Under-representation of key demographic groups in opioid use disorder trials.

Drug Alcohol Depend Rep

Department of Psychiatry, School of Medicine, Columbia University, and New York State Psychiatric Institute, New York, New York, United States.

Published: September 2022

Background: The extent to which clinical trials of medications for opioid use disorder (MOUD) are representative or not is unknown. Some patient characteristics modify MOUD effectiveness; if these same characteristics differ in distribution between the trial population and usual-care population, this could contribute to lack of generalizability-a discrepancy between trial and usual-care effectiveness. Our objective was to identify interpretable, multidimensional subgroups who were prescribed MOUD in substance use treatment programs in the US but who were not represented or under-represented by clinical trial participants.

Methods: This was a secondary descriptive analysis of trial and real-world data. The trial data included twenty-seven US opioid treatment programs in the National Drug Abuse Treatment Clinical Trials Network, N = 2,199 patients. The real-world data included US substance use treatment programs that receive public funding, N = 740,015 patients. We characterized real-world patient populations who were non-represented and under-represented in the trial data in terms of sociodemographic and clinical characteristics that could modify MOUD effectiveness.

Results: We found that 10.7% of MOUD patients in TEDS-A were not represented in the three clinical trials. As expected, pregnant MOUD patients (n = 19,490) were not represented. Excluding pregnancy, education and marital status from the characteristics, 2.6% of MOUD patients were not represented. Patients aged 65 years and older (n = 11,204), and those 50-64 years who identified as other (non-White, non-Black, and non-Hispanic) race/ethnicity or multi-racial (n = 7,281) were under-represented.

Conclusions: Quantifying and characterizing non- or under-represented subgroups in trials can provide the data necessary to improve representation in future trials and address research-to-practice gaps.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524855PMC
http://dx.doi.org/10.1016/j.dadr.2022.100084DOI Listing

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