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The American Journal of Managed Care November 2018
A Randomized, Pragmatic, Pharmacist-Led Intervention Reduced Opioids Following Orthopedic Surgery
David H. Smith, PhD, RPh; Jennifer L. Kuntz, PhD; Lynn L. DeBar, PhD, MPH; Jill Mesa; Xiuhai Yang, MS; Jennifer Schneider, MPH; Amanda Petrik, MS; Katherine Reese, PharmD; Lou Ann Thorsness, RPh; David Boardman, MD; and Eric S. Johnson, PhD
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Anupam B. Jena, MD, PhD; Jacquelyn W. Chou, MPP, MPL; Lara Yoon, MPH; Wade M. Aubry, MD; Jan Berger, MD, MJ; Wayne Burton, MD; A. Mark Fendrick, MD; Donna M. Fick, RN, PhD; David Franklin, BA; Rebecca Killion, MA; Darius N. Lakdawalla, PhD; Peter J. Neumann, ScD; Kavita Patel, MD, MSHS; John Yee, MD, MPH; Brian Sakurada, PharmD; and Kristina Yu-Isenberg, PhD, MPH, RPh
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Daniel B. Wolfson, MHSA, Executive Vice President and COO, ABIM Foundation
Cost of Pharmacotherapy for Opioid Use Disorders Following Inpatient Detoxification
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Overdose Risk for Veterans Receiving Opioids From Multiple Sources
Guneet K. Jasuja, PhD; Omid Ameli, MD, MPH; Donald R. Miller, ScD; Thomas Land, PhD; Dana Bernson, MPH; Adam J. Rose, MD, MSc; Dan R. Berlowitz, MD, MPH; and David A. Smelson, PsyD
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Kelly Gao; Gene Pellerin, MD; and Laurence Kaminsky, PhD
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Jennifer Elston Lafata, PhD; Carrie A. Miller, PhD, MPH; Deirdre A. Shires, PhD; Karen Dyer, PhD; Scott M. Ratliff, MS; and Michelle Schreiber, MD
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Patricia R. Salber, MD, MBA; Christobel E. Selecky, MA; Dirk Soenksen, MS, MBA; and Thomas Wilson, PhD, DrPH
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Jinyoung Shin, MD, PhD; Hyeonyoung Ko, MD, MPH; Jeong Ah Kim, BS; Yun-Mi Song, MD, PhD; Jin Seok Ahn, MD, PhD; Seok Jin Nam, MD, PhD; and Jungkwon Lee, MD, PhD

Overdose Risk for Veterans Receiving Opioids From Multiple Sources

Guneet K. Jasuja, PhD; Omid Ameli, MD, MPH; Donald R. Miller, ScD; Thomas Land, PhD; Dana Bernson, MPH; Adam J. Rose, MD, MSc; Dan R. Berlowitz, MD, MPH; and David A. Smelson, PsyD
Among veterans in Massachusetts, receipt of opioids from multiple sources, with or without benzodiazepines, was associated with worse opioid-related outcomes.
Cohort Eligibility Criteria

We defined a veteran as anyone filling a prescription for any Schedule II through V substance at a Massachusetts VHA pharmacy (19,479 veterans). Prescriptions for Massachusetts residents filled outside the state were not included. Further, those with a non-Massachusetts residential zip code (n = 201) or insufficient prescription data to ascertain dual care status (n = 2412) were excluded, resulting in a sample of 16,866 veterans. No veterans were excluded due to age, race, or gender.

Primary Independent Variable

Veterans were categorized into those filling prescriptions for opioids and/or benzodiazepines at VHA pharmacies only (“VHA-only”; n = 9238) or those filling such prescriptions at both VHA and non-VHA pharmacies (“dual care users”; n = 7628). Opioids used for medication-assisted therapy for opioid use disorder, such as buprenorphine, were included (eAppendix Table [eAppendix available at ajmc.com]). For veterans with dual care use, we quantified the number of switches between VHA and non-VHA fills as a measure of the extent of discoordination in the patients’ care. The index date was defined as the earliest date during the study period that a veteran filled an opioid or benzodiazepine prescription, whether inside or outside of the VHA.

Outcomes

We examined 3 outcomes after the index prescription date: nonfatal opioid overdose, fatal opioid overdose, and all-cause mortality. Nonfatal overdose was identified in 2 ways. First, any individual who had an ambulance encounter related to opioid overdose was included. The algorithm that was used to identify opioid-related overdoses in the emergency medical system data was the result of collaboration between MDPH and the CDC.18,19 The second was an ED visit, outpatient observation, or inpatient hospital discharge with an International Classification of Diseases, Ninth Revision code containing a diagnosis code for opioid poisoning. Fatal opioid-related overdoses were defined using the International Classification of Diseases, Tenth Revision codes for mortality. These multiple cause-of-death fields were then used to identify an opioid-related death. All-cause deaths were identified using death certificates that are filed with the Massachusetts Registry of Vital Records and Statistics and contain the official cause of death and manner of death assigned by physicians and medical examiners. All outcomes were chronologically sequenced to ensure that none occurred before the index opioid use date.

Covariates

Covariates included age, gender, high-dose opioid therapy (defined as exceeding 50 morphine milligram equivalents per day, on average, in at least 1 month),20 concurrent opioid/benzodiazepine use (defined by overlapping prescriptions of at least 1 day),3,21 and rural status of pharmacy and residence (defined as having a rural designation in Massachusetts). Demographics were gathered from all Chapter 55 data sources; when a conflict was found, it was resolved based on a hierarchy of reliability developed by the Chapter 55 team. The PMP data provided pharmacy location, generic codes of drugs, quantity, and dose.22 Additional information was obtained from the APCD on homelessness, which is almost certainly an undercount of all persons experiencing homelessness, and comorbidities, including the mental and physical components of the Elixhauser comorbidity index (eAppendix Table).23 Although the APCD contains health and pharmacy insurance claims data from across the state, it does not include service records from the VHA. Because of concerns that this may lead to differential completeness of data between the study groups, addition of homelessness and comorbidities in the form of an index (which would not have been available to us for VHA-only patients) was analyzed in a separate model.

Data Analysis

Covariates and outcomes were compared between VHA-only and dual care users, using χ2 and t tests to detect differences. Logistic regression models were constructed for the 3 outcomes, including (1) an unadjusted model and (2) an adjusted model including terms for demographics, high-dose opioid therapy, and concurrent opioid/benzodiazepine use. A third model also included terms for homelessness and Elixhauser comorbidity index. These models were repeated, limiting to veterans who received opioids only. We also examined interaction terms between dual care use and homelessness because of a suspected heterogeneity of effect. Odds ratios (ORs) generated from these logistic regression models, although not entirely intuitive to some readers, are generally similar to more intuitive measures when examining rare outcomes. Analyses were performed using SAS Studio version 3.5 (SAS Corporation; Cary, North Carolina).


 
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