Innovative Research Methods: Using Simulation to Evaluate Health Care Policy.

Statement Health care policies have the potential to improve patient outcomes, access to care, and reduce health disparities. However, new policy is often tested in the field, where unintended consequences are paid for by patients. In this perspective, we argue that health care simulations, which can elucidate the potential for policy to hinder clinicians' ability to provide high-quality care, are a complement to large-scale policy evaluations in the field.

Physicians' perspectives on percutaneous left atrial appendage occlusion for patients with atrial fibrillation.

BACKGROUND: There has been an increasing uptake of transcatheter left atrial appendage occlusion (LAAO) for stroke reduction in atrial fibrillation. OBJECTIVES: To investigate the perceptions and approaches among a nationally representative sample of physicians. METHODS: Using the American Medical Association Physician Masterfile, we selected a random sample of 500 physicians from each of the specialties: general cardiologists, interventional cardiologists, electrophysiologists, and vascular neurologists. The participants received the survey by mail up to three times from November 9, 2021 to January 14, 2022. In addition to the questions about experiences, perceptions, and approaches, physicians were randomly assigned to 1 of the 4 versions of a patient vignette: white man, white woman, black man, and black woman, to investigate potential bias in decision-making. RESULTS: The top three reasons for considering LAAO were: a history of intracranial bleeding (94.3%), a history of major extracranial bleeding (91.8%), and gastrointestinal lesions (59.0%), whereas the top three reasons for withholding LAAO were: other indications for long-term oral anticoagulation (87.7%), a low bleeding risk (77.0%), and a low stroke risk (65.6%). For the reasons limiting recommendations for LAAO, 59.8% mentioned procedural risks, 42.6% mentioned "limiting efficacy data comparing LAAO to NOAC" and 32.8% mentioned "limited safety data comparing LAAO to NOAC." There was no difference in physicians' decision-making by patients' race, gender, or the concordance between patients' and physicians' race or gender. CONCLUSIONS: In the first U.S. national physician survey of LAAO, individual physicians' perspectives varied greatly, which provided information that will help customize future educational activities for different audiences. CONDENSED ABSTRACT: Although diverse practice patterns of LAAO have been documented, little is known about the reasoning or perceptions that drive these variations. Unlike prior surveys that were directed to Centers that performed LAAO, the current survey obtained insights from individual physicians, not only those who perform the procedures (interventional cardiologists and electrophysiologists) but also those who are closely involved in the decision-making and referral process (general cardiologists and vascular neurologists). The findings identify key evidence gaps and help prioritize future studies to establish a consistent and evidence-based best practice for AF stroke prevention.

Longitudinal Patterns in Testosterone Prescribing After US FDA Safety Communication in 2014.

BACKGROUND: The objective of this study was to describe changes in testosterone prescribing following a 2014 US Food and Drug Administration (FDA) safety communication and how changes varied by physician characteristics. METHODS: Data were extracted from a 20% random sample of Medicare fee-for-service administrative claims data from 2011 through 2019. The sample included 1,544,604 unique male beneficiaries who received evaluation and management (E&M) services from 58,819 unique physicians that prescribed testosterone between 2011 and 2013. Patients were categorized based on presence of coronary artery disease (CAD) and non-age-related hypogonadism. Physician characteristics were identified in the OneKey database and included specialty and affiliations with teaching hospitals, for-profit hospitals, hospitals in integrated delivery networks, and hospitals in the top decile of case mix index. Linear segmented models described how testosterone prescriptions changed following a 2014 FDA safety communication and how changes were associated with physician and organizational characteristics. RESULTS: Among 65,089,560 physician-patient-quarter-year observations, mean (standard deviation) age ranged from 72.16 (5.84) years for observations without CAD or non-age-related hypogonadism to 75.73 (6.92) years with CAD and without non-age-related hypogonadism. Following the safety communication, immediate changes in off-label testosterone prescription levels fell by 0.22 percentage points (pp) (95% confidence interval [CI] -0.33 to -0.11) for patients with CAD and by -0.16 pp (95% CI -0.19 to -0.16) for patients without CAD. A similar change was noticed in on-label prescribing levels. Off-label testosterone prescription quarterly trend, however, increased for patients with CAD and without CAD; on-label testosterone prescription trends declined for both groups. Declines in off-label prescribing were larger when treated by primary care physicians vs. non-primary care physicians, and physicians affiliated with teaching compared to nonteaching hospitals. Physician and organizational characteristics were not associated with changes in on-label prescribing. CONCLUSION: On-label and off-label testosterone therapy declined following the FDA safety communication. Certain physician characteristics were associated with changes in off-label, but not on-label, prescribing.

Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method.

BACKGROUND: Lifelong oral anticoagulation is recommended in patients with atrial fibrillation (AF) to prevent stroke. Over the last decade, multiple new oral anticoagulants (OACs) have expanded the number of treatment options for these patients. While population-level effectiveness of OACs has been compared, it is unclear if there is variability in benefit and risk across patient subgroups. METHODS: We analyzed claims and medical data for 34,569 patients who initiated a nonvitamin K antagonist oral anticoagulant (non-vitamin K antagonist oral anticoagulant (NOAC); apixaban, dabigatran, and rivaroxaban) or warfarin for nonvalvular AF between 08/01/2010 and 11/29/2017 from the OptumLabs Data Warehouse. A machine learning (ML) method was applied to match different OAC groups on several baseline variables including, age, sex, race, renal function, and CHA2DS2 -VASC score. A causal ML method was then used to discover patient subgroups characterizing the head-to-head treatment effects of the OACs on a primary composite outcome of ischemic stroke, intracranial hemorrhage, and all-cause mortality. RESULTS: The mean age, number of females and white race in the entire cohort of 34,569 patients were 71.2 (SD, 10.7) years, 14,916 (43.1%), and 25,051 (72.5%) respectively. During a mean follow-up of 8.3 (SD, 9.0) months, 2,110 (6.1%) of patients experienced the composite outcome, of whom 1,675 (4.8%) died. The causal ML method identified 5 subgroups with variables favoring apixaban over dabigatran; 2 subgroups favoring apixaban over rivaroxaban; 1 subgroup favoring dabigatran over rivaroxaban; and 1 subgroup favoring rivaroxaban over dabigatran in terms of risk reduction of the primary endpoint. No subgroup favored warfarin and most dabigatran vs warfarin users favored neither drug. The variables that most influenced favoring one subgroup over another included Age, history of ischemic stroke, thromboembolism, estimated glomerular filtration rate, Race, and myocardial infarction. CONCLUSIONS: Among patients with AF treated with a NOAC or warfarin, a causal ML method identified patient subgroups with differences in outcomes associated with OAC use. The findings suggest that the effects of OACs are heterogeneous across subgroups of AF patients, which could help personalize the choice of OAC. Future prospective studies are needed to better understand the clinical impact of the subgroups with respect to OAC selection.

A Simple and Interpretable Mortality-Based Value Metric for Condition- or Procedure-Specific Hospital Performance Reporting.

Objective: To develop a simple, interpretable value metric (VM) to assess the value of care of hospitals for specific procedures or conditions by operationalizing the value equation: Value = Quality/Cost. Patients and Methods: The present study was conducted on a retrospective cohort from 2015 to 2018 drawn from the 100% US sample of Medicare inpatient claims. The final cohort comprised 637,341 consecutive inpatient encounters with a cancer-related Medicare Severity-Diagnosis Related Grouping and 13,307 consecutive inpatient encounters with the International Classification of Diseases, Ninth Revision or International Classification of Diseases, Tenth Revision procedure code for partial or total gastrectomy. Claims-based demographic and clinical variables were used for risk adjustment, including age, sex, year, dual eligibility, reason for Medicare entitlement, and binary indicators for each of the Elixhauser comorbidities used in the Elixhauser mortality index. Risk-adjusted 30-day mortality and risk-adjusted encounter-specific costs were combined to form the VM, which was calculated as follows: number needed to treat = 1/(Mortalitynational - Mortalityhospital), and VM = number needed to treat × risk-adjusted cost per encounter. Results: Among hospitals with better-than-average 30-day cancer mortality rates, the cost to prevent 1 excess 30-day mortality for an inpatient cancer encounter ranged from $71,000 (best value) to $1.4 billion (worst value), with a median value of $543,000. Among hospitals with better-than-average 30-day gastrectomy mortality rates, the cost to prevent 1 excess 30-day mortality for an inpatient gastrectomy encounter ranged from $710,000 (best value) to $95 million (worst value), with a median value of $1.8 million. Conclusion: This simple VM may have utility for interpretable reporting of hospitals' value of care for specific conditions or procedures. We found substantial inter- and intrahospital variation in value when defined as the costs of preventing 1 excess cancer or gastrectomy mortality compared with the national average, implying that hospitals with similar quality of care may differ widely in the value of that care.

