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Adverselifeeventsareasocioeconomicdisadvantageandpsychopathologyandresilienceinyoungchildren-theimportanceofriskfactorsaccumulationandprotectivefactorsspecificity.pdf

O R I G I N A L C O N T R I B U T I O N

Adverse life events, area socioeconomic disadvantage, and psychopathology and resilience in young children: the importance of risk factors’ accumulation and protective factors’ specificity

Eirini Flouri • Nikos Tzavidis • Constantinos Kallis

Received: 6 March 2009 / Accepted: 21 September 2009 / Published online: 10 October 2009

� Springer-Verlag 2009

Abstract Few studies on resilience in young children

model risk appropriately and test theory-led hypotheses

about its moderation. This study addressed both issues. Our

hypothesis was that for preschool children’s emotional/

behavioral adjustment in the face of contextual risk pro-

tective factors should be located in the cognitive domain.

Data were from the first two sweeps of the UK’s Millen-

nium Cohort Study. The final study sample was 4,748

three-year-old children clustered in 1,549 Lower layer

Super Output Areas in nine strata. Contextual risk was

measured at both area (with the Index of Multiple Depri-

vation) and family (with proximal and distal adverse life

events experienced) level. Moderator variables were par-

enting, verbal and non-verbal ability, developmental

milestones, and temperament. Multivariate multilevel

models—that allowed for correlated residuals at both

individual and area level—and univariate multilevel mod-

els estimated risk effects on specific and broad psychopa-

thology. At baseline, proximal family risk, distal family

risk and area risk were all associated with broad psy-

chopathology, although the most parsimonious was the

proximal family risk model. The area risk/broad psycho-

pathology association remained significant even after

family risk was controlled but not after family level

socioeconomic disadvantage was controlled. The cumula-

tive family risk was more parsimonious than the specific

family risks model. Non-verbal ability moderated the effect

of proximal family risk on conduct and emotional prob-

lems, and developmental milestones moderated the effect

of proximal family risk on conduct problems. The findings

highlight the importance of modeling contextual risk

appropriately and of locating in the cognitive domain

factors that buffer its effect on young children’s

adjustment.

Keywords Hierarchical data � MCS � Multilevel models � Multivariate multilevel models � Psychopathology � Resilience � Strengths and Difficulties Questionnaire

Introduction

In the classic approach to cumulative contextual risk in

child psychiatry [1], organismic characteristics as well as

proximal and distal qualities of the environment are mod-

eled collectively. For each environmental construct, a

dichotomous classification of risk exposure is determined,

typically by a statistical cut off (e.g., greater than one

standard deviation above the mean, upper quartile, etc.) or

on the basis of a conceptual categorization (e.g., being

below the poverty line). Cumulative risk is then calculated

by a simple summation of the multiple risk categories.

These risk categories are usually not weighted. In this

approach, therefore, risk is viewed as an accumulation of

stressors, and the number of risks that children experience

carries more gravity than the experience of any particular

E. Flouri (&) Department of Psychology and Human Development,

Institute of Education, University of London,

25 Woburn Square, London WC1H 0AA, UK

e-mail: [email protected]

N. Tzavidis

Social Statistics and Centre for Census and Survey Research,

University of Manchester, Manchester, UK

e-mail: [email protected]

C. Kallis

London School of Hygiene and Tropical Medicine,

University of London, London, UK

e-mail: [email protected]

123

Eur Child Adolesc Psychiatry (2010) 19:535–546

DOI 10.1007/s00787-009-0068-x

risk. Indeed, cumulative risk indexes have been noted for

their potential to capture the natural covariation of risk

factors. For example, physical risk factors, such as poor

housing quality, noise and pollution are strongly interre-

lated as are psychosocial risk parameters such as family

turmoil and violence [2]. Furthermore, aggregate variables

of risk are more stable than any individual measure, and

there is increased power to detect effects because errors of

measurement decrease as scores are summed and degrees

of freedom are preserved [3]. Cumulative risk measures are

consistently found to explain more variance in children’s

outcomes than single factors [4–7].

Despite this, few studies [8] have examined the rela-

tionship between multiple risk exposure and young chil-

dren’s psychopathology using a cumulative risk approach.

In fact, even studies with school-aged children have yet to

deal with several issues with respect to the modeling of

cumulative risk. First, studies have yet to establish con-

vincingly the effect of the timing of cumulative contextual

risk on psychopathology [4, 9, 10]. Second, with few

exceptions [4, 11–14], studies do not examine the func-

tional form of cumulative contextual risk’s effect on psy-

chopathology. There is evidence for a linear effect whereby

increments in risk factors have a steady, additive impact on

mental health problems in school-aged children [5].

However, as few researchers actually report whether their

investigations included appropriate tests for nonlinear

patterns of cumulative risk, this ignores the possibility of a

nonlinear relationship that might manifest itself as an

acceleration [14] or a leveling-off [13] of problems at a

critical level of risk. Third, despite the renewed interest in

neighborhood effects on children’s development [15], only

few studies [16] compare family and neighborhood risk.

Fourth, although it is possible that the effect of one type or

one level of risk on psychopathology is conditional upon

the value of another, interactions between types and levels

of risk on child psychopathology are not usually studied.

Fifth and finally, with few exceptions [4, 8, 17, 18], most

studies do not examine factors that protect from negative

outcomes in children exposed to cumulative contextual

risk. The dearth of such research is unfortunate as various

protective factors have been identified as moderating the

impact of specific contextual risk. These factors are

grouped under two domains, namely, individual attributes

and connections to competent and caring adults in the

family and the community [19, 20].

Moderation of risk

To the best of our knowledge, the only study modeling the

moderation of cumulative contextual risk in preschool

children’s emotional and behavioral adjustment [8] found

that low vagal tone during tasks protected from the effect

of multiple risks, and concluded that this is because low

vagal tone during tasks may reflect regulatory capacities

that allow children to engage with learning opportunities.

This suggests that children’s advanced cognitive develop-

ment may be the factor that moderates the effect of con-

textual risk on emotional and behavioral outcomes.

Although previous research has certainly located in the

cognitive domain protective factors for school-aged chil-

dren’s emotional and behavioral adjustment in the face of

cumulative contextual risk [4, 18], studies have yet to

establish the importance of cognitive protective factors for

preschool children’s adjustment in the face of cumulative

contextual risk or test the hypothesis that for preschool

children’s adjustment in the face of cumulative contextual

risk factors promoting resilience should be located in the

cognitive rather than the biological or the social domain.

This study was undertaken to meet this aim, i.e., to test

that for preschool children’s adjustment in the face of

cumulative contextual risk factors promoting resilience

should be located in the cognitive domain. In doing so, it

also extended in several ways prior work on the role of

contextual risk in child psychopathology. First, it used

items from a well-validated measure of multiple family

risks. However, since only a selection of the original items

could be used, it also tested appropriately for the effect or

risk accumulation and risk specificity when modeling the

effect of family risk on child psychopathology. Second, it

measured both distal and proximal family risk to test for

the effect of the timing of risk on psychopathology. Third,

it measured area risk and compared area with family risk

effects. Fourth, it searched for an appropriate functional

form of both area and family risk’s effect on psychopa-

thology. Fifth and finally, it tested for the presence of

interaction effects between proximal, distal and area risk

on psychopathology.

