Factors predicting access to medications for opioid use disorder for housed and unhoused patients: A machine learning approach.

ABSTRACT:

Background

Opioid use disorder (OUD) is a growing public health crisis, with opioids involved in an overwhelming majority of drug overdose deaths in the United States in recent years. While medications for opioid use disorder (MOUD) effectively reduce overdose mortality, only a minority of patients are able to access MOUD; additionally, those with unstable housing receive MOUD at even lower rates.

Objective

Because MOUD access is a multifactorial issue, we leverage machine learning techniques to assess and rank the variables most important in predicting whether any individual receives MOUD. We also seek to explain why persons experiencing homelessness have lower MOUD access and identify potential targets for action.

Methods

We utilize a gradient boosted decision tree

SUBMITTER: Esguerra A 

PROVIDER: S-EPMC11433129 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature