Skip Navigation

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (23)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Storr, C. L.
Right arrow Articles by Anthony, J. C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Storr, C. L.
Right arrow Articles by Anthony, J. C.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Am J Epidemiol 2004; 160:126-130.
Copyright © 2004 by the Johns Hopkins Bloomberg School of Public Health

ORIGINAL CONTRIBUTIONS

Early Childhood Misbehavior and the Estimated Risk of Becoming Tobacco-dependent

Carla L. Storr1 , Beth A. Reboussin2 and James C. Anthony1

1 Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
2 School of Medicine, Wake Forest University, Winston-Salem, NC.

Received for publication September 30, 2003; accepted for publication February 10, 2004.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In this study, the authors focused on signs of early childhood misbehavior that might be linked to the risk of becoming tobacco-dependent. Standardized teacher ratings of misbehavior were obtained for an epidemiologic sample of first graders entering an urban mid-Atlantic public school system in 1985 and 1986. Fifteen years later, 1,692 of the students were reassessed (nearly 75% of the original sample). As adults, 962 participants indicated that they had tried tobacco at least once; 66% of the 962 had become daily users. Latent class analysis of items on the Fagerström Test for Nicotine Dependence gave evidence of three classes pertinent to tobacco dependence syndrome in smokers by young adulthood: one nondependent class of smokers (50% of smokers), a class of smokers experiencing a moderate number of dependence features (31%), and a third class that was more severely affected (19%), as manifest in the need to smoke immediately after waking and smoking when ill. With or without adjustment for covariates, higher levels of teacher-rated childhood misbehavior at entry into primary school were associated with a modest excess risk of becoming tobacco-dependent by young adulthood (risk ratio = 1.6, 95% confidence interval: 1.1, 2.5). Interventions that seek to improve childhood behavior might reduce early onset tobacco smoking and risk of tobacco dependence among smokers.

behavior; child; cohort studies; longitudinal studies; risk; smoking; tobacco use disorder

Abbreviations: Abbreviation: FTND, Fagerström Test for Nicotine Dependence.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Many tobacco smokers experience a dependence syndrome with co-occurring mental disturbances (obsession-like cravings or urges), disturbances of behavior (compulsion-like repetitions of smoking), and sometimes observable features of neuroadaptation, such as pharmacologic tolerance to nicotine (1). Tobacco dependence may be rooted in an underlying diathesis (e.g., genetic vulnerability shaped by later experience). We studied early childhood signs of misbehavior that might be linked to a risk of becoming tobacco-dependent (2–4).


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Population, sample, and nonresponse
In 1985–1986, 2,311 pupils entered first-grade classrooms in 19 primary schools selected from an urban public school system in a mid-Atlantic US state (5–7). Over 15 years later, nearly 75 percent of the surviving pupils were traced and consented to an interview (n = 1,692), including 154 incarcerated participants. Another 12 percent of the sample was located, but 142 young adults chose not to be interviewed and 133 were not interviewed because of logistic problems (e.g., living out of state with no telephone number, military postings overseas).

A central computerized school database provided data on each child’s sex, birth date, eligibility for a subsidized lunch program, and race/ethnicity (table 1). After exclusion of 32 decedents (verified by National Death Index searches through 2002), there were independent associations of smoking with being male and being nonminority but not with year of entry into first grade ("cohort"), receipt of subsidized lunches, or level of childhood misbehavior ({alpha} = 0.05). Mean age at follow-up interview was 21 years (range, 19–24 years). Protocols were approved by cognizant institutional review boards. Signed consent was obtained from parents initially and from participants at the adult interview.


View this table:
[in this window]
[in a new window]
  		TABLE 1. Characteristics of the baseline and follow-up samples in relation to occurrence of tobacco smoking among students in an urban public school system, 1985–2002*
 
Assessment of early childhood misbehavior
First-grade teachers completed the Teacher Observation of Classroom Adaptation—Revised (8) near the end of the fall quarter for 1,925 pupils (approximately 89 percent of first graders enrolled in these schools). Test-retest correlations were above 0.60 for each subscale of the test (Cronbach’s {alpha} > 0.80). The childhood misbehavior subscale consisted of 10 six-point Likert items on behaviors such as starting fights, breaking rules, taking others’ property, lying, teasing classmates, being stubborn, yelling at or hurting others, and having trouble accepting authority. In accordance with prior research, we sorted participants into subgroups based on tertiles of the summary score (8).

