Describing and Predicting Drug Epidemics

Law enforcement can play a valuable role within a harm reduction paradigm, but this possibility is often overlooked. This paper reviews a framework for thinking about harm reduction goals, and illustrates how some harm reduction perspectives are more receptive than others to a prominent law enforcement role. Five specific roles for law enforcement are then outlined: partnerships with treatment and other interventions, constraining supply, time-focused intervention early in an epidemic, reducing control costs and associated harms, and exploiting drug markets’ inherent adaptability.

The author of the present article makes three points. Firstly, drug-related measures, such as the number of users, have changed rapidly over time, suggesting that they are not merely symptoms of underlying trends in the economy, demographics or other aggregates that change more slowly over time. Secondly, drug markets are subject to a wide range of feedback effects that can induce non-linearity into dynamic behaviour. Thirdly, there are at least five classes of drug epidemic models that reflect such non-linear dynamic behaviour. Some of those classes tend to be optimistic about the ability of drug control interventions to reduce use; others are pessimistic. It is hoped that the present article and, in particular, the typology, will inform and elevate the debate about drug policy, though it is unlikely to resolve that debate because of the inability to demonstrate empirically which classes of model is (are) more accurate.

Everingham and Rydell's [1] Markov chain model of cocaine demand is modified and updated in light of recent data. Key insights continue to hold, e.g., that the proportion of cocaine demand stemming from heavy vs. light users changed dramatically over the 1980s. New insights emerge, e.g., pertaining to the average duration of a career of heavy use (about 12 years) and the negative relationship between levels of heavy use and epidemic "infectivity" or the number of new initiates per current user per year. This illustrates how simple modeling can yield insights directly relevant to managing complex drug control policy questions.

No abstract available.