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Journal of Adolescent Health 49 (2011) 621–626

www.jahonline.org

Original article

Adolescents with Low Intelligence Are at Risk of Functional SomaticSymptoms: The TRAILS StudyEva M. Kingma, Karin A.M. Janssens, M.Sc., Manon Venema, Johan Ormel, Ph.D.,Peter de Jonge, Ph.D., and Judith G.M. Rosmalen, Ph.D.*Interdisciplinary Center for Psychiatric Epidemiology, University Medical Center Groningen, University of Groningen, The Netherlands

Article history: Received October 15, 2010; Accepted April 26, 2011

A B S T R A C T

Purpose: Low intelligence is a risk factor for functional somatic symptoms (FSSs) in adults, but it is unknownwhether a similar association exists in adolescents. We hypothesized that low intelligence may lead to FSS,and that this association is mediated by low school performance. In addition, we hypothesized that thismediation is particularly present in adolescents who perceive high parental expectations.Methods: This study was performed in a general population cohort from the TRacking Adolescents’ Individ-ual Lives Survey, using data from the first wave (n � 2,230, mean age � 11.09 years, SD � .56, 50.8% girls),econdwave (n � 2,149, mean age � 13.65 years, SD � .53, 51.0% girls), and third wave (n � 1,816, mean age16.25 years, SD � .72, 53.3% girls). Intelligence was measured using the Wechsler Intelligence Scale for

Children—Revised, which resulted in an intelligence quotient (IQ) for each participant. FSSs were measuredby the Somatic Complaints Scale of the Youth Self-Report. School performance was assessed by teacherreports and perceived parental expectations by adolescent reports. Structural equationmodelingwas used totest our hypotheses.Results:We found a significant negative association between IQ and FSS in the whole group (� � �.24). Thisssociationwas significant in the groupperceivinghighparental expectations (� � �.37), but not in the grouperceiving low parental expectations. The association between IQ and FSS was not mediated by schoolerformance.onclusions: Low intelligence is associated with a higher predisposition for FSS in adolescents, especially inhose adolescents perceiving high parental expectations.

� 2011 Society for Adolescent Health and Medicine. All rights reserved.

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Functional somatic symptoms (FSS), defined as somaticymptoms not conclusively explained by known organic pathol-gy, are frequently seen in health care [1,2]. The etiology of FSS istill elusive, although it is becoming increasingly clear that it isultifactorial, involving complex interactions between biologi-al, psychological, and social factors [3–5]. Intelligence might bene factor contributing to the etiology of FSS. We previouslyound a negative association between intelligence and FSS in aeneral adult population cohort, which was partly mediated byn unfavorable work situation [6].

* Address correspondence to: Judith G.M. Rosmalen, Ph.D., InterdisciplinaryCenter for Psychiatric Epidemiology, University Medical Center Groningen, Uni-

pversity of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands.

E-mail address: J.G.M.Rosmalen@med.umcg.nl (J.G.M. Rosmalen).

1054-139X/$ - see front matter � 2011 Society for Adolescent Health and Medicine. Adoi:10.1016/j.jadohealth.2011.04.022

It is not clear whether the association between intelligencend FSS is generalizable to the adolescent population. Moreover,t is unknownwhich factors would mediate this association, andhether these factors differ from those observed in adults. Mostdults spend a great amount of time at work, whereas adoles-ents spend much time at school. Therefore, instead of an unfa-orable work situation in adults, an unfavorable school situationight mediate the association between intelligence and FSS indolescents. Indeed, intelligence is themost important predictorf school performance [7], whereas low school performance inurn has been associated with FSS [8]. Thus, low school perfor-ance may mediate the association between intelligence andSS in adolescents.However, it is plausible that low intelligence and low school

erformance do not result in FSS in all adolescents. The burden of

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E.M. Kingma et al. / Journal of Adolescent Health 49 (2011) 621–626622

low school performancemight depend on the norms of the socialenvironment of the adolescent. A study on chronic fatigue syn-drome (CFS), a syndrome defined by the existence of a clusterf FSS, suggested that parents might play an important role.arental expectations of the intelligence of adolescents withFS were significantly higher than parental expectations ofhe intelligence of adolescents without CFS [9]. The authorssuggested that these high parental expectations might con-tribute to the development and maintenance of CFS [9]. Itmight be possible that when parental expectations cannot bemet by the adolescent, it may lead to distress and conse-quently FSS in the adolescent.