Assessment of tobacco involvement and dependence
Trained interviewers assessed tobacco experiences as part of a 90-minute interview conducted in young adulthood. Participants were classified as smokers if they indicated that they had ever smoked a tobacco product, even just a puff. Tobacco dependence was based on the young adult’s responses to six standardized items on the Fagerström Test for Nicotine Dependence (FTND) (9), which has been validated against biochemical indices (10, 11). Dependence features assessed by the FTND are: 1) "How soon after waking up do you smoke your first cigarette?"; 2) "Do you find it difficult to refrain from smoking in places where it is forbidden?"; 3) "Which cigarette would you hate to give up?"; 4) "How many cigarettes a day do you smoke?"; 5) "Do you smoke more frequently during the first hours after waking than during the rest of the day?"; and 6) "Do you smoke if you are so ill that you are in bed most of the day?". Current smokers (n = 593) reported on the most recent interval of smoking. Former smokers (those who had not smoked in the month prior to assessment) (n = 369) were asked about the interval during which they were smoking the most.

Analysis
After initial cross-classification analysis, the relative odds of tobacco involvement associated with levels of childhood misbehavior were estimated by means of logistic regression models in which other covariates were held constant. Latent class analysis was used to identify subgroups of smokers on the basis of their patterns of response to the FTND items (12–15). Alternative forms of the unobserved latent class variable were fitted to the data using Latent GOLD, version 2.0 (Statistical Innovations, Inc., Belmont, Massachusetts) (16). Conventional goodness-of-fit statistics were used in the model choice process, with probes for the standard assumption of local independence between observed variables, once the latent classes had been specified (17, 18).

Respondents were assigned to a specific tobacco dependence class based on modal class membership probabilities: a nondependent class of smokers, a class of smokers experiencing a moderate number of dependence features, and a third class of persons more severely affected. A multinomial logistic regression model yielded estimates of association linking earlier childhood misbehavior with risk of becoming tobacco-dependent among persons who had smoked at least once, before and after adjustment for covariates (e.g., age, sex, and minority group status).

In a postanalysis exploratory stage, we explored male-female differences in the strength of the observed association. We found that models with covariate terms for primary school intervention status did not yield appreciably different estimates. To accommodate the clustering of students within schools, we used a variant of the Huber-White sandwich estimator of variance to obtain robust standard errors and variance estimates (19).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
In young adulthood, 962 respondents reported that they had smoked tobacco (at least a puff), and 66 percent of them smoked tobacco daily. A higher level of childhood misbehavior was modestly associated with cumulative occurrence of tobacco use by young adulthood (table 1).

Latent class models were fitted to the FTND responses for persons who had smoked at least once. A three-class model fitted the data best, with the classification accuracy proportion being acceptably high for each class: 0.93 for class 1, 0.86 for class 2, and 0.85 for class 3. Classes differed mainly in relation to a severity dimension (increase in the prevalence of items from class to class). Class 1 included one half of our ever smokers, those reporting very few dependence features (the mean value was between 0 and 1). The mean number of dependence features reported by smokers in class 3 (an estimated 19 percent of the ever smokers in this sample) was 3.5. The intermediate class, class 2, included 31 percent of the ever smokers, who appeared to have developed a less severe form of tobacco dependence (mean number of features = 2.2). Alternately, class 2 may be a prodromal class of persons who are more susceptible to a future shift toward class 3 or are more likely to quit smoking than class 3.

With respect to the primary study hypothesis about early childhood misbehavior relative to later risk of becoming tobacco-dependent, the evidence from multinomial regression analysis was supportive, but the association was no more than modest (table 2). Estimates based on a model including a product term for sex indicated that females with teacher ratings in the highest tertile of misbehavior were almost twice as likely to have developed the more severe tobacco dependence syndrome as females in the lowest tertile. Smaller differences, some opposite, were found when class 2 smokers were contrasted with class 1 smokers. Males with higher misbehavior ratings were more likely to be in both tobacco dependence syndrome classes (table 2).


View this table:
[in this window]
[in a new window]
  		TABLE 2. Occurrence of tobacco dependence syndrome in young adulthood and estimated relative risk of smokers’ developing tobacco dependence syndrome, by level of childhood misbehavior, among students in an urban public school system, 1985–2002*
 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The results of this study provide modest support for a link between early childhood misbehavior and risk of tobacco dependence by young adulthood. It seems remarkable that a 10-item teacher rating of misbehavior obtained soon after the start of first grade is predictive.

