The role of comorbidities, medications, and social determinants of health in understanding urban-rural outcome differences among patients with heart failure.

PURPOSE: There is now a 20% disparity in all-cause, excess deaths between urban and rural areas, much of which is driven by disparities in cardiovascular death. We sought to explain the sources of these disparities for Medicare beneficiaries with heart failure with reduced ejection fraction (HFrEF). METHODS: Using a sample of Medicare Parts A, B, and D, we created a cohort of 389,528 fee-for-service beneficiaries with at least 1 heart failure hospitalization from 2008 to 2017. The primary outcome was 30-day mortality after discharge; 1-year mortality, readmissions, and return emergency room (ER) admissions were secondary outcomes. We used hierarchical, logistic regression modeling to determine the contribution of comorbidities, guideline-directed medical therapy (GDMT), and social determinants of health (SDOH) to outcomes. RESULTS: Thirty-day mortality rates after hospital discharge were 6.3% in rural areas compared to 5.7% in urban regions (P < .001); after adjusting for patient health and GDMT receipt, the 30-day mortality odds ratio for rural residence was 1.201 (95% CI 1.164-1.239). Adding the SDOH measure reduced the odds ratio somewhat (1.140, 95% CI 1.103-1.178) but a gap remained. Readmission rates in rural areas were consistently lower for all model specifications, while ER admissions were consistently higher. CONCLUSIONS: Among patients with HFrEF, living in a rural area is associated with an increased risk of death and return ER visits within 30 days of discharge from HF hospitalization. Differences in SDOH appear to partially explain mortality differences but the remaining gap may be the consequence of rural-urban differences in HF treatment.

Racial differences in low value care among older adult Medicare patients in US health systems: retrospective cohort study.

OBJECTIVE: To characterize racial differences in receipt of low value care (services that provide little to no benefit yet have potential for harm) among older Medicare beneficiaries overall and within health systems in the United States. DESIGN: Retrospective cohort study SETTING: 100% Medicare fee-for-service administrative data (2016-18). PARTICIPANTS: Black and White Medicare patients aged 65 or older as of 2016 and attributed to 595 health systems in the United States. MAIN OUTCOME MEASURES: Receipt of 40 low value services among Black and White patients, with and without adjustment for patient age, sex, and previous healthcare use. Additional models included health system fixed effects to assess racial differences within health systems and separately, racial composition of the health system's population to assess the relative contributions of individual patient race and health system racial composition to low value care receipt. RESULTS: The cohort included 9 833 304 patients (6.8% Black; 57.9% female). Of 40 low value services examined, Black patients had higher adjusted receipt of nine services and lower receipt of 20 services than White patients. Specifically, Black patients were more likely to receive low value acute diagnostic tests, including imaging for uncomplicated headache (6.9% v 3.2%) and head computed tomography scans for dizziness (3.1% v 1.9%). White patients had higher rates of low value screening tests and treatments, including preoperative laboratory tests (10.3% v 6.5%), prostate specific antigen tests (31.0% v 25.7%), and antibiotics for upper respiratory infections (36.6% v 32.7%; all P<0.001). Secondary analyses showed that these differences persisted within given health systems and were not explained by Black and White patients receiving care from different systems. CONCLUSIONS: Black patients were more likely to receive low value acute diagnostic tests and White patients were more likely to receive low value screening tests and treatments. Differences were generally small and were largely due to differential care within health systems. These patterns suggest potential individual, interpersonal, and structural factors that researchers, policy makers, and health system leaders might investigate and address to improve care quality and equity.

Virtual Access to Subspecialty Care.

Telehealth programs existed in many subspecialities before the COVID-19 pandemic, and the public health event motivated many subspecialties to reflect on how current technologies could be leveraged to benefit patient outcomes and increase health-care access. This article reviews the history and current state of telehealth access in many areas of subspecialty care. Primary care physicians (PCPs) may be unaware of the telehealth services and options local subspecialists offer. To best serve patients, PCPs could partner with subspecialists to develop processes to link patients to the right subspecialist at the right time and in the right visit type.

Factors Influencing Telemedicine Use at a Northern New England Cancer Center During the COVID-19 Pandemic.

PURPOSE: To characterize the use of telemedicine for oncology care over the course of the COVID-19 pandemic in Northern New England with a focus on factors affecting trends. METHODS: We performed a retrospective observational study using patient visit data from electronic health records from hematology-oncology and radiation-oncology service lines spanning the local onset of the pandemic from March 18, 2020, through March 31, 2021. This period was subdivided into four phases designated as lockdown, transition, stabilization, and second wave. Generalized linear mixed regression models were used to estimate the effects of patient characteristics on trends for rates of telemedicine use across phases and the effects of visit type on patient satisfaction and postvisit ER or hospital admissions within 2 weeks. RESULTS: A total of 19,280 patients with 102,349 visits (13.1% audio-only and 1.4% video) were studied. Patient age (increased use in age < 45 and 85 years and older) and urban residence were associated with higher use of telemedicine, especially after initial lockdown. Recent cancer therapy, ER use, and hospital admissions in the past year were all associated with lower telemedicine utilization across pandemic phases. Provider clinical department corresponded to the largest differences in telemedicine use across all phases. ER and hospital admission rates in the 2 weeks after a telehealth visit were lower than those in in-person visits (0.7% v 1.3% and 1.2% v 2.7% for ER and hospital use, respectively; P < .001). Patient satisfaction did not vary across visit types. CONCLUSION: Telemedicine use in oncology during the COVID-19 pandemic varied according to the phase and patient, medical, and health system factors, suggesting opportunities for standardization of care and need for attention to equitable telemedicine access.