Family Resources and Effects on Child Behavior Problem Interventions: A Cumulative Risk Approach
Family Resources and Effects on Child Behavior Problem Interventions: A Cumulative Risk Approach
● John Kjøbli 0 1
0 Regional Center for Child and Adolescent Mental Health, Eastern and Southern Norway , P.O. Box 4623, Nydalen 0405 Oslo , Norway
1 Norwegian Center for Child Behavioral Development , P.O. Box 7053, Majorstuen 0306 Oslo , Norway
Family resources have been associated with health care inequality in general and with social gradients in treatment outcomes for children with behavior problems. However, there is limited evidence concerning cumulative risk-the accumulation of social and economic disadvantages in a family-and whether cumulative risk moderates the outcomes of evidence-based parent training interventions. We used data from two randomized controlled trials evaluating high-intensity (n = 137) and lowintensity (n = 216) versions of Parent Management Training -Oregon (PMTO) with a 50:50 allocation between participants receiving PMTO interventions or regular care. A nine-item family cumulative risk index tapping socioeconomic resources and parental health was constructed to assess the family's exposure to risk. Autoregressive structured equation models (SEM) were run to investigate whether cumulative risk moderated child behaviors at posttreatment and follow-up (6 months). Our results showed opposite social gradients for the treatment conditions: the children exposed to cumulative risk in a pooled sample of both PMTO groups displayed lower levels of behavior problems, whereas children with identical risk exposures who received regular care experienced more problems. Furthermore, our results indicated that the social gradients differed between PMTO interventions: children exposed to cumulative risk in the low-intensity (five sessions) Brief Parent Training fared equally well as their high-resource counterparts, whereas children exposed to cumulative risk in the high-intensity PMTO (12 sessions) experienced vastly better treatment effects. Providing evidence-based parent training seem to be an effective way to counteract health care inequality, and the more intensive PMTO treatment seemed to be a particularly effective way to help families with cumulative risk.
Family resources ● Social risk ● Cumulative risk ● Behavior problems ● Health care inequality ●; Evidence-based parent training interventions
It is well established that behavioral problems in childhood
(i.e., conduct problems, oppositional behaviors, and
inattentive problems) negatively impact children’s long-term
well-being through an association with school problems,
work problems, social exclusion, and poor health (Maughan
et al. 1985; Rutter et al. 1970; Sroufe et al. 2009). As with
many other mental health-related problems, a social
gradient has been established for behavior problems.
Specifically, a family’s lack of social and economic resources has
been found to be a social risk factor for the development
and prevalence of such problems (Bøe et al. 2012; Mazza
et al. 2016; Piotrowska et al. 2015; Sameroff et al. 1998).
Furthermore, it has been recognized that there is a social
gradient in the outcomes of mental health care services in
general (i.e. health care inequality) and in the parent training
interventions targeting behavior problems (Leijten et al.
2013; Lundahl et al. 2006; Pescosolido et al. 2013).
Knowing that behavioral problems are one of the most
frequent reasons for referrals to mental health services
(Storvoll 1997; World Health Organization 2003), health
care inequality in the services provided to children with
behavior problems implies that we will fail to help high-risk
children and that the effectiveness of these services will be
Health inequality remains a major societal challenge, and
extensive research has examined how health care systems
exacerbate these disparities (Spencer and Grace 2016).
Service use, patient adherence, and service outcomes have
been acknowledged as elements that are important for
understanding health care inequality (Alegría et al. 2011),
and lack of family resources has been an important focus of
health care inequality research (Muntaner et al. 2013). In
caring for behavior problems, social gradient approaches
have commonly focused on family resources in the form of
socioeconomic status (SES), typically assessed in terms of
parental income and education level (Leijten et al. 2013).
SES has been proposed as a “fundamental cause” of health
inequality, structuring (un-) favorable mechanisms across
contexts and diseases (Link and Phelan 1995; Muntaner
et al. 2013). This implies that high SES families enjoy a vast
number of flexible assets that they can use to their
advantage to implement protective strategies and produce
favorable treatment outcomes.
