cancer anxiety, and concerns about side effects). To facilitate meaningful interpretation of these findings, we also conducted supplemental analyses using ANCOVA to examine whether a dichotomous indicator of affective forecasting (0 = expectations of no change or increased stress on medication, 1 = expectations of reduced stress on medication) was associated with preferences and intentions while controlling for covariates. Cohen’s d, the standardized mean difference, was used as an indicator of effect size (Cohen, 1988).

Related to our third goal, we examined whether affective forecasting was associated with decisions made at follow-up. At the three-month follow-up, only 2 participants had already initiated a chemoprevention medication (responses A and B), so these categories were merged with the considering medication category (response C) to yield a dichotomous decision variable (0 = taking or considering medication, 1 = decided against medication). Binary logistic regression was used to examine whether affective forecasting (continuous difference score) and the covariates were associated with decision making at follow-up. To facilitate meaningful interpretation of these findings, we also repeated analyses using the dichotomous indicator of affective forecasting, which allowed for straightforward comparisons of whether having favorable vs. neutral/negative affective forecasts about medication were associated with the odds of opting out against medication.

Finally, we tested a mediational model to examine whether behavioral preferences and intentions mediated the relationship between affective forecasting and reports of decision making at follow-up. To simplify interpretation, the two indicators of preferences and intentions were combined into a single mediator using principal axis factoring. Mediation analyses controlled for the effects of the covariates on decisions made. As mediation with a dichotomous outcome variable requires transforming all unstandardized beta weights to a common metric, the Herr (2013) method was used to determine the proportion of the total effect explained by the mediator. As there are several methods for evaluating the statistical significance of an indirect effect, both the Sobel (1982) test and the Preacher and Hayes (2008) bias-corrected bootstrapping method were used.

Results

Affective Forecasting

On the affective forecasting questions, participants predicted that their health-related stress would be an average of M = 3.05 (SD = 1.12) on the 1–6 rating scale if taking a breast cancer prevention medication (Tamoxifen: M = 3.15, SD = 1.20; Raloxifene: M = 2.95, SD = 1.19), relative to a predicted health-related stress of M = 2.25 (SD = 0.98) if opting against medication. The raw difference between predicted stress on medication versus not on medication (affective forecasting difference score) was thus 0.80 raw points, which was equivalent to a d = 0.81 SD unit increase in forecasted health-related stress if opting for medication, t(660) = 18.31, p < .001. The predicted level of change varied considerably across participants, ranging from −3 (anticipated lower stress on medication) to +5 (anticipated higher stress on medication). As summarized in Figure 1, less than 10% of participants (n = 57) believed that taking a chemoprevention medication would reduce their health-related stress.

Figure 1.

Figure 1

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Affective forecasting: How women at elevated risk of breast cancer expected taking a breast cancer chemoprevention medication to impact their health-related stress, relative to taking no medication. N = 661.

Decision Preferences and Intentions

Cross-sectional analyses supported the hypothesized relationship between affective forecasting and participants’ preferences and behavioral intentions for using a breast cancer chemoprevention medication. Affective forecasts of increased health-related stress on a chemoprevention medication were correlated with lower preferences (r = −.24, p < .001) and intentions (r = −.23, p < .001) for use of medication. As shown in the hierarchical regression analyses in Table 2, affective forecasting continued to explain preferences (p < .001) and intentions (p < .001) when controlling for covariates, meaning that these findings could not be attributed to confounding effects of age, numeracy, breast cancer anxiety, breast cancer risk, perceived risk, or concerns about side effects. Similarly, in ANCOVA analyses examining affective forecasting categorically, preferences (d = 0.74, F(1,653) = 27.90, p < .001) and intentions (d = 0.79, F(1,653) = 31.46, p < .001) for use of medication were higher among participants with favorable affective forecasts about taking medication (Preferences: M = 2.59, SD = 0.94; Intentions: M = 2.85, SD = 0.97) than among participants expecting no change or increased health-related stress on medication (Preferences: M = 1.90, SD = 0.93; Intentions: M = 2.09, SD = 0.95).

Table 2.

