|Category Reference for Presentations|
|AHE||Applied Health Economics||DEC||Decision Psychology and Shared Decision Making|
|HSP||Health Services, and Policy Research||MET||Quantitative Methods and Theoretical Developments|
* Candidate for the Lee B. Lusted Student Prize Competition
Method: We compared counseling and one-year smoking cessation rates before and after implementation of systematic smoking cessation intervention in 2 Swiss university hospitals for 457 smokers hospitalized for ACS. We further compared smoking cessation rates in 96 smokers with ACS from a third Swiss university hospital not providing systematic smoking intervention during the entire study period. In the observation phase, clinicians requested a specialized smoking cessation intervention based on their appraisal of the patient’s needs. In the intervention phase, a resident physician trained in motivational interviewing offered help for smoking cessation to all smokers. After discharge, smokers also received four telephone counseling sessions over two months.
Result: In the observational phase (August 2009 to October 2010), 24% (N=47/225) of smokers received a specialized smoking cessation intervention. In the intervention phase (November 2010 to February 2012), 84% (N=188/223) had an intervention (p<0.001) and 76% had at least one telephone counseling. In the intervention phase, less than 2% of smokers refused the intervention and 14% were discharged before the resident could approach them. The median duration of counseling in the hospital was 50 minutes (interquartile range 25 minutes). At one year, data on smoking status were available for 96% of participants, while abstinence was validated by expired CO measurement in 80% of quitters. In the observation phase 42% had stopped smoking vs. 52% in intervention phase (p=0.05). In the control hospital without systematic smoking intervention, contemporary smoking cessation rates at one year were 45% for the observation period and 40% for the intervention period (p=0.6).
Conclusion: A proactive strategy offering a specialized smoking cessation intervention based on motivational interviewing to all smoking patients hospitalized for ACS is well accepted. Compared to a reactive strategy, it significantly increases the delivery of smoking cessation interventions and smoking abstinence one year after an ACS.
Method: 144 currently practising GPs were recruited at 4 General Practice conferences in Australia. GPs completed a paper-based survey consisting of patient cases in which absolute risk and three key indicators related to absolute CVD risk (blood pressure, cholesterol and age) were varied.
Result: For patient cases in which the levels of absolute risk and individual risk factors were inconsistent, GPs seemed more likely to base their treatment decision on individual risk factors than absolute risk. More specifically, GPs were less likely to prescribe medication for high absolute risk (i.e. >15% risk of a cardiovascular event over the next 5 years, the treatment threshold in Australia) if the case presented a patient with lower blood pressure in comparison with high blood pressure (4% vs 93%, p<0.001). In addition, GPs were more likely to prescribe blood pressure lowering medication for low or moderate absolute risk (<10%) if the patient case had high blood pressure in comparison with lower blood pressure (79% vs 0%, p<0.001). This pattern was less pronounced for cholesterol lowering drugs. There were no differences in the way GPs prescribed for patient cases ranging in ages from 45 to 72 years at similar risk. However, GPs prescribed less medication for patients aged 86 years compared to those aged 72 (e.g. cholesterol lowering drugs: 29% vs. 49%, p<0.001). GP characteristics (gender, age, years in practice, practice size, stated use of absolute risk) did not predict their pattern of prescribing.
Conclusion: GPs seem more likely to base their CVD treatment decision making on individual risk factors than absolute risk. This effect seems to be stronger for blood pressure lowering medication compared to cholesterol lowering drugs.
Methods: With guidance from a steering committee of potential users, we aimed to develop a flexible web-based simulation model that could be applied to various study designs (i.e. parallel groups, pre-post, or hypothetical cases to assist in designing a cost-effective DM program). The model would be designed to predict medical resource use, survival, quality of life and associated costs across time for simulated patients representing clinical characteristics and unit costs specified by the user.
Results: We developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure (TEAM-HF) Cost-Effectiveness Model. To generate simulated sets of patients defined by the user, we incorporated a multivariate distribution wherein the global correlation structure was derived from several randomized trials and prospective cohort studies in heart failure. We also used these empirical data to modify the Seattle Heart Failure Model (SHFM), an externally validated prognostic model that incorporates 15 demographic, clinical and laboratory variables, as well as benefits with evidence-based medications and devices, to generate long-term survival estimates. In our modification of the SHFM, we applied calibrated Gompertz-based hazard functions for competing causes of death (i.e. sudden death, heart failure, other cause). We used data from a recent randomized trial in heart failure (HF-ACTION) to generate model parameters for medical resource use and health utilities as a function of SHFM scores. The model applies Monte Carlo simulations to generate patient-level estimates, which are then averaged across cohorts. To extend application of the TEAM-HF model to a broader user group, we developed a user-friendly, web-based interface that allows individuals to specify characteristics of their patient cohort(s), study design, DM program, unit costs and apply other options (e.g. discount rates, time horizons) relevant to conducting cost-effectiveness analyses.
Conclusion: The TEAM-HF Cost-Effectiveness Model is available at no cost at www.team-hf.org to assist users in estimating the long-term cost effectiveness of disease management programs in heart failure.
Method: A total of 1,456 young women ages 18-26, none of whom had received any HPV vaccine doses, were recruited from a national online survey panel to participate in the study. All participants were given basic non-quantitative information on cervical cancer, HPV infection and HPV vaccination, drawn from the CDC website. In addition, a subset of participants was randomly assigned to receive quantitative information on the absolute reduction in cervical cancer risk expected from HPV vaccination (i.e., for every 4,000 women receiving the HPV vaccine, about how many would be “saved” from cervical cancer) based on published clinical trial evidence. Next, all participants completed a questionnaire, in which they reported their intentions to receive the HPV vaccine, their estimates of the vaccine’s absolute risk-reduction benefit, and their perceptions of the credibility of the information they received.
Result: Participants receiving quantitative ARR information, compared to those in the no-ARR control group, reported lower estimates of the HPV vaccine’s absolute risk-reduction benefit (t=-15.4, p<.001) but higher intention to get vaccinated (t=2.91, p=.004). This paradoxical finding occurred because the mere presence of quantitative ARR information increased message credibility (t=4.91, p<.001). The positive impact of ARR information on vaccination intention was completely mediated by perceived credibility (Sobel (1982) test of mediation: z=4.62, p<.001). In contrast, participants’ estimates of ARR had no effect on vaccination intentions, even among those scoring highest on numeracy.
Conclusion: ARR information increased vaccination intention, despite reducing estimates of risk reduction benefit. While participants generally understood the ARR information, they appeared to find it hard to evaluate (Hsee 1996). As noted by Halovorsen (2010) “…one may understand the calculus of risk reductions but still find it difficult to judge whether a particular risk reduction is good or bad.” Instead, the mere presence of quantitative information (rather than the specific numbers) appears to serve as a peripheral cue to increase message credibility and vaccination intentions.
Purpose: Geriatric diabetes care guidelines encourage individualized glycemic targets (i.e., A1C goal) for older patients based on patient life expectancy (LE) and preferences. We pilot tested a web-based decision support tool which provides individualized prognostic information from a geriatric diabetes complication model, and elicits patient preferences.
Method: We randomized physicians and their patients to the decision support tool, with a 3:1 recruitment ratio. Patients were ≥ 65 years, had A1C ≥ 6.5%, and no dementia. Prior to a clinic visit, intervention patients interacted with the tool, which generated a summary for their physician that included individual patient's LE estimates, risks of developing complications, treatment preferences, and screening for geriatric conditions. Control patients received an educational pamphlet about A1C. Physicians and patients were surveyed before and after the visit.
Result: Intervention (N=75) and control patients (N=25) were similar by gender (77% female), age (mean 74 years), ethnicity/race (89% black) and diabetes duration (mean 16 years). Baseline knowledge of A1C goals by patients was low (35%). Compared to controls, intervention patients were more likely to have their physician report an A1C discussion during a visit (91% vs. 76%, p=0.06) and were more likely to have their physician report that the patient knew their A1C goal (81% vs. 60%, p=0.03). Patient decisional conflict scores declined more for intervention (52.7→24.5, p<0.01) than control patients (51.2→36.6, p=0.03). Compared to controls, more intervention patients had their physician shift their A1C goal by at least 0.5% (49% vs. 28%, p=0.06). Among the intervention patients, we found that the percentage with an intensive goal (A1C goal ≤ 7.0%) increased from 49% to 56% (p=0.23). The movement towards intensive goals occurred only in patients with longer LE (53% to 62%); no change occurred in patients with shorter LE (41%). Among the control patients, we found that the percentage with an intensive goal declined from 68% to 60%. This occurred in patients with both shorter and longer LE.
Conclusion: A personalized decision support tool incorporating prognostic information and patient preferences encouraged active discussion regarding A1C goal selection, decreased patients' decisional conflict, and had a tendency to increase appropriate personalization of A1C goals based on LE estimates. A larger, longitudinal clinical trial is needed to evaluate the intervention effects over time.
Methods: Fifty-six general practitioners (GPs) from 30 practices in Southern Denmark were cluster-randomized (using practices as clusters) to use either POL or ARR as effectiveness measures when informing patients about effectiveness of statin treatment for primary prevention cardiovascular disease (CVD). The prognosis without treatment was presented as life expectancy or 10-year mortality risk, respectively. Patients’ redemption of statin prescriptions was recorded in regional prescription database three months after the consultation. The COMRADE questionnaire was used to measure patients’ confidence in their decision and satisfaction with the communication.
Results: Of 240 patients included in the study, 112 were allocated to POL-information and 128 to ARR. The groups were balanced with respect to patient as well as GP characteristics. Patients redeeming a statin prescription totaled 6 (5.4%) in the POL-group and 32 (25%) in the ARR-group (p<0.001). The mean scores of patients’ confidence in decision on a scale from 1 to 5 were 4.17 (95% CI 4.00-4.34) for POL-patients and 4.05 (95% CI 3.89-4.22) for ARR-patients. The mean scores for satisfaction with communication in the consultation were 4.41 (95% CI 4.27-4.55) and 4.23 (95% CI 4.09-4.39) respectively.
Conclusion: The proportion of patients redeeming a statin prescription may be substantially lower when patients are informed about its effectiveness in terms of POL than ARR, but the level of confidence in decision and satisfaction with communication may not be influenced.