Strengths of this study included a predefined study base, standardized ratings of childhood misbehavior by the first-grade teacher, and independent standardized assessment of tobacco dependence in young adulthood. Our latent class approach required no arbitrary specification of cutoff scores for designating tobacco-dependent cases versus noncases.

Major limitations were the incomplete participation in young adulthood, possible unmeasured underlying susceptibility factors (e.g., familial influences), and concerns about the FTND. Although the FTND is widely respected for clinical use, its psychometric properties may not hold up in community samples (20, 21). Future work would benefit from qualitative research (22, 23) and refinements encompassing other features of nicotine dependence (24–27).

Previous studies have consistently reported an increased risk of subsequent drug use and dependence associated with conduct problems and antisocial behavior in childhood (28–31), and an association of nicotine dependence with conduct problems was found in a cross-sectional survey of young adults (24). Long-term relations between aggressive, unconventional, and impulsive behaviors and drug involvement have also been found (32–34). However, different pathways between early childhood misbehavior and tobacco involvement may exist. Psychiatric symptoms and cognitive disabilities may be manifest as aggressive behaviors, and smoking may be a response to impulsive tendencies that often co-occur with aggression or misbehavior. Distress and failure to adopt responsible conventional roles and behaviors may be important mediators linking childhood misbehavior to later tobacco dependence (34, 35).

In conclusion, this study supports the notion that early childhood misbehavior can help predict who will become a tobacco smoker and develop tobacco dependence by young adulthood. Evidence from several randomized trials now suggests that programs designed to reduce childhood misbehavior may delay the onset of tobacco use (5, 36).


   ACKNOWLEDGMENTS
 
This study was funded by grants DA09897 and DA04392 from the National Institute on Drug Abuse (Principal Investigator: J. C. Anthony) and grant MH38725 from the National Institute of Mental Health (Principal Investigator: S. G. Kellam), including a supplemental award from the National Institute on Drug Abuse. Dr. B. A. Reboussin was supported by Career Development Award DA16279 from the National Institute on Drug Abuse.

The authors thank the teachers and administrators of the Baltimore City public school system and the numerous colleagues involved in the prevention program.


   NOTES
 
Correspondence to Dr. Carla Storr, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, 8th Floor, Baltimore, MD 21205 (e-mail: cstorr{at}jhsph.edu). Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Committee on Nomenclature and Statistics, American Psychiatric Association. Diagnostic and statistical manual of mental disorders, Fourth Edition (DSM-IV). Washington, DC: American Psychiatric Association, 1994.
  2. Robins LN. Sturdy childhood predictors of adult antisocial behavior: replications from longitudinal studies. Psychol Med 1978;8:611–22.[Web of Science][Medline]
  3. Block J, Block JH, Keyes S. Longitudinally foretelling drug usage in adolescence: early childhood personality and environmental precursors. Child Dev 1988;59:336–55.[CrossRef][Web of Science][Medline]
  4. Tremblay RE, Masse B, Perron D, et al. Early disruptive behavior, poor school achievement, delinquent behavior, and delinquent personality: longitudinal analyses. J Consult Clin Psychol 1992;60:64–72.[CrossRef][Web of Science][Medline]
  5. Kellam SG, Anthony JC. Targeting antecedents to prevent tobacco smoking: findings from an epidemiologically based randomized field trial. Am J Public Health 1998;99:1490–5.
  6. Kellam SG, Werthamer-Larsson L, Dolan LJ, et al. Developmental epidemiologically based preventive trials: baseline modeling of early target behaviors and depressive symptoms. Am J Community Psychol 1991;19:563–84.[CrossRef][Web of Science][Medline]
  7. Chilcoat HD, Anthony JC. Impact of parent monitoring on initiation of drug use through late childhood. J Am Acad Child Adolesc Psychiatry 1996;35:91–100.[CrossRef][Web of Science][Medline]
  8. Werthamer-Larsson L, Kellam S, Wheeler L. Effect of first-grade classroom environment on shy behavior, aggressive behavior, and concentration problems. Am J Community Psychol 1991;19:585–602.[CrossRef][Web of Science][Medline]
  9. Heatherton TF, Kozlowski LT, Frecker RC, et al. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br J Addict 1991;86:1119–27.[CrossRef][Web of Science][Medline]
  10. Kozlowski LT, Porter CQ, Orleans CT, et al. Predicting smoking cessation with self-reported measures of nicotine dependence: FTQ, FTND, and HSI. Drug Alcohol Depend 1994;34:211–16.[CrossRef][Web of Science][Medline]
  11. Payne TJ, Smith PO, McCracken LM, et al. Assessing nicotine dependence: a comparison of the Fagerström Tolerance Questionnaire (FTQ) with the Fagerström Test for Nicotine Dependence (FTND) in a clinical sample. Addict Behav 1994;19:307–17.[CrossRef][Web of Science][Medline]
  12. Lazarsfeld PF. The logical and mathematical foundation of latent structure analysis. In: Stouffer SA, Guttman L, Suchman EA, et al, eds. Measurement and prediction. Princeton, NJ: Princeton University Press, 1950:362–412.
  13. McCutcheon AL. Latent class analysis. (Sage university paper series on quantitative applications in the social sciences, no. 07-064). Newberry Park, CA: Sage Publications, 1987.
  14. Clogg CC. Latent class models. In: Arminger G, Clogg CC, Sobel ME, eds. Handbook of statistical modeling for the social and behavioral sciences. New York, NY: Plenum Publishing Corporation, 1995:311–59.
  15. Hagenaars JA, McCutcheon AL, eds. Applied latent class analysis. Cambridge, United Kingdom: Cambridge University Press, 2002.
  16. Vermunt JK, Magidson J. Latent GOLD 2.0 user’s guide. Belmont, MA: Statistical Innovations, Inc, 2000.
  17. Bandeen-Roche K, Huang GH, Munoz B, et al. Determination of risk factor associations with questionnaire outcomes: a methods case study. Am J Epidemiol 1999;150:1165–78.[Abstract/Free Full Text]
  18. Magidson J, Vermunt JK. Latent class cluster analysis. In: Hagenaars JA, McCutcheon AL, eds. Applied latent class analysis. Cambridge, United Kingdom: Cambridge University Press, 2000.
  19. Stata Corporation. Stata statistical software, release 7.0. Reference manual. College Station, TX: Stata Corporation, 2000.
  20. Haddock CK, Lando H, Klesges RC, et al. A study of the psychometric and predictive properties of the Fagerström Test for Nicotine Dependence in a population of young smokers. Nicotine Tob Res 1999;1:59–66.[Abstract/Free Full Text]
  21. Lichtenstein E, Mermelstein RJ. Some methodological cautions in the use of the Tolerance Questionnaire. Addict Behav 1986;11:439–42.[CrossRef][Web of Science][Medline]
  22. Nichter M, Nichter M, Thompson PJ, et al. Using qualitative research to inform survey development on nicotine dependence among adolescents. Drug Alcohol Depend 2002;68(suppl 1):S41–56.
  23. Johnson JL, Bottorff JL, Moffat B, et al. Tobacco dependence: adolescents’ perspectives on the need to smoke. Soc Sci Med 2003;56:1481–92.[CrossRef][Web of Science][Medline]
  24. Breslau N, Fenn N, Peterson EL. Early smoking initiation and nicotine dependence in a cohort of young adults. Drug Alcohol Depend 1993;33:129–37.[CrossRef][Web of Science][Medline]
  25. Storr CL, Zhou H, Liang KY, et al. Empirically-derived latent classes of tobacco dependence syndromes observed in recent-onset tobacco smokers: epidemiological evidence from a national probability sample survey. Nicotine Tob Res (in press).
  26. O’Loughlin J, DiFranza J, Tarasuk J, et al. Assessment of nicotine dependence symptoms in adolescents: a comparison of five indicators. Tob Control 2002;11:354–60.[Abstract/Free Full Text]
  27. Etter JF, Le Houezec J, Perneger TV. A self-administered questionnaire to measure dependence on cigarettes: the cigarette dependence scale. Neuropsychopharmacology 2003;28:359–70.[CrossRef][Web of Science][Medline]
  28. Bardone AM, Moffitt TE, Caspi A, et al. Adult physical health outcomes of adolescent girls with conduct disorder, depression, and anxiety. J Am Acad Child Adolesc Psychiatry 1998;37:594–601.[CrossRef][Web of Science][Medline]
  29. Ohannessian CM, Stabenau JR, Hesselbrock VM. Childhood and adulthood temperament and problem behaviors and adulthood substance use. Addict Behav 1995;20:77–86.[CrossRef][Web of Science][Medline]
  30. Fergusson DM, Lynskey MT. Conduct problems in childhood and psychosocial outcomes in young adulthood: a prospective study. J Emot Behav Disord 1998;6:2–18.[CrossRef]
  31. Coffey C, Carlin JB, Lynskey M, et al. Adolescent precursors of cannabis dependence: findings from the Victorian Adolescent Health Cohort Study. Br J Psychiatry 2003;182:330–6.[Abstract/Free Full Text]
  32. Brook JS, Newcomb MD. Childhood aggression and unconventionality: impact on later academic achievement, drug use, and workforce involvement. J Genet Psychol 1995;156:393–410.[Web of Science][Medline]
  33. Juon HS, Ensminger ME, Sydnor KD. A longitudinal study of developmental trajectories to young adult cigarette smoking. Drug Alcohol Depend 2002;66:303–14.[CrossRef][Web of Science][Medline]
  34. Brook JS, Whiteman M, Finch S, et al. Aggression, intrapsychic distress, and drug use: antecedent and intervening processes. J Am Acad Child Adolesc Psychiatry 1995;34:1076–84.[CrossRef][Web of Science][Medline]
  35. Murphy JM, Horton NJ, Monson RR, et al. Cigarette smoking in relation to depression: historical trends from the Stirling County Study. Am J Psychiatry 2003;160:1663–9.[Abstract/Free Full Text]
  36. Storr CL, Ialongo NS, Kellam SG, et al. A randomized controlled trial of two primary school intervention strategies to prevent early onset tobacco smoking. Drug Alcohol Depend 2002;66:51–60.[CrossRef][Web of Science][Medline]

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Psychosom. Med.Home page
J. D. Flory and S. B. Manuck
Impulsiveness and Cigarette Smoking
Psychosom Med, May 1, 2009; 71(4): 431 - 437.
[Abstract] [Full Text] [PDF]

Home page
 Arch Gen PsychiatryHome page
H. C. Wilcox, C. L. Storr, and N. Breslau
Posttraumatic Stress Disorder and Suicide Attempts in a Community Sample of Urban American Young Adults
Arch Gen Psychiatry, March 1, 2009; 66(3): 305 - 311.
[Abstract] [Full Text] [PDF]

Home page
 Am J EpidemiolHome page
V. S. Harder, E. A. Stuart, and J. C. Anthony
Adolescent Cannabis Problems and Young Adult Depression: Male-Female Stratified Propensity Score Analyses
Am. J. Epidemiol., September 15, 2008; 168(6): 592 - 601.
[Abstract] [Full Text] [PDF]

Home page
 Arch Gen PsychiatryHome page
R. M. Crum, K. M. Green, C. L. Storr, Y.-F. Chan, N. Ialongo, E. A. Stuart, and J. C. Anthony
Depressed Mood in Childhood and Subsequent Alcohol Use Through Adolescence and Young Adulthood
Arch Gen Psychiatry, June 1, 2008; 65(6): 702 - 712.
[Abstract] [Full Text] [PDF]

Home page
 Arch Gen PsychiatryHome page
P. L. Reed, J. C. Anthony, and N. Breslau
Incidence of Drug Problems in Young Adults Exposed to Trauma and Posttraumatic Stress Disorder: Do Early Life Experiences and Predispositions Matter?
Arch Gen Psychiatry, December 1, 2007; 64(12): 1435 - 1442.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. PsychiatryHome page
C. L. Storr, N. S. Ialongo, J. C. Anthony, and N. Breslau
Childhood Antecedents of Exposure to Traumatic Events and Posttraumatic Stress Disorder
Am J Psychiatry, January 1, 2007; 164(1): 119 - 125.
[Abstract] [Full Text] [PDF]

Home page
 Am J EpidemiolHome page
P. L. Reed, C. L. Storr, and J. C. Anthony
Drug Dependence Enviromics: Job Strain in the Work Environment and Risk of Becoming Drug-Dependent
Am. J. Epidemiol., March 1, 2006; 163(5): 404 - 411.
[Abstract] [Full Text] [PDF]

Home page
 Arch Gen PsychiatryHome page
N. Breslau, B. A. Reboussin, J. C. Anthony, and C. L. Storr
The Structure of Posttraumatic Stress Disorder: Latent Class Analysis in 2 Community Samples
Arch Gen Psychiatry, December 1, 2005; 62(12): 1343 - 1351.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (23)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Storr, C. L.
Right arrow Articles by Anthony, J. C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Storr, C. L.
Right arrow Articles by Anthony, J. C.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?