OBJECTIVE: To determine the comparative safety of mood stabilizers with respect to risk of preeclampsia, placental abruption, growth restriction, and preterm birth.
METHODS: A cohort study was carried out using Medicaid Analytic eXtract data for pregnant women linked to live born infants enrolled from 2000 to 2010. Exposure to lamotrigine, valproate, topiramate, carbamazepine, oxcarbazepine, and lithium during the first 20 weeks of pregnancy was assessed. The reference group did not fill a prescription for an anticonvulsant or lithium during the 3 months prior to conception or the first half of pregnancy. Women who continued mood stabilizer monotherapy after 20 weeks were also compared to those who discontinued. Risk ratios (RRs) and 95% CIs were estimated with propensity score stratification to control for confounding.
RESULTS: Among 1,472,672 pregnancies, 10,575 (0.7%) were exposed to anticonvulsant mood stabilizer or lithium monotherapy and 917 (0.06%) were exposed to polytherapy. In unadjusted analyses, exposure to each specific mood stabilizer and polytherapy was associated with increased risks of all adverse outcomes considered compared to no exposure (RR ranged from 1.15 to 1.56). However, these RR estimates were not meaningfully elevated with adjustment for confounding (0.89 to 1.16). Continuation of mood stabilizers was not associated with an increased risk for any outcomes compared to discontinuation and was associated with a reduced risk of placental abruption and growth restriction.
CONCLUSIONS: Anticonvulsant mood stabilizers and lithium are not associated with an increased risk of placenta-mediated complications or preterm birth after accounting for confounding by indication.
Importance: Prescription opioid use is common among patients with moderate to severe knee osteoarthritis before undergoing total knee replacement (TKR). Preoperative opioid use may be associated with worse clinical and safety outcomes after TKR.
Objective: To determine the association of preoperative opioid use among patients 65 years and older with mortality and other complications at 30 days post-TKR.
Design, Setting, And Participants: This cohort study used claims data from January 1, 2010, to December 31, 2014, from a random sample of US Medicare enrollees 65 years and older who underwent TKR. Based on opioid dispensing in 360 days prior to TKR, patients were classified as continuous (≥1 opioid dispensing in each of the past 12 months) or intermittent (any dispensing of opioids in the past 12 months but not continuous use) opioid users or as opioid-naive patients (no opioids dispensed in the past 12 months). Data analyses were conducted from October 3, 2017, to November 8, 2018.
Main Outcomes and Measures: Primary outcomes included in-hospital mortality and 30-day post-TKR mortality, hospital readmission, and revision operation. Secondary safety outcomes at 30 days post-TKR included opioid overdose and vertebral and nonvertebral fracture. Multivariable Cox proportional hazards models estimated hazard ratios (HRs) and 95% CIs.
Results: Of 316 593 patients (mean [SD] age, 73.9 [5.8] years; 214 677 [67.8%] women) who underwent TKR, 22 895 (7.2%) were continuous opioid users, 161 511 (51.0%) were intermittent opioid users, and 132 187 (41.7%) were opioid naive. In-hospital mortality occurred in 276 patients (0.09%). At 30 days post-TKR, 828 patients (0.26%) died, 16 786 patients (5.30%) had hospital readmission, and 921 patients (0.29%) had a revision operation. All primary and secondary outcomes occurred more frequently among continuous opioid users compared with opioid-naive patients. Compared with opioid-naive patients and after adjusting for demographic characteristics, combined comorbidity score, number of different prescription medications, and frailty, continuous opioid users had greater risk of revision operations (HR, 1.63; 95% CI, 1.15-2.32), vertebral fractures (HR, 2.37; 95% CI, 1.37-4.09), and opioid overdose (HR, 4.82; 95% CI, 1.36-17.07) at 30 days post-TKR. However, after adjusting covariates, there were no statistically significant differences in in-hospital (HR, 1.18; 95% CI, 0.73-1.90) or 30-day (HR, 1.05; 95% CI, 0.73-1.51) mortality between continuous opioid users and opioid-naive patients.
Conclusions and Relevance: After adjusting for baseline risk profiles, including comorbidities and frailty, continuous opioid users had a higher risk of revision operations, vertebral fractures, and opioid overdose at 30 days post-TKR but not of in-hospital or 30-day mortality, compared with opioid-naive patients. These results highlight the need for better understanding of patient characteristics associated with chronic opioid use to optimize preoperative assessment of overall risk after TKR.