A more finely graded family resource approach may
involve measurement of a wide array of social and
economic resources, including parental mental and somatic
health, that are associated with SES, (i.e., cumulative risk).
Cumulative risk denotes a situation in which several social
risk factors operate together. To give an example,
comprehending a risk factor often involves envisioning the
circumstances that accompany it. For instance, is having a
single mother a risk if she has good health, a good income,
and only one child? Probably not. However, if the single
mother is poor, undereducated, and has three children, the
picture is different. This example supports the fact that
human beings often contend with constellations of risk
factors rather than isolated instances of adverse
circumstances (Seifer et al. 1992). In parent training, parents are
the agents of change in their children (Forgatch and
Patterson 2010). Thus, it is likely that limitations in parents’
access to resources create strain and stress on family life
which in turn may create health care inequality.
Social gradients and thus health care inequality have
been identified in interventions for behavior problems
(Lundahl et al. 2006). In a meta-analysis, Reyno and
McGrath (2006) found that single parent status, family size,
low income, low education, and parental mental health
issues diminished the effects of parent training. In another
meta-analysis, Lundahl et al. (2006) found that low SES
families benefitted less from parent training, particularly
when the mode of treatment was group therapy. However,
findings regarding family resources and the outcomes of
parent training are inconsistent (Deković et al. 2011). For
instance, low educational level, low marital satisfaction,
maternal depression, and a lack of psychological resources
have been found to enhance the benefits of treatment (Berlin
et al. 1998; Gardner et al. 2009; Lundahl et al. 2006).
These conflicting findings regarding separate family risks
have been taken as evidence that it is not the quality but the
quantity of resources that is relevant to family functioning
and children’s behavior (Rutter 2000; Sameroff et al. 1993;
see Stolk et al. 2008, p. 57). However, we know of only two
previous studies that have investigated how cumulative risk
influences the effects of parent training interventions for
addressing child behavior problems. In their study of a
program aimed at reducing behavioral problems among
children aged 1 to 3 years, Stolk et al. (2008) found no
associations between cumulative risk and treatment effects.
In studying another parenting intervention intended to
promote cognitive development among low-birthweight
infants, Liaw and Brooks-Gunn (1994) found that
cumulative risk did not moderate treatment effects on behavior
The social gradients in parent training outcomes are
likely grounded in rings of social influence ranging from
societal macro-level factors to micro-level factors such as
individual characteristics and client-practitioner interactions
(Spencer and Grace 2016). Hence, family resources and
cumulative risk are proxies for different change
mechanisms that operate at different levels of health care inequality
and account for more complex situational decisions,
rationalizations, and reasons for actions following parent
training intervention. Given the findings of the above review,
there is no consensus on whether family resources moderate
the benefits of parent training interventions. Several change
mechanisms are likely to vary according to family resources
and thus impact treatment outcomes for behavior problems.
Following intervention, social network is one group of
mechanisms that likely affects social gradients in treatment
outcomes. According to Thoits (2011), socially graded
network mechanisms may affect parents’ coping strategies.
Low-resource families have less access to secondary
networks of significant others who can offer various types of
beneficial support, such as information and advice on
interventions, encouragement, social influence, and role
modeling based on past experiences (Smith and Christakis
2008; Thoits 2011). Sociocultural mechanisms may be
another group of mechanisms that can reduce intervention
benefits in socially graded patterns. Low-resource parents
may hold more negative attitudes toward treatments and
professional advice. Their norms, practices, and values may
adhere less strictly to the parenting practices recommended
in parent training (Gillies 2006), as parenting interventions
may more closely match the resources and realities of
middle-class households (Zilberstein 2016). Skill
acquisition in parent training may also be less suited to
lowresource parents’ modes of learning and loci of control
(Pescosolido et al. 2013; Zilberstein 2016). Moreover,
lowresource parents have been found to behave in a less
selfassertive way when interacting with professionals, leading
to differential outcomes that may disfavor their children
(Gengler 2014; Weininger and Lareau 2003). Finally,
different practical mechanisms, in the form of social and
economic stressors (Conger et al. 1992), may reduce
lowresource families’ potential to gain from interventions.
