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Daina B Esposito,1 Vibha CA Desai,1 Judith J Stephenson,1 M Soledad Cepeda,2 Jennifer G Lyons,1 Crystal N Holick,1 Gregory P Wedin,3 Stephan Lanes1 On behalf of members of the REMS Program Companies Metrics Subteam1HealthCore, Inc., Wilmington, DE, USA; 2Janssen Research and Development, Titusville, NJ, USA; 3Upsher-Smith Laboratories, LLC, Maple Grove, MN, USACorrespondence: Judith J StephensonHealthCore Inc, 123 Justison Street, Suite 200, Wilmington, DE, 19801, USATel +1 302-547-5770Email jstephenson@healthcore.comBackground/Rationale: The US Food and Drug Administration (FDA) approved a Risk Evaluation and Mitigation Strategy (REMS) for extended release/long-acting (ER/LA) opioids in 2012. The purpose of this study was to assess patient knowledge of the safe use of these products following implementation of the REMS and to determine possible effects of the REMS, including impact on medication access.Objective: To assess patient knowledge of safe use of ER/LA opioids and use of REMS patient education tools such as the Medication Guide (MG) and Patient Counseling Document (PCD).Methods: This was a cross-sectional survey of commercially insured (Commercial) and Medicare Advantage-insured (Medicare) adults with ≥ 1 pharmacy claim for an ER/LA opioid (10/01/2015 – 02/28/2017) in the HealthCore Integrated Research Database and Medicaid-insured (Medicaid) adult members of a research panel, about their knowledge of safe use of ER/LA opioids and receipt/comprehension of the MG and PCD.Results: Survey respondents consisted of 382 Commercial, 43 Medicare and 40 Medicaid adults. While ≥ 95% of respondents received and read the MG, fewer were aware of the PCD (Commercial: 47%, Medicare: 65%, Medicaid: 53%). Almost 75% of the knowledge questions were answered correctly by ≥ 80% of all respondents; fewer respondents recognized that use of opioids as directed can lead to death (Commercial: 73%, Medicare: 56%, Medicaid: 63%), the MG should be read at each dispensing (Commercial: 78%, Medicare: 53%, Medicaid: 75%), opioids should not be stored in the medicine cabinet (Commercial: 77%, Medicare: 79%, Medicaid: 58%), missed doses should not be taken as soon as possible (Commercial: 56%, Medicare: 51%, Medicaid: 50%), and pills should not be crushed (Commercial: 85%, Medicare: 67%, Medicaid: 52%).Conclusion: Although most respondents reported reading and understanding the MG and exhibited knowledge of safe use of ER/LA opioids, providers’ use of the PCD and increased understanding of safe use core messages need reinforcement.Keywords: Risk Evaluation and Mitigation Strategy, opioids, patient knowledge

Background Observational data may inform novel drug development programs by identifying previously unappreciated, clinical benefits of existing drugs. Several preclinical and clinical studies have suggested emergent therapeutic utility of drugs acting on the N-methyl-d-aspartate (NMDA) receptor, a subtype of glutamate receptors, including the antidementia drug memantine. Methods Using a self-controlled cohort study design, the association of exposure to the NMDA receptor antagonist memantine with the incidence of all observed disease outcomes in four US administrative claims databases, spanning from January 2000 through January 2019, was assessed. The databases used in this study were the IBM MarketScan® Commercial Database (CCAE), the IBM MarketScan® Multi-State Medicaid Database (MDCD), the IBM MarketScan® Medicare Supplemental Database (MDCR), and the Optum© De-Identified Clinformatics® Data Mart Database. Outcomes were defined according to the unique Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) classification system codes and required a diagnosis on two or more distinct dates. Of 20,953 outcomes assessed, only those for which memantine was associated with a ≥ 50% reduction in risk in two or more databases were included. A meta-analysis with random effects was used to pool data across the databases. Results Overall, 312,336 patients were exposed to memantine during the study. After removing conditions related to dementia and memory loss, 60 outcomes met the threshold criteria. Results fell into five disease categories: mental disorders, substance use disorders, pain, gastrointestinal and colon disorders, and demyelinating disease. The bulk of findings fell into the first two groups, with 28 outcomes related to mental disorders and 24 related to substance use disorders. Conclusion The present results confirm that NMDA receptor antagonism may have broader therapeutic utility than previously recognized. Further observational and clinical research may be warranted to explore the therapeutic benefit of NMDA antagonists for the outcomes found in this study. Supplementary Information The online version contains supplementary material available at 10.1007/s40263-020-00789-3.

Introduction It is critical to assess who is being treated with a new marketed drug like esketamine to understand how it is used in the real‐world setting and the effects of the medication. Methods Retrospective analysis using two large U.S. health care databases that included commercially insured and Medicaid patients. Patients treated with esketamine were identified and their baseline characteristics described and compared with the baseline characteristics of patients with treatment resistant depression (TRD) and with patients undergoing transcranial magnetic stimulation (TMS). To quantify the differences, standardized mean differences were calculated. Results In the commercially insured database, 418 patients were treated with esketamine and 830,047 patients were in the TRD group. Large differences in baseline characteristics were observed. Patients in the esketamine group were more likely to have severe depression, suicidal thoughts, and prior treatments with TMS or electroconvulsive therapy than the TRD control group. Patients in the esketamine group had more comorbid psychiatric conditions (anxiety disorder, posttraumatic stress disorders, substance use disorders) and higher exposure to antipsychotics, antiepileptics, hypnotics and sedatives. In terms of general health, patients in the esketamine group had many more outpatient visits, were more likely to have chronic pain and higher Charlson comorbidity scores, a predicator of mortality. Results were similar for both the Medicaid and TMS populations. Conclusion Patients treated with esketamine have a higher burden of disease than other patients with TRD. In any real‐world comparative effectiveness or safety study these differences need to be understood and accounted for to produce valid results.

Background An important component of a systematic strategy for safety surveillance is prospective identification of anticipated serious adverse events (SAEs). Developing a structured approach to identify anticipated events and estimating their incidence can help align the safety strategy and the safety surveillance efforts. Methods We developed a novel approach to identify anticipated events for a hypothetical randomized, double-blind, controlled trial in subjects with bipolar disorder using the adverse events reported in the placebo arm of trials from the ClinicalTrials.gov database. We searched the ClinicalTrials.gov database for all trials on bipolar depression with similar inclusion/exclusion criteria and study duration as our hypothetical study. The frequencies of anticipated events in placebo arms were abstracted from each trial and 95% confidence intervals (CI) were calculated using the Clopper–Pearson method. Meta-analysis with a random effects model was performed to obtain a summary estimate and 95% CI for the events identified in more than one trial. Results A total of 129 clinical trials were initially identified, and 18 were ultimately selected as they met all the selection criteria. There were 69 unique anticipated SAEs identified, and 13 out of 69 were reported in at least 2 clinical trials. The top 5 anticipated SAEs for our study were: (1) hospitalization, psychiatric symptom (3.57%); (2) suicidal behavior, overdose (3.57%), (3) cholecystitis (2.86%); (4) fall (2.86%); (5) road traffic accident, injury (2.86%). Conclusion We successfully identified the anticipated events from registered trials that included a population similar to our trial. This method for identifying anticipated events could be applied to other disease areas.

Introduction Opioid overdose deaths in the United States have climbed sharply over the past two decades. Simultaneously, increased awareness of inadequately treated chronic pain has resulted in increased opioid analgesic prescribing. The correlation between these two phenomena has led policymakers to posit that they are causally linked, and to implement policy changes supporting safe opioid prescribing. Purpose To evaluate the impact of its Opioid Analgesic Risk Evaluation and Mitigation Strategy (REMS) program, the US Food and Drug Administration (FDA) requested the opioid manufacturers responsible for implementing that program provide information regarding opioid policy changes from 2016 to 2018. FDA also requested a survey of state requirements for pain and opioid prescribing continuing education (CE), the number of prescribers affected by those requirements, the extent to which a REMS-compliant CE program would meet each state's requirements, and the number of relevant CE programs available. Results Results indicate that 527 federal and state opioid-related policies (statutes, rules/regulations, and guidelines) were approved during the 2016-2018 study period. While the largest number of these policies focused on prescription drug monitoring programs, 170 specifically imposed limits on opioid prescribing and an additional 35 specifically referred to, or incorporated, the Centers for Disease Control and Prevention opioid prescribing guideline. We also found that 46 states and the District of Columbia mandated some amount of pain or opioid prescribing CE for prescribers renewing their licenses. These mandates potentially affected as many as 1.7 million prescribers. In 69% of cases, a REMS-compliant CE program would fully meet the state mandates for various types of prescribers. Conclusion The severity and complexity of the problems of pain management and opioid overdose have led to large-scale intervention by policymakers. Assessing the impact of these changes is difficult, at best, but will be necessary if interventions are to be refined to increase their effectiveness.

Background/rationale Little is known about the reasons for visiting multiple doctors/pharmacies, known as doctor/pharmacy shopping, to obtain opioids. Objective To investigate patients' self-reported reasons for doctor/pharmacy shopping and assess whether doctor/pharmacy shopping behavior can be used as a surrogate measure of opioid abuse/misuse. Methods We conducted a cross-sectional web-based survey among adult patients with ≥2 pharmacy claims for immediate-release or extended-release/long-acting opioids between 7/1/2015 and 12/31/2016, identified from a large United States (US) commercial claims database. Patients were classified into no, mild, moderate, or severe shopping categories based on their claims. Reasons for doctor/pharmacy shopping and opioid abuse/misuse were determined from patient responses to the Prescription Opioid Misuse and Abuse Questionnaire. Results A random sample of 10,081 patients was invited to participate in the survey and 1085 (11%) completed surveys. The most frequently reported reasons for doctor/pharmacy shopping were convenience, availability, price, and multiple morbidities requiring pain management. Among patients in the no, minimal, moderate, and severe shopping categories, only 7.8%, 8.5%, 11.8% and 12.6% reported opioid abuse/misuse, respectively. Conclusion In this commercially-insured population, patient-reported reasons for doctor/pharmacy shopping do not suggest opioid abuse/misuse. Less than 15% of patients with shopping behavior in the past 3 months reported any reasons attributable to opioid abuse/misuse, indicating that shopping behavior in this population may not be a good surrogate for abuse/misuse.

Background Understanding how patients are treated in the real-world is vital to identifying potential gaps in care. We describe the current pharmacologic treatment patterns for the treatment of depression. Methods Patients with depression were identified from four large national claims databases during 1/1/2014–1/31/2019. Patients had ≥2 diagnoses for depression or an inpatient hospitalization with a diagnosis of depression. Patients were required to have enrollment in the database ≥1 year prior to and 3 years following their first depression diagnosis. Treatment patterns were captured at the class level and included selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors, tricyclic antidepressants, other antidepressants, anxiolytics, hypnotics/sedatives, and antipsychotics. Treatment patterns were captured during all available follow-up. Results We identified 269,668 patients diagnosed with depression. The proportion not receiving any pharmacological treatment during follow-up ranged from 29 to 52%. Of the treated, approximately half received ≥2 different classes of therapy, a quarter received ≥3 classes and more than 10% received 4 or more. SSRIs were the most common first-line treatment; however, many patients received an anxiolytic, hypnotic/sedative, or antipsychotic prior to any antidepressive treatment. Treatment with a combination of classes ranged from approximately 20% of first-line therapies to 40% of fourth-line. Conclusions Many patients diagnosed with depression go untreated and many others receive a non-antidepressant medication class as their first treatment. More than half of patients received more than one type of treatment class during the study follow up, suggesting that the first treatment received may not be optimal for most patients.

Daina B Esposito, 1 M Soledad Cepeda, 2 Crystal N Holick, 1 Caitlin Knox, 1 Vibha CA Desai, 1 Nianya Liu, 1 Shiva-Krishna Vojjala, 1 Jennifer G Lyons, 1 Gregory P Wedin, 3 Stephan Lanes 1 On Behalf of the REMS Program Companies Metrics Subteam 1HealthCore, Inc, Andover, MA, USA; 2Janssen Research and Development, Titusville, NJ, USA; 3Upsher-Smith Laboratories, LLC, Maple Grove, MN, USACorrespondence: Stephan LanesSafety and Epidemiology, HealthCore, Inc., 300 Brickstone Square, 8th Floor, Suite 801A, Andover, MA 01810, USATel +1 302-384-0673Fax +1 978-247-6650Email slanes@healthcore.comIntroduction: The United States (US) Food and Drug Administration (FDA) required a Risk Evaluation and Mitigation Strategy (REMS) for extended-release and long-acting (ER/LA) opioid analgesics on 09 July 2012.Methods: This study compared the incidence of opioid overdose before (July 2010-June 2012) and after (July 2013-September 2016) the initiation of the Risk Evaluation and Mitigation Strategy (REMS) for extended-release and long-acting (ER/LA) opioid analgesics. We identified patients with ≥ 1 ER/LA opioid dispensing in either time period in national data from the HealthCore Integrated Research DatabaseSM (HIRD) and in United States (US) Medicaid claims data from four states. We described each population, calculated the incidence rate (IR) of opioid overdose, and assessed crude and propensity score adjusted incidence rate ratios (IRR) comparing the overdose rate after vs before implementation of the REMS.Results: A total of 121,229 commercially insured and 11,488 Medicaid patients were included in the analysis. Rates of overdose were substantially higher in Medicaid patients than in the commercially insured patients (IR 192.0, 95% confidence interval [CI] 162.60– 225.18 versus 102.60, 95% CI 93.0– 112.93 in the active period). The IRRs for opioid overdose were 1.01 (95% CI 0.87– 1.17) in the commercially insured population and 0.70 (95% CI 0.52– 0.93) in Medicaid.Conclusion: This leveling off of overdose rates among commercially insured patients and decline among Medicaid patients is encouraging, but it is difficult to disentangle the specific impact of the REMS from many other ongoing initiatives with similar goals.Keywords: opioid, overdose, REMS, Medicaid, claims

