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Background: Understanding medication use patterns for patients with COVID-19 will provide needed insight into the evolution of COVID-19 treatment over the course of the SARS-CoV-2 pandemic and aid clinical management considerations. Objectives: To systematically determine most frequently used medications among COVID-19 patients overall and by hospitalization status. Secondary objective was use measurement of medications considered potential therapeutic options. Methods: Retrospective cohort study was performed using data from the University of California COVID Research Data Set (UC CORDS) patients between March 10, 2020, and December 31, 2020. Main outcomes were percentages of patients prescribed medications, overall, by age group, and by comorbidity based on hospitalization status for COVID-19 patients. Use percentage by month of COVID-19 diagnosis was measured. Cumulative count of potential therapeutic options was measured over time. Results: Dataset included 22 896 unique patients with COVID-19 (mean [SD] age, 42.4 [20.4] years; 12 154 [53%] women). Most frequently used medications in patients overall were acetaminophen (21.2%), albuterol (14.9%), ondansetron (13.9%), and enoxaparin (10.8%). Dexamethasone use increased from fewer than 50 total hospitalized patients through April who had received the medication, to more than 500 patients by mid-August. Cumulative count of enoxaparin users was the largest throughout the study period. Conclusion and Relevance: In this retrospective cohort study, across age and comorbidity groups, predominant utilization was for supportive care therapy. Dexamethasone and remdesivir experienced large increases in use. Conversely, hydroxychloroquine and azithromycin use markedly dropped. Medication utilization rapidly shifted toward more evidence-concordant treatment of patients with COVID-19 as rigorous study findings emerged.

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence in the evaluation of new treatments or products. Real-world clinical trials or pragmatic trials often differ from traditional clinical trials in the use of open-label or nonblinded treatments delivered by real-world clinicians in community practice settings. Blinding and standardization of treatment may sometimes be necessary for internal validity, but they may also obscure or distort meaningful differences between treatments. When investigators consider whether blinding of clinicians, patients, or assessors is necessary, we suggest they consider several specific questions: Will clinicians, patients, and assessors have expectations or preferences regarding benefits or adverse effects? How might those expectations affect treatment uptake, treatment adherence, or assessment of outcomes? Will expectations differ in the settings where trial results will be applied? How would blinding of treatment reduce biases? How would blinding obscure true differences between treatments? How would procedures necessary for blinding reduce acceptability or increase risk of trial participation? When investigators consider how strictly treatments should be standardized, we suggest they consider several specific questions: How would treatment effectiveness or safety vary according to clinician experience or expertise? What level of experience or expertise is available in potential trial settings and settings where trial results would be applied? Is some level of standardization necessary for valid inference? Considering any special vulnerabilities of the study population, is some level of standardization necessary to assure participant safety?

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. The RWE label has been used to refer to a variety of departures from the methods of traditional randomized controlled trials. Recognizing this complexity and potential confusion, the National Academies of Science, Engineering, and Medicine convened a series of workshops to clarify and address questions regarding the use of RWE to evaluate new medical treatments. Those workshops identified three specific dimensions in which RWE studies might differ from traditional clinical trials: use of real-world data (data extracted from health system records or data captured by mobile devices), delivery of real-world treatment (open-label treatments delivered in community settings by community practitioners), and real-world treatment assignment (including nonrandomized comparisons and variations on random assignment such as before-after or stepped-wedge designs). For any RWE study, decisions regarding each of these dimensions depends on the specific research question, characteristics of the potential study settings, and characteristics of the settings where study results would be applied.

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. RWE studies often rely on real-world data (RWD), including data extracted from healthcare records or data captured by mobile phones or other consumer devices. Global assessments of RWD sources are not helpful in assessing whether any specific RWD element is fit for any specific purpose. Instead, evidence generators and evidence consumers should clearly identify the specific health state or clinical phenomenon of interest and then consider each step between that clinical phenomenon and its representation in a research database. We propose specific questions regarding potential error or bias affecting each of those steps: Would a person experiencing this clinical phenomenon present for care in this setting or interact with this recording device? Would this clinical phenomenon be accurately recognized or assessed? How might the recording environment or tools affect accurate and consistent recording of this clinical phenomenon? Can data elements from different sources be harmonized, both technically (same format) and semantically (same meaning)? Can the original data elements be consistently reduced to a useful clinical phenotype? Addressing these questions requires a range of clinical, organizational, and technical expertise. Transparency regarding each step in the creation of RWD is essential if evidence consumers are to rely on RWE studies.