Parents with cumulative risk are less likely to live in
traditional two-parent families, and they might have poorer
health and less money to pay for transportation expenses
and child care, all of which could limit their access to the
practical and social resources needed to participate in
intervention and integrate the parenting strategies learned
into their daily lives.
Although there is a general consensus that parent training
interventions work better for high-resource families, some
scholars have found compensatory effects (i.e., more
positive effects) of parent training for the low-resource families
(Leijten et al. 2013). Thus, several mechanisms are likely to
be involved in beneficial compensatory patterns. Initial
problem severity is a factor that could impact the benefits of
treatment (Leijten et al. 2013) in that more severely troubled
children may have more room for improvement. Similarly,
the severity of a child’s problem might impact parental
motivation and readiness to change (Baydar et al. 2003;
DiClemente and Velasquez 2002). Features of the
interventions could create compensatory effects for children
from families with cumulative risk. Evidence-based
interventions are based on a curriculum and follow a structured
progression. Thus, there might be less room for
highresource parents to influence the treatment situation in an
evidence-based intervention, where the teaching of core
components is somewhat fixed. In that regard,
evidencebased interventions might promote equality of care by
ensuring that effective practices are provided to both rich
and poor families (Cochrane 2004; Kristiansen and Mooney
2004). Moreover, the parent training interventions in focus
are based on the Structured Interaction Learning model
(SIL) of behavioral change (Forgatch and Patterson 2010).
According to the SIL, family resources, and thus cumulative
risk, affect the development of behavior problems by
disrupting parenting style. Hence, children exposed to
cumulative risk may have behavior problems that are more
strongly induced by social risk and disrupted parenting.
This implies that the SIL-based PMTO interventions might
work better for families with cumulative risk, as their
children’s behavior problem etiology might be more
influenced by social risk environments and thus more in line
with the PMTO curriculum. Relatedly, low-resource parents
have more often been found to adhere to negative parenting
styles (Elstad and Stefansen 2014). Hence, the parenting
focus in interventions may be better suited to low-resource
parents at the pre-intervention stage if they, to a greater
degree than high-resource parents, lack parenting skills and
abilities. If so, the practical focus on and rehearsal of
parenting practices, particularly in the high-intensive
intervention (explained in more detail below), could add a
compensatory mechanism to the treatment experience of
cumulative risk families.
In this study, we examined two evidence-based
interventions differing in intensity (i.e., dosage and scope): the
highintensity PMTO Parent Group (hereafter called PMTO) and the
low-intensity PMTO short form Brief Parent Training (BPT;
when discussed together hereafter, these are called PMTO
interventions). We focused on exposure to environments that
were characterized by a lack of family social and economic
resources in which we assessed the quantity of family
resources, i.e., cumulative risk. The primary question raised was
whether cumulative risk moderated the treatment effects of the
PMTO interventions. Thus we elaborated on the conditions
under which quantitative aspects of family resources exacerbate
or ameliorate health care inequalities in (1) parent training vs.
regular care and (2) a two-case comparison of the low-intensity
BPT intervention and the high-intensity PMTO intervention.
We used data from two randomized experiments evaluating
PMTO and BPT interventions. They were designed as pretest
(T1), posttest (T2; 8 weeks and 12 weeks after pretest for
BPT and PMTO, respectively) and follow-up (T3; 6 months
after post-test) parallel-group randomized trials with a 50:50
allocation ratio for the intervention and comparison groups.
This implies that children and their parents were randomized
to either one of the PMTO intervention groups or to the
comparison groups receiving the regular care offered in the
Norwegian health care system for children with behavior
problems. The participants were randomized after completing
the pretest questionnaire. Data collection occurred from 2007
to 2008 for BPT and from 2008 to 2009 for PMTO.
