Association between severe acute respiratory syndrome coronavirus 2 antibody status and reinfection: A case-control study nested in a Colorado-based prospective cohort study.

The association between the presence of detectable antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and SARS-CoV-2 reinfection is not well established. The objective of this study was to determine the association between antibody seronegativity and reinfection. METHODS: Participants in Colorado, USA, were recruited between June 15, 2020, and March 28, 2021, and encouraged to complete SARS-CoV-2 molecular ribonucleic acid (RNA) and serology testing for antibodies every 28 days for 10 months. Participants with reinfections (positive SARS-CoV-2 RNA test ≥ 90 days after the first positive RNA test) were matched to controls without reinfections by age, sex, date of the first positive RNA test, date of the last serology test, and serology test type. Using conditional logistic regression, case patients were compared to control patients on the last serologic test result, with adjustment for demographic and clinical confounders. RESULTS: The cohort (n = 4,235) included 2,033 participants with ≥ 1 positive RNA test, of whom 120 had reinfection. Among the 80 case patients who could be matched, the last serologic test was negative in 12 of the cases (15.0 %) whereas the last serologic test was negative in 77 of 1,034 (7.5 %) controls. Seronegativity (adjusted OR [aOR] 2.24; 95 % CI 1.07, 4.68), Hispanic ethnicity (aOR 1.87; 95 % 1.10, 3.18), and larger household size (aOR 1.15; 95 % 1.01, 1.30 for each additional household member) were associated with reinfection. CONCLUSIONS: Seronegative status, Hispanic ethnicity, and increasing household size were associated with reinfection. Serologic testing could be considered to reduce vaccine hesitancy in higher risk populations.

Comparing Cannabis Use for Pain to Use for Other Reasons in Primary Care Patients.

BACKGROUND: Medical cannabis is commonly used for chronic pain, but little is known about differences in characteristics, cannabis use patterns, and perceived helpfulness among primary care patients who use cannabis for pain versus nonpain reasons. METHODS: Among 1688 patients who completed a 2019 cannabis survey administered in a health system in Washington state, where recreational use is legal, participants who used cannabis for pain (n = 375) were compared with those who used cannabis for other reasons (n = 558) using survey and electronic health record data. We described group differences in participant characteristics, use patterns, and perceptions and applied adjusted multinomial logistic and modified Poisson regression. RESULTS: Participants who used cannabis for pain were significantly more likely to report using applied (50.7% vs 10.6%) and beverage cannabis products (19.2% vs 11.6%), more frequent use (47.1% vs 33.1% for use ≥2 times per day; 81.6% vs 69.7% for use 4 to 7 days per week), and smoking tobacco cigarettes (19.2% vs 12.2%) than those who used cannabis for other reasons. They were also significantly more likely to perceive cannabis as very/extremely helpful (80.5% vs 72.7%), and significantly less likely to use cannabis for nonmedical reasons (4.8% vs 58.8%) or report cannabis use disorder symptoms (51.7% vs 61.1%). DISCUSSION: Primary care patients who use cannabis for pain use it more frequently, often in applied and ingested forms, and have more co-use of tobacco, which may differentially impact safety and effectiveness. These findings suggest the need for different approaches to counseling in clinical care.

An individual segmented trajectory approach for identifying opioid use patterns using longitudinal dispensing data.

PURPOSE: The aim of this study is to use electronic opioid dispensing data to develop an individual segmented trajectory approach for identifying opioid use patterns. The resulting opioid use patterns can be used for examining the association between opioid use and drug overdose. METHODS: We retrospectively assembled a cohort of members on long-term opioid therapy (LTOT) between January 1, 2006 and June 30, 2019 who were 18 years and older and enrolled in one of three health care systems in the US. We have developed an individual segmented trajectory analysis for identifying various opioid use patterns by scanning over the follow-up and finding distinct opioid use patterns based on variability measured with coefficient of variation and trends of milligram morphine equivalents levels. RESULTS: Among 31, 865 members who were on LTOT between January 1, 2006 and June 30, 2019, 58.3% were female, and the average age was 55.4 years (STD = 15.4). The study population had 152 557 person-years of follow-up, with an average follow-up of 4.4 years per enrollment per person (STD = 3.4). This novel approach identified up to 13 distinct patterns including 88 756 episodes of "stable" pattern (42.1%) with an average follow-up of 11.2 months, 29 140 episodes of "increasing" pattern (13.8%) with an average follow-up of 6.0 months, 13 201 episodes of ≤10% dose reduction (6.3%) with an average follow-up of 10.4 months, 7286 episodes of 11%-20% dose reduction (3.5%) with an average follow-up of 5.3 months, 4457 episodes of 21%-30% dose reduction (2.1%) with an average follow-up of 4.0 months, and 9903 episodes of >30% dose reduction (4.7%) with an average follow-up of 2.6 months. CONCLUSIONS: A novel approach was developed to identify 13 distinct opioid use patterns using each individual's longitudinal dispensing data and these patterns can be used in examining overdose risk during the time that these patterns are ongoing.

