Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment.

ABSTRACT:

Objective

To operationalize an intersectionality framework using a novel statistical approach and with these efforts, improve the estimation of disparities in access (i.e., wait time to treatment entry) to opioid use disorder (OUD) treatment beyond race.

Data source

Sample of 941,286 treatment episodes collected in 2015-2017 in the United States from the Treatment Episodes Data Survey (TEDS-A) and a subset from California (n = 188,637) and Maryland (n = 184,276), states with the largest sample of episodes.

Study design

This retrospective subgroup analysis used a two-step approach called virtual twins. In Step 1, we trained a classification model that gives the probability of waiting (1 day or more). In Step 2, we identified subgroups with a higher probability of diffe

SUBMITTER: Kong Y 

PROVIDER: S-EPMC8928038 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature