Changes in opioid prescribing and prescription drug monitoring program utilization following electronic health record integration-Massachusetts, 2018.

OBJECTIVE: In this study, we explored key prescription drug monitoring program-related outcomes among clinicians from a broad cohort of Massachusetts healthcare facilities following prescription drug monitoring program (PDMP) and electronic health record (EHR) data integration. METHODS: Outcomes included seven-day rolling averages of opioids prescribed, morphine milligram equivalents (MMEs) prescribed, and PDMP queries. We employed a longitudinal study design to analyze PDMP data over a 15-month study period which allowed for six and a half months of pre- and post-integration observations surrounding a two-month integration period. We used longitudinal mixed effects models to examine the effect of EHR integration on each of the key outcomes. RESULTS: Following EHR integration, PDMP queries increased both through the web-based portal and in total (0.037, [95% CI = 0.017, 0.057] and 0.056, [95% CI = 0.035, 0.077]). Both measures of clinician opioid prescribing declined throughout the study period; however, no significant effect following EHR integration was observed. These results were consistent when our analysis was applied to a subset consisting only of continuous PDMP users. CONCLUSIONS: Our results support EHR integration contributing to PDMP utilization by clinicians but do not support changes in opioid prescribing behavior.

Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011-2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors.

BACKGROUND: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. METHODS: We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. RESULTS: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. CONCLUSION: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Are There Differences in Opioid Prescriptions to Urban and Rural Patients by Oral and Maxillofacial Surgeons in Massachusetts From 2011 to 2021?

PURPOSE: Oral-maxillofacial surgeons (OMSs) are frequent prescribers of opioid analgesics. It remains unclear if prescription patterns differ for urban versus rural patients, given potential differences in access to and delivery of care. This study aimed to characterize urban-rural differences in opioid analgesic prescriptions to patients in Massachusetts by OMSs from 2011 to 2021. METHODS: This retrospective cohort study used the Massachusetts Prescription Monitoring Program database to identify Schedule II and III opioid prescriptions by providers with specialty of oral and maxillofacial surgery from 2011 to 2021. The primary predictor variable was patient geography (urban/rural) and secondary predictor was year (2011-2021). The primary outcome variable was milligram morphine equivalent (MME) per prescription. Secondary outcome variables were days' supply per prescription and number of prescriptions received per patient. Descriptive and linear regression statistics were performed to analyze differences in prescriptions to urban and rural patients each year and throughout the study period. RESULTS: The study data, which includes OMS opioid prescriptions (n = 1,057,412) in Massachusetts from 2011 to 2021, ranged annually between 63,678 and 116,000 prescriptions to between 58,000 and 100,000 unique patients. The cohorts each year ranged between 48 and 56% female with mean ages between 37 and 44 years. There were no differences in the mean number of patients per provider in urban and rural populations in any year. The study sample had a large majority of urban patients (>98%). MME per prescription, days' supply per prescription, and prescriptions received per patient were all generally similar between urban and rural patients each year, with the largest MME per prescription difference in 2019 (87.3 for rural to 73.9 for urban patients, P < .01). From 2011 to 2021, all patients had a steady decrease in MME per prescription (ß = -6.64, 95% confidence interval: -6.81, -6.48; R2 = 0.39) and day's supply per prescription (ß = -0.1, 95% confidence interval: -0.1, -0.09; R2 = 0.37). CONCLUSION: In Massachusetts, there were similar opioid prescribing patterns by oral and maxillofacial surgeons to urban and rural patients from 2011 to 2021. There has also been a steady decrease in the duration and total dosage of opioid prescriptions to all patients. These results are consistent with multiple statewide policies over the last several years aimed at curbing opioid overprescribing.

Treatment Experiences for Patients Receiving Buprenorphine/Naloxone for Opioid Use Disorder: A Qualitative Study of Patients' Perceptions and Attitudes.

