Predicting opioid use disorder and associated risk factors in a Medicaid managed care population.

Medicaid managed care organizations are developing comprehensive strategies to reduce the impact of opioid use disorder (OUD) among their members. The goals of this study were to develop and validate a predictive model of OUD and to predict future OUD diagnosis, resulting in proactive, person-centered outreach.
We utilized machine learning methodology to select a multivariate logistic regression and identify predictors.
Using 2016-2018 data, we used a staged approach to test and validate the predictive accuracy of our model. We identified OUD, the dependent variable, using an industry-standard definition. We included a series of patient demographic, chronic condition, social determinants of health (SDOH), opioid-related, and health utilization indicators captured in administrative data.
Caucasian (odds ratio [OR], 1.65), male (OR, 1.57), and younger (aged 40-64 years compared with 18-39 years: OR, 0.75) members had greater odds of being diagnosed with an OUD. Members with an SDOH vulnerability had 26% higher odds than those without a documented issue. From a prescribing perspective, we found that having an opioid dose of 120 morphine milligram equivalents and contiguous 5-day supply increased odds of OUD by 1.87 times, and an opioid supply of 30 days or longer increased the odds of OUD by 1.56 times.
We built the necessary machine learning infrastructure to identify members with greater than 50% probability of developing OUD. The generated list strategically informs and guides person-centered care and interventions. Through application of these results, we strive to proactively reduce OUD-related structural barriers and prevent OUD from occurring.