Therefore, we hypothesized that adolescents with low intel-ligence and consequently low school performance are especiallyat risk for FSS when they perceive high parental expectations oftheir achievements. We studied our hypotheses in a large popu-lation cohort of adolescents.

Methods

Sample and procedure

This study is part of the TRacking Adolescents’ IndividualLives Survey (TRAILS). TRAILS is a longitudinal cohort study ofDutch adolescents. The studywas approved by the Dutch CentralCommittee on Research Involving Human Subjects. Data fromthe first, second, and third assessmentwaves are involved in thisstudy. These assessment waves were conducted betweenMarch2001 and July 2002, between September 2003 and December2004, and between September 2005 and August 2008, respec-tively. For the sample selection, five municipalities in the Northof the Netherlands were asked to give information from thecommunity register of all citizenswhowere born between Octo-ber 1, 1989 and September 30, 1990 (first twomunicipalities), orbetween October 1, 1990 and September 30, 1991 (last threemunicipalities), yielding 3,483 names. Next, all 135 primaryschools (including schools for special education) within thesemunicipalities were asked to participate in TRAILS. School par-ticipationwas a requirement for participants and their parents tobe approached by the TRAILS staff. In all, 123 schools (90.4% ofthe schools accommodating 90.3% of the children) agreed toparticipate in the study. Of the 3,145 remaining eligible children,210were excluded because theywere either unable or incapableto participate owing to severe mental retardation or a seriousphysical illness or handicap, or when no Dutch-speaking parentor parent surrogatewas available (Turkish andMoroccanparentswho were unable to speak Dutch were interviewed in their ownlanguage) [10]. After fully explaining the procedures, writteninformed consent from the parents was obtained. At the secondand third assessment waves, informed consent was obtainedfrom the adolescents themselves. Of all adolescents who wereapproached (n� 3,145), 76% (n� 2,230,mean age� 11.09 years,D � .56, 50.8% girls) were enrolled in the study. Detailed infor-ation about sample selection and analysis of nonresponse biasas been reported elsewhere [10]. Primary schools that partici-ated in TRAILS were comparable with other primary schools inhe Netherlands with regard to the percentage of childrenwith aow socioeconomic background (16.1% and 15.3%, respectively)11]. Ten percent (n � 230) of the sample had at least one parentorn in a non-western country, among which were Suriname20.0%), Dutch Antilles (16.0%), Indonesia (16.0%), Morocco

6.5%), Turkey (5.5%), and other countries (36.0%). Of the 2,230 S

aseline participants, 96.4% (n � 2,149, mean age � 13.65 years,D� .53, 51% girls) participated in the first follow-up assessment

(T2),whichwas held 2 to 3 years after assessmentwave 1 (T1). Ofthe 2,149 participants at T2, 81.4% (n � 1,816, mean age � 16.25years, SD � .72, 53.3% girls) participated in the third assessmentave (T3), which was held 2 to 3 years after T2.

easurements

ntelligence. At baseline, the intelligence of the participantswaseasured by psychologists using the shortened version of theechsler Intelligence Scale for Children–Revised (WISC-R) [12],hich resulted in an intelligence quotient (IQ) for each partici-ant. Because the TRAILS cohort consists of a large group ofdolescents, measuring intelligence of all adolescents with theechsler Intelligence Scale for Children, third edition (WISC-III)

13] was found to be too labor-intensive. Therefore, we used ahortened version of the Wechsler Intelligence Scale for Chil-ren, the WISC-R [12]. The WISC-R is suitable for participants ofge between 6 years and 16 years 11 months [14] and is age-tandardized to compare different age groups with each other15]. The shortened version of the WISC-R consists of a vocabu-ary subtest and a block design subtest. TheWISC-Rwas assessedn a quiet environment and participants were given 30 minuteso complete both subtests.