Background: Although buprenorphine/naloxone has been demonstrated to be an effective treatment for patients with opioid use disorder (OUD), treatment retention has been a challenge. This study extends what is presently a limited literature regarding patients' experiences with this medication and the implications for treatment retention. Methods: The study was conducted as a qualitative investigation of patients in treatment for OUD at the time of the study. Forty-three patients (27 men, 15 women, mean age 34.7) were recruited from three clinical settings, a community health center, an academically-based treatment site, and an independent substance abuse treatment facility. Most patients had returned to use in the past after attempts to become abstinent. Results: Patients generally reported positive experiences with this medication noting it helped to reduce opioid cravings quickly. As important considerations for treatment retention, patients emphasized a firm commitment to achieving abstinence when beginning treatment and a prescriber who is informed about and attentive to their emotional state. Diverging attitudes did exist regarding treatment duration as some patients were accepting of long-term treatment while others desired a relatively brief option. Among patients who had returned to use, potentially important issues emerged pertaining to the absence of patient outreach for missed medication appointments and inadequate discharge planning following stays at rehabilitation facilities. Conclusions: While results regarding the importance of patient motivation and strong patient-prescriber relationships have been noted in previous studies, other findings regarding opportunities to improve patient outreach and coordination of care have received relatively less attention and warrant further consideration.

Long-term patient outcomes following buprenorphine/naloxone treatment for opioid use disorder: a retrospective analysis in a commercially insured population.

Background: While buprenorphine/naloxone (buprenorphine) has been demonstrated to be an effective medication for treating opioid use disorder (OUD), an important question exists about how long patients should remain in treatment.Objective: To examine the relationship between treatment duration and patient outcomes for individuals with OUD who have been prescribed buprenorphine.Methods: We conducted a retrospective, longitudinal study using the Massachusetts All Payer Claims Database, 2013 to 2017. The study comprised over 2,500 patients, approximately one-third of whom were female, who had been prescribed buprenorphine for OUD. The outcomes were hospitalizations and emergency room (ER) visits at 36 months following treatment initiation and 12 months following treatment discontinuation. Patients were classified into four groups based on treatment duration and medication adherence: poor adherence, duration <12 months; good adherence, duration <6 months; good adherence, duration 6 to 12 months, and good adherence, duration >12 months. We conducted analyses at the patient level of the relationship between duration and outcomes.Results: Better outcomes were observed for patients whose duration was greater than 12 months. Patients in the other groups had higher odds of hospitalization at 36 months following treatment initiation: poor adherence (2.71), <6 months (1.53), and 6 to 12 months (1.42). They also had higher odds of ER visits: poor adherence (1.69), <6 months (1.51), and 6 to 12 months (1.30). Similar results were observed following treatment discontinuation.Conclusions: OUD treatment with buprenorphine should be continued for at least 12 months to reduce hospitalizations and ED visits.

A Novel Imputation Approach for Sharing Protected Public Health Data.

Objectives. To develop an imputation method to produce estimates for suppressed values within a shared government administrative data set to facilitate accurate data sharing and statistical and spatial analyses. Methods. We developed an imputation approach that incorporated known features of suppressed Massachusetts surveillance data from 2011 to 2017 to predict missing values more precisely. Our methods for 35 de-identified opioid prescription data sets combined modified previous or next substitution followed by mean imputation and a count adjustment to estimate suppressed values before sharing. We modeled 4 methods and compared the results to baseline mean imputation. Results. We assessed performance by comparing root mean squared error (RMSE), mean absolute error (MAE), and proportional variance between imputed and suppressed values. Our method outperformed mean imputation; we retained 46% of the suppressed value's proportional variance with better precision (22% lower RMSE and 26% lower MAE) than simple mean imputation. Conclusions. Our easy-to-implement imputation technique largely overcomes the adverse effects of low count value suppression with superior results to simple mean imputation. This novel method is generalizable to researchers sharing protected public health surveillance data. (Am J Public Health. 2021; 111(10):1830-1838. https://doi.org/10.2105/AJPH.2021.306432).

Opioid Prescribing After Implementation of Single Click Access to a State Prescription Drug Monitoring Program Database in a Health System's Electronic Health Record.

