Screening and early psychological intervention for depression in schools

Introduction

With an estimated prevalence of up to 2.5% in chil-dren and up to 8.3% in adolescents, depression is afrequent condition in underage groups, with highrecurrence rates, often-poor psychosocial and aca-demic outcomes, and an increased risk for othermental disorders [4]. Furthermore, clinically relevantdepressive symptoms that do not meet criteria formajor depressive disorders are found in up to 30% of

the adolescents [31]. By the age of 18, about one inevery four adolescents has had at least one depressiveepisode [6, 21], and most adults with recurrentdepression have their initial depressive episodes asteenagers [26].

It is not surprising, therefore, that in the last twodecades several studies have examined the possi-bilities of preventing and treating child and ado-lescent depression with psychological interventions[31]. Although most prevention studies have found

Pim CuijpersAnnemieke van StratenNiels SmitsFilip Smit

Screening and early psychologicalintervention for depression in schools

Systematic review and meta-analysis

Accepted: 27 February 2006Published online: 29 March 2006

j Abstract Depression in childrenand adolescents is considerablyundertreated, and the school maybe a good setting for identifyingand treating depression. We con-ducted a meta-analysis of studiesin which students were screenedfor depression, and those withdepressive symptoms were treatedwith a psychological intervention.Only randomised controlled trialswere included. Eight studies metthe inclusion criteria. Five studiesfocused on younger children(7–14 years) and three studieswere aimed at adolescents(12–19 years). In total 5803 stu-dents were screened, of whom7.2% were included in the inter-vention studies (95% CI: 7.1–7.3).The ‘numbers-needed-to-screen’was 31 (95% CI: 27–32), whichmeans that 31 students had to bescreened in order to generate onesuccessfully treated case of

depression. The effects of thepsychological treatments at post-test were compared to controlconditions in the 8 studies com-prising 12 contrast groups, with atotal of 413 students. The meaneffect size was 0.55 (95% CI: 0.35–0.76). There were not enoughstudies to examine whether spe-cific psychotherapies were supe-rior to other psychotherapies.Although the number of studies issmall and their quality is limited,screening and early intervention atschools may be an effective strat-egy to reduce the burden of dis-ease from depression in childrenand adolescents. More research onthe (negative) effects of theseinterventions is needed.

j Key words depression –children – adolescents –psychological treatment –meta-analysis

ORIGINAL CONTRIBUTIONEur Child Adolesc Psychiatry (2006)15:300–307 DOI 10.1007/s00787-006-0537-4

EC

AP

537

P. Cuijpers (&) Æ A. van StratenN. Smits Æ F. SmitDept. of Clinical PsychologyVrije UniversiteitVan der Boechorststraat 11081 BT Amsterdam, The NetherlandsTel.: +31-20/44-48757Fax: +31-20/44-48758E-Mail: [email protected]

disappointing results [24], treatment studies in thisarea generally have positive findings [12, 20, 25, 27],and psychotherapy is generally considered to be thefirst treatment for most depressed youth [1].

One specific group of treatment studies has usedthe school system to screen for clinically relevantdepression among students, and treat those sufferingfrom depression. The school is one of the few settingsthrough which all or nearly all children and adoles-cents can be reached, and because depression in theseage groups is considerably undertreated [37], thismay offer a strategy to improve treatment and healthoutcomes.

In this study, we will conduct a systematic reviewand meta-analysis of studies in which children andadolescents at school are screened for depression, andthose with clinically relevant depressive symptom-atology are treated. We will examine how many stu-dents have to be screened for one positive outcome(‘numbers needed to screen’) [3, 28], and how effec-tive the treatments are compared to control condi-tions.