Association of initial opioid prescription duration and an opioid refill by pain diagnosis: Evidence from outpatient settings in ten US health systems.

OBJECTIVE: The Centers for Disease Control and Prevention's 2022 Clinical Practice Guideline for Prescribing Opioids for Pain cautioned that inflexible opioid prescription duration limits may harm patients. Information about the relationship between initial opioid prescription duration and a subsequent refill could inform prescribing policies and practices to optimize patient outcomes. We assessed the association between initial opioid duration and an opioid refill prescription. METHODS: We conducted a retrospective cohort study of adults ≥19 years of age in 10 US health systems between 2013 and 2018 from outpatient care with a diagnosis for back pain without radiculopathy, back pain with radiculopathy, neck pain, joint pain, tendonitis/bursitis, mild musculoskeletal pain, severe musculoskeletal pain, urinary calculus, or headache. Generalized additive models were used to estimate the association between opioid days' supply and a refill prescription. RESULTS: Overall, 220,797 patients were prescribed opioid analgesics upon an outpatient visit for pain. Nearly a quarter (23.5%) of the cohort received an opioid refill prescription during follow-up. The likelihood of a refill generally increased with initial duration for most pain diagnoses. About 1 to 3 fewer patients would receive a refill within 3 months for every 100 patients initially prescribed 3 vs. 7 days of opioids for most pain diagnoses. The lowest likelihood of refill was for a 1-day supply for all pain diagnoses, except for severe musculoskeletal pain (9 days' supply) and headache (3-4 days' supply). CONCLUSIONS: Long-term prescription opioid use increased modestly with initial opioid prescription duration for most but not all pain diagnoses examined.

Prescription Opioid Dose Reductions and Potential Adverse Events: a Multi-site Observational Cohort Study in Diverse US Health Systems.

BACKGROUND: In response to the opioid crisis in the United States, population-level prescribing of opioids has been decreasing; there are concerns, however, that dose reductions are related to potential adverse events. OBJECTIVE: Examine associations between opioid dose reductions and risk of 1-month potential adverse events (emergency department (ED) visits, opioid overdose, benzodiazepine prescription fill, all-cause mortality). DESIGN: This observational cohort study used electronic health record and claims data from eight United States health systems in a prescription opioid registry (Clinical Trials Network-0084). All opioid fills (excluding buprenorphine) between 1/1/2012 and 12/31/2018 were used to identify baseline periods with mean morphine milligram equivalents daily dose of  ≥ 50 during six consecutive months. PATIENTS: We identified 60,040 non-cancer patients with  ≥ one 2-month dose reduction period (600,234 unique dose reduction periods). MAIN MEASURES: Analyses examined associations between dose reduction levels (1- < 15%, 15- < 30%, 30- < 100%, 100% over 2 months) and potential adverse events in the month following a dose reduction using logistic regression analysis, adjusting for patient characteristics. KEY RESULTS: Overall, dose reduction periods involved mean reductions of 18.7%. Compared to reductions of 1- < 15%, dose reductions of 30- < 100% were associated with higher odds of ED visits (OR 1.14, 95% CI 1.10, 1.17), opioid overdose (OR 1.41, 95% CI 1.09-1.81), and all-cause mortality (OR 1.39, 95% CI 1.16-1.67), but lower odds of a benzodiazepine fill (OR 0.83, 95% CI 0.81-0.85). Dose reductions of 15- < 30%, compared to 1- < 15%, were associated with higher odds of ED visits (OR 1.08, 95% CI 1.05-1.11) and lower odds of a benzodiazepine fill (OR 0.93, 95% CI 0.92-0.95), but were not associated with opioid overdose and all-cause mortality. CONCLUSIONS: Larger reductions for patients on opioid therapy may raise risk of potential adverse events in the month after reduction and should be carefully monitored.

