  Opioid overdose rates have reached an epidemic level and state-level policy
innovations have followed suit in an effort to prevent overdose deaths.
State-level drug law is a set of policies that may reinforce or undermine each
other, and analysts have a limited set of tools for handling the policy
collinearity using statistical methods. This paper uses a machine learning
method called hierarchical clustering to empirically generate "policy bundles"
by grouping states with similar sets of policies in force at a given time
together for analysis in a 50-state, 10-year interrupted time series regression
with drug overdose deaths as the dependent variable. Policy clusters were
generated from 138 binomial variables observed by state and year from the
Prescription Drug Abuse Policy System. Clustering reduced the policies to a set
of 10 bundles. The approach allows for ranking of the relative effect of
different bundles and is a tool to recommend those most likely to succeed. This
study shows that a set of policies balancing Medication Assisted Treatment,
Naloxone Access, Good Samaritan Laws, Medication Assisted Treatment,
Prescription Drug Monitoring Programs and legalization of medical marijuana
leads to a reduced number of overdose deaths, but not until its second year in
force.