Method

j Identification and selection of studies

Studies were traced by means of several methods.First, we used a large database of 766 papers on thepsychological treatment of depression in general. Thisdatabase was developed through a comprehensiveliterature search (from 1966 to June 2005) in which weexamined 4,661 abstracts in Pubmed (1,127 abstracts),Psycinfo (1,225), Embase (925) and the CochraneCentral Register of Controlled Trials (1,384). Weidentified these abstracts by combining terms indic-ative of psychological treatment (psychotherapy,psychological treatment, cognitive therapy, behaviourtherapy, interpersonal therapy, reminiscence, life re-view) and depression (both MeSH-terms and text-words). For this database, we also collected theprimary studies from 22 meta-analyses of psycho-logical treatment of depression [7, 8], including fivemeta-analyses of psychological treatment of depres-sion in children and adolescents [11, 12, 25, 27, 33].For the current study, we examined the abstracts ofthese 766 studies, and selected the ones which focusedon psychological treatments in children and adoles-cents.

In addition, we examined the references of majorreviews of the field [2, 13, 21, 34], we reviewed thereference lists of retrieved papers, and we enteredeach study into the ISI Web of Science database tofind papers that had subsequently cited them. Thesepapers were then evaluated for possible inclusion.

We included studies in which (-) a systematicscreening procedure was used (-) at school (-) toidentify students up to 18 years of age (-) with adepressive disorder or an elevated level of depressivesymptomatology, (-) in which the effects of a psy-chological treatment of identified subjects was (-)compared to a control condition (-) in a randomisedcontrolled trial. No language restrictions were ap-plied.

The methodological quality of the studies was as-sessed using four basic criteria [15]: allocation toconditions is done by an independent person; ade-quacy of random allocation concealment to respon-dents; blinding of assessors of outcomes; andcompleteness of follow-up data.

j Analyses

In this study, we focused on two outcomes: the meaneffect of the interventions and the numbers-needed-to-screen (NNS). The mean effect is used in most meta-analyses of treatments for mental disorders [7, 8] andgives an estimate of the overall effect of the interven-tions. The numbers needed to screen indicates thenumber of subjects that have to be screened in order togenerate one positive outcome, which in our case is onesuccessfully treated case of depression [3, 28]. The NNSgives an indication of the effort that is needed to screenin routine practice and the relative number of positiveoutcomes of screening in routine practice.

First, we examined whether the treatments of de-pressed students were effective. We calculated effectsizes (d) by subtracting (at post-test) the averagescore of the control group (Me) from the averagescore of the experimental group (Mc) and dividing theresult by the average of the standard deviations of theexperimental and control group (SDec). An effect sizeof 0.5 thus indicates that the mean of the experimentalgroup is half a standard deviation larger than themean of the control group. Effect sizes of .56–1.2 canbe assumed to be large, while effect sizes of .33–.55 aremoderate, and effect sizes of 0–.32 are small [23].

In the calculations of effect sizes we only usedthose instruments that explicitly measure depression(Table 1). When means and standard deviationswere not reported, we used other statistics (t-value,P-value) to calculate effect sizes. If more than onedepression measure was used, the mean of the effectsizes was calculated, so that each study (or contrastgroup) had only one effect size. In some studies, morethan one experimental condition was compared to acontrol condition. In these cases, the number ofsubjects in the control condition was evenly dividedover the experimental conditions so that each subjectwas used only once in the meta-analyses.

P. Cuijpers et al. 301Psychological intervention, systematic review and meta-analysis

Tab

le1

Sele

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al.,

1995

[5]

CES-

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161.

CBT

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ns;

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ns

Pre;

post

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171

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[19]

114

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ions

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of10

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Pre;

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ence

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302 European Child & Adolescent Psychiatry (2006) Vol. 15, No. 5� Steinkopff Verlag 2006

To calculate pooled mean effect sizes, we used thecomputer program Comprehensive Meta-analysis(version 2.2.021), developed for support in meta-anal-ysis. We decided to calculate mean effect sizes both withthe random and the fixed effects model in all analyses,but because we found little indications of heterogeneitywe only report the results of the fixed effects model.

As indicator of homogeneity, we calculated theQ-statistic. We also calculated the I2-statistic which isanother indicator of heterogeneity [16], in percent-ages. A value of 0% indicates no observed heteroge-neity, and larger values show increasing heterogeneity,with 25% as low, 50% as moderate, and 75% as highheterogeneity.