Importantly, the recruitment and data-collection procedures
were similar for both the BPT and PMTO groups. The
participating children and families came from all five Norwegian
health regions. The families had contacted the services
themselves or had been referred by a primary care agency
(e.g., child health clinics, child welfare agencies, schools or
kindergartens). To mirror the regular referral procedures used
in Norwegian health care services at that time, no formal
screenings were used in this study. Thus, the inclusion of
children and their families was based on practitioners’ clinical
opinions after they had consulted with eligible parents. The
participants were the parents (or caretakers) of children
between the ages of 3 and 12 years.
Participant characteristics in the PMTO and BPT samples
have been examined in a study by Tømmeraas 2016. Both
samples overall contained participants that had lower
economic and social resources compared to the Norwegian
population of families with children. Regarding baseline
differences between the two samples, the participant
characteristics differed between the BPT and the PMTO sample.
The descriptive statistics and baseline differences between
the samples are described in Table 1. As Table 1 shows, the
PMTO participants were clearly different from the BPT
sample as they had more family risks and higher levels of
behavior problems. Finally, we examined baseline
differences related to treatment conditions in the two samples in
terms of demographic and child behavior differences. In the
PMTO sample there were no significant baseline differences
between those receiving PMTO or the alternative of regular
care. In the BPT sample, one difference emerged. Parent in
the regular care group had on average higher education
levels compared to the BPT group, t(185) = 2.47, p = 0.1.
Both BPT and PMTO are part of a comprehensive
evidence-based intervention program called TIBIR
Table 1 Descriptive statistics
(means, standard deviations) and
baseline group differences
(chisquare and t-tests)
(Norwegian acronym; Early Initiatives for Children at
Risk), which was developed to prevent and treat behavior
problems in children (Solholm et al. 2013). PMTO targets
children at moderate to high risk and is an intensive
intervention consisting of 12 weekly sessions of 2.5 h each. The
reduction of negative parenting and teaching and the
rehearsal of positive parenting skills are central to PMTO;
parents practice their parenting skills through role-play and
participate in discussions. PMTO focuses on the following
parenting skills: positive involvement, skill encouragement,
family problem solving, monitoring, and effective
discipline, which includes mild contingent sanctioning through
ignoring and time-outs (or cool-downs). Moreover, much
emphasis is placed on parent emotional control to reduce
coercive interaction cycles between parents and children.
BPT is a low-intensity intervention that targets children
between low and moderate risk and consists of up to five 1-h
sessions. In these sessions, parents are taught only the most
exigent parenting practices, much less time is devoted to skill
rehearsal, and positive involvement and effective discipline
are emphasized. Important differences between the two
interventions are difference in dosage and comprehensiveness
and the fact that PMTO is a group therapy. Both BPT and
PMTO group and individual therapies have been tested in
randomized effectiveness trials and have been shown to be
more effective for reducing behavior problems than the
practices regularly used in the Norwegian health care system
for children with behavior problems (Kjøbli et al. 2013;
Kjøbli and Ogden 2012; Ogden and Hagen 2008).
Regular care consisted of the following approaches: 63
families (35%) received no treatment, 25 families received
a Household income in USD divided by 1000. b Non-Western immigrant
b Parent education level scale ranging from 1 (elementary school) to 4 (higher university degree)
c Eyberg Child Behavior Inventory—intensity scale (ECBI) 36 item version (raw scores)
***p < 0.001; **p < 0.01; *p < 0.05
help from school-based psychological services, 21 families
received counseling from public health nurses, 22 families
received counseling from public social workers in Norway’s
welfare services, 2 families received behavioral counseling,
2 families received Marte Meo (Aarts 2000), and 4 families
received other treatments (Kjøbli et al. 2013; Kjøbli and
Ogden 2012). Overall, the regular care given to the
comparison group varied in its content and intensity, and none
of the participants in this group received other
Children’s externalizing behavior problems were measured
with a 22-item version of the Eyberg Child Behavior
Intensity scale (ECBI). We used this abbreviated version of
the ECBI because previous studies have indicated that brief
versions of the ECBI have better psychometric properties
than the original scale (Hukkelberg et al. 2016). We created
a child behavior problem latent construct, ECBI, based on
three parcels of sum scores taken from the ECBI scale:
inattentive behavior, 4 items (“Easily gets distracted”, “Has
problems with concentration”); oppositional behavior, 10
items (“Does not follow rules without threat of punishment”,
“Argues about rules”); and conduct problems, 8 items
(“argues with similarly aged friends”, “destroys things”).