OBJECTIVE: Long-term opioid prescribing has increased amid concerns over effectiveness and safety of its use. We examined long-term prescription opioid use among patients with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA) and ankylosing spondylitis (AS), compared with patients with hypertension (HTN).
METHODS: We used Truven MarketScan, a US commercial claims database (2003-2014) and identified RA, SLE, PsA and AS cohorts, each matched by age and sex to patients with HTN. We compared long-term opioid prescription use during 1 year of follow-up and used multivariable Poisson regression model to estimate the relative risk (RR) of receiving opioid prescriptions based on underlying disease cohort.
RESULTS: We identified 181 710 RA (mean age 55.3±13.1, 77% female), 45 834 SLE (47.1±13.1, 91% female), 30 307 PsA (49.7±11.5, 51% female), 7686 AS (44.6±12.0, 39% female) and parallel numbers of age-matched and sex-matched patients with HTN. The proportion of patients receiving long-term opioid prescriptions, and other measures of opioid prescriptions were higher among rheumatic disease cohorts and highest in patients with AS. AS was associated with the highest RR of receiving long-term opioid prescriptions (RR 2.73, 95% CI 2.60 to 2.87) versus HTN, while RRs were 2.21 (2.16 to 2.25) for RA, 1.94 (1.87 to 2.00) for PsA and 1.82 (1.77 to 1.88) for SLE.
CONCLUSIONS: Patients with rheumatic disease have higher rates of long-term opioid prescriptions, and patients with AS have the highest risk of receiving opioid prescriptions versus patients with HTN. Further studies investigating the effectiveness of disease-targeted treatments on decreasing opioid use in these four rheumatic diseases may provide strategies for reducing prescription opioids.
Importance: Accumulating evidence indicates that there is an increased risk of cardiovascular disease among patients with psoriatic disease. Although an emerging concern that the risk of atrial fibrillation (AF) may also be higher in this patient population adds to the growing support of initiating early interventions to control systemic inflammation, evidence on the comparative cardiovascular safety of current biologic treatments remains limited.
Objective: To evaluate the risk of AF and major adverse cardiovascular events (MACE) associated with use of ustekinumab vs tumor necrosis factor inhibitors (TNFi) in patients with psoriasis or psoriatic arthritis.
Design, Setting, and Participants: This cohort study included data from a nationwide sample of 78 162 commercially insured patients in 2 US commercial insurance databases (Optum and MarketScan) from September 25, 2009, through September 30, 2015. Patients were included if they were 18 years or older, had psoriasis or psoriatic arthritis, and initiated ustekinumab or a TNFi therapy. Exclusion criteria included history of AF or receipt of antiarrhythmic or anticoagulant therapy during the baseline period.
Exposures: Initiation of ustekinumab vs TNFi therapy.
Main Outcomes and Measures: Incident AF and MACE, including myocardial infarction, stroke, or coronary revascularization.
Results: A total of 60 028 patients with psoriasis or psoriatic arthritis (9071 ustekinumab initiators and 50 957 TNFi initiators) were included in the analyses. The mean (SD) age was 46 (13) years in Optum and 47 (13) in MarketScan, and 29 495 (49.1%) were male. Overall crude incidence rates (reported per 1000 person-years) for AF were 5.0 (95% CI, 3.8-6.5) for ustekinumab initiators and 4.7 (95% CI, 4.2-5.2) for TNFi initiators, and for MACE were 6.2 (95% CI, 4.9-7.8) for ustekinumab initiators and 6.1 (95% CI, 5.5-6.7) for TNFi initiators. The combined adjusted hazard ratio for incident AF among ustekinumab initiators was 1.08 (95% CI, 0.76-1.54) and for MACE among ustekinumab initiators was 1.10 (95% CI, 0.80-1.52) compared with TNFi initiators.
Conclusions and Relevance: No substantially different risk of incident AF or MACE after initiation of ustekinumab vs TNFi was observed in this study. This information may be helpful when weighing the risks and benefits of various systemic treatment strategies for psoriatic disease.
Importance: Guidelines restricting use of calcium-based phosphate binders in all patients with end-stage renal disease owing to their potential contribution to increased cardiovascular risk shifted prescribing from calcium acetate toward the costlier sevelamer carbonate products.
