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The American Journal of Managed Care May 2017
Drivers of Excess Costs of Opioid Abuse Among a Commercially Insured Population
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Jason Shafrin, PhD; Felicia Forma, BSc; Ethan Scherer, PhD; Ainslie Hatch, PhD; Edward Vytlacil, PhD; and Darius Lakdawalla, PhD
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Prescription Opioid Registry Protocol in an Integrated Health System
G. Thomas Ray, MBA; Amber L. Bahorik, PhD; Paul C. VanVeldhuisen, PhD; Constance M. Weisner, DrPH, MSW; Andrea L. Rubinstein, MD; and Cynthia I. Campbell, PhD, MPH
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Gerald Cochran, PhD; Adam J. Gordon, MD, MPH; Walid F. Gellad, MD, MPH; Chung-Chou H. Chang, PhD; Wei-Hsuan Lo-Ciganic, PhD, MS, MSPharm; Carroline Lobo, MS; Evan Cole, PhD; Winfred Frazier, MD; Ping

Prescription Opioid Registry Protocol in an Integrated Health System

G. Thomas Ray, MBA; Amber L. Bahorik, PhD; Paul C. VanVeldhuisen, PhD; Constance M. Weisner, DrPH, MSW; Andrea L. Rubinstein, MD; and Cynthia I. Campbell, PhD, MPH
A flexible population-based prescription opioid registry was established for addressing a broad range of critical public health questions relating to prescription opioid use.
Among 2,480,030 adult KPNC members in 2011, 455,693 (18.3%) had at least 1 opioid fill at a KPNC pharmacy. Individuals with opioid use were different from individuals without opioid use on every characteristic, including being more likely to be female, older, white, have a chronic medical or psychiatric condition, and to have a diagnosis of opioid abuse/dependence or nonopioid substance use disorder (Table 1). 

The 455,693 individuals with opioid use had 474,045 unique episodes occurring in some part of 2011. Due to the 180-day gaps used when creating episodes, few individuals had more than 1 episode in 2011: 18,352 individuals had 2 episodes covering any part of 2011, and the rest had 1. After retaining the most severe episode per person, there were 112,089 long-term opioid episodes, 71,011 episodic episodes, and 272,593 acute episodes (Table 2). Long-term users were, on average, aged 56 years, and 61% were female. Forty percent of individuals with long-term opioid use received at least 1 diagnosis for a psychiatric disorder in 2011, 3% were diagnosed with opioid abuse/dependence, and 7% were diagnosed with a nonopioid substance use disorder. 

On average, long-term episodes were 1609 days long and included 54 opioid fills (Table 3). Because the opioid data spanned 2008 to 2014, it is possible that some episodes began prior to 2008 or continued after 2014; thus, episode durations may be underestimated. Among long-term opioid episodes, 27% began prior to July 1, 2008, and ended after June 30, 2014, and may be both left and right truncated. Another 17% of long-term episodes may have been left truncated only, and 24% may have been right truncated only.

Among individuals with long-term use, the mean MDDE was 38.98 mg, with the highest MDDE for long-acting Schedule II opioids. However, use of those opioids was highly skewed (median MDDE was 0), with only 28% of long-term users using any long-acting Schedule II opioids. Among individuals with episodic and acute use, on the other hand, Schedule III opioids had the highest mean MDDE. Individuals with episodic use used at lower levels than long-term users, and tended to have substantial gaps between fills; therefore, they had a much lower mean MDDE (5.37 mg). Individuals with long-term use also had higher mean MDDE than episodic or acute users. 

Sedative/hypnotics were used by 76% of individuals with long-term opioid use during their episodes, and for an average of 34% of episode days. Among individuals with acute use, 16% used sedative/hypnotics during their (much shorter) acute episode.

Among all individuals using prescription opioids in 2011, 175,558 (39%) were opioid naïve. Of these, 85,305 had continuous KPNC membership from 2008 to 2014 (n = 81,809), or until death (n = 3496), and were the analytic sample for initiating long-term opioid use (Table 4). Multivariate analysis indicated that individuals at least 80 years of age were more likely to become long-term users than individuals younger than 50 years of age. Compared with whites, Asians and Hispanics were less likely to become long-term users (odds ratio [OR], 0.41; 95% confidence interval [CI], 0.35-0.47, and OR, 0.67; 95% CI, 0.60-0.75, respectively) (Table 5). Individuals in more-deprived neighborhoods were more likely to become long-term users than those in the least-deprived neighborhoods (most-deprived neighborhood: OR, 1.26; 95% CI, 1.12-1.43). 

Numerous conditions were associated with long-term opioid use, including chronic pain (OR, 2.57; 95% CI, 2.26-2.93), nonopioid substance use disorders (OR, 2.25; 95% CI, 1.89-2.69), psychiatric disorders (OR, 1.22; 95% CI, 1.12-1.33), and arthritis (OR, 1.41; 95% CI, 1.31-1.52). Use of sedatives/hypnotics was associated with increased odds of becoming a long-term user (OR, 1.67; 95% CI, 1.54-1.81, vs no use). Even after adjusting for diagnosed conditions, inpatient hospital days and use of nonopioid medications in the prior year remained predictive of the long-term user. On the other hand, outpatient office visits in the prior year were associated with lower odds of becoming a long-term user. 

DISCUSSION

This study developed a protocol for an EHR-based prescription opioid registry that can be used to address important research questions about prescription opioid use in noncancer patients on a population level. The current paper also addressed initial questions about the characteristics of prescription opioid users and what predicts initiating long-term use.

