Using a Criminally Involved Population to Examine the Relationship Between Race/Ethnicity,Structural Disadvantage,and Methamphetamine Use

Abstract

Limited empirical focus has been given to identifying individual and structural correlates of methamphetamine use. Although race (i.e., being White) is one of the most distinguishing characteristics of methamphetamine users, few studies have examined whether race/ethnicity is a significant predictor of such illicit drug use. Research has also shown that cocaine and opiate use is associated with disadvantage; however, studies have yet to examine the relationship between structural disadvantage and methamphetamine use. Using national data from the Arrestee Drug Abuse Monitoring program, this study examines the prevalence of methamphetamine and explores the relationship between race/ethnicity, structural disadvantage, and methamphetamine use. Findings reveal that race/ethnicity and structural disadvantage are significant predictors of methamphetamine use. Additionally, findings show an interactive effect between race/ethnicity, structural disadvantage, and methamphetamine use. Implications for policy and practice are discussed.

Keywords

methamphetamine use race ethnicity structural disadvantage ADAM

Introduction

Methamphetamine use throughout the United States has received a considerable amount of attention over the past several years. A recent study by the National Association of Counties (Hansell, 2006b) revealed that more than half of the counties surveyed (58%) identified methamphetamine as the leading drug problem in the area, followed by cocaine (19%), marijuana (17%), and heroin (3%). In spite of increasing levels of methamphetamine use among the general population (National Institute on Drug Abuse, 2002, 2006), some researchers caution against claims that present methamphetamine as an epidemic. King (2006) reviewed data from various national drug monitoring surveys and found that methamphetamine use rates have remained relatively stable over the years. Additionally, researchers have found that methamphetamine is a problem only in some regions of the country, specifically western and southwestern states (Hansell, 2006b; Hunt, Kuck, & Truitt, 2006; Kyle & Hansell, 2005; National Institute on Drug Abuse, 2006; Pennell, Ellett, Rienick, & Grimes, 1999).

To date, few empirical studies have examined the “methamphetamine problem.” Although much anecdotal evidence suggests methamphetamine use is closely linked to criminal activity, few empirical studies have examined methamphetamine use among a criminally involved population. In fact, most information about methamphetamine use is descriptive in nature and comes from surveys of the general population (e.g., adult and student self-report surveys of drug use), treatment admissions, and drug-related emergency department visits. For years, technical reports from the Arrestee Drug Abuse Monitoring (ADAM) program were used to monitor drug use among a criminally involved population (see Herz, 2000; Zhang, 2004). Unfortunately, the majority of these reports failed to identify the individual characteristics statistically associated with methamphetamine use, net the effect of various factors. Also, researchers have found that crime and drug use are not simply influenced by one’s individual attributes but can be predicted, in part, by the structural disadvantage to which one is exposed (Lo, 2003; Pratt & Cullen, 2005). Although disadvantage is a significant predictor of cocaine and heroin use, the relationship between methamphetamine use and structural disadvantage is virtually unknown. The aforementioned relationships are particularly important given the contrast in sociodemographics of methamphetamine, cocaine, and heroin users (Hunt, 2006; Kyle & Hansell, 2005) and the varying nature of specific drug markets (open vs. closed markets; Pennell et al., 1999).

Although a tremendous amount of time and resources have been spent by the criminal justice system in efforts to reduce the accessibility of methamphetamine (U.S. Department of Justice, 2006), little is known about the individual offenders who use this particular illicit drug and the context of offenders’ drug use. The current study is an exploratory analysis of the intersection of race/ethnicity, context, and methamphetamine use. We rely on a sample of male arrestees to examine the individual predictors of offenders’ methamphetamine use and place empirical focus on race/ethnicity given its critical role in drug use. Furthermore, we explore the relationship between methamphetamine use and structural disadvantage to identify whether use is most prone in high or low levels of disadvantage. Findings from this study will not only provide a more comprehensive review of the relationship between methamphetamine use and crime but also inform current substance abuse treatment and crime control efforts.

Literature Review

According to the National Household Survey on Drug Abuse, approximately 10.4 million people in 2005 tried methamphetamine, compared with 8.8 million people in 2000 and 1.8 million in 1994 (National Institute on Drug Abuse, 2002, 2006). These rates are particularly important, as research suggests that methamphetamine use leads to an increase in aggression and violence (Baskin-Sommers & Sommers, 2006; Shrem & Halkitis, 2008). In fact, various studies report that methamphetamine users are more likely than non–methamphetamine users to have contact with the criminal justice system (i.e., previously arrested and/or incarcerated; Molitor et al., 1999; National Institute on Drug Abuse, 2006; Pennell et al., 1999; Rodriguez, Katz, Webb, & Schaefer, 2005). Furthermore, law enforcement officials indicate that increases in robbery, burglary, identify theft, fraud, simple assaults, and domestic violence crimes can be attributed to methamphetamine use (Kyle & Hansell, 2005).

Data from various sources, including the National Survey on Drug Use and Health, Monitoring the Future, the Treatment Episode Data Set, and the ADAM program, report that methamphetamine users are characteristically different from crack, powder cocaine, and heroin users (Hunt et al., 2006). This is not surprising because, historically, methamphetamine has been differentiated from other street drugs because of the lack of appeal from the typical drug addict. Although, in recent decades, methamphetamine has become attractive to a broad new consumer base (Gonsalves, Sapp, & Huss, 2007), users remain atypical from other illicit drug users. One attribute of methamphetamine users that appears to be different from other drug users is race. In contrast to the large volume of racial and ethnic minorities affected by cocaine and heroin use over the years, studies have consistently found that the overwhelming majority of methamphetamine users are White (Hunt, 2006; Hunt et al., 2006; Kyle & Hansell, 2005; Rodriguez et al., 2005). The following section expands on the relationship between race and methamphetamine use.

The Nexus Between Race and Methamphetamine Use

A historical review of the methamphetamine drug market reveals its close tie to Whites. During the early 1960s, an underground methamphetamine market began to emerge in California’s Bay Area as a result of pharmaceutical companies removing several forms of methamphetamine from the domestic market. The market was quickly controlled by a number of motorcycle gangs, specifically the Hells Angels (Miller, 1997). Members of these motorcycle gangs were instrumental in spreading methamphetamine throughout the west coast (Anglin, Burke, Perrochet, Stamper, & Dawud-Noursi, 2000; Meredith, Jaffe, Ang-Lee, & Saxon, 2005). The term crank is reminiscent of the days when motorcycle gang members would hide the drug in their crank cases. The history and development of the methamphetamine drug market is important for two reasons. Not only were White motorcycle gangs primarily responsible for the distribution of the drug, but they also achieved this through transactions between members and people they knew. One could argue that this led to the beginning of the closed market of methamphetamine sales.

Over the years, methamphetamine has been commonly referred to as a “White person’s drug” (Hunt et al., 2006; Pennell et al., 1999; U.S. Department of Health and Human Services, 2005). In 2002, the Drug Abuse Warning Network data showed that 65% of methamphetamine-related emergency department visits involved White patients (U.S. Department of Health and Human Services, 2004). Furthermore, data from the Treatment Episode Data Set revealed that illicit drugs like crack, powder cocaine, and heroin were more likely than methamphetamine to attract a larger percentage of racial/ethnic minorities (Hunt et al., 2006). Additional research on drug treatment has found that those who seek treatment for methamphetamine are overwhelmingly White (Calkins, 2003; U.S. Department of Health and Human Services, 2005). Although race appears to be a distinguishing characteristic among methamphetamine users in the general population, data from offenders and criminal justice officials show varying findings regarding the methamphetamine use–race nexus.

Developed in the late 1980s as a national program for gauging the relationship between drugs and crime, the ADAM program produced the only national data source for examining drug use among a representative sample of recently booked arrestees. Various empirical studies over the years have relied on ADAM data to report arrestees’ varying drug(s) use, drug acquisition behavior, and drug trends (Golub, Johnson, Taylor, & Liberty, 2002; Golub, Liberty, & Johnson, 2005; Griffin & Rodriguez, 2011; Lo, 2003; Riley, 1997; Riley et al., 2001; Rodriguez et al., 2005; Yacoubian, 2003). According to ADAM data, Whites and Asian/Pacific Islanders report the highest rate of methamphetamine use, followed by Hispanics and African American arrestees (National Institute on Drug Abuse, 2003). Two studies that relied on western regional ADAM data revealed that the majority of methamphetamine users were White (Pennell et al., 1999; Rodriguez et al., 2005). For example, Rodriguez et al. (2005) found that Hispanic, Black, and Native American arrestees in Phoenix and Tucson, Arizona, were less likely to test positive for methamphetamine than Whites, net the effect of other social demographic characteristics and legal factors.¹

Although the overrepresentation of Whites among methamphetamine users has been documented by several sources, a few studies have reported changes in demographics among users. Pennell et al. (1999) found that a growing number of Hispanic arrestees in the West tested positive for methamphetamine, yet a relatively low number of Blacks tested positive for the drug. According to the National Association of Counties (Kyle & Hansell, 2005), levels of methamphetamine have increased among Hispanic and American Indian populations. Also, the Community Epidemiology Work Group reported increasing levels of methamphetamine use among African Americans and Hispanics (National Institute on Drug Abuse, 2007). As previously discussed, individual attributes such as race/ethnicity have been distinguishing characteristics of specific illicit drug users. However, the role of race, drug use, and crime is often confounded by macro-level factors such as racial/ethnic composition and economic development (Sampson & Wilson, 1995). We now turn our attention to the relationship between drug use and structural disadvantage.

