Opioid Prescription Clusters Associated With Early or Unplanned Military Separation
Authors: Nghiem Vi T , Larson Mary Jo , Adams Rachel Sayko , Moresco Natalie , Highland Krista B 
Published: August 2023

ABSTRACT

Introduction
Early/unplanned military separation in Active Component U.S. service members can result in reduced readiness during periods of high-tempo combat and increased demand for health care services within the Military Health System and Veterans Administration. Although current assessment tools leverage prescription data to determine deployment-limiting medication receipt and the need for interventions or waivers, there is a lack of understanding regarding opioid prescription patterns and subsequent early/unplanned military separation after return from deployment. As such, understanding these relationships could support future tool development and strategic resourcing. Therefore, the goal of the present study was to identify unique 12-month opioid prescription patterns and evaluate their relationship with early/unplanned military separation in Active Component service members who returned from deployment.

Materials and Methods
This retrospective, IRB-approved cohort study included data from 137,654 Active Component Army service members who returned from deployment between 2007 and 2013, received a post-deployment (index) opioid prescription, and had at least 1 year of Active Component service post-opioid initiation. A k-means clustering analysis identified clusters using opioid prescription frequency, median dose, median days supply, and prescription breaks (≥30 days) over the 12-month post-initiation (monitoring) period. A generalized additive model examined whether cluster membership and additional covariates were associated with early/unplanned separation.

Results
In addition to the single opioid prescription (38%), the cluster analysis identified five clusters: brief/moderate dose (25%), recurrent breaks (16%), brief/high dose (11%), long/few prescriptions (8%), and high prescription frequency (2%). In the generalized additive model, the probability of early/unplanned military separation was higher for the high prescription frequency cluster (74%), followed by recurrent breaks (45%), long/few prescriptions (37%), brief/moderate dose (30%), and brief/high dose (29%) clusters, relative to the single prescription (21%) cluster. The probability of early/unplanned separation was significantly higher for service members with documented substance use disorders, mental health conditions, or traumatic brain injuries during the monitoring periods. Service members assigned male were more likely to have an early/unplanned separation relative to service members assigned female. Latinx service members and service members whose race was listed as Other were less likely to experience early/unplanned separation relative to white service members. Relative to Junior Officers, Junior Enlisted and Senior Enlisted service members were more likely to experience early/unplanned separation, but Senior Officers were less likely.

Conclusions
Further evaluation to support the integration of longitudinal opioid prescription patterns into existing tools (e.g., a screening tool for deployment-limiting prescriptions) may enable more timely intervention and support service delivery to mitigate the probability and impact of early/unplanned separation.