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

This cross-sectional study examines and compares the time taken from the accelerated approval of cancer and noncancer drugs to the initiation of confirmatory studies in the US.

Objective To compare measures of spatial access to care commonly used by policy makers and researchers with the more comprehensive enhanced two-step floating catchment area (E2SFCA) method. Study setting Fourteen southwestern Pennsylvania counties. Study design We estimated spatial access to buprenorphine-waivered prescribers using three commonly used measures-Euclidean travel distance to the closest prescriber, travel time to the closest provider, and provider-to-population ratios-and the E2SFCA. Unlike other measures, the E2SFCA captures provider capacity, potential patient volume, and travel time to prescribers. Data collection/extraction methods We measured provider capacity as the number of buprenorphine prescribers listed at a given address in the Drug Enforcement Agency's 2020 Controlled Substances Act Registrants Database, and we measured potential patient volume as the number of nonelderly adults in a given census tract as reported by the 2018 American Community Survey. We estimated travel times between potential patients and prescribers with Bing Maps and Mapbox application programming interfaces. We then calculated each spatial access measure using the R programming language. We used each measure of spatial access to identify census tracts in the lowest quintile of spatial access to prescribers. Principal findings The Euclidean distance, travel time, and provider-to-population ratio measures identified 48.3%, 47.2%, and 69.9% of the census tracts that the E2SFCA measure identified as being in the lowest quintile of spatial access to care, meaning that these measures misclassify 30%-52% of study area census tracts as having sufficient spatial access to buprenorphine prescribers. Conclusions Measures of spatial access commonly used by policy makers do not sufficiently accurately identify geographic areas with relatively low access to prescribers of buprenorphine. Using the E2SFCA in addition to the commonly used measures would allow policy makers to precisely target interventions to increase spatial access to opioid use disorder treatment and other types of health care services.

Study objective More than 17 million people have gained health insurance coverage through the Patient Protection and Affordable Care Act’s Medicaid expansion. Few studies have examined heterogeneity within the Medicaid expansion population. We do so based on time-varying patterns of emergency department (ED) and ambulatory care use, and characterize diagnoses associated with ED and ambulatory care visits to evaluate whether certain diagnoses predominate in individual trajectories. Method We used group-based multitrajectory modeling to jointly estimate trajectories of ambulatory care and ED utilization in the first 12 months of enrollment among Pennsylvania Medicaid expansion enrollees from 2015 to 2017. Results Among 601,877 expansion enrollees, we identified 6 distinct groups based on joint trajectories of ED and ambulatory care use. Mean ED use varied across groups from 3.4 to 48.7 visits per 100 enrollees in the first month and between 2.8 and 44.0 visits per 100 enrollees in month 12. Mean ambulatory visit rates varied from 0.0 to 179 visits per 100 enrollees in the first month and from 0.0 to 274 visits in month 12. Rates of ED visits did not change over time, but rates of ambulatory care visits increased by at least 50% among 4 groups during the study period. Groups varied on chronic condition diagnoses, including mental health and substance use disorders, as well as diagnoses associated with ambulatory care visits. Conclusion We found substantial variation in rates of ED and ambulatory care use across empirically defined subgroups of Medicaid expansion enrollees. We also identified heterogeneity among the diagnoses associated with these visits. This data-driven approach may be used to target resources to encourage efficient use of ED services and support engagement with ambulatory care clinicians.

Limited information exists about testing for HIV, HBV, and HCV among Medicaid enrollees after initiating an episode of medication treatment for opioid use disorder (MOUD), despite guidelines recommending such testing. Our objectives were to estimate testing prevalence and trends for HIV, HBV, and HCV among Medicaid enrollees initiating MOUD and examine enrollee characteristics associated with testing.We conducted a serial cross-sectional study of 505,440 initiations of MOUD from 2016 to 2019 among 361,537 Medicaid enrollees aged 12-64 years in 11 states. Measures of MOUD initiation; HIV, HBV, and HCV testing; comorbidities; and demographics were based on enrollment and claims data. Each state used Poisson regression to estimate associations between enrollee characteristics and testing prevalence within 90 days of MOUD initiation. We pooled state-level estimates to generate global estimates using random effects meta-analyses.From 2016 to 2019, testing for HIV increased from 20% to 25%, HBV from 22% to 25%, HCV from 24% to 27%, and for all three conditions from 15% to 19%. Adjusted rates of testing for all three conditions were lower among enrollees who were male (vs. non-pregnant females), living in a rural area (vs. urban area), and initiating methadone or naltrexone (vs. buprenorphine). Associations between enrollee characteristics and testing were heterogeneous across states.Among Medicaid enrollees in 11 states, testing for HIV, HBV, and HCV increased among enrollees initiating MOUD; however, approximately three-quarters of enrollees were not tested for each condition, and testing rates differed by enrollee characteristics.