The second outcome we focused on was the‘numbers-needed-to-screen’ (NNS) [3, 28]. We tookseveral steps in order to calculate the NNS.

First, we examined in each study the total numberof students who were screened, and the proportion ofthis total number of subjects who were randomised tothe experimental or control condition.

It appeared not to be possible to give pooled esti-mates of the number of students for whom parentalinformed consent is received, because parental consentwas asked in some studies before the screening, while inothers informed consent was asked after the screening,but before the intervention. The studies also did nothave comparable procedures on more elaborate inter-views for those who score positively on the screeninginstrument. Therefore, we could only examine the finalpooled proportion of students who met the inclusioncriteria and were randomised to conditions.

Next, we selected the studies which reporteddichotomous outcomes. These dichotomous out-comes could indicate the proportion of subjects whoscored below a certain score on a questionnaire or itcould indicate the proportion of subjects who recov-ered from a depressive episode.

In the next step, we calculated for each comparisongroup how many positive outcomes would have beenrealised in the randomised group (compared to thecontrol group), if they had all received the interven-tion. Thus, we had an indication of how many positiveoutcomes are generated by the screening plus inter-vention, compared to no intervention. This numberallowed us to calculate the NNS for each study, andthe pooled NNS.

Results

j Description of studies

We retrieved a total of 56 papers on treatment studiesof child and adolescent depression. Most of these(N=44; 78.6%) were excluded because they did not

systematically screen subjects for depression at school.One study (two papers) [10, 17] was excluded becausethe subjects were not randomly assigned to conditions,and two studies were excluded because the subjectswere not selected on the basis of predefined levels ofdepressive symptomatology [30, 36]. The remainingeight studies met the inclusion criteria and were in-cluded in the current study. Selected characteristics ofthe included studies are described in Table 1.

The included age groups in the eight studies rangedfrom 7 to 19, with 5 studies focusing on youngerchildren (7–14 years) and three studies aimed atadolescents (12–19 years). The CDI was used in fivestudies as a screening instrument, while the RADS wasused in three studies (one used both the CDI and theRADS). In all studies, an intervention based on cog-nitive behaviour therapy was examined, while twostudies also examined relaxation training. The numberof treatment sessions ranged from 8 to 16. Moststudies (6 of 8) were conducted in the United States. Inonly two studies, a follow-up measurement longerthan three months after the intervention was con-ducted. All but one study reported sufficient statistics(mean and standard deviations) to calculate effect si-zes directly. Four studies used a waiting list controlcondition, and the other four studies used a treatment-as-usual control condition. In all but one study, stu-dents were selected for the intervention on the basis ofa high score on a self-report measure of depression. Inthe other study [5], a diagnostic interview was con-ducted and only students were included in the trialwho did not meet criteria for a major depressive dis-order (but who had subthreshold depression).

The quality of the included studies was not opti-mal. It was not clear in any of the studies whetherallocation to conditions was done by an independentperson. Random allocation concealment to respon-dents was not possible (when a waiting list controlgroup was used) [8, 17, 28, 31]; or it was not reportedwhether this was done adequately [4, 18, 21, 33].Blinding of the assessors of outcomes was done inonly three studies [8, 31, 33]. Drop-out, however, wasno higher than 21% in any of the studies, and waseven zero in two studies [17, 33].

j Effects of psychological interventions at post-test

We could compare the effects of the psychologicaltreatments at post-test to control conditions in the 8studies with 12 contrast groups (Table 2), totalling413 students. The mean effect size was 0.55 (95% CI:0.35–0.76). We have plotted the effect sizes and 95%confidence intervals of the individual contrast groupsin Figure 1. The heterogeneity in this meta-analysiswas very low (Q=12.32 n.s.; I2=10.7%).


Since the weight of one study (the one focussingon subthreshold depression [4]) was considerable(37%), we conducted a new meta-analysis in whichthis study was excluded. This resulted in a some-what larger effect size (d=0.72; 95% CI: 0.45–0.99),while heterogeneity was still very low (Q=8.82 n.s.;I2=0).

We could calculate the longer term effects of theinterventions compared to (care-as-usual) controlconditions in only two studies [5, 34]. In the firststudy [5] an effect size d of 0.12 was found afterone year, and in the other study [34], an effect sized of 0.64 was found. Since the number of effectsizes was too small and the follow-up periods dif-

Table 2 Proportion of students randomized to conditions, proportion of gained positive outcomes, and numbers of needed to screen

Positive outcomesa

N Nrand /1000 Exp Contr Npos NNS

Clarke et al., 1995 1652 91 –De Cuyper, 2004 630 32 –Kahn et al., 1990 1293 53 n CBT 15/17 1/6 37 27

n Relaxation training 11/17 1/6 27 37n Self-modelling treatment 12/17 1/6 29 34

Lamb et al., 1998 222 185 –Liddle & Spence, 1990 380 82 –Reynolds & Coats, 1987 754 40 n CBT 5/6 0/5 28 36

n Relaxation training 6/8 0/5 26 38Stark et al., 1987 372 78 n Self control therapy 7/9 1/5 45 22

n Behavioural problem solving 6/10 1/5 31 32Weisz et al., 1997 500 96 n CBT 8/17 5/32 33 30OVERALL 5803 72 32 3195% CI 70.8–73.2 21–43 27–32

Study name Statistics for each study Std diff in means and 95% CI

Std diff Lower Upper Weight in means limit limit Z-Value p-Value (Fixed)

Clarke, 1995 0,320 -0,002 0,642 1,947 0,052 37,02De Cuyper, 2004 0,388 -0,501 1,277 0,855 0,392 4,86Kahn, 1990 - CBT 1,130 0,144 2,116 2,245 0,025 3,95Kahn, 1990 - REL 1,170 0,180 2,160 2,316 0,021 3,92Kahn, 1990 - SMT 1,230 0,234 2,226 2,420 0,016 3,87Lamb, 1998 0,430 -0,163 1,023 1,420 0,156 10,91Liddle, 1990 0,680 -0,201 1,561 1,513 0,130 4,95Reynolds, 1987 - CBT 1,640 0,389 2,891 2,570 0,010 2,46Reynolds, 1987 - REL 1,760 0,540 2,980 2,827 0,005 2,58Stark, 1987 - SCT 0,740 -0,387 1,867 1,287 0,198 3,02Stark, 1987 - BPS 0,830 -0,284 1,944 1,460 0,144 3,10Weisz, 1997 0,350 -0,254 0,954 1,135 0,256 10,52

0,578 0,372 0,783 5,515 0,000

-2,00 -1,00 0,00 1,00 2,00

Favours control Favours treatment

Meta Analysis

Fig. 1 Effects of school-based treatments of depression

a number of positive outcomes/total groupAbbreviations: Nrand/1000: number of subjects randomised to conditions per1000 screened subjects; Exp: experimental group; Contr: control group; Npos:number of gained positive outcomes per 1000 screened (number of subjects

with a positive outcome who would not have a positive outcome without theintervention, per 1,000 subjects screened); NNS: number needed to screen inorder to have one additional positive outcome


fered, we did not try to integrate these results in ameta-analysis.

j Numbers needed to screen

First, we calculated for each study the proportion ofscreened subjects who met inclusion criteria for theintervention trial, who gave informed consent(including parental informed consent) and who wereactually randomised. In Table 2, the number ofrandomised subjects is reported per 1,000-screenedstudents. As can be seen, this number was very inhigh in one study (185 students per 1,000), but rangedin the other studies from 32 to 96 per 1,000, with amean of 72 (95% CI: 70.8–73.2).

Next, we selected the studies in which dichotomousoutcomes at post-test were presented, indicating howmany subjects in the experimental condition wereimproved or recovered, compared to subjects in thecontrol conditions. This resulted in four studies [17,28, 31, 33], with eight comparisons between experi-mental and control conditions. The criteria that wereused in these four studies to decide whether or not asubject had improved or recovered are presented inTable 1. These criteria differed considerably perstudy, and we expected that the pooling of these datawould result in a strongly heterogeneous outcome.However, this was not the case. The pooled OR was3.97 (95% CI: 2.12–7.11), with very low heterogeneity(Q=1.12 n.s.; I2=0).

Then we calculated for each of the eight compari-son groups how many positive outcomes would havebeen realised in the randomised group (compared tothe control group), if they had all received the inter-vention. This number ranged from 26 to 45 per 1,000-screened students, with a pooled average of 32 (95%CI: 21–43).

Finally, we calculated the number of subjects thathave to be screened in order to generate one positiveoutcome. This NNS ranged from 22 to 38, with anaverage of 31 (95% CI: 27–32).

Discussion

In this study, we found promising results of inter-ventions in which school populations are screened fordepression and treatment is provided to the ones withhigh levels of depressive symptomatology. The meaneffect size of these interventions is 0.55, which can beconsidered moderate to high. And the number ofstudents that have to be screened in order to generateone positive outcome was found to be 31, whichseems quite low. This indicates that in one class ofabout 30 students, depression in one child can be

relieved with a screening plus early interventionprocedure. This can be considered to be very low,considering the burden of disease from depressionand the often poor outcome. In a school with 1,000students, the screening and intervention procedurewould result in 32 positive outcomes.

Although these results seem quite promising, thisstudy has several important limitations. First, thenumber of studies eligible for inclusion was small andtheir quality was not optimal, which was furtherlimited by the fact that in several of these studiesstudents from only one school were screened. How-ever, the meta-analysis of the effects of these inter-vention programs resulted in a relatively stable meaneffect size with few indications of heterogeneity. Thenumber of studies for which we could calculate thenumbers-needed-to-screen was even smaller.

A second major limitation was the absence in moststudies of follow-up measurements, because of whichwe could not get clear evidence about the longer-termeffects of these programs.

Third, in only one of the eight included studies wasa diagnostic interview used to establish the presenceor absence of a major depressive disorder in partici-pating students. In the other studies, inclusion wasbased on a high score on a selfrating depression scale.Therefore, we cannot be sure that participating stu-dents in the interventions actually suffered fromclinically relevant depression. However, because thestudents were sufficiently motivated to participate inthe interventions and they and their parents gaveinformed consent, we can assume that the studentssomehow benefited from the intervention and that themajority did have clinically relevant depressivesymptoms, although they probably did not meet alldiagnostic criteria for a major depressive disorder.

Due to these limitations, the results of these anal-yses should be considered with caution. Despite theselimitations, however, our study gave clear indicationsthat screening and early intervention in schools maybe an effective strategy to reduce the burden of dis-ease from depression in children and adolescents.And there is no doubt that further research in thisarea is warranted.

However, even when further research does indeedindicate that screening and early intervention iseffective, several questions have to be answered beforesuch interventions can be used in routine practice.One important question that has to be answered iswhether screening and early intervention may havenegative effects [14]. For example, it may be wellpossible that a screening instrument might indicatethat a student is depressed, while this is not the case.It is not currently known what consequences this mayhave for this student and his or her parents. Likewise,a positive score on a screening instrument may lead to


stigmatisation of the student. And it is not clear howmany students participate in the intervention withouthaving any benefit from it. Before routine applicationof this type of intervention is considered, we also needto know more about the longer-term effects, the ef-fects in routine practice, and the cost-effectiveness.

Although cognitive behavioural intervention wereexamined in all studies, there were not enough studies

to examine whether specific psychotherapies weresuperior to other psychotherapies. More research isneeded to examine this issue.

Depression is an important health problem, espe-cially in children and adolescents, and the results ofour study are very promising. This should create animpetus for more research on the possibilities of earlyscreening and intervention.

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Screening and early psychological intervention for depression in schools