Illicit Drug Use and Disadvantage

Prior studies of the relationship between illicit drug use and community characteristics have shown that drug use, in general, is not equally distributed across communities. Early studies of drug use and its distribution indicate that levels of opiate and cocaine use were highest in socially and economically impoverished areas (Brown & Silverman, 1974; Chein, Gerard, Lee, & Rosenfeld, 1964; DuPont & Greene, 1973; Inciardi, 1974, 1979; McBride & McCoy, 1981). Years later, the arrival of crack cocaine during the mid-1980s transformed many inner-city landscapes where violence became a by-product of the crack cocaine drug market. Given the important role of structural factors in drug use and crime, it is important to account for such macro-level characteristics in studies of drug use. In fact, a meta-analysis by Pratt and Cullen (2005) found that factors related to concentrated disadvantage (e.g., high levels of racial heterogeneity, economic deprivation, and high rates of family disruption) were among the strongest and most stable macro-level predictors of crime.

Recent studies examining the relationship between disadvantage and drug use, albeit not methamphetamines, have found a positive relationship between the two (Boardman, Finch, Ellison, Williams, & Jackson, 2001; Ford & Beveridge, 2006; Galea, Rudenstine, & Vlahov, 2005; Jang & Johnson, 2001). For example, Ford and Beveridge (2004) found that neighborhood disadvantage, minority concentration, and density were strongly related to increased levels of visible drug sales in neighborhoods. Lo (2003) also found that arrestees residing in structurally disadvantaged areas were more likely to be chronic drug users, especially of heroin and cocaine. However, Saxe et al. (2001) found that although visible drug use is most prevalent in disadvantaged areas, drug use is nearly equally distributed in advantaged and disadvantaged communities. It is important to note that Saxe and colleagues did not distinguish between specific illicit drug use as their dependent variable was an overall drug use measure. Although not discussed by the authors, it is possible that the effect of disadvantage in their study may be attributed, in part, to the relationship between race and drug acquisition methods.

Riley’s (1997) work on drug markets showed that Blacks were more likely to purchase drugs outdoors and within their neighborhood. On the other hand, Whites and Hispanics were more likely to purchase drugs indoors and travel outside their neighborhood to acquire drugs, making them less visible. Prior research examining drug markets suggests that methamphetamine is sold in a market that is characteristically different from the crack and heroin drug markets. For example, government reports have shown that methamphetamine dealers are more likely to sell indoors than are dealers of crack and heroin (Office of National Drug Control Policy, 2002; Pennell et al., 1999). Rodriguez et al. (2005) also found that methamphetamine users were more likely to get the drug for free. Differences in acquisition methods are likely rooted in the historical evolution of these drugs. That is, White biker gangs were instrumental in the establishment of the methamphetamine drug market, whereas the crack cocaine market has its roots in Black urban centers. This difference in acquisition behavior has led to a methamphetamine market that more closely resembles a closed market instead of an open market (i.e., where product is sold out in the open and on the street). In combination with research that suggests visible drug use and activity is more likely to take place in disadvantaged areas (Ford & Beveridge, 2004, 2006), it is likely that methamphetamine use will be low in areas where the open drug market (e.g., crack and heroin) is most prevalent. Recent work by Griffin and Rodriguez (2011) also shows how drug acquisition behavior of arrestees is highly influenced by location (indoor vs. outdoor) and place (in their neighborhood vs. outside their neighborhood).

Beyond basic descriptives of the relationship between large geographic regions and methamphetamine use and distribution, little is known about the relationship between structural disadvantage and methamphetamine use. One recent study, using the Adolescent Health data, found that marijuana, cocaine, and methamphetamine users were more likely to experience functional poverty than those who did not use illicit drugs (Iritani, Hallfors, & Bauer, 2007). In their review of methamphetamine use among arrestees in Arizona, Rodriguez et al. (2005) found that arrestees from areas characterized by a higher percentage of unemployed residents, Spanish-speaking households, and college-educated residents were less likely to use methamphetamine. Based on the aforementioned studies and the minimal empirical research that exists on methamphetamine use and crime, we seek to expand work in this area and provide greater insight on the intersection of race/ethnicity, structural disadvantage, and methamphetamine use among a criminally involved population.

The Present Study

An empirical focus on the individual and macro-level predictors of methamphetamine use among arrestees can expand current knowledge of the methamphetamine problem in America. Thus, we draw on prior research on drug use, specifically methamphetamine use, and test the following hypotheses:

Hypothesis 1: Methamphetamine use will vary based on the race and ethnicity of arrestees, leading to higher rates of use among White arrestees.