Introduction There is a significant need for disease-modifying therapies to treat and prevent dementia, including Alzheimer's disease. Availability of real-world observational information and new analytic techniques to analyze large volumes of data can provide a path to aid drug discovery. Methods Using a self-controlled study design, we examined the association between 2181 medications and incidence of dementia across four US insurance claims databases. Medications associated with ≥50% reduction in risk of dementia in ≥2 databases were examined. Results A total of 117,015,066 individuals were included in the analysis. Seventeen medications met our threshold criteria for a potential protective effect on dementia and fell into five classes: catecholamine modulators, anticonvulsants, antibiotics/antivirals, anticoagulants, and a miscellaneous group. Discussion The biological pathways of the medications identified in this analysis may be targets for further research and may aid in discovering novel therapeutic approaches to treat dementia. These data show association not causality.

Alexander M Walker,1 Lisa B Weatherby,1 M Soledad Cepeda,2 Daniel C Bradford3 1World Health Information Science Consultants, Dedham, MA, USA; 2Janssen Research and Development, Titusville, NJ, USA; 3Advanced Analytics, IQVIA, Plymouth Meeting, PA, USABackground: Doctor and pharmacy shopping (“Shopping”) for opioids is related to opioid abuse and is associated with opioid overdose and death. Lacking identifiers for prescribers and pharmacies, many data resources (notably the US FDA’s Sentinel System) cannot evaluate Shopping. We used data in which presumptive Shopping could be identified. We investigated whether US health insurance claims data could perform as well as Shopping to identify people with evidence for opioid abuse.Methods: In this cross-sectional study, we examined health insurance claims from 164,923 persons with at least two dispensing of opioids in 18 months, the first occurring in 2012. Evidence for the presence of a possible opioid abuse disorder was drawn from predictive patterns of drug fills, diagnoses and care-seeking identified in a companion research project, and Shopping was determined using a published index. The prevalence of presumptive opioid abuse was examined across levels of Shopping. The comparison between Shopping and insurance-claims-derived covariates in the detection of apparent opioid abuse was examined in multiple regression analyses.Results: Despite a strong correlation between presumptive opioid abuse and Shopping, most persons with extensive Shopping did not manifest presumptive opioid abuse, and half of the population with presumptive opioid abuse did not exhibit Shopping. As Shopping ranged from “None” to “Extensive,” the prevalence of presumptive opioid abuse increased from 0.28 to 5.0 per 100. The discriminating power of Shopping for identifying opioid abuse could be replaced using insurance claims data.Conclusion: The results suggest that patient characteristics that can be inferred from insurance claims data provide as complete discrimination of persons with presumptive opioid abuse as does a full assessment of doctor and pharmacy shopping. The inference rests on patterns of health services and drug dispensing that are indicative of doctor–pharmacy shopping and of opioid abuse. There was no direct evaluation of patients. The extent to which the conclusions are generalizable beyond the study population – Americans with health insurance coverage in the early part of this decade – is uncertain in a quantitative sense. The qualitative conclusion is that diagnostic data in health insurance databases can be predictive of behaviors consistent with opioid abuse and that more elaborate indices such as doctor and pharmacy shopping may add little.Registration: ClinicalTrials.gov study number: NCT02668549.Keywords: doctor and pharmacy shopping, opioids, abuse, screening, prediction

We used real world data to summarize comorbidities and interventions among patients newly diagnosed with autism spectrum disorder (ASD). Data were derived from two claims-based US healthcare databases; Medicaid and Optum to construct a retrospective cohort of 36,000 patients. Attention-Deficit-Hyperactivity-Disorder (ADHD) was the most common co-morbidity (Medicaid: 50.09%; Optum: 44.16%), followed by mood disorder (Medicaid: 16.56% and Optum: 17.47%). Most patients received at least one type of treatment. Behavioral therapy was common (74.64% in Medicaid and 71.97% in Optum). More than half the cohorts received at least 1 pharmacotherapy. However, pharmacotherapies were diverse. Combination therapy and therapy switching was common. Understanding the clinical diversity and complexity of patients with ASD is an important first step in understanding unmet therapeutic needs.

Following a partial response of first-line antidepressant therapy for the treatment of major depressive disorder (MDD), there is a choice to augment treatment with another agent or switch to a different antidepressant.To report the prevalence and compare the characteristics of patients switching from their initial selective serotonin reuptake inhibitor or serotonin-norepinephrine reuptake inhibitor (SSRI/SNRI) to a new SSRI/SNRI versus those augmenting SSRI/SNRI therapy with a second-generation antipsychotic (SGA).MDD patients receiving first-line SSRI/SNRI treatment were identified from a large US-based claims database during 2000-2019. Patients augmenting therapy with an SGA were compared with those who switched to a new SSRI/SNRI. The date of the treatment change was the index date. Previously diagnosed comorbid conditions, medication use and demographics were captured. Treatment patterns following the index date were also captured. Standardized differences (StdDiff) were used to quantify dissimilarities between the two groups.There were 4572 SGA add-on and 24,409 switching patients identified. SGA augmentation patients had more severe disease (diagnosed severe recurrent major depression: 24.7% vs. 9.5%, StdDiff = 0.41) and more diagnosed psychiatric conditions, including: suicidal thoughts (10.7% vs. 3.2%, StdDiff = 0.29), post-traumatic stress disorder (6.1% vs. 2.6%, StdDiff = 0.17) and alcohol abuse (5.4% vs. 2.7%, StdDiff = 0.14). SGA augmentation patients had higher rates of prior use of anxiolytics (37.4% vs. 28.2%, StdDiff = 0.20) and anticonvulsants (26.0% vs. 13.1%, StdDiff = 0.33).Patients adding an SGA to their SSRI/SNRI therapy appeared to have more severe depression and comorbid psychiatric profile than those switching their SSRI/SNRI. These differences are important to consider and adequately control for in any future comparative outcome research between these two groups.

