Street-drug lethality index: A novel methodology for predicting unintentional drug overdose fatalities in population research

Abstract

BackgroundEmerging evidence suggests the composition of local illicit drug markets varies over time and the availability and relative lethality of illicit drugs may contribute to temporal trends in overdose mortality. Law enforcement drug seizures represent a unique opportunity to sample the makeup of local drug markets. Prior research has associated shifts in the types of drugs seized and trends in unintentional drug overdose mortality. The present report builds on this work by demonstrating a novel methodology, the Street-Drug Lethality Index, which may serve as a low-lag predictor of unintentional overdose deaths. MethodsData included administrative records of law enforcement drug seizures and unintentional drug overdose deaths in Ohio from 2009 -to- 2018. Death records and lab results from drug seizures occurring during the calendar year 2017 were transformed via the described procedure to create lethality indices for individual drugs. These indices were then summed annually to create the independent variable for a linear regression model predicting unintentional overdose deaths for all years during the study period. ResultsThe regression model explained 93 % of the year-to-year variance in unintentional overdose fatalities (slope = 0.009480; CI = 0.007369 to 0.011590; t10 = 10.355942; P = 0.000007; Y = 11.808982 + 0.009480X, r2 = 0.931). ConclusionThese findings contribute to a growing body of evidence that changes in the composition of the drug supply may predict trends in unintentional overdose mortality. The proposed methodology might inform future overdose prevention and response efforts as well as research.