OBJECTIVES Limited information is available regarding provider- and patient panel-level factors associated with primary care provider (PCP) adoption/prescribing of medication for opioid use disorder (MOUD). METHODS We assessed a retrospective cohort from 2015 to 2018 within the Pennsylvania Medicaid Program. Participants included PCPs who were Medicaid providers, with no history of MOUD provision, and who treated ≥10 Medicaid enrollees annually. We assessed initial MOUD adoption, defined as an index buprenorphine/buprenorphine-naloxone or oral/extended release naltrexone fill and sustained prescribing, defined as ≥1 MOUD prescription(s) for 3 consecutive quarters from the PCP. Independent variables included provider- and patient panel-level characteristics. RESULTS We identified 113 rural and 782 urban PCPs who engaged in initial adoption and 36 rural and 288 urban PCPs who engaged in sustained prescribing. Rural/urban PCPs who issued increasingly larger numbers of antidepressant and antipsychotic medication prescriptions had greater odds of initial adoption and sustained prescribing (P

ImportanceEmergency department (ED)–based initiation of buprenorphine has been shown to increase engagement in outpatient treatment and reduce the risk of subsequent opioid overdose; however, rates of buprenorphine treatment in the ED and follow-up care for opioid use disorder (OUD) remain low in the US. The Opioid Hospital Quality Improvement Program (O-HQIP), a statewide financial incentive program designed to increase engagement in OUD treatment for Medicaid-enrolled patients who have ED encounters, has the potential to increase ED-initiated buprenorphine treatment.ObjectiveTo evaluate the association between hospitals attesting to an ED buprenorphine treatment O-HQIP pathway and patients’ subsequent initiation of buprenorphine treatment.Design, Setting, and ParticipantsThis cohort study included Pennsylvania patients aged 18 to 64 years with continuous Medicaid enrollment 6 months before their OUD ED encounter and at least 30 days after discharge between January 1, 2016, and December 31, 2020. Patients with a claim for medication for OUD 6 months before their index encounter were excluded.ExposuresHospital implementation of an ED buprenorphine treatment O-HQIP pathway.Main Outcomes and MeasuresThe main outcome was patients’ receipt of buprenorphine within 30 days of their index OUD ED visit. Between August 2021 and January 2023, data were analyzed using a difference-in-differences method to evaluate the association between hospitals’ O-HQIP attestation status and patients’ treatment with buprenorphine after ED discharge.ResultsThe analysis included 17 428 Medicaid-enrolled patients (female, 43.4%; male, 56.6%; mean [SD] age, 37.4 [10.8] years; Black, 17.5%; Hispanic, 7.9%; White, 71.6%; other race or ethnicity, 3.0%) with OUD seen at O-HQIP–attesting or non–O-HQIP–attesting hospital EDs. The rate of prescription fills for buprenorphine within 30 days of an OUD ED discharge in the O-HQIP attestation hospitals before the O-HQIP intervention was 5%. The O-HQIP attestation was associated with a statistically significant increase (2.6 percentage points) in prescription fills for buprenorphine within 30 days of an OUD ED discharge (β, 0.026; 95% CI, 0.005-0.047).Conclusions and RelevanceIn this cohort study, the O-HQIP was associated with an increased initiation of buprenorphine in patients with OUD presenting to the ED. These findings suggest that statewide incentive programs may effectively improve outcomes for patients with OUD.

BACKGROUND: Survivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual’s risk of death after a nonfatal opioid overdose. OBJECTIVE: To predict risk of death after a nonfatal opioid overdose. DESIGN AND PARTICIPANTS: This retrospective cohort study included 9686 Pennsylvania Medicaid beneficiaries with an emergency department or inpatient claim for nonfatal opioid overdose in 2014–2016. The index date was the first overdose claim during this period. EXPOSURES, MAIN OUTCOME, AND MEASURES: Predictor candidates were measured in the 180 days before the index overdose. Primary outcome was 180-day all-cause mortality. Using a gradient boosting machine model, we classified beneficiaries into six subgroups according to their risk of mortality (

OBJECTIVE: This study sought to characterize recent trends in mental health visits of adult outpatients to primary care physicians (PCPs), specialty mental health providers (SMHPs), and other providers (non–primary care physicians, specialists other than SMHPs, nurse practitioners, and physician assistants). Trends determined by degree of patients’ psychological distress and in the types of treatments received within different settings were also examined. METHODS: Data were from the household component of the nationally representative Medical Expenditure Panel Survey for the 2008–2011 and 2012–2015 periods for adults ages ≥18 years (N=13,111) who had a mental health outpatient visit. Bivariate logistic regression was used to compare means between the two periods. RESULTS: The percentage of adults having mental health outpatient visits increased between the two periods, largely driven by an increase in visits with providers other than SMHPs and PCPs, which rose from 11.9% (N=667) to 15.5% (N=1,048). Outpatient mental health visits with PCPs decreased from 29.0% (N=1,802) to 26.8% (N=1,945). The proportion of respondents with mental health outpatient visits increased both among those with high psychological distress and among those with low or no psychological distress (from 30.7% [N=1,332] to 36.2% [N=1,491] and from 6.0% [N=4,516] to 6.9% [N=5,772], respectively). The percentage of respondents receiving only psychotropic medication decreased over the two periods. CONCLUSIONS: Mental health outpatient visits for adults increased between 2008 and 2015, and visits with SMHPs remained relatively stable during that time. A greater understanding of recent trends in types of outpatient mental health services may help identify targets for future mental health workforce studies.

Objective To examine whether low-income children's use of preventive dental services is linked to variation in state Medicaid policies that affect parents' access to dental care in Medicaid. Data sources Medical Expenditure Panel Survey (2011-2016), Area Health Resources File, and Medicaid adult dental coverage policies. Study design We conducted a quasi-experimental analysis using linked parent-child dyads in low-income families (≤125 percent of the Federal Poverty Level). We assessed whether expansions of Medicaid to low-income adults under the Affordable Care Act were associated with increases in the use of preventive dental services among low-income children when state Medicaid programs did vs did not cover these services for adults. Principal findings Over the study period, 37.8 percent of low-income children received at least one annual preventive dental visit. We found no change in children's receipt of preventive dental care associated with Medicaid expansions in states that covered (1.26 percentage points; 95% CI: -3.74 to 6.27) vs did not cover preventive dental services for adults (3.03 percentage points; 95% CI: -2.76 to 8.81). (differential change: -1.76 percentage points; 95% CI: -8.09, 4.56). However, our estimates are imprecise, with wide confidential intervals that are unable to rule out sizable effects in either direction. Conclusion We did not find an association between Medicaid expansions with concurrent coverage of preventive dental services for adults and children's use of these services. Factors other than parental access to dental benefits through Medicaid may be more salient determinants of preventive dental care use among low-income children.

New prescription medications are a primary driver of spending growth in the United States. For patients with severe mental illnesses, second generation antipsychotic (SGA) medications feature prominently. However, many SGAs are costly, particularly before generic entry, and some may increase the risk of diabetes. Because physicians play a prominent role in new prescription adoption, understanding their prescribing behaviors is policy-relevant. Several features of prescription data, such as different antipsychotic choice sets over time, variable physician prescription volumes, and correlation among drug choices within physicians, complicate inferences. We propose a multivariate Bayesian hierarchical model with piecewise random effects to characterize the diffusion of new antipsychotic drugs. This model captures the complex prescriber-specific relationships among the different diffusion processes and takes advantage of the Bayesian paradigm to quantify uncertainty for all parameters straightforwardly. To evaluate the prescribing patterns for each physician, we propose various indices to identify early new SGA adopters. A sample of nearly 17,000 U.S. physicians whose antipsychotic drug prescribing information was collected between January 1, 1997 and December 31, 2007 illustrates the methods. Determinants of high prescription rates and adoption speeds of new SGAs included physician sex, age, hospital affiliation, physician specialty, and office location. Large within- and between-provider variations in prescribing patterns of new SGAs were identified. Early adopters for one drug were not early adopters for another drug.

The health of women and children affected by opioid use disorder is a priority for state Medicaid programs. Little is known about longer-term outcomes among Medicaid-enrolled children exposed to op...

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.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.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.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.

ImportanceThe 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.ObservationsState 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 RelevancePolicy 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.

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.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.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.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.

ImportanceMedicaid 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.ObjectiveTo 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 ReviewAnalyses 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.FindingsMedicaid 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 RelevanceMedicaid 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.

The time lag encountered when accessing health-care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one's opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions.A retrospective cohort study in Pennsylvania, USA among Pennsylvania Medicaid beneficiaries aged 18-64 years who initiated opioid prescriptions between July 2017 and September 2018 (318 585 eligible beneficiaries (mean age = 39 ± 12 years, female = 65.7%, White = 62.2% and Black = 24.9%).We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time.Among eligible beneficiaries, 0.61% had one or more occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time, including consistent low-risk (63%), consistent medium-risk (25%) and consistent high-risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high- to medium-risk (3%) and another group that increased from medium- to high-risk over time (5%).More than 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.

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).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.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.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.National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

Background Few studies have assessed prescription opioid supply preceding death in individuals dying from unintentional prescription opioid overdoses, or described the characteristics of these individuals, particularly among Veterans. Objectives To describe the history of prescription opioid supply preceding prescription opioid overdose death among Veterans. Methods In a national cohort of Veterans who filled ≥1 opioid prescriptions from the Veterans Health Administration (VA) or Medicare Part D during 2008–2013, we identified deaths from unintentional or undetermined-intent prescription opioid overdoses in 2012–2013. We captured opioid prescriptions using both linked VA and Part D data, and VA data only. Results Among 1181 decedents, 643 (54.4%) had prescription opioid supply on the day of death, and 735 (62.2%) within 30 days based on linked data, compared to 40.1% and 46.7%, respectively, using VA data alone. Decedents with prescription opioid supply were significantly older and less likely to have alcohol or illicit drugs as co-occurring substances involved in the overdose. Using linked data, 241 (20.4%) decedents lacked prescription opioid supply within a year of death. Conclusions Many VA patients who die from prescription opioid overdose receive opioid prescriptions outside VA or not at all. It is important to supplement VA with non-VA data to more accurately measure prescription opioid exposure and improve opioid medication safety.

BACKGROUND: The opioid epidemic has disproportionately affected rural areas, where a limited number of health care providers offer medication-assisted treatment (MAT), the mainstay of treatment for opioid use disorder (OUD). Rural residents with OUD may face multiple barriers to engagement in MAT including long travel distances. OBJECTIVE: To examine the degree to which rural residents with OUD are engaged with primary care providers (PCPs), describe the role of rural PCPs in MAT delivery, and estimate the association between enrollee distance to MAT prescribers and MAT utilization. DESIGN: Retrospective cohort study. PARTICIPANTS: Medicaid-enrolled adults diagnosed with OUD in 23 rural Pennsylvania counties. MAIN MEASURES: Primary care utilization, MAT utilization, distance to nearest possible MAT prescriber, mean distance traveled to actual MAT prescribers, and continuity of pharmacotherapy. KEY RESULTS: Of the 7930 Medicaid enrollees with a diagnosis of OUD, a minority (18.6%) received their diagnosis during a PCP visit even though enrollees with OUD had 4.1 visits to PCPs per person-year in 2015. Among enrollees with an OUD diagnosis recorded during a PCP visit, about half (751, 50.8%) received MAT, most of whom (508, 67.6%) received MAT from a PCP. Enrollees with OUD with at least one PCP visit were more likely than those without a PCP visit to receive MAT (32.7% vs. 25%; p

Unlike demand studies in other industries, models of provider demand in health care often must omit a price, or any other factor that equilibrates the market such as a waiting time. Estimates of the consumer response to quality may consequently be attenuated, if the limited capacity of individual physicians prevents some consumers from obtaining higher quality. We propose a tractable method to address this problem by adding a congestion effect to standard discrete-choice models. We show analytically how this can improve forecasts of the consumer response to quality. We then apply this method to the market for heart surgery, and find that the attenuation bias in estimated quality effects can be important empirically.

Cost sharing in traditional Medicare can consume a substantial portion of the income of beneficiaries who do not have supplemental insurance from Medicaid, an employer, or a Medigap plan. Near-poor Medicare beneficiaries (with incomes more than 100 percent but less than 200 percent of the federal poverty level) are ineligible for Medicaid but frequently lack alternative supplemental coverage, resulting in a supplemental coverage "cliff" of 25.8 percentage points just above the eligibility threshold for Medicaid (100 percent of poverty). We estimated that beneficiaries affected by this supplemental coverage cliff incurred an additional $2,288 in out-of-pocket spending over the course of two years, used 55 percent fewer outpatient evaluation and management services per year, and filled fewer prescriptions. Lower prescription drug use was partly driven by low take-up of Part D subsidies, which Medicare beneficiaries automatically receive if they have Medicaid. Expanding eligibility for Medicaid supplemental coverage and increasing take-up of Part D subsidies would lessen cost-related barriers to health care among near-poor Medicare beneficiaries.