Association Between Opioid Dose Reduction Rates and Overdose Among Patients Prescribed Long-Term Opioid Therapy.

BACKGROUND: Tapering long-term opioid therapy is an increasingly common practice, yet rapid opioid dose reductions may increase the risk of overdose. The objective of this study was to compare overdose risk following opioid dose reduction rates of ≤10%, 11% to 20%, 21% to 30%, and >30% per month to stable dosing. METHODS: We conducted a retrospective cohort study in three health systems in Colorado and Wisconsin. Participants were patients ≥18 years of age prescribed long-term opioid therapy between January 1, 2006, and June 30, 2019. Five opioid dosing patterns and drug overdoses (fatal and nonfatal) were identified using electronic health records, pharmacy records, and the National Death Index. Cox proportional hazard regression was conducted on a propensity score-weighted cohort to estimate adjusted hazard ratios (aHRs) for follow-up periods of 1, 3, 6, 9, and 12 months after a dose reduction. RESULTS: In a cohort of 17 540 patients receiving long-term opioid therapy, 42.7% of patients experienced a dose reduction. Relative to stable dosing, a dose reduction rate of >30% was associated with an increased risk of overdose and the aHR estimates decreased as the follow-up increased; the aHRs for the 1-, 6- and 12-month follow-ups were 5.33 (95% CI, 1.98-14.34), 1.81 (95% CI,1.08-3.03), and 1.49 (95% CI, 0.97-2.27), respectively. The slower tapering rates were not associated with overdose risk. CONCLUSIONS: Patients receiving long-term opioid therapy exposed to dose reduction rates of >30% per month had increased overdose risk relative to patients exposed to stable dosing. Results support the use of slow dose reductions to minimize the risk of overdose.

Spinal cord injury and prescribed opioids for pain: a scoping review.

OBJECTIVE: Spinal cord injury (SCI) is a life-altering neurological condition affecting physical and psycho-social functioning and associated high rates of pain. Thus, individuals with SCI may be more likely to be exposed to prescription opioids. A scoping review was conducted to synthesize published research findings on post-acute SCI and prescription opioid use for pain, identify literature gaps, and propose recommendations for future research. METHODS: We searched 6 electronic bibliographic databases (PubMed [MEDLINE], Ovid [MEDLINE], EMBASE, Cochrane Library, CINAHL, PsychNET) for articles published from 2014 through 2021. Terms for "spinal cord injury" and "prescription opioid use" were used. Included articles were in English and peer reviewed. Data were extracted using an electronic database by 2 independent reviewers. Opioid use risk factors for chronic SCI were identified and a gap analysis was performed. RESULTS: Of the 16 articles included in the scoping review, a majority were conducted in the United States (n = 9). Most articles lacked information on income (87.5%), ethnicity (87.5%), and race (75%). Prescription opioid use ranged from 35% to 64% in articles reporting this information (n = 7 articles, n = 3675 participants). Identified risk factors for opioid use included middle age, lower income, osteoarthritis diagnosis, prior opioid use, and lower-level spinal injury. Limited reporting of diversity in study populations, absence of risk of polypharmacy, and limited high quality methodology were identified gaps. CONCLUSIONS: Future research should report data on prescription opioid use in SCI populations, with additional demographics such as race, ethnicity, and income, given their importance to risk outcomes.

Update of a Multivariable Opioid Overdose Risk Prediction Model to Enhance Clinical Care for Long-term Opioid Therapy Patients.

BACKGROUND: Clinical opioid overdose risk prediction models can be useful tools to reduce the risk of overdose in patients prescribed long-term opioid therapy (LTOT). However, evolving overdose risk environments and clinical practices in addition to potential harmful model misapplications require careful assessment prior to widespread implementation into clinical care. Models may need to be tailored to meet local clinical operational needs and intended applications in practice. OBJECTIVE: To update and validate an existing opioid overdose risk model, the Kaiser Permanente Colorado Opioid Overdose (KPCOOR) Model, in patients prescribed LTOT for implementation in clinical care. DESIGN, SETTING, AND PARTICIPANTS: The retrospective cohort study consisted of 33, 625 patients prescribed LTOT between January 2015 and June 2019 at Kaiser Permanente Colorado, with follow-up through June 2021. MAIN MEASURES: The outcome consisted of fatal opioid overdoses identified from vital records and non-fatal opioid overdoses from emergency department and inpatient settings. Predictors included demographics, medication dispensings, substance use disorder history, mental health history, and medical diagnoses. Cox proportional hazards regressions were used to model 2-year overdose risk. KEY RESULTS: During follow-up, 65 incident opioid overdoses were observed (111.4 overdoses per 100,000 person-years) in the study cohort, of which 11 were fatal. The optimal risk model needed to risk-stratify patients and to be easily interpreted by clinicians. The original 5-variable model re-validated on the new study cohort had a bootstrap-corrected C-statistic of 0.73 (95% CI, 0.64-0.85) compared to a C-statistic of 0.80 (95% CI, 0.70-0.88) in the updated model and 0.77 (95% CI, 0.66-0.87) in the final adapted 7-variable model, which was also well-calibrated. CONCLUSIONS: Updating and adapting predictors for opioid overdose in the KPCOOR Model with input from clinical partners resulted in a parsimonious and clinically relevant model that was poised for integration in clinical care.

Opioid Overdose Risk Following Hospital Discharge Among Individuals Prescribed Long-Term Opioid Therapy: a Risk Interval Analysis.

BACKGROUND: Individuals prescribed long-term opioid therapy (LTOT) have increased risk of readmission and death after hospital discharge. The risk of opioid overdose during the immediate post-discharge time period is unknown. OBJECTIVE: To examine the association between time since hospital discharge and opioid overdose among individuals prescribed LTOT. DESIGN: Self-controlled risk interval analysis. PARTICIPANTS: Adults prescribed LTOT with at least one hospital discharge at a safety-net health system and a non-profit healthcare organization in Colorado. MAIN MEASURES: We identified individuals prescribed LTOT who were discharged from January 2006 through June 2019. The outcome was a composite of fatal and non-fatal opioid overdoses during a 90-day post-discharge observation period, identified using electronic health record (EHR) and vital statistics data. Risk intervals included days 0-6 after index and subsequent hospital discharges. Control intervals ranged from days 7 to 89 after index discharge and included all other time during the observation period that did not fall within a risk interval or time readmitted during a subsequent hospitalization, which was excluded. Poisson regression was used to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI) for overdose events during risk in comparison to control intervals. KEY RESULTS: We identified 7695 adults (63.3% over 55 years, 59.4% female, 20.3% Hispanic) who experienced 9499 total discharges during the study period. Twenty-one overdoses occurred during their observation periods (1174 per 100,000 person-years [9 in risk, 12 in control]). Overdose risk was significantly higher during the risk interval in comparison to the control interval (IRR 6.92; 95% CI 2.92-16.43). CONCLUSION: During the first 7 days after hospital discharge, individuals prescribed LTOT appear to be at elevated risk for opioid overdose. Clarifying mechanisms of overdose risk may help inform in-hospital and post-discharge prevention strategies.

The association between buprenorphine treatment duration and mortality: a multi-site cohort study of people who discontinued treatment.

BACKGROUND AND AIMS: Buprenorphine is an effective medication for opioid use disorder that reduces mortality; however, many patients are not retained in buprenorphine treatment, and an optimal length of treatment after which patients can safely discontinue treatment has not been identified. This study measured the association between buprenorphine treatment duration and all-cause mortality among patients who discontinued treatment. Secondary objectives were to measure the association between treatment duration and drug overdose and opioid-related overdoses. DESIGN: Multi-site cohort study. SETTING: Eight US health systems. PARTICIPANTS: Patients who initiated and discontinued buprenorphine treatment between 1 January 2012 and 31 December 2018 (n = 6550). Outcomes occurring after patients discontinued buprenorphine treatment were compared between patients who initiated and discontinued treatment after 8-30, 31-90, 91-180, 181-365 and > 365 days. MEASUREMENTS: Covariate data were obtained from electronic health records (EHRs). Mortality outcomes were derived from EHRs and state vital statistics. Non-fatal opioid and drug overdoses were obtained from diagnostic codes. Four sites provided cause-of-death data to identify fatal drug and opioid-related overdoses. Adjusted frailty regression was conducted on a propensity-weighted cohort to assess associations between duration of the final treatment episode and outcomes. FINDINGS: The mortality rate after buprenorphine treatment was 1.82 per 100 person-years (n = 191 deaths). In regression analyses with > 365 days as the reference group, treatment duration was not associated with all-cause mortality and drug overdose (P > 0.05 for both). However, compared with > 365 days of treatment, 91-180 days of treatment was associated with increased opioid overdose risk (hazard ratio = 2.94, 95% confidence interval = 1.11-7.79). CONCLUSIONS: Among patients who discontinue buprenorphine treatment, there appears to be no treatment duration period associated with a reduced risk for all-cause mortality. Patients who discontinue buprenorphine treatment after 91-180 days appear to be at heightened risk for opioid overdose compared with patients who discontinue after > 365 days of treatment.

Opioid Dose Trajectories and Associations With Mortality, Opioid Use Disorder, Continued Opioid Therapy, and Health Plan Disenrollment.

Importance: Uncertainty remains about the longer-term benefits and harms of different opioid management strategies, such as tapering and dose escalation. For instance, opioid tapering could help patients reduce opioid exposure to prevent opioid use disorder, but patients may also seek care elsewhere and engage in nonprescribed opioid use. Objective: To evaluate the association between opioid dose trajectories observed in practice and patient outcomes. Design, Setting, and Participants: This retrospective cohort study was conducted in 3 health systems in Colorado and Wisconsin. The study population included patients receiving long-term opioid therapy between 50 and 200 morphine milligram equivalents between August 1, 2014, and July 31, 2017. Follow-up ended on December 31, 2019. Data were analyzed from January 2020 to August 2022. Exposures: Group-based trajectory modeling identified 5 dosing trajectories over 1 year: 1 decreasing, 1 high-dose increasing, and 3 stable. Main Outcomes and Measures: Primary outcomes assessed after the trajectory period were 1-year all-cause mortality, incident opioid use disorder, continued opioid therapy at 1 year, and health plan disenrollment. Associations were tested using Cox proportional hazards regression and log-binomial models, adjusting for baseline covariates. Results: A total of 3913 patients (mean [SD] age, 59.2 [14.4] years; 2767 White non-Hispanic [70.7%]; 2237 female patients [57.2%]) were included in the study. Compared with stable trajectories, the decreasing dose trajectory was negatively associated with opioid use disorder (adjusted hazard ratio [aHR], 0.40; 95% CI, 0.29-0.55) and continued opioid therapy (site 1: adjusted relative risk [aRR], 0.39; 95% CI, 0.34-0.44), but was positively associated with health plan disenrollment (aHR, 1.66; 95% CI, 1.24-2.22). The decreasing trajectory was not associated with mortality (aHR, 1.28; 95% CI, 0.87-1.86). In contrast, the high-dose increasing trajectory was positively associated with mortality (aHR, 2.19; 95% CI, 1.44-3.32) and opioid use disorder (aHR, 1.81; 95% CI, 1.39-2.37) but was not associated with disenrollment (aHR, 0.90; 95% CI, 0.56-1.42) or continued opioid therapy (site 1: aRR, 0.98; 95% CI, 0.94-1.03). Conclusions and Relevance: In this cohort study, decreasing opioid dose was associated with reduced risk of opioid use disorder and continued opioid therapy but increased risk of disenrollment compared with stable dosing, whereas the high-dose increasing trajectory was associated with an increased risk of mortality and opioid use disorder. These findings can inform opioid management decision-making.

Sex Differences in Comorbid Mental and Substance Use Disorders Among Primary Care Patients With Opioid Use Disorder.

