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.

BACKGROUND: Family caregivers provide assistance with health care tasks for many older adults with chronic illnesses. The difficulty they experience in providing this assistance, and related implications for their well-being, have not been well described. OBJECTIVE: The objectives of this study are: (1) to describe caregiver's health care task difficulty (HCTD), (2) determine the characteristics associated with HCTD, and (3) explore the association between HCTD and caregiver well-being. DESIGN: This is a cross-sectional study. PARTICIPANTS: Baseline sample of caregivers to older (aged 65+years) multimorbid adults enrolled in an ongoing cluster-randomized controlled trial (N=308). MAIN MEASURES:The HCTD scale (0-16) is comprised of questions measuring self-reported difficulty in assisting older adults with eight health care tasks, including taking medication, visiting health care providers, and managing medical bills. Caregivers were categorized using this scale into no, low, medium, and high HCTD groups. We used ordinal logistic regression and multivariate linear regression analyses to examine the relationships between HCTD, caregiver self-efficacy, caregiver strain (Caregiver Strain Index), and depression (Center for Epidemiological Studies Depression Scale), controlling for patient and caregiver socio-demographic and health factors. KEY RESULTS: Caregiver age and number of health care tasks performed were positively associated with increased HCTD. The quality of the caregiver's relationship with the patient, and self-efficacy were inversely associated with increased HCTD. A onepoint increase in self-efficacy was associated with a significant lower odds of reporting high HCTD (OR, 0.64; 95% CI, 0.54, 0.77).Adjusted linear regression models indicated that high HCTD was independently associated with significantly greater caregiver strain (B, 2.7; 95% CI, 1.12, 4.29) and depression (B, 3.01; 95% CI, 1.06, 4.96). CONCLUSIONS: This study demonstrates that greater HCTD is associated with increased strain and depression among caregivers of multimorbid older adults. That caregiver self-efficacy was strongly associated with HCTD suggests health-system-based educational and empowering interventions might improve caregiver well-being.

BACKGROUND Self-care management is recognized as a key component of care for multi-morbid older adults, but the characteristics of those most likely to participate in Chronic Disease Self-Management (CDSM) programs and how to maximize participation in such programs are unknown. OBJECTIVES To identify individual factors associated with attending CDSM programs in a sample of multi-morbid older adults. RESEARCH DESIGN Participants in the intervention arm of a matched-pair cluster-randomized controlled trial of the Guided Care model were invited to attend a six-session CDSM course. Logistic regression was used to identify factors independently associated with attendance. SUBJECTS All subjects (N=241) were 65 years or older, were at high risk for health care utilization, and were not homebound. MEASURES Baseline information on demographics, health status, health activities, and quality of care was available for CDSM participants and non-participants. Participation was defined as attendance at five or more CDSM sessions. RESULTS 22.8% of multi-morbid older adults who were invited to CDSM courses participated in five or more sessions. Having better physical health (OR[95% CI] = 2.3[1.1–4.8]) and rating one’s physician poorly on support for patient activation (OR[95% CI] = 2.8[1.3–6.0]) were independently associated with attendance. CONCLUSIONS Multi-morbid older adults who are in better physical health and who are dissatisfied with their physicians’ support for patient activation are more likely to participate in CDSM courses.

Context Public policy regarding family caregiving for disabled older adults is affected by their estimated number, their attributes, and the services provided. The available national surveys, however, do not have a uniform approach to ascertaining the number of family caregivers, so their estimated number varies widely. Methods This article looks at nationally representative, population-based surveys of family caregivers conducted between 1985 and 2010 to find methods pertinent to ascertaining the number of caregivers. The surveys’ design, definition of disability, and approach to identifying and defining caregivers of disabled adults aged sixty-five and older were identified, and cross-survey estimates were compared. Findings Published estimates of the numbers of caregivers of older disabled adults ranged from 2.7 million to 36.1 million in eight national surveys conducted between 1992 and 2009. The surveys were evenly divided between caregivers identified by disabled older adults (n = 4, “disability surveys”) and self-identified (n = 4, “caregiver self-identification surveys”). The estimated number of family caregivers of disabled adults aged sixty-five and older was, on average, 4.8 million in disability surveys and 24.4 million in caregiver self-identification surveys. Conclusions The number of family caregivers of disabled older adults estimated by national surveys varied substantially. Greater consistency in defining caregivers could yield more informative estimates and also advance policy efforts to more effectively monitor and support family caregivers.