Supplemental material, HCUP_Supplemental_Appendices for Emergency Department Visits for Psychotropic-Related Adverse Drug Events in Older Adults With Alzheimer Disease, 2013-2014 by Aryana Sepassi and Jonathan H. Watanabe in Annals of Pharmacotherapy

BACKGROUND:Public attention and recent US Congressional activity has intensified focus on escalating medication prices. However, the actual cost of medication use extends beyond the up-front cost of purchasing medicines. It also encompasses the additional medical costs of morbidity and mortality resulting from nonoptimized medication regimens, including medication nonadherence. OBJECTIVES:Applying the most current nationally representative data sources, our goal was to estimate the cost of prescription drug-related morbidity and mortality in the United States. METHODS:Total costs of nonoptimized prescription drug use and average pathway costs for a patient who experienced a treatment failure (TF), a new medical problem (NMP), or a TF and NMP were modeled in Microsoft Excel (Microsoft Corporation, Redmond, WA) and TreeAge Pro Healthcare, v2014 (TreeAge Software, Inc, Williamstown, MA), respectively. RESULTS:The estimated annual cost of prescription drug-related morbidity and mortality resulting from nonoptimized medication therapy was $528.4 billion in 2016 US dollars, with a plausible range of $495.3 billion to $672.7 billion. The average cost of an individual experiencing TF, NMP, or TF and NMP after initial prescription use were $2481 (range: $2233, $2742), $2610 (range: $2374, $2848) and $2572 (range: $2408, $2751), respectively. CONCLUSIONS:The estimated annual cost of drug-related morbidity and mortality resulting from nonoptimized medication therapy was $528.4 billion, equivalent to 16% of total US health care expenditures in 2016. We propose expansion of comprehensive medication management programs by clinical pharmacists in collaborative practices with physicians and other prescribers as an effective and scalable approach to mitigate these avoidable costs and improve patient outcomes.

BackgroundPrevious estimates of the economic burden of Crohn's disease (CD) varied widely from $2.0 to $18.2 billion per year (adjusted to 2015 $US). However, these estimates do not reflect recent changes in pharmaceutical treatment options and guidelines.ObjectiveThe goal of this study was to update cost estimates of Crohn's disease based on a representative sample of the US population from the most recent 11 years (2003-2013) of the Medical Expenditure Panel Survey (MEPS). A secondary aim described expenditure trends in respondents with and without Crohn's disease pre-post FDA approvals of new biologics and the American College of Gastroenterology Crohn's disease treatment guidelines.MethodsAverage annual expenditures (total, prescription, inpatient, and outpatient) were evaluated using a pooled cross-sectional design. Respondent data from the most recent 11 years (2003-2013) of MEPS were analyzed. Two-part generalized linear models with power-link were used to estimate the average annual expenditures per patient adjusted to multiple covariates. Confidence intervals (CI) were estimated using bootstrap methods. Difference-in-differences estimations were performed to compare the changes in health care expenditures pre-post FDA approvals of new biologics and the American College of Gastroenterology Crohn's disease treatment guidelines.ResultsThe annual aggregate economic burden of CD was $6.3 billion in the US. Respondents with CD had higher total (+$6442; 95% CI: $4864 to $8297), prescription (+$3283; 95% CI: $2289 to $4445), inpatient (+$1764; 95% CI: $748 to $3551), and outpatient (+$1191; 95% CI: $592 to $2160) expenditures compared to respondents without CD. In the difference-in-differences estimation, respondents with CD had significantly higher total (P=0.001) and prescription (P&nbsp

Health technology assessment is a form of health policy research that provides policymakers with information relevant to decisions about policy alternatives. Findings from cost-effectiveness analysis (CEA) are one of the important aspects of health technology assessment. Nevertheless, the more advanced method of value of information (VOI), which is recommended by the International Society for Pharmacoeconomics and Outcomes Research and Society for Medical Decision Making Modeling Good Research Practices Task Force, has rarely been applied in CEA studies in Asia. The lack of VOI in Asian CEA studies may be due to limited understanding of VOI methods and what VOI can and cannot help policy decision makers accomplish. This concept article offers audiences a practical primer in understanding the calculation, presentation, and policy implications of VOI. In addition, it provides a rapid survey of health technology assessment guidelines and literature related to VOI in Asia and discusses the future directions of VOI use in Asia and its potential barriers. This article will enable health economists, outcomes researchers, and policymakers in Asia to better understand the importance of VOI analysis and its implications, leading to the appropriate use of VOI in Asia.