OBJECTIVES: To determine the effect of one-click integration of a state's prescription drug monitoring program (PDMP) on the number of PDMP searches and opioid prescriptions, stratified by specialty. METHODS: Our large health system worked with the state department of public health to integrate the PDMP with the electronic health record (EHR), which enabled providers to query the data with a single click inside the EHR environment. We evaluated Schedule II or III opioid prescriptions reported to the Massachusetts PDMP 6 months before (November 15, 2017-May 15, 2018) and 6 months after (May 16, 2018, to November 16, 2018) integration. Search counts, prescriptions, patients, morphine milligram equivalents, as well as prescriber specialty were compared. RESULTS: There were 3,185 unique prescribers with a record of a Schedule II and/or III opioid prescription in both study periods that met inclusion criteria. After integration, the number of PDMP searches increased from 208,684 in the pre-integration phase to 298,478 searches in the post-integration phase (+43.0%). The number of opioid prescriptions dispensed decreased by 4.8%, the number of patients receiving a prescription decreased by 5.1%, and the mean morphine milligram equivalents (MMEs) per prescriber decreased by 5.4%. There were some notable specialty-specific differences in these measures. CONCLUSIONS: Integration of the PDMP into the EHR markedly increased the number of searches but was associated with modest decreases in opioids prescribed and patients receiving a prescription. Single click EHR integration of the PDMP, if implemented broadly, may be a way for states to significantly increase PDMP utilization.

A machine learning based two-stage clinical decision support system for predicting patients' discontinuation from opioid use disorder treatment: retrospective observational study.

BACKGROUND: Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. OBJECTIVE: To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation. METHODS: For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients' demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure. RESULTS: A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients' adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models' discriminative power (p-value < 0.001) based on the C-statistic. We also constructed interpretable decision classification rules using the decision tree model. CONCLUSION: Machine learning models can predict which patients are most at-risk of premature treatment discontinuation with reasonable discriminative power. The proposed machine learning framework can be used as a tool to help inform a clinical decision support system following further validation. This can potentially help prescribers allocate limited healthcare resources optimally among different groups of patients based on their vulnerability to treatment discontinuation and design personalized support systems for improving patients' long-term adherence to OUD treatment.

Opioid days' supply limits: an interrupted time-series analysis of opioid prescribing before and following a Massachusetts law.

Background: In 2016, Massachusetts passed the first-in-the-nation law limiting opioid naïve adults and all minors to a 7-day supply of opioids when prescribed in the outpatient setting.Objective: We hypothesized this policy would be associated with declines in the percent of opioid prescriptions with more than a 7-day supply among opioid naïve adults and minors for select prescriber groups.Methods: Interrupted time series analyses were conducted using measures from the Massachusetts Prescription Monitoring Program database for 2015 through 2017 (n = 13,672,325 opioid prescriptions; 54% to females). Outcomes were the monthly percent of opioid prescriptions greater than 7 days' supply in opioid naïve adults and in minors among select prescriber groups. Model estimates of the pre-policy trend, the average changes in the level pre/post-implementation, and the trend changes post-implementation were assessed.Results: Pre-policy trends showed significant monthly declines in the percent of opioid prescriptions greater than 7 days' supply for all prescriber groups. Policy implementation was associated with significant reduction in the level for opioid naïve adults among surgeons (-2.92%, p < .01), dentists (-0.23%, p < .01), and general medical providers (-2.22%, p = .04), and for minors among all-included prescribers (-2.97%, p < .01) and surgeons (-3.8%, p < .01). Post-implementation changes in trends were not significant except among opioid naïve adults for dentists (0.02%, p = .04).Conclusion: Within a context of significant reductions occurring in opioid prescriptions greater than 7 days' supply during this period, the Massachusetts policy was associated with further declines for opioid naïve adults and minors among select prescriber groups.

Patterns of buprenorphine/naloxone prescribing: an analysis of claims data from Massachusetts.

Background: The brand name Suboxone and its generic formulation buprenorphine/naloxone is a medication for treating opioid use disorder. While this medication has been shown to be effective, little research has examined the extent to which it is being prescribed and under what circumstances.Objective: This study examined patterns of prescription claims for buprenorphine/naloxone in terms of volume and associated clinical conditions.Methods: The study was conducted using a statewide database comprising pharmacy and medical claims that were covered by commercial health insurance plans in Massachusetts between 2011 and 2015. Trends in prescription volume for buprenorphine/naloxone were assessed based on the annual number of patients with a prescription for buprenorphine/naloxone. To examine clinical conditions associated with buprenorphine/naloxone prescriptions, patients' pharmacy claims were linked to their medical claims within the prior three months. For patients with common pain-related conditions, the odds they were prescribed buprenorphine/naloxone rather than oxycodone, a widely used opioid for pain management, were also examined.Results: The number of patients with a buprenorphine/naloxone prescription increased substantially during the study period, from approximately 25,000 in 2011 to over 39,000 in 2015. The most common clinical condition associated with buprenorphine/naloxone prescribing was opioid use disorder, but a substantial percentage of prescriptions were preceded by diagnoses that included pain or were for pain alone.Conclusion: A substantial increase in the number of patients with a prescription for buprenorphine/naloxone was observed. While buprenorphine/naloxone is most frequently prescribed for opioid use disorder, clinicians also appear to prescribe it for pain, particularly for patients who may be at elevated risk for opioid use disorder.

Unsolicited Reporting to Prescribers of Opioid Analgesics by a State Prescription Drug Monitoring Program: An Observational Study with Matched Comparison Group.

Objective: State prescription drug monitoring programs (PDMPs) can help detect individuals with multiple provider episodes (MPEs; also referred to as doctor/pharmacy shopping), an indicator of prescription drug abuse and/or diversion. Although unsolicited reporting by PDMPs to prescribers of opioid analgesics is thought to be an important practice in reducing MPEs and the potential harm associated with them, evidence of its effectiveness is mixed. This exploratory research evaluates the impact of unsolicited reports sent by Massachusetts' PDMP to the prescribers of persons with MPEs. Methods: Individuals with MPEs were identified from PDMP records between January 2010 and July 2011 as individuals having Schedule II prescriptions (at least one prescription being an opioid) from four or more distinct prescribers and four or more distinct pharmacies within six months. Based on available MA-PDMP resources, an unsolicited report containing the patient's 12-month prescription history was sent to prescribers of a subset of patients who met the MPE threshold; a comparison group closely matched on demographics and baseline prescription history, whose prescribers were not sent a report, was generated using propensity score matching. The prescription history of each group was examined for 12 months before and after the intervention. Results: There were eighty-four patients (intervention group) whose prescribers received an unsolicited report and 504 matched patients (comparison group) whose prescribers were not sent a report. Regression analyses indicated significantly greater decreases in the number of Schedule II opioid prescriptions (P < 0.01), number of prescribers visited (P < 0.01), number of pharmacies used (P < 0.01), dosage units (P < 0.01), total days' supply (P < 0.01), total morphine milligram equivalents (MME; P < 0.01), and average daily MME (P < 0.05) for the intervention group relative to the comparison group. A post hoc analysis suggested that the observed intervention effects were greater for individuals with an average daily dose of less than 100 MMEs. Conclusions: This study suggests that PDMP unsolicited reporting to prescribers can help reduce risk measures in patients' prescription histories, which may improve health outcomes for patients receiving opioid analgesics from multiple providers.

Patient outcomes following buprenorphine treatment for opioid use disorder: A retrospective analysis of the influence of patient- and prescriber-level characteristics in Massachusetts, USA.

