Diagnostic profiles associated with long-term opioid therapy in active duty servicemembers

Abstract

Introduction: Over-prescription of opioids has diminished in recent years; however, certain populations remain at high risk. There is a dearth of research evaluating prescription rates using specific multimorbidity patterns. Objective: To identify distinct clinical profiles associated with opioid prescription and evaluate their relative odds of receiving long-term opioid therapy. Design: Retrospective analysis of the complete military electronic health record. We assessed demographics and 26 physiological, psychological, and pain conditions present during initial opioid prescription. Latent class analysis (LCA) identified unique clinical profiles using diagnostic data. Logistic regression measured the odds of these classes receiving long-term opioid therapy. Setting: All electronic health data under the TRICARE network. Participants: All servicemembers on active duty during fiscal years 2016 through 2019 who filled at least one opioid prescription. Main Outcome Measures: Number and qualitative characteristics of LCA classes; odds ratios (ORs) from logistic regression. We hypothesized that LCA classes characterized by high-risk contraindications would have significantly higher odds of long-term opioid therapy. Results: A total of N = 714,446 active duty servicemembers were prescribed an opioid during the study window, with 12,940 (1.8%) receiving long-term opioid therapy. LCA identified five classes: Relatively Healthy (82%); Musculoskeletal Acute Pain and Substance Use Disorders (6%); High Pain, Low Mental Health Burden (9%); Low Pain, High Mental Health Burden (2%), and Multisystem Multimorbid (1%). Logistic regression found that, compared to the Relatively Healthy reference, the Multisystem Multimorbid class, characterized by multiple opioid contraindications, had the highest odds of receiving long-term opioid therapy (OR = 9.24; p