An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured individuals.

OBJECTIVES: Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation. METHODS: This retrospective study used United States (US) 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models. RESULTS: A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models' discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply. CONCLUSION: ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.

Updated core competencies in pharmacoepidemiology to inform contemporary curricula and training for academia, government, and industry.

PURPOSE: The first paper to specify the core content of pharmacoepidemiology as a profession was published by an ISPE (International Society for Pharmacoepidemiology) workgroup in 2012 (Jones JK et al. PDS 2012; 21[7]:677-689). Due to the broader and evolving scope of pharmacoepidemiology, ISPE considers it important to proactively identify, update and expand the list of core competencies to inform curricula of education programs; thus, better positioning pharmacoepidemiologists across academic, government (including regulatory), and industry positions. The aim of this project was to update the list of core competencies in pharmacoepidemiology. METHODS: To ensure applicability of findings to multiple areas, a working group was established consisting of ISPE members with positions in academia, industry, government, and other settings. All competencies outlined by Jones et al. were extracted from the initial manuscript and presented to the working group for review. Expert-based judgments were collated and used to identify consensus. It was noted that some competencies could contribute to multiple groups and could be directly or indirectly related to a group. RESULTS: Five core domains were proposed: (1) Epidemiology, (2) Clinical Pharmacology, (3) Regulatory Science, (4) Statistics and data science, and (5) Communication and other professional skills. In total, 55 individual competencies were proposed, of which 25 were new competencies. No competencies from the original work were dropped but aggregation or amendments were made where considered necessary. CONCLUSIONS: While many core competencies in pharmacoepidemiology have remained the same over the past 10 years, there have also been several updates to reflect new and emerging concepts in the field.

Trajectories of Pre-Exposure Prophylaxis (PrEP) Adherence Among Commercially Insured Individuals.

We used group-based trajectory models to identify four distinct trajectory patterns of adherence to pre-exposure prophylaxis (PrEP) among 20,696 users. Only 44.5% were consistently PrEP adherent, with younger age, being female, or having substance use disorder or depression associated with early discontinuation. Public health efforts are needed to improve PrEP adherence.

Proceedings of the 2023 Cannabis Clinical Outcomes Research Conference.

The Consortium for Medical Marijuana Clinical Outcomes Research, a multi-university collaboration established by the state of Florida in the USA, hosted its third annual Cannabis Clinical Outcomes Research Conference (CCORC) in May 2023. CCORC was held as a hybrid conference, with a scientific program consisting of in-person sessions, with some sessions livestreamed to virtual attendees. CCORC facilitated and promoted up-to-date research on the clinical effects of medical cannabis, fostering collaboration and active involvement among scientists, policymakers, industry professionals, clinicians, and other stakeholders. Three themes emerged from conference sessions and speaker presentations: (1) disentangling conflicting evidence for the effects of medical cannabis on public health, (2) seeking solutions to address barriers faced when conducting clinical cannabis research - especially with medical cannabis use in special populations such as those who are pregnant, and (3) unpacking the data behind cannabis use and mental health outcomes. The fourth annual CCORC is planned for the summer of 2024 in Florida, USA.

Incident mental health episodes after initiation of gabapentinoids vs. dopamine agonists for early-onset idiopathic restless legs syndrome.

Limited long-term safety information exists for gabapentinoid treatment of idiopathic restless legs syndrome (RLS). We estimated incident mental health-related emergency department visits and hospitalizations with a primary mental health diagnosis (primary outcome) among early-onset idiopathic RLS patients following first-line treatment initiation and examined outcome risk with gabapentinoids compared with dopamine agonists (DAs). A retrospective cohort study was conducted using administrative claims data from 2012 to 2019. Adults with early-onset (18-44 years) idiopathic RLS initiating either gabapentinoids or DAs within 60 days of new diagnosis were followed up to two years. Incidence rates were calculated and a log-binomial regression model with propensity score weighting estimated relative risk of the outcome and of substance use disorders (SUDs) as a secondary analysis with gabapentinoids. Among a cohort of 6,672 patients, 4,986 (74.7%) initiated DAs and 1,686 (25.3%) gabapentinoids. Incidence of the primary outcome (49.8 [95% CI 40.8-69.3] per 1,000 person-years) and SUDs (49.5 [95% CI 40.6-59.9] per 1,000 person-years) were higher in the gabapentinoid group compared with the DA group. A statistically significant risk of mental health diagnoses with gabapentinoids was not detected, but SUD risk was significant after covariate adjustment. High-risk mental health comorbidities (i.e., SUDs) should be considered when initiating RLS treatments.

