|BEC||Behavioral Economics||ESP||Applied Health Economics, Services, and Policy Research|
|DEC||Decision Psychology and Shared Decision Making||MET||Quantitative Methods and Theoretical Developments|
* Candidate for the Lee B. Lusted Student Prize Competition
Purpose: Many approaches used by physicians during the medical encounter have the potential to affect patient adherence to recommendations for preventive health services. Persuasion is one approach defined as a principal method of inducing compliance (Chayes et al. 1995). However, more recent findings suggest that the use of persuasion may be detrimental (Barton et al. 2009). We evaluate the frequency with which physicians use persuasion when recommending colorectal cancer (CRC) screening, patients’ perceptions of physician use of persuasion, and how each impacts adherence to physician-recommended CRC screening.
Method: Direct observation of periodic health exams (N=415) in 2007-2009 among primary care patients aged 50-80 due for CRC screening. Qualitative content analyses were used to code office visit audio-recordings for physician use of persuasion (Siminoff et al. 2011). A post-visit survey collected patient perceptions of the use of persuasion by their physician (Burgoon et al. 1984). Post-visit CRC screening use was compiled via claims data. Generalized estimating equations were used to evaluate the association of coded and perceived persuasion with each other as well as with CRC screening.
Result: Content analyses revealed that persuasion occurred in 73% of the visits. Among visits with observer-coded persuasion, most frequently used was argument (45%), followed by argument and refutation combined (21%). Patient perceptions of physician persuasion were significantly (p<0.05) associated with coded physician use of persuasion. Regardless of whether persuasion was observer-coded or patient-reported, neither was associated with subsequent CRC screening use.
Conclusion: While persuasion is frequently used when physicians recommend CRC screening and patients acknowledge when their physician attempts to persuade them, our findings indicate that persuasion is not associated with screening use. Further research is needed to better understand patient perceptions of physician persuasion and better ways to communicate recommendations for potentially life-saving preventive services.
Purpose:Despite guidelines on cancer management, the increasing availability of targeted therapies has deeply challenged classical patterns of cancer treatment. Our objective was to analyze the relative influence of efficacy, tolerability, adherence and route of chemotherapy administration on medical decision-making.
Method:A Discrete Choice Experiment was performed among 203 French physicians involved in cancer treatment (i.e oncologists, haematologists and physicians qualified in oncology). In a questionnaire of six scenarios, respondents were asked to choose between two treatments which differed with respect to four attributes: efficacy, tolerability, adherence and route of administration. Three of those attributes (efficacy, tolerability and adherence) had two modalities (good vs. moderate) and the later (route of administration) had three modalities (intravenous, oral and oral with a patient support program). To analyze the effect of the therapeutic goal on physicians’ preferences, the six scenarios were first presented for curative setting then for palliative setting. The attributes presented in the questionnaire were drawn from a literature review submitted to expert opinion. The effects of each attribute on physicians’ preferences were analyzed using conditional logistic regression models.
Result:The efficacy attribute was the predominant criteria in choosing a chemotherapy treatment either in curative setting (moderate vs. good: β=-2.1145, p <0.0001) or in palliative setting (moderate vs. good: β=-1.0628, p <0.0001). The route of administration had a positive effect in palliative setting, for which physicians preferred the oral route (β=0.6125, p<.0.003) particularly in the haematologists group. Removing the efficacy attribute of the model, we found that tolerability (moderate vs. good: β=-1.2277, p<0.0001) and adherence had also significant effects on decision (moderate vs. good: β=-1.2228, p<0.0001) but only for curative treatment, and that the oral route with a patient support program remained decisive in palliative setting (β=0.431, p<0.0001).
Conclusion:Our results highlights a consensus on the priority of the efficacy attribute reflecting a good compliance of physicians to guidelines. On condition of equivalent efficacy between two treatments, the oral route of administration was the only criteria considered in palliative setting. This is consistent with the priority to maintain patient’s quality of life by staying at home at the advanced-stage of disease. Financial disclosure: Funding for the study was provided by GlaxoSmithKline and had no influence on the study design, execution and publication of results.
Purpose: Women who are at high risk for breast cancer have the option of taking drugs that can reduce their risk (e.g. Tamoxifen). One question is what factors determine women’s interest in chemoprevention. All else equal, women who have higher breast cancer risk should show more interest in chemoprevention. However, women’s anxiety about breast cancer may also play a significant role in this decision, above and beyond actual or perceived risk.
Method: 623 women who were at above average risk for breast cancer (Gail score > 1.66) were recruited to participate in a test of a decision aid (DA) for Tamoxifen. All women read a decision aid, which provided them with their personalized breast cancer risk (i.e. Gail score), and also provided tailored statistics about the risks and benefits of chemoprevention. Women were asked to report their perceived risk level, as well as their anxiety about developing breast cancer. Finally, women were asked about their interest in chemoprevention.
Result: Actual risk (Gail score) did not predict interest in chemoprevention (p > .05). However, both women’s perception of risk and anxiety about breast cancer significantly predicted interest in chemoprevention. Regression analyses revealed that anxiety was a relatively strong predictor of interest, even when controlling for both actual and perceived risk (b = .31, p < .01). By contrast, perceived risk was a significant yet much smaller predictor of interest, when controlling for actual risk and anxiety (b = .13, p < .01).
Conclusion: In the context of chemoprevention, actual risk does not predict interest in chemoprevention, and perceived risk only weakly predicts interest. By far the strongest predictor of interest in chemoprevention was anxiety about breast cancer: Women with more anxiety were more likely to be interested in chemoprevention, regardless of their actual or perceived risk. These data reveal that anxiety can play an important role in decision-making about chemoprevention, and can potentially bias patients. It could be helpful for DAs to provide information that decreases anxiety in low-risk individuals, so that they do not undergo medical interventions unnecessarily. On the other hand, it may be necessary to modestly raise anxiety in high-risk individuals, so that they are moved to act.
Purpose: Regular exercise offers an important solution to the growing burden of obesity-related chronic disease. We evaluated the use of commitment contracts and nudges to promote habitual exercise, focusing on the duration of the contracts and the heterogeneity of individual responses to these behavioral economic devices.
Methods: A randomized controlled trial examined the use of a web-based tool for creating exercise commitment contracts for 3,179 adults (aged 18-77) between September, 2010 and April, 2011. Individuals were randomized to be shown different default contract durations (8 weeks, 12 weeks, or 20 weeks) which they could easily change if they wished. After also choosing the number of exercise sessions per week (frequency) and financial penalty for failing to complete each week, each individual who ultimately signed a contract was followed for the duration of the contract, with weekly reports of their success in meeting exercise goals. For this analysis, follow-up through 13 weeks was available for 1,268 individuals representing 12,574 person-weeks. We analyzed the data using nonlinear multivariable regressions based on a theoretical model of active choice in the context of nudges.
Results: Longer duration nudges increased the mean duration of contracts chosen (13.5 weeks, 14.7 weeks, 18.6 weeks) without altering the likelihood of signing a contract (~70% for all arms), chosen exercise frequency (3.98, 3.93, 3.94 sessions per week), or chosen financial incentives ($6.90, $6.09, $6.81 per week). Based on our active choice model, more than 40% of users were highly susceptible to contract duration nudges, with the greatest effect for individuals interested in contract durations near the nudged defaults. For individuals signing contracts, those nudged to longer contract durations completed statistically more exercise, though this was largely attributable to longer follow-up as success rates did not vary across nudges. Approximately 40% did not complete any exercise sessions (early drop-outs). Early drop-outs were more likely to have accepted the exact nudged duration presented to them.
Conclusions: Individuals can be “nudged” to select contracts with more total exercise. Random use of nudges also causes individuals to reveal two related aspects of their true colors: 1) their activity/passivity of exercise choice; and 2) their likelihood of failing to live up to their exercise commitments. Recognition of such heterogeneity can guide the design of more efficient exercise interventions.
Purpose: Because real-world patients may not exhibit the same level of medication adherence seen in clinical trials, the effectiveness of medications in routine practice may differ. Cost-effectiveness analysis (CEA) models often do not incorporate adherence variation. Furthermore, the Markovian assumption does not allow adherence history to affect future event probabilities. We created a framework incorporating adherence history into a Markov model using the example of Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER).
Method: Prescription claims records for primary prevention statin users were obtained using the IMS LifeLink Health Plan Claims Database. Yearly adherence was measured as the proportion of days covered (PDC) for three years following statin initiation and was categorized as A0 (PDC=0), A1 (0<PDC≤.33), A2 (.33<PDC≤.66), or A3 (PDC>.66). Yearly adherence transitions were incorporated into a Markov microsimulation using TreeAge software. Tracker variables and global matrices stored adherence transitions which were used to adjust statin costs and subsequent probabilities of cardiovascular events over the patient’s lifetime. Statin effectiveness was adjusted between 0% (level A0) and 100% (level A3) of trial-based risk reduction. 10,000 microsimulations were used to estimate incremental cost-effectiveness ratios (ICERs) as US dollars per quality-adjusted life-year (QALY). The model was an extension of the authors’ previously published JUPITER CEA model in which adherence was not incorporated ("adherence-naïve").
Result: Among 27,862 new statin users, 58% began the first year of statin use in level A3, while 20% and 22% were in levels A2 and A1, respectively. By year three, we found a significant decrease in adherence. 32% of patients were in level A3, 15% in A2, 20% in A1 and 33% in A0. The model incorporating adherence resulted in an ICER of $23,459/QALY while the ICER of the adherence-naïve model was $11,127/QALY. Patient subgroup analysis revealed that the ICER for patients beginning in level A1 was $52,214/QALY while the ICER for patients beginning in level A3 was $17,578/QALY. The ICER for patients remaining in level A3 for three years was $8,347/QALY.
Conclusion: Patient-level simulations that include adherence behavior reveal value differences not seen in a cohort model based on the “average” patient. In the interest of patient-centered outcomes research and personalized medicine, this approach adds insight to how patient subgroups may benefit from adherence-improving interventions.