Publications

Substance use disorder (SUD) is a complex chronic condition requiring a multi-disciplinary approach to both research and treatment. Randomized controlled trials (RCTs) are gold standard methodologies for inferring causal relationships between an intervention and treatment outcomes but often face challenges in generalizability, scalability and real-world implementation. Target trial emulation (TTE) is a powerful methodological framework that uses observational or real-world data sources to emulate the methodology of these gold standard target trials to complement the learning from RCTs and enhance translation to real world evidence. An additional methodological innovation is the translational testing of clinical- and community-based digital health systems to provide new insights into SUD in the real world and provide scalable access to therapeutic resources. To explore these methodological innovations in SUD research, the National Institute on Drug Abuse Center for the Clinical Trials Network convened a variety of experts for a virtual workshop titled "Target Trial Emulation in Observational Research and Translational Testing of Advanced Digital Health Tools for Substance Use Disorder Prevention and Treatment." This article summarizes the discourse of the workshop, focused on three thematic areas: TTE using real-world healthcare data, SUD evidence from nationwide data sources that may be useful in TTE analyses, and translational testing of clinical- and community-based digital health systems. The workshop also highlighted various exemplars of digital health systems that demonstrate success in translational research addressing SUDs, key methodological and translational challenges, importance of rigorous study design, robust data linkages and expanding use of common data elements, and the integration of digital health tools to enhance causal inference and clinical impact. Future research directions are outlined to refine these approaches, address barriers, and maximize the utility of real-world data in shaping effective SUD prevention and treatment strategies.

IMPORTANCE: There are large racial and ethnic differences in the use of medications for opioid use disorder (MOUD). Whether differences in geographic availability of MOUD providers (defined in this study as buprenorphine prescribers, methadone dispensing opioid treatment programs, and naltrexone prescribers) contribute to these differences in Medicaid is unknown.

OBJECTIVE: To examine differential geographic availability of MOUD in Medicaid and whether it is associated with MOUD use.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study analyzed the geographic availability of Medicaid prescribers of MOUD in 2021, spanning 10 states (Delaware, Kentucky, Maryland, Maine, Michigan, North Carolina, Pennsylvania, Tennessee, Virginia, and West Virginia) in the Medicaid Outcomes Distributed Research Network. The study population included Medicaid enrollees aged 18 to 64 not enrolled in Medicare and their MOUD providers. The data analysis was conducted from December 2022 to April 2026.

EXPOSURES: Geographic availability was measured at the zip code level (number of MOUD providers available in Medicaid within a 15-minute drive time per 100 Medicaid enrollees).

MAIN OUTCOMES AND MEASURES: Main outcomes included the probability of buprenorphine, methadone, and naltrexone use as a function of enrollee race and ethnicity, whether they had above-median geographic availability, and interactions between above-median geographic availability and race and ethnicity.

RESULTS: The sample included 8 081 899 Medicaid enrollees; 472 409 (5.8%) had an OUD diagnosis. The population was 58.7% female and 41.3% male. Among the study population, 11.7% were aged 18 to 20 years, 39.5% were aged 21 to 34 years, 21.4% were aged 35 to 44 years, 14.5% were aged 45 to 54 years, and 12.9% were aged 55 to 64 years. Overall, 7.3% of the sample were Hispanic enrollees, 27.4% were non-Hispanic Black enrollees, 56.2% were non-Hispanic White enrollees, and 7.2% were enrollees from other racial and ethnic groups. Overall, 13 575 buprenorphine prescribers, 516 methadone dispensers, and 4801 naltrexone prescribers billed Medicaid. Median Medicaid MOUD providers available within a 15-minute drive were 0.89 per 100 enrollees for buprenorphine, 0.03 for methadone, and 0.32 for naltrexone. Above-median geographic availability of methadone was associated with a 0.99 (95% CI, 0.54-1.42)-percentage point increase in methadone use for non-Hispanic White enrollees; there was no such increase for non-Hispanic Black or Hispanic enrollees. Evidence of similar differences was limited for naltrexone. Above-median availability of buprenorphine was not associated with increased use of MOUD for any racial or ethnic group.

CONCLUSIONS AND RELEVANCE: In this cross-sectional study of 10 state Medicaid programs, greater geographic availability of MOUD was associated with increased use only for methadone and, to a lesser extent, naltrexone. No racial and ethnic groups experienced gains in use associated with improved access. Additional strategies beyond addressing geographic access may be needed to close racial and ethnic gaps in MOUD.

OBJECTIVES: The 2023 Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations for 10-year cardiovascular disease (CVD) were developed by the American Heart Association to improve upon the 2013 Pooled Cohort Equations (PCEs). This study sought to compare risk reclassification using the PREVENT equations with and without the use of optional predictors of hemoglobin A1c (HbA1c) and urine albumin-creatinine ratio (UACR).

RESEARCH DESIGN AND METHODS: This was a cross-sectional study of adults aged 30 to 79 years participating in the National Health and Nutrition Examination Survey 2015-2020. Ten-year CVD estimates stratified by diabetes status using the base PREVENT equations and the enhanced PREVENT equations including HbA1c and UACR were examined.

RESULTS: The study included 8,293 participants (weighted mean age 51 years; 52% female; 13% with diabetes) representing 147.9 million US adults. Estimated 10-year CVD risks using the base and enhanced PREVENT equations were within 5 percentage points for 99% of adults without diabetes and 82% of adults with diabetes. Individuals with HbA1c ≥9.0% had a higher enhanced equations' mean risk of 17.8% (95% CI, 15.9%-19.7%) compared to the base equations' mean risk of 11.8% (95% CI, 10.7%-12.9%). This was mirrored with UACR, as individuals UACR ≥300 mg/g had a higher enhanced equations' mean risk at 36.1% (95% CI, 33.2%-39.1%) compared to the based equations' mean risk of 23.4% (95% CI, 20.0%-26.9%).

CONCLUSIONS: Inclusion of HbA1c and UACR in the PREVENT equations provides opportunity for greater individualization of CVD risk estimation in adults with diabetes but little benefit in populations without diabetes.

BACKGROUND: Physical restraints and antipsychotics should only be used as a last resort in hospitalized individuals at risk of harm to themselves or others. Since verbal reorientation and de-escalation are first-line management in these scenarios, patients with limited English proficiency (LEP) may be at higher risk of physical restraint and antipsychotic use. We aimed to examine the relationship between English proficiency, physical restraints, and antipsychotic use in hospitalized adults.

METHODS: Retrospective cohort study of adults discharged from an academic medical center between 1/2019 and 6/2023. LEP was defined by non-English primary language listed in the electronic medical record. The primary outcome was use of either physical restraints or antipsychotics during the hospitalization, defined by orders and pharmacy charges, respectively. Multivariable generalized estimating equations were used to examine the relationship between primary language and outcomes, controlling for demographic, hospitalization, and clinical characteristics.

RESULTS: The cohort consisted of 132,767 hospitalizations (mean age 62.9 years, 48.3% female, 10.8% with LEP). Either physical restraints or antipsychotics were used in 14,802 (11.2%) hospitalizations. Unadjusted outcome rates in LEP and non-LEP groups were similar (10.1% vs. 11.3% for either physical restraints or antipsychotics, 7.9% vs. 8.9% for physical restraints, and 4.9% vs. 4.9% for antipsychotics). However, LEP patients had higher adjusted odds of either physical restraint or antipsychotic use (aOR 1.18; 95% CI, 1.08-1.28), physical restraint use (aOR 1.30; 95% CI 1.17-1.43), and antipsychotic use (aOR 1.12; 95% CI 1.01-1.25). Subgroup analysis among patients with diagnosed delirium revealed an even stronger association between LEP and the primary outcome (aOR 1.42; 95% CI 1.22-1.65).

CONCLUSION: This single center study found language-based disparities in orders for physical restraints and antipsychotics among hospitalized patients. Given the potential risks associated with both physical restraints and antipsychotics, causes and mitigators of this disparity should be investigated.

IMPORTANCE: State Medicaid prescription cap policies (ie, limiting the monthly number of covered prescriptions) may impede access to medications for opioid use disorder (OUD) and other chronic conditions. Yet, these policies remain understudied among those who become subject to caps at age 21 years.

OBJECTIVE: To evaluate the association of prescription cap policies with medication and acute care use among young adults with OUD.

DESIGN, SETTING, AND PARTICIPANTS: This study identified a cohort of young adults diagnosed with OUD using T-MSIS Analytic Files from January 1, 2016, to December 31, 2021. Data analysis was conducted from July 2025 to December 2025. The study compared outcomes between prescription cap and noncap states using a difference-in-differences analysis where a 2-month policy phase-in window was applied before and after age 21 years and effects estimated across the full follow-up period and the early (months 3-6), mid (months 7-9), and late (months 10-12) periods since the 21st birthday.

EXPOSURES: Becoming exposed to prescription caps at age 21 years.

MAIN OUTCOMES AND MEASURES: Monthly use (any and count) of buprenorphine, overall prescriptions, inpatient hospitalizations, and emergency department (ED) visits 12 months before vs after participant reached the age of 21.

RESULTS: This study analyzed 15 526 individuals from 26 non-prescription cap states and 1769 from 8 states with prescription cap policies. Most individuals were female (noncap states, 8156 [52.5%]; cap states, 1033 [58.4%]) and White (noncap states, 9512 [61.3%]; cap states, 705 [39.9%]). The baseline monthly prevalence for noncap and cap states was 39.3% vs 40.2% for any prescription receipt, 7.5% vs 3.1% for buprenorphine receipt, 3.2% vs 4.8% for hospitalizations, and 14.1% vs 18.7% for ED visits. After adjustment, cap policies were associated with a 4.7% (95% confidence limit [CL], -9.9% to -0.2%) lower prevalence of any prescription receipt and 12.7% (95% CL, -18.7%, -6.7%) fewer total monthly prescriptions 10 to 12 months after participants reached the age of 21. Cap states had more hospitalizations during postperiod months 10 to 12 (6.0%; 95% CL, 0.3%-10.0%) and more ED visits in postperiod months 3 to 6 (4.7%; 95% CL, 1.0%-10.0%) and months 7 to 9 (8.3%; 95% CL, 3.3%-13.3%). Buprenorphine use did not significantly change after cap implementation.

CONCLUSIONS AND RELEVANCE: In this cohort study, Medicaid prescription caps were associated with lower overall use of prescription medications and greater frequency of acute care use among young adults with OUD.