unctional somatic symptoms. FSS were measured by the So-atic Complaints scale of the Youth Self-Report [16] at all threessessment waves (T1, T2, and T3). This scale contains ninetems, which refer to somatic complaints without a knownmed-cal cause (aches/pains, headaches, nausea, eye problems, skinroblems, stomachache, and vomiting) or without obvious rea-on (overtiredness and dizziness). For each item, participantsad to respond on a 3-point scale (0 � did not experience theomplaint in the preceding 6 months, 1 � experienced the com-laint sometimes or a little bit in the preceding 6 months, 2 �xperienced the complaint often or a lot in the preceding 6onths) [17–19]. Factor analysis indicated that two items (eyeroblems and skin problems) had low factor loadings at bothssessment waves for both girls and boys. This suggests thathese two items did not represent the underlying construct veryell in our sample and were therefore excluded. For the analy-es, we composed sum scores for FSS.

chool performance. Baseline school performance was rated byhe participants’ teacher for two school subjects: Dutch languagend mathematics. We derived a mean score of school perfor-ance by adding the scores obtained in both the subjects and

hen dividing the total by two. School performance at both fol-ow-up waves was rated by the participants’ teacher for fivegroups of) school subjects: Dutch language, foreign languagesFrench, English, and German), geography and history, mathe-atics, and other exact sciences (biology, physics, and chemis-

ry). For each (groupof) school subject(s), performancewas ratedn a 5-point scale (1 � insufficient performance, to 5 � excellenterformance). We derived a mean score of school performancey adding the scores obtained in all five (groups of) subjects andhen dividing the total by five.

erceived parental expectations. At baseline, perceived parentalxpectations were assessed with two items from the EMBU-C (a

wedish acronym for my memories of upbringing) for Children

E.M. Kingma et al. / Journal of Adolescent Health 49 (2011) 621–626 623

[20]. The EMBU-C has been developed to assess children’s per-ception of parental rearing practices [20]. The two items were“Do you think your father has high expectations as far as yourschool results, sport achievements and so on are concerned?”and “Do you think your mother has high expectations as far asyour school results, sport achievements and so on are con-cerned?” Possible answers could be no, never � 1; yes, some-times � 2; yes, often � 3; yes, most of the time � 4. These fourpossible answers were merged to form the following two largercategories: low perceived parental expectations � 1–2 and highperceived parental expectations � 3–4. In particular, we wereinterested in high parental expectations; therefore, we used dataof the parent with the highest expectations, as perceived by theadolescent.

Description of themodel. Tomodel the relationships between IQ,school performance, and FSS, we first composed a trait and state(T&S) model of FSS (Figure 1), based on models of Duncan-Joneset al [21]. At each time point, FSSswere determined by two latentvariables: trait FSS (Tr1, Tr2, and Tr3) and state FSS (St1, St2, andSt3). Trait FSS is stable over time and reflects unchanged riskfactors. State FSS represents the variance not accounted for bytrait FSS and therefore reflects changes in symptom scores overtime (partly caused by error variance). A necessary assumptionto identify the model is that the T&S components are equal ateach time point (Tr1 � Tr2 � Tr3; St1 � St2 � St3). The T&Smodel further includes autoregressive effects of the states (Au1and Au2), meaning that the immediate preceding state value hasa direct effect on the next state value. The T&S model is consid-ered to represent the reality better than a completely autoregres-sivemodel, in which all stability in FSS scores is explained by thevalue of the preceding FSS score.

After having composed the T&S model for FSS, we connectedthismodel to themeasured variables school performance and IQ,as is depicted in Figures 2, 3, and 4.Wemodeled one correlation:between school performance at T1 and state FSS at T1. Further-more, we modeled 14 regression effects: a direct effect of IQ ontrait FSS; three direct effects of IQ on school performance (T1, T2,

Figure 1. The trait and statemodel. Note that trait functional somatic symptoms(FSS) at assessment wave 1 (Tr1) � trait FSS at assessment wave 2 (Tr2) � traitFSS at assessmentwave 3 (Tr3) and state FSS at T1 (St1)� state FSS at assessmentwave 2 (St2) � state FSS at assessment wave 3 (St3). The autoregressive effect atassessment wave 1 (Au1) and the autoregressive effect at assessment wave 2(Au2) may differ across time points. T1 � assessment wave 1, T2 � assessmentwave 2, T3 � assessment wave 3.

and T3); two direct effects from school performance at T1 on

school performance at T2, and from school performance at T2on school performance at T3; two contemporaneous effects ofschool performance on state FSS (at T2 and T3); two contempo-raneous effects of state FSS on school performance (at T2 and T3);two lagged effects of school performance on state FSS; and twolagged effects of state FSS on school performance. To identify themodel, it was necessary to remove the contemporaneous pathfrom FSS on school performance at T2.

Statistical analyses. Descriptive statistics were calculated bySPSS version16.0 forWindows (SPSS Inc, Chicago, IL). In addition,we tested our hypotheses by structural equation modeling per-formed by Mplus version 6.0 for Windows (MuthÊn & MuthÊn,Los Angeles, CA). Model fits were considered good when thecomparative fit index (CFI) and the Tucker–Lewis index (TLI)were greater than .95, and the rootmean square error of approx-imation (RMSEA) was smaller than .05. Ideally, the �2 should benonsignificant (p � .05), but larger samples increase the likeli-hood of obtaining significant p values [22]. After testing themodel in the total sample, we tested the model in two groupsbased on perceived parental expectations (high vs. low). In addi-tion, for the total group and for the two subgroups, we added allrequested indirect effects between IQ and state FSS. Further-more, we performed multiple imputation analyses by Mplus,including 10 imputed data sets, as a sensitivity analysis to test for

Figure 2. Relation between IQ, school performance, and FSS for the total group ofadolescents. The standardized estimates (� and standard error) are depicted. Thedotted lines represent the paths that are not significant. T1� assessmentwave 1,T2 � assessment wave 2, T3 � assessment wave 3.

the influence of missing values.

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E.M. Kingma et al. / Journal of Adolescent Health 49 (2011) 621–626624

Results

Sample characteristics

Table 1 presents the scale scores on IQ, FSS, and school per-formance of the adolescents at the three assessment waves. Allthese variableswerenormally distributed. Table 1 shows that theprevalence of FSS declined during the waves. When categorizingperceived parental expectations as high or low, 1,139 adoles-cents (51.9%; 56.5% boys) perceived high parental expectationsand 1,056 (48.1%; 41.1% boys) perceived low parental expecta-tions.

Model for the total sample

The model is depicted in Figure 2; only significant path esti-mates are given. The model fit was good (model fit: �2 (df � 3) �3.72, p � .29; CFI � 1.00; TLI � .99; RMSEA � .01). The estimatedrait variance in FSS ranged from27% (.522) to 29% (.542). Accord-ng to the model, there was a significant negative associationetween IQ and trait FSS. Furthermore, IQ was significantly pos-tively associatedwith school performance at T1 and T2,whereashe associationbetween IQ and school performance at T3wasnotignificant. School performance at T1 predicted school perfor-ance at T2, and school performance at T2 predicted school

Figure 3. Relation between IQ, school performance, and FSS for adolescentserceiving high parental expectations. The standardized estimates (� and stan-ard error) are depicted. The dotted lines represent the paths that are notignificant. T1 � assessment wave 1, T2 � assessment wave 2, T3 � assessmentwave 3.

erformance at T3. With regard to the paths between school m

performance and state FSS, no path was significant. Thus, therewere no significant indirect paths between IQ and state FSSthrough school performance. State FSS at T1 predicted state FSSat T2, and state FSS at T2 predicted state FSS at T3. Estimatesremained essentially the same after multiple imputation analy-ses.

Model for adolescents perceiving high parental expectations

The model is depicted in Figure 3; only significant path esti-mates are given. The model fit was good (model fit: �2 (df � 3) �5.63, p � .13; CFI � .99; TLI � .98; RMSEA � .03). The estimatedtrait variance in FSS ranged from25% (.502) to 28% (.532). Accord-ing to the model, there was a significant negative associationbetween IQ and trait FSS. Furthermore, IQ was significantly pos-itively associated with school performance at T1 and T2, but notwith school performance at T3. School performance at T1 pre-dicted school performance at T2, but school performance at T2did not predict school performance at T3. With regard to thepaths between school performance and state FSS, no path wassignificant. Thus, there were no significant indirect paths be-tween IQ and state FSS through school performance. State FSS atT1 predicted state FSS at T2, and state FSS at T2 predicted stateFSS at T3. After fixing all paths between school performance andstate FSS at zero, themodel fitwas good (model fit: �2 (df� 11)�10.79, p � .46; CFI � 1.00; TLI � 1.00; RMSEA � .02), and the

Figure 4. Relation between IQ, school performance, and FSS for adolescentsperceiving low parental expectations. The standardized estimates (� and stan-ard error) are depicted. The dotted lines represent the paths that are notignificant. T1 � assessment wave 1, T2 � assessment wave 2, T3 � assessmentwave 3.

odel did not alter significantly (model fit: ��2 (df � 8) � 5.16,

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E.M. Kingma et al. / Journal of Adolescent Health 49 (2011) 621–626 625

p � .74). Estimates remained essentially the same after multipleimputation analyses.

