Discussion
death, SUD
To the best of our knowledge, no other cohort study has investigated the association of pharmacological treatments and outcomes in patients with MAUD during a long-term follow-up period. This study provides insight concerning the association of different medications, generally used in persons with MAUD, with long-term health outcomes, such as risk of hospitalization and death. We found that, compared with personal nonuse periods, lisdexamphetamine was the only medication studied that was associated with a statistically significant beneficial finding in all 3 outcomes (SUD hospitalization, any hospitalization or death, and all-cause mortality). Benzodiazepines, antidepressants, and antipsychotics were associated with an increase in risk of any hospitalization or death. Benzodiazepines and antidepressants were also associated with an increase in risk of SUD hospitalization and the use of benzodiazepines was associated with a higher risk of death.Currently there are no officially approved pharmacotherapies for MAUD and, despite promising medication candidates, studies are often limited by small and selected cohorts as well as low treatment retention or completion rates. The most consistent positive findings have been demonstrated with stimulant-agonist treatments as well as naltrexone and topiramate, and less consistent benefits have been observed for antidepressants bupropion and mirtazapine.The main strengths of this study are large population size of almost 14 000 persons with nationwide coverage of people with diagnosed MAUD. Previous studies concerning the effectiveness of medications for MAUD are mostly randomized clinical trials limited by small sample sizes, low participant retention, and low treatment adherence rates. The median follow-up time in this study was 3.9 years. Overall, the results are generalizable for real-world patients and offer new and useful information on the association of medications widely used in persons with MAUD with long-term health outcomes. We analyzed the main outcomes by using within-individual design where each individual acts as his or her own control. The method eliminates selection bias by accounting for factors remaining constant for an individual. In addition, we used data on actually purchased medications instead of data on prescriptions given to patients. Drug use was modeled with the PRE2DUP method, which is known to estimate drug use-periods with high accuracy.