U.S. Counties’ Vulnerability to Methamphetamine Labs

• DOI: 10.1177/0022042614559841
• Author: Monica Cain,Cretson Leo Dalmadge
• Published date: 01 April 2015
• Date: 01 April 2015
• Subject Matter: Articles

Journal of Drug Issues

2015, Vol. 45(2) 118 –132

© The Author(s) 2014

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DOI: 10.1177/0022042614559841

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Article

U.S. Counties’ Vulnerability to

Methamphetamine Labs

Cretson Leo Dalmadge1 and Monica Cain1

Abstract

This national study analyzes county-level risk factors for methamphetamine manufacture. Neural

network and probit models are used to test the effectiveness of county-level characteristics in

predicting methamphetamine production levels. Data on all 3,143 counties are drawn from

the U.S. DEA’s Clandestine Laboratory Surveillance System, the 2000 U.S. Census and health

service resources from the 2004 Area Resource File, and the Uniform Crime Reporting Program

(UCRP) for the period 2002-2005. The resulting model accurately predicted methamphetamine

production levels 85% of the time. The leading variables were existing methamphetamine

problems, seizures in contiguous counties, families with “female head of household,” home

value, and “percentage of White population.” Several variables that factored heavily in earlier

single-community studies had very little impact in this national study. This study’s results suggest

a new approach to assessing community vulnerability to drug manufacturer and a need to

refocus efforts in fighting the problem.

Keywords

methamphetamine manufacture, county-level risk factors, neural network

Introduction

Public health and safety officials want to understand whether the next wave of illicit drug use will

affect their communities. Ideally, localities could assess their vulnerability to the next new drug

or drug market using a flexible prediction model. By anticipating the community resource

demands introduced by drug use, distribution, and manufacture, officials might be better able to

respond to these new challenges.

The need to identify practical tools to track and predict current and future drug markets moti-

vates this study. In this article, we describe a framework for modeling community-level vulner-

ability to public health and safety threats, using the recent example of methamphetamine uptake

and manufacturing. The study uses 2002-2005 county-level methamphetamine laboratory sei-

zure data and community characteristics to develop a measure of the vulnerability to metham-

phetamine manufacture for each county in the United States.

Artificial neural network models (ANNs) are a simple-to-use methodology for predicting cat-

egorical outcomes and for evaluating the relative importance of independent variables. These

models have been shown to be especially useful when outcomes result from complex causal

1Winston–Salem State University, NC, USA

Corresponding Author:

Cretson Leo Dalmadge, Winston–Salem State University, 601 S. Martin Luther King Jr. Drive, Winston–Salem, NC

27110, USA.

Email: dalmadgec@wssu.edu

559841JODXXX10.1177/0022042614559841Journal of Drug IssuesDalmadge and Cain

research-article2014