BACKGROUND AND AIMS: Opioid use disorder (OUD) is treatable with buprenorphine/naloxone (buprenorphine), but many patients discontinue treatment prematurely. The aim of this study was to assess the influence of patient- and prescriber-level characteristics relative to several patient outcomes following the initiation of buprenorphine treatment for OUD. DESIGN: This was a retrospective observational investigation. We used the Public Health Data Warehouse from the Massachusetts Department of Public Health to construct a sample of patients who initiated buprenorphine treatment between 2015 and 2019. We attributed each patient to a prescriber based on information from prescription claims. We used multilevel models to assess the influence of patient- and prescriber-level characteristics on each outcome. SETTING: Massachusetts, USA. PARTICIPANTS: The study cohort comprised 37 955 unique patients and 2146 prescribers. Among patients, 64.6% were male, 52.6% were under the age of 35 and 82.2% were White, non-Hispanic. For insurance coverage, 72.1% had Medicaid. MEASUREMENTS: The outcome measures were poor medication continuity, treatment discontinuation and opioid overdose, all assessed within a 12-month follow-up period that began with a focal prescription for buprenorphine. Each patient had a single follow-up period. Poor medication continuity was defined as medication gaps totaling more than 7 days during the initial 180 days of buprenorphine treatment and treatment discontinuation was defined as having a medication gap for 2 consecutive months within the 12-month follow-up period. FINDINGS: The patient-level rates for poor medication continuity, treatment discontinuation and opioid overdose were 59.7% [95% confidence interval (CI) = 59.2-60.2], 57.4% (95% CI = 56.9-57.9) and 10.3% (95% CI = 10.0-10.6), respectively, with 1.1% (95% CI = 1.0-1.2) experiencing a fatal opioid overdose. At the patient level, after adjustment for covariates, adverse outcomes were associated with race/ethnicity as both Black, non-Hispanic and Hispanic patients had worse outcomes than did White, non-Hispanic patients (Black, non-Hispanic -- poor continuity: 1.50, 95% CI = 1.34-1.68; discontinuation: 1.44, 95% CI = 1.30-1.60; Hispanic -- poor continuity: 1.21, 95% CI = 1.12-1.31; discontinuation: 1.38, 95% CI = 1.28-1.48). Patients with insurance coverage through Medicaid also had worse outcomes than those with commercial insurance (poor continuity: 1.18, 95% CI = 1.11-1.26; discontinuation: 1.09, 95% CI = 1.03-1.16; overdose: 1.98, 95% CI = 1.75-2.23). Pre-treatment mental health conditions and other types of chronic illness were also associated with worse outcomes (History of mental health conditions -- poor continuity: 1.11, 95% CI = 1.06-1.17; discontinuation: 1.05, CI = 1.01-1.10; overdose: 1.47, 95% CI = 1.36-1.60; Chronic health conditions -- poor continuity: 1.15, 95% CI = 1.05-1.27; discontinuation: 1.15, 95% CI = 1.05-1.26; overdose: 1.83, 95% CI = 1.60-2.10; History of substance use disorder other than for opioids -- poor continuity: 1.54, 95% CI = 1.46-1.62; discontinuation: 1.54, 95% CI = 1.47-1.62; overdose: 1.93, 95% CI = 1.80-2.07). At the prescriber level, after adjustments for covariates, adverse outcomes were associated with clinical training, as primary care physicians had higher rates of adverse outcomes than psychiatrists (poor continuity: 1.12, 95% CI = 1.02-1.23; discontinuation: 1.04, 95% CI = 1.01-1.09). A larger prescriber panel size, based on number of patients being prescribed buprenorphine, was also associated with higher rates of adverse outcomes (poor continuity: 1.36, 95% CI = 1.27-1.46; discontinuation: 1.21, 95% CI = 1.14-1.28; overdose: 1.10, 95% CI = 1.01-1.19). Between 9% and 15% of the variation among patients for the outcomes was accounted for at the prescriber level. CONCLUSIONS: Patient- and prescriber-level characteristics appear to be associated with patient outcomes following buprenorphine treatment for opioid use disorder. In particular, patients' race/ethnicity and insurance coverage appear to be associated with substantial disparities in outcomes, and prescriber characteristics appear to be most closely associated with medication continuity during early treatment.

Prescribed stimulant medications: Trends in the last decade, pre and post COVID-19 response.

Background: Recent studies indicate that COVID-19 has had a significant impact on access and continuity to opioid and benzodiazepine medications; little is known about its effect on access to and utilization of stimulant medications. Objective: To investigate trends of dispensed stimulant medications in relation to the COVID-19 pandemic response. Methods: Stimulant prescriptions dispensed during 2011-2021 were analyzed using the Massachusetts Prescription Drug Monitoring Program (PDMP), the state's data repository for all controlled substance medications dispensed to residents from retail pharmacies and out of state mail-order pharmacies. Statewide trends were estimated by age group, sex, and stimulant-naïve patients (individuals with no stimulant prescription in the prior one-year period). Results: Overall, stimulant prescriptions increased 70% from 2011 to 2021. Wide differences by sex and age groups were found pre and post COVID response periods. Between 2019 and 2021, stimulant prescriptions for males 12-18 years old decreased 14.6% compared to 0.9% for females. Female stimulant-naïve patients ages 25-34 increased more than males between 2019 and 2021 (11.6% compared to <1%, respectively) and females ages 35-44 increased 4.1% while males decreased by 2.7%. Conclusions: Administrators, clinicians, and policy makers should closely monitor stimulant prescribing trends, a critical step in improving access to and quality of care.

