The CKD Stress Scale: Development and identification of patients at risk.

AIMS: Develop a novel chronic kidney disease (CKD)-specific stress scale and examine associations with patient characteristics. MATERIALS AND METHODS: Adults with CKD stages 1 - 5 enrolled in a cross-sectional survey. Eight questions assessed patients' thoughts and feelings of stress related to CKD (CKD Stress Scale). Patients also reported their knowledge of CKD, barriers to CKD health, and demographics. The scale was evaluated using exploratory factor analysis and Cronbach's alpha. Associations were examined via linear regression. RESULTS: 245 participant enrolled with a mean age of 60 years and a mean estimated glomerular filtration rate (eGFR) of 34 mL/min/1.73m2; 49% were women (match percentage in Table 1), 74% White, 14% African American. A one-factor model of CKD Stress exhibited high internal consistency (α = 0.89). In bivariate analyses, higher CKD Stress was associated with lower eGFR, younger age, African American race (compared to White), and having a high school education or some college (compared to college degree or higher). Adjusting for these characteristics, as well as income and knowledge about CKD, only lower eGFR (b = -0.01; 95% CI [-0.01, -0.001]), younger age (b = -0.01; 95% CI [-0.01, -0.003]), African American race (b = 0.35, 95% CI [0.10, 0.60]), and receiving a high school education or some college (b = 0.20, 95% CI [0.01, 0.39]) were independently associated with more CKD-specific stress. Concurrent validity was supported by associations between stress and perceived barriers to care. CONCLUSION: Our CKD Stress Scale exhibits excellent internal reliability and identified where future educational interventions may benefit from tailoring for at-risk patients.

Grouping Promotes Equality: The Effect of Recipient Grouping on Allocation of Limited Medical Resources.

Decisions about allocation of scarce resources, such as transplant organs, often entail a trade-off between efficiency (i.e., maximizing the total benefit) and fairness (i.e., dividing resources equally). In three studies, we used a hypothetical scenario for transplant-organ allocation to examine allocation to groups versus individuals. Study 1 demonstrated that allocation to individuals is more efficient than allocation to groups. Study 2 identified a factor that triggers the use of fairness over efficiency: presenting the beneficiaries as one arbitrary group rather than two. Specifically, when beneficiaries were presented as one group, policymakers tended to allocate resources efficiently, maximizing total benefit. However, when beneficiaries were divided into two arbitrary groups (by hospital name), policymakers divided resources more equally across the groups, sacrificing efficiency. Study 3 replicated this effect using a redundant attribute (prognosis) to create groups and found evidence for a mediator of the grouping effect--the use of individualizing information to rationalize a more equitable allocation decision.

Medical Maximizing-Minimizing Predicts Patient Preferences for High- and Low-Benefit Care.

Purpose. People vary in their general preferences for more v. less health care, and the validated Medical Maximizing-Minimizing Scale (MMS) reliably measures this orientation. Medical maximizers (people scoring highly on the MMS) prefer to receive more health care visits, medications, tests, and treatments, whereas minimizers prefer fewer services. However, it is unclear how maximizing-minimizing preferences relate to willingness to pursue appropriate health care. We hypothesized that minimizers are at increased risk of rejecting evidence-based high-benefit care and that maximizers are at risk of wanting low-benefit care. Design. In total, 785 US adults recruited through an online panel expressed preferences to receive or forgo a health care intervention in 18 hypothetical scenarios. In 8 scenarios, the intervention was high benefit per evidence-based guidelines. In the remaining 10 scenarios, the intervention was low benefit. We assessed associations between participants' MMS score and their preferences for medical intervention in each scenario using regression analyses that adjusted for hypochondriasis, health risk tolerance, health status, and demographic variables. Results. MMS score was significantly associated with preferences in all 18 scenarios after adjusting for other variables. The MMS uniquely explained 11% of the variance in preferences for high-benefit care and 29% of the variance in preferences for low-benefit care. Differences between strong minimizers (10th percentile) and strong maximizers (90th percentile) across the 18 scenarios ranged from 5.6 to 32.3 points on a 1 to 100 preference scale. Conclusions. The MMS reliably predicts people's willingness to pursue appropriate care, both when appropriate care means taking high-benefit actions and when appropriate care means avoiding low-benefit actions. Targeting and tailoring messages according to maximizing-minimizing preferences might increase the effectiveness of both efforts to reduce overutilization of low-benefit services and campaigns to support uptake of high-benefit care.