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P1: GMX The Journal of Primary Prevention [jpp]

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Style file version Nov 28th, 2002

C 2003) The Journal of Primary Prevention, Vol. 24, No. 2, Winter 2003 (°

Community Risk and Protective Factors and Adolescent Substance Use Scott P. Hays,1,2 Carol E. Hays,1,3 and Peter F. Mulhall1

This paper researches the impact of the contextual characteristics of the community on self-reported 8th grade ATOD use. The study addresses a criticism of past research by relying on objective measures of community contextual characteristics and aggregated data from self-reported, individual substance use surveys. By analyzing 40 counties in the state of Illinois, we test the results of multivariate models for youth use of tobacco, alcohol and other drugs. Results indicate that community disorganization is an important risk factor for ATOD use while family supports is an important protective factor. Contrary to expectations, greater economic constraints decreases, rather than increases, substance use. Findings regarding the other variables were mixed. KEY WORDS: substance abuse; prevention; community; Illinois; “ATOD.”

INTRODUCTION Research on the causes of substance abuse has evolved from focusing on characteristics of individuals to recognizing the importance of the context of the community in which individuals live. In the latter approach, risk and protective factors for substance use derive from the interaction between individuals and their social environment (Brofenbrenner, 1979; Felner & Felner, 1989; Springer, Sale, Sambrano, Turner, & Hermann, 1998; Weiczorek, Donovan and Backus, 1997; Kumpfer and Turner, 1991). For example, Weiczorek, Donovan and Backus found that community alcohol “consequences,” such as DUI related incidents, correlate 1 Center

for Prevention Research and Development, University of Illinois at Urbana—Champaign, Champaign, Illinois. 2 Address correspondence to Scott P. Hays, 510 Devonshire Dr., Champaign, Illinois, 61820; e-mail: [email protected]. 3 Present address: Community Systems Investments International, Louisville, Kentucky. 125 C 2003 Human Sciences Press, Inc. 0278-095X/03/1200-0125/0 °


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with contextual characteristics, poverty, alcohol availability, and the adult alcohol probation population. Other correlates included family dysfunction and school separation (1997). Sociological studies have shown how community contextual characteristics influence a variety of social pathologies. Brooks-Gunn, Duncan, Klebanov, & Sealand (1993) have shown that merely living in a more affluent neighborhood as opposed to a very poor neighborhood results in fewer teen pregnancies, less truancy, and other undesirable social outcomes. A study of over 350 Massachusetts towns examined the effects of multiple community level risk and protective social indicators on reports of substance abuse consequences and found that towns with a diversity of non-profit, community-based organizational activities tend to have reduced risk for of substance abuse (Kreiner et al., 2001). The Center for Substance Abuse and Prevention Community Partnership Program recently released a preliminary report that indicated that communities with antidrug coalitions were able to significantly reduce ATOD use among males in 24 communities compared to 24 comparison communities that did not have these organizations (Center for Substance Abuse Prevention, 1999). However, this research has not directly examined the relationship of community risk and protective factors on self-reported youth substance use. Studies of how individual substance use or abuse behaviors stem from contextual factors have been limited by two different methodological shortcomings. First, some studies do not rely on actual community level measures of the social and demographic environment but instead rely on youth perceptions of their own relationship to that environment (Kumpfer and Turner 1991; Pollard, Hawkins and Arthur, 1998). Other studies, in contrast, use objective community level measures but do not have individual, self-reported measures of substance use as the dependent variable. Rather, they rely on proxy measures of consequences of substance use such as treatment admissions or incarceration rates to indicate community level substance use (Kriener, et al., in press; Weiczorek, Donovan and Backus, 1997). This study focuses on the community as the unit of analysis, specifically 40 Illinois counties, and seeks to overcome both of these limitations in order to advance our understanding of the relationship between contextual risk factors and substance use behavior. First, we rely on objective indicators to measure risk and protective factors and, second, we employ an aggregated indicator of self-reported alcohol, tobacco and drug use among 8th graders derived from representative samples of youth to measure substance use. The study examines the effect of the county level risk and protective factors on self-reported early adolescent substance use. Our model considers three risk factors: housing vacancies, economic constraints, and single-parent family structure. It also considers three protective factors: resources and supports for families, local regulations, and student academic test performance. We examine these risk and protective factors and their relationship to the use of alcohol, tobacco and other drugs. The next section reviews each factor and the measures for each and presents hypotheses for the research.


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COMMUNITY RISK FACTORS Housing Vacancies Community disorganization has been shown to place youth at risk for substance use (Haggerty, 1989). Hawkins, Catalano and Miller (1992) list community disorganization as one of 17 risk factors associated with substance use. We measure the general concept of community disorganization as the rate of housing vacancy in a county. Greater vacancy rates indicate both a level of instability and undesirability of that community, and perhaps a population loss that indicates the negative factors associated with neighborhood transitions and transient populations (Hawkins, Catalano and Miller, 1992). We hypothesize that counties with greater housing vacancies will have greater rates of self-reported youth ATOD use. Economic Constraints The effects of a community’s economic constraints on ATOD use are somewhat more difficult to predict. Research seems equivocal with one study suggesting that community economic resources may be a protective factor against some negative outcomes such as teen pregnancy and leaving school (Brooks-Gunn, et al., 1993). Moreover, higher poverty and unemployment rates may indicate community decline and which may translate into a lack of opportunity for the community’s youth, and youth living in such communities are more likely to become involved in antisocial behavior (Hawkins, Catalano and Miller 1992; Connell and Aber, 1995). Extreme economic deprivation has been shown to increase risk for later development of alcoholism and drug problems (Hawkins, Catalano and Miller, 1992). In contrast, evidence also suggests that youth access to greater economic resources may have detrimental effects, as well, facilitating access to substances and earlier onset of their use among children of higher status and better educated parents (Illinois Bureau of Substance Abuse Prevention, 1998). Our measure is, therefore, not a single indicator of poverty or level of income, but a composite measure of the lack of economic resources in the community. This measure consists of the county’s rate of unemployment, the percent of families living below the poverty level, the county’s percent of low-income students, and percent of female-headed households with no husband present who are below poverty. We hypothesize that counties with greater economic constraints will have greater rates of self-reported youth ATOD use. Single Female Parent Family Structure Research shows that family structure can also influence detrimental youth behaviors such as youth substance use. Single female parent family structure is


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particularly important since among single parents, single male parents account for only about 15% of the total (although this has been one of the fastest growing family types in the U.S.) (Meyer & Garasky, 1993). While single female parenting may not be a risk factor, per se, such a family structure is often associated with a variety of situations that increase risk among children. McLanahan (1999) found three reasons that single female parent families increase risk: first, single female parent families have fewer financial resources to devote to children’s upbringing; second, there is less time and energy for the nurturing and supervision of children; third, due to reduced income among single female headed families, there is usually reduced access to community resources that could supplement and support parents’ efforts (McLanahan, 1999). Brooks-Gunn, et al. (1993) found that the concentration of poor, female headed families is correlated with a lack of adult supervision and monitoring which is associated with several problematic youth outcomes. Block, Crockett, and Vicate (1991) found greater risk for substance use for youth reporting not living with both parents, and Kandel and Andrews (1987) found that poor adult socialization is associated with greater youth substance abuse. Therefore, family structure, particularly being a child in a single female parent household, puts children at greater risk of substance use. Single female parent family structure is measured directly as the county’s percent of single, female-headed households with children under the age of 16. We hypothesize that counties with greater rates of single female parent families with children under 16 years old will have greater rates of self-reported youth ATOD use. COMMUNITY PROTECTIVE FACTORS Child Care Availability While the effect of internal family dynamics such as bonding, communication patterns, and parenting skills are clearly important in models of youth substance use and problem behaviors (Kumpfer & Turner, 1991; Kandel & Andrews 1987), the community’s response to family stress and needs must also be considered. Resources and supports for families, including day care availability, after school programs for youth, and pro-social youth development opportunities are associated with lower rates of family stress and disorganization (Brooks-Gunn, et al., 1993; Felner, et al., 1994). Connell and Aber (1995) posit that many communities are hard-pressed to support the kinds of institutions that provide primary services and opportunities to youth, such as alternative activities for the out of school hours, and this leads to several social pathologies. Due to the prevalence of other risk factors confronting youth in communities with few family support services, youth services may be critical to reducing opportunities and pressures to initiate drug use (Connell & Aber, 1995). Research has shown that youth that spend more than 3 hours unsupervised after school each day have been found to have higher rates of