Introduction Peripheral inhibition of tumor necrosis factor (TNF)‐α, outside of the central nervous system, may result in clinical improvement of Alzheimer's disease (AD) outcomes. TNF‐α inhibitors (TNFIs) are effective treatments for various autoimmune conditions and may be effective for preventing and/or treating AD. The objective of this study was to compare the risk of dementia and AD in patients initiating methotrexate versus those initiating TNFIs. Methods Insurance claims data from databases of commercially insured and Medicare‐eligible patients were used to estimate the risk of dementia and AD within patients with rheumatoid arthritis (RA) initiating a TNFI versus initiation of methotrexate. A sensitivity analysis included all patients without the RA diagnosis requirement. The at‐risk period spanned from the index date until a diagnosis of the outcome, loss‐to‐follow‐up, or receipt of the comparator drug. Patients were matched 1‐to‐1 using propensity scores. A Cox proportional hazards model was used to estimate the hazard ratio (HR). Negative controls were used to calibrate the results. Results A total of 11,092 new TNFI patients and 44,023 new methotrexate patients were identified, and 8925 from each group were matched. The outcome of dementia occurred in 1.4% of patients in both groups. The calibrated results from the Cox regression found no difference between the two groups (commercially insured database: calibrated HR = 0.69, 95% confidence interval = 0.45 to 1.05; Medicare‐only database: 1.14, 0.66 to 1.96). Results were similar in all sensitivity analyses: outcome of AD and including patients without RA. Discussion No significant difference for the risk of dementia or AD was seen between patients initiating a TNFI versus methotrexate. Although this study cannot conclude whether use of TNFIs is protective against dementia and AD compared with receiving no treatment, there was no evidence that it is more protective than the active comparator methotrexate.

ObjectiveTo conduct a retrospective analysis of sequential cross-sectional data of opioid prescribing practices in patients with no prior history of opioid use.MethodsIndividuals filling an oral opioid prescription who had 1 year of prior observation were identified from four different administrative claims databases for the period between January 1, 2002, and December 31, 2018: IBM MarketScan® Commercial Database (CCAE), Multi-State Medicaid Database (MDCD), Medicare Supplemental Database (MDCR), and Optum© De-Identified Clinformatics® Data Mart Database. Outcomes included incidence of new opioid use and characteristics of patients’ first opioid prescription, including dispensed morphine milligram equivalent (MME) per day, total MME dispensed, total MME ≥300, and days’ supply of prescription for ≤3 or ≥30 days.ResultsThere were 40,600,696 new opioid users identified. The incidence of new opioid use in the past 17 years ranged from 6% to 11% within the two commercially insured databases. Incidence decreased over time in MDCD and was consistently higher in MDCR. Total MME dispensed decreased in MDCD and increased in CCAE, with no major changes in the other databases. The proportion of patients receiving ≥30-day prescriptions decreased and the proportion of patients receiving ≤3-day prescriptions increased in MDCD, while ≥30-day prescriptions in the Optum database dramatically increased (low of 3.0% in 2003 to peak of 16.9% in 2017).ConclusionsOpioid prescribing practices varied across different populations of insured individuals during the past 17 years. The most substantial changes in opioid prescriptions over time have occurred in MDCD, with reductions in use across multiple metrics.

Background Major depressive and bipolar disorders are associated with impaired quality of life and high economic burden. Although progress has been made in our understanding of the underlying pathophysiology and the development of novel pharmacological treatments, a large unmet need remains for finding effective treatment options. The purpose of this study was to identify potential new mechanisms of actions or treatment targets that could inform future research and development opportunities for major depressive and bipolar disorders. Methods A self-controlled cohort study was conducted to examine associations between 1933 medications and incidence of major depressive and bipolar disorders across four US insurance claims databases. Presence of incident depressive or bipolar disorders were captured for each patient prior to or after drug exposure and incident rate ratios were calculated. Medications that demonstrated ≥50% reduction in risk for both depressive and bipolar disorders within two or more databases were evaluated as potential treatment targets. Results Eight medications met our inclusion criteria, which fell into three treatment groups: drugs used in substance use disorders; drugs that affect the cholinergic system; and drugs used for the management of cardiovascular-related conditions. Limitations This study was not designed to confirm a causal association nor inform current clinical practice. Instead, this research and the methods employed intended to be hypothesis generating and help uncover potential treatment pathways that could warrant further investigation. Conclusions Several potential drug targets that could aid further research and discovery into novel treatments for depressive and bipolar disorders were identified.

Background: The treatment landscape for multiple sclerosis (MS) is quickly evolving. Understanding real-world treatment patterns of patients is necessary to identifying potential gaps in care.Methods: Patients with incident MS were identified from a large national claims database during 1/1/2014-6/30/2019. Patients had ≥2 diagnoses for MS or an inpatient hospitalization with a primary diagnosis of MS. Patients were required to have enrollment in the database ≥1 year prior to and ≥1 year following their first MS diagnosis. Treatment sequences were captured for all available disease modifying therapies (DMTs) during all available follow-up. Presence of comorbid conditions were captured during the one year prior to and following (and including) the index date; absolute change in prevalence from the pre- to post-index periods was calculated. Results: We identified 5,691 patients with incident MS. Common comorbidities included physical symptoms (e.g., pain, weakness, fatigue), mental health conditions (anxiety, depression), and cardiovascular/metabolic conditions (hypertension, hyperlipidemia, diabetes, obesity). Just 1,994 (35.0%) of patients received a DMT at any time during follow-up. Of those receiving a DMT, 28.2% went on to receive a second line of therapy, 5.8% received a third, and just 0.9% went on to a fourth line. Use of more than one DMT concomitantly occurred in just 1.8% of all treated patients. Glatiramer and dimethyl fumarate were by far the most common first-line treatments received accounting for nearly 62% of patients receiving a DMT. Conclusion: Approximately two-thirds of patients newly diagnosed with MS did not receive a DMT and the disease is accompanied by a significant comorbid burden.

Objective To assess changes in mortality rates in extended-release and long-acting (ER/LA) opioid analgesics after the implementation of the Risk Evaluation and Mitigation Strategy (REMS). Setting All drug poisoning deaths in three states: Florida, Oregon, and Washington. Data were obtained through state vital records offices and the Researched Abuse, Diversion and Addiction-Related Surveillance System Medical Examiner Program. Methods Using cause-of-death literal text from death certificates, individual opioid active pharmaceutical ingredients (APIs) involved in each death were identified using rules-based natural language processing. Population-adjusted and prescriptions dispensed–adjusted mortality rates were calculated for all ER/LA opioid analgesic and individual opioid APIs. Rates before and after implementation of the REMS were compared. Rate changes were compared with rates from two APIs with little or no inclusion in the REMS: benzodiazepines and hydrocodone. Results The mean ER/LA opioid analgesic population-adjusted mortality rate significantly decreased in all three states (FL: P = 0.003; OR: P = 0.003; WA: P Conclusions The population-adjusted mortality rate of ER/LA opioid analgesics has decreased in three states. Notably, the contributions to mortality rates by individual opioid analgesics were not uniform across the three states in this study. However, these changes were not generally distinct from changes in mortality rates where comparator substances were involved.

