Journal of Rural Community Psychology
Vol E14 No 1
Adolescent Substance Use in Rural
and Small Urban Communities
Ruth W. Edwards, Linda R. Stanley,
Beverly S. Marquart & Randall C. Swaim
ABSTRACT
This study used a nationally representative sample of 193 predominantly white
communities to compare both mean adolescent substance use by level of rurality and the
variability of community-wide adolescent substance use across levels of rurality. Using
data from 181,351 adolescents, grade-adjusted lifetime and last month prevalence were
computed for 7th – 12th grades and separately for 8th, 10th, and 12th grades. Mean drug
use was compared for significant differences across 4 levels of rurality using the survey
commands of Stata®, and box plots were used to compare the dispersion of community drug
use across ruralities. Many similarities in mean substance use across level of rurality were
found. As grade increased, more differences were found, with most of those differences
showing greater use in the larger areas. In addition, there was greater variability in use rates
for nearly all substances for remote and medium rural communities than for small urban
communities. The findings suggest that reports of rural adolescent substance use equaling or
exceeding that of urban adolescents are only partially true. More importantly, the findings
show the complexity behind adolescent substance use rates in various types of
communities that cannot simply be captured by level of rurality.
INTRODUCTION
Rural areas have undergone considerable economic, technological and social change in
recent decades. Economically, rural family income has fallen further behind urban family
income, and the rural-urban gap in child poverty rates has increased (Economic Research
Service, 2003). The majority of rural communities are not as “isolated” as they once were.
More than ever, rural communities are coming under urban influence due, in part, to
improvements in transportation and access to mass media and telecommunications
(Salamon, 2003). In addition, many rural communities are dependent upon and influenced
by economic forces that are beyond their direct control. One result is the introduction of
more urban norms, values, and beliefs, altering rural communities at their core (Mulkey &
Beaulieu, 1995). For example, rural communities have seen an increase in divorce and
single-parent families, and large, intergenerational families residing near one another are
largely a thing of the past (MacTavish & Salamon, 2003). Consistent with these changes,
several recent studies have found that rural and urban substance use rates have converged
and, for some substances, rural use rates now exceed urban rates (Cronk & Sarvela, 1997;
National Center on Addiction and Substance Abuse, 2000; SAMHSA, 2004; Scheer,
Borden, & Donnermeyer, 2000). In a study comparing substance use prevalence for rural
and urban seniors using Monitoring the Future (MTF) data (1976-1992), urban students
had greater prevalence for most illicit substances in 1976, but by 1992, rural use of illicit
substances was similar to urban use (Cronk & Sarvela, 1997). In a recent report that
combines data from national surveys, numerous studies, and interviews with experts, the
National Center on Addiction and Substance Abuse (National Center on Addiction and
Substance Abuse, 2000) found that rural eighth graders were 104% more likely to use
amphetamines including methamphetamines and 50% more likely to use cocaine, and the
use of other drugs such as marijuana and alcohol were more prevalent in rural
communities. For tenth graders, rural use rates exceeded urban rates for every drug,
except ecstasy and marijuana.
Patterns of substance use in rural communities warrant further investigation for several
reasons. First, though more attention has recently been given to rural substance use
problems, this study is unique from other studies in that it compares not only mean youth
substance use across ruralities, but also the variation or distribution of substance use
across communities by level of rurality. Rural communities, especially those that are
geographically isolated, may exhibit more internal homogeneity than urban areas, but they
can differ considerably from one another on any number of dimensions (e.g., socioeconomic
conditions, ethnicity and cultural traditions, historical events, religiousness, and
accessibility to drugs (Edwards, 1997)). Thus, two rural communities that are similar in
size and geographic location may exhibit very different patterns of adolescent substance
use. In addition, in small isolated rural communities, a single individual or a small group of
individuals can have a major effect on the prevalence of substance use community-wide
(Edwards, 1995). This study compares the variability of community-wide adolescent
substance use across levels of rurality using a nationally representative sample of schools
located in 193 communities, the largest-scale study of substance use and rurality that has
been conducted to our knowledge.
Second, classifications of rurality have been inconsistent across studies, and in many cases,
the definition of rural may not have been a valid indicator of a community’s actual level of
rurality. Although the Bureau of the Census defines rural communities as those
communities with 2,500 or fewer residents, geographical proximity to urban or to other
rural communities is also an indicator of rurality (Edwards, 1997; Patton, 1989;
Scaramella & Keyes, 2001). For example, a community with 2,500 residents that has easy
access to an urban area is likely quite distinct from another community that also has 2,500
residents but is isolated from other communities. This study combines a community’s
population size with its geographical location and accessibility to categorize communities
into four levels of rurality. Comparisons are then made across level of rurality, grade level,
and substance.
METHOD
Sample
Data for this study were collected from a sample of 193 communities within the contiguous
U.S. between 1996 and 2000. Non-metropolitan counties within each of the four FBI crime
report regions (West, South, Midwest, Northeast) were first identified and then classified
into three groups (based on 1990 census data): nonadjacent (to a metro area) counties with
largest place less than 2,500 population; adjacent counties with either largest place less
than 2500 or counties with largest place from 2500 to 20,000; and counties with largest
place of 20,000 to 50,000. Metropolitan counties, having largest place of 50,000 or greater,
were classified into a fourth group.
Predominantly white communities (defined as over 60% white population) within each of
the rurality categories were proportionately drawn to their representation in each of the
four regions and each state within those regions, where possible. Where it was not possible
to match representation for a given state (due to problems in legislative restrictions of
protocol of consent or other recruiting difficulties), communities in nearby states within the
same region were substituted. Of the communities recruited, the participation rate was
approximately 70%. Table 1 contains the number of communities in each rural and
regional stratum.
Table 1 Number of Communities and Students in Each Rurality and Region Strata
Number of Communities in Each Classification of Rurality and Region
Level of Rurality
Remote
Medium Rural
Small urban
Metro
Region Total
South
21
26
30
2
79
Midwest
26
23
21
2
72
West
12
10
10
2
34
Northeast
1
1
4
2
8
Rurality Total
60
60
65
8
Number of Students in Each Classification of Rurality and Region
Level of Rurality
Remote
Medium Rural
Small urban
Metro
Region Total
South
5,863
19,065
52,679
7,521
85,128
Midwest
6,611
11,368
36,973
698
55,650
West
3,061
7,391
15,596
3,749
29,797
Northeast
182
930
8,019
29,797
10,776
Rurality Total
15,717
38,754
113,267
13,613
181,351
Note: Metro communities are used primarily for comparison purposes. Because it was difficult to
receive permission to survey students in metro communities, only 2 metro communities in each region
were surveyed.
Within each community, surveys were administered at a single public high school and the
public feeder junior-high/middle school(s). In the relatively small percentage of cases where
there was more than one high school in the community, the high school determined to be
the most representative socio-demographically of the community was chosen. The final
sample consisted of 181,351 students in grades 7 through 12, with 75.6% white students,
6.6% African-American, 5% Mexican-American, and all other ethnicity categories being
2.1% or less. Table 1b shows the number of students in each rural and regional stratum.
Instrument
Students were given the Community Drug and Alcohol Survey (CDAS). The CDAS is a 99item survey that asks a variety of questions related to substance use, school adjustment,
relationships with family and peers, and other individual risk factors in substance use. The
CDAS is a variation of the American Drug and Alcohol Survey™, which has been in use
since the mid-1980s (E.R. Oetting & Beauvais, 1990; Eugene R. Oetting, Beauvais, &
Edwards, 1985). Its measures have been through rigorous reliability and validity analysis
and it is one of the instruments listed in SAMHSA’s Measures and Instruments Resource
guide (SAMHSA, 2007).
Measures
Although rural communities were first selected for the sample based on population and
proximity to a metropolitan area, the level of rurality was further refined by adding
accessibility in travel time. A rural community that is very small may, in fact, have easy
access to a larger metropolitan community, making it less rural than its population and/or
its miles from the larger area would seem to indicate. Because Census data do not report
distances to major services and travel times to larger population centers, questions
measuring accessibility were incorporated into community assessment surveys that were
completed once a community was included in the sample. Questions included miles from an
Interstate exit, drive time to nearest metropolitan area, and distance and obstacles to
driving to the nearest big town.
