Building Drug Intelligence Networks to Combat the Opioid Crisis in Rural Communities: A Collaborative Intelligence-Led Policing Strategy

By Andy Hochstetler1,2, David J. Peters1, Kyle Burgason1, Jeff Bouffard1, Glenn Sterner III3, Shannon Monnat

Introduction: The goal of this project was to identify rural jurisdictions with high drug overdose rates and collaborate with resourced mentors to create law enforcement intelligence responses to local opioid problems. Methods: The first part of the strategy was to control for known contributors to opioid death that are largely beyond the control of law enforcement, and thereby focus on jurisdictions that are outliers in terms of drug problems. (Work products include a Non-Metropolitan County Opioid Overdose Calculator that allows one to examine how demographics and other county conditions affect overdose risk.) Use of maps of drug overdose deaths identified high overdose places by drug type with a classification technique to group places with like drug problems (latent profile analysis) and a multiple regression data analysis to identify outliers. A survey of law enforcement agencies provided an understanding of intelligence resources available in rural areas and agencies. This information informed recruitment of enthusiastic participant agencies. A small scale, intelligence strategy appropriate to resource deprived, rural departments was developed in collaboration with participating agencies, leaving them great flexibility in design. Evaluation of outcomes included a survey, qualitative interviews providing anecdotal feedback, and official data that each department had decided would speak to successful implementation. Conclusions: Variables significantly predicting death rates include population, indicators of ethnic diversity, natural resource amenities, and labor market characteristics. Lagged indicators of drug deaths and prescribing rates are the most consistently significant and convincing block of variables as predictors of current death. The most successful departments implemented efforts based on what they had learned in previous collaborations with better resourced areas, where efforts led to arrests and judges supported use of intelligence in court proceedings, and either information sharing or use of electronic surveillance was supported such as using cell phone opening software. Also, closed network iPads were used in relation to controlled buys, search warrants, pre and post raids, evidence and picture recording during searches, overdose mapping, surveillance photos and messaging to the narcotics officer, confidential informant files and referencing files, and notes from scenes. ODMAP can inform efforts but proved difficult to use on mobile devices, lagging in time, and imprecise to use as daily actionable intelligence. Funds can be well spent in rural places, but investments in departments with little resource slack, lacking in administrative capacity, and where there are few personnel or hours of investment to spare are risky and make for difficult collaborations. Analytics and predictive problem solving are near impossible. Therefore, immediate and accessible intelligence for patrol officers without investment in analytics likely should be the goal.

Ames, IA: Iowa State University, 2023. 71p.