Model for adolescents perceiving low parental expectations

The model is depicted in Figure 4; only significant paths aregiven. The model fit was good (model fit: �2 (df � 3) � 2.86, p �.41; CFI � 1.00; TLI � 1.00; RMSEA � .01). The estimated traitvariance in FSS ranged from31% (.562) to 33% (.572). According tohe model, there was no significant association between IQ andrait FSS. Furthermore, IQ was significantly positively associatedith school performance at T1 and T2, whereas the associationetween IQ and school performance at T3 was not significant.chool performance at T1 predicted school performance at T2,nd school performance at T2 predicted school performance at3. With regard to the paths between school performance andtate FSS, no path was significant. Thus, there were no signif-cant indirect paths between IQ and state FSS through schoolerformance. State FSS at T1 did not predict state FSS at T2, buttate FSS at T2 predicted state FSS at T3. After fixing all pathsetween school performance and state FSS at zero, the modelt was good (model fit: �2 (df � 11) � 11.59, p � .40; CFI � .99;LI � .99; RMSEA � .02), and the model did not alter signifi-

cantly (model fit: ��2 (df � 8) � 8.73, p � .37). Estimatesremained essentially the same after multiple imputation anal-yses.

Discussion

This studydemonstrates that low intelligence is associatedwithahigherpredisposition forFSS. Schoolperformancedidnotmediatethe association between intelligence and state FSS. The associationbetween intelligence and trait FSS was significant for adolescentsperceiving high but not for those perceiving low parental expecta-tions.

There are several strengths of this study. First, we used a largelongitudinal population cohort without applying strict inclusioncriteria. Therefore, results are likely to be generalizable to adoles-cents from the general population. Second, we used structuralequation models with trait and state FSS to test our hypotheses.Thesemodels arepreferredbecause they aremultidimensional andinclude FSS scores at specific time points, while also taking intoaccount the underlying risk factor for FSS. Third, instead of usingarbitrary cut-off points that might result in inevitable loss of infor-mation andpower [23],we strengthened our analyses by the use ofcontinuousmeasures for FSS.

Besides strengths, there are also limitations that should be con-sidered when interpreting our results. First, the WISC-R measures

Table 1Sample characteristics

Measure Valid N

IQ T1 2,221FSS T1 2,115FSS T2 2,015FSS T3 1,636School performance T1 1,914School performance T2 1,327School performance T3 886

FSS � functional somatic symptoms; T1 � assessment waintelligence quotient.

only two subtests for intelligence. Although intelligencewas there-

fore not measured fully breadthwise, theWISC-R includes a verbalsubtest (vocabulary test) and a performance subtest (block designtest), measuring verbal skills as well as spatial skills. Second, weonlymeasured intelligence at the first wave. However, intelligenceis a stable factor from the age of 10–12 years onward [24]. A thirdlimitation is the fact that FSS were measured by self-reports. It istherefore not certain whether FSS were truly not the result of con-ventionalmedical conditions.However,wepreviously performedafactor analysis, which indicated that we were measuring one un-derlying construct [11]. In addition, it was explicitly stated in thequestionnaire that the FSS had to occur without medical cause orwithout obvious reason. It may be argued that this complex state-ment was more difficult to understand for adolescents with lowintelligence. Such bias would decrease the reporting of FSS in ado-lescents with low intelligence, which would imply that our resultsmight be an underestimation of the negative association betweenintelligence and FSS.

To the best of our knowledge, we are the first to study thedirect association between intelligence and FSS in adolescents.We found a negative association between intelligence and FSS inadolescents, which was not mediated by school performance.One study, comparing children who complain often of FSS withchildren who never or sometimes complain of FSS, found lowschool performance in childrenwho complain often of FSS [8]. Incontrast to our general population study, the aforementionedstudy focused on a clinical sample with severe problems.