Communities were then classified into four levels of rurality: remote, medium rural, small
urban, and metro. A remote community has a population less than 2,500 and is located
more than 2 hours driving time from a metropolitan area. A medium rural community
either has a population between 2,500 and 20,000 or a population less than 2,500 but is
located less than 2 hours driving time from a metropolitan area. A small urban community
has a population between 20,000 and 50,000 while a metro community has population
greater than 50,000.
For surveyed substances, lifetime and last month use are calculated from questions asking
whether the student has ever tried a substance and how many times it was used in the last
month. Missing data comprised between .3% – 3.2% of the data for lifetime prevalence and
.9% – 2.5% for last month prevalence.
Procedure
Surveys were conducted between the years 1996 and 2000 with passive parental consent
and procedures that ensured complete confidentiality. In order to ensure that the selfreported data is reasonably trustworthy and represents an accurate picture of the
respondents’ behaviors and attitudes, 40 different internal consistency checks were made
on each completed survey prior to using the survey data for analyses. If there were three
or more inconsistencies (e.g., indicating no lifetime use of marijuana but reporting use of
marijuana in the last month and illogical estimates of relative harm of drug use) including
endorsement of a fake drug, the student’s survey was discarded. Approximately 1% of
surveys were discarded for inconsistent responses.
Analysis
Grade-adjusted lifetime and last month prevalence were computed for 7th – 12th grades
and separately for 8th, 10th, and 12th grades. Because the sample is stratified and
clustered, mean drug use is compared for significant differences across levels of rurality
using the survey commands of Stata® statistical software. This method accounts for
the effects of clustering that are due to the hierarchical nature of the data. The sample
was specified in Stata as being stratified by rurality and region with the primary
sampling unit (PSU) being a community.
Lifetime and last month substance use were then aggregated, adjusting for grade,
across 10th – 12th grade students within a community to compute the percentage of
students within the community sample that had tried a substance or had used the
substance in the last month. Box plots were used to compare the dispersion of
community drug use across ruralities. Metro communities are not included in the
boxplots because of their relatively small number. The box plots consist of a box that
gives the interquartile range (25th to 75th percentiles), the median (a line in the box),
reasonable upper and lower boundaries (designated by horizontal lines 1.5 box-lengths
from the upper or lower edge of the box), outliers (designated by a circle and 1.5 to 3
box-lengths from the upper or lower edge of the box), and extreme values (designated
by an asterisk and more than 3 box-lengths from the upper or lower edge of the box).
We present boxplots for last month prevalence of drunkenness, marijuana, inhalants,
methamphetamines, cocaine, psychedelics, and ecstasy.
RESULTS
Lifetime and Last Month Prevalence
Table 2 gives 7th – 12th grade student lifetime and last month prevalence rates for all
substances included in the survey. Few significant differences across ruralities were
found for either lifetime or last month prevalence rates. No differences were found for
either of the alcohol measures. Lifetime prevalence for psychedelics (including LSD and
other psychedelics), ketamines, cigarettes, and smokeless tobacco differed significantly
across ruralities. For ketamines and the psychedelic measures, the percentage trying
the substance was greater in small urban and metro communities. For cigarettes and
smokeless tobacco, the rates were greatest in remote and medium rural communities.
Significant differences in the same directions were found for these substances for last
month (or daily) prevalence. In addition, last month marijuana use was significantly
higher in small urban and metro communities.
Table 2 Grade-Adjusted Lifetime and Last Month Prevalence for
7th – 12th Grades by Level of Rurality
Lifetime Prevalence
Alcohol
Got Drunk
Marijuana
Inhalants
Methamphet.
