Colleen M. Carey, PhD; Anupam B. Jena, MD, PhD; Michael L. Barnett, MD, MS
Grant Support: By NIH Early Independence Award 1DP5OD017897-01 (Dr. Jena).
Disclosures: Dr. Jena reports grants from the NIH during the conduct of the study and personal fees from Pfizer, Hill-Rom Services, Bristol-Myers Squibb, Novartis Pharmaceuticals, Vertex Pharmaceuticals, and Precision Health Economics outside the submitted work. Dr. Barnett has a patent for a method using physician social networks based on common patients to predict cost and intensity of care in hospitals, with royalties paid. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-3065.
Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that her spouse has stock options/holdings with Targeted Diagnostics and Therapeutics. Darren B. Taichman, MD, PhD, Executive Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer and Johnson & Johnson.
Reproducible Research Statement:Study protocol: Not applicable. Statistical code: Available from Dr. Carey (e-mail, firstname.lastname@example.org). Data set: Available through a Data Use Agreement with Centers for Medicare & Medicaid Services (www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/Data-Disclosures-Data-Agreements/Overview.html).
Corresponding Author: Michael L. Barnett, MD, Harvard T.H. Chan School of Public Health, Department of Health Policy and Management, 677 Huntington Avenue, Boston, MA 02115; e-mail, email@example.com.
Current Author Addresses: Dr. Carey: 298 Martha van Rensselaer Hall, Cornell University, Ithaca, NY 14850.
Dr. Jena: Harvard Medical School, Department of Health Care Policy, 180 Longwood Avenue, Boston, MA 02115.
Dr. Barnett: Harvard T.H. Chan School of Public Health, Department of Health Policy and Management, 677 Huntington Avenue, Boston, MA 02115.
Author Contributions: Conception and design: C.M. Carey, A.B. Jena, M.L. Barnett.
Analysis and interpretation of the data: C.M. Carey, A.B. Jena, M.L. Barnett.
Drafting of the article: C.M. Carey, A.B. Jena, M.L. Barnett.
Critical revision for important intellectual content: C.M. Carey, A.B. Jena, M.L. Barnett.
Final approval of the article: C.M. Carey, A.B. Jena, M.L. Barnett.
Statistical expertise: C.M. Carey.
Administrative, technical, or logistic support: A.B. Jena, M.L. Barnett.
Collection and assembly of data: C.M. Carey.
Providers are increasingly being expected to examine their patients' opioid treatment histories before writing new opioid prescriptions. However, little evidence exists on how patterns of potential opioid misuse are associated with subsequent adverse outcomes nationally.
To estimate how a range of patterns of potential opioid misuse relate to adverse outcomes during the subsequent year.
Observational study comparing outcomes for Medicare enrollees with potential opioid misuse patterns versus those for beneficiaries with no such patterns, adjusting for patient characteristics.
Medicare, 2008 to 2012.
A 5% sample of beneficiaries who had an opioid prescription without a cancer diagnosis.
Several measures for opioid misuse were defined on the basis of drug quantity, overlapping prescriptions, use of multiple prescribers or pharmacies, and use of out-of-state prescribers or pharmacies. The primary outcome was a diagnosis of opioid overdose in the year after a 6-month index period. Secondary outcomes included subsequent opioid-related or overall mortality.
Overall, 0.6% to 8.5% of beneficiaries fulfilled a misuse measure. Subsequent opioid overdose was positively associated with successively greater numbers of prescribers or pharmacies or higher opioid quantities during the index period. For example, patients who obtained opioids from 2, 3, or 4 prescribers were increasingly more likely to have an opioid overdose (adjusted absolute risk per 1000 beneficiary-years [aAR], 3.5 [95% CI, 3.3 to 3.7]; 4.8 [CI, 4.5 to 5.2]; or 6.4 [CI, 5.8 to 6.9], respectively) than those with a single prescriber (aAR, 1.9 [CI, 1.8 to 2.0]). Subsequent overdose risk increased meaningfully with any deviation in the single prescriber–single pharmacy opioid use pattern. All misuse measures examined had a positive association with subsequent opioid overdose and death.
Risk estimates provide measures of association and may not generalize to non-Medicare populations.
To fully assess patients' opioid overdose risk, clinicians should examine a wide range of misuse patterns.
National Institutes of Health.
Table 1. Characteristics of the Study Population, by Possible Opioid Misuse Measures*
Trends in measures of possible opioid misuse, by Medicare eligibility category, 2008 to 2012.
Prevalence of measures of possible opioid misuse, as defined in Methods, from 2008–2012. Each point represents the prevalence of a measure of possible misuse during a half-year. The blue lines represent the trend for the Medicare population enrolled because of disability, and the orange lines represent all other Medicare beneficiaries.
State-level prevalence of possible opioid misuse measures, 2008 to 2012.
States are divided into quartiles by prevalence of any misuse measure, as defined in Methods, among prescription opioid users in each state between 2008 and 2012. Higher quartiles are colored darker on the map, and the key shows the range of prevalence of any misuse measure for each quartile.
Association between opioid use patterns and subsequent opioid-related adverse outcomes.
The figure shows the association between successively greater numbers of prescribers, pharmacies, days supplied, or misuse measures (as defined in Methods) and opioid-related outcomes in the following year. The top set of panels examines subsequent opioid overdose, the middle set subsequent death within 30 days of an overdose, and the bottom set subsequent overall mortality. In each panel, the point shows the adjusted absolute risk from a logistic regression controlling for patient covariates described in Methods. The bars around each point report the 95% CI for this estimate. aAR = adjusted absolute risk per 1000 beneficiary-years.
Table 2. Association of Possible Misuse Measures With Subsequent Opioid-Related Adverse Outcomes*
Table 3. Association of Possible Misuse Measures With Subsequent Opioid Overdose in the Subpopulations of Persons Aged ≥65 Years and Disabled Persons*
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Carey CM, Jena AB, Barnett ML. Patterns of Potential Opioid Misuse and Subsequent Adverse Outcomes in Medicare, 2008 to 2012. Ann Intern Med. ;168:837–845. doi: 10.7326/M17-3065
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Published: Ann Intern Med. 2018;168(12):837-845.
Published at www.annals.org on 22 May 2018
Healthcare Delivery and Policy, Tobacco, Alcohol, and Other Substance Abuse.
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