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© 2007 American Public Health Association
DOI: 10.2105/AJPH.2005.081307
RESEARCH AND PRACTICE |
The authors are with the Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor.
Correspondence: Requests for reprints should be sent to Yange Xue, PhD, School of Public Health, University of Michigan, 1420 Washington Heights, M5525 SPH II, Ann Arbor, MI 48109-2029 (e-mail: yxue{at}umich.edu).
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ABSTRACT |
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Objectives. We examined the association between neighborhood characteristics and cigarette use among adolescents and explored the protective effects of participation in prosocial activities to better understand strengths in adolescents’ lives and help identify protective factors for the prevention of adolescent smoking.
Methods. We interviewed ninth graders who had grade point averages of 3.0 or lower and who were not developmentally disabled. Participants’ addresses were geocoded so that interview data could be linked to 1990 US census data on neighborhood characteristics.
Results. Neighborhood disadvantage and the percentage of Black residents in a neighborhood had different effects on cigarette smoking among Black and White adolescents. Living in a neighborhood with a high percentage of Black residents had favorable effects for Blacks but not for Whites. For both groups, a low percentage of Black residents was a risk factor for cigarette use, and risk effects were higher in the more disadvantaged neighborhoods. Involvement in prosocial activities moderated neighborhood risks.
Conclusions. Neighborhood effects on adolescent cigarette use were contingent upon both contextual and individual characteristics. Participation in prosocial activities had a protective effect among adolescents in high-risk neighborhoods. Engaging adolescents in such activities may help offset the adverse effects of living in a disadvantaged neighborhood.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Approximately 80% of adult smokers began smoking before the age of 18 years.1 This trend in early initiation of cigarette smoking, together with the adverse consequences of smoking,1–3 suggests that understanding factors associated with adolescent smoking remains a public health priority. Previous studies have mostly focused on individual, family, peer, and school influences4; few researchers have investigated neighborhood effects on cigarette use.
As predicted by social disorganization theories,5,6 growing evidence has demonstrated that neighborhood sociodemographic attributes are associated with adolescent risk behaviors. Disadvantaged neighborhoods have been found to be a risk factor for delinquency,7–9 adolescent sexuality and childbearing,10–12 and low educational attainment.11,13 Furthermore, neighborhood racial/ethnic diversity is associated with adolescent criminal activity.8
Findings from the few studies examining the relationship between neighborhood disadvantage and substance use are mixed. Substance use is often identified as occurring more frequently in poor urban neighborhoods.14,15 One study that linked neighborhood characteristics with adult drug use demonstrated that neighborhood disadvantage was associated with increased drug use.16 By contrast, a study of Chicago adolescents indicated that neighborhood poverty was not related to cigarette use,17 and other studies have reported no association between neighborhood disadvantage and adolescent substance use.18,19
Researchers have also found that the effects of neighborhood disadvantage on cigarette use differ according to race/ethnicity and neighborhood racial composition. For example, Diez Roux et al.,20 in a study of young adults, found that neighborhood disadvantage was associated with smoking prevalence rates among Whites but not among Blacks. Similarly, Tseng et al.21 found that neighborhood disadvantage was a risk factor for smoking among White women but not among Black women. In addition, Reardon et al.17 found that a high percentage of Black residents in a neighborhood was associated with a lower risk of adolescent smoking initiation, regardless of race. Together, these findings suggest that specific characteristics of neighborhoods can either promote or discourage cigarette use among young people.
Most previous research on neighborhood effects has examined only direct effects (i.e., effects are assumed to be the same for all) of neighborhood disadvantage, but not all adolescents exposed to risk factors experience negative outcomes. Resilience theory, which focuses on positive adjustment among individuals exposed to risks, provides 1 way to study this issue.22–25 Individuals who avoid negative outcomes may overcome these risks as a result of compensatory or protective factors (i.e., assets or resources).22
Involvement in prosocial activities (i.e., activities associated with organized groups that help children and adolescents develop skills) has been found to be one of the factors that protect young people from risks associated with cigarette use.24,25 Researchers have found that adolescents who are more involved in such activities are less likely to smoke cigarettes.26–29 Research has also demonstrated favorable effects of prosocial involvement on academic achievement,30–33 sexual risk behavior,34,35 antisocial behavior,36 criminal behavior,29,37 and drug and alcohol use.27,38–42 Many of these studies, however, involved predominantly White samples.26,27 Moreover, to our knowledge no study has investigated the protective role of participation in prosocial activities in relation to neighborhood-level risks.
We investigated whether neighborhood disadvantage and racial composition are associated with adolescent cigarette smoking after we controlled for individual characteristics, parental and peer influences, and participation in prosocial activities. More important, we examined whether neighborhood effects on cigarette use are moderated by prosocial participation. Most researchers investigating prosocial participation have focused on the particular type of activity or the setting (e.g., school, church, and community).34,43,44 Because different activities may ameliorate neighborhood risks in different ways, we examined overall prosocial participation as well as involvement in specific types of activities. We included parental and peer substance use as control variables in the analysis because parental and peer influences have been recognized as factors in smoking behaviors45–49 and may be confounded with neighborhood effects.
Our sample was composed of urban youths at low levels of academic achievement who were at risk for cigarette use.50–53 Although including only young people with low school achievement may limit the generalizability of our results, we viewed this strategy as beneficial in that it could potentially uncover critical risk and protective factors unique to members of this population, whose cigarette use may be influenced by different factors than those influencing their higher-achieving counterparts.
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METHODS |
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Sample and Procedures
We derived data from the first year of a longitudinal study of young people at risk of school dropout; the study was conducted in 4 public schools in Flint, Mich.54 The sample included 824 ninth graders who had grade point averages (GPAs) of 3.0 or lower in eighth grade and were not diagnosed as emotionally or developmentally disabled. Trained interviewers collected data during 50- to 60-minute face-to-face interviews conducted in 1994–1995. After being interviewed, participants were asked to complete a pencil-and-paper questionnaire gathering information on sensitive topics such as substance use. The sample consisted of 681 Black and 143 White students, and 50% of the participants were girls.
Measures
Data for all individual-level measures were collected through face-to-face interviews and self-report questionnaires. Data from the 1990 US census were used in computing neighborhood-level measures.
Cigarette use. Cigarette use was assessed through the students’ answer to the question, "How often have you smoked during the past 30 days?" Participants responded on a 7-point Likert scale (1 = not at all, 2 = less than 1 cigarette per day, 3 = 1 to 5 cigarettes per day, 4 = about one half of a pack per day, 5 = about 1 pack per day, 6 = about 1.5 packs per day, 7 = 2 packs or more per day).
Individual-level measures. As individual-level variables, we assessed demographic characteristics, parental and peer influences, and participation in prosocial activities. Demographic characteristics included age, gender (1 = male, 0 = female), race/ethnicity (1 = Black, 0 = White), and family socioeconomic status (SES). Codes developed by the National Opinion Research Center were used to assess family SES according to the highest occupational prestige score of either parent.55,56 SES scores in our sample ranged from 29.28 to 64.38 (mean = 39.90, SD = 10.42).
Participants provided data on parental and peer substance use. The measure of parental substance use consisted of the mean of 13 items assessing alcohol use and drug use by the adult or adults raising the participant (Cronbach 
= 0.74). Examples included how often he or she gets drunk, has been arrested for using or having drugs, or has been treated for a drug problem. Participants responded on a 5-point Likert scale ranging from never (1) to very often (5). The measure of peer substance use consisted of the mean of 13 items assessing number of friends involved in substance use, including marijuana use at least once a month, drug use at school, and arrest for selling drugs (Cronbach = 0.83). Items were rated on a 5-point Likert scale ranging from none (1) to all (5).
Young people’s prosocial involvement was measured through questions about their participation in school, community, and church activities. Within each setting, they reported all activities in which they had participated in the previous year. For each activity, participants used a 4-point scale (1=hardly ever to 4=most of the time) to indicate how often they attended; they also reported how many months they had been involved and whether they had held any leadership positions. Responses to the 3 questions were standardized and summed for all of the participants. The resulting scores were then summed for each participant and each activity setting: extracurricular activities in school, community activities, and church activities. We also created a score representing overall involvement in prosocial activities by summing the standardized scores for all 3 domains. Higher scores on these measures indicated higher levels of participation.
Neighborhood-level variables. The addresses of our participants were geocoded so that they could be linked to 1990 US census data. In this study, we operationalized neighborhoods (n = 143) as census block groups. On average, there were 6 students per block group. We constructed a measure of concentrated neighborhood disadvantage by conducting an oblique factor analysis of the census data, including the poverty rate, percentage of residents receiving public assistance, percentage of female-headed families, unemployment ratio, and percentage of people with less than a high school degree. Higher values on this measure represented more disadvantaged neighborhoods.
We also created variables indicating the racial/ethnic compositions of neighborhoods. Two neighborhood categories were created: neighborhoods with a high concentrations of Blacks (above 90%) and neighborhoods with a low concentrations of Blacks (less than 10%). Neighborhoods in which 10% to 90% of residents were Black served as the reference group.
