Poor Sleep Quality and Other Risk Factors for Unemployment Among Patients on Opioid Agonist Treatment

Abstract

Purpose: Patients with opioid use disorder (OUD) face high rates of unemployment, putting them at higher risk of treatment nonadherence and poor outcomes, including overdose death. The objective of this study was to investigate sleep quality and its association with other biopsychosocial risk factors for unemployment in patients receiving opioid agonist treatment (OAT) for OUD. Methods: Using a cross-sectional survey design, participants from 3 OAT programs for OUD completed questionnaires to measure sleep quality (Pittsburgh Sleep Quality Index [PSQI]); pain disability; catastrophic thinking; injustice experience; quality of life; and self-assessed disability. Spearman’s rank correlation was used to test for associations between sleep quality and other study variables. Results: Thirty-eight participants completed the study, with mean age 45.6 ± 10.9 years, 27 (71.1%) males, and 16 (42.1%) reporting a high school diploma/equivalent certification as the highest level of academic attainment. Poor sleep quality (defined as PSQI > 5) was identified in 29 participants (76.3%) and was positively correlated with pain disability ( r = 0.657, P < .01), self-assessed disability ( r = 0.640, P < .001), symptom catastrophizing ( r = 0.499, P < .001), and injustice experience ( r = 0.642, P < .001), and negatively correlated with quality of life ( r = −0.623, P < .001). Conclusions: There was a high prevalence of poor sleep quality in patients with OUD on OAT and this was associated with multiple known risk factors for unemployment. These findings warrant the consideration of regular screening for sleep problems and the inclusion of sleep-related interventions to improve sleep quality, decrease the unemployment rate, and enhance the recovery process for individuals with OUD undergoing OAT.

Associations Between Patient-Reported Experiences with Opioid Use Disorder Treatment and Unmet Treatment Needs and Discontinuation Among Virginia Medicaid Members

“…We included demographic variables previously found to influence treatment retention and unmet needs during treatment. [31][32][33][34][35][36] Specifically, these demographic factors included gender (male/female), age (21-34, 35-54, 55+ years old), race/ethnicity (non-Hispanic white, non-Hispanic Black/African American, or other), marital status (single, married, or separated/widowed/divorced), highest educational attainment (less than high school, high school, or some college or more), and current employment status (employed, not employed but looking for work, or retired/student/homemaker). All except age and gender were based on survey self-report; age and gender were drawn from enrollment files.…”