Method

Participants and procedure

Data were obtained from the first two sweeps of the

Millennium Cohort Study (MCS), a longitudinal survey

drawing its sample from all live births in the UK over a

period of 12 months, beginning on 1 September 2000 in

England and Wales, and 3 months later in Scotland and

Northern Ireland. The sample was drawn slightly later in

Scotland and Northern Ireland so as not to coincide with

other surveys being carried out on families with babies in

these areas at the same time. Sweep 1 took place when the

children were aged 9 months, and Sweep 2 took place when

the children were around 3 years. The sample design

536 Eur Child Adolesc Psychiatry (2010) 19:535–546

123

allowed for disproportionate representation of families liv-

ing in areas of child poverty in Northern Ireland, Scotland

and Wales and in areas with high ethnic minority

populations in England. In all there were nine strata: Eng-

land-advantaged, England-disadvantaged, England-ethnic,

Wales-advantaged, Wales-disadvantaged, Scotland-advan-

taged, Scotland-disadvantaged, Northern Ireland-advan-

taged, and Northern Ireland-disadvantaged. Data on child

psychopathology were collected at Sweep 2 from the main

respondent. Although the MCS is a study of 19,244 children,

there were complete data on broad psychopathology for a

total of 9,736 children clustered in Lower layer Super Output

Areas (LSOAs). LSOAs are built from groups of Output

Areas (typically 4–6), and are constrained by the boundaries

of the Standard Table wards used for 2001 Census outputs.

They have, on average, 1,500 residents. For example, there

are 175,434 Output Areas and 34,378 LSOAs in England

and Wales. In this study, LSOAs, rather than the electoral

wards on which the MCS survey design was built, were used

to get smaller ‘neighborhoods’ and more up-to-date eco-

logical correlates. For one child, there was no information on

the LSOA at Sweep 2 and, therefore, this case was removed

from our analysis.

Measures

Area contextual risk was measured with the Index of

Multiple Deprivation (IMD), a weighted area level aggre-

gation of specific dimensions of deprivation. The dimen-

sions of deprivation used to construct the English IMD

2004, for instance, were (1) income deprivation, (2)

employment deprivation, (3) health deprivation and dis-

ability, (4) education, skills and training deprivation, (5)

barriers to housing and services, (6) living environment

deprivation and (7) crime [21]. As the various UK coun-

tries’ indices of multiple deprivation are not comparable,

IMD ranks were used, which for the purposes of this study

were standardized. As discussed above, the geography we

worked with was at LSOA level and the IMD score was

measured at LSOA level. Many families changed LSOAs

between Sweeps 1 and 2 (of the 19,244 cohort members,

52.4% did not change LSOA between Sweeps 1 and 2, 44%

did, and for 3.6% there was missing information about

LSOA at Sweep 1), but most remained within the same

LSOA. The IMD scores at Sweeps 1 and 2 were highly

correlated (the correlation between the IMD ranks at

Sweeps 1 and 2 was 0.911), and we, therefore, used IMD at

Sweep 1 in all our analyses.

Distal (birth to Sweep 1) and proximal (Sweep 1 to

Sweep 2) family contextual risk was measured with a

subset of Tiet et al.’s [18] Adverse Life Events Scale. This

scale is composed of 25 possible events for which children

had little or no control over and is a modification of the

Life Events Checklist (LEC) [22–24], a psychometrically

valid [22] measure of exposure to potentially traumatic

events developed at the National Center for Posttraumatic

Stress Disorder (PTSD) to facilitate the diagnosis of

PTSD. The self-report version of the Adverse Life Events

Scale has been used with children as young as 9 years

[25], and measures exposure to adverse life events at both

family (e.g., ‘negative change in parents’ financial situa-

tion’), and child (e.g., ‘got seriously ill or injured’, ‘lost a

close friend’) levels. In view of this study’s research aims

the version of the scale used comprised items which (a)

measured only family level risk, (b) were developmentally

appropriate, (c) could be reconstructed from the MCS

data, and (d) measured both distal and proximal risk. In

all, information about eight adverse life events was

available both between birth and Sweep 1 (‘distal family

risk’) and between Sweeps 1 and 2 (‘proximal family

risk’) in MCS. The eight events corresponded to the fol-

lowing Adverse Life Events Scale items: ‘family member

died’, ‘family member was seriously injured’, ‘negative

change in parents’ financial situation’, ‘family member

had mental/emotional problem’, ‘family moved’, ‘got a

new brother or sister’, ‘one of the parents went to jail’, and

‘parents separated’. In contrast to the high correlation in

IMD scores at Sweeps 1 and 2, the correlation between

family risk at Sweep 1 and family risk at Sweep 2 was

0.201.

Broad and specific psychopathology was assessed with

the main caregiver’s report of the Strengths and Difficulties

Questionnaire (SDQ), a 25-item 3-point (ranging from 0 to

2) scale measuring four difficulties (hyperactivity, emo-

tional symptoms, conduct problems, and peer problems), as

well as prosocial behavior [26, 27]. Each subscale has five

items. A total difficulties (broad psychopathology) scale is

calculated by summing the scores for hyperactivity, emo-

tional symptoms, conduct problems and peer problems

(http://www.sdqinfo.com). The SDQ has both good test–

retest reliability [28] and excellent concurrent and dis-

criminant validity [29, 30].

The child level covariates were age and sex, and the

family level covariates, all measured at Sweep 1, were

family structure, maternal psychological distress, and, to

index family’s socioeconomic status, maternal social class

and qualifications. Maternal psychological distress was

measured with nine items from the Malaise Inventory [31],

a psychometrically valid [32] measure of depressed mood.

Maternal social class, measured with the National Statistics

Socio-economic Classification (NS-SEC), ranged from 1

(‘high managerial’/‘professional’) to 7 (‘routine’). Moth-

ers’ highest qualifications were grouped into six major

categories, ranging from 1 to 6 (‘level 5’, i.e., first/higher

degree), roughly equivalent to National Vocational Quali-

fication (NVQ) levels.