According to prior studies, White motorcycle gangs were responsible for the initial wave of methamphetamine distribution in the United States (Anglin et al., 2000; Meredith et al., 2005). Also, a higher rate of methamphetamine use among Whites relative to other racial and ethnic groups has been reported in both the general and criminal justice populations (Hunt et al., 2006; Kyle & Hansell, 2005; Pennell et al., 1999; U.S. Department of Health and Human Services, 2005). Although rates of methamphetamine use among minority groups (e.g., African Americans, Hispanics, and American Indians) have increased (Glittenberg & Anderson, 1999; National Institute on Drug Abuse, 2007; Oetting et al., 2000), we expect methamphetamine use to be most prevalent among Whites than other racial/ethnic groups, net the effects of extralegal and legal characteristics, including age, educational attainment, employment status, and prior record. A test of this hypothesis will reveal whether race is a significant predictor of methamphetamine use as well as identify other individual attributes of the typical methamphetamine user among a criminally involved population.

Hypothesis 2: Structural disadvantage will be a significant predictor of methamphetamine use, producing lower levels of methamphetamine use among arrestees who reside in highly disadvantaged areas.

To date, little is known about the relationship between structural disadvantage and methamphetamine use. The only study to date to examine the relationship between methamphetamine use and community factors found lower rates of confirmed methamphetamine use among arrestees who lived in economically deprived communities (Rodriguez et al., 2005). Unfortunately, this study is not generalizable to a national criminal justice population as it only included arrestees in one southwestern state (i.e., Arizona). Despite the limited work in this area, we hypothesize a negative relationship between methamphetamine use and structural disadvantage. That is, arrestees throughout the United States who experience higher levels of structural disadvantage will be less likely to use methamphetamine than those who do not live in such areas. Although it is possible that the mean level of methamphetamine use will not vary by structural disadvantage, given the vastly different areas where methamphetamine users reside, we believe users are not more likely to reside in economically deprived areas. Not only are the sociodemographics of methamphetamine users not consistent with the population of drug users found in economically disadvantaged areas (e.g., young and Black), but the production and distribution of methamphetamine is not confined to lower class areas. Furthermore, the presence of methamphetamine in nonurban areas makes an association between the drug and economically deprived areas less robust. Based on these factors, we expect to find higher levels of methamphetamine use among those who do not live in places characterized by structural disadvantage.

Hypothesis 3: The effect of race and ethnicity in methamphetamine use will differ based on the levels of structural disadvantage. In particular, minority arrestees who live in disadvantaged areas will be less likely to use methamphetamine than White arrestees who live in similar places.

Because no study that we are aware of has examined the intersection of race/ethnicity, structural disadvantage, and methamphetamine use, we set out to explore this relationship. Based on the hypothesized direct effect of race and structural disadvantage on methamphetamine use, we expect methamphetamine use to be significantly lower among racial and ethnic minority groups than among Whites who live in disadvantaged areas. Furthermore, this proposed relationship is consistent with government reports that indicate Black and Hispanic youth may experience more social disadvantage yet do not necessarily use illicit drugs at higher rates than White youth (National Institute on Drug Abuse, 2003). A test of this hypothesis will allow us to disentangle the effects of race/ethnicity, structural disadvantage, and methamphetamine use among arrestees as well as provide valuable information about who is using methamphetamine and where such users reside.

Method

We rely on two data sources to examine the relationship between race/ethnicity, structural disadvantage, and methamphetamine use.

Data

The 2000-2003 national data set of male arrestees from the ADAM program were used for these analyses (N = 23,368).² The ADAM program was formerly funded by the National Institute of Justice and designed to collect information from arrestees on an array of topics. The program was designed to monitor national drug use trends and provide local jurisdictions with information on drug use, treatment needs, and drug markets. Interview data and urine samples were collected within 48 hours of the arrest, providing data on sociodemographics, prior involvement with the criminal justice system, drug dependence and abuse measures, treatment experience(s), drug markets, and self-reported and confirmed drug use. The ADAM program implemented a probability-based sampling plan so that “sites can be assured that data truly represent the arrestee population, not simply an unspecified proportion of that population” (Hunt & Rhodes, 2001, p. 6).³ It is important to note that drug use from this population does not represent drug use among the general population but, rather, individuals who have been arrested and booked for alleged crime(s). As previously discussed, the ADAM data have been used by an array of previous studies to examine the prevalence of drug use in a criminal justice population. Because the focus of this study is on the prevalence of methamphetamine use among a criminally involved population, the data source is ideally suited for the current study.

To explore the effect of structural disadvantage on methamphetamine use, arrestees’ residential zip codes were used to link zip code–level data from the 2000 Census (U.S. Bureau of the Census, 2000; Summary Tape File 3). Arrestees in our sample represent 271 zip codes throughout the ADAM sites in the United States.⁴

Sample

ADAM data were collected from booking facilities across 43 different sites. Only male arrestees who provided a urine sample and had a valid residential zip code within the corresponding jurisdiction were included in this study.⁵

Measures

We rely on urinalyses (UAs) results from arrestees to measure the dependent variable. Methamphetamine use is coded as a binomial variable (positive UA for methamphetamine = 1; negative UA for methamphetamine = 0).⁶ Predictors at the individual level include race/ethnicity (dummy-coded variables for Hispanic/Latinos and Blacks, with Whites as the omitted category) and age at the time of arrest. We also include measures of education (less than high school education = 1; high school education = 0) and employment status (working = 0; not working = 1). Legal factors are measured by the inclusion of most serious offense at arrest (violent offense = 1; nonviolent offense = 0)⁷ and prior arrest history (yes = 1; no = 0).

Using zip code as the unit of analysis, a structural disadvantage index was created using principal components analysis with one factor. Macro-level variables used in the construction of the index include percentage unemployed, percentage receiving public assistance, percentage living in poverty, percentage of female-headed households with children younger than 18 years, median household income, and percentage with less than high school education. The eigenvalue for the factor is 3.98 with the factor dominated by high loading factors (>.80), with a slightly lower factor loading for less than high school education. Each arrestee was assigned a score on the index based on his zip code of residence. Table 1 presents the independent and dependent variables used in the analyses, along with the corresponding coding scheme.

Table 1.

Coding Scheme and Descriptive Statistics.

Individual data (N = 20,395)
Dependent variables
Methamphetamine use	Positive UA for methamphetamine = 1	23.0%
	Negative UA for methamphetamine = 0	77.0%
Independent variables
Race/ethnicity^a	White (reference category)	45.2%
	Black	24.1%
	Hispanic/Latino	30.7%
Age	Age at arrest	Mean = 31.77; SD = 10.412
Less than high school education	Not a high school graduate = 1	27.7%
	High school graduate = 0	72.3%
Married	Married = 1	23.0%
	Not married = 0	77.0%
Unemployed	Unemployed = 1	26.1%
	Employed = 0	73.9%
Most serious arrest charge	Violent offense = 1	28.2%
	Nonviolent =0	71.8%
Prior arrest	Yes = 1	84.2%
	No = 0	15.8%
Community data (N = 271)
Community disadvantage index^b
Percent unemployed		.879
Percent receiving public assistance		.809
Percent living in poverty		.950
Percent female headed households with children under 18 years old		.842
Median household income		−.801
Percent with less than high school education		.721

Note. UA = urinalysis.

Separate dummy variables for race/ethnicity.

Alpha values are provided for community disadvantage index data.

Analytical Strategy

To properly analyze the data given their nested structure (i.e., arrestees within zip codes), a hierarchical modeling technique is used to analyze the data. Multilevel models allow for an examination of both individual- and macro-level effects on the dependent variable (Raudenbush & Bryk, 2002).⁸ Because the dependent variable is dichotomous (methamphetamine use), a hierarchical generalized linear model is used to estimate the impact of individual factors (Level 1) and structural disadvantage (Level 2) on methamphetamine use.

Findings

Descriptive statistics show that the average age of arrestees in this study was 32 years (see Table 1). Among the three racial/ethnic groups under examination, the largest proportion of arrestees was White (45.2%), followed by Hispanic/Latino (30.7%) and Black (24.1%). The majority of cases in the study involved individuals who were arrested for nonviolent offenses (71.8%) and had been arrested in the past (84.2%). They were generally unmarried (77%), employed (73.9%), and had at least a high school education (72.3%). Twenty-three percent of arrestees tested positive for methamphetamine. The structural disadvantage index ranged from −1.60 to 2.86.

Table 2 contains all 43 ADAM sites and the percentage of arrestees who tested positive for methamphetamine. To review the geographic distribution of the sites, we have categorized the sites according to the Census Bureau’s regions and divisions. Because levels of methamphetamine use were relatively low to nonexistent in various sites, only sites with significant levels of methamphetamine use were included in the analysis (see Table 2; sites in boldface were included in the analysis). Additionally, only the zip codes with a sufficient size of arrestees (a minimum of 40) were included in the multilevel analyses.⁹

Table 2.

Methamphetamine Use Among Arrestees: ADAM Sites (2000-2003).

Region/Division/Site	Percentage Positive
West	21.8
Pacific	25.3
Portland	22.5
San Diego	33.2
Los Angeles	24.5
San Jose	32.7
Sacramento	34.0
Anchorage	0.9
Seattle	12.0
Spokane	22.8
Honolulu	39.2
Mountain	18.5
Phoenix	29.0
Denver	3.3
Albuquerque	8.4
Tucson	9.0
Las Vegas	21.1
Salt Lake City	20.9
Rio Arriba	1.7
Midwest	5.6
West North Central	13.3
Omaha	19.3
Kansas City	1.0
Minneapolis	2.7
Des Moines	21.9
Woodbury County	16.6
East North Central	0.7
Indianapolis	1.3
Detroit	0.0
Chicago	0.5
Cleveland	0.4
South	4.3
West South Central	6.2
Houston	1.0
New Orleans	0.9
Dallas	4.5
San Antonio	2.7
Laredo	0.0
Oklahoma City	11.8
Tulsa	15.1
East South Central	0.8
Birmingham	0.8
South Atlantic	0.9
Washington, D.C.	0.5
Fort Lauderdale	0.0
Miami	0.1
Atlanta	1.5
Charlotte-Metro	0.6
Tampa	1.7
Northeast	0.2
Middle Atlantic	0.2
New York	0.2
Philadelphia	0.2
Albany—Capital Area	0.0
New England	0.0
Boston	0.0

Note. ADAM = Arrestee Drug Abuse Monitoring. Entries in boldface represent the sites included in the analyses. Los Angeles and Woodburry County were not included in the analyses because of insufficient number of arrestees across zip codes.

A review of the data shows much higher levels of methamphetamine use in the West region and relatively low rates in the Midwest, South, and Northeast regions. Out of the nine ADAM sites in the pacific division, seven sites had levels of methamphetamine use by arrestees exceeding 12%, with San Diego, San Jose, Sacramento, and Honolulu having rates of methamphetamine use higher 30%. ADAM sites within the mountain division of the West region show that five of the seven sites had methamphetamine use rates higher than 8%. The Phoenix, Las Vegas, and Salt Lake City sites all had methamphetamine use rates more than 20%. In the Midwest region, only two sites (Omaha and De Moines) had substantial levels of methamphetamine use. Similarly, only two sites (Oklahoma City and Tulsa) in the South region had levels of methamphetamine use higher than 10%.

Predicting Methamphetamine Use Among Arrestees

Because the current focus of the study is to examine levels of methamphetamine use across various landscapes, it is critical to first determine whether the mean methamphetamine use of arrestees varied across geographic areas (i.e., zip codes). For this reason an intercepts-only model was first estimated (see Table 3, Model 1). The significant random effects component for the intercept indicates that the rate of methamphetamine use varies across zip codes (p < .001). Given this variation across areas, a random coefficient model including only the individual-level measures was estimated (see Table 3, Model 2).

Table 3.

HGLM Log Odds of Methamphetamine Use (Weighted Estimates).

	Model 1	Model 2	Model 3	Model 4
Intercept^a	.240***	−.1.471	−1.463***	−1.468***
	(.007)	(.056)	(.052)	(.052)
Community-level indicator (N = 271)
Disadvantage index			−.312***	−.348***
			(.063)	(.068)
Individual-level indicators (N = 20,395)
Black		−1.838***	−1.846***	−1.747***
		(.090)	(.091)	(.093)
Cross-level interaction
Disadvantage index				−.296***
				(.090)
Latino		−.439***	−.439***	−.447***
		(.058)	(.058)	(.057)
Cross-level interaction
Disadvantage index				.046
				(.068)
Age		−.010***	−.010***	−.010***
		(.002)	(.002)	(.002)
Violent offense		−.475***	−.475***	−.475***
		(.055)	(.055)	(.055)
Previous arrest		.965***	.962***	.960***
		(.058)	(.057)	(.057)
No high school education		.045	.045	.042
		(.041)	(.041)	(.041)
Married		−.068	−.068	−.068
		(.049)	(.049)	(.049)
Unemployed		.426***	.428***	.430***
		(.045)	(.045)	(.045)

Note. HGLM = hierarchical generalized linear model. Data presented in the analyses are weighted. However, it is important to note that none of the coefficients substantially differed when models were estimated without sampling weights. Model 1: χ² = 1978.67, df = 270; Model 2: χ² = 2174.09, df = 270; Model 3: χ² = 2129.15, df = 269; Model 4: χ² = 2132.57, df = 269. Standard errors in parentheses.

White arrestees represent the reference category.

***

p < .001.

The findings suggest that age and race/ethnicity had a negative effect on methamphetamine use. As predicted, Hispanic/Latino and Black arrestees had lower log odds of testing positive for methamphetamine use than White arrestees. In particular, the odds for Hispanics/Latinos testing positive for methamphetamine were .64 times (exp[−.439]) lower than the odds for Whites. The odds of testing positive were even lower for Blacks (.16 = exp[−1.838]) compared with Whites. Violent offenders were .62 times (exp[−.475]) less likely to test positive for methamphetamine than nonviolent offenders. A prior arrest was associated with higher odds of methamphetamine use, with such arrestees being 2.62 times (exp[.965]) more likely than arrestees with no prior arrests to use methamphetamine. Unemployment was associated with higher log odds of methamphetamine use, with an odds ratio of 1.53 (exp[.426]). Education and marital status had no effect on the log odds of testing positive for methamphetamine.

