Data Science Approaches in Criminal Justice and Public Health Research: Lessons Learned From Opioid Projects

Published date01 May 2021
Date01 May 2021
Subject MatterArticles
Journal of Contemporary Criminal Justice
2021, Vol. 37(2) 175 –191
© The Author(s) 2021
Article reuse guidelines:
DOI: 10.1177/1043986221999858
Data Science Approaches
in Criminal Justice and
Public Health Research:
Lessons Learned From
Opioid Projects
Tammy L. Anderson1, Ellen A. Donnelly1,
Chris Delcher2, and Yanning Wang3
The persistence of the nation’s opioid epidemic has called on criminal justice
and public health agencies to collaborate more than ever. This epidemiological
criminology framework highlights the surveillance of public health and safety, often
using data science approaches, to inform best practices. The purpose of our article
is to delineate the main benefits and challenges of adopting data science approaches
for epidemiological criminology partnerships, research, and policy. We offer
“lessons learned” from our opioid research in Delaware and Florida to advise future
researchers, especially those working closely with policymakers and practitioners in
translating science into impactful best practices. We begin with a description of our
projects, pivot to the challenges we have faced in contributing to science and policy,
and close with recommendations for future research, public advocacy, and practice.
opioids, data science, criminal justice, public health
The current U.S. opioid epidemic is now entering its third decade. The Centers for
Disease Control and Prevention (2012) established its origin in the late 1990s
1University of Delaware, Newark, SE, USA
2University of Kentucky, Lexington, KY, USA
3University of Florida, Gainesville, FL, USA
Corresponding Author:
Tammy L. Anderson, Department of Sociology & Criminal Justice, University of Delaware, Newark,
DE 19716, USA.
999858CCJXXX10.1177/1043986221999858Journal of Contemporary Criminal JusticeAnderson et al.
176 Journal of Contemporary Criminal Justice 37(2)
following increases in opioid prescribing, which expanded to illegal opioids in the late
2010s. The Trump administration declared opioid abuse a public health emergency in
2017 (ONDCP 2020). Drug-related treatment admissions, arrests, and incarceration
remain high today (Centers for Disease Control and Prevention, 2020; U.S. Department
of Justice, National Institute of Corrections, 2017). The exponential growth in fatal
and nonfatal drug overdoses since the 1980s has increased the pressure to find new and
effective approaches to address this crisis (Jalal et al., 2018). The popular catch phrase
among criminal justice stakeholders—“We can’t arrest our way out of this problem”—
reflects a potential shift in policy approaches that enhance collaborations between
criminal justice and public health agencies. Efforts include training police to revive
people who have overdosed (Banta-Green et al., 2013), creating diversionary pro-
grams that send people struggling with addiction to treatment instead of court (Str eisel
et al., 2019), and adding public health staff to law enforcement agencies to increase
communication and intelligence sharing across states (HIDTA Program, 2020).
This public health-criminal justice approach, otherwise called an epidemiological
criminology framework (Akers & Lanier, 2009), presents new challenges to the aca-
demic–practitioner partnership as research, policies and interventions will invariably
have to straddle multiple domains, goals, and expectations. For example, the
Department of Health and Human Services (2015) outlined three main initiatives to
reduce opioid deaths: decrease unsafe prescribing of prescription (Rx) opioids, distrib-
ute naloxone widely, and make medication-assisted treatment (MAT) more readily
available across populations, including people in criminal justice settings (U.S.
Department of Health & Human Services, Office of the Assistant Secretary for
Planning and Evaluation, 2015).
To date, improved surveillance has helped law enforcement reduce overprescribing
from fraudulent pain management clinics (i.e., pill mills) and diversion activities (U.S.
Department of Justice, Drug Enforcement Administration, 2020) and administer nal-
oxone to overdose victims. Correctional officials are making MAT available to prison-
ers, whereas probation and parole officers are monitoring a higher portion of their
clientele in treatment (Substance Abuse and Mental Health Services Administration,
2019). The National Institute of Drug Abuse established the Justice Community
Opioid Innovation Network (JCOIN) with substantial funding to community public
health projects in criminal justice settings to treat and reduce opioid addiction (U.S.
Department of Health & Human Services, National Institutes of Health, 2019b). To
support these efforts, public health and criminal justice agencies need robust surveil-
lance data and have increasingly relied on data science to gain insights and direct
resources for this epidemic (National Center for Injury Prevention and Control Board
of Scientific Counselors, 2018).
As demands for quick, effective, and data-driven solutions mount, data science
technologies are needed for epidemiological criminology studies (Hogle, 2016; Lynch,
2018; Perdue et al., 2018). A working definition of data science highlights that it is an
“interdisciplinary field of study that draws on quantitative and analytical processes
using large scale and complex data sets” (U.S. Department of Health & Human
Services, National Institutes of Health, 2019a). Data science approaches to addressing

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