Publications

BACKGROUND: Despite evidence that individuals with opioid use disorder (OUD) have a lower risk of mortality when using evidence-based medications for OUD (MOUD), only 20 % of people with OUD receive MOUD. Black patients are significantly less likely than White patients to initiate MOUD. We measured the association between various facilitators and barriers to initiation, including criminal justice, human services, and health care factors, and variation in initiation of MOUD by race.

METHODS: We used data from a comprehensive, linked data set of health care, human services, and criminal justice programs from Allegheny County in Western Pennsylvania to measure disparities in MOUD initiation by race in the first 180 days after an OUD diagnosis, as well as mediation by potential facilitators and barriers to treatment, among Medicaid enrollees. This is a cross-sectional analysis.

RESULTS: Among 6374 Medicaid enrollees who met study criteria, Black enrollees were 18.2 percentage points less likely than White enrollees to start MOUD after controlling for gender, age, and Medicaid eligibility (95 % CI: -21.5 % - -14.8 %). Each day in the emergency department or county jail was associated with a decrease in the likelihood of initiation, as was the presence of a non-OUD substance use disorder diagnosis or participation in intensive non-MOUD treatment. Mediators accounted for approximately one-fifth of the variation in initiation related to race.

CONCLUSIONS: Acute care facilities and settings in which people with OUD are incarcerated may have an opportunity to increase the use of MOUD overall and close the racial gap in initiation.

OBJECTIVE: Off-label utilization of second-generation antipsychotic medications may expose patients to significant risks. The authors examined the prevalence, temporal trends, and factors associated with off-label utilization of second-generation antipsychotics among publicly insured adults.

METHODS: A retrospective repeated panel was used to examine monthly off-label utilization of second-generation antipsychotics among fee-for-service Medicare, Medicaid, and dually eligible White, Black, and Latino adult beneficiaries filling prescriptions for second-generation antipsychotics in California, Georgia, Mississippi, and Oklahoma from July 2008 through June 2013.

RESULTS: Among 301,367 users of second-generation antipsychotics, between 36.5% and 41.9% had utilization that was always off-label. Payer did not modify effects of race-ethnicity on off-label utilization. Compared with Whites, Blacks had lower monthly odds of off-label utilization in all four states, and Latinos had lower odds of utilization in California and Georgia. Payer was associated with off-label utilization in California, Mississippi, and Oklahoma. California Medicaid beneficiaries were 1.12 (95% confidence interval=1.10-1.13) times as likely as dually eligible beneficiaries to have off-label utilization. Off-label utilization increased relative to the baseline year in all states, but a downward trend followed in three states.

CONCLUSIONS: Off-label utilization of second-generation antipsychotics was prevalent despite the drugs' cardiometabolic risks and little evidence of their effectiveness. The lower likelihood of off-label utilization among patients from racial-ethnic minority groups might stem from prescribers' efforts to minimize risks, given a higher baseline risk for these groups, or from disparities-associated factors. Variation among payers suggests that payer policies can affect off-label utilization.

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, multistate measures estimating the burden of OUD in pregnancy, medication for OUD (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 to 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 1000 to 54.1 per 1000 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 1000 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.

BACKGROUND: Older adults receive treatment for fall injuries in both inpatient and outpatient settings. The effect of persistent polypharmacy (i.e. using multiple medications over a long period) on fall injuries is understudied, particularly for outpatient injuries. We examined the association between persistent polypharmacy and treated fall injury risk from inpatient and outpatient settings in community-dwelling older adults.

METHODS: The Health, Aging and Body Composition Study included 1764 community-dwelling adults (age 73.6 ± 2.9 years; 52% women; 38% black) with Medicare Fee-For-Service (FFS) claims at or within 6 months after 1998/99 clinic visit. Incident fall injuries (N = 545 in 4.6 ± 2.9 years) were defined as the initial claim with an ICD-9 fall E-code and non-fracture injury, or fracture code with/without a fall code from 1998/99 clinic visit to 12/31/08. Those without fall injury (N = 1219) were followed for 8.1 ± 2.6 years. Stepwise Cox models of fall injury risk with a time-varying variable for persistent polypharmacy (defined as ≥6 prescription medications at the two most recent consecutive clinic visits) were adjusted for demographics, lifestyle characteristics, chronic conditions, and functional ability. Sensitivity analyses explored if persistent polypharmacy both with and without fall risk increasing drugs (FRID) use were similarly associated with fall injury risk.

RESULTS: Among 1764 participants, 636 (36%) had persistent polypharmacy over the follow-up period, and 1128 (64%) did not. Fall injury incidence was 38 per 1000 person-years. Persistent polypharmacy increased fall injury risk (hazard ratio [HR]: 1.31 [1.06, 1.63]) after adjusting for covariates. Persistent polypharmacy with FRID use was associated with a 48% increase in fall injury risk (95%CI: 1.10, 2.00) vs. those who had non-persistent polypharmacy without FRID use. Risks for persistent polypharmacy without FRID use (HR: 1.22 [0.93, 1.60]) and non-persistent polypharmacy with FRID use (HR: 1.08 [0.77, 1.51]) did not significantly increase compared to non-persistent polypharmacy without FRID use.

CONCLUSIONS: Persistent polypharmacy, particularly combined with FRID use, was associated with increased risk for treated fall injuries from inpatient and outpatient settings. Clinicians may need to consider medication management for FRID and other fall prevention strategies in community-dwelling older adults with persistent polypharmacy to reduce fall injury risk.

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.

OBJECTIVES: Little is known about relationships between opioid- and gabapentinoid-use patterns and healthcare expenditures that may be affected by pain management and risk of adverse outcomes. This study examined the association between patients' opioid and gabapentinoid prescription filling/refilling trajectories and direct medical expenditures in US Medicare.

METHODS: This cross-sectional study included a 5% national sample (2011-2016) of fee-for-service beneficiaries with fibromyalgia, low back pain, neuropathy, or osteoarthritis newly initiating opioids or gabapentinoids. Using group-based multitrajectory modeling, this study identified patients' distinct opioid and gabapentinoid (OPI-GABA) dose and duration patterns, based on standardized daily doses, within a year of initiating opioids and/or gabapentinoids. Concurrent direct medical expenditures within the same year were estimated using inverse probability of treatment weighted multivariable generalized linear regression, adjusting for sociodemographic and health status factors.

RESULTS: Among 67 827 eligible beneficiaries (mean age ± SD = 63.6 ± 14.8 years, female = 65.8%, white = 77.1%), 11 distinct trajectories were identified (3 opioid-only, 4 gabapentinoid-only, and 4 concurrent OPI-GABA trajectories). Compared with opioid-only early discontinuers ($13 830, 95% confidence interval = $13 643-14 019), gabapentinoid-only early discontinuers and consistent low-dose and moderate-dose gabapentinoid-only users were associated with 11% to 23% lower health expenditures (adjusted mean expenditure = $10 607-$11 713). Consistent low-dose opioid-only users, consistent high-dose opioid-only users, consistent low-dose OPI-GABA users, consistent low-dose opioid and high-dose gabapentinoid users, and consistent high-dose opioid and moderate-dose gabapentinoid users were associated with 14% to 106% higher healthcare expenditures (adjusted mean expenditure = $15 721-$28 464).

CONCLUSIONS: Dose and duration patterns of concurrent OPI-GABA varied substantially among fee-for-service Medicare beneficiaries. Consistent opioid-only users and all concurrent OPI-GABA users were associated with higher healthcare expenditures compared to opioid-only discontinuers.

BACKGROUND: Once-daily extended-released memantine with donepezil (hereafter memantine/donepezil) may improve medication adherence but has a 60-fold higher cost compared with combined generic components. Little is known about factors associated with prescribing memantine/donepezil. We examined the association between pharmaceutical industry payments to physicians and prescribing memantine/donepezil in Medicare.

METHODS: A cross-sectional study was conducted. Using 2015-2016 Centers for Medicare and Medicaid Services Open Payments and Part D prescription databases, we identified unique physicians who prescribed ≥11 memantine/donepezil prescriptions from 2015 to 2016. Outcome variable was the number of memantine/donepezil prescriptions written per physician per year. The key independent variable was physician receipt of industry payments defined in 2 models: (1) number of payments and (2) amount of payment ($100 units) for memantine/donepezil received per physician per year. Multivariable Poisson regression was used, adjusting for potential confounders.

RESULTS: Among 4,895 unique eligible physicians in 2015-2016, the median number of memantine/donepezil prescriptions per physician per year was 19.5 (25th percentile 13, 75th percentile 32). Physicians received between 0 and 75 payments per year (median 1, 25th percentile 0, 75th percentile 2.5) for memantine/donepezil, totaling an average of $92 per year (median $10.5, 25th percentile $0, 75th percentile $33.20). Every 1 additional payment received was associated with a 2% increase in new memantine/donepezil prescriptions prescribed per physician per year (rate ratio [RR] 1.02, 95% confidence interval [CI] 1.02-1.02). Every $100 increase in payment for memantine/donepezil was associated with a 0.3% increase in new memantine/donepezil prescriptions prescribed per physician per pear (RR 1.003, 95% CI 1.002-1.004).

CONCLUSIONS: Receipt of industry payments for memantine/donepezil was independently associated with increased likelihood of physician prescribing memantine/donepezil in Medicare.

BACKGROUND: Florida's House Bill 21 (HB21), implemented into law on July 1, 2018, limited opioid prescriptions for acute pain to a 3-day supply. While the law has been associated with a decrease in opioid prescribing for acute pain, its effect on opioid volume dispensed at the plan level remains unknown. OBJECTIVES: To assess the impact of HB21 on the total volume dispensed of oral Schedule II opioids. We evaluated the change from before to after the law's implementation in (1) total number of opioid units dispensed per month and (2) total morphine milligram equivalent (MMEs) dispensed per month. METHODS: Pharmacy claims from July 2017 to June 2019 were analyzed from a private health plan serving a large Florida employer. We summed the number of units and the total MMEs dispensed for each month per 1,000 enrollees. Units were defined as the total quantity of tablets/capsules dispensed for each Schedule II oral opioid prescription. We used interrupted time series (ITS) models, accounting for autocorrelation, to determine any immediate change after the policy implementation and to estimate trends before and after the policy. RESULTS: We identified 16,226 prescriptions of oral Scheduled II opioids dispensed to 6,315 enrollees over a 2-year period. The HB21 law was associated with an immediate but not statistically significant decrease of 110.25 units dispensed per 1,000 enrollees in the month after implementation (95% CI: -218.84, -1.67; P = 0.06). There was an immediate but not statistically significant decrease of 1,456.29 MMEs dispensed per 1,000 enrollees following HB21 implementation (95% CI: -2,983.87, 71.29; P = 0.07). There were no significant changes in the slopes of the trends for total number of opioid units and total MMEs dispensed after HB21. CONCLUSIONS: Despite substantial lower quantities, there were no significant immediate reductions in total opioid units and MMEs dispensed in the year following the implementation of HB21. Our findings can inform other health plans on the potential effect of such restrictive laws and policies in other states where preexisting declining trends might have a higher impact than restriction policies. Future studies are needed to evaluate long-term intended and unintended consequences, including effects on patients' access to care, resulting from this type of restrictive law. DISCLOSURES: No outside funding supported this study. The authors report no conflicts of interest. Preliminary results of this study were presented at the Virtual ISPOR 2020 Conference held May 18-20, 2020.

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 (< 25th percentile of the risk score, 25th to < 50th, 50th to < 75th, 75th to < 90th, 90th to < 98th, ≥ 98th). We then measured receipt of medication for opioid use disorder (OUD), risk mitigation interventions (e.g., prescriptions for naloxone), and prescription opioids filled in the 180 days after the index overdose, by risk subgroup.

KEY RESULTS: Of eligible beneficiaries, 347 (3.6%) died within 180 days after the index overdose. The C-statistic of the mortality prediction model was 0.71. In the highest risk subgroup, the observed 180-day mortality rate was 20.3%, while in the lowest risk subgroup, it was 1.5%. Medication for OUD and risk mitigation interventions after overdose were more commonly seen in lower risk groups, while opioid prescriptions were more likely to be used in higher risk groups (both p trends < .001).

CONCLUSIONS: A risk prediction model performed well for classifying mortality risk after a nonfatal opioid overdose. This prediction score can identify high-risk subgroups to target interventions to improve outcomes among overdose survivors.

BACKGROUND AND AIMS: Little is known about opioid and gabapentinoid (OPI-GABA) use duration and dose patterns' associations with adverse outcome 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 (2011-16), 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 ± standard deviation (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%; hazard ratio (HR) = 1.47, 95% confidence interval (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.