A prediction model for asthma exacerbations after stopping asthma biologics.

BACKGROUND: Little is known regarding the prediction of the risks of asthma exacerbation after stopping asthma biologics. OBJECTIVE: To develop and validate a predictive model for the risk of asthma exacerbations after stopping asthma biologics using machine learning models. METHODS: We identified 3057 people with asthma who stopped asthma biologics in the OptumLabs Database Warehouse and considered a wide range of demographic and clinical risk factors to predict subsequent outcomes. The primary outcome used to assess success after stopping was having no exacerbations in the 6 months after stopping the biologic. Elastic-net logistic regression (GLMnet), random forest, and gradient boosting machine models were used with 10-fold cross-validation within a development (80%) cohort and validation cohort (20%). RESULTS: The mean age of the total cohort was 47.1 (SD, 17.1) years, 1859 (60.8%) were women, 2261 (74.0%) were White, and 1475 (48.3%) were in the Southern region of the United States. The elastic-net logistic regression model yielded an area under the curve (AUC) of 0.75 (95% confidence interval [CI], 0.71-0.78) in the development and an AUC of 0.72 in the validation cohort. The random forest model yielded an AUC of 0.75 (95% CI, 0.68-0.79) in the development cohort and an AUC of 0.72 in the validation cohort. The gradient boosting machine model yielded an AUC of 0.76 (95% CI, 0.72-0.80) in the development cohort and an AUC of 0.74 in the validation cohort. CONCLUSION: Outcomes after stopping asthma biologics can be predicted with moderate accuracy using machine learning methods.

Emulating the GRADE trial using real world data: retrospective comparative effectiveness study.

OBJECTIVE: To emulate the GRADE (Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study) trial using real world data before its publication. GRADE directly compared second line glucose lowering drugs for their ability to lower glycated hemoglobin A1c (HbA1c). DESIGN: Observational study. SETTING: OptumLabs® Data Warehouse (OLDW), a nationwide claims database in the US, 25 January 2010 to 30 June 2019. PARTICIPANTS: Adults with type 2 diabetes and HbA1c 6.8-8.5% while using metformin monotherapy, identified according to the GRADE trial specifications, who also used glimepiride, liraglutide, sitagliptin, or insulin glargine. MAIN OUTCOME MEASURES: The primary outcome was time to HbA1c ≥7.0%. Secondary outcomes were time to HbA1c >7.5%, incident microvascular complications, incident macrovascular complications, adverse events, all cause hospital admissions, and all cause mortality. Propensity scores were estimated using the gradient boosting machine method, and inverse propensity score weighting was used to emulate randomization of the treatment groups, which were then compared using Cox proportional hazards regression. RESULTS: 8252 people were identified (19.7% of adults starting the study drugs in OLDW) who met eligibility criteria for the GRADE trial (glimepiride arm=4318, liraglutide arm=690, sitagliptin arm=2993, glargine arm=251). The glargine arm was excluded from analyses owing to small sample size. Median times to HbA1c ≥7.0% were 442 days (95% confidence interval 394 to 480 days) for glimepiride, 764 (741 to not calculable) days for liraglutide, and 427 (380 to 483) days for sitagliptin. Liraglutide was associated with lower risk of reaching HbA1c ≥7.0% compared with glimepiride (hazard ratio 0.57, 95% confidence interval 0.43 to 0.75) and sitagliptin (0.55, 0.41 to 0.73). Results were consistent for the secondary outcome of time to HbA1c >7.5%. No significant differences were observed among treatment groups for the remaining secondary outcomes. CONCLUSIONS: In this emulation of the GRADE trial, liraglutide was statistically significantly more effective at maintaining glycemic control than glimepiride or sitagliptin when added to metformin monotherapy. Generating timely evidence on medical treatments using real world data as a complement to prospective trials is of value.

