Abstract

Background

Opioid agonist therapy (OAT) has been shown to reduce mortality in patients with opioid use disorder (OUD), yet mortality in individuals receiving OAT remains higher than in an age- and gender-matched population.

Objective

To identify baseline risk factors in patients who engaged in buprenorphine treatment that are associated with this elevated risk of death.

Design

We performed a retrospective cohort study from January 1, 2007, to December 31, 2018, using a centralized clinical data registry within a multi-hospital health system in Boston, MA, USA.

Participants

All adult patients who had ≥ 2 consecutive encounters with sublingual buprenorphine on the active medication list from January 1, 2007, to December 31, 2018.

Main Measures

We abstracted several sociodemographic, clinical, and healthcare use characteristics from the clinical data registry. The primary outcome was all-cause mortality and the secondary outcome was opioid overdose-related mortality. We performed multivariable cox regression to identify baseline characteristics independently associated with these outcomes.

Key Results

Of 5948 patients in the cohort, the majority were white (80.7%) and male (59.7%), with a mean age of 38.2 years. The all-cause mortality rate was 24.0 deaths per 1000 person-years. Baseline characteristics independently associated with an increased hazard of all-cause mortality included homelessness (adjusted hazard ratio [aHR] = 1.39; 95% confidence interval [CI] = 1.09, 1.78), an opioid on the active medication list (aHR = 1.28; 95% CI = 1.08, 1.52), and entry into the cohort during an inpatient hospitalization (aHR = 1.43; 95% CI = 1.18, 1.73). Homelessness was also associated with an increased hazard of opioid overdose-related mortality (aHR = 1.77; 95% CI = 1.25, 2.50).

Conclusions

We identified several novel and potentially modifiable predictors of mortality among patients engaging in buprenorphine treatment who remain at an increased risk of death compared with the general population. Understanding these risk factors can assist healthcare providers in risk stratification and inform the design of targeted interventions to improve outcomes in a high-risk patient population.