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substance use, behavior problems, depression, and lower self esteem and academic efficacy (Mulhall, Stone & Stone, 1996; Mertens, 1998). At the community level, reliable data on afterschool and other programs for youth is not available and, in any case, would be quite difficult to collect with any degree of accuracy. However, reliable data on child care availability is available based on licenses and since many day care centers support afterschool programs as well, we posit that child care availability serves as a more general indicator of the concept of community support for families. For our measure, we rely on the county’s total number of childcare slots for children under five years old as a proportion of the under 5-year-old population at the county level as an indicator of more general resources and supports for families. We hypothesize that counties with greater child care availability will have lower rates of self-reported youth ATOD use. Tobacco Regulation Local regulations regarding alcohol and tobacco price, consumption and use significantly influence youth ATOD use and abuse (National Institutes of Health, 1995; Holder, 1998; Chaloupka & Saffer, 1992). Policy approaches to alcohol and tobacco control reflect and reinforce a community norm opposed to substance use and abuse but they also simultaneously affect all members of the community rather than a select few (Klitzner, 1998; ANR 1999). Other policies found to be effective include raising the minimum drinking age (O’Malley & Wagenaar 1991; Klitzner, Stewart & Fisher 1993; Wagenaar 1993) and mandating server training (Wagenaar & Holder 1991; Holder & Wagenaar 1994). Raising taxes has been shown to be effective in reducing tobacco use, particularly among youth (Lewit, Coate and Grossman, 1981; Coate and Grossman 1988; Saffer and Grossman 1987; Manning, Blumberg and Moulton 1995). Research has also shown that restricting youth access by enforcing ordinances regulating retail sales to youth can reduce youth smoking prevalence rates (Jason, et al., 1999). Clean Indoor Air ordinances that restrict the places where tobacco may be smoked, particularly those that target public places, have been shown to reduce cigarette demand (Chaloupka and Saffer, 1992). While no codification of alcohol ordinances exist, we can measure tobacco regulation, and assume that tobacco regulation indicates the general level of support for regulating ATOD more generally. Our measure is based on data collected for the Illinois Liquor Control Commission. The data was drawn from a survey conducted by researchers at the Office of Social Science Research at the University of Illinois at Chicago. The researchers surveyed county and municipal governments inquiring about the existence of several tobacco control policies and about enforcement of those policies. Since the number of municipalities regulating tobacco to any extent was very small, and the number that had several regulations in place was even


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smaller, we created a dichotomous variable with 1 indicating that there is some level of tobacco regulation and 0 no regulation. We hypothesize that counties that regulate tobacco will have lower rates of self-reported youth ATOD use. Student Academic Test Performance Numerous studies show that better school performance reduces the likelihood of youth substance abuse (Bloch, Crockett and Vicate, 1991). Student performance in school is directly and indirectly related to the quality of education provided to students at that school. In other words, high quality schools provide students with a high quality education that provides them opportunities to learn and succeed (Felner, et al., 1997). On the other hand, academic failure can be one of the most prevalent and potent risk factors associated with individual youth ATOD use and abuse (Hawkins, Catalano & Miller 1992). We seek to measure the quality of a community’s schools at the county level. Although we understand that controversies abound regarding student assessments through standardized testing and school quality (Linn, 2000), student academic test performance is the best available and most generally accepted aggregate indicator of the quality of education in a county. To obtain county level measures of student academic test performance, we aggregated student performance within each county on the Illinois Goals Assessment Progress (IGAP), a test administered to all Illinois students in selected grades to measure the quality of local education. County-level aggregate IGAP scores are highly correlated across the reading, writing, and mathematics component of the test, as well as across the three grades (6th, 8th, and 10th) to which it is administered. Thus, we created a composite index from all of these scores (9 in all) to indicate the aggregate levels of academic performance of a county’s students. Importantly, we contend that aggregating test scores in this way creates a measure of the environmental variable of educational quality; our study does not relate individual academic performance with individual behavioral outcomes. We hypothesize that counties with higher levels of overall student academic performance will have lower rates of self-reported youth ATOD use.

