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.

Time to publication of cost-effectiveness analyses for medical devices.

OBJECTIVES: Academic researchers and physicians have called for greater use of cost-effectiveness analyses in informing treatment and reimbursement decisions. This study examines the availability of cost-effectiveness analyses for medical devices, in terms of both the number of studies and when studies are published. STUDY DESIGN: Analysis of the number of years between FDA approval/clearance and publication for cost-effectiveness analyses of medical devices in the United States published between 2002 and 2020 (n = 86). METHODS: Cost-effectiveness analyses of medical devices were identified using the Tufts University Cost-Effectiveness Analysis Registry. Studies in which the model and manufacturer of the medical device used in the intervention were identifiable were linked to FDA databases. Years between FDA approval/clearance and publication of cost-effectiveness analyses were calculated. RESULTS: A total of 218 cost-effectiveness analyses of medical devices in the United States published between 2002 and 2020 were identified. Of these studies, 86 (39.4%) were linked to FDA databases. Studies examining devices approved via premarket approval were published a mean of 6.0 years after the device received FDA approval (median, 4 years), whereas studies examining devices that were cleared via the 510(k) process were published a mean of 6.5 years after the device received FDA clearance (median, 5 years). CONCLUSIONS: There are few studies describing the cost-effectiveness of medical devices. Most of these studies' findings are not published until several years after the studied devices received FDA approval/clearance, meaning that decision makers will likely not have evidence of cost-effectiveness when making initial decisions related to newly available medical devices.

Association Between Regulatory Submission Characteristics and Recalls of Medical Devices Receiving 510(k) Clearance.

Importance: Most regulated medical devices enter the US market via the 510(k) regulatory submission pathway, wherein manufacturers demonstrate that applicant devices are "substantially equivalent" to 1 or more "predicate" devices (legally marketed medical devices with similar intended use). Most recalled medical devices are 510(k) devices. Objective: To examine the association between characteristics of predicate medical devices and recall probability for 510(k) devices. Design, Setting, and Participants: In this exploratory cross-sectional analysis of medical devices cleared by the US Food and Drug Administration (FDA) between 2003 and 2018 via the 510(k) regulatory submission pathway, linear probability models were used to examine associations between a 510(k) device's recall status and characteristics of its predicate medical devices. Public documents for the 510(k) medical devices were collected using FDA databases. A text extraction algorithm was applied to identify predicate medical devices cited in 510(k) regulatory submissions. Algorithm-derived metadata were combined with 2003-2020 FDA recall data. Exposures: Citation of predicate medical devices with certain characteristics in 510(k) regulatory submissions, including the total number of predicate medical devices cited by the applicant device, the age of the predicate medical devices, the lack of similarity of the predicate medical devices to the applicant device, and the recall status of the predicate medical devices. Main Outcomes and Measures: Class I or class II recall of a 510(k) medical device between its FDA regulatory clearance date and December 31, 2020. Results: The sample included 35 176 medical devices, of which 4007 (11.4%) were recalled. The applicant devices cited a mean of 2.6 predicate medical devices, with mean ages of 3.6 years and 7.4 years for the newest and oldest, respectively, predicate medical devices. Of the applicant devices, 93.9% cited predicate medical devices with no ongoing recalls, 4.3% cited predicate medical devices with 1 ongoing class I or class II recall, 1.0% cited predicate medical devices with 2 ongoing recalls, and 0.8% cited predicate medical devices with 3 or more ongoing recalls. Applicant devices citing predicate medical devices with 3 or more ongoing recalls were significantly associated with a 9.31-percentage-point increase (95% CI, 2.84-15.77 percentage points) in recall probability compared with devices without ongoing recalls of predicate medical devices, or an 81.2% increase in recall probability relative to the mean recall probability. A 1-SD increase in the total number of predicate medical devices cited by the applicant device was significantly associated with a 1.25-percentage-point increase (95% CI, 0.62-1.87 percentage points) in recall probability, or an 11.0% increase in recall probability relative to the mean recall probability. A 1-SD increase in the newest age of a predicate medical device was significantly associated with a 0.78-percentage-point decrease (95% CI, 1.29-0.30 percentage points) in recall probability, or a 6.8% decrease in recall probability relative to the mean recall probability. Conclusions and Relevance: This exploratory cross-sectional study of 510(k) medical devices cleared by the FDA between 2003 and 2018 demonstrated significant associations between 510(k) submission characteristics and recalls of medical devices. Further research is needed to understand the implications of these associations.

Physician Network Connections Associated With Faster De-Adoption of Dronedarone for Permanent Atrial Fibrillation.

