Geographic variation in the use of continuous outpatient inotrope infusion therapy and beta blockers.

BACKGROUND: Continuous outpatient inotrope infusion therapy (COIIT) can be used as palliative or interim treatment in patients with advanced heart failure (AHF). Despite widespread use, there is a relative lack of data informing best practices. This study aimed to examine whether patterns of COIIT use differed by region and to explore whether observed differences influenced clinical outcomes. METHODS: Retrospective study of AHF patients receiving COIIT from May 2009 through June 2016. The primary outcome was regional difference, the secondary outcome was persistence (duration) on therapy. Cox proportional hazards model was used to calculate hazard ratios for treatment regimens. RESULTS: There were 3,286 patients, mean (SD) age 61.9 (14.4) years and 74.0% (2,433) male. Inotrope selection and beta blocker use varied by region by chi square (χ2 (21) = 166.9, p < 0.001). Persistence was greater on milrinone compared to dobutamine (HR (for discontinuation) 0.54, CI 0.41-0.70, p < 0.001). Concurrent beta-blocker was associated with greater persistence for patients receiving milrinone (HR 0.13, CI 0.08-0.20, p < 0.001) and dobutamine (HR 0.36, CI 0.18-0.71, p < 0.001). CONCLUSIONS: Patterns of COIIT use varied by region, and variations in use were associated with differences in clinical outcomes.

Where Do We Go From Here? A Framework for Using Susceptible-Infectious-Recovered Models for Policy Making in Emerging Infectious Diseases.

OBJECTIVES: Throughout the coronavirus disease 2019 pandemic, susceptible-infectious-recovered (SIR) modeling has been the preeminent modeling method to inform policy making worldwide. Nevertheless, the usefulness of such models has been subject to controversy. An evolution in the epidemiological modeling field is urgently needed, beginning with an agreed-upon set of modeling standards for policy recommendations. The objective of this article is to propose a set of modeling standards to support policy decision making. METHODS: We identify and describe 5 broad standards: transparency, heterogeneity, calibration and validation, cost-benefit analysis, and model obsolescence and recalibration. We give methodological recommendations and provide examples in the literature that employ these standards well. We also develop and demonstrate a modeling practices checklist using existing coronavirus disease 2019 literature that can be employed by readers, authors, and reviewers to evaluate and compare policy modeling literature along our formulated standards. RESULTS: We graded 16 articles using our checklist. On average, the articles met 6.81 of our 19 categories (36.7%). No articles contained any cost-benefit analyses and few were adequately transparent. CONCLUSIONS: There is significant room for improvement in modeling pandemic policy. Issues often arise from a lack of transparency, poor modeling assumptions, lack of a system-wide perspective in modeling, and lack of flexibility in the academic system to rapidly iterate modeling as new information becomes available. In anticipation of future challenges, we encourage the modeling community at large to contribute toward the refinement and consensus of a shared set of standards for infectious disease policy modeling.

A US Population Health Survey on the Impact of COVID-19 Using the EQ-5D-5L.