OBJECTIVE: The authors sought to characterize the 3-year prevalence of mental disorders and nonnicotine substance use disorders among male and female primary care patients with documented opioid use disorder across large U.S. health systems. METHODS: This retrospective study used 2014-2016 data from patients ages ≥16 years in six health systems. Diagnoses were obtained from electronic health records or claims data; opioid use disorder treatment with buprenorphine or injectable extended-release naltrexone was determined through prescription and procedure data. Adjusted prevalence of comorbid conditions among patients with opioid use disorder (with or without treatment), stratified by sex, was estimated by fitting logistic regression models for each condition and applying marginal standardization. RESULTS: Females (53.2%, N=7,431) and males (46.8%, N=6,548) had a similar prevalence of opioid use disorder. Comorbid mental disorders among those with opioid use disorder were more prevalent among females (86.4% vs. 74.3%, respectively), whereas comorbid other substance use disorders (excluding nicotine) were more common among males (51.9% vs. 60.9%, respectively). These differences held for those receiving medication treatment for opioid use disorder, with mental disorders being more common among treated females (83% vs. 71%) and other substance use disorders more common among treated males (68% vs. 63%). Among patients with a single mental health condition comorbid with opioid use disorder, females were less likely than males to receive medication treatment for opioid use disorder (15% vs. 20%, respectively). CONCLUSIONS: The high rate of comorbid conditions among patients with opioid use disorder indicates a strong need to supply primary care providers with adequate resources for integrated opioid use disorder treatment.

Development and implementation of a prescription opioid registry across diverse health systems.

Objective: Develop and implement a prescription opioid registry in 10 diverse health systems across the US and describe trends in prescribed opioids between 2012 and 2018. Materials and Methods: Using electronic health record and claims data, we identified patients who had an outpatient fill for any prescription opioid, and/or an opioid use disorder diagnosis, between January 1, 2012 and December 31, 2018. The registry contains distributed files of prescription opioids, benzodiazepines and other select medications, opioid antagonists, clinical diagnoses, procedures, health services utilization, and health plan membership. Rates of outpatient opioid fills over the study period, standardized to health system demographic distributions, are described by age, gender, and race/ethnicity among members without cancer. Results: The registry includes 6 249 710 patients and over 40 million outpatient opioid fills. For the combined registry population, opioid fills declined from a high of 0.718 per member-year in 2013 to 0.478 in 2018, and morphine milligram equivalents (MMEs) per fill declined from 985 MMEs per fill in 2012 to 758 MMEs in 2018. MMEs per member declined from 692 MMEs per member in 2012 to 362 MMEs per member in 2018. Conclusion: This study established a population-based opioid registry across 10 diverse health systems that can be used to address questions related to opioid use. Initial analyses showed large reductions in overall opioid use per member among the combined health systems. The registry will be used in future studies to answer a broad range of other critical public health issues relating to prescription opioid use.

Comparison of Medical Cannabis Use Reported on a Confidential Survey vs Documented in the Electronic Health Record Among Primary Care Patients.

Importance: Patients who use cannabis for medical reasons may benefit from discussions with clinicians about health risks of cannabis and evidence-based treatment alternatives. However, little is known about the prevalence of medical cannabis use in primary care and how often it is documented in patient electronic health records (EHR). Objective: To estimate the primary care prevalence of medical cannabis use according to confidential patient survey and to compare the prevalence of medical cannabis use documented in the EHR with patient report. Design, Setting, and Participants: This study is a cross-sectional survey performed in a large health system that conducts routine cannabis screening in Washington state where medical and nonmedical cannabis use are legal. Among 108 950 patients who completed routine cannabis screening (between March 28, 2019, and September 12, 2019), 5000 were randomly selected for a confidential survey about cannabis use, using stratified random sampling for frequency of past-year use and patient race and ethnicity. Data were analyzed from November 2020 to December 2021. Exposures: Survey measures of patient-reported past-year cannabis use, medical cannabis use (ie, explicit medical use), and any health reason(s) for use (ie, implicit medical use). Main Outcomes and Measures: Survey data were linked to EHR data in the year before screening. EHR measures included documentation of explicit and/or implicit medical cannabis use. Analyses estimated the primary care prevalence of cannabis use and compared EHR-documented with patient-reported medical cannabis use, accounting for stratified sampling and nonresponse. Results: Overall, 1688 patients responded to the survey (34% response rate; mean [SD] age, 50.7 [17.5] years; 861 female [56%], 1184 White [74%], 1514 non-Hispanic [97%], and 1059 commercially insured [65%]). The primary care prevalence of any past-year patient-reported cannabis use on the survey was 38.8% (95% CI, 31.9%-46.1%), whereas the prevalence of explicit and implicit medical use were 26.5% (95% CI, 21.6%-31.3%) and 35.1% (95% CI, 29.3%-40.8%), respectively. The prevalence of EHR-documented medical cannabis use was 4.8% (95% CI, 3.45%-6.2%). Compared with patient-reported explicit medical use, the sensitivity and specificity of EHR-documented medical cannabis use were 10.0% (95% CI, 4.4%-15.6%) and 97.1% (95% CI, 94.4%-99.8%), respectively. Conclusions and Relevance: These findings suggest that medical cannabis use is common among primary care patients in a state with legal use, and most use is not documented in the EHR. Patient report of health reasons for cannabis use identifies more medical use compared with explicit questions about medical use.