Although we found an association between low intelligence andrisk of FSS, as we hypothesized, results differed somewhat from ourexpectations. High intelligencewas significantly associatedwith highschool performance in primary school, but this association decreasedin the subsequentmeasurementwaves inwhich adolescentswere insecondary school. Several explanations for this decrease can be pro-posed. Adolescents in secondary school are in an educational levelmatching their intelligence, explaining the decreasing strength be-tween intelligence and school performance over time. Moreover,schoolperformance inadolescencepartlydependsonmotivation [25]that might influence school performance more strongly than intelli-gence. Another explanation might be found in the way we analyzedour data. Part of the effect between intelligence and school perfor-manceat the secondand thirdwavesmightbe included in theautore-gressive paths between school performances at differentwaves. Logi-cally, this results in less strong effects over time between intelligenceandschoolperformance.Whenweperformedsimple regressionanal-yses between intelligence and school performance at each wave, wefound the same decrease in strength between intelligence and schoolperformance over time, indicating that the autoregressive paths arenot responsible for the decreasing correlation between intelligence

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E.M. Kingma et al. / Journal of Adolescent Health 49 (2011) 621–626626

Our results show that low school performance is not a riskactor for FSS in adolescents perceiving high parental expecta-ions. However, we found a significant negative association be-ween intelligence and FSS in adolescents perceiving high paren-al expectations, whereas we found no association betweenntelligence and FSS in adolescents perceiving low parental ex-ectations. These results seem quite similar to the findings inrevious research, where parental expectations were higher indolescents with CFS as compared with healthy controls [9].owever, that particular study was a small cross-sectional case-ontrol study, whereas we studied a large longitudinal popula-ion cohort. Moreover, because we studied expectations relatedo both parents, we were able to identify the parent with theighest expectations, as perceived by the adolescent, whereashe previous study looked at the view of only one parent. Ouresults suggest that perceived parental expectations influencehe association between intelligence and FSS through factorsther than school performance. In a previous cross-sectionaltudy on perfectionismandwell-being in adolescents [26], itwasound that adolescents who perceive parental pressure reportore somatic complaints and had higher motivation to avoid

ailure [26]. Adolescents who perceive high parental expecta-ions or pressure are thought to experience emotional distress26], which could be reflected in FSS. Alternatively, adolescentsith low intelligence may tend to smoke more often and oftenave a high body mass index, which in turn are associated withore FSS [6]. Perhaps, adolescentswith low intelligencewho areistressed, owing to highly perceived parental expectations,end to smoke more and engage more often in unhealthy eatingehaviors, resulting in FSS.In conclusion, low intelligence is associated with a higher

redisposition for FSS in adolescents, especially in those adoles-ents perceiving high parental expectations. These results sug-est that the social context is important for a healthy develop-ent during adolescence. Clinicians dealing with adolescentsith FSS could consider possible interactions between intelli-ence and perceived parental expectations, so as to understandhe development of FSS in adolescents. In those cases in whicharental factors are suspected to contribute to the developmentf FSS, it might be important to involve parents in the treatment.

cknowledgments

This research is part of the TRacking Adolescents’ Individualives Survey (TRAILS). Participating centers of TRAILS include vari-usdepartments of theUniversityMedical Center andUniversity ofroningen, the Erasmus UniversityMedical Center, Rotterdam, theniversity of Utrecht, the Radboud Medical Center, Nijmegen, andhe Parnassia Bavo group, all in the Netherlands. TRAILS has beennancially supportedby various grants from theNetherlandsOrga-ization for Scientific Research NWO (Medical Research Councilrogram grant GB-MW 940-38-011; ZonMW Brainpower grant00-001-004; ZonMw Risk Behavior and Dependence grants 60-0600-98-018 and 60-60600-97-118; ZonMw Culture and Healthrant 261-98-710; Social Sciences Council medium-sized invest-ent grants GB-MaGW 480-01-006 and GB-MaGW 480-07-001;ocial Sciences Council project grants GB-MaGW 457-03-018, GB-aGW452-04-314, and GB-MaGW452-06-004; NWO large-sized

nvestment grant 175.010.2003.005); the Sophia Foundation foredical Research (projects 301 and 393), the Dutch Ministry of

ustice (WODC), the European Science Foundation (EuroSTRESS

roject FP-006), and the participating universities. The authors are

rateful to all adolescents, their parents and teachers who partici-ated in this research, and to everyonewhoworked on this projectnd made it possible. They also thank Professor Tineke Oldehinkelor critical reading.

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