Objective Refilling an opioid prescription early is an important risk factor of prescription opioid abuse and misuse; we aimed to understand the scope of this behavior. This study was conducted to quantify the prevalence and distribution of early refills among patients prescribed opioids. Methods We conducted a retrospective cohort study utilizing dispensed prescription records. Patients filling one or more prescription opioids were identified and followed for one year. Early refills were defined as having a second prescription filled ≥15% early relative to the days’ supply of the previous prescription for the same opioid (according to the National Drug Code [NDC]). The distribution of the number of early refills and patient characteristics were assessed. Results A total of 60.6 million patients met the study criteria; 28.8% had two or more opioid prescriptions for the same opioid during follow-up. Less than 3% of all patients receiving an opioid had an early refill. Approximately 10% of those with two or more opioid prescriptions for the same drug had an early refill. For patients with multiple fills (N = 1.5 million with extended-release long-acting [ER/LA] opioids; N = 17.1 million with immediate-release short-acting [IR/SA] opioids), early refills were more common among patients with an ER/LA opioid (18.5%) compared with an IR/SA opioid (8.7%). Three-quarters of patients with an early refill had only one (70.9% and 78.4% for ER/LA and IR/SA, respectively). Conclusion Refilling an opioid prescription with the same opioid early is an infrequent behavior within all opioid users, but more common in ER/LA users. Patients who refilled early tended to do so just once.

Introduction Opioid overdose deaths in the United States have climbed sharply over the past two decades. Simultaneously, increased awareness of inadequately treated chronic pain has resulted in increased opioid analgesic prescribing. The correlation between these two phenomena has led policymakers to posit that they are causally linked, and to implement policy changes supporting safe opioid prescribing. Purpose To evaluate the impact of its Opioid Analgesic Risk Evaluation and Mitigation Strategy (REMS) program, the US Food and Drug Administration (FDA) requested the opioid manufacturers responsible for implementing that program provide information regarding opioid policy changes from 2016 to 2018. FDA also requested a survey of state requirements for pain and opioid prescribing continuing education (CE), the number of prescribers affected by those requirements, the extent to which a REMS-compliant CE program would meet each state’s requirements, and the number of relevant CE programs available. Results Results indicate that 527 federal and state opioid-related policies (statutes, rules/regulations, and guidelines) were approved during the 2016–2018 study period. While the largest number of these policies focused on prescription drug monitoring programs, 170 specifically imposed limits on opioid prescribing and an additional 35 specifically referred to, or incorporated, the Centers for Disease Control and Prevention opioid prescribing guideline. We also found that 46 states and the District of Columbia mandated some amount of pain or opioid prescribing CE for prescribers renewing their licenses. These mandates potentially affected as many as 1.7 million prescribers. In 69% of cases, a REMS-compliant CE program would fully meet the state mandates for various types of prescribers. Conclusion The severity and complexity of the problems of pain management and opioid overdose have led to large-scale intervention by policymakers. Assessing the impact of these changes is difficult, at best, but will be necessary if interventions are to be refined to increase their effectiveness.

Background Treatment for depressive disorders often requires subsequent interventions. Patients who do not respond to antidepressants have treatment-resistant depression (TRD). Predicting who will develop TRD may help healthcare providers make more effective treatment decisions. We sought to identify factors that predict TRD in a real-world setting using claims databases. Methods A retrospective cohort study was conducted in a US claims database of adult subjects with newly diagnosed and treated depression with no mania, dementia, and psychosis. The index date was the date of antidepressant dispensing. The outcome was TRD, defined as having at least three distinct antidepressants or one antidepressant and one antipsychotic within 1 year after the index date. Predictors were age, gender, medical conditions, medications, and procedures 1 year before the index date. Results Of 230,801 included patients, 10.4% developed TRD within 1 year. TRD patients at baseline were younger; 10.87% were between 18 and 19 years old versus 7.64% in the no-TRD group, risk ratio (RR) = 1.42 (95% confidence interval [CI] 1.37-1.48). TRD patients were more likely to have an anxiety disorder at baseline than non-TRD patients, RR = 1.38 (95% CI 1.35-1.14). At 3.68, fatigue had the highest RR (95% CI 3.18-4.25). TRD patients had substance use disorders, psychiatric conditions, insomnia, and pain more often at baseline than non-TRD patients. Conclusion Ten percent of subjects newly diagnosed and treated for depression developed TRD within a year. They were younger and suffered more frequently from fatigue, substance use disorders, anxiety, psychiatric conditions, insomnia, and pain than non-TRD patients.

Introduction Epilepsy is a neurological disease characterized by recurrent, unprovoked seizures and its impact on biological, cognitive, psychological, and social outcomes. An unmet need for finding effective treatment options exists. Identifying medical diagnoses present prior to a diagnosis of epilepsy is an important step in increasing our understanding of how people with epilepsy may respond to therapy, help guide clinicians in managing associated comorbid conditions, and inform future research. Methods A population-based retrospective comparative cohort study was conducted using administrative claims data to explore differences in medical diagnoses prior to an initial diagnosis of epilepsy between patients with and without drug-resistant epilepsy (DRE) identified within one-year post diagnosis by evaluating standardized mean differences between the groups. Results A total of 205,183 patients with newly diagnosed epilepsy were identified. Of those, 4.1% (n = 8340) were considered drug resistant one-year post diagnosis. Pain and mood disorders were the common physical and psychiatric diagnoses in both cohorts. Differences between the newly diagnosed epilepsy and DRE cohorts were observed. Patients in the DRE cohort were younger, had more encounters with the healthcare system, and higher burden of disease for both physical (e.g., headache, neuropathy, muscular-skeletal disorders, and traumatic brain injury) and psychiatric diagnoses (e.g., depression, anxiety, bipolar disorder, suicidal thoughts, drug dependency, and sleep disorders). Conclusion Physical and psychiatric diagnoses are common one year prior to first diagnosis of epilepsy in administrative claims data. Compared to patients without DRE, those who develop DRE within one-year post initial diagnosis demonstrated a higher burden of disease.