Percutaneous Left Atrial Appendage Occlusion in Comparison to Non-Vitamin K Antagonist Oral Anticoagulant Among Patients With Atrial Fibrillation.

Background This study aimed to compare percutaneous left atrial appendage occlusion (LAAO) with non-vitamin K antagonist oral anticoagulants among patients with atrial fibrillation. Methods and Results Using a US administrative database, 562 850 patients with atrial fibrillation were identified, among whom 8397 were treated with LAAO and 554 453 were treated with non-vitamin K antagonist oral anticoagulants between March 13, 2015 and December 31, 2018. Propensity score overlap weighting was used to balance baseline characteristics. The primary outcome was a composite end point of ischemic stroke or systemic embolism, major bleeding, and all-cause mortality. The mean age was 76.4±7.6 years; 280 097 (49.8%) were female. Mean follow-up was 1.5±1.0 years. LAAO was associated with no significant difference in the risk of the primary composite end point (hazard ratio [HR], 0.93 [0.84-1.03]), or the secondary outcomes including ischemic stroke/systemic embolism (HR, 1.07 [0.81-1.41]), and intracranial bleeding (HR, 1.08 [0.72-1.61]). LAAO was associated with a higher risk of major bleeding (HR, 1.22 [1.05-1.42], P=0.01) and a lower risk of mortality (HR, 0.73 [0.64-0.84], P<0.001). The lower risk of mortality associated with LAAO was most pronounced in patients with a prior history of intracranial bleeding. Conclusions In comparison to non-vitamin K antagonist oral anticoagulants, LAAO was associated with no significant difference in the risk of the composite outcome and a lower risk of mortality, which suggests LAAO might be a reasonable option in select patients with atrial fibrillation. The observation of higher bleeding risk associated with LAAO highlights the need to optimize postprocedural antithrombotic regimens as well as systematic efforts to assess and address bleeding predispositions.

PreScription DigitaL ThErapEutic for Patients with Insomnia (SLEEP-I): a protocol for a pragmatic randomised controlled trial.

INTRODUCTION: Cognitive behavioural therapy for insomnia (CBT-I) is effective at treating chronic insomnia, yet in-person CBT-I can often be challenging to access. Prior studies have used technology to bridge barriers but have been unable to extensively assess the impact of the digital therapeutic on real-world patient experience and multidimensional outcomes. Among patients with insomnia, our aim is to determine the impact of a prescription digital therapeutic (PDT) (PEAR-003b, FDA-authorised as Somryst; herein called PDT) that provides mobile-delivered CBT-I on patient-reported outcomes (PROs) and healthcare utilisation. METHODS AND ANALYSIS: We are conducting a pragmatically designed, prospective, multicentre randomised controlled trial that leverages Hugo, a unique patient-centred health data-aggregating platform for data collection and patient follow-up from Hugo Health. A total of 100 participants with insomnia from two health centres will be enrolled onto the Hugo Health platform, provided with a linked Fitbit (Inspire 2) to track activity and then randomised 1:1 to receive (or not) the PDT for mobile-delivered CBT-I (Somryst). The primary outcome is a change in the insomnia severity index score from baseline to 9-week postrandomisation. Secondary outcomes include healthcare utilisation, health utility scores and clinical outcomes; change in sleep outcomes as measured with sleep diaries and a change in individual PROs including depressive symptoms, daytime sleepiness, health status, stress and anxiety. For those allocated to the PDT, we will also assess engagement with the PDT. ETHICS AND DISSEMINATION: The Institutional Review Boards at Yale University and the Mayo Clinic have approved the trial protocol. This trial will provide important data to patients, clinicians and policymakers about the impact of the PDT device delivering CBT-I on PROs, clinical outcomes and healthcare utilisation. Findings will be disseminated to participants, presented at professional meetings and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04909229.

Approvals and Timing of New Formulations of Novel Drugs Approved by the US Food and Drug Administration Between 1995 and 2010 and Followed Through 2021.