Eur Child Adolesc Psychiatry (2010) 19:535–546 537

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The moderator variables examined were developmental

milestones, temperament and parenting, all assessed in

Sweep 1 by the main respondent, and verbal and non-

verbal ability, measured in Sweep 2. Developmental mile-

stones are a set of functional skills or age-specific tasks that

most children can do at a certain age range. In the MCS

developmental milestones assessing communicative ges-

tures as well as fine and gross motor coordination typical

for a 9-month-old child were measured with 12 3-point

scales from the Denver Developmental Screening Test

[33], the most popular tool to screen for potential devel-

opmental problems. A high score on the test indicated

developmental delay. Temperament or individual differ-

ences in reactivity and self-regulation that are assumed to

have a relatively enduring, biological basis, was measured

in the MCS with items selected from the Carey Infant

Temperament Scale [34, 35], used also in the Avon Lon-

gitudinal Study of Parents and Children (ALSPAC) and the

National Longitudinal Survey of Youth. The items inclu-

ded in the MCS measured three dimensions of tempera-

ment, namely mood (measured with five items such as ‘is

pleasant’), adaptability (measured with five items such as

‘is rarely or almost never wary of strangers’) and regularity

(measured with four items such as ‘gets sleepy at about the

same time’). Parenting was measured with four 5-point

scales, originally derived from the European Longitudinal

Study of Pregnancy and Childhood, and used in other UK

longitudinal studies (such as the ALSPAC), assessing to

what extent the main respondent agreed that ‘it is important

to develop a regular pattern of feeding and sleeping with a

baby’, ‘babies need to be stimulated if they are to develop

well’, ‘talking, even to a young baby, is important’, and

‘cuddling a baby is very important’. The four-item scale for

the whole MCS at Sweep 1 yielded a Cronbach’s alpha of

0.67, with a high scale score indicating negligent parenting.

Finally, verbal and non-verbal ability were measured with

the naming vocabulary subtest of the British Ability Scales

(BAS) and with the School Readiness Composite (SRC) of

the Revised Bracken Basic Concept Scale, respectively.

The BAS Naming Vocabulary subtest measures children’s

expressive language skills, and the SRC measures ‘readi-

ness’ for formal education. Both assessments were indi-

vidually administered in Sweep 2 by survey team members

in computer-assisted interviews. The BAS Naming

Vocabulary subtest is part of a cognitive test battery

designed for children aged 3–17 [36]. Children are asked to

name a series of pictures of everyday items. There are 36

items in total, and the assessment is terminated if five

successive items are answered incorrectly. In the MCS, the

test was not administered to children who did not speak

English. The SRC comprises six subtests of the Revised

Bracken Basic Concept Scale measuring children’s

knowledge of those ‘readiness’ concepts that parents and

teachers traditionally teach children in preparation for

formal education [37]. The test has been designed for

children in the age range of 2.5–7 years and 11 months.

The six subtests of the SRC comprise the assessment of

children’s basic concepts of colors, letters, numbers/

counting, sizes, comparisons and shapes.

Models

The structure of the MSC data with children clustered

within LSOAs dictates the use of statistical models that

appropriately account for the nested structure. One class of

statistical models that provide valid inferences in the case

of hierarchical data is multilevel random effects models

[38]. To be consistent with the hierarchical structure of the

MCS data, we describe multilevel models using a two-level

structure under which children are clustered within LSOAs

at Sweep 2. The simplest two-level model for total diffi-

culties is a random intercepts model described by

yij ¼ xTij b þ uj þ eij; i ¼ 1; . . .; n; j ¼ 1; . . .; d ð1Þ

where yij is the response variable (total difficulties) for

child i in LSOA j, xij denotes a set of explanatory variables

that can be defined at child, family and LSOA level, uj denotes a vector of random effects associated with LSOA j

and eijk denotes the level 1 (child level) residual term. As information for each child was available for each of

the five SDQ subscales, allowing for a different model for

each SDQ subscale may provide a deeper insight. How-

ever, it is possible that responses to the different SDQ

subscales are correlated. To account for the existence of

correlation in each child’s responses, we fitted multivariate

response multilevel models that allowed for the error terms

of the different models to be correlated. Let us denote by i

the subscript referring to the child, by j the subscript

referring to the LSOA, and by l the subscript denoting a

specific SDQ subscale. The multivariate response multi-

level model is then defined as

yijl ¼ xTijlbl þ ujl þ eijl; i ¼ 1; . . .; n; j ¼ 1; . . .; d; l ¼ 1; . . .; 5

ð2Þ

where eijl * N(0, Re) with Re denoting the variance covariance matrix between the level 1 error terms and

ujl * N(0, Ru) with Ru denoting the variance covariance matrix between the level 2 error terms. Modeling specific

psychopathologies in a multivariate way offers a more

flexible modeling framework as it can accommodate dif-

ferent covariates for the different SDQ subscales as well as

allow for the correlation between unobserved factors

affecting the scores on the different SDQ subscales at

different hierarchical levels. When compared with a single

multilevel model for broad psychopathology, a multivariate

538 Eur Child Adolesc Psychiatry (2010) 19:535–546

123

multilevel model offers an additional advantage with

respect to the handling of missing data. In particular, when

modeling broad psychopathology, a missing value in one of

the items comprising total difficulties on the SDQ results in

a missing total difficulties value. This is not the case with

multivariate models, since missing values in some of the

SDQ subscales do not result in the omission of the corre-

sponding unit from the analysis. In this way, the estimates

obtained from the multivariate multilevel model are

efficient.

The complex survey design of the MCS was accounted

for by conditioning on the design variables, i.e., the vari-

ables used in designing the MCS survey. Therefore, in the

models described below, stratification was accounted for

by including a set of dummy variables representing the

nine strata of the MCS, whereas clustering was accounted

for by running multilevel models. The models assumed that

data were missing at random (MAR) [38].

Results

We used STATA version 10 and MLwiN to run random

intercepts models (see Table 1 for a model summary),

described by (1), allowing the constant to vary at level 2,

i.e., LSOA at Sweep 2. We first estimated an empty

model for total difficulties in the 9,735 children with valid

data on total difficulties. This model included only the

design variables to adjust for the effects of stratification.

The two-level empty model showed that the average total

difficulties score as reflected in the intercept was 8.219

(SE = 0.102). The child level variance component (level

1 variance) was 23.367 (SE = 0.353), and the variance

due to differences in LSOAs was 0.850 (SE = 0.166).

This suggests a significant between LSOA variation.

Using the two variance components to partition the var-

iance across levels we found that the intra-cluster corre-

lation coefficient was 0.035. This suggests that controlling

only for stratification 3.5% of the variance in total diffi-

culties scores was attributable to the area level. Therefore,

although small the intra-cluster correlation justified the

use of hierarchical modeling. We first investigated the

contextual risk/total difficulties association by running

three baseline models, one with proximal family risk, one

with distal family risk and one with area risk. Area risk

(b = -0.938, SE = 0.083), distal family risk (b = 0.852,

SE = 0.060), and proximal family risk (b = 0.715,

SE = 0.043) were all related to total difficulties. As

expected, the amount of variance due to differences in

areas explained in this model when area risk was added to

Table 1 Model summary (broad psychopathology)

a The design variables used

were the MCS strata defined by

country (England, Scotland,

Wales and Northern Ireland)

and disadvantage status

according to the Child Poverty

Index (CPI). For England, there

was an additional stratum, i.e.