Data Analysis
Given that students were nested within neighborhoods, we used 2-level hierarchical linear models to account for individual variation within neighborhoods as well as variation between neighborhoods.57 Level 1 units were individuals, and level 2 units were neighborhoods. Hierarchical linear modeling software was used in conducting all analyses.58 We began the analysis by constructing an unconditional model of adolescent cigarette use that did not include any predictors, allowing us to partition variance in cigarette use into between-and within-neighborhood proportions.
The first model (individual-level model) included all of the individual-level variables (age, gender, race/ethnicity, family SES, parental substance use, peer substance use, and prosocial activities). The second model (neighborhood-level model) added neighborhood variables (concentrated disadvantage and percentage of Black residents) for the intercept, the race slope, and the prosocial participation slope from the individual-level model.
The model for the Black slope assessed differences in neighborhood effects according to race/ethnicity. The model for the prosocial involvement slope tested whether prosocial activities moderated neighborhood-level risks when such risks were identified. We also included an interaction term for the 2 neighborhood measures in the model.
Models 3 and 4 were similar to models 1 and 2 with the exception that we separated prosocial activities into different categories to determine whether they functioned differently across neighborhoods. In our analyses, we converted all continuous variables to z scores to facilitate interpretation of regression coefficients.
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RESULTS |
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Sample Characteristics
Descriptive statistics for the sample overall and for each racial/ethnic group are shown in Table 1
. The average age of the participants was 15 years. Whites and Blacks were comparable in terms of age, gender, family SES, parental and peer substance use, and overall involvement in prosocial activities. However, when activities were separated by domain results showed that Whites were less likely to be involved in church activities than were Blacks (t813 = –0.29, P < .01). The 2 groups did not differ in regard to participation in school or community activities. White students reported more cigarette use than did Black students (t801 = 3.77, P < .001).
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On average, White participants lived in more-advantaged neighborhoods than did Black participants (t822 = 5.17, P < .001). White participants tended to live in neighborhoods with low concentrations of Black residents, whereas Blacks tended to live in neighborhoods with high concentrations of Black residents (t822 = 26.12, P < .001).
Individual-Level Predictors
Results from the unconditional model demonstrated significant between-neighborhood variation in youth cigarette use (
2 = 178.44, P < .05), and 3.4% of the variance was attributable to neighborhood differences. The individual-level model (Table 2, model 1) indicated that rates of cigarette use were lower among Black participants than among White participants after we controlled for other variables. Older participants reported more cigarette use than did younger participants. Both parental substance use and peer substance use were associated with increased cigarette use, but the association was stronger for peer use. Gender and SES were not related to cigarette use.
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Higher levels of prosocial participation were associated with less cigarette use (b=–0.106, P<.001) after we controlled for all other variables (Table 2
, model 1). We found no interaction between race/ethnicity and participation in prosocial activities. When we separated prosocial activities into different types of activities (Table 2, model 3), we found that school (b=–0.074, P<.05) and church (b=–0.058, P<.05) activities were associated with reduced cigarette use, whereas community activities were not.
Neighborhood Effects on Cigarette Smoking
To test neighborhood effects, we included neighborhood variables for the intercept, race slope, and prosocial participation slope from the individual-level model (Table 2, model 2). Results showed that neighborhood effects were different for Black and White participants (Figure 1). The values on the y-axis in Figure 1 represent standardized scores for the Likert scale categories of cigarette use. Living in a neighborhood with a high percentage of Black residents had favorable effects on cigarette smoking among Black participants (b = –0.810, P < .001) but not among White participants (b = 0.170, P < .05).
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Moreover, we found an interaction between concentrated neighborhood disadvantage and low percentage of Black residents (b=0.283, P<.05). Students living in neighborhoods with a low percentage of Black residents were at the highest risk for cigarette use (b=0.600, P<.01), regardless of race. The risk associated with a low percentage of Black residents was higher in more disadvantaged neighborhoods than in less disadvantaged neighborhoods. After neighborhood factors had been taken into account, the racial/ethnic difference in cigarette use disappeared.
Protective Effects of Prosocial Activities
Results from the prosocial participation slope model indicated that prosocial activities moderated neighborhood effects among both White and Black participants (Figure 2 and Table 2). On average, higher levels of prosocial involvement were associated with less cigarette smoking in neighborhoods with fewer Black residents (b = –0.488, P < .001). This favorable effect was especially pronounced in disadvantaged neighborhoods with low percentages of Black residents (b = –0.277, P < .10). In other words, prosocial activities mitigated the adverse effects of a low percentage of Black residents and neighborhood disadvantage.
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To examine the protective effects of specific types of prosocial activities, we assessed participation in school, church, and community activities separately (Table 2
, models 3 and 4). Participation in school and community activities had a protective effect in regard to cigarette use that was similar to the effect of overall prosocial participation. Specifically, participation in school (b =–0.319, P < .01) and community (b = –0.228, P < .05) activities had favorable effects on cigarette use among students living in neighborhoods with a low percentage of Black residents. School and community activities were most protective for young people living in disadvantaged neighborhoods with a low percentage of Black residents (b = –0.237, P < .10, and b = –0.270, P < .05, respectively). Church activities had protective effects on cigarette use among adolescents living in neighborhoods with a high percentage of Black residents (b = –0.160, P < .01) but not among adolescents living in neighborhoods with a low percentage of Black residents.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Our findings demonstrate that neighborhood contexts are related to cigarette smoking among adolescents. Similar to previous findings from studies of neighborhood effects on other adolescent problem behaviors,5,59–62 our results revealed that a small percentage of the variance (3.4%) in adolescent cigarette smoking was accounted for by neighborhood context. Consistent with national surveys,63,64 we found that Black adolescents reported less cigarette use than White adolescents.
Our results also provide evidence that neighborhood factors may help explain these racial/ethnic differences. It is noteworthy that differences in smoking rates between Black and White participants disappeared after neighborhood factors had been taken into account. In other words, neighborhood factors and their interaction with race/ethnicity helped explain racial/ethnic differences in adolescent smoking.
Moreover, race/ethnicity-specific differences in neighborhood effects were observed. Our findings suggest that neighborhood effects on adolescent smoking were contingent upon both contextual and individual characteristics. Living in a predominantly Black neighborhood appeared to protect Black youths, but not White youths, from cigarette use. By contrast, living in a predominantly White neighborhood was associated with more cigarette use among both Black and White adolescents, and this was especially the case in more disadvantaged neighborhoods.
Previous research has documented that the social environments of Black youths are less conducive to smoking than are those of White youths.65 White youths have been shown to be more affected by peer pressure than are Black youths,66 and Black youths are more likely to encounter parental disapproval of smoking than are White youths.65,67 These factors may account for the different contextual effects on cigarette smoking observed among White and Black youths. We also found that both peer and parental substance use were associated with adolescent cigarette use and that peer use had a greater influence than parental use. These findings are consistent with those of past research.46–49
Our results may appear contradictory to theories of neighborhood effects suggesting that adolescents living in disadvantaged minority neighborhoods exhibit increased problem behaviors.5,68 Previous studies on cigarette use, however, indicate patterns of contextual effects similar to ours.17,69,70 Johnson and Hoffmann,69 for instance, found that a higher percentage of minority students in a school led to reductions in smoking initiation rates among minority students in the school and helped account for racial/ethnic differences in initiation rates. Kandel et al.70 found that higher school minority enrollments had protective effects on smoking among all students.
As mentioned, Reardon et al.17 found that a high percentage of Black residents in a neighborhood was associated with a reduced risk of smoking initiation regardless of race. Other researchers have found that neighborhood disadvantage is a risk factor for adult cigarette use among Whites but not among Blacks.20,21 Taken together, these findings suggest that neighborhood context may not influence adolescent smoking in the same way it influences delinquency, violence, or other problem behaviors. However, at the same time, these findings suggest that neighborhood context may be a critical factor in adolescent smoking.
We found that involvement in prosocial activities may play a pivotal role in helping young people overcome risk exposures. The cross-level interaction observed between prosocial participation and neighborhood factors supports the protective benefits of prosocial participation. Overall, prosocial involvement had the most favorable effects in neighborhoods at the highest level of risk for adolescent smoking (i.e., disadvantaged neighborhoods with low concentrations of Black residents). Involvement in prosocial activities mitigated the risks associated with living in a neighborhood with a low percentage of Black residents and concentrated disadvantage. We found that school and community activities had protective effects similar to those of overall prosocial involvement, but church activities were more protective for youths living in predominantly Black neighborhoods than for those in other neighborhoods.
Limitations
Several limitations of this study should be noted. First, because our findings were based on a sample of low-achieving students from a medium-sized city, they may not be generalizable to all urban populations. However, the nature of our sample allowed collection of detailed data on risk and protective factors among a group of at-risk young people not typically studied. Thus, our results may be most relevant for urban youths who are at high risk for negative outcomes because of their low school achievement prior to entering high school.
Second, although our data were collected more than a decade ago and may be somewhat dated, they provide new and useful insights into adolescent cigarette use. The smoking rate in our sample in 1994 was 24.7%, and the most recent population estimate (i.e., 2005) indicates that 23% of high school students smoke cigarettes.71 This 1.7% difference, rather than being a cohort effect, may be because of characteristics of our sample. Our sample also provided a unique opportunity to study neighborhood effects on adolescent cigarette use, an area that has been understudied.