Importance: New formulations of prescription drugs can improve convenience and tolerability for patients, but they also constitute manufacturer strategies to extend brand-name drug market exclusivity periods. Objective: To examine whether new formulations of brand-name novel drugs were associated with novel drugs' sales and/or therapeutic value, as well as characterize first new formulations' approval timing relative to the novel drug's generic approval. Design Setting and Participants: This cross-sectional study used the Drugs@FDA database to identify all novel tablet and capsule drugs approved by the US Food and Drug Administration (FDA) between 1995 and 2010 and followed through December 31, 2021. Exposures: Novel drugs' blockbuster status, defined as annual sales of $1 billion or greater, and therapeutic value, measured by (1) accelerated approval status, (2) World Health Organization Model Lists of Essential Medicines inclusion, (3) innovativeness, and (4) clinical usefulness. Main Outcomes and Measures: Approval of a new formulation and timing relative to a novel drug's first generic's approval. Results: Among the 206 novel drugs in tablet or capsule form approved by the FDA from 1995 to 2010, 81 (39.3%) were followed by an FDA-approved new formulation, and 167 (81.1%) had a generic version as of December 31, 2021. In multivariable analyses, new formulations were statistically significantly more likely among blockbuster drugs vs not (58.2% vs 27.6%; adjusted odds ratio [AOR], 4.72; 95% CI, 2.26-9.87; P < .001) and those granted accelerated approval vs not (50.0% vs 37.6%; AOR, 5.48; 95% CI, 1.52-19.67; P = .009), and less likely among orphan products vs not (11.8% vs 44.8%; AOR, 0.13; 95% CI, 0.03-0.52; P = .004). Essential medicine listing vs no listing (47.8% vs 36.9%; AOR, 1.32; 95% CI, 0.52-3.34; P = .56), first-in-class or advance-in-class status vs addition-to-class status (37.8% vs 40.5%; AOR, 0.71; 95% CI, 0.32-1.58; P = .40), and categorization as clinically useful vs not useful (40.9% vs 44.8%; AOR, 0.81; 95% CI, 0.34-1.92; P = .64) were not associated with increased likelihood of a new formulation. First new formulations were statistically significantly less likely to be approved after the novel drug's first generic approval (84.6% vs 15.4%; P < .001). Conclusions and Relevance: In this cross-sectional study of novel drugs in tablet or capsule form approved by the FDA between 1995 and 2010, manufacturers pursued new formulations of best-selling brand-name drugs and those granted accelerated approval but did so less frequently once generic competitors entered the market. Other measures of therapeutic value were not associated with new formulations.

Acute pain pathways: protocol for a prospective cohort study.

INTRODUCTION: Opioid analgesics are often used to treat moderate-to-severe acute non-cancer pain; however, there is little high-quality evidence to guide clinician prescribing. An essential element to developing evidence-based guidelines is a better understanding of pain management and pain control among individuals experiencing acute pain for various common diagnoses. METHODS AND ANALYSIS: This multicentre prospective observational study will recruit 1550 opioid-naïve participants with acute pain seen in diverse clinical settings including primary/urgent care, emergency departments and dental clinics. Participants will be followed for 6 months with the aid of a patient-centred health data aggregating platform that consolidates data from study questionnaires, electronic health record data on healthcare services received, prescription fill data from pharmacies, and activity and sleep data from a Fitbit activity tracker. Participants will be enrolled to represent diverse races and ethnicities and pain conditions, as well as geographical diversity. Data analysis will focus on assessing patients' patterns of pain and opioid analgesic use, along with other pain treatments; associations between patient and condition characteristics and patient-centred outcomes including resolution of pain, satisfaction with care and long-term use of opioid analgesics; and descriptive analyses of patient management of leftover opioids. ETHICS AND DISSEMINATION: This study has received approval from IRBs at each site. Results will be made available to participants, funders, the research community and the public. TRIAL REGISTRATION NUMBER: NCT04509115.

Shared Decision-Making for Patients Hospitalized with Acute Myocardial Infarction: A Randomized Trial.