Objective: To compare cardiovascular events and mortality between patients with end-stage renal disease (ESRD) undergoing hemodialysis receiving sevelamer vs calcium acetate in real-world practice.
Design, Setting, and Participants: An observational cohort study was conducted using the United States Renal Data System linked to Medicare claims data (May 1, 2012, to December 31, 2013). Data analysis was performed from October 2017 to September 2018. Participants included patients 65 years or older with ESRD within 180 days after starting hemodialysis (sevelamer, 2647; calcium acetate, 2074).
Exposures: New use of sevelamer (calcium-free phosphate binder) vs calcium acetate (calcium-based phosphate binder).
Main Outcomes and Measures: Hazard ratios (HRs) with 95% CIs were estimated for fatal or nonfatal cardiovascular events (myocardial infarction or ischemic stroke: primary outcome) and all-cause mortality (secondary outcome) using Cox proportional hazards regression with fine stratification on the propensity score to control for potential confounders, including phosphorus and calcium levels.
Results: After propensity score weighting, 2639 patients initiating sevelamer treatment (1184 men [44.9%]; mean [SD] age, 75.6 [6.9] years) and 2065 patients initiating calcium acetate treatment (930 men [45.0%]; mean [SD] age, 75.5 [7.1] years) were included in the analysis. Crude incidence rates (IRs) for cardiovascular events of 458 per 1000 person-years for sevelamer and 464 per 1000 person-years for calcium acetate were observed. After propensity score fine-stratification weighting, HRs of 0.96 (95% CI, 0.84-1.10) for cardiovascular events were observed. Results were consistent within subgroups of age (<75 y: primary outcome, HR, 1.02; 95% CI, 0.85-1.24; vs ≥75 years: primary outcome, HR, 0.83; 95% CI, 0.69-1.01) and sex (primary outcome in men: HR, 1.02; 95% CI, 0.83-1.26).
Conclusions and Relevance: The results of the study do not suggest increased cardiovascular safety of sevelamer in the routine clinical practice of patients with ESRD compared with calcium acetate; this study's findings suggest that well-designed, long-term, randomized clinical trials are needed.
BACKGROUND: To the extent that outcomes are mediated through negative perceptions of generics (the nocebo effect), observational studies comparing brand-name and generic drugs are susceptible to bias favoring the brand-name drugs. We used authorized generic (AG) products, which are identical in composition and appearance to brand-name products but are marketed as generics, as a control group to address this bias in an evaluation aiming to compare the effectiveness of generic versus brand medications.
METHODS AND FINDINGS: For commercial health insurance enrollees from the US, administrative claims data were derived from 2 databases: (1) Optum Clinformatics Data Mart (years: 2004-2013) and (2) Truven MarketScan (years: 2003-2015). For a total of 8 drug products, the following groups were compared using a cohort study design: (1) patients switching from brand-name products to AGs versus generics, and patients initiating treatment with AGs versus generics, where AG use proxied brand-name use, addressing negative perception bias, and (2) patients initiating generic versus brand-name products (bias-prone direct comparison) and patients initiating AG versus brand-name products (negative control). Using Cox proportional hazards regression after 1:1 propensity-score matching, we compared a composite cardiovascular endpoint (for amlodipine, amlodipine-benazepril, and quinapril), non-vertebral fracture (for alendronate and calcitonin), psychiatric hospitalization rate (for sertraline and escitalopram), and insulin initiation (for glipizide) between the groups. Inverse variance meta-analytic methods were used to pool adjusted hazard ratios (HRs) for each comparison between the 2 databases. Across 8 products, 2,264,774 matched pairs of patients were included in the comparisons of AGs versus generics. A majority (12 out of 16) of the clinical endpoint estimates showed similar outcomes between AGs and generics. Among the other 4 estimates that did have significantly different outcomes, 3 suggested improved outcomes with generics and 1 favored AGs (patients switching from amlodipine brand-name: HR [95% CI] 0.92 [0.88-0.97]). The comparison between generic and brand-name initiators involved 1,313,161 matched pairs, and no differences in outcomes were noted for alendronate, calcitonin, glipizide, or quinapril. We observed a lower risk of the composite cardiovascular endpoint with generics versus brand-name products for amlodipine and amlodipine-benazepril (HR [95% CI]: 0.91 [0.84-0.99] and 0.84 [0.76-0.94], respectively). For escitalopram and sertraline, we observed higher rates of psychiatric hospitalizations with generics (HR [95% CI]: 1.05 [1.01-1.10] and 1.07 [1.01-1.14], respectively). The negative control comparisons also indicated potentially higher rates of similar magnitude with AG compared to brand-name initiation for escitalopram and sertraline (HR [95% CI]: 1.06 [0.98-1.13] and 1.11 [1.05-1.18], respectively), suggesting that the differences observed between brand and generic users in these outcomes are likely explained by either residual confounding or generic perception bias. Limitations of this study include potential residual confounding due to the unavailability of certain clinical parameters in administrative claims data and the inability to evaluate surrogate outcomes, such as immediate changes in blood pressure, upon switching from brand products to generics.
