Patient-Clinician Decision Making for Stable Angina: The Role of Health Literacy

Background: Stable angina patients have difficulty understanding the tradeoffs between treatment alternatives. In this analysis, we assessed treatment planning conversations for stable angina to determine whether inadequate health literacy acts as a barrier to communication that may partially explain this difficulty. Methods: We conducted a descriptive analysis of patient questionnaire data from the PCI Choice Trial. The main outcomes were the responses to the Decisional Conflict Scale and the proportion of correct responses to knowledge questions about stable angina. We also conducted a qualitative analysis on recordings of patient-clinician discussions about treatment planning. The recordings were coded with the OPTION12 instrument for shared decision-making. Two analysts independently assessed the number and types of patient questions and expressions of preferences. Results: Patient engagement did not differ by health literacy level and was generally low for all patients with respect to OPTION12 scores and the number of questions related to clinical aspects of treatment. Patients with inadequate health literacy had significantly higher decisional conflict. However, the proportion of knowledge questions answered correctly did not differ significantly by health literacy level. Conclusions: Patients with inadequate health literacy had greater decisional conflict but no difference in knowledge compared to patients with adequate health literacy. Inadequate health literacy may act as a barrier to communication, but gaps were found in patient engagement and knowledge for patients of all health literacy levels. The recorded patient-clinician encounters and the health literacy measure were valuable resources for conducting research on care delivery

Can delivery systems use cost-effectiveness analysis to reduce healthcare costs and improve value? [version 1; referees: 2 approved]

Understanding costs and ensuring that we demonstrate value in healthcare is a foundational presumption as we transform the way we deliver and pay for healthcare in the U.S. With a focus on population health and payment reforms underway, there is increased pressure to examine cost-effectiveness in healthcare delivery. Cost-effectiveness analysis (CEA) is a type of economic analysis comparing the costs and effects (i.e. health outcomes) of two or more treatment options. The result is expressed as a ratio where the denominator is the gain in health from a measure (e.g. years of life or quality-adjusted years of life) and the numerator is the incremental cost associated with that health gain. For higher cost interventions, the lower the ratio of costs to effects, the higher the value. While CEA is not new, the approach continues to be refined with enhanced statistical techniques and standardized methods. This article describes the CEA approach and also contrasts it to optional approaches, in order for readers to fully appreciate caveats and concerns. CEA as an economic evaluation tool can be easily misused owing to inappropriate assumptions, over reliance, and misapplication. Twelve issues to be considered in using CEA results to drive healthcare delivery decision-making are summarized. Appropriately recognizing both the strengths and the limitations of CEA is necessary for informed resource allocation in achieving the maximum value for healthcare services provided

Literature-Based Appraisal of Racial/Ethnic Cardiovascular Health Care Disparities

Abstract available at publisher's website

Identifying appropriate comparison groups for health system interventions in the COVID‐19 era

Abstract Introduction COVID‐19 has created additional challenges for the analysis of non‐randomized interventions in health system settings. Our objective is to evaluate these challenges and identify lessons learned from the analysis of a medically tailored meals (MTM) intervention at Kaiser Permanente Northwest (KPNW) that began in April 2020. Methods We identified both a historical and concurrent comparison group. The historical comparison group included patients living in the same area as the MTM recipients prior to COVID‐19. The concurrent comparison group included patients admitted to contracted non‐KPNW hospitals or admitted to a KPNW facility and living outside the service area for the intervention but otherwise eligible. We used two alternative propensity score methods in response to the loss of sample size with exact matching to evaluate the intervention. Results We identified 452 patients who received the intervention, 3873 patients in the historical comparison group, and 5333 in the concurrent comparison group. We were able to mostly achieve balance on observable characteristics for the intervention and the two comparison groups. Conclusions Lessons learned included: (a) The use of two different comparison groups helped to triangulate results; (b) the meaning of utilization measures changed pre‐ and post‐COVID‐19; and (c) that balance on observable characteristics can be achieved, especially when the comparison groups are meaningfully larger than the intervention group. These findings may inform the design for future evaluations of interventions during COVID‐19

