Data Science Approaches in Criminal Justice and Public Health Research: Lessons Learned From Opioid Projects. (May 2021)

• Record Type: Journal Article
• Authors: Anderson, Tammy L.; Donnelly, Ellen A.; Delcher, Chris; Wang, Yanning
• Other Names: Burgess-Proctor Amanda guest-editor.; Ranjan Sheetal guest-editor.
• Is Part Of: Journal of contemporary criminal justice. Volume 37:Number 2(2021)
• Page Start: 175
• Page End: 191
• Publication Date: 2021-05
• Subjects: opioids -- data science -- criminal justice -- public health; Criminal justice, Administration of -- Periodicals; Criminal justice, Administration of -- United States -- Periodicals; 364.973
• DOI: 10.1177/1043986221999858
• Languages: English
• ISSNs: 1043-9862

Abstract

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.