Clinical documentation of patient-reported medical cannabis use in primary care: Toward scalable extraction using natural language processing methods.

Background: Most states have legalized medical cannabis, yet little is known about how medical cannabis use is documented in patients' electronic health records (EHRs). We used natural language processing (NLP) to calculate the prevalence of clinician-documented medical cannabis use among adults in an integrated health system in Washington State where medical and recreational use are legal. Methods: We analyzed EHRs of patients ≥18 years old screened for past-year cannabis use (November 1, 2017-October 31, 2018), to identify clinician-documented medical cannabis use. We defined medical use as any documentation of cannabis that was recommended by a clinician or described by the clinician or patient as intended to manage health conditions or symptoms. We developed and applied an NLP system that included NLP-assisted manual review to identify such documentation in encounter notes. Results: Medical cannabis use was documented for 16,684 (5.6%) of 299,597 outpatient encounters with routine screening for cannabis use among 203,489 patients seeing 1,274 clinicians. The validated NLP system identified 54% of documentation and NLP-assisted manual review the remainder. Language documenting reasons for cannabis use included 125 terms indicating medical use, 28 terms indicating non-medical use and 41 ambiguous terms. Implicit documentation of medical use (e.g., "edible THC nightly for lumbar pain") was more common than explicit (e.g., "continues medical cannabis use"). Conclusions: Clinicians use diverse and often ambiguous language to document patients' reasons for cannabis use. Automating extraction of documentation about patients' cannabis use could facilitate clinical decision support and epidemiological investigation but will require large amounts of gold standard training data.

Naloxone Co-Dispensing with Opioids: a Cluster Randomized Pragmatic Trial.

BACKGROUND: Although naloxone prevents opioid overdose deaths, few patients prescribed opioids receive naloxone, limiting its effectiveness in real-world settings. Barriers to naloxone prescribing include concerns that naloxone could increase risk behavior and limited time to provide necessary patient education. OBJECTIVE: To determine whether pharmacy-based naloxone co-dispensing affected opioid risk behavior. Secondary objectives were to assess if co-dispensing increased naloxone acquisition, increased patient knowledge about naloxone administration, and affected opioid dose and other substance use. DESIGN: Cluster randomized pragmatic trial of naloxone co-dispensing. SETTING: Safety-net health system in Denver, Colorado, between 2017 and 2020. PARTICIPANTS: Seven pharmacies were randomized. Pharmacy patients (N=768) receiving opioids were followed using automated data for 10 months. Pharmacy patients were also invited to complete surveys at baseline, 4 months, and 8 months; 325 survey participants were enrolled from November 15, 2017, to January 8, 2019. INTERVENTION: Intervention pharmacies implemented workflows to co-dispense naloxone while usual care pharmacies provided usual services. MAIN MEASURES: Survey instruments assessed opioid risk behavior; hazardous drinking; tobacco, cannabis, and other drug use; and knowledge. Naloxone dispensings and opioid dose were evaluated using pharmacy data among pharmacy patients and survey participants. Intention-to-treat analyses were conducted using generalized linear mixed models accounting for clustering at the pharmacy level. KEY RESULTS: Opioid risk behavior did not differ by trial group (P=0.52; 8-month vs. baseline adjusted risk ratio [ARR] 1.07; 95% CI 0.78, 1.47). Compared with usual care pharmacies, naloxone dispensings were higher in intervention pharmacies (ARR 3.38; 95% CI 2.21, 5.15) and participant knowledge increased (P=0.02; 8-month vs. baseline adjusted mean difference 1.05; 95% CI 0.06, 2.04). There was no difference in other substance use by the trial group. CONCLUSION: Co-dispensing naloxone with opioids effectively increased naloxone receipt and knowledge but did not increase self-reported risk behavior. TRIAL REGISTRATION: Registered at ClinicalTrials.gov ; Identifier: NCT03337100.