BACKGROUND: Physicians' professional networks are an important source of new medical information and have been shown to influence the adoption of new treatments, but it is unknown how physician networks impact the de-adoption of harmful practices. METHODS: We analyzed changes in physicians' use of dronedarone after the PALLAS trial (Palbociclib Collaborative Adjuvant Study; November 2011) showed that dronedarone increased the risk of death from cardiovascular events among patients with permanent atrial fibrillation. Deidentified administrative claims from the OptumLabs Data Warehouse were combined with physicians' demographic information from the Doximity database and publicly available data on physicians' patient-sharing relationships compiled by the Centers for Medicare and Medicaid Services. We used a linear probability model with an interrupted linear time trend specification to model the impact of the PALLAS trial on physicians' dronedarone usage between 2009 and 2014. RESULTS: Before the PALLAS trial, the use of dronedarone was increasing by 0.22 percentage points per quarter (95% CI, 0.19-0.25) in our Medicare Advantage sample (N=343 429 patient-quarter observations) and 0.63 percentage points per quarter (95% CI, 0.52-0.75) in our commercially insured sample (N=44 402 patient-quarter observations). After the PALLAS trial and subsequent United States Food and Drug Administration black box warning, physicians in the Medicare Advantage sample with an above-median number of network connections to other physicians decreased their quarterly usage of dronedarone by 0.12 percentage points more per quarter (95% CI, -0.20 to -0.04; P=0.031) than physicians with equal to or below the median number of network connections. Similar patterns existed in the commercially insured sample (P=0.0318). CONCLUSIONS: After controlling for a wide range of patient, physician, and geographic characteristics, physicians with a greater number of network connections were faster de-adopters of dronedarone for patients with permanent atrial fibrillation after the PALLAS trial and subsequent United States Food and Drug Administration black box warning detailed the harmfulness of dronedarone for these patients. Policies for improving physicians' responsiveness to new medical information should consider utilizing the influence of these important professional network relationships.

Uptake of evidence by physicians: De-adoption of erythropoiesis-stimulating agents after the TREAT trial.

BACKGROUND: Variation in de-adoption of ineffective or unsafe treatments is not well-understood. We examined de-adoption of erythropoiesis-stimulating agents (ESA) in anemia treatment among patients with chronic kidney disease (CKD) following new clinical evidence of harm and ineffectiveness (the TREAT trial) and the FDA's revision of its safety warning. METHOD: We used a segmented regression approach to estimate changes in use of epoetin alfa (EPO) and darbepoetin alfa (DPO) in the commercial, Medicare Advantage (MA) and Medicare fee-for-service (FFS) populations. We also examined how changes in both trends and levels of use were associated with physicians' characteristics. RESULTS: Use of DPO and EPO declined over the study period. There were no consistent changes in DPO trend across insurance groups, but the level of DPO use decreased right after the FDA revision in all groups. The decline in EPO use trend was faster after the TREAT trial for all groups. Nephrologists were largely more responsive to evidence than primary care physicians. Differences by physician's gender, and age were not consistent across insurance populations and types of ESA. CONCLUSIONS: Physician specialty has a dominant role in prescribing decision, and that specializations with higher use of treatment (nephrologists) were more responsive to new evidence of unsafety and ineffectiveness.

Physician variation in the de-adoption of ineffective statin and fibrate therapy.

OBJECTIVE: To describe physicians' variation in de-adopting concurrent statin and fibrate therapy for type 2 diabetic patients following a reversal in clinical evidence. DATA SOURCES: We analyzed 2007-2015 claims data from OptumLabs® Data Warehouse, a longitudinal, real-world data asset with de-identified administrative claims and electronic health record data. STUDY DESIGN: We modeled fibrate use among Medicare Advantage and commercially insured type 2 diabetic statin users before and after the publication of the ACCORD lipid trial, which found statins and fibrates were no more effective than statins alone in reducing cardiovascular events among type 2 diabetic patients. We modeled fibrate use trends with physician random effects and physician characteristics such as age and specialty. DATA EXTRACTION: We identified patient-year-quarters with one year of continuous insurance enrollment, type 2 diabetes diagnoses, and fibrate use. We designated the physician most responsible for patients' diabetes care based on evaluation and management visits and prescriptions of glucose-lowering drugs. PRINCIPAL FINDINGS: Fibrate use increased by 0.12 percentage points per quarter among commercial patients (95% CI, 0.10 to 0.14) and 0.17 percentage points per quarter among Medicare Advantage patients (95% CI, 0.13 to 0.20) before the trial and then decreased by 0.16 percentage points per quarter among commercial patients (95% CI, -0.18 to -0.15) and 0.05 percentage points per quarter among Medicare Advantage patients (95% CI, -0.06 to -0.03) after the trial. However, 45% of physicians treating commercial patients and 48% of physicians treating Medicare Advantage patients had positive trends in prescribing following the trial. Physicians' characteristics did not explain their variation (pseudo R2  = 0.000). CONCLUSION: On average, physicians decreased fibrate prescribing following the ACCORD lipid trial. However, many physicians increased prescribing following the trial. Observable physician characteristics did not explain variations in prescribing. Future research should examine whether physicians vary similarly in other de-adoption settings.