To examine the effect of structural disadvantage on the mean rate of methamphetamine use across geographic areas, the structural disadvantage index was incorporated as a predictor of the intercept in each zip code (see Table 3, Model 3). The sociodemographic and legal characteristic estimates were similar to the estimates reported in Model 2. As predicted, the disadvantage index significantly influenced the mean rate of methamphetamine use. The negative effect of the index on methamphetamine use indicates that arrestees from areas characterized by higher levels of structural disadvantage were associated with lower log odds of methamphetamine use than those from areas with lower levels of disadvantage.

An examination of the cross-level interaction between race/ethnicity and structural disadvantage on methamphetamine use showed significant effects for Blacks but not Hispanics/Latinos (see Table 3, Model 4). Specifically, Black arrestees who lived in areas with high levels of structural disadvantage had lower log odds of methamphetamine use than similarly situated Whites. To more easily convey these differences, predicted probabilities were plotted for methamphetamine use among Black and White arrestees across levels of disadvantage. As shown in Figure 1, the levels of methamphetamine use were highest among White arrestees who lived in areas characterized by low levels of disadvantage and lowest in those areas with high levels of disadvantage. The levels of methamphetamine use among Black arrestees were lower than Whites, regardless of the level of structural disadvantage.

Figure 1.

Predicted probability of methamphetamine use by disadvantage.

Discussion and Conclusion

Research on drug use and communities, for the most part, has found a positive relationship between structural disadvantaged and drug use, specifically for cocaine and heroin use (Boardman et al., 2001; Esbensen & Huizinga, 1990; Ford & Beveridge, 2006; Galea et al., 2005; Jang & Johnson, 2001). Unfortunately, most of the information about individual and community correlates of methamphetamine use is descriptive in nature (Hansell, 2006a; Pennell et al., 1999). Although local, state, and federal agencies have launched efforts aimed at reducing methamphetamine use, little is known about the individuals who use the drug and where such users reside. This study set out to fill this void in the extant research by examining the relationship between race/ethnicity, structural disadvantage, and methamphetamine use among a nationally representative sample of arrestees.

Findings from this study provide valuable insight on the relationship between methamphetamine use and crime. Analyses reveal that both race/ethnicity and structural disadvantage are significant predictors of methamphetamine use among arrestees. Although increasing levels of methamphetamine use among racial/ethnic groups have been documented, our findings revealed White arrestees were more likely than Black and Hispanic/Latino arrestees to test positive for methamphetamine. Furthermore, methamphetamine use rates were lower among arrestees who reside in highly disadvantaged areas. In fact, an interactive effect between race/ethnicity, structural disadvantage, and methamphetamine use indicates that Whites who live in disadvantaged and advantaged areas were more likely to test positive for methamphetamine than similarly situated Blacks.

The findings from this study have important implications for research and practice. Much of the prior research on substance abuse has found a positive relationship between cocaine and heroin use and socially disadvantaged communities (Brown & Silverman, 1974; Chein et al., 1964; DuPont & Green, 1973; Inciardi, 1974, 1979; McBride & McCoy, 1981). However, the casual relationship between illicit drug use, such as cocaine and heroin, and structural disadvantage does not hold true for methamphetamine use. Based on the current analysis, we find that methamphetamine use is negatively related to structural disadvantage, most likely because of the closed nature of the methamphetamine drug market. The closed nature of this drug market is such that it will not thrive in disadvantage areas where visible open drug markets are norm. Accordingly, we propose that the methamphetamine drug market will thrive in large part through social networks. Prior research indicates that methamphetamine is more likely sold “clandestinely,” that is, sold indoors, typically in a private residence, unlike powder and crack cocaine drug markets (Office of National Drug Control Policy, 2002; Zhang, 2004). Additionally, Rodriguez et al. (2005) found that methamphetamine users were more likely to obtain their drug as a gift, during noncash transactions. Thus, research that has emerged suggests that the methamphetamine market is more of a closed network whereby the drug is being sold between friends, or at least between people who know and trust each other. One would expect methamphetamine use rates to increase slowly over time among other racial and ethnic groups because the drug is not primarily being sold out in the open or on the streets.

The findings reported here are also relevant for law enforcement agencies attempting to curb methamphetamine use and related crime. Our findings suggest that methamphetamine is most likely being used by Whites in less disadvantaged areas. To contextualize these findings and help direct law enforcement efforts, the authors examined the three zip codes with the highest rates of methamphetamine use. These three zip codes were strikingly similar, with more than half of the arrestees in each zip code testing positive for methamphetamine. When we examined the 2000 Census data for these three zip codes, we found that 85% of the residents were White, compared with 75.1% of all residents in the United States. Just more than 8% of residents in these zip codes were living in poverty, 11.6% had less than high school education, and their median income was $45,764. At the national level, 12.4% of residents lived in poverty, 19.6% had less than high school education, and their median income was $41,994. Thus, residents in these three zip codes were, on average, more White and advantaged than the average citizen in America. Additionally, the unemployment rate was slightly higher in these three zip codes (4.5% compared with 3.7%). These data convey how areas characterized by high methamphetamine use are not areas typically regarded as high drug use areas as evidenced by the lower rates of structural disadvantage indicators (i.e., high levels of poverty, large minority population, etc). When a significant number of law enforcement resources are directed and concentrated in highly disadvantaged areas, such strategies will be less effective in reducing methamphetamine use. Thus, law enforcement efforts may have more of an impact if directed toward networks of users instead of specific locations. Given that methamphetamine users are more likely to reside in less disadvantaged areas and closed nature of the drug market, law enforcement strategies that rely on undercover operations as well as on informants should be more successful than location-based strategies (i.e., cracking down on “hot spots”). In the end, any effort aimed at reducing methamphetamine use will need to consider the different nature of the methamphetamine drug market and its relationship to neighborhoods and communities.

Finally, this research reaffirms the relationship between methamphetamine use and nonviolent offending. This is promising for the substance abuse treatment community given the increasing number of treatment options available for nonviolent offenders, including drug courts and mandatory drug treatment. To target the population most at risk of using methamphetamine, treatment must be made available for offenders across various geographical areas (i.e., disadvantaged and nondisadvantaged).

Despite the new insight presented by this research, several limitations of the study are worth noting. First, rates of methamphetamine use in ADAM sites located in eastern states were not analyzed in the study. As such, our findings cannot be generalized to arrestees in that geographical area. Unfortunately, because of the low cell count (and in some cases, nonpresence of methamphetamine use in certain ADAM sites), data from eastern ADAM sites were not included. This is an important exclusion, because anecdotal evidence suggests that methamphetamine use is increasing in the east coast. Second, this study only examined methamphetamine use among adult male arrestees. The exclusion of females is relevant in part because of their increasing involvement in crime and drug use. Because our study also excluded juveniles from the analysis, we are unable to compare rates of methamphetamine use among juvenile delinquents with those of students who take part in general surveys (e.g., Add Health and Monitoring the Future data). Third, only those geographic areas that had 40 or more arrestees were included in the analyses. By excluding areas with fewer than 40 arrestees, it is likely that we eliminated many rural zip codes from the analyses. Thus, we are unable to establish whether rates of methamphetamine use are similar in rural and urban areas as reported by prior research (Hansell, 2006b; National Institute on Drug Abuse, 2006).

Future research must continue to examine how individual and community characteristics affect methamphetamine use among the criminally involved. Future studies should also explore how the methamphetamine drug market is different from the cocaine and heroin drug markets, especially how acquisition methods vary across community types. For example, studies that examine how methamphetamine is sold (e.g., indoors, outdoors, within their neighborhood) and whether sellers of methamphetamine are also involved in other drug markets (e.g., cocaine and heroin) will be particularly useful in our understanding of the methamphetamine problem. Also, future research should attempt to analyze methamphetamine use in rural settings (see Haight et al., 2005; Stoops, Tindall, Mateyoke-Scrivner, & Leukefeld, 2005). Research should begin to examine whether the social networks of those involved in the methamphetamine drug market are different from the networks of those involved in other drug markets. Studies that examine the differences between the methamphetamine drug market and the cocaine and heroin drug markets will be particularly insightful in providing the larger context for the intersection of race/ethnicity, community dynamics, and illicit drug use. In summary, methamphetamine use has become the number one drug problem in many communities across the country. Our study found that both race and structural disadvantage significantly influence methamphetamine use among arrestees, thus adding to the much-needed research on methamphetamine and the intersection of race, crime, and drugs.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research and/or authorship of this article.

Notes

Author Biographies

Andrew M. Fox is a doctoral student in the School of Criminology and Criminal Justice and a graduate research assistant for the Center for Violence Prevention and Community Safety at Arizona State University. He received his BA in sociology from Point Loma Nazarene University in 2005 and his MS degree in criminology and criminal justice from Arizona State University in 2008. His research interests include social network analysis, gangs, drug markets, and communities.

Nancy Rodriguez is an associate professor in the School of Criminology and Criminal Justice at Arizona State University. Her research interests include juvenile justice, sentencing processes, and drug abuse. She also conducts research in the area of restorative justice. Her recent work has appeared in Criminology, Justice Quarterly, and Journal of Research in Crime & Delinquency.

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