Cocaine
Crack
Psychedelics
LSD
Other Psychedelics
Ecstasy
Ephedrine
Amphetamines
Downers
PCP
Ketamines
Heroin
Other Narcotics (not heroin)
Quaaludes
Tranquilizers
Amyl, ethyl or butyl nitrates
Legal Stimulants
Ritalin
Steroids (last year)
Cigarettes (daily)
Smokeless Tobacco (daily)
Last Month Prevalence
Remote Rural
Med
Small
Urban Metro Remote Rural
Med. Small
Urban Metro
73.2
43.1
30.3
14.8
6.0
6.0
4.8
10.0**
.7**
8.1**
13.1
1.7
8.4
4.7
2.5
1.5**
2.6
7.5
1.4
5.0
2.5
17.6
5.1
2.4
55.1**
28.4**
70.2
38.7
33.3
14.0
5.7
6.3
4.7
11.9
9.6
8.1
12.9
1.6
8.6
4.7
2.7
2.1
2.7
8.2
1.4
5.9
2.7
16.0
5.7
2.3
49.5
16.4
40.0
20.7
19.2
6.0
2.6
2.8
2.0
5.9
4.5
3.5
N/A
.5
3.2
2.7
1.0
.7
1.3
N/A
.4
3.1
.9
7.9
2.9
2.0
13.8
2.0
69.1
39.5
30.9
15.0
6.3
6.0
5.4
10.0
7.2
7.4
12.5
1.5
8.6
4.7
2.4
1.3
2.7
7.4
1.4
5.2
2.4
16.2
4.8
2.2
52.2
23.7
70.4
39.1
35.3
13.0
5.7
7.3
5.0
15.5
10.5
12.3
13.4
1.5
8.6
4.2
2.8
2.2
2.6
8.1
1.4
5.2
2.8
13.7
5.3
2.7
46.6
13.3
42.4
22.0
16.7*
6.3
3.1
2.6
2.1
5.0**
3.0**
3.8**
N/A
.5
3.0
2.6
1.0
.5**
1.3
N/A
.5
2.5
.9
8.8
2.7
2.1
16.0**
4.9**
39.0
20.3
16.9
6.8
3.0
2.7
2.4
4.8
3.0
3.3
N/A
.5
3.2
2.6
.9
.4
1.4
N/A
.4
2.7
.9
8.1
2.5
2.0
14.5
4.1
42.3
21.5
20.8
5.6
2.4
3.3
2.1
8.1
5.1
5.8
N/A
.4
3.0
2.3
1.0
.7
1.1
N/A
.4
2.7
.8
6.7
2.5
2.5
10.0
1.2
*: P < .05
**: P < .01
Lifetime and Last Month Prevalence: 8th, 10th and 12th Grades
Table 3 presents lifetime and last month prevalence separately for 8th, 10th, and 12th
graders for a subset of the drugs in Table 2. Interestingly, as grade increases, the number
of significant differences across ruralities also increased. In eighth grade, crack and
steroids were significantly more likely to have been tried by metro students while smokeless
tobacco was more likely to have been tried by students living in remote and medium rural
communities. Psychedelic lifetime use was also higher in metro communities (P < .07)
while lifetime cigarette use was greater (P < .10) in remote and medium rural communities.
For last month use, steroid, crack (P < .10), and psychedelic use were higher in the larger
communities, while daily smokeless tobacco and cigarette use (P < .10) became greater as
the size of the community decreased.
Table 3
Grade-Adjusted Lifetime and Last Month Prevalence for Grades 8, 10, and 12 by Level of Rurality
Lifetime Prevalence
Last Month Prevalence
Med.
Remote Rural
Small
Urban
Metro
Med.
Remote Rural
Small
Urban
Metro
27.5
21.6
14.6
4.1
4.3
4.1*
6.9a
10.3
1.1
5.1
2.7
1.9
1.1
2.6
4.5
2.9*
47.7a
22.7**
25.8
21.3
16.5
4.6
4.6
4.9
7.4
10.8
1.1
5.8
3.1
2.1
1.3
3.0
5.1
2.4
47.2
19.6
24.0
22.8
15.9
4.4
4.5
4.7
8.4
10.2
1.1
6.1
3.3
2.1
1.5
2.9
5.3
2.4
43.1
12.1
24.7
23.9
15.7
5.2
5.8
6.4
10.5
10.2
1.3
6.6
3.5
2.7
1.9
3.2
5.9
3.9
39.5
9.2
12.2
12.7
8.0
2.1
2.2
2.0 a
3.7**
N/A
.3
2.2
1.5
.9
.3
1.6
N/A
2.7**
11.1a
3.6**
12.7
11.9
8.9
2.5
2.4
2.4
3.8
N/A
.3
2.3
2.0
1.0
.4
1.7
N/A
2.3
9.8
2.7
11.7
13.5
8.2
2.4
2.3
2.4
4.9
N/A
.4.
2.5
2.0
.9
.5
1.7
N/A
2.2
9.2
1.1
13.1
15.1
8.2
2.6
3.1
3.3
6.2
N/A
.5
2.6
2.0
1.1
.7
1.7
N/A
3.7
5.8
.6
53.2
36.1*
15.7
7.8
6.8
5.8
13.2*
14.8
1.7
11.0
6.2
3.1a
1.9**
2.9
10.2
2.3
61.8*
31.1**
48.6
38.1
16.2
7.5
7.0
6.6
12.5
15.1
1.5
11.7
6.7
2.9
1.3
3.2
10.0
2.4
59.0
27.8
48.9
41.6
14.6
6.9
7.2
5.3
14.7
15.1
1.8
10.9
6.0
3.5
2.4
2.9
10.5
2.4
56.5
19.0
49.7
43.0
13.9
6.7
8.8
5.8
18.7
16.4
2.0
10.2
6.3
4.2
2.8
2.6
10.3
3.1
54.1
15.3
27.5
20.4*
6.0
4.1
2.8
2.6*
6.5**
N/A
.4
3.8
3.6
1.2
.7
1.2
N/A
1.7
19.2*
5.4**
25.9
21.8
6.7
3.8
3.3
3.0
6.3
N/A
.5
4.5
4.0
1.1
.5
1.6
N/A
1.8
18.1
5.4
26.7
25.3
5.6
3.1
3.2
2.1
7.2
N/A
.6
4.2
3.6
1.4
.9
1.4
N/A
2.0
16.5
2.3
29.2
27.0
5.5
3.0
4.2
2.5
10.9
N/A
.7
3.9
4.0
1.6
1.0
1.3
N/A
2.5
11.9
1.4
Got Drunk
Marijuana
Inhalants
Methamphetamines
Cocaine
Crack
Psychedelics
Ecstasy
Ephedrine
Amphetamines
Downers
PCP
Ketamines
Heroin
Narcotics (not heroin)
Steroids (last year)
Cigarettes (daily)
Smokeless Tobacco (daily)
67.9
42.9**
12.6
8.4
9.2*
5.1*
13.2**
16.8a
2.0
11.9
6.3
2.1**
1.6**
2.1*
10.4**
1.6**
66.2
37.4**
62.3
45.6
13.4
8.4
9.2
6.2
14.2
16.0
2.7
12.3
6.5
2.9
1.6
2.6
10.8
2.7
63.5
31.5
63.5
51.0
11.6
8.3
10.3
5.1
18.8
18.0
2.7
13.2
6.8
3.5
3.3
2.7
13.2
2.2
62.1
23.9
62.7
52.8
11.8
9.3
12.7
6.1
23.9
20.2
2.4
15.6
6.8
4.2
3.1
3.7
14.3
3.6
59.4
23.1
37.6
22.8a
4.1*
3.9
4.2
2.1*
5.3**
N/A
.5*
3.7
3.4
.7
.5*
.8
N/A
1.3**
24.5
8.3**
32.6
23.2
4.1
3.6
3.6
2.3
5.6
N/A
1.1
3.8
3.2
.9
.6
1.2
N/A
1.9
23.2
6.1
36.0
27.3
3.1
3.2
3.9
1.6
7.5
N/A
.9
4.3
3.6
.9
1.1
1.0
N/A
1.7
23.2
3.6
38.4
27.7
3.0
2.7
4.6
1.8
10.1
N/A
.5
4.5
3.4
1.0
.7
1.1
N/A
2.9
19.8
3.1
*
**
8th Grade
Got Drunk
Marijuana
Inhalants
Methamphetamines
Cocaine
Crack
Psychedelics
Ecstasy
Ephedrine
Amphetamines
Downers
PCP
Ketamines
Heroin
Narcotics (not heroin)
Steroids (last year)
Cigarettes (daily)
Smokeless Tobacco (daily)
10th Grade
Got Drunk
Marijuana
Inhalants
Methamphetamines
Cocaine
Crack
Psychedelics
Ecstasy
Ephedrine
Amphetamines
Downers
PCP
Ketamines
Heroin
Narcotics (not heroin)
Steroids (last year)
Cigarettes (daily)
Smokeless Tobacco (daily)
12th Grade
: P < .05
: P < .01.