OBJECTIVES: State Medicaid programs are the largest single provider of healthcare for pregnant persons with opioid use disorder (OUD). Our objective was to provide comparable, multi-state measures estimating the burden of OUD in pregnancy, medication for opioid use disorder (MOUD) in pregnancy, and related neonatal and child outcomes. METHODS: Drawing on the Medicaid Outcomes Distributed Research Network (MODRN), we accessed administrative healthcare data for 1.6 million pregnancies and 1.3 million live births in 9 state Medicaid populations from 2014–2017. We analyzed within- and between-state prevalences and time trends in the following outcomes: diagnosis of OUD in pregnancy, initiation and continuity of MOUD in pregnancy, Neonatal Opioid Withdrawal Syndrome (NOWS), and well-child visit utilization among children with NOWS. RESULTS: OUD diagnosis increased from 49.6 per 1,000 to 54.1 per 1,000 pregnancies, and the percentage of those with any MOUD in pregnancy increased from 53.4% to 57.9%, during our study time period. State-specific percentages of 180-day continuity of MOUD ranged from 41.2% to 84.5%. The rate of neonates diagnosed with NOWS increased from 32.7 to 37.0 per 1,000 live births. State-specific percentages of children diagnosed with NOWS who had the recommended well-child visits in the first 15 months ranged from 39.3% to 62.5%. CONCLUSIONS: Medicaid data, which allow for longitudinal surveillance of care across different settings, can be used to monitor OUD and related pregnancy and child health outcomes. Findings highlight the need for public health efforts to improve care for pregnant persons and children affected by OUD.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877–0.892 vs. C-statistic = 0.871; 95%CI = 0.863–0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

Highlights • No CVD benefits gains from early insulin initiation in young T2D individuals. • CVD possibly pre-exists or concurrent to insulin initiation or first-line therapy. • Young T2D individuals needs additional management of hypertension and dyslipidemia. • CVD risk before insulin initiation is examined by innovative methods in large cohort., Aims We evaluated the relationship between the timing of insulin initiation and cardiovascular diseases (CVD) risk in Pennsylvania Medicaid enrollees with type 2 diabetes (T2D). Methods We included 17,873 enrollees (age 47.4 ± 10.3 years; range 18–64 years) initially treated with non-insulin glucose-lowering agents (GLAs) in 2008–2016. Based on clinical guidelines, we identified early (N = 1,158; 6%; insulin initiation ≤ 6 months after first-line GLAs), in-time (N = 569; 3%; 6–12 months), delayed (N = 2,761; 15%; >12 months), and non-insulin users (N = 13,385; 75%). The Prentice-Williams-Peterson (PWP) models with inverse probability weighting estimated CVD risk across the four groups and the change in risk after insulin initiation. Results Regardless of time to insulin initiation, insulin users had higher CVD risks after first-line GLAs than non-insulin users (aHR: early: 2.0 [1.5–2.5], in-time: 1.8 [1.2–2.6], delayed: 1.9 [1.6–2.3]). However, we found only a borderline increase in CVD risk after insulin initiation vs. before in early (aHR: 1.4 [1.1–1.8]) and delayed users (aHR: 1.3 [1.0–1.7]), and no increase in in-time users (aHR: 1.3 [0.9–2.0]). Conclusions We observed no gains in CVD benefits from insulin initiation in the early stages of pharmacotherapy possibly because CVD developed before insulin initiation. Additional management of hypertension and dyslipidemia may be important to reduce CVD risk in this young and middle-aged T2D cohort.