THE DEPENDENT VARIABLE IN EXISTING RESEARCH: AN EMPIRICAL CRITIQUE Efforts to integrate community characteristics into ATOD prevention have taken two approaches. The first approach relies on the individual as the unit of analysis. For example, a recent study examining risk and protective factors in multiple domains among adolescent youth relies on individual level data and found that communities and families clearly influence ATOD use (Pollard, Hawkins, & Arthur, 1999). In fact, much of the social ecology research focuses on how individual


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youths perceive and interact with these external “domains” (Springer et al., 1998; Kumpfer and Turner 1991; Pollard, Hawkins and Arthur, 1999). While these individual level models of ATOD use consider the effect of the individual’s relationship to and participation in the community in a larger ecological model of substance abuse, youth perceptions of their social environment may or may not reflect actual community level characteristics. The second approach relies on aggregate community (usually city or county) indicator data, providing an objective measure of community characteristics, but these studies tend to rely on aggregated, surrogate indicators of community level substance use or abuse (such as treatment admissions or alcohol related criminal activity) as the dependent variable. For example, Wieczorek, Donovan, and Backus, (1997) applied the Hawkins, Catalano and Miller (1992) framework to measured indicators of youth substance use in New York counties. Using multiple regression, they found significant relationships between an array of youth alcohol and drug consequence measures and aggregated youth drug treatment and juvenile delinquency rates. A limitation of this measure as an indicator of individual substance use is that treatment admissions depend not only on the extent of the substance abuse problem in a community but on the number of treatment slots available, as well as the proportion of users who recognize the need for treatment. Similarly, law enforcement-related surrogate substance use measures depend on the aggressiveness of enforcement as well as the extent of the actual problem. AGGREGATE SELF-REPORTED ATOD USE One of the barriers to examining youth substance use at the aggregate level has been the lack of an adequate measure of prevalence of ATOD use among young adolescents across an adequate number of communities. We attempt to overcome this barrier, examining the effect of aggregated county contextual indicators on selfreported 8th grade use of tobacco, alcohol, and “other drugs” (primarily marijuana, in most cases) across 40 communities. The independent variables described above are generally drawn from readily available data sources, including the U.S. Census and other state archival sources. The unique challenge presented by modeling the relationship between aggregated community indicators and youth self-reported ATOD prevalence rates is to create an accurate county level measure of youth substance use. Simple mathematics makes this a quite difficult task. Samples are typically drawn to represent a state, and as such, are not representative when disaggregated to lower geographic levels. Therefore, such a sample does not allow for assessing the influence of the varying community context across a state as diverse as Illinois. Alternatively, separate communities or even schools may implement such studies, but the coverage of such data may be quite sparse and not representative of a county or community at large much less several communities in a state.