CONCLUSIONS: In this study, we observed that use of generics was associated with comparable clinical outcomes to use of brand-name products. These results could help in promoting educational interventions aimed at increasing patient and provider confidence in the ability of generic medicines to manage chronic diseases.
PURPOSE: Bootstrapping can account for uncertainty in propensity score (PS) estimation and matching processes in 1:1 PS-matched cohort studies. While theory suggests that the classical bootstrap can fail to produce proper coverage, practical impact of this theoretical limitation in settings typical to pharmacoepidemiology is not well studied.
METHODS: In a plasmode-based simulation study, we compared performance of the standard parametric approach, which ignores uncertainty in PS estimation and matching, with two bootstrapping methods. The first method only accounted for uncertainty introduced during the matching process (the observation resampling approach). The second method accounted for uncertainty introduced during both PS estimation and matching processes (the PS reestimation approach). Variance was estimated based on percentile and empirical standard errors, and treatment effect estimation was based on median and mean of the estimated treatment effects across 1000 bootstrap resamples. Two treatment prevalence scenarios (5% and 29%) across two treatment effect scenarios (hazard ratio of 1.0 and 2.0) were evaluated in 500 simulated cohorts of 10 000 patients each.
RESULTS: We observed that 95% confidence intervals from the bootstrapping approaches but not the standard approach, resulted in inaccurate coverage rates (98%-100% for the observation resampling approach, 99%-100% for the PS reestimation approach, and 95%-96% for standard approach). Treatment effect estimation based on bootstrapping approaches resulted in lower bias than the standard approach (less than 1.4% vs 4.1%) at 5% treatment prevalence; however, the performance was equivalent at 29% treatment prevalence.
CONCLUSION: Use of bootstrapping led to variance overestimation and inconsistent coverage, while coverage remained more consistent with parametric estimation.
OBJECTIVES: To examine the rate of incident malignancies excluding non-melanoma skin cancer (NMSC) in patients with rheumatoid arthritis (RA) newly treated with tocilizumab versus other biologic drugs.
METHODS: We conducted a cohort study using data from 3 U.S. insurance claims databases - Medicare (2010-2015), 'IMS' PharMetrics Plus (2011-2015) and Truven 'MarketScan' (2011-2015). Adults with RA who newly started tocilizumab or a TNF inhibitor (TNFi) after failing a different TNFi, abatacept or tofacitinib were included. The primary outcome was development of any malignancies excluding NMSC. For confounding control, tocilizumab starters were propensity score (PS)-matched to TNFi starters with a variable ratio of 1:3 within each database. Hazard ratios (HR) from the 3 PS-matched cohorts were combined by an inverse variance-weighted, fixed-effects model. We conducted a secondary analysis where we compared tocilizumab initiators with abatacept initiators.
RESULTS: We included 13,102 tocilizumab initiators PS-matched to 26,727 TNFi initiators in all three databases. The incidence rate of malignancies per 1,000 person-years ranged from 8.27 (IMS) to 23.18 (Medicare) in the tocilizumab group and from 9.64 (MarketScan) to 21.46 (Medicare) in the TNFi group. The risk of incident malignancies was similar between tocilizumab and TNFi initiators across all three databases, with a combined HR of 0.98 (95%CI 0.80-1.19) in tocilizumab versus TNFi. The secondary analysis comparing tocilizumab versus abatacept showed similar results (combined HR 0.97, 95%CI 0.74-1.27).
CONCLUSIONS: This large multi-database cohort study found no difference in the risk of malignancies excluding NMSC in RA patients who newly started tocilizumab compared with TNFi or abatacept.
OBJECTIVE: To evaluate the variation in long-term opioid use in osteoarthritis (OA) patients according to geography and health care access.
METHODS: We designed an observational cohort study among OA patients undergoing total joint replacement (TJR) in the Medicare program (2010 through 2014). The independent variables of interest were the state of residence and health care access, which was quantified at the primary care service area (PCSA) level as categories of number of practicing primary care providers (PCPs) and categories of rheumatologists per 1,000 Medicare beneficiaries. The percentage of OA patients taking long-term opioids (≥90 days in the 360-day period immediately preceding TJR) within each PCSA was the outcome variable in a multilevel, generalized linear regression model, adjusting for case-mix at the PCSA level and for policies, including rigor of prescription drug monitoring programs and legalized medical marijuana, at the state level.
RESULTS: A total of 358,121 patients with advanced OA, with a mean age of 74 years, were included from 4,080 PCSAs. The unadjusted mean percentage of long-term opioid users varied widely across states, ranging from 8.9% (Minnesota) to 26.4% (Alabama), and this variation persisted in the adjusted models. Access to PCPs was only modestly associated with rates of long-term opioid use between PCSAs with highest (>8.6) versus lowest (<3.6) concentration of PCPs (adjusted mean difference 1.4% [95% confidence interval 0.8%, 2.0%]), while access to rheumatologists was not associated with long-term opioid use.
CONCLUSION: We note a substantial statewide variation in rates of long-term treatment with opioids in OA, which is not fully explained by the differences in access to health care providers, varying case-mix, or state-level policies.
Methodologic research evaluating confounding due to socioeconomic status (SES) in observational studies of medications is limited. We identified 7,109 patients who initiated brand or generic atorvastatin from Medicare claims (2011-2013) linked to electronic medical records and census data. We created a propensity score (PS) containing only claims-based covariates and augmented it with additional claims-based proxies for SES, ZIP code, and block group level SES. Cox models with PS fine-stratification and weighting were used to compare rates of a cardiovascular end point and emergency department visits. Adjustment with only claims-based variables substantially improved balance on all SES variables compared with the unadjusted. Although inclusion of SES in PS models further improved balance on SES variables compared with models with claims-based covariates only, it did not materially change point estimates for either outcome. Inclusion of claims-based proxies may mitigate confounding by SES when aggregate-level SES information is unavailable.
OBJECTIVE: To investigate the rate of serious bacterial, viral or opportunistic infection in patients with rheumatoid arthritis (RA) starting tocilizumab (TCZ) versus tumour necrosis factor inhibitors (TNFi) or abatacept.
METHODS: Using claims data from US Medicare from 2010 to 2015, and IMS and MarketScan from 2011 to 2015, we identified adults with RA who initiated TCZ or TNFi (primary comparator)/abatacept (secondary comparator) with prior use of ≥1 different biologic drug or tofacitinib. The primary outcome was hospitalised serious infection (SI), including bacterial, viral or opportunistic infection. To control for >70 confounders, TCZ initiators were propensity score (PS)-matched to TNFi or abatacept initiators. Database-specific HRs were combined by a meta-analysis.
RESULTS: The primary cohort included 16 074 TCZ PS-matched to 33 109 TNFi initiators. The risk of composite SI was not different between TCZ and TNFi initiators (combined HR 1.05, 95% CI 0.95 to 1.16). However, TCZ was associated with an increased risk of serious bacterial infection (HR 1.19, 95% CI 1.07 to 1.33), skin and soft tissue infections (HR 2.38, 95% CI 1.47 to 3.86), and diverticulitis (HR 2.34, 95% CI 1.64 to 3.34) versus TNFi. An increased risk of composite SI, serious bacterial infection, diverticulitis, pneumonia/upper respiratory tract infection and septicaemia/bacteraemia was observed in TCZ versus abatacept users.
CONCLUSIONS: This large multidatabase cohort study found no difference in composite SI risk in patients with RA initiating TCZ versus TNFi after failing ≥1 biologic drug or tofacitinib. However, the risk of serious bacterial infection, skin and soft tissue infections, and diverticulitis was higher in TCZ versus TNFi initiators. The risk of composite SI was higher in TCZ initiators versus abatacept.