Regression Model Explaining Behavioral Preferences and Intentions for Taking a Breast Cancer Chemoprevention Medication

Predictor Behavioral Preference
Behavioral Intention
B B
Step 1
 Older Age −.01       −.00      
 Higher Numeracy .03       .03      
 Higher Breast Cancer Anxiety .25*** .30***
 Higher Breast Cancer Risk .07*     .09**  
 Higher Perceived Risk .11*** .13***
 Concerned about Side Effects −.34*** −.22**  
R2 = .14*** R2 = .15***
Step 2
 Older Age −.01       −.00      
 Higher Numeracy .03       .04      
 Higher Breast Cancer Anxiety .22*** .26***
 Higher Breast Cancer Risk .08*     .10**  
 Higher Perceived Risk .12*** .14***
 Concerned about Side Effects −.27*** −.15      
 Affective Forecast of Higher Stress on Chemoprevention Medication −.18*** −.18***
ΔR2 = .04*** ΔR2 = .04***
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Note. N = 661. B = unstandardized beta weight.

*

p < .05

**

p < .01

***

p < .001

Decision Making at Three-month Follow-up

At the three-month follow-up, 54.9% (n = 202) of participants had decided against taking a chemoprevention medication. Only 0.5% (n = 2) had initiated a chemoprevention medication (solely Raloxifene), though 44.6% (n = 164) were still weighing doing so; we merged these latter two groups together in subsequent analyses. As hypothesized, logistic regression analyses showed that affective forecasting was associated with medication decision making at the three-month follow-up, including when controlling for covariates (see Table 3), odds ratio = 1.34, p = .008. In further logistic regression analyses examining affective forecasting categorically, the percentage of participants opting against medication at follow-up was lower among participants with favorable affective forecasts about taking medication (29.0%) than among participants expecting no change or increased health-related stress on medication (57.3%), odds ratio = 2.66, p = .02, again while controlling for covariates. Thus, affective forecasting was uniquely predictive of subsequent medication decision making.

Table 3.

Logistic Regression Model for Deciding Against a Breast Cancer Chemoprevention Medication at Three-Month Follow-up

Predictor B Wald χ2 Odds Ratio 95% CI
Lower Upper
Step 1
 Older Age .04 2.14 1.04 0.99 1.09
 Higher Numeracy .18 1.98 1.20 0.93 1.53
 Higher Breast Cancer Anxiety   −.46** 8.89 0.63 0.46 0.85
 Higher Breast Cancer Risk −.01   0.01 0.99 0.82 1.20
 Higher Perceived Risk −.08   0.92 0.93 0.79 1.08
 Concerned about Side Effects .41 3.53 1.51 0.98 2.33
Step 2
 Older Age .04 2.97 1.04 0.99 1.09
 Higher Numeracy .17 1.76 1.19 0.92 1.53
 Higher Breast Cancer Anxiety   −.41** 6.90 0.66 0.49 0.90
 Higher Breast Cancer Risk −.01   0.02 0.99 0.81 1.20
 Higher Perceived Risk −.07   0.74 0.93 0.80 1.09
 Concerned about Side Effects .33 2.18 1.39 0.90 2.16
 Affective Forecast of Higher Stress on Chemoprevention Medication     .29** 7.04 1.34 1.08 1.66
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Note. N = 368. B = unstandardized beta weight.

*

p < .05

**

p < .01

***

p < .001

Mediation Analyses

The proposed mediational model was supported (see Figure 2). In mediation analyses using the Herr (2013) method, behavioral preferences and intentions were found to explain 61% of the relationship between affective forecasting and medication decision making at the three-month follow-up (i.e., an unstandardized beta of .18 without controlling for behavioral preferences and intentions, changing to .07 after controlling for them, leading to a (.18 − .07)/.18 = .61 reduction in the coefficient representing the association between affective forecasting and the medication decision). The indirect effect was statistically significant using the Sobel test, Z = 4.59, p < .001, as well as the bias-corrected bootstrapping method, t(358) = 3.36, p <.001. In summary, affective forecasting was predictive of participants’ decision making about chemoprevention medications at the three-month follow-up, with much of the relationship explained by the behavioral preferences and intentions that we assessed.

Figure 2.

Figure 2

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Affective forecasting model of medication decision making in breast cancer chemoprevention. Affective forecasting that chemoprevention medications would increase health-related stress was associated with a greater likelihood of opting out against medication at follow-up. Behavioral preferences and intentions were found to explain the relationship between affective forecasting and medication decision making. Each coefficient is an unstandardized beta (B) transformed to a common metric. The indirect effect was statistically significant, p < .001. Analyses controlled for the covariates of age, numeracy, breast cancer risk, perceived risk, breast cancer anxiety, and concerns about side effects.