BACKGROUND: The COVID-19 pandemic has resulted in negative impacts on the economy, population health, and health-related quality-of-life (HRQoL). OBJECTIVE: To assess the impact of COVID-19 on US population HRQoL using the EQ-5D-5L. DESIGN: We surveyed respondents on physical and mental health, demographics, socioeconomics, brief medical history, current COVID-19 status, sleep, dietary, financial, and spending changes. Results were compared to online and face-to-face US population norms. Predictors of EQ-5D-5L utility were analyzed using both standard and post-lasso OLS regressions. Robustness of regression coefficients against unmeasured confounding was analyzed using the E-Value sensitivity analysis. SUBJECTS: Amazon MTurk workers (n=2776) in the USA. MAIN MEASURES: EQ-5D-5L utility and VAS scores by age group. KEY RESULTS: We received n=2746 responses. Subjects 18-24 years reported lower mean (SD) health utility (0.752 (0.281)) compared with both online (0.844 (0.184), p=0.001) and face-to-face norms (0.919 (0.127), p<0.001). Among ages 25-34, utility was worse compared to face-to-face norms only (0.825 (0.235) vs. 0.911 (0.111), p<0.001). For ages 35-64, utility was better during pandemic compared to online norms (0.845 (0.195) vs. 0.794 (0.247), p<0.001). At age 65+, utility values (0.827 (0.213)) were similar across all samples. VAS scores were worse for all age groups (p<0.005) except ages 45-54. Increasing age and income were correlated with increased utility, while being Asian, American Indian or Alaska Native, Hispanic, married, living alone, having history of chronic illness or self-reported depression, experiencing COVID-19-like symptoms, having a family member diagnosed with COVID-19, fear of COVID-19, being underweight, and living in California were associated with worse utility scores. Results were robust to unmeasured confounding. CONCLUSIONS: HRQoL decreased during the pandemic compared to US population norms, especially for ages 18-24. The mental health impact of COVID-19 is significant and falls primarily on younger adults whose health outcomes may have been overlooked based on policy initiatives to date.

Characterizing the Validity and Real-World Utility of Health Technology Assessments in Healthcare: Future Directions Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling".

With their article, Grutters et al raise an important question: What do successful health technology assessments (HTAs) look like, and what is their real-world utility in decision-making? While many HTAs are published in peer-reviewed journals, many are considered proprietary and their attributes remain confidential, limiting researchers' ability to answer these questions. Models for economic evaluations like cost-effectiveness analyses (CEAs) synthesize a wide range of evidence, are often statistically and mathematically sophisticated, and require untestable assumptions. As such, there is nearly universal agreement among researchers that enhancing transparency is an important issue in health economic modeling. However, the definition of transparency and guidelines for its implementation vary. Model registration combined with a linked database of model-based economic evaluations has been proposed as a solution, whereby registered models and their accompanying technical and nontechnical documentation are sourced into a single publicly-available repository, ideally in a standardized format to ensure consistent and complete representation of features, code, data sources, results, validation exercises, and policy recommendations. When such a repository is ultimately created, modelers will not have to reinvent the wheel for every new drug launched or new treatment pathway. These more open and transparent approaches will have substantial implications for model accuracy, reliability, and validity, improving trust and acceptance by healthcare decision-makers.

Cost-effectiveness analysis of dapagliflozin versus glimepiride as monotherapy in a Chinese population with type 2 diabetes mellitus.

OBJECTIVE: To evaluate the long-term cost-effectiveness of dapagliflozin (a novel sodium-glucose co-transporter-2 inhibitor) versus glimepiride (a widely used sulfonylurea), when applied as monotherapy in patients with type 2 diabetes mellitus (T2DM) in China. METHODS: Literature screening, meta-analysis and indirect comparison were used to compare efficacy and safety between dapagliflozin and glimepiride. Direct medication costs and medical expenditure on treating diabetes related comorbidities were calculated based on published and local sources and reported in 2015 Chinese Renminbi (RMB). A discount rate of 3% was applied to both costs and health effects. The Cardiff model, an economic model designed to evaluate the cost-effectiveness of comparator therapies in diabetes, was used to generate outputs including macrovascular and microvascular complications, diabetes-specific mortality, costs and quality-adjusted life years (QALYs) over a time horizon of 40 years from the health provider perspective. Univariate and probabilistic sensitivity analyses were performed to assess uncertainty in the model results. RESULTS: Compared with glimepiride, patients on dapagliflozin gained 1.01 QALYs, at a cost saving of RMB 49,065 in our simulated cohort. This resulted in a cost saving of RMB 48,585 per QALY gained with dapagliflozin. The cost-effectiveness results were robust to various sensitivity analyses including probabilistic sensitivity analysis (PSA). CONCLUSIONS: Compared with glimepiride, dapagliflozin as monotherapy for T2DM is a more cost-effective treatment for T2DM patients on monotherapy in China. The weight control has been identified as the major contributor for the higher cost-effectiveness of dapagliflozin.