National trends in emergency conditions through the Omicron COVID‐19 wave in commercial and Medicare Advantage enrollees

Abstract Objective To evaluate trends in emergency care sensitive conditions (ECSCs) from pre‐COVID (March 2018–February 2020) through Omicron (December 2021–February 2022). Methods This cross‐sectional analysis evaluated trends in ECSCs using claims (OptumLabs Data Warehouse) from commercial and Medicare Advantage enrollees. Emergency department (ED) visits for ECSCs (acute appendicitis, aortic aneurysm/dissection, cardiac arrest/severe arrhythmia, cerebral infarction, myocardial infarction, pulmonary embolism, opioid overdose, pre‐eclampsia) were reported per 100,000 person months from March 2018 to February 2022 by pandemic wave. We calculated the percent change for each pandemic wave compared to the pre‐pandemic period. Results There were 10,268,554 ED visits (March 2018−February 2022). The greatest increases in ECSCs were seen for pulmonary embolism, cardiac arrest/severe arrhythmia, myocardial infarction, and pre‐eclampsia. For commercial enrollees, pulmonary embolism visit rates increased 22.7% (95% confidence interval [CI], 18.6%–26.9%) during Waves 2−3, 37.2% (95% CI, 29.1%–45.8%] during Delta, and 27.9% (95% CI, 20.3%–36.1%) during Omicron, relative to pre‐pandemic rates. Cardiac arrest/severe arrhythmia visit rates increased 4.0% (95% CI, 0.2%–8.0%) during Waves 2−3; myocardial infarction rates increased 4.9% (95% CI, 2.1%–7.8%) during Waves 2−3. Similar patterns were seen in Medicare Advantage enrollees. Pre‐eclampsia visit rates among reproductive‐age female enrollees increased 31.1% (95% CI, 20.9%–42.2%), 23.7% (95% CI, 7.5%,–42.3%), and 34.7% (95% CI, 16.8%–55.2%) during Waves 2−3, Delta, and Omicron, respectively. ED visits for other ECSCs declined or exhibited smaller increases. Conclusions ED visit rates for acute cardiovascular conditions, pulmonary embolism and pre‐eclampsia increased despite declines or stable rates for all‐cause ED visits and ED visits for other conditions. Given the changing landscape of ECSCs, studies should identify drivers for these changes and interventions to mitigate them

Co‐Occurrence of Social Risk Factors and Associated Outcomes in Patients With Heart Failure

Background Among patients with heart failure (HF), social risk factors (SRFs) are associated with poor outcomes. However, less is known about how co‐occurrence of SRFs affect all‐cause health care utilization for patients with HF. The objective was to address this gap using a novel approach to classify co‐occurrence of SRFs. Methods and Results This was a cohort study of residents living in an 11‐county region of southeast Minnesota, aged ≥18 years with a first‐ever diagnosis for HF between January 2013 and June 2017. SRFs, including education, health literacy, social isolation, and race and ethnicity, were obtained via surveys. Area‐deprivation index and rural‐urban commuting area codes were determined from patient addresses. Associations between SRFs and outcomes (emergency department visits and hospitalizations) were assessed using Andersen‐Gill models. Latent class analysis was used to identify subgroups of SRFs; associations with outcomes were examined. A total of 3142 patients with HF (mean age, 73.4 years; 45% women) had SRF data available. The SRFs with the strongest association with hospitalizations were education, social isolation, and area‐deprivation index. We identified 4 groups using latent class analysis, with group 3, characterized by more SRFs, at increased risk of emergency department visits (hazard ratio [HR], 1.33 [95% CI, 1.23–1.45]) and hospitalizations (HR, 1.42 [95% CI, 1.28–1.58]). Conclusions Low educational attainment, high social isolation, and high area‐deprivation index had the strongest associations. We identified meaningful subgroups with respect to SRFs, and these subgroups were associated with outcomes. These findings suggest that it is possible to apply latent class analysis to better understand the co‐occurrence of SRFs among patients with HF