OBJECTIVE: To evaluate the risk of venous thromboembolism (VTE) in rheumatoid arthritis (RA) patients receiving tofacitinib versus those receiving tumor necrosis factor (TNF) inhibitors.
METHODS: RA patients who were initiating treatment with tofacitinib or a TNF inhibitor and had not previously received any biologic agent or tofacitinib were identified from the Truven MarketScan database (2012-2016) or Medicare claims (parts A, B, and D) database (2012-2015). Patients were followed up until treatment discontinuation, treatment switch, insurance disenrollment, or administrative censoring. The outcome of VTE was identified using inpatient claims for pulmonary embolism or deep vein thrombosis. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were determined using a Cox proportional hazards model after accounting for confounding through propensity score-based fine-stratification weighting. HRs were pooled across databases using the inverse variance meta-analytic method.
RESULTS: A total of 34,074 RA patients (mean age 50 years; 5.6% tofacitinib initiators) and 17,086 RA patients (mean age 71 years; 5.8% tofacitinib initiators) were identified from the Truven and Medicare databases, respectively. The crude incidence rates of VTE per 100 person-years were 0.60 (95% CI 0.26-1.19) and 0.34 (95% CI 0.27-0.41) in Truven and 1.12 (95% CI 0.45-2.31) and 0.92 (95% CI 0.76-1.11) in Medicare for patients receiving tofacitinib and patients receiving TNF inhibitors, respectively. Propensity score-adjusted HRs showed no significant differences in the risk of VTE between tofacitinib-treated and TNF inhibitor-treated patients in either database, with a pooled HR of 1.33 (95% CI 0.78-2.24).
CONCLUSION: Occurrence of VTE in a total of 50,865 RA patients initiating treatment with tofacitinib or a TNF inhibitor was infrequent (<1 per 100 person-years). We observed a numerically higher, but statistically nonsignificant, risk of VTE in RA patients receiving tofacitinib versus those receiving TNF inhibitors.
INTRODUCTION: Lawyer-submitted reports may have unintended consequences on safety signal detection in spontaneous adverse event reporting systems.
OBJECTIVE: Our objective was to assess the impact of lawyer-submitted reports primarily for one adverse event (AE) on the ability to detect a signal of disproportional reporting for another AE for the same drug in the US FDA Adverse Event Reporting System (FAERS).
METHODS: FAERS reports from January 2004 to September 2015 were used to estimate yearly cumulative proportional reporting ratios (PRRs) for three known drug-AE pairs-isotretinoin-birth defects, atorvastatin-rhabdomyolysis, and rosuvastatin-rhabdomyolysis-with and without lawyer-submitted reports. Isotretinoin and atorvastatin have been the subject of high-profile tort litigation regarding other AEs. A lower bound of the 95% confidence interval (CI) of one or more based on three or more reports defined a signal.
RESULTS: Cumulative PRRs met signaling criteria in all analyses. For isotretinoin, lawyer-submitted reports increased PRRs for birth defects before 2008, with the largest increase in 2006 (2.9 [95% CI 2.4-3.5] to 3.3 [95% CI 2.8-3.9]); lawyer-submitted reports decreased PRRs for birth defects after 2011, with the largest decrease in 2013 (2.2 [95% CI 2.0-2.5] to 1.9 [95% CI 1.7-2.1]). For atorvastatin, lawyer-submitted reports reduced PRRs for rhabdomyolysis after 2013, with the largest decrease in 2015 (18.0 [95% CI 17.1-19.1] to 15.4 [95% CI 14.5-16.2]). Lawyer-submitted reports had little impact on PRRs for rosuvastatin and rhabdomyolysis.
CONCLUSIONS: Inclusion of lawyer-submitted reports in FAERS did not meaningfully distort known safety signals for two drugs subject to high-profile tort litigation for other AEs.
OBJECTIVE: Compare and validate 5 algorithms to detect aberrant behavior with opioids: Opioid Misuse Score, Controlled Substance-Patterns of Utilization Requiring Evaluation (CS-PURE), Overutilization Monitoring System, Katz, and Cepeda algorithms.
STUDY DESIGN AND SETTING: We identified new prescription opioid users from 2 insurance databases: Medicaid (2000-2006) and Clinformatics Data Mart (CDM; 2004-2013). Patients were followed 1 year, and aberrant opioid behavior was defined according to each algorithm, using Cohen's kappa to assess agreement. Risk differences were calculated comparing risk of opioid-related adverse events for identified aberrant and nonaberrant users.
RESULTS: About 3.8 million Medicaid and 4.3 million CDM patients initiated prescription opioid use. Algorithms flagged potential aberrant behavior in 0.02% to 12.8% of initiators in Medicaid and 0.01% to 7.9% of initiators in CDM. Cohen's kappa values were poor to moderate (0.00 to 0.50 in Medicaid; 0.00 to 0.30 in CDM). Algorithms varied substantially in their ability to predict opioid-related adverse events; the Overutilization Monitoring System had the highest risk differences between aberrant and nonaberrant users (14.0% in Medicaid; 13.4% in CDM), and the Katz algorithm had the lowest (0.96% in Medicaid; 0.47% in CDM).
CONCLUSIONS: In 2 large databases, algorithms applied to prescription data had varying accuracy in identifying increased risk of adverse opioid-related events.
BACKGROUND: The approval of new oral disease-modifying drugs (DMDs), such as fingolimod, dimethyl fumarate (DMF), and teriflunamide, has considerably expanded treatment options for relapsing forms of multiple sclerosis (MS). However, data describing the use of these agents in routine clinical practice are limited.
OBJECTIVE: To describe time trends and identify factors associated with oral DMD treatment initiation and switching among individuals with MS.
METHODS: Using data from a large sample of commercially insured patients, we evaluated changes over time in the proportion of MS patients who initiated treatment with an oral DMD and who switched from an injectable DMD to an oral DMD between 2009 and 2014 in the United States. We evaluated predictors of oral DMD use using conditional logistic regression in 2 groups matched on calendar time: oral DMD initiators matched to injectable DMDs initiators and oral DMD switchers matched to those who switched to a second injectable DMD.
RESULTS: Our cohort included 7,576 individuals who initiated a DMD and 1,342 who switched DMDs, of which oral DMDs accounted for 6% and 39%, respectively. Oral DMD initiation and switching steadily increased from 5% to 16% and 35% to 84%, respectively, between 2011 and 2014, with DMF being the most commonly used agent. Of the potential predictors with clinical significance, a recent neurologist consultation (OR = 1.60; 95% CI = 1.20-2.15) and emergency department visit (OR = 1.43; 95% CI = 1.01-2.01) were significantly associated with oral DMD initiation. History of depression was noted to be a potential predictor of oral DMD initiation; however, the estimate for this predictor did not reach statistical significance (OR = 1.35; 95% CI = 0.99-1.84). No clinically relevant factors measured in our data were associated with switching to an oral DMD.
CONCLUSIONS: Oral DMDs were found to be routinely used as second-line treatment. However, we identified few factors predictive of oral DMD initiation or switching, which implies that their selection is driven by patient and/or physician preferences.
DISCLOSURES: This study was funded by CVS Caremark through an unrestricted research grant to Brigham and Women's Hospital. Shrank and Matlin were employees of, and shareholders in, CVS Health at the time of the study; they report no financial interests in products or services that are related to the subject of this study. Spettell is an employee of, and shareholder in, Aetna. Chitnis serves on clinical trial advisory boards for Novartis and Genzyme-Sanofi; has consulted for Bayer, Biogen Idec, Celgene, Novartis, Merck-Serono, and Genentech-Roche; and has received research support from NIH, National Multiple Sclerosis Society, Peabody Foundation, Consortium for MS Centers, Guthy Jackson Charitable Foundation, EMD-Serono, Novartis Biogen, and Verily. Desai reports receiving a research grant from Merck for unrelated work. Gagne is principal investigator of a research grant from Novartis Pharmaceuticals Corporation to the Brigham and Women's Hospital and has received grant support from Eli Lilly, all for unrelated work. He is also a consultant to Aetion and Optum. Minden reports grants from Biogen and other fees from Genentech, EMD Serano, Avanir, and Novartis, unrelated to this study. The other authors have no conflicts to report. This study was presented as a poster at the International Society for Pharmacoepidemiology 32nd Annual Meeting; August 25-28, 2016; Dublin, Ireland.