Background Applying disease-specific guidelines to people with multimorbidity may result in complex regimens that impose treatment burden. Objectives To describe and validate a measure of health care treatment difficulty (HCTD) in a sample of older adults with multimorbidity. Research Design Cross-sectional and longitudinal secondary data analysis Subjects Multimorbid adults ages ≥65 from primary care clinics Measures We generated a scale (0–16) of self-reported difficulty with 8 health care tasks(HCTD) and conducted factor analysis to assess its dimensionality and internal consistency. To assess predictive ability, cross-sectional associations of HCTD and number of chronic diseases, and conditions that add to health status complexity (falls, visual, and hearing impairment), patient activation, patient-reported quality of chronic illness care (Patient Assessment of Chronic Illness Care; PACIC), mental and physical health (SF-36) were tested using statistical tests for trend (n=904). Longitudinal analyses of the effects of change in HCTD on changes in the outcomes were conducted among a subset (n=370) with≥1 follow-up at 6 and/or 18 months. All models were adjusted for age, education, sex, race and time. Results Greater HCTD was associated with worse mental and physical health (Cuzick’s test for trend (P<0.05), and patient-reported quality of chronic illness care (P<0.05). In longitudinal analysis, increasing patient activation was associated with declining HCTD over time (P<0.01). Increasing HCTD over time was associated with declining mental (P<0.001) and physical health (P=0.001) and patient-reported quality of chronic illness care (P<0.05). Conclusions The findings of this study establish the construct validity of the HCTD scale.

To quantify and contextualize the risk for coronavirus disease 2019 (COVID-19)related hospitalization and illness severity in type 1 diabetes. RESEARCH DESIGN AND METHODS We conducted a prospective cohort study to identify case subjects with COVID-19 across a regional health care network of 137 service locations. Using an electronic health record query, chart review, and patient contact, we identified clinical factors influencing illness severity. RESULTS We identified COVID-19 in 6,138, 40, and 273 patients without diabetes and with type 1 and type 2 diabetes, respectively. Compared with not having diabetes, people with type 1 diabetes had adjusted odds ratios of 3.90 (95% CI 1.75-8.69) for hospitalization and 3.35 (95% CI 1.53-7.33) for greater illness severity, which was similar to risk in type 2 diabetes. Among patients with type 1 diabetes, glycosylated hemoglobin (HbA 1c), hypertension, race, recent diabetic ketoacidosis, health insurance status, and less diabetes technology use were significantly associated with illness severity. CONCLUSIONS Diabetes status, both type 1 and type 2, independently increases the adverse impacts of COVID-19. Potentially modifiable factors (e.g., HbA 1c) had significant but modest impact compared with comparatively static factors (e.g., race and insurance) in type 1 diabetes, indicating an urgent and continued need to mitigate severe acute respiratory syndrome coronavirus 2 infection risk in this community. The medical community currently lacks sufficient data to adequately mitigate the impact of the novel coronavirus disease 2019 (COVID-19) in the type 1 diabetes community. At present, our knowledge is largely extrapolated from recent retrospective analyses of hospitalized patients (1-5), which have strongly suggested "diabetes" increases risk for COVID-19 morbidity and mortality. These studies did not, however, distinguish between type 1 diabetes and type 2 diabetesdtwo pathophysiologically distinct conditions. Although reports of COVID-19 in type 1 diabetes are emerging, the scope of these investigations to date has been limited by including only hospitalized

<i>Objective: To quantify and contextualize the risk for COVID-19 related hospitalization and illness severity in type 1 diabetes.</i> <p> </p> <p><i>Research Design and Methods: We conducted a prospective cohort study to identify COVID-19 cases across a regional healthcare network of 137 service locations. Using an electronic health record query, chart review, and patient contact, we identified clinical factors influencing illness severity. </i></p> <p> </p> <p><i>Results: We identified COVID-19 in 6,138, 40, and 273 patients without diabetes and with type 1 and type 2 diabetes, respectively. Compared with not having diabetes, people with type 1 diabetes had adjusted odds ratios (ORs) of 3.90 (95% CI 1.75-8.69) for hospitalization and 3.35 (95% CI 1.53-7.33) for greater illness severity, which was similar to risk in type 2 diabetes. Among type 1 diabetes patients, glycosylated hemoglobin (HbA1c), hypertension, race, recent diabetic ketoacidosis (DKA), health insurance status, and less diabetes technology use were significantly associated with illness severity.</i></p> <p> </p> <h2>Conclusions: Diabetes status, both type 1 and type 2, independently increases the adverse impacts of COVID-19. Potentially modifiable factors (e.g., HbA1c) had significant but modest impact compared to comparatively static factors (e.g. race, insurance) in type 1 diabetes indicating an urgent and continued need to mitigate SARS-CoV-2 infection risk in this community.</h2>

GC improved the quality of chronic illness care received by multimorbid care recipients but did not improve caregivers' depressive symptoms, affect, or productivity.

Objectives To assess the validity of the Work Productivity and Activity Impairment questionnaire as adapted for caregiving (WPAI:CG) to measure productivity loss (hours missed from work, impairment while at work, and impairment in regular activities) due to unpaid caregiving for medically complex older adults. Methods The WPAI:CG was administered along with the Caregiver Strain Index (CSI) and Center for Epidemiologic Studies Depression Scale (CESD) to a caregiving population (N = 308) enrolled with their older, medically complex care-recipient in a cluster-randomized controlled study. Correlation coefficients were calculated between each productivity variable derived from the WPAI:CG and CSI/CESD scores. Nonparametric tests for trend across ordered groups were carried out to examine the relationship between each productivity variable and the intensity of the caregiving. Results Significant positive correlations were found between work productivity loss and caregiving-related strain (r = 0.45) and depression (r = 0.30). Measures of productivity loss were also highly associated with caregiving intensity (P < 0.05) and care-recipient medical care use (P < 0.05). The average employed caregiver reported 1.5 hours absence from work in the previous week and 18.5% reduced productivity while at work due to caregiving. Employed and nonemployed caregivers reported 27.2% reduced productivity in regular activities in the previous week. Conclusion The results indicate high convergent validity of the WPAI:CG questionnaire. This measure could facilitate research on the cost-effectiveness of caregiver-workplace interventions and provide employers and policy experts with a more accurate and comprehensive estimate of caregiving-related costs incurred by employers and society.