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To rectify this dilemma, we rely on a pooled, cross-sectional survey data collection method. Following the method of an innovative study of state politics by Erickson, Wright and McIver (1993) who pooled data from 10 years of CBS-New York Times national surveys to create a self-reported political ideology variable for all 50 states, we pooled available ATOD prevalence data from several school surveys of 8th graders implemented over a maximum span of five years in communities across the state of Illinois to generate adequate sample sizes of 8th grade use in 40 counties. Analysis indicated no temporal trends in reported use from the surveys over this time. We relied on self-reported ATOD use during the past 30 days, since the risk profiles of “experimenters” who report less frequent use are different from those reporting 30 day or more frequent use (Block, Crockett, & Vicate, 1991). The ATOD use questions across the surveys we used were quite comparable. The question was asked of youth in two ways. One version was asked as the “frequency of use” of alcohol, tobacco and marijuana, for which we coded thirty day or more frequent use as “1” and less frequent use as “0” and calculated the percentage of “1’s” for each county. The second question asked whether the student had used any of these substances in the last thirty days. Again, coding dichotomously, our variable is the percentage of 1’s in the county. To create sufficiently representative samples in each county, we combined self-reports of youth ATOD use from five consecutive years of school surveys. Focusing on 8th grade use, first we estimated the number of 8th graders in each county and then calculated the appropriate sample size for the size needed to be representative of the population in each county. Then we determined the number of survey respondents available from each survey source. Since the sources were not fully random at the county level, we used a smaller error ratio (plus or minus two percent as opposed to three percent) to determine the required sample size, increasing the number of respondents needed for each county. Nonetheless, data was either not available or insufficient to have confidence in the county level aggregate total for most Illinois counties. In the end, we were able to create a representative county level variable indicating the mean use of tobacco, alcohol and other drugs for 40 of Illinois’102 counties. Due to its size and contextual complexity, Cook, the county in which Chicago is located, was excluded from all analysis. FINDINGS Table I shows the bivariate correlation matrix among all of the variables in the analysis. The analysis indicates a high degree of intercorrelation among alcohol, tobacco and marijuana use. This suggests that among 8th grade students, students using one substance are likely to also use other substances as well. Among the other bivariate relationships between the dependent and the other independent variables in the model, housing vacancies is positively related to


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Table I. Bivariate Correlations for all Variables in the Modela Tobacco Alcohol Tobacco 1.00 Alcohol .855∗∗ Other drugs .732∗∗ Housing .347∗∗ vacancies Economic .154 constraints Single female .180 parent families Child care −.267 availability Tobacco −.125 regulation Student academic −.207 test performance a ∗

Student Other Housing Economic Single female Child care Tobacco academic test drugs vacancies constraints parent families availability regulation performance

1.00 .741∗∗ 1.00 .209 .157 .055

1.00

.096

.657 ∗∗

1.00 .493∗∗

.201

.450

.042

−.173

−.179

−.069

−.076

−.145

−.062

−.130

.262

−.267

−.343

∗

−.143

1.00 .111

1.00

−.105 ∗∗

−.554

−.290 ∗∗

−.696

−.132

1.00 .132

1.00

All table entries are Pearson Correlation coefficients. Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

∗∗

reported tobacco use, (r = .347, p < .05) providing support for the first hypothesis. A larger percent of single female parent families is positively related to greater other drug use (r = .450, p < .01) and higher student academic test performance is negatively related to other drug use (r = −.343, p < .05), both confirming our hypotheses. While none of the other relationships achieve statistical significance, we note the signs of the coefficients are in the predicted directions (positive for risk factors and negative for protective factors) for all coefficients. Among the independent variables some strong bivariate relationships are notable. Economic constraints are positively related to housing vacancies (r = .657, p < .01), single female parent families (r = .493, p < .01), and negatively related to student academic test performance (r = −.554, p < .01), all consistent with expectations about the effect of a poor economy. Having more single parent households is negatively related to student academic test performance (r = −.696, p < .01). These findings suggest that economic constraints in a county influence many of the other variables in our analysis. Given that, further analysis is needed to control for these interrelationships and more accurately examine the influence of economic constraints. The initial bivariate findings then, while intriguing, give us some confidence in proceeding to a multivariate analysis of this data.

Tobacco Table II shows the multivariate regression model for tobacco use. This model explains just over 29% of the variance in self-reported tobacco use among 8th graders (R 2 adj. = .291, p < .01). Turning to the variables in the model, three


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Hays, Hays, and Mulhall Table II. Multivariate Regression Results for Youth Tobacco Use Unstandardized coefficients

Constant Housing vacancies Economic constraints Single female parent families Child care availability Tobacco regulation Student academic test performance

Standardized coefficients

B

Std. Error

Beta

T

Sig

.104 .017 −.052 .015 −.001 −.040 −.020

.081 .005 .018 .009 .000 .023 .014

.713∗∗ −.738∗∗ .333 −.424∗∗ −.261 −.287

1.282 3.546 −2.919 1.647 2.883 −1.750 −1.409

.209 .001 .006 .109 .007 .089 .168

∗ Coefficient is significant at the 0.05 level (2-tailed). ∗∗ Coefficient is significant at the 0.01 level (2-tailed).