A Natural Language Processing Algorithm for Classifying Suicidal Behaviors in Alzheimer's Disease and Related Dementia Patients: Development and Validation Using Electronic Health Records Data.

This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) and Deep Learning (DL) techniques to identify and classify documentation of suicidal behaviors in patients with Alzheimer's disease and related dementia (ADRD). We utilized MIMIC-III and MIMIC-IV datasets and identified ADRD patients and subsequently those with suicide ideation using relevant International Classification of Diseases (ICD) codes. We used cosine similarity with ScAN (Suicide Attempt and Ideation Events Dataset) to calculate semantic similarity scores of ScAN with extracted notes from MIMIC for the clinical notes. The notes were sorted based on these scores, and manual review and categorization into eight suicidal behavior categories were performed. The data were further analyzed using conventional ML and DL models, with manual annotation as a reference. The tested classifiers achieved classification results close to human performance with up to 98% precision and 98% recall of suicidal ideation in the ADRD patient population. Our NLP model effectively reproduced human annotation of suicidal ideation within the MIMIC dataset. These results establish a foundation for identifying and categorizing documentation related to suicidal ideation within ADRD population, contributing to the advancement of NLP techniques in healthcare for extracting and classifying clinical concepts, particularly focusing on suicidal ideation among patients with ADRD. Our study showcased the capability of a robust NLP algorithm to accurately identify and classify documentation of suicidal behaviors in ADRD patients.

Acceptability of an mHealth App for Monitoring Perinatal and Postpartum Mental Health: Qualitative Study With Women and Providers.

BACKGROUND: Up to 15% of pregnant and postpartum women commonly experience undiagnosed and untreated mental health conditions, such as depression and anxiety, which may result in serious health complications. Mobile health (mHealth) apps related to mental health have been previously used for early diagnosis and intervention but not among pregnant and postpartum women. OBJECTIVE: This study aims to assess the acceptability of using mHealth to monitor and assess perinatal and postpartum depression and anxiety. METHODS: Focus group discussions with pregnant and postpartum women (n=20) and individual interviews with health care providers (n=8) were conducted to inform the acceptability of mHealth and determine its utility for assessing perinatal and postpartum mood symptoms. Participants were recruited via purposive sampling from obstetric clinics and the surrounding community. A semistructured interview guide was developed by an epidemiologist with qualitative research training in consultation with an obstetrician. The first author conducted all focus group discussions and provider interviews either in person or via Zoom (Zoom Video Communications, Inc) depending on the COVID-19 protocol that was in place during the study period. All interviews were audio recorded with consent; transcribed; and uploaded for coding to ATLAS.ti 8 (ATLAS.ti Scientific Software Development Gmb H), a qualitative data analysis and retrieval software. Data were analyzed using the deductive content analysis method using a set of a priori codes developed based on the interview guide. Methodological rigor and quality were ensured by adopting a systematic approach during the implementation, data collection, data analysis, and reporting of the data. RESULTS: Almost all women and providers had downloaded and used at least 1 health app. The respondents suggested offering short questions in layperson language that could be understood by women of all educational levels and offering no more than 2 to 3 assessments per day at preferred timings decided by the women themselves. They also suggested that the women themselves receive the alerts first, with other options being family members, spouses, or friends if the women themselves did not respond within 24 to 72 hours. Customization and snooze features were strongly endorsed by women and providers to improve acceptability and utility. Women mentioned competing demands on their time during the postpartum period, fatigue, privacy, and the security of mental health data as concerns. Health care professionals highlighted the long-term sustainability of app-based mood assessment and monitoring as an important challenge. CONCLUSIONS: The findings from this study show that mHealth would be acceptable to pregnant and postpartum women for monitoring mood symptoms. This could inform the development of clinically meaningful and inexpensive tools for facilitating the continuous monitoring of, the early diagnosis of, and an early intervention for mood disorders in this vulnerable population.