N = 368. * p < .05 ** p < .01 *** p < .001

Discussion

In a large sample of women at elevated risk of developing breast cancer, the present investigation found that affective forecasting was associated with decision making about breast cancer chemoprevention medications. Prior studies have called attention to the fact that few women opt to use breast cancer chemoprevention medications (see Fagerlin et al., 2011; Waters et al., 2009, 2010). In the present investigation, participants received an individually tailored decision aid on the risks and benefits of chemoprevention medications. As hypothesized, when asked to forecast the impact of medication on their lives, few participants (<10%) reported believing that initiating a chemoprevention medication would reduce their health-related stress, while most thought medication would increase their stress (see Figure 1). Also as hypothesized, those with more favorable affective forecasts about taking chemoprevention medications had stronger intentions and preferences for medication cross-sectionally and were less likely to have opted against using a medication at the three month follow-up. As well, the hypothesized mediational model was supported (see Figure 2), with preferences and intentions explaining 61% of the relationship between affective forecasting and decision making at follow-up. Findings have implications for future research aimed at reducing cancer morbidity and mortality by understanding decision making.

The present investigation extends upon a broader body of research and theory on the importance of affective forecasting in decision making. Basic research in this arena has shown that affective forecasting fuels decision making in many life domains (Mathieu & Gosling, 2012; Wilson & Gilbert, 2013), but few of those studies have examined healthcare decisions in older populations, despite growing public health significance (Halpern & Arnold, 2008; Rhodes & Strain, 2008; Zikmund-Fisher et al., 2010). As the pipeline from basic research discoveries toward public health solutions has historically moved slowly (Berwick, 2003), there have been calls for translational research to bridge that gap (Woolf, 2008). A few studies have shown the importance of affective forecasting for other health promotion behaviors (Chapman & Coups, 2006; Dillard et al., 2010; Ruby et al., 2011; Walsh & Kiviniemi, 2014), and this is the first investigation of which we are aware to show that affective forecasting is related to medication decision making, and more specifically, related to decision making about breast cancer chemoprevention. Thus, these findings contribute to understanding the emotional pathways that may influence decision making in cancer prevention.

Our research also raises the question of why participants’ affective forecasts about taking chemoprevention medications were so negative (see Figure 1). One possibility is that the women are accurate that taking a chemoprevention medication would increase their health-related stress. However, high-powered randomized controlled trials have repeatedly failed to find evidence of exacerbated stress in at-risk women taking chemoprevention medications (Day et al., 1999, 2001; Fisher et al., 1998; Land et al., 2006). Alternatively, it is possible that participants’ affective forecasts about taking chemoprevention medications may have been negatively biased. Affective forecasting research has identified three cognitive biases that can routinely lead people to have unduly negative perceptions of stressful scenarios, like initiating a new medication (for a review, see Wilson & Gilbert, 2013). One, people focus on the most stressful aspect of the situation (a bias called focalism, Hoerger et al., 2010; Kahneman et al., 2006; Wilson et al., 2000), which might involve focusing disproportionately on the possibility of side effects or other concerns. Two, people underestimate their resilience in coping with stressful scenarios (variously called immune neglect, adaptation neglect, or coping fallacy; Gilbert et al., 1998; Hoerger, 2012; Hoerger et al., 2009), meaning they might adjust to taking a medication more easily than imagined. Three, stress in the moment, including perhaps learning new risk information through a decision aid, may lead people to overestimate future stress (dysphoric forecasting bias; Hoerger, Quirk et al., 2012; Wenze et al., 2012). Our research shows that affective forecasting about chemoprevention medications is negative, and more research appears warranted to begin to determine whether these expectations are relatively realistic versus negatively biased, for whom, and under what circumstances.