Background Depression that does not respond to antidepressants is treatment-resistant depression (TRD). TRD definitions include assessments of treatment response, dose and duration, and implementing these definitions in claims databases can be challenging. We built a data-driven TRD definition and evaluated its performance. Methods We included adults with depression, ≥1 antidepressant, and no diagnosis of mania, dementia, or psychosis. Subjects were stratified into those with and without proxy for TRD. Proxies for TRD were electroconvulsive therapy, deep brain, or vagus nerve stimulation. The index date for subjects with proxy for TRD was the procedure date, and for subjects without, the date of a randomly selected visit. We used three databases. We fit decision tree predictive models. We included number of distinct antidepressants, with and without adequate doses and duration, number of antipsychotics and psychotherapies, and expert-based definitions, 3, 6, and 12 months before index date. To assess performance, we calculated area under the curve (AUC) and transportability. Results We analyzed 33,336 subjects with no proxy for TRD, and 3,566 with the proxy. Number of antidepressants and antipsychotics were selected in all periods. The best model was at 12 months with an AUC = 0.81. The rule transported well and states that a subject with ≥1 antipsychotic or ≥3 antidepressants in the last year has TRD. Applying this rule, 15.8% of subjects treated for depression had TRD. Conclusion The definition that best discriminates between subjects with and without TRD considers number of distinct antidepressants (≥3) or antipsychotics (≥1) in the last year.

Aim Assess the association of flossing with periodontitis. Materials and Methods This was a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) years 2011-2014. We used three categories of flossing: 0–1, 2–4 and ≥5 days in the past week and the CDC definition of periodontitis. We calculated odds ratios controlling for age, gender, smoking, drinking, income and dentist visits. Results A total of 6939 adult subjects were included, 35% flossed ≤1 time a week, and 40% had periodontitis. After adjustment, the odds of periodontitis were 17% lower for subjects who flossed >1 time a week than for subjects who flossed less often (odds ratio=0.83, 95% CI 0.72–0.97). A dose response was not observed. Men were twice as likely as women to have periodontitis. Younger subjects, non-smokers and subjects with the highest incomes had lower odds of having periodontitis. Conclusions Flossing was associated with a modestly lower prevalence of periodontitis. Older age, being male, smoking, low income and less frequent dental visits were associated with a higher prevalence of periodontitis. Flossing 2–4 days a week could be as beneficial as flossing more frequently. This is a cross-sectional study so a causal relation between flossing and periodontitis cannot be established.

Reports suggest low levels of cholesterol are associated with depression. However, results have not been replicated, the direction of the associations among types of cholesterol levels is not consistent, there is large study heterogeneity, and many studies have small samples.The objective of the study was to assess the association of cholesterol with depression.This is a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES). The NHANES is a research program that collects health information from a representative U.S.We included subjects aged ≥18 years who responded to NHANES surveys from 2009 to 2015. Subjects were classified as having major depression if the Patient Health Questionnaire scores were ≥10. Exposures were total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride levels. We considered the age, body mass index, gender, smoking, alcohol use, health status, and exposure to statins and antipsychotics as potential confounders. To assess the association of the exposures with depression, we used decision tree and logistic regression models.A total of 19,527 subjects were analyzed, and 8% had depression. Subjects with depression were more likely to be women and smokers, and to have higher body mass index, poor health, higher levels of total cholesterol and triglycerides and lower levels of high-density lipoprotein cholesterol than subjects with no depression. After adjustment, low levels of total cholesterol (129 mg/dL) were associated with decreased risk of depression compared with higher levels, OR = 0.64 and 95% CI (0.42-0.98).This large population-based study found no association of low cholesterol or any other lower type of cholesterol levels with increased risk of depression. These findings are generalizable to the U.S.

Healthcare professionals increasingly rely on observational healthcare data, such as administrative claims and electronic health records, to estimate the causal effects of interventions. However, limited prior studies raise concerns about the real-world performance of the statistical and epidemiological methods that are used. We present the “OHDSI Methods Benchmark” that aims to evaluate the performance of effect estimation methods on real data. The benchmark comprises a gold standard, a set of metrics, and a set of open source software tools. The gold standard is a collection of real negative controls (drug-outcome pairs where no causal effect appears to exist) and synthetic positive controls (drug-outcome pairs that augment negative controls with simulated causal effects). We apply the benchmark using four large healthcare databases to evaluate methods commonly used in practice: the new-user cohort, self-controlled cohort, case-control, case-crossover, and self-controlled case series designs. The results confirm the concerns about these methods, showing that for most methods the operating characteristics deviate considerably from nominal levels. For example, in most contexts, only half of the 95% confidence intervals we calculated contain the corresponding true effect size. We previously developed an “empirical calibration” procedure to restore these characteristics and we also evaluate this procedure. While no one method dominates, self-controlled methods such as the empirically calibrated self-controlled case series perform well across a wide range of scenarios.

This study sought to: 1. determine the frequency of rehospitalization with diagnosis of suicidal ideation or suicide attempt (SI/SA) within a year and how often patients had multiple rehospitalizations; 2. identify the time period for which the risk of rehospitalization is highest; and 3. determine the characteristics of patients with multiple rehospitalizations. We conducted a retrospective cohort study of adults with depression using 4 US health claims databases. We defined hospitalization as an inpatient or emergency room visit with codes indicating a suicide attempt or suicidal thoughts using a validated algorithm. Rates of rehospitalization with SI or SA were analyzed together and separately, including multiple re-hospitalizations with SI/SA. Across all databases 121,065 patients were hospitalized with a diagnosis of SI/SA. Rates of rehospitalization within a year ranged from 7.96% to 11.24%. The risk of rehospitalization with SI/SA is highest during the first month. Nearly 50% of rehospitalizations occurred within 3 months after initial hospitalization. Patients with rehospitalization(s) had more anxiety disorders, sleep disorders and substance use disorders than patients without. Among patients with depression hospitalized for SI/SA, rehospitalization for SI/SA within a year is not uncommon. Risk of rehospitalization with a diagnosis of SI/SA is highest during the first month.

Additional file 1. APPENDIX Fig. 1. Study design illustration for drug eras, switching and combination therapy classification. A) Drug eras are illustrated assuming a 30-day supply for each medication fill and allowing for a 30-day gap between the end of supply and the next fill. The drug era ends if another fill is not received within this gap. (B) If drug eras of two classes overlap at least 30 days (Drug Class B and Drug Class C) then it is classified as combination therapy, otherwise it is a switch between two classes (Drug Class A to Drug Class B).

