| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
RESEARCH AND PRACTICE |
Denise B. Kandel is with the Department of Sociomedical Sciences, Mailman School of Public Health, and the Department of Psychiatry, College of Physicians and Surgeons, Columbia University, and the New York State Psychiatric Institute, New York, NY. At the time of the study, Gebre-Egziabher Kiros was with the Department of Sociomedical Sciences, Mailman School of Public Health. Christine Schaffran is with the New York State Psychiatric Institute. Mei-Chen Hu is with the Department of Sociomedical Sciences, Mailman School of Public Health.
Correspondence: Requests for reprints should be sent to Denise B. Kandel, PhD, Columbia University, 1051 Riverside Dr, Unit 20, New York, NY 10032 (e-mail: dbk2{at}columbia.edu).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Objectives. We sought to identify individual and contextual predictors of adolescent smoking initiation and progression to daily smoking by race/ethnicity.
Methods. We used data from the National Longitudinal Study of Adolescent Health to estimate the effects of individual (adolescent, family, peer) and contextual (school and state) factors on smoking onset among nonsmokers (n = 5374) and progression to daily smoking among smokers (n = 4474) with multilevel regression models.
Results. Individual factors were more important predictors of smoking behaviors than were contextual factors. Predictors of smoking behaviors were mostly common across racial/ethnic groups.
Conclusions. The few identified racial/ethnic differences in predictors of smoking behavior suggest that universal prevention and intervention efforts could reach most adolescents regardless of race/ethnicity. With 2 exceptions, important contextual factors remain to be identified.
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
We investigated ethnic-specific predictors of smoking initiation and progression to daily smoking with data from the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative sample of adolescents. Our study replicated our previous research on a different national sample10 and extended it by considering contextual school-level and state-level variables in addition to individual characteristics as predictors of the outcomes of interest within a multilevel statistical framework.
Predictors of adolescent smoking behavior that have been investigated have focused mainly on personal, peer, family, and sociodemographic characteristics.3,10 Peer smoking is one of the strongest predictors of adolescent smoking initiation and persistence.5,11–14 Parental smoking and low levels of parent-child closeness also predict smoking initiation and persistence.15–19 Problem behaviors, depression, low self-esteem, and poor academic performance are other predictors of smoking initiation and persistence and may be more important for persistence than initiation. However, smoking may predict depression rather than the reverse.20,21
Selected tobacco advertising and promotion, public policies, and primary prevention efforts also may play a role in promoting or reducing smoking,3,4,22–28 although interventions for curtailing youth access appear to have no impact.29 While cross-sectional studies consistently report that increased taxation is associated with reductions in rates of smoking and extensiveness (number of days per month a person smokes/number of cigarettes smoked) of smoking,26,27,30,31 with rare exceptions,32 longitudinal investigations find no effect on either onset or escalation of smoking.33,34 To the extent that advertising and promotional activities target specific racial/ethnic groups,4 youths of different race/ethnicity may respond differently to changes in price and public policies.26
The surgeon general’s report Tobacco Use Among Racial/Ethnic Minority Groups4 underscored the paucity and the methodological limitations of studies on racial/ethnic differences in the determinants of adolescent smoking. With few exceptions, most studies are cross-sectional and confound precursors and consequences of smoking. In addition to studies based on local samples,7,15,19,35 we have identified 4 longitudinal studies that were based on national samples: the National Longitudinal Survey of Youth (NLSY),10 Add Health,36 the National Education Longitudinal Study,37 and the Teenage Attitudes and Practices Survey.38 The results are somewhat inconsistent across these studies, but overall, they point to few racial/ethnic differences in the predictors of various smoking behaviors. Hoffman and Johnson37 were the first, to our knowledge, to consider the effects of school-level variables on daily cigarette initiation among different racial/ethnic groups. They identified strong protective effects of percentage minority enrollment in school among minorities; academic competitiveness was a risk for all groups.