Pragmatic Model and Faculty Toolkit for PharmD Student Engagement in Social and Administrative Pharmacy Research.

OBJECTIVE: We aim to describe the development of a pharmacy student workgroup as an experiential education model to provide social and administrative pharmacy research opportunities and provide a toolkit for faculty seeking to increase student research engagement via this model. METHODS: Three pharmacy faculty with diverse training backgrounds but a common interest in opioid medications established a workgroup named the Opioid Research Workgroup. The workgroup consisted of first-year pharmacy students, research interns, and advanced graduate trainees. A hierarchical leadership model of supervision was implemented, whereby students reported progress on research tasks directly to an advanced graduate trainee leading a project team. To understand students' perspectives on the research experience and educational outcomes, students were asked to complete an anonymous voluntary survey after a year of participation. RESULTS: Since its establishment, the workgroup has published multiple conference abstracts, manuscripts, and grants. Students' overall satisfaction with the Workgroup on a scale of 1-5, 5 being very high, was 4.69. The successful scalability and longevity of this model are dependent on administrative support that protects faculty resources. The toolkit provided offers resources for those interested in adapting this model. CONCLUSION: Our experience with the pragmatic model of pharmacy student engagement in research proved successful in terms of research output and student training experience. Although the model can be applied across a variety of health science clinical and research topics, and faculty can leverage this approach to increase productivity in research output, faculty must ensure that resources are available to support this effort.

Protocol of a Combined Cohort and Cross-Sectional Study of Persons Receiving Medical Cannabis in Florida, USA: The Medical Marijuana and Me (M3) Study.

Significant knowledge gaps regarding the effectiveness and safety of medical cannabis (MC) create clinical challenges for MC physicians, making treatment recommendations and patients choosing treatment among the growing number of options offered in dispensaries. Additionally, data describing the characteristics of people who use MC and the products and doses they receive are lacking. The Medical Marijuana and Me (M3) Study was designed to collect patient-centered data from MC users. We aim to describe preferred MC use patterns that patients report as "most effective" for specific health conditions and symptoms, identify user characteristics associated with such use patterns, characterize adverse effects, including cannabis use disorder, identify products and patient characteristics associated with adverse effects, describe concurrent prescription medication use, and identify concomitant medication use with potential drug-MC interaction risk. Among MC initiators, we also aim to quantify MC use persistence and identify reasons for discontinuation, assess MC utilization pattern trajectories over time, describe outcome trajectories of primary reasons for MC use and determine factors associated with different trajectories, track changes in concomitant substance and medication use after MC initiation, and identify factors associated with such changes. M3 is a combined study comprised of: (1) a prospective cohort of MC initiators completing surveys at enrollment, 3 months, and 9 months after MC initiation and (2) a cross-sectional study of current MC users. A multidisciplinary committee including researchers, physicians, pharmacists, patients, and dispensary personnel designed and planned study protocols, established study measures, and created survey questionnaires. M3 will recruit 1,000-1,200 participants aged ≥18 years, with ∼50% new and ∼50% current MC patients from MC clinics across Florida, USA. Study enrollment started in May 2022 and will continue until the target number of patients is achieved. Survey domains include sociodemographic characteristics, physical and mental health, cannabis use history, reasons for MC use and discontinuation, MC products and use patterns, concurrent use of prescription medications and other substances, and side effects. Data collected in the M3 Study will be available for interested researchers affiliated with the Consortium for Medical Marijuana Clinical Outcomes Research. The M3 Study and Databank will be the largest cohort of current and new MC users in Florida, USA, which will provide data to support MC-related health research necessary to inform policy and clinical practice and ultimately improve patient outcomes.

Enhancing active learning of medication errors in the PharmD curriculum through a multi-perspective video-based experience.

INTRODUCTION: We created and assessed an immersive instructional series of video-based activities for pharmacy student evaluation of medication errors via root cause analysis (RCA). METHODS: A novel series of video vignettes showed a medication error from the perspectives of each healthcare team member involved. Students were engaged in a series of activities to guide them through RCA interspersed with the vignettes. A pre/post-assessment tool measured student-perceived skills and attitudes in medication error prevention and handling. Per item pre/post-mean scores were compared using Mann-Whitney U tests with Bonferroni correction. RESULTS: From N = 270 students, 231 and 163 completed the anonymous pre- and post-assessment, respectively. Most students positively endorsed attitude items at both assessment intervals, with no significant changes in mean for "learning how to improve patient safety is an appropriate use of time in pharmacy school" (pre-assessment = 4.26; post-assessment = 4.23). However, there were significant improvements in the skills items "I am confident in my ability to analyze a case to find the root causes of an error" (pre = 3.44; post = 3.85) and "I can identify the key factors in systems and processes that could lead to a medication error" (pre = 3.55; post = 3.88). CONCLUSIONS: Pharmacy students reported significantly improved self-perceived skills in handling and preventing medication errors, but not in attitudes, following the immersive instructional activity. There are opportunities to expand such an immersive instructional series in an interprofessional setting, which may yield different findings.

Clinician barriers, perceptions, and practices in treating patients with hepatitis C virus and substance use disorder in the United States.

The likelihood of clinicians prescribing direct-acting antiviral (DAA) therapy for patients with chronic hepatitis C virus (HCV) and substance use disorder (SUD) was assessed via a survey emailed throughout the United States to clinicians (physicians and advanced practice providers) in gastroenterology, hepatology, and infectious disease specialties. Clinicians' perceived barriers and preparedness and actions associated with current and future DAA prescribing practices of HCV-infected patients with SUD were assessed. Of 846 clinicians presumably receiving the survey, 96 completed and returned it. Exploratory factor analyses of perceived barriers indicated a highly reliable (Cronbach alpha = 0.89) model with five factors: HCV stigma and knowledge, prior authorization requirements, and patient- clinician-, and system-related barriers. In multivariable analyses, after controlling for covariates, patient-related barriers (P < 0.01) and prior authorization requirements (P < 0.01) were negatively associated with the likelihood of prescribing DAAs. Exploratory factor analyses of clinician preparedness and actions indicated a highly reliable (Cronbach alpha = 0.75) model with three factors: beliefs and comfort level; action; and perceived limitations. Clinician beliefs and comfort levels were negatively associated with the likelihood of prescribing DAAs (P = 0.01). Composite scores of barriers (P < 0.01) and clinician preparedness and actions (P < 0.05) were also negatively associated with the intent to prescribe DAAs. Conclusion: These findings underscore the importance of addressing patient-related barriers and prior authorization requirements-significant problematic barriers-and improving clinicians' beliefs (e.g., medication-assisted therapy should be prescribed before DAAs) and comfort levels for treating patients with HCV and SUD to enhance treatment access for patients with both HCV and SUD.

Using Joinpoint regression for drug utilization research: Tutorial and case study of prescription opioid use in the United States.

PURPOSE: Drug utilization researchers are often interested in evaluating prescribing and medication use patterns and trends over a specified period of time. Joinpoint regression is a useful methodology to identify any deviations in secular trends without a preconceived notion of where these break points might occur. This article provides a tutorial on the use of joinpoint regression, within Joinpoint software, for the analysis of drug utilization data. METHODS: The statistical considerations for whether a joinpoint regression analytical technique is a suitable approach are discussed. Then, we offer a tutorial as an introduction on conducting joinpoint regression (within Joinpoint software) through a step-by-step application, which is a case study developed using opioid prescribing data from the United States. Data were obtained from public files available through the Centers for Disease Control and Prevention from 2006 to 2018. The tutorial provides parameters and sample data needed to replicate the case study and it concludes with general considerations for the reporting of results using joinpoint regression in drug utilization research. RESULTS: The case study evaluated the trend of opioid prescribing in the United States from 2006 to 2018, where time points of significant variation (one in 2012 and another in 2016) are detected and interpreted. CONCLUSIONS: Joinpoint regression is a helpful methodology for drug utilization for the purposes of conducting descriptive analyses. This tool also assists with corroborating assumptions and identifying parameters for fitting other models such as interrupted time series. The technique and accompanying software are user-friendly; however, researchers interested in using joinpoint regression should exercise caution and follow best practices for correct measurement of drug utilization.

Partnering with recovery community centers to build recovery capital by improving access to reproductive health.

BACKGROUND: People with substance use disorders (SUD), especially opioid use disorder (OUD) have the highest rates of unintended pregnancies (80-95%) and report unmet reproductive health needs. Women of childbearing age have some of the highest death rates from opioids and are notably rising the most rapidly, and when pregnancy does occur overdose is one of the leading causes of maternal mortality. There are numerous gender-based health disparities and social determinants of health shaped by the distribution of power and privilege that influence the risk trajectories of people who can get pregnant or are pregnant with a substance use disorder (SUD). PURPOSE: The purpose of this paper is to describe how reproductive health is essential to recovery and building recovery capital for people who can get pregnant, (1) introduce a pilot implementation science study working with trained peer support coaches to promote reproductive autonomy in the community, and (2) make policy and advocacy recommendations relevant to the new reproductive health landscape in the United States. We will also describe the adaptation and feasibility of the initial pilot study where we partnered with a recovery community center to train peer recovery coaches to provide low barrier resources (contraception, pregnancy tests and prenatal vitamins) and referrals to health care. METHODS: This initiative is the merging of best practices in recovery and community-based global reproductive health, to empower people with SUD who can get pregnant in an implementation science framework. The pilot study will last 3 months in each city and aims to (1) assess and describe the effectiveness of the training of local peer recovery coaches on the link between recovery capital and reproductive health, and (2) assess the feasibility, acceptability, appropriateness, scalability, sustainability, and uptake and reach of low barrier reproductive health resources (pregnancy tests, prenatal vitamins, and emergency contraception). In this paper we are only reporting the initial findings regarding adaptation and feasibility. FINDINGS: Informed by qualitative interviews with stakeholders and participants, the method of contraception was adapted from injectable to emergency to meet the needs and context of the community with SUD. Early outcomes such as uptake and acceptability indicate that this is a feasible model with peer recovery coaches and recovery community centers, with the greatest uptake of emergency contraception and pregnancy tests. CONCLUSION: Considering recent policies limiting access to reproductive health, innovative community-based solutions are needed to engage and empower people who can get pregnant or are pregnant while in active drug use and in recovery. Providing low barrier reproductive health items by people with lived experience with SUD can serve as a valuable harm reduction model and improve recovery capital. CLINICAL RELEVANCE: This is the first study to propose a methodology and context to implement a community-based study merging best practices in recovery with those in reproductive health with the potential to improve recovery capital and maternal/child health trajectories for people with SUD.

Proceedings of the 2022 Cannabis Clinical Outcomes Research Conference.

The Consortium for Medical Marijuana Clinical Outcomes Research, a multi-university collaboration established by the state of Florida in the USA, hosted its second annual Cannabis Clinical Outcomes Research Conference (CCORC) in May 2022. CCORC was held as a hybrid conference, with a scientific program consisting of in-person and virtual sessions. CCORC fostered and disseminated current research on clinical outcomes of medical marijuana while stimulating collaboration and engagement between the scientific community, policymakers, industry representatives, clinicians, and other interested stakeholders. Three themes emerged from conference sessions and speakers: (1) disentangling research findings comparing use and outcomes of medical and nonmedical cannabis, (2) addressing barriers and promoting facilitators for clinical cannabis research, and (3) resolving uncertainties around cannabis dosing. The third annual CCORC is planned for the summer of 2023 in Florida, USA.