ethnic minority indicating wards

which, in the 1991 Census of

Population, had an ethnic

minority indicator of at least

30%. In other words, at least

30% of their total population

fell into the categories ‘Black’

or ‘Asian’

Models Specification

Model 1 Design variables a

Model 2 Model 1 ? area random effect

Model 3.1 Model 2 ? proximal family risk

Model 3.2 Model 2 ? distal family risk

Model 3.3 Model 2 ? area risk

Model 4 Model 2 ? area risk ? measures of family risk (proximal and distal family risk)

Model 5 Model 4 ? family level fixed effects (family structure, mother’s qualifications,

mother’s social class and mother’s psychological distress)

Model 6 Model 5 ? child level fixed effects (gender, age) ? moderator variables (mood,

regularity, adaptability, non-verbal ability, verbal ability, developmental delay,

negligent parenting)

Model 7.1 Model 6 ? quadratic effect for area risk

Model 7.2 Model 6 ? quadratic effect for distal family risk

Model 7.3 Model 6 ? quadratic effect for proximal family risk

Model 8.1 Model 6 ? interaction between proximal family risk and distal family risk

Model 8.2 Model 6 ? interaction between proximal family risk and area risk

Model 8.3 Model 6 ? interaction between distal family risk and area risk

Model 9.1 Model 6 ? interaction between proximal family risk and mood

Model 9.2 Model 6 ? interaction between proximal family risk and regularity

Model 9.3 Model 6 ? interaction between proximal family risk and adaptability

Model 9.4 Model 6 ? interaction between proximal family risk and non-verbal ability

Model 9.5 Model 6 ? interaction between proximal family risk and verbal ability

Model 9.6 Model 6 ? interaction between proximal family risk and developmental delay

Model 9.7 Model 6 ? interaction between proximal family risk and parenting

Eur Child Adolesc Psychiatry (2010) 19:535–546 539

123

the empty model was reduced (0.697, SE = 0.152). To

compare the goodness of fit of these three baseline

models, we used an appropriate statistic that takes into

account that the models are not nested. Using the

Bayesian Information Criterion (BIC), a function of the

likelihood, the number of observations and the number of

free parameters of each model [39], we found that the

most parsimonious model was the proximal family risk

model. The association of area risk, distal family risk and

proximal family risk with total difficulties remained sig-

nificant even when all three risk variables were entered in

the same model. The amount of variance due to differ-

ences in areas explained in this model was further

reduced (0.606, SE = 0.140), but was still significant.

This suggests that although child psychopathology scores

differed by area partly because of differences in area level

socioeconomic disadvantage, even with area socioeco-

nomic disadvantage adjusted there was still area level

variance that remained unexplained.

In the next step, the family level variables of family

structure, and mother’s qualifications, social class and

psychological distress were added. With these variables in

the model, the effect of area risk on total difficulties

became nonsignificant. The amount of variance in total

psychopathology scores due to differences in area

explained in the model became nonsignificant (0.090,

SE = 0.093). Taken together, these two findings suggest

that both the area differences in total difficulties and the

effect of area risk on total difficulties operated via family

characteristics. The effect of distal and proximal family

risk, on the other hand, remained significant (b = 0.238,

SE = 0.065, and b = 0.376, SE = 0.044, respectively)

suggesting that family contextual risk predicts child psy-

chopathology directly and independently of family struc-

ture, socioeconomic status or maternal psychopathology.

Next, the full model (Model 6) was introduced. This

added the child level control variables and the proposed

moderators. The final study sample obtained after omitting

cases with missing values on both broad psychopathology

and all its predictors was 4,748 children clustered in 1,549

LSOAs. The number of children per LSOA ranged from 1

to 41, with an average of 3.1 children per LSOA. The

distribution of the 4,748 children in the nine MCS strata in

Sweep 2 was as follows: England-advantaged: 1,847;

England-disadvantaged: 884; England-ethnic: 139; Wales-

advantaged: 319; Wales-disadvantaged: 421; Scotland-

advantaged: 434; Scotland-disadvantaged: 264; Northern

Ireland-advantaged: 220, and Northern Ireland-disadvan-

taged: 220. As can be seen in Table 2, although negligent

parenting was marginally significant, all the child level

moderator variables were statistically significant in pre-

dicting broad psychopathology. In this final model, the

child level variance component (level 1 variance) was

16.409 (SE = 0.359), and the variance due to differences

in LSOAs was 0.203 (SE = 0.133).

As explained above, the initial MCS sample is 19,244

children. Of these, 9,736 had information about broad

psychopathology, our main response variable, and of these,

4,748 children were the final study sample. As expected,

these 4,748 children differed from the 14,496 children not

included in the final study sample in most of the study

variables. In particular, with regards to differences in

Sweep 1 variables, compared with their counterparts those

in the final study sample tended to live in less disadvan-

taged areas, in intact families, and with mothers who were

less negligent, less distressed, more educated and of higher

social class. With regards to differences in Sweep 2 vari-

ables, compared with their counterparts those in the study

sample tended to present with less broad psychopathology,

be female, have easier temperament, higher verbal and

non-verbal cognitive ability, and have less developmental

delay (all differences were statistically significant at

P \ 0.001; results available from the authors). However, there was no difference between the two groups in age or in

number of either proximal or distal family adverse life

events experienced.

Comparing cumulative and specific family risk

specifications

We could not compare the proximal cumulative risk

specification with the proximal specific risk specification

as one item of the proximal Adverse Life Events Scale

(‘one of the parents went to jail’) was dropped due to

collinearity. However, the comparison of the distal

cumulative with the distal specific risk specification

showed that, after applying a Bonferroni correction

(alpha of 0.05/8 adverse events = 0.00625), none of the

eight specific distal risks were significantly associated

with broad psychopathology. The only distal risk item

approaching significance was ‘negative change in parents’

financial situation’ (b = 0.421, SE = 0.158). As the BIC

for the cumulative risk model (27,176.89) was lower than

that for the specific risk model (27,216.41), we concluded

that the cumulative risk model specification should be

preferred, and therefore this risk specification was used for

the remaining analysis.

Investigating the appropriate functional

form of contextual risk’s effect and testing

for moderator effects

We further investigated the appropriate functional form of

contextual risk’s effect on broad psychopathology by

introducing quadratic terms for contextual risk separately

in the full model (Model 6). None of the three quadratic

540 Eur Child Adolesc Psychiatry (2010) 19:535–546

123

terms for area, distal family, and proximal family risk were

significant. We also tested if the effect of one type of risk

was conditional upon the value of another. We entered

these two-way interactions between distal, proximal and

area risk variables separately in the full model, but neither

interaction term was significant. This suggests that the

effect of proximal family risk on broad psychopathology

did not depend on the level of distal risk, and that area risk

did not moderate the effect of either proximal or distal risk

on broad psychopathology.

