Risk of Suicidal Ideation and Behavior Following Early-Onset Idiopathic Restless Legs Syndrome Treatment.

PURPOSE: To estimate incidence rates of suicidal ideation and behavior following treatment initiation with gabapentinoids or dopamine agonists (DAs) in patients with newly diagnosed early-onset idiopathic restless legs syndrome (RLS) and to examine suicidal behavior risk, comparing between those receiving gabapentinoids and DAs. METHODS: A new user retrospective cohort study using MarketScan claims data from 2012 to 2019 was conducted. Exposures were monotherapy gabapentinoids or DAs initiated within 60 days of new RLS diagnosis. Three varying outcome measures of suicidality were examined and incidence rates were calculated for each. A log-binomial regression model the estimated relative risk (RR) of the outcomes with gabapentinoids. Propensity score weighting adjusted for baseline covariates, including age, substance use disorders, hyperlipidemia, antipsychotic use, hypnotic/sedative use, and mood stabilizer use, which were most imbalanced before weighting. RESULTS: The cohort included 6672 patients, with 4986 (74.7%) initiating a DA and 1686 (25.3%) initiating a gabapentinoid. Incidence rates for all outcome measures were higher in the gabapentinoid group (suicidality: 21.6 vs. 10.7 per 1000 person-years; suicidality with self-harm: 23.0 vs. 11.1 per 1000 person-years; overdose- and suicide-related events: 30.0 vs. 15.5 person-years). Associated risk of suicidality (adjusted RR, 1.27 [95% CI, 0.86-1.88]); suicidality with self-harm (adjusted RR, 1.30 [95% CI, 0.89-1.90]); or overdose- and suicide-related events (adjusted RR, 1.30 [95% CI, 0.93-1.80]) was not significant with gabapentinoids. CONCLUSIONS: Incidence rates for suicidal ideation and behavior were higher among the gabapentinoid group, although increased risk was not detected after adjustment. A possible signal cannot be ruled out given limitations of the data and rarity of the outcome.

A Review of Concordance and Quality in Clinical Guidelines for Hormonal Contraceptives to Mitigate Drug-Drug Interactions in Women With Epilepsy.

PURPOSE: Concomitant use of hormonal contraceptive agents (HCAs) and enzyme-inducting antiepileptic drugs (EIAEDs) may lead to contraceptive failure and unintended pregnancy. This review identified and evaluated concordance and quality of clinical treatment guidelines related to the use of HCAs in women with epilepsy (WWE) receiving EIAEDs. METHODS: Relevant clinical guidelines were identified across four databases and were independently evaluated for quality utilizing the AGREE-II protocol instrument. Quality in this context is defined as the rigor and transparency of the methodologies used to develop the guideline. Guidelines were further assessed in terms of concordance and discordance with the latest body of knowledge concerning the use of hormonal contraception in the presence of EIAEDs. RESULTS: A total of n = 5 guidelines were retrieved and evaluated. Overall guideline scores ranged from 17% to 92%, while individual domain scores ranged from 0% to 100%. Contraceptive guidelines consistently recommended the use of intrauterine systems and long-acting injectables in the presence of EIAEDs, recommended against the use of oral, transdermal, and vaginal ring contraceptives, and differed regarding recommendations related to implants. Guidelines agreed regarding recommendations that women treated with EIAEDs should receive intrauterine systems and long-acting injectables; however, the suggested frequency of administration of injectable contraceptives differed. The use of intrauterine systems in this population is supported by evidence, but there is uncertainty surrounding the use of long-acting injectables and contraceptive implants. CONCLUSIONS: To mitigate the risk of unintended pregnancy and its consequences, recommendations related to implants and long-acting injectable contraceptives should be evidence-based.

End-of-Life Healthcare Utilization in Lewy Body Dementia.

Background: Lewy body dementia (LBD) is the second most common neurodegenerative dementia in the US, presenting unique end-of-life challenges. Objective: This study examined healthcare utilization and care continuity in the last year of life in LBD. Methods: Medicare claims for enrollees with LBD, continuously enrolled in the year preceding death, were examined from 2011-2018. We assessed hospital stays, emergency department (ED) visits, intensive care unit (ICU) admissions, life-extending procedures, medications, and care continuity. Results: We identified 45,762 LBD decedents, predominantly female (51.8%), White (85.9%), with average age of 84.1 years (SD 7.5). There was a median of 2 ED visits (IQR 1-5) and 1 inpatient stay (IQR 0-2). Higher age was inversely associated with ICU stays (Odds Ratio [OR] 0.96; 95% Confidence Interval [CI] 0.96-0.97) and life-extending procedures (OR 0.96; 95% CI 0.95-0.96). Black and Hispanic patients experienced higher rates of ED visits, inpatient hospitalizations, ICU admissions, life-extending procedures, and in-hospital deaths relative to White patients. On average, 15 (7.5) medications were prescribed in the last year. Enhanced care continuity correlated with reduced hospital (OR 0.72; 95% CI 0.70-0.74) and ED visits (OR 0.71; 95% CI 0.69-0.87) and fewer life-extending procedures (OR 0.71; 95% CI 0.64-0.79). Conclusions: This study underscored the complex healthcare needs of people with LBD during their final year, which was influenced by age and race. Care continuity may reduce hospital and ED visits and life-extending procedures.

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.

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.

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.

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.