The U.S. COVID-19 County Policy Database: a novel resource to support pandemic-related research.

BACKGROUND: It is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. There has been limited systematic evaluation of variation in U.S. local COVID-19-related policies. This study introduces the U.S. COVID-19 County Policy (UCCP) Database, whose objective is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies. METHODS: In January-March 2021, we collected an initial wave of cross-sectional data from government and media websites for 171 counties in 7 states on 22 county-level COVID-19-related policies within 3 policy domains that are likely to affect health: (1) containment/closure, (2) economic support, and (3) public health. We characterized the presence and comprehensiveness of policies using univariate analyses. We also examined the correlation of policies with one another using bivariate Spearman's correlations. Finally, we examined geographical variation in policies across and within states. RESULTS: There was substantial variation in the presence and comprehensiveness of county policies during January-March 2021. For containment and closure policies, the percent of counties with no restrictions ranged from 0% (for public events) to more than half for public transportation (67.8%), hair salons (52.6%), and religious gatherings (52.0%). For economic policies, 76.6% of counties had housing support, while 64.9% had utility relief. For public health policies, most were comprehensive, with 70.8% of counties having coordinated public information campaigns, and 66.7% requiring masks outside the home at all times. Correlations between containment and closure policies tended to be positive and moderate (i.e., coefficients 0.4-0.59). There was variation within and across states in the number and comprehensiveness of policies. CONCLUSIONS: This study introduces the UCCP Database, presenting granular data on local governments' responses to the COVID-19 pandemic. We documented substantial variation within and across states on a wide range of policies at a single point in time. By making these data publicly available, this study supports future research that can leverage this database to examine how policies contributed to and continue to influence pandemic-related health and socioeconomic outcomes and disparities. The UCCP database is available online and will include additional time points for 2020-2021 and additional counties nationwide.

Buprenorphine Use Trends Following Removal of Prior Authorization Policies for the Treatment of Opioid Use Disorder in 2 State Medicaid Programs.

Importance: State Medicaid programs have implemented initiatives to expand treatment coverage for opioid use disorder (OUD); however, some Medicaid programs still require prior authorizations (PAs) for filling buprenorphine prescriptions. Objective: To evaluate the changes in buprenorphine use for OUD among Medicaid enrollees in states that completely removed buprenorphine PA requirements. Design Setting and Participants: This retrospective cross-sectional study analyzed the immediate and trend changes on buprenorphine use during 2013 to 2020 associated with removal of PA requirements using a controlled interrupted time series analysis to account for autocorrelation. Data were collected from Medicaid State Drug Utilization Data for 2 states (California and Illinois) that completely removed a buprenorphine PA during the study period, and buprenorphine prescriptions for OUD treatment were identified among Medicaid enrollees. Main Outcomes and Measures: Quarterly total number of buprenorphine prescriptions for each state was calculated, and stratification analyses were conducted by dosage form (films and tablets). Results: Among the 2 state Medicaid programs (California and Illinois) that removed buprenorphine PAs, there was a total of 702 643 and 415 115 eligible buprenorphine prescription claims, respectively. After removing PA requirements for buprenorphine, there was an immediate increase that was not statistically significant (rate ratio [RR], 1.11; 95% CI, 0.76-1.61) in the number of all buprenorphine prescriptions in California and a statistically significant increase (RR, 6.99; 95% CI, 4.67-10.47) in the number of all buprenorphine prescriptions in Illinois relative to the change in the control states (Alabama, Florida, Idaho, Kansas, Mississippi, Nevada, South Dakota, and Wyoming). Additionally, there was a statistically significant decreasing trend in the number of all buprenorphine prescriptions in California (RR, 0.88; 95% CI, 0.82-0.94) and a statistically significant increasing trend in Illinois (RR, 1.11; 95% CI, 1.05-1.19) relative to the trend in control states. Conclusions and Relevance: In this cross-sectional study, removal of buprenorphine PA requirements was associated with a statistically significant increase in the number of buprenorphine prescription fills among Medicaid populations in 1 of the 2 included states.