We examined key school and public policies targeted toward preventing or reducing smoking—together with individual personal and interpersonal factors—and their effects on smoking initiation and transition to daily smoking. We estimated multilevel logistic models that take into account the clustering of students within schools and schools within states, and we assessed the importance of contextual factors on the outcomes of interest. Initiation and daily smoking captured 2 essential developmental stages of smoking.3,15,39,40 While these stages may be influenced by different factors,15,39,40 few unique predictors of stages of smoking have been identified to date.41
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Our study was based on 4 analytical samples restricted to non-Hispanic Black, Hispanic, and non-Hispanic White youths. Two cross-sectional samples included all ethnic-eligible Wave 1 subjects (n = 18 933) and the subsample of those who had ever smoked at least 1 cigarette (n = 10 564). Two longitudinal samples were selected from the 12 158 ethnic-eligible adolescents who were reinterviewed at Wave 2 and who had complete smoking data at both waves: (1) Wave 1 never smokers for predicting smoking onset (n = 5374) and (2) Wave 1 smokers who had never been daily smokers for predicting transition to daily smoking by Wave 2 (n = 4474). Cases excluded from the longitudinal analysis included those without weights (genetic sample of siblings and co-twins = 1168); those who were not White, Black, or Hispanic (n = 1141); those who were missing smoking data at either wave (n = 269); and those who ever smoked daily by Wave 1 (n = 2310). Females composed 50% of the sample for initiation and 51% of the sample for daily smoking. Mean ages were 14.8 years (SD = 1.7) for initiation and 15.0 years (SD = 1.6) for daily smoking at Wave 1. At each wave, Add Health sampling weights that correct for unequal probabilities of sample selection were applied.44 Information about Add Health is available at http://www.cpc.unc.edu/projects/addhealth/.
Definition of Variables
All individual variables, except onset of smoking and daily smoking, were based on Wave 1 adolescents’ reports. Parental education was based on parents’ reports when they were interviewed and on adolescents’ reports otherwise.
Definitions of Individual Characteristics
= 0.78).43 = 0.85). = 0.84); restless sleep and crying spells were not questioned. = 0.82).
Definitions of Contextual School Variables
Definitions of Contextual State Variables
These definitions are from the Wave 1 Tobacco Contextual Database (37 states are represented).
Statistical Analysis
We fitted ethnic-specific survival models to the cross-sectional Wave 1 data to plot the hazards of smoking initiation by age and the hazards of becoming a daily smoker among those who had ever smoked at least 1 cigarette by years since smoking onset. In a second step, we implemented multilevel logistic regressions to estimate the effects of individual characteristics (adolescent, family, peers) and contextual variables (school, state) on the 2 smoking outcomes of interest. The multilevel models took into account the 3-level hierarchical structure of the data for adolescents (level 1) sampled within schools (level 2) and states (level 3).46,47 Ignoring the clustering of adolescents within schools and schools within states may generate improper estimates of standard errors, because students sampled from the same school or state share common characteristics or exposure to school and community factors.
We estimated a 3-level hierarchical logistic regression model with MLwiN version 1.10.0007
 | (1)) |
where pijk is the probability of smoking initiation (or transition to daily smoking) for the ith adolescent in the jth school and the kth state; , ß, and 
k’s are parameters to be estimated; x, y, and zk’s are observed individual-, school-, and state-level predictor variables, respectively; and vk and ujk denote random effects that are assumed to be independently normally distributed with means equal to 0 and variances 
v2 and u2, respectively. The random effect vk represents unobserved state-level factors that affect smoking behavior and are shared by all students living in the same state; and ujk represents unobserved school-level factors shared by students who attend the same school. The fixed coefficient ß0 represents the hazard of smoking when all the predictors are equal to 0 with random effects vk = ujk = 0.
