Predictors of Postdeployment Prescription Opioid Receipt and Long-term Prescription Opioid Utilization Among Army Active Duty Soldiers (vol 184, E101, 2019)

MILITARY MEDICINE(2019)

Cited 2|Views13
No score
Abstract
Introduction: Little is known about long-term prescription opioid utilization in the Military Health System. The objectives of this study were to examine predictors of any prescription opioid receipt, and predictors of long-term opioid utilization among active duty soldiers in the year following deployment. Materials and Methods: The analytic sample consisted of Army active duty soldiers returning from deployment to Operation Enduring Freedom, Operation Iraqi Freedom, or Operation New Dawn in fiscal years 2008-2014 (N = 540,738). The Heckman probit procedure was used to jointly examine predictors of any opioid prescription receipt and long-term opioid utilization (i.e., an episode of 90 days or longer where days-supply covered at least two-thirds of days) in the postdeployment year. Predictors were based on diagnoses and characteristics of opioid prescriptions. Results: More than one-third of soldiers (34.8%, n = 188,211) had opioid receipt, and among those soldiers, 3.3% had long-term opioid utilization (or 1.1% of the cohort, n = 6,188). The largest magnitude predictors of long-term opioid utilization were receiving a long-acting opioid within the first 30 days of the episode, diagnoses of chronic pain (no specified source), back/neck pain, or peripheral/central nervous system pain, and severe pain score in vital records. Conclusions: Soldiers returning from deployment were more likely to receive an opioid prescription than the overall active duty population, and 1.1% initiated a long-term opioid episode. We report a declining rate of opioid receipt and long-term opioid utilization among Army members from fiscal years 2008-2014. This study demonstrates that the most important predictors of opioid receipt were not demographic factors, but generally clinical indicators of acute pain or physical trauma.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined