Diabetes Distress in Young Adults With Youth-Onset Type 2 Diabetes: TODAY2 Study Results

OBJECTIVE To assess the prevalence of high diabetes distress and associated factors in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY2) study cohort of young adults with youth-onset type 2 diabetes. RESEARCH DESIGN AND METHODS Participants completed the Diabetes Distress Scale (DDS) at end-of-study visits. Factors examined for association with high distress were demographic (sex, race/ethnicity, age, education, income), medical (HbA1c, BMI, complications), psychological (depressive and anxiety symptoms), and social (number in household, offspring, health care coverage, established with diabetes care provider). Univariate logistic regression identified factors associated with high distress that were controlled for in multivariate logistic regressions. RESULTS Of 438 participants, 66% were female (mean age 26.8 years, 18% non-Hispanic White, 37% non-Hispanic Black, 38% Hispanic). High distress (DDS ≥2) was reported by 105 (24%) participants. Subscales identified 40% with high regimen distress and 29.7% with high emotional burden. A greater percentage of those with high distress were female (P = 0.002), diagnosed with hypertension (P = 0.037) and retinopathy (P = 0.005), treated with insulin, had higher HbA1c, and had moderate to severe depressive and anxiety symptoms (all P < 0.001). In multivariate analyses, female sex (P < 0.001), HbA1c (P < 0.001), anxiety symptoms (P = 0.036), and lack of health care coverage (P = 0.019) were associated with high distress, after controlling for potential confounders. Moderate to severe depressive symptoms were associated with high regimen distress (P = 0.018) and emotional burden (P < 0.001); insulin treatment was associated with high emotional burden (P = 0.027). CONCLUSIONS Future research should identify modifiable factors associated with high diabetes distress in young adults with youth-onset type 2 diabetes that may inform distress interventions with this medically vulnerable group.

Financial Burdens of Out-of-Pocket Spending Among Medicare Fee-for-Service Beneficiaries: Role of the “Big Four” Chronic Health Conditions

While Medicare is the universal source of health care coverage for Americans aged 65 years or older, the program requires significant cost sharing in terms of out-of-pocket (OOP) spending. We conducted a retrospective study using data from 2016 to 2018 Medicare Current Beneficiary Surveys of elderly community-dwelling beneficiaries ( n = 10,431) linked with administrative data to estimate OOP spending associated with the “big four” chronic diseases (cardiovascular disease, cancer, diabetes, and chronic lung disease). We estimated a generalized linear model adjusting for predisposing, enabling, and need factors to estimate annual OOP spending. We found that beneficiaries with any of the “big four” chronic conditions spent 15% ( p < .001) higher OOP costs and were 56% more likely to spend ≥20% of annual income on OOP expenditure (adjusted odds ratio = 1.56; p < .001) compared with those without any of those conditions. OOP spending appears to be heterogeneous across disease types and changing by conditions over time.

Factors affecting the uptake of community-based health insurance in Ethiopia: a systematic review

<p class="abstract">The goal of health care financing in Ethiopia is achieving universal health care coverage by community-based health insurance which was expected to cover more than eighty percent of the population. The aim was to minimize catastrophic out-of-pocket health service expenditure. We systematically reviewed factors affecting the uptake of community-based health insurance in Ethiopia. We searched various databases by 09 to 10 March 2019. We included articles regardless of their publication status with both quantitative and qualitative approaches. The factors determining the uptake of community-based health insurance in Ethiopia were found to be demographic and socio-economic, and health status, and health service-related issues. Among demographic and socio-economic factors, the report of the studies regarding gender and age was not consistent. However, income, education, community participation, marriage, occupation, and family size were found to be significant predictors and were positively related to the uptake of the scheme.<strong> </strong>Concerning health status and health service-related factors; illness experience, benefit package, awareness level, previous out of pocket expenditure for health care service, and health service status (quality, adequacy, efficiency, and coverage) were significantly and positively related but the premium amount, self-rated health status and bureaucratic complexity were found to be negative predictors. To achieve universal health care coverage through community-based health insurance, special attention should be given to community-based intervention.</p>

Under Careful Construction: Combining Findings, Arguments, and Values Into Robust Health Care Coverage Decisions

Background: Health care coverage decisions deal with health care technology provision or reimbursement on a national level. The coverage decision outcome, i.e., the publicly available document with reasons for the decision, may contain various elements: quantitative calculations like cost and clinical effectiveness analyses and formalised and non-formalised qualitative considerations. We know little about the process of combining these heterogeneous elements into robust decision outcomes.Methods: In this study, we describe a model for combining different elements into coverage decision outcomes. We build on two qualitative cases of coverage appraisals at the Dutch National Health Care Institute, for which we analysed observations at committee meetings (n=2, with field notes taken) and analysis of audio files (n=3), interviews with appraisal committee members (n=10 in seven interviews) and with Institute employees (n=5 in three interviews).Results: We conceptualise decision outcomes as combinations of elements, specifically (quantitative) findings and (qualitative) arguments and values. Our model contains three steps: 1) identifying elements; 2) designing the combinations of elements, which entails articulating links, broadening the scope of designed combinations, and black-boxing links; and 3) testing these combinations and choosing one as the final decision outcome. Conclusions: The proposed model highlights decision makers’ expertise in composing both elements and combinations. It also provides additional rationales for facilitating appeals and engaging patients and the public. Future research efforts could further explore the relationship between robustness and decision combination strength.