Objective: Adherence to guideline-recommended medications after acute myocardial infarction (AMI) is suboptimal. Patient fidelity to treatment regimens may be related to their knowledge of the risk of death following AMI, the pros and cons of medications, and to their involvement in treatment decisions. Shared decision-making may improve both patients' knowledge and involvement in treatment decisions. Methods: In a pilot trial, patients hospitalized with AMI were randomized to the use of the AMI Choice conversation tool or to usual care. AMI Choice includes a pictogram of the patient's estimated risk of mortality at 6 months with and without guideline-recommended medications, ie, aspirin, statins, beta-blockers, and angiotensin-converting enzyme inhibitors. Primary outcomes were patient knowledge and conflict with the decision made assessed via post-encounter surveys. Secondary outcomes were patient involvement in the decision-making process (observer-based OPTION12 scale) and 6-month medication adherence. Results: Patient knowledge of the expected survival benefit from taking medications was significantly higher (62% vs 16%, p<0.0001) in the AMI Choice group (n = 53) compared to the usual care group (n = 53). Both groups reported similarly low levels of conflict with the decision to start the medications (13 (SD 24.2) vs 16 (SD 22) out of 100; p=0.16). The extent to which clinicians in the AMI Choice group involved their patients in the decision-making process was high (OPTION12 score 53 out of 100, SD 12). Medication adherence at 6-months was relatively high in both groups and not different between groups. Conclusion: The AMI Choice conversation tool improved patients' knowledge of their estimated risk of short-term mortality after an AMI and the pros and cons of treatments to reduce this risk. The effect on patient fidelity to recommended medications of using this SDM tool and of SDM in general should be tested in larger trials enrolling patients at high risk for nonadherence. Trial Registration Number: NCT00888537.

Association of stay-at-home orders and COVID-19 incidence and mortality in rural and urban United States: a population-based study.

OBJECTIVE: We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States. DESIGN: We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports. INTERVENTIONS: Issuance of stay-at-home orders. PRIMARY AND SECONDARY OUTCOME MEASURES: Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility. RESULTS: Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%-6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties. CONCLUSION: Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

Many Medicare Beneficiaries Do Not Fill High-Price Specialty Drug Prescriptions.

For high-price drugs, Medicare Part D beneficiaries who do not receive a low-income subsidy must pay a percentage of the drug's price for each medication fill. Without that subsidy, which lowers out-of-pocket spending, beneficiaries typically pay hundreds or thousands of dollars for a single fill. We estimated the proportion of Part D beneficiaries in fee-for-service Medicare, with and without a subsidy, who do not initiate treatment (that is, do not fill a new prescription) with high-price Part D drugs newly prescribed for four conditions. Examining 17,076 new prescriptions issued between 2012 and 2018 for Part D beneficiaries from eleven geographically diverse health systems, we found that beneficiaries receiving subsidies were nearly twice as likely to obtain the prescribed drug within ninety days as those without subsidies. Among beneficiaries without subsidies, we observed noninitiation for 30 percent of prescriptions written for anticancer drugs, 22 percent for hepatitis C treatments, and more than 50 percent for disease-modifying therapies for either immune system disorders or hypercholesterolemia. Our findings support current legislative efforts to increase the accessibility of high-price medications by reducing out-of-pocket expenses under Medicare Part D, particularly for beneficiaries without low-income subsidies.

Management and Outcomes of Acute Myocardial Infarction-Cardiogenic Shock in Uninsured Compared With Privately Insured Individuals.

BACKGROUND: There are limited data on uninsured patients presenting with acute myocardial infarction-cardiogenic shock (AMI-CS). This study sought to compare the management and outcomes of AMI-CS between uninsured and privately insured individuals. METHODS: Using the National Inpatient Sample (2000-2016), a retrospective cohort of adult (≥18 years) uninsured admissions (primary payer-self-pay or no charge) were compared with privately insured individuals. Interhospital transfers were excluded. Outcomes of interest included in-hospital mortality, temporal trends in admissions, use of cardiac procedures, do-not-resuscitate status, palliative care referrals, and resource utilization. RESULTS: Of 402 182 AMI-CS admissions, 21 966 (5.4%) and 93 814 (23.3%) were uninsured and privately insured. Compared with private insured individuals, uninsured admissions were younger, male, from a lower socioeconomic status, had lower comorbidity, higher rates of acute organ failure, ST-segment elevation AMI-CS (77.3% versus 76.4%), and concomitant cardiac arrest (33.8% versus 31.9%; all P<0.001). Compared with 2000, in 2016, there were more uninsured (adjusted odds ratio, 1.15 [95% CI, 1.13-1.17]; P<0.001) and less privately insured admissions (adjusted odds ratio, 0.85 [95% CI, 0.83-0.87]; P<0.001). Uninsured individuals received less frequent coronary angiography (79.5% versus 81.0%), percutaneous coronary intervention (60.8% versus 62.2%), mechanical circulatory support (54% versus 55.5%), and had higher palliative care (3.8% versus 3.2%) and do-not-resuscitate status use (4.4% versus 3.2%; all P<0.001). Uninsured admissions had higher in-hospital mortality (adjusted odds ratio, 1.62 [95% CI, 1.55-1.68]; P<0.001) and resource utilization. CONCLUSIONS: Uninsured individuals have higher in-hospital mortality and lower use of guideline-directed therapies in AMI-CS compared with privately insured individuals.