Key Points Question Are states that chose not to expand Medicaid under the Patient Protection and Affordable Care Act associated with reduced emergency access to acute care hospitals? Findings In this cross-sectional study of acute care hospital availability in all 50 US states and the District of Columbia, states that did not expand Medicaid experienced worsened emergency access to acute care hospitals compared with states that expanded Medicaid. Meaning This study found reduced emergency access to acute care hospitals in states that did not expand Medicare, which could negatively impact the quality of care for time-sensitive conditions such as acute myocardial infarction, stroke, sepsis, and trauma., This cross-sectional study uses hospital-level Medicaid and geospatial data to analyze the association of state expansion of Medicaid under the Patient Protection and Affordable Care Act with emergency access to acute care hospitals., Importance State decisions not to expand Medicaid under the Patient Protection and Affordable Care Act could reduce emergency access to acute care hospitals. Objective To determine the relationship between state Medicaid expansion and emergency access to acute care hospitals in the United States. Design, Setting, and Participants This cross-sectional study linked hospital-level data from the Centers for Medicare & Medicaid Services from 2007 to 2017 to US Census data for all 50 US states and the District of Columbia. Geospatial analyses and difference-in-differences regression models were used to compare temporal changes in the size of the population without 30-minute access to acute care hospitals between 32 states that expanded Medicaid with the population without access in 19 that did not, before and after expansion. Analyses focused on the total population and those with low incomes; secondary analyses examined emergency access to safety-net hospitals. Exposures State-level Medicaid expansion. Main Outcomes and Measures Population without emergency access to an acute care hospital, defined as living outside a 30-minute drive of any hospital. Results States that did not expand Medicaid experienced an increase in the population without access to hospitals overall (without expansion: 6.76% to 6.79% [0.03%]; vs with expansion: 5.65% to 5.35% [–0.30%]; difference-in-differences, 0.33%; 95% CI, 0.33%-0.34%; P

BACKGROUND AND AIMS: Little is known about opioid and gabapentinoid (OPI-GABA) use duration and dose patterns’ associations with adverse outcomes risks. We examined associations between OPI-GABA dose and duration trajectories and subsequent drug overdose. DESIGN: Retrospective cohort study. SETTING: US Medicare. PARTICIPANTS: Using a 5% sample (2011–2016), we identified 71,005 fee-for-service Medicare beneficiaries with fibromyalgia, low back pain, neuropathy, and/or osteoarthritis initiating OPIs and/or GABAs (mean age±SD=65.5±14.5 years, female=68.1%, white=76.8%). MEASUREMENTS: Group-based multi-trajectory models identified distinct OPI-GABA use patterns during the year of OPI and/or GABA initiation, based on weekly average standardized daily dose (i.e., OPIs=morphine milligram equivalent, GABAs=minimum effective daily dose). We estimated models with three to 12 trajectories and selected the best model based on Bayesian information criterion (BIC) and Nagin’s criteria. We estimated risk of time to first drug overdose diagnosis within 12 months following the index year, adjusting for socio-demographic and health factors using inverse probability of treatment weighted multivariable Cox proportional hazards models. FINDINGS: We identified 10 distinct trajectories (BIC=−1,176,954; OPI-only=3, GABA-only=3, OPI-GABA=4). Compared with OPI-only early discontinuers (40.6% of the cohort), 1-year drug overdose risk varied by trajectory group: consistent low-dose OPI-only users (16.6%; HR=1.47, 95%CI=1.19–1.82), consistent high-dose OPI-only users (1.8%; HR=4.57, 95%CI=2.99–6.98), GABA-only early discontinuers (12.5%; HR=1.39, 95%CI=1.09–1.77), consistent low-dose GABA-only users (11.0%; HR=1.44, 95%CI=1.12–1.85), consistent high-dose GABA-only users (3.1%; HR=1.43, 95%CI=0.94–2.17), early discontinuation of OPIs and consistent low-dose GABA users (6.9%; HR=1.24, 95%CI=0.90–1.69), consistent low-dose OPI-GABA users (3.4%; HR=2.49, 95%CI=1.76–3.52), consistent low-dose OPI and high-dose GABA users (3.2%; HR=2.46, 95%CI=1.71–3.53), and consistent high-dose OPI and moderate-dose GABA users (0.9%; HR=7.22, 95%CI=4.46–11.69). CONCLUSIONS: Risk of drug overdose varied substantially among US Medicare beneficiaries on different use trajectories of opioids and gabapentinoids. High-dose opioid-only users and all consistent opioid and gabapentinoid users (regardless of doses) had more than double the risk of subsequent drug overdose compared with opioid-only early discontinuers.