achieve statistical significance below the .05 level. Consistent with the bivariate findings, and confirming our hypothesis, housing vacancies are strongly and positively related to tobacco use (r = .713, p < .01) and greater child care availability is negatively and significantly related to tobacco use (r = −.424, p < .01) However, rather than being weak and non-significant, economic constraints now becomes strongly and negatively related to tobacco use (r = −.738, p < .01) indicating less tobacco use in counties with greater economic constraints. Conversely, this suggests more tobacco use in more economically viable counties. While contradicting our hypothesis, this does suggest that tobacco use is a function of its cost and the ability to purchase it. Hawkins, Catalano and Miller (1992) suggest that only extreme poverty is associated with greater substance use, while the findings surrounding milder levels of poverty are somewhat more ambiguous. Notably, while the other coefficients fail to meet the required levels of significance, the signs are in the predicted directions (although the findings for tobacco regulation would meet a standard of p < .10 and the findings for single female parent families would only slightly exceed that standard). That is, more tobacco use in counties with more single female parent families and less in counties with greater regulation and higher student academic test performance.

Alcohol Table III shows the results of the alcohol use model. This model is also significant and explains just under a quarter of the variance in self-reported 8th grade alcohol use (Adj. R 2 = .245, p < .05). Similar to tobacco, greater housing vacancies in a county are related to greater reported alcohol use (r = .626, p < .01) and stronger greater child care availability are related to decreasing alcohol use (r = −.302, p < .05). In the alcohol use model, student academic test performance is significantly and negatively related to greater use, providing support for our hypothesis (r = −.429, p = .05). Again contradicting our hypothesis, greater


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Table III. Multivariate Regression Results for Youth Alcohol Use Unstandardized coefficients



B

Std. Error

Beta

T

Sig

.129 .015 −.059 .014 −.023 .000 −.030

.085 .005 .016 .010 .000 .024 .015

.626∗∗ −.842∗∗ .302 −.363∗ −.273 −.429∗

1.531 3.019 −3.228 1.449 2.390 −1.775 −2.038

.135 .005 .003 .157 .023 .085 .050


economic constraints lead to less alcohol use (r = −.829, p < .01), reinforcing the notion of a greater accessibility of alcohol to youth in a more affluent communities. While the directions of the effect of single female parent families (positively associated with alcohol use) and tobacco regulation (negatively associated with alcohol use) are consistent with our hypothesized predictions, they fail to achieve statistical significance and will not be further noted here. Other Drug Use Table IV reports the findings for reported “other drug” use among 8th graders. First of all, we should point out that while the questions regarding drug use include multiple controlled substances, surveys which asked about these substances separately indicate that marijuana is by far the most prevalent illicit drug of choice among 8th graders (reported use of marijuana varies from about 8% to 15%, while reported use of harder drugs (cocaine, LSD, etc.) rarely exceeds 1%). The R 2 value indicates that this model explains 40% of the variance in drug use (Adj. R 2 = .398, p < .001). Table IV. Multivariate Regression Results for Youth Other Drug Use Unstandardized coefficients