a
: P < .10
For 10th graders, lifetime and last month use of marijuana and psychedelics were
significantly greater in the larger communities while lifetime and daily use of smokeless
tobacco were significantly greater in smaller communities. Lifetime use of ketamines was
greater in small urban and metro communities while daily cigarette use was significantly
less in the largest communities.
By 12th grade, lifetime use of marijuana, psychedelics, PCP, ketamines, narcotics and
steroids and last month use of marijuana, psychedelics, and ketamines, and last year use of
steroids were significantly greater in the larger communities. Smokeless tobacco lifetime
and daily use were significantly greater in the smaller communities. There were also
significant differences in crack use across ruralities, with lifetime use being lowest in
remote and small urban communities and last month use being lowest in small urban
communities.
Community Dispersion by Rurality
Figure 1 presents boxplots of community last month drunkenness, community last month
use of marijuana, inhalants, methamphetamines, and psychedelics, and lifetime use of
ecstasy by rurality for 10th – 12th graders. For each substance, remote communities had a
greater range of substance use than small urban communities for both lifetime and last
month prevalence. For example, in remote communities, the lifetime use of marijuana
ranged from 5% to 74% with an interquartile range of 18% while the range for small
urban communities was from 22% to 62% with an interquartile range of 10%. Lifetime
use of ecstasy in remote communities ranged from 0% to 40% while for small urban
communities the range was from 8% to 26%. In addition, the interquartile range was grer
for remote communities than for small urban communities for all substance use measures
except psychedelics, where the interquartile ranges were the same. Finally, there were
more outliers (circles) and extreme values (asterisks) among the remote and medium rural
communities than the small urban communities.
Figure 1
Boxplots Showing Community Dispersion of Substance Use
DISCUSSION
Using a sample of 193 communities that vary by level of rurality, this study found many
similarities in mean substance use across different levels of rurality. Comparisons by
rurality at three grade levels, 8th, 10th and 12th, for 18 measures of lifetime substance use
and 16 measures of last month use showed no significant differences for approximately
70% of those comparisons. This is consistent with a number of studies that have found
many similarities between rural and urban communities (Cronk & Sarvela, 1997;
Donnermeyer & Scheer, 2001; Edwards, 1997). In addition, lifetime and last month use
rates for many substances were similar to those found by Monitoring the Future (Johnston,
O'Malley, Bachman, & Schulenberg, 2005) during this time period. Where differences
were found (inhalants, psychedelics, ecstasy, crack, and amphetamines), there were also
differences in the measures used for those substances. For example, the Community Drug
and Alcohol Survey specifically excluded legal stimulants from the category of
amphetamines whereas the MTF survey did not.
The fewest number of differences were found across levels of rurality for 8th graders.
Lifetime prevalence of crack and steroids and last month use of psychedelics were higher in
metro areas while lifetime and last month prevalence of smokeless tobacco were several
times greater in remote areas than in metro areas. These results differ from the National
Center on Addiction and Substance Abuse (CASA) analysis of 1999 Monitoring the Future
data(National Center on Addiction and Substance Abuse, 2000) that found 8th graders in
rural areas (defined as counties with no city of over 50,000) were significantly more likely
to have used marijuana in the past month than their peers in large metropolitan areas
(defined as areas over one million in population) and were more likely to have used
marijuana, cocaine, and crack in the past year than their large city counterparts.
Several reasons may account for the differences found. In the CASA study, rural areas
were defined as counties with no city over 50,000 in population, and accessibility to a
metropolitan area was not accounted for in defining these areas as “rural”. In our study,
there are three categories of rurality for communities of less than 50,000 that take into
account both differences in size and differences in accessibility to larger, more urban areas.