Third, selection of at-risk youths for a study of resilience raises concerns that regression to the mean may be a plausible alternative explanation for our resilience findings.22 This issue was somewhat mitigated in our sample because we excluded eighth graders only at the very top of the GPA distribution as a means of reducing regression effects. In addition, significant numbers of our participants had GPAs above 3.0 by their senior year, and GPAs were more normally distributed in their senior year than in eighth grade,72 resulting in the sample being more heterogeneous in regard to the selection variable over time.
Fourth, most of our data were dependent on self-reported information, which may suffer from social desirability effects. However, the paper-and-pencil format used to collect sensitive information on substance use behaviors reduced this concern somewhat. In addition, we included neighborhood census data, which also mitigated self-reporting bias.
Fifth, our outcome measure focused only on past-month cigarette use. More-detailed smoking data (e.g., smoking stage, such as trying first cigarette, smoking on a nondaily basis, etc.) may provide critical information on the effects of neighborhoods on adolescent smoking. Our findings suggest the need for additional neighborhood context research that addresses smoking behaviors in more detail. Similarly, although we focused on the protective effects of involvement in prosocial activities, other assets and resources may also help protect young people from neighborhood risks. Future research examining other individual (e.g., self-esteem), family (e.g., parental support), or neighborhood (e.g., collective efficacy) factors that may help deter adolescent cigarette use would be helpful.
Finally, we were unable to analyze our data separately by race/ethnicity and gender because of our limited population of White students. Future research that examines gender- and race/ethnicity-specific neighborhood effects would be informative.
Implications for Prevention
Despite these limitations, our findings support resilience theory and add to the literature on risk and protective factors that affect at-risk youths who reside in urban areas. Our results have several implications for smoking prevention efforts aimed toward Black as well as White adolescents. In addition to underlining the importance of an ecological approach to prevention, our findings suggest that prevention efforts designed to engage adolescents in prosocial activities may be a useful strategy in disadvantaged neighborhoods.
Researchers have found, for example, that involvement in school clubs and sport teams,30,31 community youth clubs or voluntary organizations, and church activities29 is associated with lower rates of cigarette use and other substance use. Therefore, prevention programs that create opportunities for young people to become involved in these activities or motivate them to become involved may be beneficial. Models designed to involve young people in prosocial activities are growing as part of the movement focusing on positive youth development.73,74 Our findings suggest that programs enhancing young people’s opportunities to take part in all types of prosocial activities may be most effective in disadvantaged White neighborhoods, whereas intervention efforts that focus on church activities may be most useful in predominantly Black neighborhoods.
Conclusions
Our study underlines the importance of neighborhood context in understanding cigarette smoking among adolescents. Moreover, our findings suggest that increased prosocial participation is protective for young people living in high-risk neighborhoods. Prevention efforts designed to engage adolescents in prosocial activities at the neighborhood level may be efficacious in reducing cigarette use.
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Acknowledgments |
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This research was funded by the National Institute on Drug Abuse (grant DA07484).
Note. The content of this article does not necessarily reflect the views or policies of the National Institute on Drug Abuse.
Human Participant Protection
This study was approved by the University of Michigan’s institutional review board. Written informed consent from the participants and passive parental consent were obtained at the beginning of data collection.
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Footnotes |
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Peer Reviewed
Contributors
Y. Xue completed the analyses and led the writing. M. A. Zimmerman directed the research design and supervised the implementation of the study. C. H. Caldwell assisted with the study and the writing.
Accepted for publication May 8, 2006.
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References |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
1. Centers for Disease Control and Prevention. Youth tobacco surveillance: United States, 2000. MMWR Morb Mortal Wkly Rep.2001;50:1–84.[Medline]
2. McGinnis JM, Foege WH. Actual causes of death in the United States. JAMA.1993;270:2207–2212.
3. Centers for Disease Control and Prevention. Smoking-attributable mortality and years of potential life lost—United States, 1984. MMWR Morb Mortal Wkly Rep.1997;46:444–451.[Medline]
4. Weinberg NZ, Rahdert E, Colliver JD, Glanz MD. Adolescent substance abuse: a review of the past 10 years. J Am Acad Child Adolesc Psychiatry.1998;37: 252–261.[CrossRef][Web of Science][Medline]
5. Leventhal T, Brooks-Gunn J. The neighborhood they live in: the effects of neighborhood residence on child and adolescent outcomes. Psychol Bull.2000; 126:309–337.[CrossRef][Web of Science][Medline]
6. Sampson RJ, Morenoff JD, Gannon-Rowley T. Assessing ‘neighborhood effects’: social processes and new directions in research. Annu Rev Sociol.2002;28: 443–478.[CrossRef][Web of Science]
7. Loeber R, Wikstrom PH. Individual pathways to crime in different types of neighborhoods. In: Farrington DP, Sampson RJ, Wikstrom, POH, eds. Integrating Individual and Ecological Aspects of Crime. Stockholm, Sweden: National Council for Crime Prevention; 1993: 169–204.
8. Sampson RJ, Groves WB. Community structure and crime: testing social-disorganization theory. Am J Sociol.1989;94:774–780.[CrossRef]
9. Simons RI, Johnson C, Beaman JJ, Conger RD, Whitbeck LB. Parents and peer groups as mediators of the effect of community structure on adolescent behavior. Am J Community Psychol.1996;24:145–171.[CrossRef][Web of Science][Medline]
10. Billy JO, Moore DE. A multilevel analysis of marital and nonmarital fertility in the US. Soc Forces.1992; 70:977–1011.[CrossRef][Web of Science]
11. Brooks-Gunn J, Duncan GJ, Klebanov PK, Sealand N. Do neighborhoods influence child and adolescent development? Am J Sociol. 1993;99:353–395.[CrossRef]
12. Crane J. The epidemic theory of ghettos and neighborhood effects on dropping out and teenage childbearing. Am J Sociol.1991;96:1126–1159.
13. Duncan GJ. Families and neighbors as sources of disadvantage in the schooling decisions of white and black adolescents. Am J Educ.1994;103:20–53.[CrossRef]
14. Anderson E. Streetwise: Race, Class, and Change in an Urban Community. Chicago, Ill: University of Chicago Press; 1990.
15. Wilson WJ. When Work Disappears: The World of the New Urban Poor. New York, NY: Vintage Books; 1996.
16. Boardman JD, Finch BK, Ellison CG, Williams DR. Neighborhood disadvantage, stress, and drug use among adults. J Health Soc Behav.2001;42: 151–165.[CrossRef][Web of Science][Medline]
17. Reardon SF, Brennan R, Buka SL. Estimating multilevel discrete-time hazard models using cross-sectional data-neighborhood effects on the onset of adolescent cigarette use. Multivariate Behav Res.2002;37: 297–330.[CrossRef][Web of Science]
18. Allison KW, Crawford I, Leone PE, et al. Adolescent substance use: preliminary examinations of school and neighborhood context. Am J Community Psychol.1999;27:111–141.[CrossRef][Web of Science][Medline]
19. Ennett ST, Flewelling RL, Lindrooth RC, Norton EC. School and neighborhood characteristics associated with school rates of alcohol, cigarette, and marijuana use. J Health Soc Behav.1997;38:55–71.[CrossRef][Web of Science][Medline]
20. Diez Roux AV, Merkin SS, Hannan P, Jacobs DR, Kiefe CI. Area characteristics, individual-level socioeconomic indicators, and smoking in young adults. Am J Epidemiol.2003;157:315–326.
21. Tseng M, Yeatts K, Millikan R, Newman B. Area-level characteristics and smoking in women. Am J Public Health.2001;91:1847–1850.
22. Fergus S, Zimmerman MA. Adolescent resilience: a framework for understanding healthy development in the face of risk. Annu Rev Public Health.2005;26: 399–419.[CrossRef][Web of Science][Medline]
23. Garmezy N, Masten AS, Tellegen A. The study of stress and competence in children: a building block for developmental psychopathology. Child Dev.1984;55: 97–111.[CrossRef][Web of Science][Medline]
24. Luthar SS, Cicchetti D, Becker B. The construct of resilience: a critical evaluation and guidelines for future work. Child Dev.2000;71:543–562.[CrossRef][Web of Science][Medline]
25. Zimmerman MA, Arunkumar R. Resiliency research: implications for schools and policy. Soc Policy Rep.1994;8:1–17.[Medline]
26. Chung HH, Elias M. Patterns of adolescent involvement in problem behaviors: relationship to self-efficacy, social competence, and life events. Am J Community Psychol.1996;24:771–784.[CrossRef][Web of Science][Medline]
27. Elder C, Leaver-Dunn D, Wang MQ, Nagy S, Green L. Organized group activity as a protective factor against adolescent substance use. Am J Health Behav.2000;24:108–113.[Web of Science]
28. Holmen TL, Barrett-Connor E, Holmen J, Bjermer L. Adolescent occasional smokers, a target group for smoking cessation? The Nord-Trøndelag Health Study, Norway, 1995–1997. Prev Med.2000;31:682–690.[CrossRef][Web of Science][Medline]
29. Steinman KJ, Zimmerman MA. Religious activity and risk behavior among African-American adolescents: concurrent and developmental effects. Am J Community Psychol.2004;33:151–161.[CrossRef][Web of Science][Medline]
30. Eccles JS, Barber BL. Student council, volunteering, basketball, or marching band: what kind of extracurricular involvement matters? J Adolesc Res. 1999; 14:10–43.