Consistent with prior literature,3,22,23 individuals who used opioids were older, more likely to be white, and were more clinically complex patients, with more medical and psychiatric conditions and substance use disorders than individuals not using opioids. Further, a considerable portion of patients was using opioids long term. Patients using opioids long term are especially important to identify, because duration of use is associated with abuse, overdose, and other AEs.3,16,21,24-26 As prior researchers found,22,27 we found individuals with long-term opioid use to be more likely than those with shorter-term use to have higher daily dosages, chronic medical or psychiatric conditions, and opioid or other substance use disorders. Given the current epidemic of misuse and overdose, identifying long-term users with population-based data can help health systems identify patients early, monitor them, and refer them to specialty services (eg, substance use treatment, pain management) as needed. 

Our analysis of “opioid-naïve” users indicated that only 4.2% went on to long-term use within 3 years, although at any given time, the percentage of long-term opioid users is quite high (25%). Although individual risk is low, at a population level, this is consistent with the high level of AEs observed in recent years. 

Predictors of developing long-term use included chronic pain, sedative/hypnotic use, psychiatric disorders, and nonopioid substance use disorders. Concurrent use of sedative/hypnotics and opioids has been shown to be associated with a substantial increased risk of death from drug overdose.8,25,26,28 Federal and health-system guidelines have focused on reducing high daily dosages, and also on restricting concurrent opioid and sedative/hypnotic use.2,29 Individuals who lived in more-deprived neighborhoods were also more likely to develop long-term opioid use; to our knowledge, this is a relationship not previously identified in other research studies. Our data do not contain information on pain severity or control. However, findings may suggest that individuals residing in more-deprived neighborhoods (which may also be a proxy for individual deprivation) have more complex health status, or fewer nonmedication treatment alternatives available—these hypotheses deserve further study.

There is increased interest in using registries to address critical clinical and policy questions.30 A goal of this project was to develop a protocol that can serve as a reference for other clinical and research teams addressing similar questions. Study algorithms can be used in health systems with pharmacy dispensation data and encounter data. For example, because our approach used the VDW, investigators from 19 other health systems in the Health Care Systems Research Network can also use the VDW to similarly address important questions about prescription opioid use. We recognize this is not without challenges, and would require adaptations, particularly for systems that have dissimilar EHR data elements or claims data. However, by sharing details about our methodology, we hope to contribute to developing harmonized approaches across systems to address the opioid epidemic. 

Limitations

Our measures of opioid and sedative/hypnotic use depend on pharmacy dispensation data, which is commonly used in the literature, and which we consider a reasonable proxy for use. Uncertainty also exists about calculating use for overlapping fills. However, in contrast to some prior studies, we make explicit our assumptions for overlapping fills. The vast majority of KPNC members fill prescriptions at KPNC pharmacies,14 but we miss potential non-KPNC pharmacy fills. Although all registry members filled an opioid prescription at KPNC, it is possible that individuals using opioids may be more likely to seek opioid prescriptions externally. Identification of medical and psychiatric conditions, and substance use disorders, is based on diagnoses recorded in the EHR as part of routine care; thus, individuals with more visits may have more opportunity to receive a diagnosis. Also, there can be truncation of episodes that began prior to 2008 or continued post 2014 and, therefore, possible underestimation of long-term episode duration. These limitations are similar to those of other EHR-data–based studies. Finally, generalizability to other systems may be limited, although study algorithms can be adapted. 

CONCLUSIONS 

This study established a population-based opioid registry that is flexible, and can be used to address important questions of prescription opioid use. Future analyses will leverage the prescription opioid registry and its algorithms to examine prescription opioid misuse, fatal and nonfatal overdose, and health service utilization and cost. Thus, with this same registry, we will be able to address a broad range of critical public health issues relating to prescription opioid use. 

Acknowledgments

The authors gratefully acknowledge Agatha Hinman, BS, for her assistance in preparing the manuscript.

Author Affiliations: Division of Research, Kaiser Permanente Medical Care Program, Northern California Region (GTR, CMW, CIC), Oakland, CA; Department of Psychiatry, University of California (ALB, CMW), San Francisco, CA; The Emmes Corporation (PCV), Rockville, MD; Kaiser Permanente, Department of Anesthesiology, Santa Rosa Medical Center (ALR), Santa Rosa, CA. 

Source of Funding: This work was funded by the National Institute on Drug Abuse, Clinical Trials Network, UG1DA040314-01S1. In addition, Dr Bahorik was supported by National Institute on Drug Abuse training grant T32DA007250.

Author Disclosures: Mr Ray has received research support on grants to Kaiser Permanente Division of Research in the past 3 years from Pfizer, Merck, Genentech, and Purdue Pharma. Dr Campbell has been supported on a subcontract to the Kaiser Permanente Division of Research by Purdue Pharma. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Authorship Information: Concept and design (GTR, ALB, CMW, CIC); acquisition of data (GTR, CIC); analysis and interpretation of data (GTR, ALB, PCV, CMW, ALR, CIC); drafting of the manuscript (GTR, ALB, PCV, CMW, ALR, CIC); critical revision of the manuscript for important intellectual content (GTR, ALB, PCV, CMW, ALR, CIC); statistical analysis (GTR); obtaining funding (ALB, CIC); and supervision (CIC). 

Address Correspondence to: G. Thomas Ray, MBA, Division of Research, Kaiser Permanente, 2000 Broadway, Oakland, CA. E-mail: tom.ray@kp.org.
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