Abrupt Discontinuation From Long-Term Opioid Therapy in Massachusetts, 2015-2018.

INTRODUCTION: In response to the opioid overdose crisis, providers were urged to taper and discontinue patients from long-term opioid therapy; however, abrupt discontinuation may lead to poor health outcomes. This study aims to determine abrupt and tapered discontinuation rates and identify the patient and provider characteristics associated with abrupt discontinuation. METHODS: Data were from the Massachusetts Prescription Monitoring Program, 2015-2018. Patients discontinued from long-term opioid therapy were included in the analysis. Differences between abrupt and tapered discontinuations were identified with bivariate correlations, and variables independently associated with abrupt discontinuation were identified using multivariable Poisson regression analyses. Data were analyzed during 2019-2021. RESULTS: In total, 277,485 patients experienced 359,320 discontinuations, of which 33.7% (n=120,964) were abrupt. Of all discontinuations, 55.7% were among female patients, and 57.9% were among patients aged >55 years. The ratio of abrupt to tapered discontinuations increased from 1:2.11 in 2015 to 1:1.75 in 2018. In bivariate analysis, prescribers with more patients receiving monthly opioid prescriptions were less likely to abruptly discontinue patients (29.0, IQR=13.9, 55.3 vs 18.8, IQR=5.84, 43.9, p<0.001), as were prescribers who wrote more monthly opioid prescriptions (36.0, IQR=16.8, 70.8 vs 25.4, IQR=7.40, 58.3, p<0.001). Multivariable results indicated that abrupt discontinuation was independently associated with male sex (RR=1.31, 95% CI=1.29, 1.1.32), younger age (RR=0.872, 95% CI=0.869, 0.874), greater distance between patient and prescriber (RR=1.0075, 95% CI=1.0072, 1.0078), and longer long-term opioid therapy duration (RR=1.021, 95% CI=1.021, 1.0122 for every month increase). CONCLUSIONS: Among all long-term opioid therapy discontinuations, abrupt discontinuation is increasing. Evidence-based approaches to managing and tapering long-term opioid therapy are urgently needed.

Patterns of patient discontinuation from buprenorphine/naloxone treatment for opioid use disorder: A study of a commercially insured population in Massachusetts.

BACKGROUND: Research has shown buprenorphine/naloxone to be an effective medication for treating individuals with opioid use disorder. At the same time, treatment discontinuation rates are reportedly high though much of the extant evidence comes from studies of the Medicaid population. OBJECTIVES: To examine the pattern and determinants of buprenorphine/naloxone treatment discontinuation in a population of commercially insured individuals. RESEARCH DESIGN: We performed a retrospective observational analysis of Massachusetts All Payer Claims Data (MA APCD) covering years 2013 through 2017. We defined treatment discontinuation as a gap of 60 consecutive days without a prescription for buprenorphine/naloxone within a time frame of 24 months from the initiation of treatment. A mixed-effect Cox proportional hazard model examined the associated risk of discontinuing treatment with baseline predictors. SUBJECTS: A total of 5134 individuals who were commercially insured during the study period. MEASURES: Buprenorphine/naloxone treatment discontinuation. RESULTS: Overall 75% of individuals had discontinued treatment within two years of initiating treatment, and median time to discontinuation was 300 days. Patients aged between 18 and 24 years (HR = 1.436, 95%, CI = 1.240-1.663) and receiving treatment from prescribers with high panel-size (HR = 1.278, 95% CI = 1.112-1.468) had higher risk of discontinuing treatment. On the contrary, patients receiving treatment from multiple prescribers had lower associated risk of treatment discontinuation. CONCLUSIONS: A substantial percentage of patients discontinue treatment well before they can typically meet criteria for sustained remission. Further investigations should assess the clinical outcomes following premature discontinuation and identify strategies for retaining patients in treatment.