BACKGROUND: Many persons with opioid use disorder (OUD) initiate medication for opioid use disorder (MOUD) with one clinic and switch to another clinic during their course of treatment. These switches may occur for referrals or for unplanned reasons. It is unknown, however, what effect switching MOUD clinics has on continuity of MOUD treatment or on overdoses. OBJECTIVE: To examine patterns of switching MOUD clinics and its association with the proportion of days covered (PDC) by MOUD, and opioid-related overdose. DESIGN: Cross-sectional retrospective analysis of Pennsylvania Medicaid claims data. MAIN MEASURES: MOUD clinic switches (i.e., filling a MOUD prescription from a prescriber located in a different clinic than the previous prescriber), PDC, and opioid-related overdose. RESULTS: Among 14,107 enrollees, 43.2% switched clinics for MOUD at least once during the 270 day period. In multivariate regression results, enrollees who were Non-Hispanic black (IRR=1.43; 95% CI=1.24–1.65; p

RESEARCH OBJECTIVE: Cost sharing is intended to mitigate the effects of moral hazard in health insurance but can have unintended consequences if patients underuse medically beneficial care that is subject to out‐of‐pocket costs. The Medicare program requires substantial cost sharing, including deductibles and coinsurance for outpatient care and prescription drugs. However, little is known about the consequences of this benefit design for low‐income Medicare beneficiaries who do not qualify for Medicaid or Part D Low‐Income Subsidies (LIS), which defray these costs. In this study, we harnessed quasi‐random variation in exposure to Medicare cost sharing, resulting from “cliffs” in cost sharing subsidies above vs. below income eligibility thresholds for Medicaid and the LIS, to assess the effects of cost sharing on low‐income beneficiaries’ out‐of‐pocket spending and use of care. STUDY DESIGN: We conducted regression discontinuity analyses using detailed household income data from the Health and Retirement Study (HRS) linked to Medicare claims. Our study design compared Medicare beneficiaries with incomes above vs. below eligibility thresholds for the Qualified Medicare Beneficiary (QMB) program, a Medicaid benefit that eliminates cost sharing in Medicare Parts A and B for beneficiaries $2,000) out‐of‐pocket medical costs, relative to beneficiaries whose income was below this threshold. Beneficiaries whose income exceeded the LIS threshold had 17% fewer prescription drug claims and were 16% more likely to have high out‐of‐pocket costs. We found no discernable differences in inpatient utilization above vs. below either threshold. CONCLUSIONS: Our findings show how cost sharing obligations in the Medicare program affect low‐income beneficiaries’ use of care: beneficiaries whose income just exceeds thresholds at which these cost sharing obligations are eliminated or appreciably reduced use substantially fewer prescription drugs and, in some cases, outpatient care. IMPLICATIONS FOR POLICY OR PRACTICE: Medicare beneficiaries with minimally different incomes can face substantially different out‐of‐pocket costs that affect their use of care. Extending Medicaid and LIS cost sharing subsidies to additional near‐poor beneficiaries, and gradually tapering assistance for individuals with modestly higher incomes, could alleviate the impacts of Medicare’s cost sharing obligations on near poor beneficiaries’ use of care. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

BACKGROUND: There is growing interest in financing housing and supportive services for homeless individuals through Medicaid. Permanent Supportive Housing (PSH), which integrates non-time-limited housing with supportive services for people who are disabled and chronically homeless, has seen rapid growth in the last decade, but clear evidence on the long-term impacts of PSH, needed to guide state efforts to finance some PSH services through Medicaid, is lacking. OBJECTIVE: Assess changes in Medicaid expenditures and utilization associated with receiving PSH. DESIGN: Cohort study using a difference-in-differences approach. PARTICIPANTS: A total of 1226 PA Medicaid enrollees who entered PSH 2011–2016 and remained in PSH for 180 days or more, and a matched comparison cohort of 970 enrollees experiencing housing instability who did not receive PSH. MAIN MEASURES: Medicaid spending in aggregate, and on behavioral and physical health services; emergency department (ED) visits and inpatient hospital stays. KEY RESULTS: Three years after PSH entry, spending decreased by an average of $145/month in the PSH cohort relative to changes in the comparison cohort (p = 0.046), with the greatest relative spending reductions occurring for residential behavioral health ($64, p < 0.001) and inpatient non-behavioral health services ($89, p = 0.001). We also found relative reductions in ED use (4.7 visits/100 person-months, p = 0.010) and inpatient hospital stays (1.6 visits/100 person-months, p < 0.001). CONCLUSIONS: These results can inform emerging state efforts to finance PSH services through Medicaid. Additional state expenditures to expand financing for PSH services could be partially offset by reductions in Medicaid spending, in part by facilitating a shift in treatment to outpatient from acute care settings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-020-06465-y.