B

Std. Error

Beta

T

Sig

.083 .013 −.056 .029 −.000 .026 −.017

.069 .004 .015 .008 .000 .020 .012

.611∗∗ −.871∗∗ .683∗∗ −.370∗∗ −.186 −.275

−1.211 3.296 −.739 3.667 2.724 −1.355 −1.463

.235 .002 .001 .001 .010 .185 .153



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The findings here are somewhat different than the other models. In support of our hypotheses, we again confirm a positive and strong effect of housing vacancies on greater drug use (r = .611, p < .01) and a strong and negative effect of greater child care availability (r = −.373, p < .05). We also confirm a strong and positive relationship between single female parent families and greater drug use (r = .683, p < .01). Contradicting our hypothesis (and consistent with the tobacco and alcohol models), we find a negative relationship of economic constraints to drug use (r = −.871, p < .001), providing further support for the notion that greater economic resources facilitate (rather than act as a protector against) substance use. While neither tobacco regulation nor student academic test performance is significantly related to drug use, both coefficients have signs in a direction consistent with our hypotheses. COMPARING THE THREE MODELS Comparing the results across the three models, several broad findings are apparent. First, slightly less than a quarter to over a third of the variance in aggregate youth-reported tobacco, alcohol and drug use can be explained with characteristics of the community in which youth live. Second, although similarities exist, different factors affect youth use of various substances in different ways. Although we attempted to test parallel models for the independent variables across the three substances, the effect of the separate model components were different for each. The lesson here is that although a high degree of correlation exists among use of tobacco, alcohol or drugs, each substance, nonetheless, has unique, underlying causal factors that should not be ignored. Consistently across the three substances examined, we confirm that the risk factor of housing vacancies does indeed increase reported ATOD use. We also confirm that the protective factor of greater child care availability reduces reported ATOD use. Since we posit that greater child care availability is an indicator of the broader notion of community supports for families, these findings support and reinforce the importance of healthy communities for reducing substance abuse and stresses the importance of the community in a broader contextual model of prevention. While we had hypothesized economic constraints as a risk factor for increased ATOD use, 8th grade students in counties with greater economic constraints reported reduced ATOD use. Although contradicting our hypothesis, we speculate that this stems from adolescents’ price consciousness and demand elasticity for tobacco, alcohol and drugs. We do not mean to imply, however, that substance abuse is only a problem for wealthier counties. Two points bear this out. First, the bivariate associations presented above reveal greater economic constraints are associated with other social problems, including greater housing vacancies and more single parent households and have a negative effect on student academic test


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performance. Therefore, in economically constrained counties, all of these are also likely to be problems, and these are generally associated with greater ATOD use. Our results suggest that the effect of a poor economy is to create a community environment more conducive to greater ATOD use, but when these community factors are accounted for by the model, economic constraints, in and of itself reduces ATOD use. Second, our measure may not be capturing extreme poverty, which Hawkins, Catalano and Miller’s (1992) review found to be associated with increased substance use. However, these authors did not find studies supporting the strong association between poverty and reduced ATOD use that we report. The risk factor of single female parent families and their association with increased drug use (and positive but not significant associations with tobacco and alcohol use) requires careful interpretation. While substance abuse research generally confirms this disturbing finding, research on single female parent families suggests that parenting style—particularly the quality of the mother-child relationship—strongly affects child development (Zaslow, et. al. 1999). That is, where parenting is both authoritative and warm, there is high monitoring and low coerciveness; there is less externalizing and internalizing behavior and greater social and academic competence in children (Hetherington 1993, 46). Moreover, the key variable may also be the presence of greater child care availability for single parents, which our model confirms to be generally associated with reducing use of all three substances. Also, we do not measure the presence of multigenerational families that can provide an important resource for single parents (ChaseLandsdale, et al. 1999). We interpret our results to mean that aggregated at the community level, single female parenting is generally associated with increasing youth drug use, yet the particular circumstances and style of single parenting is likely the most important consideration. Tobacco regulation had a significant and strong relationship to greater alcohol use and was negatively but weakly associated with tobacco use. The effect on drug use was weak and non-significant but negative, consistent with our hypothesis. Thus, stricter regulation can reduce substance use, and the fact that tobacco regulatory stringency is negatively related to alcohol use perhaps reflects that the stringency of the tobacco ordinance reflects a more general community norm opposed to substance use and less stringent ordinances reflect a more accepting norm for alcohol and tobacco. We interpret this to mean that a more permissive tobacco policy might reflect a community norm accepting of tobacco use, and by association, alcohol use, but drug use is not permissible in any county, and therefore bears no association to the strength of these regulatory ordinances. The findings concerning the relationship between student academic test performance and substance use suggest limited support for the notion that a quality education is related to substance use. In fact, this variable was only significantly and positively related to alcohol use. This finding reflects the complexity of the relationship between student academic test performance and substance use. Our