Measuring Primary Care Exam Length Using Electronic Health Record Data.

BACKGROUND: Physicians' time with patients is a critical input to care, but is typically measured retrospectively through survey instruments. Data collected through the use of electronic health records (EHRs) offer an alternative way to measure visit length. OBJECTIVE: To measure how much time primary care physicians spend with their patients, during each visit. RESEARCH DESIGN: We used a national source of EHR data for primary care practices, from a large health information technology company. We calculated exam length and schedule deviations based on timestamps recorded by the EHR, after implementing sequential data refinements to account for non-real-time EHR use and clinical multitasking. Observational analyses calculated and plotted the mean, median, and interquartile range of exam length and exam length relative to scheduled visit length. SUBJECTS: A total of 21,010,780 primary care visits in 2017. MEASURES: We identified primary care visits based on physician specialty. For these visits, we extracted timestamps for EHR activity during the exam. We also extracted scheduled visit length from the EHR's practice management functionality. RESULTS: After data refinements, the average primary care exam was 18.0 minutes long (SD=13.5 min). On average, exams ran later than their scheduled duration by 1.2 minutes (SD=13.5 min). Visits scheduled for 10 or 15 minutes were more likely to exceed their allotted time than visits scheduled for 20 or 30 minutes. CONCLUSIONS: Time-stamped EHR data offer researchers and health systems an opportunity to measure exam length and other objects of interest related to time.

Patient and provider-level factors associated with changes in utilization of treatments in response to evidence on ineffectiveness or harm.

High-quality health care not only includes timely access to effective new therapies but timely abandonment of therapies when they are found to be ineffective or unsafe. Little is known about changes in use of medications after they are shown to be ineffective or unsafe. In this study, we examine changes in use of two medications: fenofibrate, which was found to be ineffective when used with statins among patients with Type 2 diabetes (ACCORD lipid trial); and dronedarone, which was found to be unsafe in patients with permanent atrial fibrillation (PALLAS trial). We examine the patient and provider characteristics associated with a decline in use of these medications. Using Medicare fee-for-service claims from 2008 to 2013, we identified two cohorts: patients with Type 2 diabetes using statins (7 million patient-quarters), and patients with permanent atrial fibrillation (83 thousand patient-quarters). We used interrupted time-series regression models to identify the patient- and provider-level characteristics associated with changes in medication use after new evidence emerged for each case. After new evidence of ineffectiveness emerged, fenofibrate use declined by 0.01 percentage points per quarter (95% CI - 0.02 to - 0.01) from a baseline of 6.9 percent of all diabetes patients receiving fenofibrate; dronedarone use declined by 0.13 percentage points per quarter (95% CI - 0.15 to - 0.10) from a baseline of 3.8 percent of permanent atrial fibrillation patients receiving dronedarone. For dronedarone, use declined more quickly among patients dually-enrolled in Medicare and Medicaid compared to Medicare-only patients (P < 0.001), among patients seen by male providers compared to female providers (P = 0.01), and among patients seen by cardiologists compared to primary care providers (P < 0.001).

Exploring the Validity of Developing an Interdisciplinarity Score of a Patient's Needs: Care Coordination, Patient Complexity, and Patient Safety Indicators.

Despite the Affordable Care Act's push to improve the coordination of care for patients with multiple chronic conditions, most measures of coordination quality focus on a specific moment in the care process (e.g., medication errors or transfer between facilities), rather than patient outcomes. One possible supplementary way of measuring the care coordination quality of a facility would be to identify the patients needing the most coordination, and to look at outcomes for that group. This paper lays the groundwork for a new measure of care coordination quality by outlining a conceptual framework that considers the interaction between a patient's interdisciplinarity, biological susceptibility, and procedural intensity. Interdisciplinarity captures the degree of specialized medical expertise needed for a patient's care and will be an important measure to estimate the number of specialists a patient might see. We then develop a preliminary measure of interdisciplinarity and run tests linking interdisciplinarity to medical mistakes, as defined by Agency for Healthcare Research and Quality's Patient Safety Indicators. Finally, we use our preliminary measure to verify that interdisciplinarity is likely to be statistically different from existing measures of comorbidity, like the Charlson score. Future research will need to build upon our findings by developing a more statistically validated measure of interdisciplinarity.