Objective Some patients who are given opioids for pain could develop opioid use disorder. If it was possible to identify patients who are at a higher risk of opioid use disorder, then clinicians could spend more time educating these patients about the risks. We develop and validate a model to predict a person's future risk of opioid use disorder at the point before being dispensed their first opioid. Methods A cohort study patient-level prediction using four US claims databases with target populations ranging between 343,552 and 384,424 patients. The outcome was recorded diagnosis of opioid abuse, dependency or unspecified drug abuse as a proxy for opioid use disorder from 1 day until 365 days after the first opioid is dispensed. We trained a regularized logistic regression using candidate predictors consisting of demographics and any conditions, drugs, procedures or visits prior to the first opioid. We then selected the top predictors and created a simple 8 variable score model. Results We estimated the percentage of new users of opioids with reported opioid use disorder within a year to range between 0.04%-0.26% across US claims data. We developed an 8 variable Calculator of Risk for Opioid Use Disorder (CROUD) score, derived from the prediction models to stratify patients into higher and lower risk groups. The 8 baseline variables were age 15-29, medical history of substance abuse, mood disorder, anxiety disorder, low back pain, renal impairment, painful neuropathy and recent ER visit. 1.8% of people were in the high risk group for opioid use disorder and had a score > = 23 with the model obtaining a sensitivity of 13%, specificity of 98% and PPV of 1.14% for predicting opioid use disorder. Conclusions CROUD could be used by clinicians to obtain personalized risk scores. CROUD could be used to further educate those at higher risk and to personalize new opioid dispensing guidelines such as urine testing. Due to the high false positive rate, it should not be used for contraindication or to restrict utilization.

Background: Among patients with chronic pain using long-term opioid therapy, the incidence of opioid abuse, addiction, overdose, and associated death are not well quantified. The range of estimates for these adverse outcomes varies drastically and may depend on how they are measured (i.e. study definitions of outcomes) and on patient characteristics and opioid-use factors (e.g. regimen, daily dose).Methods: Based on a review of the literature, the US Food and Drug Administration (FDA) required companies that manufacture and sell extended-release/long-acting (ER/LA) opioids conduct as a postmarketing requirement (PMR) a series of observational studies to estimate the rates of treatment-emergent misuse, abuse, addiction, overdose, and death using validated measures. The companies formed a consortium, the Opioid PMR Consortium (OPC), to conduct the studies.Results: The FDA initially requested four observational studies (a cohort study, a questionnaire validation study, a code validation study, and a doctor-shopping validation study), but in order to achieve the FDA's goals of the 4 studies, OPC and FDA agreed to 10 observational studies (a prospective cohort study, a retrospective database cohort study, three questionnaire validation studies, two code validation studies, and three doctor-shopping validation studies). The studies are continuing through 2020.Conclusions: A series of 10 observational studies was or are being conducted in response to the FDA's postmarketing requirement. All studies have been feasible to conduct, although a validated algorithm for measuring abuse and addiction in databases was not successful.

Background/Rationale Little is known about the reasons for visiting multiple doctors/pharmacies, known as doctor/pharmacy shopping, to obtain opioids. Objective To investigate patients’ self-reported reasons for doctor/pharmacy shopping and assess whether doctor/pharmacy shopping behavior can be used as a surrogate measure of opioid abuse/misuse. Methods We conducted a cross-sectional web-based survey among adult patients with ≥2 pharmacy claims for immediate-release or extended-release/long-acting opioids between 7/1/2015 and 12/31/2016, identified from a large United States (US) commercial claims database. Patients were classified into no, mild, moderate, or severe shopping categories based on their claims. Reasons for doctor/pharmacy shopping and opioid abuse/misuse were determined from patient responses to the Prescription Opioid Misuse and Abuse Questionnaire. Results A random sample of 10,081 patients was invited to participate in the survey and 1085 (11%) completed surveys. The most frequently reported reasons for doctor/pharmacy shopping were convenience, availability, price, and multiple morbidities requiring pain management. Among patients in the no, minimal, moderate, and severe shopping categories, only 7.8%, 8.5%, 11.8% and 12.6% reported opioid abuse/misuse, respectively. Conclusion In this commercially-insured population, patient-reported reasons for doctor/pharmacy shopping do not suggest opioid abuse/misuse. Less than 15% of patients with shopping behavior in the past 3 months reported any reasons attributable to opioid abuse/misuse, indicating that shopping behavior in this population may not be a good surrogate for abuse/misuse.

Background Parkinson’s disease is a disorder growing in prevalence, disability, and deaths. Healthcare databases provide a ‘real-world’ perspective for millions of individuals. We envisioned helping accelerate drug discovery by using these databases. Objectives The objectives of this study were to assess the association of marketed medications with the risk of parkinsonism in four US claims databases and to evaluate the consistency of the association of β-adrenoreceptor modulation with parkinsonism. Methods The study was conducted using a self-controlled cohort design in which subjects served as their own control. The time from treatment initiation until discontinuation or end of observation was the exposed period and a similar time preceding medication was the unexposed period. Medications were studied at ingredient and class level. The incidence rate ratio (IRR) and combined IRR were calculated. Results We assessed 2181 drugs and 117,015,066 people. Diphenhydramine, isradipine, methylphenidate, armodafinil, and modafinil were associated with reduced risk for parkinsonism in at least two databases. Armodafinil, modafinil, methylphenidate, and the β-agonist albuterol were associated with a 56%, 54%, 39%, and 17% reduction in the risk of having parkinsonism, respectively. Isradipine results were heterogeneous and no significant association was found. Propranolol was associated with a 32% increased risk, the only β-adrenoceptor antagonist (β-blocker) associated with an increased risk. Conclusions Armodafinil, modafinil, and methylphenidate were associated with a decreased risk of parkinsonism, as were β-agonists. Of the β-blockers, only propranolol was associated with increased risk. Healthcare database analyses that incorporate scientific rigor provide insight and direction for drug discovery efforts. These findings show association not causality; however, they offer considerable support to the association between β-adrenergic receptor modulation and risk of Parkinson’s disease. Electronic supplementary material The online version of this article (10.1007/s40261-019-00830-4) contains supplementary material, which is available to authorized users.

Objective. Opioid abuse is a serious public health concern. In response, the Food and Drug Administration (FDA) determined that a risk evaluation and mitigation strategy (REMS) for extended-release and long-acting (ER/LA) opioids was necessary to ensure that the benefits of these analgesics continue to outweigh the risks. Key components of the REMS are training for prescribers through accredited continuing education (CE), and providing patient educational materials. Methods. The impact of this REMS has been assessed using diverse metrics including evaluation of prescriber and patient understanding of the risks associated with opioids; patient receipt and comprehension of the medication guide and patient counseling document; patient satisfaction with access to opioids; drug utilization and changes in prescribing patterns; and surveillance of ER/LA opioid misuse, abuse, overdose, addiction, and death. Results and Conclusions. The results of these assessments indicate that the increasing rates of opioid abuse, addiction, overdose, and death observed prior to implementation of the REMS have since leveled off or started to decline. However, these benefits cannot be attributed solely to the ER/LA opioid analgesics REMS since many other initiatives to prevent abuse occurred contemporaneously. These improvements occurred while preserving patient access to opioids as a large majority of patients surveyed expressed satisfaction with their access to opioids.