Table 2 Broad psychopathology

Predictors Model 2 Model 4 Model 6

Coeff. SE Coeff. SE Coeff. SE

Constant 8.219 0.102 7.250 0.174 13.749 1.371

Stratum (Ref: England-advantaged)

England-disadvantaged 1.927 0.170 0.849 0.186 0.315 0.208

England-ethnic 2.732 0.289 1.983 0.294 0.630 0.405

Wales-advantaged -0.155 0.257 -1.609 0.289 -0.528 0.321

Wales-disadvantaged 1.276 0.224 -0.406 0.266 0.021 0.302

Scotland-advantaged -0.357 0.234 21.595 0.257 -0.178 0.281

Scotland-disadvantaged 1.115 0.268 -0.403 0.293 0.188 0.334

Northern Ireland-advantaged 20.715 0.286 22.142 0.318 20.844 0.360

Northern Ireland-disadvantaged 1.194 0.260 -0.435 0.298 20.854 0.360

Standardized IMD rank 20.776 0.081 -0.039 0.095

Proximal adverse life events 0.581 0.044 0.394 0.054

Distal adverse life events 0.666 0.061 0.308 0.079

Family structure (Ref: two natural parents)

Natural mother only 0.260 0.208

Other -0.959 1.828

Mother’s NS-SEC (Ref: high managerial/professional)

Low managerial/professional -0.047 0.229

Intermediate 0.098 0.259

Small emp and self-employed 20.824 0.361

Low supervisory and technical 0.517 0.336

Semi routine 0.728 0.275

Routine 0.553 0.310

Mother’s highest qualifications (Ref: level 5)

Level 4 -0.426 0.306

Level 3 0.372 0.330

A/AS Level 0.345 0.333

Level 2 0.920 0.314

Level 1 1.623 0.364

Mother’s psychological distress 0.472 0.040

Girl 20.755 0.121

Age 0.669 0.327

Mood 20.153 0.019

Regularity 20.134 0.023

Adaptability 20.123 0.018

Non-verbal ability 20.038 0.006

Verbal ability 20.072 0.017

Developmental delay 0.049 0.025

Negligent parenting 0.073 0.042

Between area variability (ru 2 ) 0.849 0.155 0.606 0.133 0.171 0.135

Within area variability (re 2 ) 23.367 0.351 22.379 0.335 16.317 0.357

Eur Child Adolesc Psychiatry (2010) 19:535–546 541

123

We finally explored if parenting, verbal and non-verbal

ability, developmental milestones, and mood, regularity

and adaptability moderate the association between con-

textual risk and young children’s broad psychopathology.

As proximal risk was the type of risk more strongly asso-

ciated with child psychopathology both at baseline and in

the full model, interaction terms with proximal family risk

and each of the proposed moderators were calculated and

entered separately in the full model (Model 6). None of the

interaction terms were significant, however, suggesting that

the effect of proximal risk on broad psychopathology was

not buffered either by child’s easy temperament or

advanced development, or by involved parenting. Table 2

presents the results of Model 6 and of the intermediate

models (Models 2 and 4) that were of particular substantive

significance.

Multivariate response models

We fitted multivariate response multilevel models descri-

bed by [8] that allowed the error terms of the different

models to be correlated and that included random area

(LSOA) effects. These multivariate response two level

models were effectively treated in MLwiN as three level

models, i.e., with the responses as the additional lower

level.

First, we ran an empty multivariate response multilevel

model (Model 10). This model included only the design

variables to adjust for the effects of stratification. The

variance partition coefficients obtained showed that 1% of

the total variation in prosocial behavior, 2% of the total

variation in emotional symptoms, 2% of the total variation

in conduct problems, 2% of the total variation in hyper-

activity, and 2% of the total variation in peer problems

were due to between LSOA variation.

We started our analysis by fitting three baseline models.

The first model examined the effect of proximal family

risk, which was significant in predicting scores in all five

SDQ subscales. The second model examined the effect of

distal risk, which was also significant in predicting scores

in all five SDQ subscales. The third model examined the

effect of area risk, which also had a significant, albeit

weaker, effect on the four difficulties and a nonsignificant

effect on prosocial behavior. The effect of proximal, distal

and area risk on all four difficulties remained significant

even when all risk variables were entered simultaneously

(Model 13). However, the effect of distal risk on prosocial

behavior became nonsignificant after adjusting for proxi-

mal and area risk suggesting that the effect of distal risk on

prosocial behavior was via its impact on proximal risk.

Subsequently, we introduced the full model (Model 14).

This added to the model including the design variables and

the three risk variables all the family- and child level fixed

effects, and all the moderator variables. The sample size in

this full model was 28,672. 1

In this model, the effect of

proximal risk on prosocial behavior became nonsignificant,

as did the effect of area risk on all four difficulties.

Although the effect of proximal risk remained significant

on all four difficulties, the effect of distal risk was signif-

icant only on hyperactivity and conduct problems.

Inspection of the between area and between children var-

iance covariance matrices (Table 3) from the various

models showed the covariances between the SDQ sub-

scales at area level became less significant as we controlled

for background characteristics. The covariances between

the SDQ subscales at child level, however, remained sig-

nificant even after controlling for a range of background

characteristics in this final model (Model 14), which jus-

tifies the use of a multivariate model.

Next, we tested if the effect of one type of risk was

conditional upon the value of another. We calculated two-

way interactions between distal, proximal and area risk

variables and entered these separately in the full model,

but none of these interaction terms was significant. This

suggests that the effect of proximal family risk on specific

psychopathology did not depend on the level of distal

risk, and that area risk did not moderate the effect of

either proximal or distal risk on any specific child

psychopathology.

Finally, we explored if parenting, verbal and non-verbal

ability, developmental milestones, and mood, regularity

and adaptability moderate the association between con-

textual risk and young children’s specific psychopathology.

As proximal risk was the type of risk more strongly asso-

ciated with child psychopathology both at baseline and in

the full model, interaction terms with proximal family risk

and each of the proposed moderators were calculated

and entered separately in the full model. The interaction

between proximal family risk and developmental

milestones predicted conduct problems (b = -0.017,

SE = 0.008), and the interaction between proximal family

risk and non-verbal ability predicted both conduct prob-

lems (b = -0.002, SE = 0.001) and emotional symptoms

(b = -0.002, SE = 0.001), but no other interaction effects

were significant. These effects remained significant even

when both interaction terms were entered in the same

model (Model 17). These findings suggest that delayed

development buffered the effect of proximal risk on con-

duct problems, and non-verbal ability buffered the effect

of proximal risk on both emotional symptoms and con-

duct problems. Although developmental delay was not

1 This was the total number of observations in the multivariate

(specific psychopathology) model. The number of observations for

each SDQ outcome was as follows: Prosocial behavior: 5,719;

Emotional symptoms: 5,966; Conduct problems: 5,954; Hyperactiv-

ity: 5,689, and Peer problems: 5,344.

542 Eur Child Adolesc Psychiatry (2010) 19:535–546

123

significantly associated with any externalizing psychopa-

thology (although it was positively associated with emo-

tional symptoms and peer problems and negatively

associated with prosocial behavior) in the full model

(Model 14), it buffered the effect of proximal adverse life

events on conduct problems. Similarly, although non-ver-

bal ability was not significantly associated with any

internalizing psychopathology (although it was positively

related to prosocial behavior and negatively related to both

conduct problems and hyperactivity) in the full model

(Model 14), it buffered the effect of proximal risk on

conduct problems but also on emotional symptoms.

Table 4 shows the model summary for specific psychopa-

thology, and Table 5 shows the results of Model 17.

Table 3 Child level and area level covariance matrices between the five SDQ subscales estimated by fitting multivariate models (Models 10, 13, and 14)

Prosocial Emotional Conduct Hyperactivity Peer

Model 10

Between area variance covariance matrix

Prosocial 0.021 (0.011)

Emotional -0.011 (0.007) 0.042 (0.008)

Conduct -0.018 (0.010) 0.050 (0.009) 0.088 (0.017)

Hyperactivity -0.018 (0.011) 0.048 (0.010) 0.080 (0.016) 0.090 (0.022)

Peer -0.009 (0.007) 0.039 (0.007) 0.051 (0.010) 0.047 (0.011) 0.043 (0.010)

Between children variance covariance matrix

Prosocial 3.302 (0.043)

Emotional -0.209 (0.024) 1.938 (0.025)

Conduct -1.246 (0.036) 0.757 (0.026) 3.937 (0.050)

Hyperactivity -1.249 (0.041) 0.715 (0.031) 2.173 (0.047) 5.202 (0.069)

Peer -0.759 (0.027) 0.611 (0.020) 0.719 (0.029) 0.745 (0.033) 2.105 (0.029)

Model 13

Between area variance covariance matrix

Prosocial 0.022 (0.011)

Emotional -0.013 (0.007) 0.032 (0.008)

Conduct -0.023 (0.009) 0.031 (0.008) 0.054 (0.015)

Hyperactivity -0.023 (0.011) 0.033 (0.009) 0.053 (0.014) 0.068 (0.002)

Peer -0.011 (0.007) 0.028 (0.006) 0.029 (0.008) 0.029 (0.010) 0.031 (0.009)

Between children variance covariance matrix

Prosocial 3.299 (0.043)

Emotional -0.200 (0.024) 1.915 (0.024)

Conduct -1.223 (0.035) 0.700 (0.026) 3.799 (0.048)

Hyperactivity -1.228 (0.041) 0.663 (0.030) 2.043 (0.045) 5.079 (0.067)

Peer -0.750 (0.027) 0.587 (0.020) 0.665 (0.028) 0.697 (0.033) 2.081 (0.028)

Model 14

Between area variance covariance matrix

Prosocial 0.005 (0.017)

Emotional -0.005 (0.009) 0.008 (0.009)

Conduct -0.006 (0.014) 0.015 (0.01) 0.029 (0.02)

Hyperactivity -0.004 (0.016) 0.029 (0.012) 0.011 (0.018) 0.024 (0.027)

Peer -0.011 (0.011) 0.007 (0.008) 0.003 (0.011) -0.003 (0.013) 0.023 (0.013)

Between children variance covariance matrix

Prosocial 2.848 (0.056)

Emotional -0.093 (0.029) 1.468 (0.028)

Conduct -0.949 (0.043) 0.375 (0.029) 2.992 (0.058)

Hyperactivity -0.871 (0.051) 0.358 (0.036) 1.478 (0.054) 4.304 (0.085)

Peer -0.500 (0.033) 0.384 (0.023) 0.361 (0.033) 0.387 (0.04) 1.668 (0.034)

Eur Child Adolesc Psychiatry (2010) 19:535–546 543

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Discussion

This study was carried out to test the hypothesis that for

preschool children’s behavioral and emotional adjustment

in the face of contextual risk, factors promoting resilience

should be located in the cognitive domain. Indeed, this

study showed that neither parenting nor temperament

buffered the effect of contextual risk on young children’s

emotional and behavioral adjustment. However, non-verbal

ability moderated the effect of cumulative proximal risk on

both emotional symptoms and conduct problems.

In testing this hypothesis, we measured and modeled

contextual risk appropriately and we, therefore, also

extended in several ways prior work on contextual risk

effects on children’s behavioral and emotional adjustment.

First, assessing with well-validated measures family con-

textual risk, we showed that, although the number of family

adversities experienced in the child’s first year (distal

family risk) did predict broad and externalizing psycho-

pathology, the number of proximal (i.e., in the second and

third year) family adversities (proximal family risk) pre-

dicted both broad and externalizing but also internalizing

psychopathology. Second, we showed that the most parsi-

monious model was the model that included cumulative

rather than specific family risk. Taken together, these

findings highlight the importance of proximal family risk in

predicting both broad and specific psychopathology, and

the importance of considering family risk accumulation

rather than specificity in predicting psychopathology in

young children. Third, we showed that although the effect

of area and the effect of area level contextual risk were

significant on broad psychopathology even after distal and

proximal family risk were controlled for, they became

nonsignificant after adjusting for maternal socioeconomic

status and mental health. This finding suggests that the

effect of area and the effect of area risk on child psycho-

pathology operate via family characteristics. The effect of

distal and proximal family risk, on the other hand,

remained significant after adjusting for these maternal

characteristics suggesting that family contextual risk pre-

dicts child psychopathology directly and independently,

and not because it is associated with parental psychopa-

thology or social class. In other words, the effect of family

contextual risk on child psychopathology transcends social

origins and genetic predispositions. Fourth and finally, by

testing for the functional form of the effect of both area and

family risk and for the interaction between the various

types and levels of risk, we joined the few other researchers

[4, 11–14] that have tested for ‘threshold’ models of mul-

tiple risk. Ours, however, was the first study to test such

effects on young children’s psychopathology.

Of course, the study’s limitations should also be

acknowledged. For example, the children of the final study

sample were clearly more privileged than those not inclu-

ded in the final study sample. However, in our modelling

framework data were treated under the MAR assumption.

Table 4 Model summary (specific psychopathology)

Models Specification

Model 10 Design variables

Model 11 Model 10 ? area random effect

Model 12.1 Model 11 ? proximal family risk

Model 12.2 Model 11 ? distal family risk

Model 12.3 Model 11 ? area risk

Model 13 Model 11 ? area risk ? measures of family risk (proximal and distal family risk)

Model 14 Model 13 ? family level fixed effects (family structure, mother’s qualifications,

mother’s social class, mother’s psychological distress) ? child level fixed effects

(age, gender) ? moderator variables (mood, regularity, adaptability, non-verbal

ability, verbal ability, developmental delay, negligent parenting)

Model 15.1 Model 14 ? interaction between proximal family risk and distal family risk

Model 15.2 Model 14 ? interaction between proximal family risk and area risk

Model 15.3 Model 14 ? interaction between distal family risk and area risk

Model 16.1 Model 14 ? interaction between proximal family risk and mood

Model 16.2 Model 14 ? interaction between proximal family risk and regularity

Model 16.3 Model 14 ? interaction between proximal family risk and adaptability

Model 16.4 Model 14 ? interaction between proximal family risk and non-verbal ability

Model 16.5 Model 14 ? interaction between proximal family risk and verbal ability

Model 16.6 Model 14 ? interaction between proximal family risk and developmental delay

Model 16.7 Model 14 ? interaction between proximal family risk and parenting

Model 17 Model 14 ? interaction between proximal family risk and developmental

delay ? interaction between proximal family risk and non-verbal ability

544 Eur Child Adolesc Psychiatry (2010) 19:535–546

123

Our study findings have important implications for the

study of both contextual risk and resilience as they suggest

that, even among young children, contextual factors that

impede adjustment are proximal rather than distal, and

should be modeled cumulatively and located in the family

rather than the area, and, importantly for the study of

resilience, that factors that promote adjustment in the face

of such contextual risk should be located among individual

Table 5 Specific psychopathology (Model 17)

Predictors Prosocial

behavior

Coeff. (SE)

Emotional

symptoms

Coeff. (SE)

Conduct

problems

Coeff. (SE)

Hyperactivity

Coeff. (SE)

Peer problems

Coeff. (SE)

Constant 6.633 (0.558) 2.306 (0.401) 3.789 (0.573) 3.989 (0.698) 2.870 (0.445)

Stratum (Ref: England-advantaged)

England-disadvantaged -0.050 (0.076) -0.032 (0.054) 0.017 (0.078) 0.078 (0.094) 0.181 (0.063)

England-ethnic 0.375 (0.139) 0.128 (0.099) 0.038 (0.146) 0.076 (0.176) 0.259 (0.117)

Wales-advantaged 0.033 (0.118) -0.046 (0.084) -0.213 (0.122) 0.118 (0.146) -0.134 (0.097)

Wales-disadvantaged 0.177 (0.109) -0.044 (0.079) 0.154 (0.114) 0.237 (0.136) -0.082 (0.091)

Scotland-advantaged -0.037 (0.105) -0.058 (0.074) -0.042 (0.108) -0.042 (0.129) -0.067 (0.086)

Scotland-disadvantaged -0.194 (0.123) -0.079 (0.088) 0.141 (0.128) 0.139 (0.153) 0.062 (0.101)

Northern Ireland-advantaged 0.024 (0.134) -0.036 (0.094) -0.232 (0.136) -0.197 (0.164) -0.187 (0.108)

Northern Ireland-disadvantaged 0.154 (0.133) -0.085 (0.094) -0.134 (0.137) -0.408 (0.164) -0.082 (0.108)

Standardized IMD rank -0.037 (0.035) -0.012 (0.025) -0.028 (0.036) 0.077 (0.043) -0.033 (0.029)

Proximal adverse life events -0.071 (0.147) 0.171 (0.105) 0.511 (0.149) 0.134 (0.181) 0.073 (0.118)

Distal adverse life events 0.025 (0.030) 0.031 (0.021) 0.096 (0.030) 0.132 (0.037) 0.035 (0.024)

Family structure (Ref: two natural parents)

Natural mother only 0.079 (0.077) 0.079 (0.054) 0.170 (0.078) 0.110 (0.095) 0.082 (0.061)

Other -0.092 (0.060) -0.131 (0.459) -0.745 (0.617) -1.340 (0.833) 0.425 (0.525)

Mother’s NS-SEC (Ref: high managerial/professional)

Low managerial/professional 0.002 (0.088) -0.065 (0.062) -0.064 (0.089) 0.120 (0.108) 0.039 (0.070)

Intermediate 0.015 (0.099) -0.136 (0.069) 0.069 (0.099) 0.280 (0.121) -0.082 (0.078)

Small emp and self-employed 0.109 (0.138) -0.159 (0.097) -0.243 (0.138) -0.173 (0.168) -0.035 (0.109)

Low supervisory and technical 0.123 (0.126) -0.123 (0.089) 0.056 (0.127) 0.445 (0.156) 0.174 (0.100)

Semi routine -0.107 (0.104) -0.037 (0.073) 0.347 (0.104) 0.332 (0.128) 0.082 (0.083)

Routine 0.017 (0.116) 0.001 (0.082) 0.173 (0.117) 0.276 (0.142) 0.173 (0.092)

Mother’s highest qualifications (Ref: level 5)

Level 4 -0.100 (0.115) -0.050 (0.081) -0.096 (0.117) -0.035 (0.140) -0.120 (0.094)

Level 3 -0.048 (0.124) -0.004 (0.088) 0.010 (0.126) 0.345 (0.152) 0.067 (0.101)

A/AS Level -0.108 (0.125) 0.022 (0.088) -0.058 (0.127) 0.272 (0.153) 0.056 (0.102)

Level 2 -0.111 (0.117) 0.096 (0.083) 0.170 (0.119) 0.495 (0.143) 0.141 (0.096)

Level 1 -0.092 (0.134) 0.311 (0.095) 0.400 (0.136) 0.722 (0.165) 0.229 (0.110)

Mother’s psychological distress -0.032 (0.014) 0.085 (0.010) 0.158 (0.015) 0.159 (0.018) 0.064 (0.012)

Girl 0.306 (0.045) -0.009 (0.032) -0.143 (0.046) -0.505 (0.056) -0.152 (0.036)

Age 0.051 (0.121) 0.109 (0.088) 0.013 (0.124) 0.580 (0.152) -0.066 (0.097)

Mood 0.064 (0.007) -0.025 (0.005) -0.053 (0.007) -0.051 (0.009) -0.017 (0.006)

Regularity 0.003 (0.008) -0.019 (0.006) -0.031 (0.009) -0.043 (0.010) -0.035 (0.007)

Adaptability 0.017 (0.007) -0.050 (0.005) -0.020 (0.007) -0.015 (0.008) -0.039 (0.005)

Non-verbal ability 0.009 (0.003) -0.000 (0.002) -0.011 (0.003) -0.016 (0.004) -0.002 (0.003)

Verbal ability 0.021 (0.007) -0.016 (0.005) -0.014 (0.007) -0.033 (0.008) -0.020 (0.005)

Developmental delay -0.076 (0.015) 0.023 (0.011) 0.029 (0.015) 0.009 (0.019) 0.026 (0.012)

Negligent parenting -0.095 (0.015) 0.005 (0.011) 0.015 (0.015) 0.035 (0.019) 0.028 (0.012)

Proximal adverse life events by developmental delay 0.004 (0.008) -0.004 (0.005) -0.017 (0.008) 0.004 (0.010) -0.002 (0.006)

Proximal adverse life events by non-verbal ability -0.001 (0.001) -0.002 (0.001) -0.002 (0.001) -0.002 (0.002) 0.001 (0.001)

Eur Child Adolesc Psychiatry (2010) 19:535–546 545

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attributes pertaining specifically to cognitive strengths.

Future studies should test this as well as the generalise-

ability of these findings in older child samples as the

adverse characteristics of a neighborhood might impact

differently upon the psychopathology of older children.

Acknowledgments This program of research was supported by a grant from the British Academy to the first two authors. The authors

are grateful to Jon Johnson, Rachel Rosenberg, and Tina Roberts for

their help with the construction of the dataset.

References

1. Rutter M (1979) Protective factors in children’s responses to

stress and disadvantage. In: Kent MW, Rolf JE (eds) Primary

prevention of psychopathology: III. Promoting social competence

and coping in children. University Press of New England,

Hanover, pp 49–74

2. Evans GW (2003) A multimethodological analysis of cumulative

risk and allostatic load among rural children. Dev Psychol

39:924–933

3. Burchinal M, Roberts J, Hooper S, Zeisel S (2000) Cumulative

risk and early cognitive development: a comparison of statistical

risk models. Dev Psychol 36:793–807

4. Flouri E, Kallis C (2007) Adverse life events and psychopa-

thology and prosocial behavior in late adolescence: testing the

timing, specificity, accumulation, gradient, and moderation of

contextual risk. J Am Acad Child Adolesc Psychiatry 46:1651–

1659

5. Deater-Deckard K, Dodge KA, Bates JE, Pettit GS (1998) Mul-

tiple risk factors in the development of externalizing behavior

problems: group and individual differences. Dev Psychopathol

10:469–493

6. Atzaba-Poria N, Pike A, Deater-Deckard K (2004) Do risk factors

for problem behaviour act in a cumulative manner? An exami-

nation of ethnic minority and majority children through an eco-

logical perspective. J Child Psychol Psychiatry 45:707–718

7. Sameroff AJ, Seifer R, Baldwin A, Baldwin C (1993) Stability of

intelligence from preschool to adolescence: the influence of

social and family risk factors. Child Dev 64:80–97

8. Sheinkopf SJ, Lagasse LL, Lester BM et al (2007) Vagal tone as

a resilience factor in children with prenatal cocaine exposure.

Dev Psychopathol 19:649–673

9. Burchinal MR, Roberts JE, Zeisel SA, Rowley SJ (2008) Social

risk and protective factors for African American children’s

academic achievement and adjustment during the transition to

middle school. Dev Psychol 44:286–292

10. Ackerman BP, Brown ED, Izard CE (2004) The relations

between persistent poverty and contextual risk and children’s

behavior in elementary school. Dev Psychol 40:367–377

11. Flaherty EG, Thompson R, Litrownik AJ et al (2006) Effect of

early childhood adversity on child health. Arch Pediatr Adolesc

Med 160:1232–1238

12. Gerard JM, Buehler C (2004) Cumulative environmental risk and

youth problem behavior. J Marriage Fam 66:702–720

13. Morales JR, Guerra NG (2006) Effects of multiple context and

cumulative stress on urban children’s adjustment in elementary

school. Child Dev 77:907–923

14. Simmons RG, Burgeson R, Carlton-Ford S, Blyth DA (1987) The

impact of cumulative change in early adolescence. Child Dev

58:1220–1234

15. Leventhal T, Brooks-Gunn J (2000) The neighborhoods they live

in: the effects of neighborhood residence on children and ado-

lescent outcomes. Psychol Bull 126:309–337

16. McCulloch A, Joshi HE (2001) Neighborhood and family influ-

ences on the cognitive ability of children in the British National

Child Development Study. Soc Sci Med 53:59–591

17. Ackerman BP, Izard CE, Schoff K, Youngstrom EA, Kogos J

(1999) Contextual risk, caregiver emotionality, and the problem

behaviors of six- and seven-year-old children from economically

disadvantaged families. Child Dev 70:1415–1427

18. Tiet QQ, Bird HR, Davies M et al (1998) Adverse life events and

resilience. J Am Acad Child Adolesc Psychiatry 37:1191–1200

19. Masten AS (2001) Ordinary magic: resilience processes in

development. Am Psychol 56:227–238

20. Masten AS, Burt KB, Roisman GI, Obradovic J, Long JD, Tellegen

A (2004) Resources and resilience in the transition to adulthood:

continuity and change. Dev Psychopathol 16:1071–1094

21. Noble M, Wright G, Smith G, Dibben C (2006) Measuring

multiple deprivation at the small area level. Environ Plann A

38:169–185

22. Brand AH, Johnson JH (1982) Note on reliability of the Life

Events Checklist. Psychol Rep 50:1274

23. Coddington RD (1972) The significance of life events as etiologic

factors in the diseases of children: I. A survey of professional

workers. J Psychosom Res 16:7–18

24. Coddington RD (1972) The significance of life events as etiologic

factors in the diseases of children: II. A study of a normal pop-

ulation. J Psychosom Res 16:205–213

25. Tiet QQ, Bird HR, Hoven CW et al (2001) Relationship between

specific adverse life events and psychiatric disorders. J Abnorm

Child Psychol 29:153–164

26. Goodman R (1994) A modified version of the Rutter Parent

Questionnaire including extra items on children’s strengths: a

research note. J Child Psychol Psychiatry 35:1483–1494

27. Goodman R (1997) The Strengths and Difficulties Questionnaire:

a research note. J Child Psychol Psychiatry 38:581–586

28. Goodman R (2001) Psychometric properties of the Strengths and

Difficulties Questionnaire. J Am Acad Child Adolesc Psychiatry

40:1337–1345

29. Goodman R, Scott S (1999) Comparing the Strengths and Diffi-

culties Questionnaire and the Child Behavior Checklist: is small

beautiful? J Abnorm Child Psychol 27:17–24

30. Goodman R, Meltzer H, Bailey V (1998) The Strengths and

Difficulties Questionnaire: a pilot study on the validity of the self-

report version. Eur Child Adolesc Psychiatry 7:125–130

31. Rutter MJ, Tizard J, Whitmore K (1970) Education, health and

behaviour. Longman, London

32. Rodgers B, Pickles A, Power C, Collishaw S, Maughan B (1999)

Validity of the Malaise Inventory in general population samples.

Soc Psychiatry Psychiatr Epidemiol 34:333–341

33. Frankenburg WK, Dodds JB (1967) Denver Developmental

Screening Test. J Paediatr 71:181–191

34. Carey W, McDevitt S (1978) Revision of the Infant Temperament

Questionnaire. Pediatrics 61:735–739

35. Carey W, McDevitt S (1995) Coping with children’s tempera-

ment: a guide for professionals. Basic Books, New York

36. Elliot CD (1983) British Ability Scales. NFER-Nelson, Windsor

37. Bracken BA (1998) Bracken Basic Concept Scale-revised.

The Psychological Corporation, Harcourt Brace and Company,

San Antonio

38. Goldstein H (2003) Multilevel statistical models, 3rd edn.

Arnold, London

39. Schwarz G (1978) Estimating the dimension of a model. Ann Stat

6:461–464

546 Eur Child Adolesc Psychiatry (2010) 19:535–546

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