Background: Insulin use, time to insulin initiation, and subsequent healthcare utilization in low-income adults with type 2 diabetes (T2DM) are poorly understood.Methods: Our objectives were to examine whether 1) insulin initiation and 2) every 1-year decrease in the time from first-line glucose-lowering agents (GLAs) to insulin initiation reduced healthcare utilization within 12 months after insulin initiation in Pennsylvania (PA) Medicaid enrollees with T2DM. We included a cohort of 12,648 PA Medicaid enrollees (age 47.3±10.3 years) with first-line non-insulin GLAs between 2008 and 2016. Insulin users (N=3,625) were matched with non-insulin users (N=9,023) on dates of 1st GLA prescription and propensity scores calculated based on baseline characteristics to account for potential confounders. Generalized estimating equations models estimated within-person changes in inpatient stays and emergency room (ER) visits 12 months after vs. 12 months before insulin initiation. We performed sensitivity analyses in young (18-45 years) and middle-aged enrollees (>45 to 64 years). Results: The average time from first-line GLAs to insulin initiation was 2.0±1.7 years. Neither insulin initiation (rate ratio [RR]:1.0 [1.0, 1.1]) nor time to insulin initiation (RR: 1.0 [1.0, 1.0]) was associated with within-person change in ER visits. In young T2DM adults, insulin users had a greater subsequent increase in inpatient stays after insulin initiation vs. non-insulin users over the same time period (RR: 1.3 [1.1, 1.5]). Conclusions: In T2DM Medicaid enrollees, no reduction in healthcare utilization was observed after insulin initiation, even in early stages of pharmacotherapy. Studies investigating primary contributors to the increased inpatient use in young insulin users with T2DM are needed.

The health of women and children affected by opioid use disorder is a priority for state Medicaid programs. Little is known about longer-term outcomes among Medicaid-enrolled children exposed to opioids in utero. We examined well-child visit use and diagnoses of pediatric complex chronic conditions in the first five years of life among children with opioid exposure, tobacco exposure, or neither exposure in utero. The sample consisted of 82,329 maternal-child dyads in the Pennsylvania Medicaid program in which the children were born in the period 2008–11 and followed up for five years. Children with in utero opioid exposure had a lower predicted probability of recommended well-child visit use at age fifteen months (42.1 percent) compared to those with tobacco exposure (54.1 percent) and those with neither exposure (55.7 percent). Children with in utero opioid exposure had a predicted probability of being diagnosed with a pediatric complex chronic condition similar to that among children with tobacco exposure and those with neither exposure (20.4 percent, 18.7 percent, and 20.2 percent, respectively). Our findings were consistent when we examined a subgroup of opioid-exposed children identified as having neonatal opioid withdrawal symptoms.

ObjectiveTo develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions.MethodsThis prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling ≥1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples. We measured 269 potential predictors including socio-demographics, health status, patterns of opioid use, and provider-level and regional-level factors in 3-month periods, starting from three months before initiating opioids until development of OUD, loss of follow-up or end of 2016. The primary outcome was a recorded OUD diagnosis or initiating methadone or buprenorphine for OUD as proxy of incident OUD. We applied elastic net, random forests, gradient boosting machine, and deep neural network to predict OUD in the subsequent three months. We assessed prediction performance using C-statistics and other metrics (e.g., number needed to evaluate to identify an individual with OUD [NNE]). Beneficiaries were stratified into subgroups by risk-score decile.ResultsThe training (n = 120,474), testing (n = 120,556), and validation (n = 120,497) samples had similar characteristics (age ≥65 years = 81.1%; female = 61.3%; white = 83.5%; with disability eligibility = 25.5%; 1.5% had incident OUD). In the validation sample, the four approaches had similar prediction performances (C-statistic ranged from 0.874 to 0.882); elastic net required the fewest predictors (n = 48). Using the elastic net algorithm, individuals in the top decile of risk (15.8% [n = 19,047] of validation cohort) had a positive predictive value of 0.96%, negative predictive value of 99.7%, and NNE of 104. Nearly 70% of individuals with incident OUD were in the top two deciles (n = 37,078), having highest incident OUD (36 to 301 per 10,000 beneficiaries). Individuals in the bottom eight deciles (n = 83,419) had minimal incident OUD (3 to 28 per 10,000).ConclusionsMachine-learning algorithms improve risk prediction and risk stratification of incident OUD in Medicare beneficiaries.

This cross-sectional study examines changes in hydroxyprogesterone caproate fills from 2010 through 2020.