31. Mahoney JL, Cairns RB. Do extracurricular activities protect against early school dropout? Dev Psychol. 1997;33:241–253.[CrossRef][Web of Science][Medline]
32. Marsh HW. Extracurricular activities—beneficial extension of the traditional curriculum or subversion of academic goals? J Educ Psychol. 1992;84:553–562.[CrossRef][Web of Science]
33. McNeal RB. Extracurricular activities and high-school dropouts. Sociol Educ.1995;68:62–80.[CrossRef][Web of Science]
34. Miller KE, Sabo DF, Farrell MP, Barnes GM, Melnick MJ. Athletic participation and sexual behavior in adolescents: the different worlds of boys and girls. J Health Soc Behav.1998;39:108–123.[CrossRef][Web of Science][Medline]
35. Ramirez-Valles J, Zimmerman MA, Newcomb MD. Sexual risk behavior among youth: modeling the influence of prosocial activities and socioeconomic factors. J Health Soc Behav.1998;39:237–253.[CrossRef][Web of Science][Medline]
36. Mahoney JL, Stattin H. Leisure activities and adolescent antisocial behavior: the role of structure and social context. J Adolesc.2000;23:113–127.[CrossRef][Web of Science][Medline]
37. Landers DM, Landers DM. Socialization via inter-scholastic athletics—its effects on delinquency. Sociol Educ.1978;51:299–303.[CrossRef][Web of Science]
38. Barber BL, Eccles JS, Stone MR. Whatever happened to the jock, the brain, and the princess? Young adult pathways linked to adolescent activity involvement and social identity. J Adolesc Res.2001;16: 429–455.
39. Borden LM, Donnemeyer JF, Scheer SD. The influence of extra-curricular activities and peer influence on substance use. Adolesc Fam Health.2001;2:12–19.
40. Catalano RF, Kosterman R, Hawkins JD, Newcomb MD, Abbott RD. Modeling the etiology of adolescent substance use: a test of the social development model. J Drug Issues.1996;26:429–455.[Web of Science][Medline]
41. Wallace JM, Williams DR. Religion and adolescent health-compromising behavior. In: Schulenberg J, Maggs JL, Hurrelmann K, eds. Health Risks and Developmental Transitions During Adolescence. Cambridge, England: Cambridge University Press; 1997:444–468.
42. Zimmerman MA, Maton KI. Lifestyle and substance use among urban black male adolescents: a cluster analytic approach. Am J Community Psychol.1992;20:121–138.[CrossRef][Web of Science][Medline]
43. Buckhalt JA, Halpin G, Noel R, Meadows ME. Relationship of drug use to involvement in school, home, and community activities: results of a large survey of adolescents. Psychol Rep.1992;70:139–146.[CrossRef][Web of Science][Medline]
44. Dodge T, Jaccard J. Participation in athletics and female sexual risk behavior: the evaluation of four causal structures. J Adolesc Res.2002;17:42–67.
45. Greenlund K, Johnson C, Webber L, Berenson G. Cigarette smoking attitudes and first use among third-through sixth-grade students: the Bogalusa Heart Study. Am J Public Health.1997;87:1345–1348.
46. Kobus K. Peers and adolescent smoking. Addiction.2003;98:s37–s55.[CrossRef]
47. Lau RR, Quadrel MJ, Hartman KA. Development and change of young adults’ preventive health beliefs and behavior: influence from parents and peers. J Health Soc Behav.1990;31:240–259.[CrossRef][Web of Science][Medline]
48. Mittelmark M, Murray D, Luepker R, Pechacek T, Pirie P, Pallonen U. Predicting experimentation with cigarettes: the Childhood Antecedents of Smoking Study (CASS). Am J Public Health.1987;77:206–208.
49. Simons-Morton BG. Prospective analysis of peer and parent influences on smoking initiation among early adolescents. Prev Sci.2002;3:275–283.[CrossRef][Medline]
50. Bryant AL, Zimmerman MA. Examining the effects of academic beliefs and behaviors on changes in substance use among urban adolescents. J Educ Psychol.2002;94:621–637.[CrossRef][Web of Science]
51. Hawkins JD, Catalano RF, Miller JY. Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: implications for substance abuse prevention. Psychol Bull.1992;112: 64–105.[CrossRef][Web of Science][Medline]
52. Maton KI, Zimmerman MA. Psychosocial predictors of substance use among urban black male adolescents. Drugs Soc.1992;6:79–114.
53. Schulenberg JE, Bachman JG, O’Malley P, Johnston LD. High school educational success and subsequent substance use: a panel analysis following adolescents into young adulthood. J Health Soc Behav.1994;35:45–62.[CrossRef][Web of Science][Medline]
54. Zimmerman MA, Schmeelk-Cone KH. A longitudinal analysis of adolescent substance use and school motivation in African American youth. J Res Adolesc.2003;13:185–210.[Web of Science]
55. Nakao K, Treas J. Computing 1989 Occupational Prestige Scores. Chicago, Ill: National Opinion Research Center; 1990. GSS methodological report 70.
56. Nakao K, Treas J. The 1989 Socioeconomic Index of Occupations: Construction of the 1989 Occupational Prestige Scores. Chicago, Ill: National Opinion Research Center; 1990. GSS methodological report 74.
57. Raudenbush SW, Bryk AS. Hierarchical Linear Models: Applications and Data Analysis Methods. 2nd ed. Thousand Oaks, Calif: Sage Publications; 2002.
58. Raudenbush SW, Bryk AS, Cheong YF, Congdon R. HLM5: Hierarchical Linear and Nonlinear Modeling. Chicago, Ill: Scientific Software International; 2001.
59. Boyle MH, Lipman EL. Do places matter? Socioeconomic disadvantage and behavior problems of children in Canada. J Consult Clin Psychol.2002;70: 378–389.[CrossRef][Web of Science][Medline]
60. Cook TD, Herman MR, Phillips M, Settersten RAJ. Some ways in which neighborhoods, nuclear families, friendship groups, and schools jointly affect changes in early adolescent development. Child Dev.2002;73: 1283–1309.[CrossRef][Web of Science][Medline]
61. Elliott D, Wilson WJ, Huizinga D, Sampson RJ, Elliott A, Rankin B. The effects of neighborhood disadvantage on adolescent development. J Res Crime Delinquency.1996;33:389–426.
62. Furstenberg FF, Cook TD, Eccles J, Elder G, Sameroff A. Managing to Make It: Urban Families and Adolescent Success. Chicago, Ill: University of Chicago Press; 1999.
63. Johnston LD, O’Malley PM, Bachman JG, eds. Monitoring the Future: National Survey Results on Drug use, 1975–2001. Volume 1: Secondary School Students. Bethesda, Md: National Institute on Drug Abuse; 2002.
64. Griesler PC, Kandel DB, Davies M. Ethnic differences in predictors of initiation and persistence of adolescent cigarette smoking in the National Longitudinal Survey of Youth. Nicotine Tob Res.2002;4:79–93.[Abstract]
65. Ellickson PL, Orlando M, Tucker JS, Klein D. From adolescence to young adulthood: racial/ethnic disparities in smoking. Am J Public Health.2004;94: 293–299.
66. Unger JB, Rohrbach LA, Cruz TB, et al. Ethnic variation in peer influences on adolescent smoking. Nicotine Tob Res.2001;3:167–176.
67. Flay BR, Hu F, Siddiqui O, et al. Differential influence of parental smoking and friends’ smoking on adolescent initiation and escalation of smoking. J Health Soc Behav.1994;35:248–265.[CrossRef][Web of Science][Medline]
68. Jencks C, Mayer S. The social consequences of growing up in a poor neighborhood. In: Lynn LE, McGeary MFH, eds. Inner-City Poverty in the United States. Washington, DC: National Academy Press; 1990:111–186.
69. Johnson RA, Hoffmann JP. Adolescent cigarette smoking in U.S. racial/ethnic subgroups: findings from the National Educational Longitudinal Study. J Health Soc Behav.2000;41:392–407.[CrossRef][Web of Science][Medline]
70. Kandel DB, Kiros G, Schaffran C, Hu M. Racial/ethnic differences in cigarette smoking initiation and progression to daily smoking: a multilevel analysis. Am J Public Health.2004;94:128–135.
71. Centers for Disease Control and Prevention. Cigarette use among high school students—United States, 1991–2005. MMWR Morb Mortal Wkly Rep.2006;55:724–726.[Medline]
72. Zimmerman MA, Caldwell CH, Bernat DH. Discrepancy between self-report and school-record grade point average: correlates with psychosocial outcomes among African American adolescents. J Appl Soc Psychol.2002;32:86–109.[CrossRef][Web of Science]
73. Zimmerman MA. A youth participation model for assessing environmental risk and protective factors and developing interventions for youth violence in Flint, Michigan. Paper presented at: 8th World Conference on Injury Prevention and Safety Promotion, April 2006, Durban, South Africa.
74. Institute of Medicine. Community Programs to Promote Youth Development. Washington, DC: National Academy Press; 2002.
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