BACKGROUND Identifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population. OBJECTIVE This study aimed to identify medical conditions that are highly predictive of poor health status using claims databases. METHODS To determine the medical conditions most highly predictive of poor health status, we used a retrospective cohort study using 2 US claims databases. Subjects were commercially insured patients. Health status was measured using a self-report health status response. All medical conditions were included in a least absolute shrinkage and selection operator regression model to assess which conditions were associated with poor versus excellent health. RESULTS A total of 1,186,871 subjects were included; 61.64% (731,587/1,186,871) reported having excellent or very good health. The leading medical conditions associated with poor health were cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), sleep disorders, seizures, male reproductive tract infections, chronic obstructive pulmonary disease, cardiomyopathy, dementia, and headaches. CONCLUSIONS Understanding the impact of disease in a commercially insured population is critical to identify subjects who may be at risk for reduced productivity and job loss. Claims database studies can measure the impact of medical conditions on the health status in a population and to assess changes overtime and could limit the need to collect prospective collection of information, which is slow and expensive, to assess disease burden. Leading medical conditions associated with poor health in a commercially insured population were the ones associated with high burden of disease such as cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), infections, chronic obstructive pulmonary disease, cardiomyopathy, and dementia. However, sleep disorders, seizures, male reproductive tract infections, and headaches were also part of the leading medical conditions associated with poor health that had not been identified before as being associated with poor health and deserve more attention.

Background: Doctor shopping, defined by filling overlapping prescriptions from more than one prescriber at more than two pharmacies, is a way to obtain scheduled medications for diversion or abuse. Little is known about how far attention deficit hyperactivity disorder (ADHD) medication shoppers travel, how often they cross state lines to fill their ADHD prescriptions and how often they pay for their medication in cash, i.e. entirely out of pocket. Objective: We sought to describe the pattern of doctor shopping for ADHD medications: how far shoppers travel, how often they cross state lines to fill their prescriptions, and how often they pay in cash. Methods: Retrospective cohort study using LRx, a large US retail prescription database. We included subjects with any ADHD medication dispensed between 2011 and 2012. Subjects were followed for 18 months. Results: Of a total of 4 402 464 subjects exposed to ADHD medications, 0.4% developed shopping behavior. Women were more likely to become shoppers. Shoppers travelled a median of 91.9 miles and non-shoppers 0.2 miles to fill their ADHD prescriptions. Almost 28% of the shoppers filled prescriptions in >1 state compared with 4.3% of non-shoppers. Of the shoppers, 27.3% paid at least one prescription in cash compared to 14.4% of the non-shoppers. Conclusions: Shoppers travelled larger distances, visited more states and paid in cash for ADHD medications more often than non-shoppers. Data sharing among prescriptions monitoring programs can improve their effectiveness and drug utilization studies should take account of cash purchases.

Background Observational studies of switching from branded to generic formulations of the same drug substance often lack appropriate comparators for the subjects who switched. Three generic formulations were deemed equivalent to Concerta: an authorized generic (AG) identical except for external packaging, and two other generics (EG). Objective Compare the incidence of a combined endpoint (switching back to Concerta, changing the use of immediate release methylphenidate (MPH), stopping all long-acting methylphenidate, or starting a new medication) among people switched from Concerta to the AG versus the EG. Methods Cohort study from the Truven CCAE database of people aged 6 to 65 diagnosed with ADHD, treated with Concerta, and switched to the EG or to the AG formulation. Results In the EG arm 24.6% and in the AG arm 19.7% of subjects switched back to Concerta. The proportion of subjects meeting the combined endpoint was 39.5% in the EG arm, 32.9% in the AG arm, a crude risk ratio of 1.20 (95% CI 0.94, 1.54). After adjustment by propensity score stratification, the adjusted odds ratio (OR) was 1.23 (95% CI 0.90, 1.70). In an unplanned analysis using a different method of adjustment, the adjusted OR was 1.00 (95% CI 0.69, 1.44). Discussion This study did not detect a difference between the proportion of people who met the study endpoint in the two study arms, i.e. between those who switched to a generic formulation that was identical to Concerta except for external packaging and those who switched to the comparison generics. The high incidence of the combined endpoint in the AG arm demonstrates the need for an appropriate comparator in studies of this type. Trial registration ClinicalTrials.gov NCT02730572

Objective Accurate representation of study findings is crucial to preserve public trust. The language used to describe results could affect perceptions of the efficacy or safety of interventions. We sought to compare the adjectives used in clinical trial reports of industry-authored and non-industry-authored research. Methods We included studies in PubMed that were randomized trials and had an abstract. Studies were classified as “non-industry-authored” when all authors had academic or governmental affiliations, or as “industry-authored” when any of the authors had industry affiliations. Abstracts were analyzed using a part-of-speech tagger to identify adjectives. To reduce the risk of false positives, the analysis was restricted to adjectives considered relevant to “coloring” (influencing interpretation) of trial results. Differences between groups were determined using exact tests, stratifying by journal. Results A total of 306,007 publications met the inclusion criteria. We were able to classify 16,789 abstracts; 9,085 were industry-authored research, and 7,704 were non-industry-authored research. We found a differential use of adjectives between industry-authored and non-industry-authored reports. Adjectives such as “well tolerated” and “meaningful” were more commonly used in the title or conclusion of the abstract by industry authors, while adjectives such as “feasible” were more commonly used by non-industry authors. Conclusions There are differences in the adjectives used when study findings are described in industry-authored reports compared with non-industry-authored reports. Authors should avoid overusing adjectives that could be inaccurate or result in misperceptions. Editors and peer reviewers should be attentive to the use of adjectives and assess whether the usage is context appropriate.

Background Identifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population. Objective This study aimed to identify medical conditions that are highly predictive of poor health status using claims databases. Methods To determine the medical conditions most highly predictive of poor health status, we used a retrospective cohort study using 2 US claims databases. Subjects were commercially insured patients. Health status was measured using a self-report health status response. All medical conditions were included in a least absolute shrinkage and selection operator regression model to assess which conditions were associated with poor versus excellent health. Results A total of 1,186,871 subjects were included; 61.64% (731,587/1,186,871) reported having excellent or very good health. The leading medical conditions associated with poor health were cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), sleep disorders, seizures, male reproductive tract infections, chronic obstructive pulmonary disease, cardiomyopathy, dementia, and headaches. Conclusions Understanding the impact of disease in a commercially insured population is critical to identify subjects who may be at risk for reduced productivity and job loss. Claims database studies can measure the impact of medical conditions on the health status in a population and to assess changes overtime and could limit the need to collect prospective collection of information, which is slow and expensive, to assess disease burden. Leading medical conditions associated with poor health in a commercially insured population were the ones associated with high burden of disease such as cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), infections, chronic obstructive pulmonary disease, cardiomyopathy, and dementia. However, sleep disorders, seizures, male reproductive tract infections, and headaches were also part of the leading medical conditions associated with poor health that had not been identified before as being associated with poor health and deserve more attention.