Models were first estimated for each racial/ethnic group separately. Next, interaction terms between race/ethnicity and statistically significant predictors (P < 0.05) in any racial/ethnic group were included in models fitted to the total sample to assess statistically significant racial/ethnic differences in risk and protective factors. The models were reestimated by retaining only the significant interactions.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
shows the distributions of predictors by race/ethnicity.
|
shows that the hazards of smoking initiation peaked earlier for White and Black youths (at 17 years) than for Hispanic youths (at 18 years). The hypothesis that the hazards of smoking initiation are equal across ethnic groups was strongly rejected (P < .001). At all ages, minority youths, especially Black youths, were less likely to start smoking than White youths.
|
shows the hypothesis that the probabilities of becoming a daily smoker are equal across ethnic groups was rejected (P < .001). Minority youths, especially Black youths, were less likely to become daily smokers than White youths. The differences among groups were sharper for daily smoking than for onset of smoking. For all groups, the hazard decreased with increasing intervals after 1 year since onset of smoking.
|
).
|
Predictors of smoking onset.
Four individual predictors of smoking onset were common across all 3 racial/ethnic groups (Table 2
). The most significant were 2 risk factors: having at least 1 friend perceived to be a current smoker and delinquent participation. The former increased the odds of smoking onset by 63% when compared with having no friends who smoke. Being in a 2-parent family and being older were protective. Of the contextual factors, 1 state-level variable—bans on vending machines from locations accessible to youths—decreased the risk of onset for all youths.
One ethnic-specific predictor emerged. Positive scholastic attitude reduced the risk of initiation among Hispanic (odds ratio [OR] = .53; P < .001) and White (OR = .59; P < .001) youths but not among Black (OR = .95; P = .32) youths. Several factors that were significant in the unadjusted models became nonsignificant when we controlled for other covariates: quality of parent-child relationship, which was indexed by parental connectedness (protective), and depression (risk).
Predictors of transition to daily smoking.
There were more common statistically significant predictors of transition to daily smoking than predictors of smoking initiation across all 3 racial/ethnic groups (Table 2). The most important common risk factors were extensiveness of smoking at the initial interview, having at least 1 friend who smokes, and lifetime smoking by 1 parent. The association of parental smoking with child smoking was the same whether or not the variables distinguished maternal from paternal smoking and the number of smoking parents in the household. Peer smoking had a stronger effect than parental smoking. One factor, parent-child connectedness, was protective.
There were 2 ethnic-specific predictors. Delinquency was a significant risk only for White (OR = 1.03; P < .01) and Black (OR = 1.03; P < .01) youths, while positive scholastic attitudes were protective only for minority youths (Black youths OR = .79; P < .05; Hispanic youths OR = .56; P < .01). Percentage minority in school significantly reduced transition to daily smoking across all racial/ethnic groups. None of the other contextual school or state variables were significant predictors of daily smoking.
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Our analyses confirmed differences in rates of adolescent smoking onset and especially progression to daily smoking among racial/ethnic groups. Hispanic youths had the highest rates of smoking onset, and White youths had the highest rates of progression to daily smoking. Black youths consistently had the lowest rates of smoking onset and progression to daily smoking. The lower rates of smoking among Black youths are consistent with most national and regional adolescent studies.7,8,10,38
Individual factors were much more important predictors of the 2 smoking outcomes than contextual factors. Only the between-school variance was significant for onset of smoking; however, 1 specific school variable—percentage minority—and 1 specific state variable—banning vending machines—had strong inverse relationships with 1 of the outcomes. Banning vending machines reduced smoking onset, whereas a higher percentage of minorities in school reduced the risk of progressing to daily smoking. Prevalence of student smoking within schools, antismoking school policy, prohibition of tobacco marketing within 500 feet of schools, and state tax did not predict the 2 smoking transitions for any of the racial/ethnic groups.
There were more common predictors than ethnic-specific predictors of adolescent smoking initiation and progression to daily smoking. Peer smoking, delinquency, single-parent family, older age, and absence of ban on vending machines predicted smoking onset across all 3 racial/ethnic groups. Frequency of smoking, parental and peer smoking, and low parent-child connectedness were common predictors of progression to daily smoking. Thus, only peer smoking was a common predictor of the 2 transitions across the 3 racial/ethnic groups. Once adolescents had experimented with cigarettes, their progression to daily smoking appeared to be independent of age. Across all 3 racial/ethnic groups, parental smoking was a risk factor only for progression to daily smoking. With regard to ethnic-specific predictors, positive school attitude was a significant protective factor for both smoking onset and progression to daily smoking among Hispanic youths, for smoking onset among White youths, and for progression to daily smoking among Black youths. Delinquency was a significant risk factor for progression to daily smoking among White and Black youths. Without controls for other covariates, except age and gender, Bauman et al.’s36 analysis of the same data set found a weak parental effect and a stronger peer-smoking effect on smoking onset among White youths.
Griesler et al.10 similarly concluded that there were more common predictors than ethnic-specific predictors of smoking initiation and persistence of smoking over a 2-year interval, a conclusion also reached by Mayhew et al. in their review.41 Our investigation and the other 2 longitudinal investigations published after Mayhew et al.’s review35,37 provide further support for this generalization. The significant common predictors identified in different investigations are not necessarily the same; this may in part be because of the fact that the studies focused on somewhat different outcomes. Griesler et al.10 focused on persistence of smoking, while our study focused on transition to daily smoking. Johnston and Hoffman35 examined the onset of daily smoking among nonsmokers. Griesler et al.10 found that interpersonal factors were important predictors of smoking initiation, and intrapersonal adolescent characteristics were important predictors of smoking persistence, with delinquency/conduct problems a common predictor of both transitions. Our results are not congruent with that conclusion. In both studies, delinquency was a common risk factor for smoking initiation. While in the earlier study delinquency also predicted persistent smoking across the 3 racial/ethnic groups, in our study, it did not predict progression to daily smoking among Hispanic youths. Furthermore, in our study, peer influence was highly significant across all 3 racial/ethnic groups for both transitions, but it was significant only for smoking initiation in the earlier study. Conversely, in our sample, parental influence was a more important predictor of transition to daily smoking than smoking initiation, while the reverse was observed earlier for persistence of smoking. Our sample, which is larger and more representative of the nation’s youth than the NLSY, may generate more stable estimates.
With 2 exceptions, we did not identify specific school or state factors of importance. The public policies regarding cigarette smoking that we considered were not significant, whether for smoking onset or progression to daily smoking, except for banning cigarette vending machines, which protected against smoking onset across all 3 racial/ethnic groups. Prohibiting vending machines and self-service displays of cigarettes, except in facilities totally inaccessible to persons under 18 years of age, was 1 of 3 key regulations that the government passed in 1996 to reduce access to tobacco products and their appeal to minors.4 This initiative appears to constitute a successful strategy for all youths. Cigarette taxation was not a significant protective factor, and it has consistently been found to be negatively associated with youths’ tobacco use in cross-sectional studies,26,27,49 although not in most longitudinal studies.33,34 At a particular point in time, smoking rates may reflect the impact of existing taxation, whereas changes in smoking behaviors may be influenced by changes in taxation.32 Of the school variables, overall smoking rates and policy toward smoking did not predict adolescent smoking behaviors, whereas a higher percentage of minorities in school had a protective effect for daily smoking onset for all youths. By contrast, Johnson and Hoffman37 found percentage minority in school to be important only for minorities. The adolescent’s immediate friendship network was a more pervasive source of influence on smoking than the larger school or state context. Similarly, Rosendahl et al.48 concluded in a Swedish study that the social microenvironment of the classroom was a more important predictor of smoking onset than schoolwide or local antismoking policies. This hypothesis could not be tested with Add Health, because classroom membership data were not available.
Changing contextual school and community factors is potentially one of the most efficient public health strategies available for prevention or reduction of adolescent smoking. While we were mostly unsuccessful in identifying such effective factors in our research, the identification of contextual factors that account for smoking behavior in adolescence represents an important area for future investigation. Furthermore, the relatively few predictors unique to specific race/ethnic groups leaves unresolved the issue of what could account for racial/ethnic differences in patterns of smoking. Our findings suggest that, by and large, universal prevention and intervention efforts would reach most adolescents, regardless of race/ethnicity. Reducing delinquent behavior would benefit almost all youths. Strengthening a positive academic orientation would be particularly beneficial for reducing minorities’ progression to daily smoking. For all adolescents, preventing smoking would have an amplified effect through the mutual influence of peers on each other, while targeting parental smoking would appear to reduce progression to chronic daily smoking. A multipronged approach that targets parental smoking and youth deviance, stresses the importance of resisting peer pressure, increases commitment to school, and reduces easy access to cigarettes should be incorporated in strategies designed to prevent smoking onset and progression to daily smoking.
|
Acknowledgments |
|---|
This research uses data from Add Health, a project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris and funded by grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgments to Ronald R. Rindfuss and Barbara Entwisle for their assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W Franklin St, Chapel Hill, NC 27516–2524 (http://www.cpc.unc.edu/addhealth/contract.html). The authors also thank the reviewers for their comments.
Human Participant Protection
The Add Health data collection and research protocol were approved by the institutional review board of the School of Public Health at the University of North Carolina at Chapel Hill. The secondary data analysis protocol was approved by the institutional review boards of the New York State Psychiatric Institute and the Columbia Presbyterian Medical Center.
|
Footnotes |
|---|
Accepted for publication July 13, 2003.
|
References |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
2. Kopstein A. Tobacco Use in America: Findings from the 1999 National Household Survey on Drug Abuse. Rockville, Md: Substance Abuse and Mental Health Services Administration, Office of Applied Studies; 2001.
3. US Dept of Health and Human Services. Preventing Tobacco Use Among Young People: A Report of the Surgeon General. Atlanta, Ga: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office of Smoking and Health; 1994.
4. US Dept of Health and Human Services. Tobacco Use Among US Racial/Ethnic Minority Groups–African-Americans, American Indians and Alaska Natives, Asian Americans and Pacific Islanders, and Hispanics: A Report of the Surgeon General. Atlanta, Ga: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office of Smoking and Health; 1998.
5. Griesler PC, Kandel DB. Ethnic differences in correlates of adolescent cigarette smoking. J Adolesc Health. 1998;23:167–180.[ISI][Medline]
6. Kandel DB, Chen K, Warner LA, Kessler RC, Grant B. Prevalence and demographic correlates of symptoms of last year dependence on alcohol, nicotine, marijuana and cocaine in the US population. Drug Alcohol Depend. 1997;44:11–29.[ISI][Medline]
7. Landrine H, Richardson JL, Klonoff EA, Flay B. Cultural diversity in the predictors of adolescent cigarette smoking: the relative influence of peers. J Behav Med. 1994;17:331–346.[ISI][Medline]
8. Nelson DE, Giovino GA, Shopland DR, Mowery PD, Mills SL, Eriksen MP. Trends in cigarette smoking among US adolescents, 1974–1991. Am J Public Health. 1995;85:34–40.
9. Levy DT, Cummings KM, Hyland A. A simulation of the effects of youth initiation policies on overall cigarette use. Am J Public Health. 2000;90:1311–1314.
10. 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 Tobacco Res. 2002;4:79–93.
11. Urberg KA, Degirmencioglu SM, Pilgram C. Close friend and group influence on adolescent cigarette smoking and alcohol use. Dev Psychol. 1997;33:834–844.[ISI][Medline]
12. Killen JD, Robinson TN, Haydel KF, et al. Prospective study of risk factors for the initiation of cigarette smoking. J Consult Clin Psychol. 1997;65:1011–1016.[ISI][Medline]
13. Alexander C, Piazza M, Mekos D, Valente T. Peers, schools, and adolescent cigarette smoking. J Adolesc Health. 2001;29:22–30.[ISI][Medline]
14. Flay BR, Hu FB, 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.[ISI][Medline]
15. Ary DV, Biglan A. Longitudinal changes in adolescent cigarette smoking behavior: onset and cessation. J Behav Med. 1988;11:361–382.[ISI][Medline]
16. Chassin L, Presson CC, Sherman SJ, Corty E, Olshavsky RW. Predicting the onset of cigarette smoking in adolescents: a longitudinal study. J Appl Soc Psychol. 1984;14:224–243.[ISI]
17. Choi WS, Pierce JP, Gilpin EA, Farkas AJ, Berry CC. Which adolescent experimenters progress to established smoking in the United States. Am J Prev Med. 1997;13:385–391.[ISI][Medline]
18. Doherty WJ, Allen W. Family functioning and parental smoking as predictors of adolescent use: a six-year prospective study. J Fam Psychol. 1994;8:347–353.
19. O’Loughlin J, Paradis G, Renaud L, Gomez LS. One year predictors of smoking initiation and of continued smoking among elementary schoolchildren in multiethnic, low-income, inner-city neighborhoods. Tobacco Control. 1998;7:268–275.
20. Goodman E, Capitman J. Depressive symptoms and cigarette smoking among teens. Pediatrics. 2000;106:748–754.
21. Wu L, Anthony JC. Tobacco smoking and depressed mood in late childhood and early adolescence. Am J Public Health. 1999;89:1837–1840.
22. DiFranza JR, Brown LJ. The Tobacco Institute’s "It’s the Law" campaign: has it halted illegal sales of tobacco to children? Am J Public Health. 1992;82:1271–1273.
23. DiFranza JR, Savageau JA, Aisquith BF. Youth access to tobacco: the effects of age, gender, vending machine locks, and "It’s the Law" programs. Am J Public Health. 1996;86:221–224.
24. Altman DG, Foster V, Rasenick-Douss L, Tye JB. Reducing the illegal sale of cigarettes to minors. JAMA. 1989;261:80–83.[Abstract]
25. Kaufman NJ, Castrucci BC, Mowery PD, Gerlach KK, Emont S, Orleans T. Predictors of change on the smoking uptake continuum among adolescents. Arch Pediatr Adolesc Med. 2002;156:581–587.
26. Chaloupka FJ, Pacula RL. Sex and race differences in young people’s responsiveness to price and tobacco control policies. Tobacco Control. 1999;8:373–377.
27. Chaloupka FJ, Grossman M. Price, Tobacco Control Policies, and Youth Smoking. Cambridge, Mass: National Bureau of Economic Research; 1996. Working Paper No. 5740.
28. Rigotti NA, DiFranza JR, Change Y, Tisdale T, Kemp B, Singer DE. The effect of enforcing tobacco-sales laws on adolescents’ access to tobacco and smoking behavior. N Engl J Med. 1997;337:1044–1051.
29. Fichtenberg CM, Glantz SA. Youth access interventions do not affect youth smoking. Pediatrics. 2002;109:1088–1092.
30. Liang L, Chaloupka FJ. Differential effects of cigarette price on youth smoking intensity. Nicotine Tobacco Res. 2002;4:109–114.
31. Ross H, Chaloupka FJ. The effect of cigarette prices on youth smoking. Health Econ. 2003;12:217–230.[ISI][Medline]
32. Tauras JA, O’Malley PM, Johnston LD. Effects of Price and Access Laws on Teenage Smoking Initiation: A National Longitudinal Analysis. Cambridge, Mass: National Bureau of Economic Research; 2001. Working Paper No. 8331.
33. Douglas SM. The duration of the smoking habit. Econ Inquiry. 1998;36:49–64.
34. DeCicca P, Kenkel D, Mathios A. Putting Out the Fires: Will Higher Taxes Reduce Youth Smoking? Aarhus, Denmark: Department of Economics, Aarhus School of Business; 2000. Working Paper No. 00–3.