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findings reveal that when other factors are controlled that isolate the effect of education, its association with 8th grade substance use generally is in fact rather tenuous, but the effect on reduced alcohol use is clear. Perhaps we are measuring causality in the other direction, since increased alcohol use may cause reduced test scores. But this is an individual phenomenon and our data does not connect individual use to individual scores and thus cannot address this question. IMPLICATIONS FOR PREVENTION Our findings offer several implications and recommendations for prevention. The primary implication for our findings is that considerations of broader community conditions must be integrated into a comprehensive approach to substance abuse prevention. While we have clearly not explained all of the variance in reported levels of youth alcohol, tobacco and other drug use in the counties in our analysis, we have explained a significant portion of it. Since we have aggregated all of our measures to the community level (county, in our case), we posit that we have measured the social environment created in such a community. Thus even variables which emphasize family factors (single female parents) and the individual (academic performance), are measured at the social environment level in our study. Therefore, these findings imply that a significant portion of youth substance use stems from the characteristics of the social environment in which youth and families live. This finding should reinforce efforts to support comprehensive community-based prevention strategies and should stimulate community-based prevention coalitions to reach out to a variety of community sectors in their efforts to reduce risk factors present in the community environment. These findings also have important implications for prevention programming which solely targets individual domains. In communities with various social, demographic, and economic problems, this unhealthy social environment might mediate the effect of prevention strategies targeting individual knowledge about drugs and decision-making skills. In other communities, the effect of such programming might be enhanced by a healthy social environment. Our findings also suggest that prevention efforts should not be based on a “one size fits all” approach. That is, since the community setting or context affects substance use, different types of communities are likely to have different types of issues they must deal with. For example, highly disorganized communities should focus on reducing vacancies and other visible signs of infrastructure decay. Communities should also examine available supports for working families including day care, after school care and pro-social youth development programs and opportunities. Efforts should also focus on the local adoption and enforcement of ordinances designed to reduce the availability of and youth’s access to alcohol and tobacco. Clearly more attention needs to be directed to adopting the appropriate mix of prevention strategies to suit communities’ differing environmental realities.


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Moreover, the findings reinforce the importance of community-based prevention efforts that target comprehensive strategies across multiple domains, and thus speak to the importance of coalition-building and collaborative efforts within communities (Pentz, Dwyer, MacKinnon, Flay, Hansen, Wang & Johnson, 1989; CSAP, 1999). Real change in any of the factors identified by our models will take collaborative efforts by many more people than only those directly engaged in substance abuse prevention efforts. A major challenge of prevention research is to continue to assess the effects of community factors - schools, neighborhoods, and broader society (e.g., media) that contribute to adolescent ATOD use. This approach will help to create a framework of understanding for the community factors that contribute to ATOD use ranging from basic socio-economic factors, to educational opportunities, laws, policies and norms. Since most children and youth do not select the community they live in or have any say in whether they remain in that community until they are near adulthood, it is incumbent upon prevention researchers to determine what influence these community factors have on youth ATOD use and related problems so that prevention coalitions can effectively address them. Finally, our findings point to the need for improving data collection efforts for needs assessment and analysis. Better and more inclusive data would also permit more sophisticated modeling techniques tested with more sophisticated methodologies. For example, youth substance use could be hierarchically modeled, integrating contextual (community) factors and individual factors into the same framework. More extensive data would also permit analysis with a structural equation model, which not only permits the specification and estimation of latent variables but also allows more accurate specification of the causal relationships among the independent variables in the model. We believe our findings shed light on the role community context exerts on aggregate youth ATOD use; yet we also recognize the data limitations of the current study. Perhaps, based on these findings, this study will pave the way for greater efforts to collect the data necessary to more fully specify youth ATOD use models and more accurately analyze the processes involved, so we may more effectively target prevention efforts to reduce youth substance use in communities throughout the country. Future efforts should focus on ways to effectively model the combined effect of the community context and individual characteristics using better techniques to inform the study of individual substance use patterns. REFERENCES Americans for Non-Smokers Rights (ANR). (1999). Clean indoor air as a youth prevention strategy. Retrieved from http://www.no-smoke.org/cia as youth access.html Becker, M. J. (1997). Meta-analysis and models of substance abuse prevention. In William F. Bukoski, Ph.D. (Ed.), Meta-analysis of drug abuse prevention programs. (NIDA Research Monograph 170) Washington, DC: U.S. Department of Health and Human Services, National Institutes of Health.


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