Opioid Use Disorder Treatment Initiation and Continuation: a Qualitative Study of Patients Who Received Addiction Consultation and Hospital-Based Providers.

BACKGROUND: Hospitalizations related to opioid use disorder (OUD) are rising. Addiction consultation services (ACS) increasingly provide OUD treatment to hospitalized patients, but barriers to initiating and continuing medications for OUD remain. We examined facilitators and barriers to hospital-based OUD treatment initiation and continuation from the perspective of patients and healthcare workers in the context of an ACS. METHODS: In this qualitative study, we sought input using key informant interviews and focus groups from patients who received care from an ACS during their hospitalization and from hospitalists, pharmacists, social workers, and nurses who work in the hospital setting. A multidisciplinary team coded and analyzed transcripts using a directed content analysis. FINDINGS: We conducted 20 key informant interviews with patients, nine of whom were interviewed following hospital discharge and 12 of whom were interviewed during a rehospitalization. We completed six focus groups and eight key informant interviews with hospitalists and hospital-based medical staff (n = 62). Emergent themes related to hospital-based OUD treatment included the following: the benefit of an ACS to facilitate OUD treatment engagement; expanded use of methadone or buprenorphine to treat opioid withdrawal; the triad of hospitalization, self-efficacy, and easily accessible, patient-centered treatment motivates change in opioid use; adequate pain control and stabilization of mental health conditions among patients with OUD contributed to opioid agonist therapy (OAT) continuation; and stable housing and social support are prerequisites for OAT uptake and continuation. CONCLUSION: Modifiable factors which facilitate hospital-based OUD treatment initiation and continuation include availability of in-hospital addiction expertise to offer easily accessible, patient-centered treatment and the use of methadone or buprenorphine to manage opioid withdrawal. Further research and public policy efforts are urgently needed to address reported barriers to hospital-based OUD treatment initiation and continuation which include unstable housing, poorly controlled chronic medical and mental illness, and lack of social support.

Perspectives among women receiving medications for opioid use disorder: Implications for development of a peer navigation intervention to improve access to family planning services.

Background Studies have consistently found high rates of unintended pregnancy among women with opioid use disorder (OUD). Few interventions have been developed to specifically engage and address the family planning (FP) needs of women in substance use disorder treatment. Objectives: Our goal was to collect formative qualitative data to identify the FP experiences, needs and service preferences of women receiving medications for OUD and to use these data to develop a FP education and navigation intervention that could be tested in diverse, resource-limited treatment settings. Methods: From August 2016 to April 2017, we conducted 21 guided qualitative interviews with women from two outpatient treatment clinics in Denver, Colorado. We recorded, transcribed, and coded all interviews. We then facilitated three focus groups (n = 16) from May to July 2017 to verify or challenge interview themes and to further inform the development of the FP intervention. Results: Most participants expressed ambivalence or low perceived risk regarding unintended pregnancy and desired more information about contraceptive methods. Many participants described mistrust or lack of engagement in the medical system and histories of trauma were a common barrier to seeking services. Focus group participants endorsed a peer-led FP navigation intervention and provided feedback to tailor existing FP educational materials to fit the specific needs of women in recovery. Conclusions/Importance: Results from this qualitative study suggest that women in recovery from OUD have unique, unmet FP education and service needs. These findings provide important information for the development of feasible and acceptable FP service delivery within diverse, resource-limited treatment settings and informed the development of a trauma-informed, peer-led FP education and navigation intervention that would be implemented in a subsequent phase of the study.