Multiple alternative measures for defining high-risk opioid use in claims data were used in the peer-reviewed literature and are summarized in Table 2.6. These measures were not used in the proposed dissertation analyses, but they deserve mention here if only to guide potential future research regarding the development of standardized MLIP enrollment criteria. One study defined high-risk opioid use as receiving 210 days supply of opioids in one year.166 Multiple studies considered aberrant opioid
utilization patterns in the form of inappropriately early opioid refills and receipt of multiple overlapping opioid prescriptions.131,143,151,152,161,167 White and colleagues measured notable opioid dose escalations over
consecutive months as markers of inappropriate opioid utilization,151 Bohnert, et al, assessed whether or
not patients received only “as needed” opioid prescriptions compared to schedule opioid dosing140, and
Liu compared considered receipt of long-acting and extended release opioid preparations as a potential risk factor.143 Interestingly, Rice was able to measure household opioid use and found that six or more household opioid prescriptions or an opioid abuse diagnosis in 12 months significantly increases a patient’s likelihood of receiving an opioid abuse diagnosis.152
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Table 2.6: Summary of Alternative Claims-Based Measures of High-Risk Opioid Use in Peer-Reviewed Literature
Study Patient population Outcome predicted
Observation
period Alternative high-risk opioid definition Edlund, et al (2007) US Veterans Opioid abuse Dx 12 months ≥210 total days supplied
Buurma, et al (2008) Netherlands "Drug-seeking" behavior 12 months >10% of all Rx filled elsewhere + >10 Rx filled elsewhere
White, et al (2009) Privately insured (ME) Opioid abuse Dx 6 months ≥1 early refill; ≥2 consecutive dose escalations Sullivan, et al (2010) Medicaid (AR) Novel risk score 6 months Novel opioid misuse scoring algorithm
Bohnert, et al (2011) US Veterans Overdose death Variable Receiving only Rx with "as needed" dosing schedule Cepeda, et al (2012, 13) Population-based "Drug-seeking" behavior 18 months ≥2 overlapping Rx from 2 prescribers at ≥3 pharmacies Peirce, et al (2012) WV residents Overdose death 6 months Concomitant opioid/benzodiazepine use
Rice, et al (2012) Privately insured Opioid abuse Dx 12 months Early refills; ≥6 household Rx fills; household opioid abuse Dx
Daubresse, et al (2013) Privately insured Novel risk score 3 months Novel opioid misuse scoring algorithm Liu, et al (2013) Privately insured N/A (researcher-defined) 12 months Overlapping opioid Rx; Overlapping
opioid/benzodiazepine Rx; LA/ER opioid use;
Yang, et al (2015) Medicaid Overdose event 90 days Overlapping opioid Rx
Note: All findings reported in the table refer to opioid utilization unless otherwise specified. US=United States; Dx=diagnosis; Rx=prescription; ME=Maine; AR=Arkansas; WV=West Virginia; LA=long-acting; ER=extended release.
Additionally, two large-scale efforts have also been made in the literature to develop claims- based aggregate opioid risk scoring systems to identify high-risk patients. Researchers in 2010 built upon the work of Parente, et. al., to develop and validate an opioid misuse score in the context of the large- scale TROUP (Trends and Risks of Opioid Use for Pain) study.133 The opioid misuse score was based on the assessment of days supply of short-acting opioids, days supply of long-acting opioids, number of pharmacies used, and number of prescribers used over a six-month period. Each of the four measures was scored on a scale of 0-2, and year-long opioid misuse scores were stratified into no misuse (0-1), possible misuse (2-4), and probable misuse (5-16). The authors validated the opioid misuse score in two insurance claims databases for years 2000-2005: one containing nearly 3.8 million privately insured beneficiaries and the other comprised of 128,000 Arkansas Medicaid beneficiaries.133 Sullivan and colleague’s opioid misuse score also had a strong, linear relationship with receipt of an opioid abuse diagnosis.
More recently, Daubresse and colleagues developed a different controlled substance misuse scoring algorithm for use in claims data.168 The controlled substance use score was based primarily on the volume of controlled substance prescriptions filled, number of pharmacies and physicians visited, and rate of utilization of controlled substances (Table 2.7). The authors assessed utilization of all Schedule II through Schedule V drug classes over a 90-day observation period. Although the authors did not explicitly validate these criteria by investigating associations between scores and controlled substance- related overdose or mortality, they did express confidence in their controlled substance score’s specificity while still identifying a sample of high utilizers of sufficient size for analysis and intervention.
Table 2.7: Controlled substance risk score component measures Source of information Weight
Volume of controlled substance claims
Assign half a point to the individual for each of their first 8 claims for a controlled substance; assign 1 point for each additional controlled substance claim thereafter Number of unique
pharmacies and prescribers
Based on the combined total of unique pharmacies and prescribers, assign 1 point for the first two unique entities; assign 1.5 points for each unique entity thereafter Rate of utilization of
controlled substances
Assign 1 point if the number of claims for controlled substances in the 3rd month of the 90-day pre-intervention is two or more than the number of claims in the 2nd month of the pre-intervention period
Source: Daubresse M, Gleason PP, Peng Y, Shah ND, Ritter ST, Alexander GC. Impact of a drug utilization review program on high-risk use of prescription controlled substances. Pharmacoepidemiol Drug Saf. Jul 24 2013.