Multivariate analyses showed that affective forecasting uniquely accounted for decision making when controlling for key covariates (see Tables 2 and 3). Prior research has suggested that older age, greater risk and perceived risk, numeracy, breast cancer anxiety, and fewer concerns about side effects may increase preferences for chemoprevention medications (Bober et al., 2004; Dillard et al., 2013; Zikmund-Fisher et al., 2008). We found no effect of age or numeracy in the present analyses. Breast cancer risk, perceived risk, and lower concerns about side effects were associated with greater preferences and intentions cross-sectionally but did not predict decision making at the three-month follow-up. Breast cancer anxiety was associated with greater preferences and intentions, as well as a reduced likelihood of opting-out against chemoprevention medications at follow-up. While the central focus of these analyses was affective forecasting, breast cancer anxiety and affective forecasting each uniquely accounted for decision making. Both findings point to the need for more research examining emotional processes implicated in cancer prevention.

The present investigation had several notable strengths, but findings were also tempered by several limitations. Strengths included the relatively novel application of affective forecasting theory toward understanding healthcare decision making, the use of a decision aid that was tailored with personalized risk information, and the implementation of an extensive screening processes (i.e., medical record reviews, mailings, and Internet-mediated cancer risk screenings) needed to obtain data from a large sample of women at elevated risk of breast cancer. However, a key limitation was that the majority of participants were white and college-educated, and follow-up studies would be useful for gauging the extent to which the same emotional processes shape decision making in samples that are more racially and ethnically diverse or have less educational training. As well, all findings were correlational, and follow-up studies piloting interventions aimed at modifying affective forecasts would support stronger causal inferences. Finally, at the three-month follow-up, only 0.5% of participants had initiated a chemoprevention medication, with many still weighing their decision and over half having explicitly decided against medication. Thus, the present findings speak more to factors underlying medication avoidance than those sparking medication initiation, and they suggest that future investigators should consider longer follow-up phases to allow more time for decision making to solidify.

The current study has implications for future research aimed at reducing the toll of breast cancer. Affective forecasting about breast cancer chemoprevention medications was negative, and future studies should aim to understand whether these forecasts are realistic or biased. An ideal study might compare at-risk women’s predicted vs. actual emotional reactions to chemoprevention medications, but such a study would be difficult to implement, given that so few women actually agree to use the medications. Alternatively, several studies have examined interventions used to improve affective forecasting accuracy (e.g., Gilbert et al., 2009; Hoerger et al., 2009, 2010), and future studies could examine whether affective forecasting interventions adapted for this context augment preferences for chemoprevention medications.

Historically, medical encounters and decision aids have often provided extensive health information, risk statistics, and graphs, but this information is often insufficient for increasing uptake of chemoprevention medications (Fagerlin et al., 2010; Melnikow et al., 2010; Rush Port, Montgomery, Heerdt, & Borgen, 2001; Stacey, O’Connor, De Grasse, & Verma, 2003). It may be useful to integrate more content addressing emotional concerns into decision interventions (Elwyn et al., 2009; Hoerger et al., 2013), particularly given that people’s emotional, gist-level understanding of the situation (e.g., “the medication sounds scary”) is increasingly thought to affect healthcare decision making (Reyna, 2008; Reyna et al., 2015; Slovic, Peters, Finucane, & MacGregor, 2005). An important next step would be to study factors underlying unfavorable affective forecasts about medication in order to address them directly in decision aids. Our affective forecasting questions queried global health-related stress regarding taking vs. not taking a chemoprevention medication, and future studies could explore more specific questions, such as forecasts about the expected stress of particular physical side effects, attending more closely to one’s health, managing a medication regimen, or other factors. Integrating emotional content into decision aids is a challenging feat (Waters et al., 2007), though non-statistical methods, such as narrative video-based content may help (Katz et al., 2009). Providing individuals with information on normative emotional reactions to life events has been shown to improve affective forecasting (Gilbert et al., 2009); thus, it could be useful for decision aids to describe typical stress levels experienced by actual patients on chemoprevention medications, including global health-related stress as well as stress regarding key concerns, such as side effects. In the wake of recent healthcare reforms, a pressing national priority is to identify treatment outcomes that matter to patients, such as the stress experienced while taking a chemoprevention medication, and provide individuals with the requisite outcome data needed to make informed healthcare decisions (Hoerger, in press; Selby, Beal, & Frank, 2012).

Acknowledgments

This work was supported by T32MH018911 from the National Institute of Mental Health (MH), U54GM104940 from the National Institute of General Medical Sciences (MH), and P50CA101451 from the National Cancer Institute (AF). The authors wish to thank Catherine Rochefort and Laura Perry who assisted with quantitatively coding some the qualitative data.

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