Estimating Long-Term Health Utility Scores and Expenditures for Cardiovascular Disease From the Medical Expenditure Panel Survey.

BACKGROUND: Long-term health utility scores and costs used in cost-effectiveness analyses of cardiovascular disease prevention and management can be inconsistent, outdated, or invalid for the diverse population of the United States. Our aim was to develop a user friendly, standardized, publicly available code and catalog to derive more valid long-term values for health utility and expenditures following cardiovascular disease events. METHODS: Individual-level Short Form-12 version 2 health-related quality of life and expenditure data were obtained from the pooled 2011 to 2016 Medical Expenditure Panel Surveys. We developed code using the R programming language to estimate preference-weighted Short Form-6D utility scores from the Short Form-12 for quality-adjusted life year calculations and predict annual health care expenditures. Result predictors included cardiovascular disease diagnosis (myocardial infarction, ischemic stroke, heart failure, cardiac dysrhythmias, angina pectoris, and peripheral artery disease), sociodemographic factors, and comorbidity variables. RESULTS: The cardiovascular disease diagnoses with the lowest utility scores were heart failure (0.635 [95% CI, 0.615-0.655]), angina pectoris (0.649 [95% CI, 0.630-0.667]), and ischemic stroke (0.649 [95% CI, 0.635-0.663]). The highest annual expenditures were for heart failure ($20 764 [95% CI, $17 500-$24 027]), angina pectoris ($18 428 [95% CI, $16 102-$20 754]), and ischemic stroke ($16 925 [95% CI, $15 672-$20 616]). CONCLUSIONS: The developed code and catalog may improve the quality and comparability of cost-effectiveness analyses by providing standardized methods for extracting long-term health utility scores and expenditures from Medical Expenditure Panel Survey data, which are more current and representative of the US population than previous sources.

Economic costs of implementing group interventions to reduce diabetes distress in adults with type 1 diabetes mellitus in the T1-REDEEM trial.

AIMS: This study evaluated the implementation costs of two group interventions, one focused on diabetes education (KnowIt) and one focused directly on diabetes distress (OnTrack), that reduced diabetes distress and HbA1C in adults with poorly controlled type 1 diabetes (T1DM) in the T1-REDEEM trial. METHODS: Resources used to provide interventions were enumerated using activity-based micro-costing methods. Costs were assigned to resources in 2017 US dollars. US median wage and benefit rates were used to calculate costs of staff time. Cost per unit change was calculated for diabetes distress and HbA1C. RESULTS: For both interventions, per participant implementation costs were approximately $250 and cost per 1.0 percentage point (11 mmol/mol) change in HbA1C was $1400. Cost per unit change in diabetes distress was $364 for KnowIt and $335 for OnTrack. No statistically significant differences in costs were observed. CONCLUSIONS: This is the first study to examine the costs of implementing interventions targeting diabetes distress in the context of T1DM. Both interventions had per participant implementation costs in the lower end of the range of previously examined diabetes self-management interventions ($219 to $5390). These inventions and their costs merit further attention because reducing diabetes distress may impact long term T1DM outcomes. CLINICAL TRIALS REGISTRATION: ClinicalTrials.govNCT02175732.