European Child & Adolescent Psychiatry [Suppl 2]13: II/25–II/31 (2004) DOI 10.1007/s00787-004-2005-3 ORIGINAL CONTRIBUTION

ECA

P 2005

■ Abstract Background Child psy-chiatric disorders are commonamong children in foster and resi-dential care, but often go unde-tected and therefore untreated.Aims To assess the Strengths andDifficulties Questionnaire (SDQ) asa potential means for improving

R. Goodman (�) · T. FordDept. of Child & Adolescent PsychiatryInstitute of Psychiatry, King’s CollegeBox Number P085London SE5 8AF, UKE-Mail: r.goodman@iop.kcl.ac.uk

T. Corbin · H. MeltzerHealth and Care DivisionOffice for National StatisticsLondon, UK

the detection of child psychiatricdisorders in the community.Method SDQ predictions and inde-pendent psychiatric diagnoseswere compared in a communitysample of 1,028 looked-after 5–17year olds from a nationwide Eng-lish survey. Results Multi-informantSDQs (parents, teachers, older chil-dren) identified individuals with apsychiatric diagnosis with a speci-ficity of 80 % and a sensitivity of85 %. The SDQ prediction worksbest when SDQs have been com-pleted by both carers and teachers.When it is only possible to haveone adult informant, carers andteachers provide information ofroughly equal predictive value. By

contrast, self-reports by 11–17 yearolds provide little extra informa-tion when there is already an adultinformant. Conclusion Using multi-informant SDQs as a regularscreening measure for looked-afterchildren could potentially increasethe detection of child psychiatricdisorders, thereby improving ac-cess to effective treatments.

■ Key words screening – mentalhealth – foster care – residentialcare – Strengths and DifficultiesQuestionnaire (SDQ)

■ Declaration of Interest: Surveyfunded by the Department ofHealth

Robert GoodmanTasmin FordTania CorbinHoward Meltzer

Using the Strengths and DifficultiesQuestionnaire (SDQ) multi-informantalgorithm to screen looked-after childrenfor psychiatric disorders

Introduction

Children and teenagers who are in foster homes or resi-dential homes at the behest of their local authority –sometimes referred to as looked-after children – are ahigh-risk group for mental health problems [2, 3, 10, 13].A recent survey of a large and representative Englishsample of looked-after children showed that 45 % had atleast one psychiatric diagnosis [11]. Providing looked-after children with timely help for any mental healthproblem is widely viewed as a high priority [12].One op-tion is to screen looked-after children regularly for indi-cations of mental health problems, channelling screen-positive individuals into more detailed assessments byexperts. In a previous study [7], we showed that theStrengths and Difficulties Questionnaire (SDQ) – a briefand freely available questionnaire – had promise as a

screening tool for detecting mental health problems inchildren living with their own families. The presentstudy examines whether the SDQ could be a suitablescreening tool for looked-after children.

Methods

■ Sample

Between 2001 and 2002, the Office for National Statisticscarried out a survey of the mental health of 5–17 yearolds looked after by local authorities (described in detailin [11]). In England, local authorities make annual re-turns to the Department of Health (DH), givinganonymised details of 1 in 3 of all looked-after children.The current sample was drawn using this database to se-lect a sample of children who were looked after on 31

II/26 European Child & Adolescent Psychiatry, Vol. 13, Supplement 2 (2004)© Steinkopff Verlag 2004

March 2001 (identified solely by a serial number). A to-tal sample of 2,500 children was drawn (approximately 1in 18 of all looked-after children), with the numbers be-ing proportional to the number of children looked afterin each authority. All directors of Local Authority SocialServices Departments in England, excluding the Isles ofScilly – a total of 149 – were contacted, informing themof the survey and asking for their participation. Of the149 local authorities, 142 (95 %) initially agreed to par-ticipate. In each participating local authority, a contactperson nominated by the director was sent all the “childsummary forms” for that local authority giving the chil-dren’s serial numbers from the DH database.The contactperson then distributed the forms to the social workersresponsible for the children concerned and asked themto complete the forms, obtain whichever consents theyfelt were necessary (e. g. consent from the foster parent,residential care home, birth parent), and then returnthem to the Office for National Statistics.

A total of 2,315 child summary forms were sent out,of which 1,796 (78 %) were returned,coming from 134 ofthe 149 local authorities (90 %). Of the 1,796 returnedforms, 662 (37 %) were ineligible. The five main reasonsfor ineligibility were carer refusal (26 %), child goingthrough adoption procedures (17 %), the local authorityrefused access (14 %), carer felt it was an inappropriatetime (13 %), summary forms arrived back too late to beallocated to interviewers (12 %). Information from up tothree sources (carer, teacher, young person) was col-lected on 1,039 of the 1,134 eligible children (over 91 %)by trained interviewers working for the Office for Na-tional Statistics. While all of the 1,039 subjects had beenin care on 31 March 2001, some had returned to their bi-ological parents by the time the survey was carried out(between October 2001 and May 2002).At the time of as-sessment, 701 were in foster families, 186 were in resi-dential care, 113 were with their natural parents, and 39were living independently. Although the sample wassupposed to be aged between 5 and 17 at the time of as-sessment, ten individuals had already reached their 18th

birthday at the time of assessment and were excludedfrom the analyses reported in this paper because theywere too old to be assessed with the SDQ, yielding a to-tal analysis sample of 1,029 children and adolescents.

■ Questionnaire measures

The SDQ is a brief questionnaire that can be adminis-tered to the parents and teachers of 4–17 year olds andto 11–17 year olds themselves [4, 5, 8]. Besides coveringcommon areas of emotional and behavioural difficul-ties, it also enquires whether the informant thinks thatthe child has a problem in these areas, and if so asksabout resultant distress and social impairment. Furtherinformation on the SDQ and copies of the questionnaire

in over 40 languages can be obtained free athttp:\\www.sdqinfo.com.

Computerised algorithms for predicting psychiatricdisorder by bringing together information on symptomsand impact from SDQs completed by multiple infor-mants have been described in detail in a previous report[9], and can easily be inspected and applied by down-loading either SAS or SPSS syntax files available fromwww.sdqinfo.com/e8.html. The algorithm makes sepa-rate predictions for three groups of disorders, namelyconduct-oppositional disorders, hyperactivity-inatten-tion disorders, and anxiety-depressive disorders. Each ispredicted to be unlikely,possible or probable.Predictionsof these three groups of disorders are combined to gen-erate an overall prediction about the presence or absenceof any psychiatric disorder, which will be used as “SDQprediction”throughout this report, unless indicated.

SDQs were counted as valid if all four of the follow-ing scores needed for the predictive algorithm could becomputed: emotional, conduct, hyperactivity and im-pact. Valid SDQs had been completed by a carer/parenton 99.6 % of the sample (1,025/1,029), by a teacher on61.5 % of the sample (633/1029), and by the young per-sons themselves for 62.8 % of the 11–17 year olds(429/683). There was at least one valid SDQ on 99.9 % ofthe sample (1,028/1,029). For this ‘any data’ sample, themean age was 12.5 years (SD 3.5), 57.4 % were male, and17 % were in residential care. The full complement ofSDQs was available for 52.4 % of the sample (539/1,029),i. e. there were both carer/parent and teacher SDQs for 5to 10 year olds, and there were carer/parent, teacher andself-report SDQs for 11 to 17 year olds. For this ‘full data’sample, the mean age was 11.3 years (SD 3.4), 54.4 %were male, and 9.5 % were in residential care. Full datawas significantly more likely for younger subjects andthose not in residential care (p < 0.001, logistic regres-sion). Gender ratio did not differ significantly betweenthe younger and older children, with males making up56.3 % of the 5–10 year olds and 51.5 % of the 11–15 yearolds (continuity adjusted chi-square = 0.97, 1df, p = 0.3)

■ Psychiatric diagnosis

All children were assigned psychiatric diagnoses on thebasis of the Development and Well-Being Assessment(DAWBA; www.dawba.com, [6]), an integrated packageof questionnaires, interviews and rating techniques de-signed to generate psychiatric diagnoses on 5–17 yearolds. Non-clinical interviewers administer a structuredinterview to parents and older children, supplementingthe structured questions with open-ended questions toget respondents to describe the problems in their ownwords. Experienced clinical raters assign ICD-10 [14]and DSM-IV [1] diagnoses after reviewing the interviewrecords and teacher questionnaires. In the validation

R. Goodman et al. II/27Screening looked-after children with the SDQ

study of the DAWBA [6], there was excellent discrimina-tion between community and clinic samples in rates ofdiagnosed disorder.Within the community sample, sub-jects with and without diagnosed disorders differedmarkedly in external characteristics and prognosis. Inthe clinic sample, there was substantial agreement be-tween DAWBA and case note diagnoses.

In the study reported here, DAWBA diagnoses weregenerated blind to the SDQ scores.For the present paper,the diagnoses are nearly all based on the research diag-nostic criteria of ICD-10. Choosing ICD-10 rather thanDSM-IV makes little difference as far as emotional andconduct-oppositional disorders are concerned, wheregroup membership is very similar whichever classifica-tion is used. It is only for the hyperactivity disorders thatthere are marked discrepancies between the two classi-fications – hence screening efficiency is reported sepa-rately for ICD-10 hyperkinetic disorders and DSM-IVattention-deficit/hyperactivity disorders (ADHD).

Results

■ Overall screening efficiency

The proportions of looked-after children with SDQ pre-dictions of ‘unlikely’,‘possible’ and ‘probable’ psychiatricdisorder were roughly a quarter,a quarter and a half.Forthe 1028 individuals with at least one valid SDQ – the ‘anydata’ sample – the exact figures were 27.7 %, 26.7 % and45.6 %.For the 539 individuals with a full complement ofSDQs – the ‘full data’ sample – the exact figures were28.4 %,25.6 %,and 46 %.The proportion of ‘probable’rat-ings was not significantly different for boys and girls ineither the ‘any data’or the ‘full data’sample.The accuracyof these SDQ predictions is shown separately for the ‘anydata’and ‘full data’samples in Table 1.In both samples,thepredictive value is substantial: fewer than 1 in 10 of the‘unlikely’cases have a disorder,as compared with arounda quarter of the ‘possible’ cases and three-quarters of the‘probable’ cases. Given the similarity between the find-ings for the ‘any data’sample and the ‘full data’sample,allfurther analyses are restricted to the ‘full data’ sample.Despite reducing sample size and therefore statisticalpower, this restriction has two main advantages. Firstly,focusing on the ‘full data’sample increases comparability

with a previous study of children from private house-holds where the screening efficiency of the SDQ wasbased on children will full data [7]. Secondly, when thereis full data on all subjects, it is possible to see how far thepredictive value changes in exactly the same cases wheninformation from one or more raters is dropped.

SDQ predictions were dichotomised into ‘positive’and ‘negative’ in order to make it possible to describe thescreening efficiency of the SDQ in the conventionalmanner in terms of specificity, sensitivity, positive pre-dictive value and negative predictive value. ‘Probable’predictions were counted as positive, whereas ‘unlikely’and ‘possible’predictions were both counted as negative.It is worth noting, however, that the majority of ‘falsenegatives’ (i. e. children with a particular diagnosis whowere not rated ‘probable’ by the SDQ) were rated ‘possi-ble’ rather than ‘unlikely’. In other words, most of thefalse negatives were partial rather than complete.For ex-ample, 33 of the 539 looked-after children with full datahad an ICD-10 diagnosis of at least one psychiatric dis-order but were not rated as ‘probable’ by the SDQ algo-rithm; 26 (79 %) of these ‘false negatives’ were rated as‘possible’ rather than ‘unlikely’ (Table 1).With this reser-vation, the screening efficiency of multi-informantSDQs for the looked-after children is presented inTable 2,which also shows the comparable values for chil-dren from private households [7].

■ Sensitivity to different diagnoses

These findings on screening efficiency apply to all diag-noses combined. How did this vary by type of psychi-atric disorder? The following analyses focus just on sen-

Table 1 Overall agreement between SDQ prediction of ‘any disorder positive’ andpsychiatric diagnosis for looked-after children with any data and full data

ICD-10 psychiatric diagnosis present

SDQ prediction At least one valid SDQ Full complement of SDQs(N = 1028) (N = 539)

Disorder unlikely 8.8% (25/285) 4.6% (7/153)

Disorder possible 25.9% (71/274) 18.8% (26/138)

Disorder probable 77.8% (365/469) 74.2% (184/248)

Looked-after sample Private household samplewith full data (N = 539) with full data (N = 7984)

from Goodman et al., 2000 [7]

Sensitivity 84.8% (80.0–89.6%) 63.3% (59.7–66.9%)

Specificity 80.1% (75.8–84.5%) 94.6% (94.1–95.1%)

Positive Predictive Value 74.2% (68.7–79.6%) 52.7% (49.3–56.1%)

Negative Predictive Value 88.7% (85.0–92.3%) 96.4% (96.0–96.8%)

Table 2 Screening efficiency (with 95 % CI) for pre-sent looked-after sample and comparison samplefrom private household survey

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sitivity since this value is likely to be of particular im-portance in deciding whether the screening efficiency isadequate to warrant a formal trial of screening. Asshown in Table 3, the sensitivity varies depending on thediagnosis, identifying between 82.7 % (anxiety disor-ders) and 100 % (less common diagnoses such as autis-tic spectrum disorders and eating disorders which arenot included in the other specific categories) of the re-spective cases. In no instance did sensitivity differ sig-nificantly between males and females.

■ Predictive efficiency by age and informant

The results presented so far have been for all ages from5 to 17 and, except for the initial analyses, have involved

predictions based on full information on each child (i. e.parent and teacher SDQs for all children,plus self-reportSDQ for 11–17 year olds). Further analyses were carriedout splitting the sample into those who had and had notreached their 11th birthday. These further analyses ex-amined how the sensitivity changed when predictionswere based on incomplete data, e. g. looking at predic-tions when just carer/parent SDQs were entered into thepredictive algorithm. Table 4 presents data on 268 chil-dren aged under 11, showing the sensitivity of SDQ pre-dictions for various broad-band diagnoses. These pre-dictions are based on the combination of carer/parentand teacher SDQs (CT), or just carer/parent SDQs (C) orjust teacher SDQs (T).For all diagnoses,CT has a greatersensitivity than either C or T.

Table 5 presents comparable data for 271 older chil-dren and adolescents (aged 11 to 17). There are morecolumns in Table 5 than in Table 4 because these youngpeople can also complete the self-report SDQ. Conse-quently, the full multi-informant prediction is based oncarer/parent, teacher and self-report SDQs (CTS). Thereare three sets of predictions based on just two of thesethree informants (CT, CS, TS) and three sets of predic-tions based on only one informant (C, T, S). For all diag-noses, CTS has the greatest sensitivity. If it is only possi-ble to collect data from two raters, CT is generally betterthan CS or TS.The main cost of dropping the self-ratingsis missing a few emotional disorders. If it is only possi-ble to collect information from one adult informant (i. e.comparing CS with TS, or comparing C with T), then it

Table 3 Sensitivity of the SDQ prediction of ‘any disorder positive’ by diagnosticgroupings

Detecting: Sensitivity

Any psychiatric disorder 84.8% (184/217)

Any conduct-oppositional disorder 87.8% (166/189)

Any hyperkinetic disorder (ICD-10) 97.7% (42/43)

Any ADHD disorder (DSM-IV) 87.9% (51/58)

Any depressive disorder 84.6% (11/13)

Any anxiety disorder 82.7% (43/52)

Less common diagnoses 100.0% (12/12)

Detecting: Sensitivity

CT C T

Any psychiatric disorder (N = 107) 82.2% (88) 51.4% (55) 59.8% (64)

Any conduct-oppositional disorder (N = 93) 84.9% (79) 54.8% (51) 65.6% (61)

Any hyperkinetic disorder (ICD-10; N = 29) 96.6% (28) 72.4% (21) 72.4% (21)

Any ADHD disorder (DSM-IV; N = 36) 86.1% (31) 61.1% (22) 63.9% (23)

Any anxiety or depressive disorder (N = 25) 80.0% (20) 56.0% (14) 44.0% (11)

C prediction draws on carer/parent SDQ; T prediction draws on teacher SDQNo differences between C and T significant (McNemar)

Table 4 Sensitivity of the SDQ prediction of ‘any dis-order positive’ for children aged 5 to 10 (N = 268)

Table 5 Sensitivity of the SDQ prediction of ‘any disorder positive’ for children aged 11 to 17 (N = 271)

Detecting: Sensitivity

CTS CT CS TS C T S

Any psychiatric disorder (N = 110) 87.3% (96) 85.5% (94) 65.5% (72) 65.5% (72) 60.0% (66) 59.1% (65) 16.4% (18)*

Any conduct-oppositional disorder (N = 96) 90.6% (87) 89.6% (86) 65.6% (63) 68.8% (66) 60.4% (58) 64.6% (62) 15.6% (15)*

Any hyperkinetic disorder (ICD-10; N = 14) 100.0% (14) 100.0% (14) 78.6% (11) 85.7% (12) 78.6% (11) 85.7% (12) 7.1% (1)*

Any ADHD disorder (DSM-IV; N = 22) 90.9% (20) 90.9% (20) 68.2% (15) 72.7% (16) 68.2% (15) 72.7% (16) 9.1% (2)*

Any anxiety or depressive disorder (N = 28) 82.1% (23) 75.0% (21) 78.6% (22) 71.4% (20) 64.3% (18) 53.6% (15) 46.4% (13)

C prediction draws on carer/parent SDQ; T prediction draws on teacher SDQ; S prediction draws on self-report SDQ* significantly lower than C or T (McNemar, p < 0.002)

R. Goodman et al. II/29Screening looked-after children with the SDQ

makes little difference whether that adult is thecarer/parent or the teacher. S is the single least usefulscreening strategy, being less sensitive than C and T forall disorders, though this difference is not significant foremotional disorders.

■ SDQ predictions for type of disorder

The SDQ algorithm generates specific predictions for‘conduct disorders’, ‘hyperactivity disorders’ and ‘emo-tional disorders’ as well as an overall prediction for ‘anydisorder.’ Table 6 shows the proportion of children withparticular clinical diagnoses who received ‘probable’SDQ predictions for each of these specific categories.Foreach psychiatric disorder, more children obtained theSDQ ‘any disorder’ rating than the more specific ratings.Detecting children with emotional and hyperactivitydisorders was particularly dependent on the presence ofcomorbidity. For example, although the SDQ algorithmdetected over 80 % of children with anxiety or depres-sion as having ‘any disorder,’ the specific prediction wasmore often a conduct than an emotional disorder.

■ Characteristics of ‘false positives’

The ‘full data’ sample included 64 children who werepredicted by the SDQ algorithm to have a ‘probable’ dis-order, but who did not have an ICD-10 psychiatric diag-nosis (the 248 minus 184 cases in the bottom right cell ofTable 1).Who were these ‘false positives’? The SDQ algo-rithm is designed so that it will not predict a ‘probable’disorder unless at least one informant has reported thecombination of a high symptom score and resultant im-pact. The perceived level of these reported problems canbe gauged from an SDQ question that asks informants torate the child’s difficulties as absent, minor, definite orsevere. Here, all 64 of the ‘false positives’ were reportedas having some difficulties by at least one informant,while 52 (81 %) were reported as having definite or se-vere difficulties by at least one informant. Of the ‘falsepositives’, 45 (70 %) had a hyperactivity score in the ‘ab-

normal’ range according to at least one informant; thecorresponding numbers scoring in the abnormal rangefor the emotional symptoms and the conduct problemssubscale were 37 (58 %) and 43 (67 %). All childrenscored in the abnormal range on at least one SDQ prob-lem scale, while 50 (78 %) scored in the abnormal rangeon at least two of the four SDQ problem scales. The ‘falsepositives’ did not differ significantly from the rest of the‘full data’ sample in either age or gender.

Of the children who were screen negative (i. e., notrated ‘probable’ by the SDQ algorithm), 11 % did have apsychiatric diagnosis according to the clinical raters (re-flecting the 7 plus 26 cases in the top and middle rightcells of Table 1, 33/291). Thus, 79 % (26/33) of the falsenegatives were ‘partial’ false negatives, since their SDQprediction was ‘possible’ rather than ‘unlikely’.

Discussion

■ Predicting the presence of a psychiatric disorder

In this representative sample of looked-after children, aprediction of ‘probable’ disorder based on multi-infor-mant SDQs had a sensitivity of 85 % and a specificity of80 % – as judged against independent diagnoses madeby clinicians on the basis of detailed interview informa-tion. The false positives were all ‘partial’ false positives,in the sense that even though they did not have a clini-cal diagnosis, they did all have a high level of symptomswith associated impact according to at least one of therespondents. The majority of the false negatives were‘partial’ false negatives, in the sense that although theywere not predicted to be ‘probable’ cases, they were pre-dicted to be ‘possible’ cases.

The SDQ’s screening efficiency for looked-after chil-dren was not the same as that previously reported for arepresentative sample of children living with their ownfamilies [7]. Thus, the sensitivity and positive predictivevalue were higher for looked-after children, while thespecificity and negative predictive value were lower.Thissort of change is what would be expected when compar-ing a high-risk (looked-after) with a low-risk (private

Table 6 Detecting broad diagnostic groupings through different SDQ predictions

Clinical Diagnosis Proportion (N) rated as “probable” by SDQ for:

Conduct Disorder Hyperactivity Disorder Emotional Disorder Any Disorder

Any psychiatric disorder N = 217 75.6% (164) 36.9% (80) 14.7% (32) 84.8 % (184)

Any conduct-oppositional disorder N = 189 83.1 % (157) 39.2% (74) 12.2% (23) 87.8% (166)

Any hyperkinetic disorder (ICD-10) N = 43 90.7% (39) 79.1 % (34) 11.6% (5) 97.7% (42)

Any ADHD disorder (DSM-IV) N = 58 82.8% (48) 63.8 % (37) 10.3% (6) 87.9% (51)

Any anxiety or depressive disorder N = 53 54.7% (29) 35.8% (19) 43.4 % (23) 81.1% (43)

Less common diagnoses N = 12 83.3% (10) 66.7% (8) 25.0% (3) 100.0% (12)

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household) sample – moving the distribution of symp-toms or impact to the right will tend to increase the pro-portion of false positives and reduce the proportion offalse negatives, as in this instance.

The sensitivity of the SDQ prediction of ‘any diagno-sis probable’ depended on which diagnosis (or diag-noses) the child had, ranging from around 80 % for anx-iety and depressive disorders, to around 90 % forconduct disorder and broadly-defined attention-deficit/hyperactivity disorder, to near 100 % for nar-rowly-defined hyperkinesis and less common disorders.

■ Predicting the type of disorder

In child mental health clinics, the SDQ algorithm canpredict the broad type of disorder – conduct, emotionalor hyperactivity – with relatively few false negatives [9].Prediction of type of disorder in a low-risk communitysample is more prone to false negatives [7]. The samewas true, to a lesser extent, for the looked-after sample.For example, a child from a clinic sample with a severedepressive conduct disorder may correctly be predictedby the SDQ algorithm to have both a conduct and anemotional disorder, whereas a looked-after child with amilder depressive conduct disorder may be predicted tohave a conduct disorder but not an emotional disorder.Likewise, looked-after children with mild hyperkineticconduct disorder may be predicted to have a conductdisorder but no hyperactivity disorder. Consequently, ifresearchers or clinicians want to detect as many emo-tional or hyperactivity disorders as possible, they wouldbe well advised to use the SDQ prediction for ‘any disor-der’ rather than for ‘emotional disorder’ or ‘hyperactiv-ity disorder’. A second-stage screening procedure canthen be used to detect which SDQ ‘positive’ childrenhave the disorder of particular interest.

■ Choice of informant

The SDQ prediction works best when SDQs have beencompleted by both carers and teachers. If it is only pos-

sible to have one adult informant, carers and teachersprovide information of roughly equal predictive value.When information has been collected from adult infor-mants, there is some value to collecting self-reports by11–17 year olds as well since this results in a modest in-crease in the detection rate for emotional disorders. Re-lying just on self-reports in the absence of adult infor-mants would detect about half of the emotionaldisorders among looked-after 11–17 year olds, butwould miss the great majority of conduct and hyperac-tivity problems.

■ Potential value in screening

The findings of this study suggest that screening withthe SDQ (carer and teacher versions) could improve thedetection and treatment of behavioural, emotional, andconcentration problems among looked-after children.Whether improved detection would be useful dependson several factors. Most obviously, there is little point inidentifying more looked-after children as having psy-chological problems if this does not lead on to access toeffective treatments. Providing appropriate treatment islikely to require considerable motivation and resourcesfrom an alliance of social services, education, and childand adolescent mental health services. It would also becritically important to ensure that the screening processdoes not do serious harm, e. g. by causing anguish to‘false positives’ or by labelling children who would havebeen better off unlabelled. It is also worth rememberingthat routine SDQ screening of looked-after childrenwould consume resources, not only in the administra-tion and scoring of the questionnaires, but also in thesubsequent assessment of screen-positive children tosee if they really have problems that warrant specialistattention. These resources might have been employedmore profitably in other ways, e. g. on improving train-ing for foster parents and residential carers, or on im-proving specialist services. Given all these uncertainties,SDQ-based screening programs for looked-after chil-dren should only be implemented after adequate pilot-ing and evaluation.

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