State Drug Policy Effectiveness: Comparative Policy Analysis of Drug Overdose Mortality

TL;DR

This study employs hierarchical clustering to identify policy bundles across US states and analyzes their impact on opioid overdose mortality over a decade, revealing effective combinations that reduce deaths after two years.

Contribution

Introduces a machine learning approach to empirically generate and evaluate policy clusters for their effectiveness in reducing overdose deaths.

Findings

Certain policy bundles significantly reduce overdose deaths after two years.

Clustering simplifies complex policy interactions into 10 distinct groups.

Policy combinations including Medication Assisted Treatment and Naloxone are most effective.

Abstract

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.