Adolescent Substance Use in Rural and Small Urban Communities

Transcription

1 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 7 th 12 th grades and separately for 8 th, 10 th, and 12 th 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 ( ), urban students

2 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. Sample METHOD Data for this study were collected from a sample of 193 communities within the contiguous U.S. between 1996 and 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

3 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 South Midwest West Northeast Rurality Total Remote Medium Rural Small urban Metro Region Total Number of Students in Each Classification of Rurality and Region Level of Rurality South Midwest West Northeast Rurality Total Remote 5,863 6,611 3, ,717 Medium Rural 19,065 11,368 7, ,754 Small urban 52,679 36,973 15,596 8, ,267 Metro 7, ,749 29,797 13,613 Region Total 85,128 55,650 29,797 10, ,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.

4 Instrument Students were given the Community Drug and Alcohol Survey (CDAS). The CDAS is a 99- item 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.

5 Analysis Grade-adjusted lifetime and last month prevalence were computed for 7 th 12 th grades and separately for 8 th, 10 th, and 12 th 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 10 th 12 th 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 (25 th to 75 th 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 7 th 12 th 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.

6 Table 2 Grade-Adjusted Lifetime and Last Month Prevalence for 7th 12th Grades by Level of Rurality Lifetime Prevalence Last Month Prevalence Med Small Med. Small Remote Rural Urban Metro Remote Rural Urban Metro Alcohol Got Drunk Marijuana * Inhalants Methamphet Cocaine Crack Psychedelics 10.0 ** ** LSD.7 ** ** Other Psychedelics 8.1 ** ** Ecstasy N/A N/A N/A N/A Ephedrine Amphetamines Downers PCP Ketamines 1.5 ** ** Heroin Other Narcotics (not heroin) N/A N/A N/A N/A Quaaludes Tranquilizers Amyl, ethyl or butyl nitrates Legal Stimulants Ritalin Steroids (last year) Cigarettes (daily) 55.1 ** ** Smokeless Tobacco (daily) 28.4 ** ** *: P <.05 **: P <.01 Lifetime and Last Month Prevalence: 8 th, 10 th and 12 th Grades Table 3 presents lifetime and last month prevalence separately for 8 th, 10 th, and 12 th 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.

7 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. Small Med. Small Remote Rural Urban Metro Remote Rural Urban Metro 8 th Grade Got Drunk Marijuana Inhalants Methamphetamines Cocaine Crack 4.1 * a Psychedelics 6.9 a ** Ecstasy N/A N/A N/A N/A Ephedrine Amphetamines Downers PCP Ketamines Heroin Narcotics (not heroin) N/A N/A N/A N/A Steroids (last year) 2.9 * ** Cigarettes (daily) 47.7 a a Smokeless Tobacco (daily) 22.7 ** ** th Grade Got Drunk Marijuana 36.1 * * Inhalants Methamphetamines Cocaine Crack * Psychedelics 13.2 * ** Ecstasy N/A N/A N/A N/A Ephedrine Amphetamines Downers PCP 3.1 a Ketamines 1.9 ** Heroin Narcotics (not heroin) N/A N/A N/A N/A Steroids (last year) Cigarettes (daily) 61.8 * * Smokeless Tobacco (daily) 31.1 ** ** th Grade Got Drunk Marijuana 42.9 ** a Inhalants * Methamphetamines Cocaine 9.2 * Crack 5.1 * * Psychedelics 13.2 ** ** Ecstasy 16.8 a N/A N/A N/A N/A Ephedrine * Amphetamines Downers PCP 2.1 ** Ketamines 1.6 ** * Heroin 2.1 * Narcotics (not heroin) 10.4 ** N/A N/A N/A N/A Steroids (last year) 1.6 ** ** Cigarettes (daily) Smokeless Tobacco (daily) 37.4 ** ** * : P <.05 ** : P <.01. a : P <.10

8 For 10 th 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 12 th 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 10 th 12 th 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

9 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, 8 th, 10 th and 12 th, 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 8 th 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 8 th 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 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 12 th grade, lifetime and last month prevalence for marijuana, ketamines, and psychedelics were higher in metro and small

10 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 12 th graders, but they also found that 12 th 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 7 th -12 th 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

11 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. REFERENCES Cronk, C. E., & Sarvela, P. D. (1997). Alcohol, tobacco, and other drug use among rural/small town and urban youth: A secondary analysis of the Monitoring the Future data set. American Journal of Public Health, 87(5), Donnermeyer, J. F., & Scheer, S. D. (2001). An analysis of substance use among adolescents from smaller places. Journal of Rural Health, 17(2), Economic Research Service. (2003, October 17, 2003). Rural income, poverty, and welfare: Rural income. Retrieved April 20, 2006, from Edwards, R. W. (1995). Alcohol, tobacco, and other drug use by youth in rural communities. Oakbrook, IL: North Central Regional Educational Laboratory. Edwards, R. W. (1997). Drug and alcohol use among youth in rural communities (Vol. 168). Rockville, MD: National Institute of Drug Abuse. Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2005). Monitoring the Future national survey results on drug use, (Vol. I: Secondary school students, pp. 680). Bethesda, MD: National Institute on Drug Abuse. MacTavish, K., & Salamon, S. (2003). What do rural families look like today? In D. L. Brown & L. E. Swanson (Eds.), Challenges for rural America in the twenty-first century (pp ). University Park, PA: The Pennsylvania State University Press. Mulkey, D., & Beaulieu, M. J. (1995). Human capital as a rural development strategy: Promise or false hope. In L. J. Beaulieu & D. Mulkey (Eds.), Investing in people: The human capital needs of rural America (pp ). San Francisco: Westview Press. National Center on Addiction and Substance Abuse. (2000). No place to hide: Rural 8th graders using drugs, smoking, drinking at higher rates than urban 8th graders. Retrieved April 7, 2004, from id=49 Oetting, E. R., & Beauvais, F. (1990). Adolescent drug use: Findings of antional and local surveys. Journal of Consulting and Clinical Psychology, 58(4), Oetting, E. R., Beauvais, F., & Edwards, R. W. (1985). The American drug and alcohol survey. Fort Collins, CO: Rocky Mountain Behavioral Science Institute, Inc.

12 Patton, H. (1989). Setting the rural health services research agenda: The congressional perspective. Health Services Research, 23, Salamon, S. (2003). From hometown to nontown: Rural community effects of suburbanization. Rural Sociology, 68(1), SAMHSA. (2004). Results from the 2003 National Survey on Drug Use and Health: National Findings. In U. S. DHHS (Ed.). Rockville, MD: SAMHSA: Office of Applied Studies. SAMHSA. (2007). SAMHSA's Measures and Instruments Resource Guide. Retrieved February 13, 2007, from lts.cfm?category=&row=&categoryid=&cfid=655964&cftoken= &search mode=allinstruments. Scaramella, L. V., & Keyes, A. W. (2001). The social contextual approach and rural adolescent substance use: Implications for prevention in rural settings. Clinical Child and Family Psychology Review, 4(3), Scheer, S. D., Borden, L. M., & Donnermeyer, J. F. (2000). The relationship between family factors and adolescent substance use in rural, suburban, and urban settings. Journal of Child and Family Studies, 9(1), 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).