35. Johnson CJ, Li D, Perry CL, et al. Fifth through eighth grade longitudinal predictors of tobacco use among a racially diverse cohort: CATCH. J Sch Health. 2002;72:58–64.[ISI][Medline]
36. Bauman KE, Carver K, Gleiter K. Trends in parent and friend influence during adolescence: the case of adolescent cigarette smoking. Addict Behav. 2001;26:349–361.[Medline]
37. Johnson RA, Hoffmann JP. Adolescent cigarette smoking in US racial/ethnic subgroups: findings from the National Education Longitudinal Study. J Health Soc Behav. 2000;41:392–407.[ISI][Medline]
38. Flint AJ, Yamada EG, Novotny TE. Black-white differences in cigarette smoking uptake: progression from adolescent experimentation to regular use. Prev Med. 1998;27:358–364.[ISI][Medline]
39. Flay BR. Youth tobacco use: risks, patterns, and control. In: Slade J, Orleans CT, eds. Nicotine Addiction: Principles and Management. New York, NY: Oxford University Press; 1993:365–384.
40. Leventhal H., Cleary P. The smoking problem: a review of the research and theory in behavioral risk modification. Psychol Bull. 1980;88:370–405.[ISI][Medline]
41. Mayhew KP, Flay BR, Mott JA. Stages in development of adolescent smoking. Drug Alcohol Depend. 2000;59:S61–S81.
42. Bearman PS, Jones J, Udry JR. The National Longitudinal Study of Adolescent Health: Research Design. Available at: http://www.cpc.unc.edu/addhealth. Accessed November 18, 2002.
43. Resnick MD, Bearman PS, Blum RW, et al. Protecting adolescents from harm: findings from the National Longitudinal Study of Adolescent Health. JAMA. 1997;278:823–832.[Abstract]
44. Chantala K, Tabor J. Strategies to Perform a Design-Based Analysis Using the Add Health Data. Carolina Population Center: University of North Carolina at Chapel Hill; 1999.
45. Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401.
46. Diggle PJ, Heagerty P, Liang KY, Zeger SL. Analysis of Longitudinal Data. 2nd ed. Oxford, England: Clarendon Press; 2002. Oxford Statistical Science Series 13.
47. Leyland AH, Goldstein H, eds. Multilevel Modeling of Health Statistics. New York, NY: Wiley; 2001.
48. Rosendahl KI, Galanti MR, Gilljam H, Bremberg S, Ahlbom A. School and class environments are differently linked to future smoking among preadolescents. Prev Med. 2002;34:649–654.[ISI][Medline]
49. US Dept of Health and Human Services. Changing Adolescent Smoking Prevalence. Bethesda, Md: National Institutes of Health, National Cancer Institute; 2001. Smoking and Tobacco Control Monograph No. 14.
This article has been cited by other articles:
|
|
 |
|
  C T C Okoli, C G Richardson, P A Ratner, and J L Johnson An examination of the smoking identities and taxonomies of smoking behaviour of youth Tob. Control, June 1, 2008; 17(3): 151 - 158. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. R.N. Else, E. S. Hishinuma, D. A. Goebert, S. T. Nishimura, and S. Baker Predicting Cigarette Use Among Hawai`i Adolescents School Psychology International, May 1, 2008; 29(2): 230 - 247. [Abstract] [PDF]
|
|
|
|
 |
|
 B. Milton, S. E. Woods, L. Dugdill, L. Porcellato, and R. J. Springett Starting young? Children's experiences of trying smoking during pre-adolescence Health Educ. Res., April 1, 2008; 23(2): 298 - 309. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Xue, M. A. Zimmerman, and C. H. Caldwell Neighborhood Residence and Cigarette Smoking Among Urban Youths: The Protective Role of Prosocial Activities Am J Public Health, October 1, 2007; 97(10): 1865 - 1872. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Panday, S. P. Reddy, R. A. C. Ruiter, E. Bergstrom, and H. de Vries Determinants of smoking among adolescents in the Southern Cape-Karoo region, South Africa Health Promot. Int., September 1, 2007; 22(3): 207 - 217. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A M A Smith and S V Subramanian Population contextual associations with heterosexual partner numbers: a multilevel analysis. Sex. Transm. Inf., June 1, 2006; 82(3): 250 - 254. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M.-C. Hu, M. Davies, and D. B. Kandel Epidemiology and Correlates of Daily Smoking and Nicotine Dependence Among Young Adults in the United States Am J Public Health, February 1, 2006; 96(2): 299 - 308. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