A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations.

Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020-March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.

Midwest rural-urban disparities in use of patient online services for COVID-19.

PURPOSE: Rural populations are disproportionately affected by the COVID-19 pandemic. We characterized urban-rural disparities in patient portal messaging utilization for COVID-19, and, of those who used the portal during its early stage in the Midwest. METHODS: We collected over 1 million portal messages generated by midwestern Mayo Clinic patients from February to August 2020. We analyzed patient-generated messages (PGMs) on COVID-19 by urban-rural locality and incorporated patients' sociodemographic factors into the analysis. FINDINGS: The urban-rural ratio of portal users, message senders, and COVID-19 message senders was 1.18, 1.31, and 1.79, indicating greater use among urban patients. The urban-rural ratio (1.69) of PGMs on COVID-19 was higher than that (1.43) of general PGMs. The urban-rural ratios of messaging were 1.72-1.85 for COVID-19-related care and 1.43-1.66 for other health care issues on COVID-19. Compared with urban patients, rural patients sent fewer messages for COVID-19 diagnosis and treatment but more messages for other reasons related to COVID-19-related health care (eg, isolation and anxiety). The frequent senders of COVID-19-related messages among rural patients were 40+ years old, women, married, and White. CONCLUSIONS: In this Midwest health system, rural patients were less likely to use patient online services during a pandemic and their reasons for its use differ from urban patients. Results suggest opportunities for increasing equity in rural patient engagement in patient portals (in particular, minority populations) for COVID-19. Public health intervention strategies could target reasons why rural patients might seek health care in a pandemic, such as social isolation and anxiety.

Rates of, and factors associated with, switching among generic levothyroxine preparations in commercially insured American adults.

IMPORTANCE: Some practice guidelines warn against generic L-thyroxine preparation switching. OBJECTIVE: To examine the rates of generic L-thyroxine preparation switching within one year of initiating L-thyroxine, and to examine factors associated with switching. DESIGN AND SETTING: Retrospective study using national data from a large administrative claims database from January 2008 through November 2018. PATIENTS: Medicare or commercially insured adults (≥18 years) who filled a generic L-thyroxine preparation. MAIN OUTCOME MEASURES: At least one switch from one generic L-thyroxine preparation to another within 1 year of L-thyroxine initiation defined by prescription fills. RESULTS: From January 2008 to November 2018, we included 483,390 patients who initiated generic L-thyroxine: mean (SD) age was 61.4 years (15.2), 75.2% were female, 72.6% were white. Within 1 year of initiating therapy, 98,013 (20%) switched to another L-thyroxine generic preparation at least once. In a multivariate logistic regression analysis, factors associated with switching included the number of pharmacies visited to fill L-thyroxine (>2 vs 1 adjusted OR [aOR] 7.15, 95% confidence interval [CI] 6.97-7.34), age ≥75 vs. <45 years (aOR 1.29, 95% CI 1.26-1.33), history of thyroid surgery (aOR 1.22, 95% CI 1.13-1.31), and first L-thyroxine fill date in 2018 vs. 2008 (aOR 3.32, 95% CI 3.14-3.51). CONCLUSIONS AND RELEVANCE: One in five patients switched among generic L-thyroxine manufacturers within one year of treatment initiation. Generic L-thyroxine switching occurred more often when more pharmacies were used to fill L-thyroxine. Given existing guideline recommendations, additional studies should clarify the impact of generic L-thyroxine switching on thyroid hormone values.