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Purpose: "Lock-in" programs (LIPs) are used by health insurers to address potential substance (eg, opioid) misuse among beneficiaries. We sought to (1) examine heterogeneity in trajectories of dispensed opioids (in average daily morphine milligram equivalents (MMEs)) over time: prior to, during, and following release from a LIP, and (2) assess associations between trajectory patterns and beneficiary characteristics., Methods: Medicaid claims were linked to Prescription Drug Monitoring Program records for a cohort of beneficiaries enrolled in the North Carolina Medicaid LIP (n = 2701). Using latent class growth analyses, we estimated trajectories of average daily MMEs of opioids dispensed to beneficiaries across specific time periods of interest., Results: Five trajectory patterns appeared to sufficiently describe underlying heterogeneity. Starting values and slopes varied across the 5 trajectory groups, which followed these overall patterns: (1) start at a high level of MMEs, end at a high level of MMEs (13.1% of cohort); (2) start medium, end medium (13.2%); (3) start medium, end low (21.5%); (4) start low, end medium (22.6%); and (5) start low, end low (29.6%). We observed strong associations between patterns and beneficiaries' demographics, substance use-related characteristics, comorbid conditions, and healthcare utilization., Conclusions: In its current form, the Medicaid LIP appeared to have limited impact on beneficiaries' opioid trajectories. However, strong associations between trajectory patterns and beneficiary characteristics provide insight into potential LIP design modifications that might improve program impact (eg, LIP integration of substance use disorder assessment and referral to treatment, assessment and support for alternate pain therapies)., (© 2018 John Wiley & Sons, Ltd.)

Purpose: This study aimed to develop hypotheses to explain the increasing tramadol utilisation, evaluate the impact of tramadol classification, and explore the trend between tramadol utilisation and related deaths in the United Kingdom., Methods: This cross-sectional study used individual patient data, the Clinical Practice Research Datalink from 1993 to 2015, to calculate monthly defined daily dose (DDD)/1000 registrants, monthly prevalence and incidence of tramadol users, annual supply days, and mean daily dose of tramadol. Aggregated-level national statistics and reimbursement data from 2004 to 2015 were also used to quantify annual and monthly tramadol DDD/1000 inhabitants and rate of tramadol-related deaths in England and Wales. Interrupted time-series analysis was used to evaluate the impact of tramadol classification in June 2014., Results: Prevalence of tramadol users increased from 23 to 97.6/10 000 registrants from 2000 to 2015. Both annual dose and annual supply days of existing tramadol users were higher than new users. Level and trend of monthly utilisation (β 2 : -12.9, β 3 : -1.6) and prevalence of tramadol users (β 2 : -6.4, β 3 : -0.37) significantly reduced after classification. Both annual tramadol utilisation and rate of tramadol-related deaths increased before tramadol classification and decreased thereafter., Conclusions: Increasing tramadol utilisation was influenced by the increase in prevalence and incidence of tramadol users, mean daily dose, and day of supply. Prevalence of tramadol users, tramadol utilisation, and reported deaths declined after tramadol classification. Future studies need to evaluate the influencing factors to ensure the safety of long-term tramadol use., (Copyright © 2017 John Wiley & Sons, Ltd.)

Purpose: To develop a complete and consistent prescription drug monitoring program (PDMP) data set for use by drug safety researchers in evaluating patterns of high-risk use and potential abuse of scheduled drugs., Methods: Using publically available data references from the US Food and Drug Administration and the Centers for Disease Control and Prevention, we developed a strategic methodology to assign drug categories based on pharmaceutical class for the majority of prescriptions in the PDMP data set. We augmented data elements required to calculate morphine milligram equivalents and assigned duration of action (short-acting or long acting) properties for a majority of opioids in the data set., Results: About 10% of prescriptions in the PDMP data set did not have a vendor-assigned drug category, and 20% of opioid prescriptions were missing data needed to calculate risk metrics. Using inclusive methods, 19 133 167 (>99.9%) of prescriptions in the PDMP data set were assigned a drug category. For the opioid category, augmenting data elements resulted in 10 760 669 (99.8%) having required values to calculate morphine milligram equivalents and evaluate duration of action properties., Conclusions: Drug safety researchers who require a complete and consistent PDMP data set can use the methods described here to ensure that prescriptions of interest are assigned consistent drug categories and complete opioid risk variable values. Copyright © 2016 John Wiley & Sons, Ltd., Competing Interests: and Conflicts of Interest: None of the authors have conflicts of interest to declare., (Copyright © 2016 John Wiley & Sons, Ltd.)

Objectives: Prescription drug monitoring programs (PDMPs) are state-based data collection systems recording controlled substance medications. Currently, 49 states have PDMPs. There are discrepancies in reporting patterns, infrastructure, and oversight between programs. We characterized aspects of each state's PDMP., Methods: A web search of each state's PDMP was conducted, and a list of all PDMP administrators was obtained. From August 1 to November 31, 2014, a link to a web-based survey was sent to each PDMP administrator. Closed-ended questions included type of access, mandatory-use programs, data sharing, proactive contact with patients or health care providers, details of pharmacy reporting, and protocols for identifying "high-risk" patients. Descriptive statistics were used for analysis., Results: We received a 100% response rate (49/49). Ninety-six percent (47/49) have a physician-accessible PDMP. Most, 68% (32/49), do not have an enrollment mandate for physicians. Prior to prescribing controlled medications, 16% (8/49) require prescribers to access their state's PDMP. More than half of states (53%, 26/49) reported patient prescriptions over the past two or more years. Most, 57% (28/59), reported a lag time of 1 week or longer for patients to appear in a PDMP database after prescription filling. A majority of states (65% 32/49) share data with at least one other state. Protocols exist to identify high-risk patients for prescription drug misuse in 55% (27/49) of states., Conclusion: Characteristics of PDMPs are heterogeneous throughout the country. Standardizing data capture, availability, and reporting would improve their usefulness for providers. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Background: Prescription drug abuse has prompted considerable concern. We evaluated a retrospective drug utilization review program to reduce controlled substance use among individuals with high-risk utilization., Methods: We analyzed pharmacy claims from a large pharmaceutical benefits manager. For each eligible member, we calculated a controlled substance score based on the number and type of claims, prescribers and pharmacies, and utilization patterns over three months. Two state health plans sent controlled substance letters to prescribers of members meeting or exceeding a plan- and pre-specified controlled substance score. Two different state health plans did not send such letters. We used a difference-in-difference design and generalized estimating equations to quantify the impact of the program on the mean difference in reduction of the controlled substance score over six months., Results: Eligible members in the intervention and comparison states had similar baseline mean controlled substance scores (19.0 vs. 18.6, p = 0.36). Adjusting for individuals' age, sex and pharmacy risk group score, reductions in the mean controlled substance score were greater in the intervention than comparison cohort (5.67 vs. 4.31, p = 0.01), corresponding with a 34.0% reduction in the intervention cohort compared to a 25.5% reduction in the comparison cohort. Changes were driven primarily by reductions in the number of controlled substance claims filled (30.5% vs. 23.1%, p = 0.01), as well as by a non-statistically significant trend towards reductions in the number of prescribers and pharmacies used (26.9% vs. 20.1%, p = 0.07)., Conclusions: Retrospective drug utilization review programs may reduce controlled substance scores and claims among individuals with patterns suggesting high-risk utilization., (Copyright © 2013 John Wiley & Sons, Ltd.)