Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders
This work addresses the opioid epidemic by providing fine-grained analysis to inform policy interventions, though it is incremental as it applies an existing method to a new domain.
The study tackled the problem of heterogeneous health states in opioid use and disorders by using probabilistic topic modeling on medical diagnosis histories to identify latent phenotypes, which were shown to predict future opioid-related outcomes and explain prescription variability.
The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed with opioid use disorder, have diverse underlying health states. Policy interventions targeting prescription opioid use, opioid use disorder, and overdose often fail to account for this variation. To identify latent health states, or phenotypes, pertinent to opioid use and opioid use disorders, we use probabilistic topic modeling with medical diagnosis histories from a statewide population of individuals who were prescribed opioids. We demonstrate that our learned phenotypes are predictive of future opioid use-related outcomes. In addition, we show how the learned phenotypes can provide important context for variability in opioid prescriptions. Understanding the heterogeneity in individual health states and in prescription opioid use can help identify policy interventions to address this public health crisis.