Publications

INTRODUCTION: Follow-up visits within 7 days of an emergency department (ED) visit related to opioid use disorder (OUD) is a key measure of treatment quality, but we know little about its protective effect on future opioid-related overdoses. The objective this paper is to examine the rate of 7-day follow-up after an OUD-related ED visit and the association with future overdoses.

METHODS: Retrospective analysis of Medicaid enrollees in 11 states that had an OUD-related ED visit from 2016 through 2018. Each state used Cox proportional hazard models to estimate the association between having a follow-up visit within 7 days of an OUD-related ED visit, and an overdose within 6 months of the ED visit. State analyses were pooled to generate global estimates using random effects meta-analysis.

RESULTS: Among 114,945 Medicaid enrollees with an OUD-related ED visit, 15.7% had a follow-up visit within 7 days. State-specific rates varied from 7.2% to 22.4% across the 11 states. Compared to those with no follow-up visit, enrollees with a follow-up visit were more likely to be female, non-Hispanic White, less likely to have had an overdose or other substance use disorder at the time of the ED visit, and much more likely to have been receiving MOUD treatment prior to the ED visit. Global estimates based on multivariate analysis showed that having a 7-day follow-up visit was associated with a lower likelihood of overdose within 6 months of the index ED visit (HR = 0.91, CI = 0.84, 0.99). However, states had considerable heterogeneity in this association, with only two states having statistically significant results.

CONCLUSIONS: Among Medicaid enrollees with OUD, having a follow-up visit 7 days after an ED visit is protective against fatal or nonfatal overdose within 6 months, although the association varies considerably across states. Although the association with future overdoses was relatively modest, both practitioners and policymakers should seek to increase the number of Medicaid enrollees with OUD who receive follow-up care within 7 days after an ED visit.

IMPORTANCE: Medicaid is the largest health insurance program by enrollment in the US and has an important role in financing care for eligible low-income adults, children, pregnant persons, older adults, people with disabilities, and people from racial and ethnic minority groups. Medicaid has evolved with policy reform and expansion under the Affordable Care Act and is at a crossroads in balancing its role in addressing health disparities and health inequities against fiscal and political pressures to limit spending.

OBJECTIVE: To describe Medicaid eligibility, enrollment, and spending and to examine areas of Medicaid policy, including managed care, payment, and delivery system reforms; Medicaid expansion; racial and ethnic health disparities; and the potential to achieve health equity.

EVIDENCE REVIEW: Analyses of publicly available data reported from 2010 to 2022 on Medicaid enrollment and program expenditures were performed to describe the structure and financing of Medicaid and characteristics of Medicaid enrollees. A search of PubMed for peer-reviewed literature and online reports from nonprofit and government organizations was conducted between August 1, 2021, and February 1, 2022, to review evidence on Medicaid managed care, delivery system reforms, expansion, and health disparities. Peer-reviewed articles and reports published between January 2003 and February 2022 were included.

FINDINGS: Medicaid covered approximately 80.6 million people (mean per month) in 2022 (24.2% of the US population) and accounted for an estimated $671.2 billion in health spending in 2020, representing 16.3% of US health spending. Medicaid accounted for an estimated 27.2% of total state spending and 7.6% of total federal expenditures in 2021. States enrolled 69.5% of Medicaid beneficiaries in managed care plans in 2019 and adopted 139 delivery system reforms from 2003 to 2019. The 38 states (and Washington, DC) that expanded Medicaid under the Affordable Care Act experienced gains in coverage, increased federal revenues, and improvements in health care access and some health outcomes. Approximately 56.4% of Medicaid beneficiaries were from racial and ethnic minority groups in 2019, and disparities in access, quality, and outcomes are common among these groups within Medicaid. Expanding Medicaid, addressing disparities within Medicaid, and having an explicit focus on equity in managed care and delivery system reforms may represent opportunities for Medicaid to advance health equity.

CONCLUSIONS AND RELEVANCE: Medicaid insures a substantial portion of the US population, accounts for a significant amount of total health spending and state expenditures, and has evolved with delivery system reforms, increased managed care enrollment, and state expansions. Additional Medicaid policy reforms are needed to reduce health disparities by race and ethnicity and to help achieve equity in access, quality, and outcomes.

BACKGROUND: In the US, Medicaid covers over 80 million Americans. Comparing access, quality, and costs across Medicaid programs can provide policymakers with much-needed information. As each Medicaid agency collects its member data, multiple barriers prevent sharing Medicaid data between states. To address this gap, the Medicaid Outcomes Distributed Research Network (MODRN) developed a research network of states to conduct rapid multi-state analyses without sharing individual-level data across states.

OBJECTIVE: To describe goals, design, implementation, and evolution of MODRN to inform other research networks.

METHODS: MODRN implemented a distributed research network using a common data model, with each state analyzing its own data; developed standardized measure specifications and statistical software code to conduct analyses; and disseminated findings to state and federal Medicaid policymakers. Based on feedback on Medicaid agency priorities, MODRN first sought to inform Medicaid policy to improve opioid use disorder treatment, particularly medication treatment.

RESULTS: Since its 2017 inception, MODRN created 21 opioid use disorder quality measures in 13 states. MODRN modified its common data model over time to include additional elements. Initial barriers included harmonizing utilization data from Medicaid billing codes across states and adapting statistical methods to combine state-level results. The network demonstrated its utility and addressed barriers to conducting multi-state analyses of Medicaid administrative data.

CONCLUSIONS: MODRN created a new, scalable, successful model for conducting policy research while complying with federal and state regulations to protect beneficiary health information. Platforms like MODRN may prove useful for emerging health challenges to facilitate evidence-based policymaking in Medicaid programs.

BACKGROUND: Follow-up after residential treatment is considered best practice in supporting patients with opioid use disorder (OUD) in their recovery. Yet, little is known about rates of follow-up after discharge. The objective of this analysis was to measure rates of follow-up and use of medications for OUD (MOUD) after residential treatment among Medicaid enrollees in 10 states, and to understand the enrollee and episode characteristics that are associated with both outcomes.

METHODS: Using a distributed research network to analyze Medicaid claims data, we estimated the likelihood of 4 outcomes occurring within 7 and 30 days post-discharge from residential treatment for OUD using multinomial logit regression: no follow-up or MOUD, follow-up visit only, MOUD only, or both follow-up and MOUD. We used meta-analysis techniques to pool state-specific estimates into global estimates.

RESULTS: We identified 90,639 episodes of residential treatment for OUD for 69,017 enrollees from 2018 to 2019. We found that 62.5% and 46.9% of episodes did not receive any follow-up or MOUD at 7 days and 30 days, respectively. In adjusted analyses, co-occurring mental health conditions, longer lengths of stay, prior receipt of MOUD or behavioral health counseling, and a recent ED visit for OUD were associated with a greater likelihood of receiving follow-up treatment including MOUD after discharge.

CONCLUSIONS: Forty-seven percent of residential treatment episodes for Medicaid enrollees are not followed by an outpatient visit or MOUD, and thus are not following best practices.

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.