Publications

Many state Medicaid officials are concerned about rising prescription drug spending, particularly drugs approved through the Food and Drug Administration's (FDA) accelerated approval pathway. The authors examined how much of Medicaid programs' accelerated approval spending is attributable to products that have demonstrated clinical benefits versus those that have not. Their findings provide support for states' concerns that pharmaceutical companies often fail to complete their required postapproval confirmatory studies within the FDA's requested timeline. But the findings also highlight one issue that policy stakeholders have not yet devoted substantial attention to: the use of surrogate endpoints involved in the postapproval confirmatory studies for most of the products in this study's sample. The granularity of the study's results enabled an analysis of the impact of different policy recommendations on both the accelerated approval pathway and Medicaid programs. These findings inform the current policy debate, suggesting that policy stakeholders might focus attention on products converting their approval on the basis of surrogate outcomes rather than on clinical outcomes.

IMPORTANCE: The US Food and Drug Administration (FDA) has an accelerated approval program that has become the subject of scholarly attention and criticism, not only for the FDA's oversight of the program but also for its implications for payers.

OBSERVATIONS: State Medicaid programs' legal obligations to provide reimbursement for accelerated approval products have created fiscal challenges for Medicaid that have been exacerbated by industry's changing use of the accelerated approval program over time. Although strategies for accelerated approval reforms have been proposed, most focus on reforming the FDA's accelerated approval pathway and product regulation without taking into account the implications of this pathway for state Medicaid programs. There is a need for policy reforms that balance the goal of speeding approval of important medicines with states' real concerns regarding spending on medications with little evidence of clinical benefits. Areas of potential reform include formulary exclusion, Medicaid rebates, value-based pricing, and consolidated purchasing or carve outs.

CONCLUSIONS AND RELEVANCE: Policy makers may wish to consider options for reforming reimbursement for accelerated approval products in addition to reforms to the FDA's operation of the pathway. Policy reform proposals can provide a range of options to evaluate trade-offs of access and pricing.

We examined trends in management of headache disorders in United States (US) emergency department (ED) visits. We conducted a cross-sectional study using 2007−2018 National Hospital Ambulatory Medical Care Survey data. We included adult patient visits (≥18 years) with a primary ED discharge diagnosis of headache. We classified headache medications by pharmacological group: opioids, butalbital, ergot alkaloids/triptans, acetaminophen/nonsteroidal anti-inflammatory drugs (NSAIDs), antiemetics, diphenhydramine, corticosteroids, and intravenous fluids. To obtain reliable estimates, we aggregated data into three time periods: 2007−2010, 2011−2014, and 2015−2018. Using multivariable logistic regression, we examined medication, neuroimaging, and outpatient referral trends, separately. Among headache-related ED visits, opioid use decreased from 54.1% in 2007−2010 to 28.3% in 2015−2018 (Ptrend < 0.001). There were statistically significant increasing trends in acetaminophen/NSAIDs, diphenhydramine, and corticosteroids use (all Ptrend < 0.001). Changes in butalbital (6.4%), ergot alkaloid/triptan (4.7%), antiemetic (59.2% in 2015−2018), and neuroimaging (37.3%) use over time were insignificant. Headache-related ED visits with outpatient referral for follow-up increased slightly from 73.3% in 2007−2010 to 79.7% in 2015−2018 (Ptrend = 0.02). Reflecting evidence-based guideline recommendations for headache management, opioid use substantially decreased from 2007 to 2018 among US headache-related ED visits. Future studies are warranted to identify strategies to promote evidence-based treatment for headaches (e.g., sumatriptan, dexamethasone) and appropriate outpatient referral and reduce unnecessary neuroimaging orders in EDs.

BACKGROUND: Gabapentinoids are increasingly prescribed to manage chronic noncancer pain (CNCP) in older adults. When used concurrently with opioids, gabapentinoids may potentiate central nervous system (CNS) depression and increase the risks for fall. We aimed to investigate whether concurrent use of gabapentinoids with opioids compared with use of opioids alone is associated with an increased risk of fall-related injury among older adults with CNCP.

METHODS AND FINDINGS: We conducted a population-based cohort study using a 5% national sample of Medicare beneficiaries in the United States between 2011 and 2018. Study sample consisted of fee-for-service (FFS) beneficiaries aged ≥65 years with CNCP diagnosis who initiated opioids. We identified concurrent users with gabapentinoids and opioids days' supply overlapping for ≥1 day and designated first day of concurrency as the index date. We created 2 cohorts based on whether concurrent users initiated gabapentinoids on the day of opioid initiation (Cohort 1) or after opioid initiation (Cohort 2). Each concurrent user was matched to up to 4 opioid-only users on opioid initiation date and index date using risk set sampling. We followed patients from index date to first fall-related injury event ascertained using a validated claims-based algorithm, treatment discontinuation or switching, death, Medicare disenrollment, hospitalization or nursing home admission, or end of study, whichever occurred first. In each cohort, we used propensity score (PS) weighted Cox models to estimate the adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) of fall-related injury, adjusting for year of the index date, sociodemographics, types of chronic pain, comorbidities, frailty, polypharmacy, healthcare utilization, use of nonopioid medications, and opioid use on and before the index date. We identified 6,733 concurrent users and 27,092 matched opioid-only users in Cohort 1 and 5,709 concurrent users and 22,388 matched opioid-only users in Cohort 2. The incidence rate of fall-related injury was 24.5 per 100 person-years during follow-up (median, 9 days; interquartile range [IQR], 5 to 18 days) in Cohort 1 and was 18.0 per 100 person-years during follow-up (median, 9 days; IQR, 4 to 22 days) in Cohort 2. Concurrent users had similar risk of fall-related injury as opioid-only users in Cohort 1(aHR = 0.97, 95% CI 0.71 to 1.34, p = 0.874), but had higher risk for fall-related injury than opioid-only users in Cohort 2 (aHR = 1.69, 95% CI 1.17 to 2.44, p = 0.005). Limitations of this study included confounding due to unmeasured factors, unavailable information on gabapentinoids' indication, potential misclassification, and limited generalizability beyond older adults insured by Medicare FFS program.

CONCLUSIONS: In this sample of older Medicare beneficiaries with CNCP, initiating gabapentinoids and opioids simultaneously compared with initiating opioids only was not significantly associated with risk for fall-related injury. However, addition of gabapentinoids to an existing opioid regimen was associated with increased risks for fall. Mechanisms for the observed excess risk, whether pharmacological or because of channeling of combination therapy to high-risk patients, require further investigation. Clinicians should consider the risk-benefit of combination therapy when prescribing gabapentinoids concurrently with opioids.

BACKGROUND: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state).

METHODS: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use.

FINDINGS: A total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose.

INTERPRETATION: A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries.

FUNDING: National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

BACKGROUND: The optimal dose of apixaban therapy to prevent asecondary venous thromboembolism (VTE) event remains unconfirmed. To investigate the effects of extended phase use of apixaban (2.5 vs. 5 mg twice daily) beyond 6 months of initial treatment on the risk of recurrent VTE and major bleeding events among patients with a history of VTE.

METHODS: A retrospective cohort analysis of two large national insurance claims databases was conducted for patients diagnosed with VTE. Cox proportional hazard models after propensity score matching were used to compare the risk of recurrent VTE and major bleeding.

RESULTS: There were no detected differences in recurrent VTE or major bleeding events between patients prescribed low versus full dose apixaban.

CONCLUSION: This study provides evidence that apixaban 2.5 mg twice daily is an alternative option for extended phase therapy for risk reduction of VTE recurrence compared to apixaban 5 mg twice daily.

INTRODUCTION: Concurrent use of prescription opioids with gabapentinoids may pose risks of serious drug interactions. Yet, little is known about the trends in and patient characteristics associated with concurrent opioid-gabapentinoid use among older Medicare opioid users with chronic noncancer pain.

METHODS: A cross-sectional study was conducted among Medicare older beneficiaries (aged ≥65 years) with chronic noncancer pain who filled ≥1 opioid prescription within 3 months after a randomly selected chronic noncancer pain diagnosis (index date) in a calendar year between 2011 and 2018. Patient characteristics were measured in the 6-month baseline before the index date, and concurrent opioid-gabapentinoid use for ≥1 day was measured in the 3-month follow-up after the index date. Multivariable modified Poisson regression hwas used to assess the trends and characteristics of concurrent opioid-gabapentinoid use. Analyses were conducted from January to June 2021.

RESULTS: Among 464,721 eligible older beneficiaries with chronic noncancer pain and prescription opioids, the prevalence of concurrent opioid-gabapentinoid use increased from 17.0% in 2011 to 23.5% in 2018 (adjusted prevalence ratio=1.48, 95% CI=1.45, 1.53). Concurrent users versus opioid-only users tended to be non-Black, low-income subsidy recipients, and Southern residents. The clinical factors associated with concurrent opioid-gabapentinoid use included having a diagnosis of neuropathic pain, polypharmacy, and risk factors for opioid-related adverse events.

CONCLUSIONS: Concurrent opioid-gabapentinoid use among older Medicare beneficiaries with chronic noncancer pain and prescription opioids has increased significantly between 2011 and 2018. Future studies are warranted to investigate the impact of concurrent use on outcomes in older patients. Interventions that reduce inappropriate concurrent use may target older patients with identified characteristics.

BACKGROUND: Low-income subsidy/dual eligibility (LIS/DE) status and disability status may be associated with high-risk medication (HRM) use but are not usually accounted for in medication-use quality measures.

OBJECTIVE: To examine the association of: 1) LIS/DE status and HRM use; and 2) disability status and HRM use, while controlling for both health plan level effects and patient characteristics for Medicare beneficiaries enrolled in Medicare Advantage Prescription Drug Plans (MA-PD) and stand-alone Prescription Drug Plans (PDP).

METHODS: This retrospective cross-sectional study used 2013 Medicare data to determine if LIS/DE status and disability status were independently associated with HRM use (using the Pharmacy Quality Alliance HRM measure) in MA-PDs and PDPs. Multivariable generalized linear mixed models assessed the association of LIS/DE and HRM use, and disability and HRM use, after adjusting for health plan effect and patient-level confounders for MA-PD and PDP beneficiaries.

RESULTS: Of 520,019 MA-PD beneficiaries, 88,693 (17.1%) were LIS/DE and 48,997 (9.4%) were disabled. Of 881,264 PDP beneficiaries, 213,096 (24.2%) were LIS/DE, and 83,593 (9.5%) were disabled. LIS/DE beneficiaries had a higher percent of HRM users compared to non-LIS/DE MA-PD (13.3% vs. 9.7%, p < 0.001) and PDP (17.1% vs. 13.2%, p < 0.001) beneficiaries. Disabled beneficiaries had a higher percent of HRM users compared to non-disabled MA-PD (17.0% vs. 9.6%, p < 0.001) and PDP (22.9% vs. 13.2%, p < 0.001) beneficiaries. Multivariable analyses showed LIS/DE (adjusted odds ratio [AOR] = 1.07; 95% CI = 1.04, 1.10) and disability (AOR = 1.38; 95% CI = 1.34, 1.42) were associated with HRM use among MA-PD and PDP beneficiaries (LIS/DE AOR = 1.14; 95% CI = 1.12, 1.16; disability AOR = 1.37; 95% CI = 1.34, 1.40).

CONCLUSIONS: The association of LIS/DE and disability with higher HRM use in both MA-PD and PDP beneficiaries, when controlling for health plan effects and patient characteristics, suggests these factors should be considered when comparing health plan performance on HRM measures.

Limited real-world evidence exists for effectiveness and safety of extended oral anticoagulation beyond 6 months of initial treatment in prevention of recurrent venous thromboembolism (VTE) and adverse major bleeding events among patients with VTE. Using MarketScan Commercial and Medicare Supplemental databases (2013-2019), we conducted a retrospective cohort study to compare the risk of recurrent VTE and major bleeding events during extended treatment among patients with VTE who completed the 6-month initial treatment and received extended oral anticoagulant treatment with apixaban, warfarin, or no extended treatment. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using Cox proportional hazards modeling with inverse probability treatment weighting. We identified 14,818 patients with extended treatment of apixaban (n = 4,338), warfarin (n = 5,298), or no extended treatment (n = 5,182). Compared with no extended treatment, apixaban use was associated with decreased risk of recurrent VTE (HR: 0.10, 95% CI: 0.04-0.26) without increased risk of major bleeding events (HR: 1.06, 95% CI: 0.52-2.17); warfarin use was associated with decreased risk of recurrent VTE (HR: 0.23, 95% CI: 0.12-0.44) but with increased risk of major bleeding events (HR: 2.64, 95% CI: 1.51-4.59). Compared with warfarin, apixaban use was associated with decreased risk of major bleeding events (HR: 0.42, 95% CI: 0.22-0.80) but no difference in risk of recurrent VTE (HR: 0.46, 95% CI: 0.15-1.36). In a real-world clinical setting, extended anticoagulation with apixaban or warfarin was associated with decreased risk of recurrent VTE compared with no extended treatment, and apixaban had a better safety profile with fewer major bleeding events compared with warfarin among commercially insured patients with VTE.

BACKGROUND AND AIMS: One-third of opioid (OPI) overdose deaths involve concurrent benzodiazepine (BZD) use. Little is known about concurrent opioid and benzodiazepine use (OPI-BZD) most associated with overdose risk. We aimed to examine associations between OPI-BZD dose and duration trajectories, and subsequent OPI or BZD overdose in US Medicare.

DESIGN: Retrospective cohort study.

SETTING: US Medicare.

PARTICIPANTS: Using a 5% national Medicare data sample (2013-16) of fee-for-service beneficiaries without cancer initiating OPI prescriptions, we identified 37 879 beneficiaries (age ≥ 65 = 59.3%, female = 71.9%, white = 87.6%, having OPI overdose = 0.3%).

MEASUREMENTS: During the 6 months following OPI initiation (i.e. trajectory period), we identified OPI-BZD dose and duration patterns using group-based multi-trajectory models, based on average daily morphine milligram equivalents (MME) for OPIs and diazepam milligram equivalents (DME) for BZDs. To label dose levels in each trajectory, we defined OPI use as very low (< 25 MME), low (25-50 MME), moderate (51-90 MME), high (91-150 MME) and very high (>150 MME) dose. Similarly, we defined BZD use as very low (< 10 DME), low (10-20 DME), moderate (21-40 DME), high (41-60 DME) and very high (> 60 DME) dose. Our primary analysis was to estimate the risk of time to first hospital or emergency department visit for OPI overdose within 6 months following the trajectory period using inverse probability of treatment-weighted Cox proportional hazards models.

FINDINGS: We identified nine distinct OPI-BZD trajectories: group A: very low OPI (early discontinuation)-very low declining BZD (n = 10 598; 28.0% of the cohort); B: very low OPI (early discontinuation)-very low stable BZD (n = 4923; 13.0%); C: very low OPI (early discontinuation)-medium BZD (n = 4997; 13.2%); D: low OPI-low BZD (n = 5083; 13.4%); E: low OPI-high BZD (n = 3906; 10.3%); F: medium OPI-low BZD (n = 3948; 10.4%); G: very high OPI-high BZD (n = 1371; 3.6%); H: very high OPI-very high BZD (n = 957; 2.5%); and I: very high OPI-low BZD (n = 2096; 5.5%). Compared with group A, five trajectories (32.3% of the study cohort) were associated with increased 6-month OPI overdose risks: E: low OPI-high BZD [hazard ratio (HR) = 3.27, 95% confidence interval (CI) = 1.61-6.63]; F: medium OPI-low BZD (HR = 4.04, 95% CI = 2.06-7.95); G: very high OPI-high BZD (HR = 6.98, 95% CI = 3.11-15.64); H: very high OPI-very high BZD (HR = 4.41, 95% CI = 1.51-12.85); and I: very high OPI-low BZD (HR = 6.50, 95% CI = 3.15-13.42).

CONCLUSIONS: Patterns of concurrent opioid and benzodiazepine use most associated with overdose risk among fee-for-service US Medicare beneficiaries initiating opioid prescriptions include very high-dose opioid use (MME > 150), high-dose benzodiazepine use (DME > 40) or medium-dose opioid with low-dose benzodiazepine use.