In addition, our study makes no distinction among communities that are greater than
50,000 population while the CASA study defines two types of metropolitan areas – large
and small - based on population.
Our results are, on the other hand, consistent with an analysis of 1976-1997 MTF data
(Donnermeyer & Scheer, 2001) that categorized adolescents into 6 types of smaller areas
based on metropolitan status and where they grew up (e.g. on a farm, in the country, in a
small town) to look at substance use among adolescents from smaller places. Although
rates of past-year illicit drug use were often similar across locations, there were some
persistent differences in substance use for youth living in different types of smaller places,
with less use for the more rural location. Large differences were found for past-year
marijuana use, with less use for the more rural location.
We also found more differences in use rates as grade increased, with most of those
differences showing greater use in the larger areas. By 12th grade, lifetime and last month
prevalence for marijuana, ketamines, and psychedelics were higher in metro and small
urban areas as was lifetime and last year use of steroids, and lifetime prevalence of
narcotics. Only smokeless tobacco showed greater use in remote and medium rural
communities than in the more urban communities. Similar to our findings, the CASA study
found that current use of marijuana, hallucinogens, LSD, MDMA, and steroids was higher
in urban areas than in rural areas among 12th graders, but they also found that 12th grade
current use rates in rural areas for cocaine, amphetamines, barbiturates, inhalants, crack
and tranquilizers were higher. Again, these differences are likely due to the differences in
both the samples and the definitions of rurality, as noted above.
An important distinction between this study and others that have compared rural and
urban substance use is the ability to look at variability in use rates across communities for
a specific level of rurality. As conjectured, we found greater variability in use rates for
nearly all substances for remote and medium rural communities than for the larger
communities. For 82% of the substance use measures for 7th-12th graders, the remote
category had the highest and/or lowest community substance use rate while the medium
rural category had 15% of either the highest or lowest community rate.
Overall, the findings from this study suggest that reports indicating equal or greater
substance use among rural compared to urban adolescents are only partially true. When
rural areas are more finely classified using differences in population and accessibility, the
most rural of those areas, while showing many similarities in drug use to larger areas, still
show less use of particular types of drugs, namely marijuana, psychedelics, and crack. In
addition, there are many rural areas that show very low or no use of nearly all substances
that were measured in the survey.
More importantly, these findings exemplify the complexity behind adolescent substance use
rates in various types of communities that cannot simply be captured by level of rurality
(Donnermeyer & Scheer, 2001). The wide range in substance use rates for rural areas is
simply one indication of the great diversity of rural areas. Rural areas differ in many
aspects, for example, by types of economies (e.g., farming, mining, tourism), in- and outmigration patterns, family structure, norms and behaviors toward substance use, social
integration/disorganization, and density of acquaintanceship. To draw conclusions or
make policy recommendations based upon mean substance use rates for rural
communities, no matter how rural is defined, is nearly meaningless.
Further research on explaining why substance use rates across seemingly similar
communities vary so significantly is the next step. This large sample of communities is
almost ideal to discover why some communities, while seeming very similar to other
communities in terms of geographical and population factors, have very different levels of
substance use. Much of the differences in substance use rates across communities can
likely be explained by differences in individual-level risk and protective factors in these
communities. For example, in a community where parents must commute long distances to
work, a lack of parental monitoring may result in high rates of substance use. However,
some of the differences in use rates across communities may be due to school and
community influences that either directly affect substance use or that mediate or are
mediated by individual effects (Scaramella & Keyes, 2001). For example, in the community
where parents must commute long distances to work and thus are less able to monitor their
children, other members of the community may take responsibility for monitoring
adolescent behavior because they know parental monitoring is lacking. An important
contribution of this research will be to discover how these individual- and community-level
variables act together to either prevent or give rise to adolescent substance use.
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Acknowledgements:
Support for this research has been provided by grants from the National Institute on Drug Abuse and the
National Institute on Alcohol Abuse & Alcoholism (R01 DA09349, Ruth W. Edwards, PI; R21 AA017267,
Linda R. Stanley & Randall C. Swaim PI’s; P50 DA07074, Eugene R. Oetting, PI).