Because of sample power issues and to maintain accurate
identification, we used parcels in our measurement model,
which is considered a better alternative than using the
observed sum scores as outcomes (Rhemtulla 2016).
To measure the quantity of exposure to family risk, we
constructed a cumulative risk index combining nine
different family social and economic resources with parental
health, as shown in Table 2. These resource indicators have
been previously acknowledged as risk factors for the
development of behavior problems and have been shown to
affect health care outcomes (Alegría et al. 2011; Kjeldsen
et al. 2014; Moffitt and Scott 2009; Narayanan and Naerde
2016; Piotrowska et al. 2015; Waldfogel et al. 2010;
Zilberstein 2016). Our cumulative risk index had indicators
similar to those used in the Sameroff cumulative risk index
(Sameroff et al. 1987). Cumulative risk indexes are usually
calculated by summing the number of dichotomized risk
factors (Evans et al. 2013; Trentacosta et al. 2008). We
computed our cumulative risk index using the nine
dichotomized indicators shown in Table 2. Parents scored
an indicator as “1” if it was present and “0” if it was absent.
Cut-offs were based on previously established limits and/or
corresponded to previous cumulative risk research or
population-validated numbers (for mental health; Evans
et al. 2013; Reedtz et al. 2008; Trentacosta et al. 2008). The
cumulative risk indicator was based on parent-reported
information assessed before treatment.
Table 2 Cumulative risk indicators, definitions, and percentages
complying with sample criteria
Description of criteria
a Organization for Economic Co-operation and Development (OECD)
equivalence poverty scale
b One-item scale ranging from 1 (excellent health status) to 5 (poor
c Adjusted SCL-5 scores ranging between 1 and 5, a higher score
indicates more anxiety and mental distress
The poverty measure, OECD poor, was based on the
OECD equivalence scale (Organisation for Economic
Cooperation and Development 2016); families with less than
50% of the median net income were coded as poor. Thus,
the different poor cut-offs were calculated as a function of
family constellation and the 2008 average population
median net income of approximately 27,500 USD. The low
education variable was a dichotomized variable derived
from a categorical education level variable counting; 1 =
elementary school; 2 = upper secondary school; 3 lower
university degree; and 4 higher university degree (>4
years). Parents that scored 1 on the education level variable
were coded as 1 in the low education cumulative risk
indicator. Parents’ mental health was measured with the
Symptom Checklist 5 (SCL-5), which measures anxiety and
depression. SCL-5 is a (very) short-form of the SCL-25
mental health index which is derived from the SCL-90
psychopathology rating scale (Derogatis 1992; Strand et al.
2003). In a Norwegian population-based sample, Strand
et al. (2003) found that the correlation between SCL-5 and
SCL-25 was 0.91. SCL-5 risk cut-offs used were based on
numbers validated and normed in a Norwegian study
(Tambs and Moum 1993). Cronbach’s alpha for the SCL-5
was 0.88. The variable measuring parents’ somatic status
was a one-item and non-validated scale ranging from 1
(“Excellent health status”) to 5 (“Very poor health status”).
Table 3 displays the bivariate correlations among the
cumulative risk indicators.
Risk seemed to cluster in our sample. Hence, being poor
was significantly correlated with all the other nine risk
factors, as shown in Table 3. Moreover, the cumulative risk
index was significantly correlated with higher baseline
1. OECD poora
2. Low education
5. Single parent
6. Caregiver ratio
7. Young parent
8. Somatic health
9. Mental health 1 1 0.25***
a Organization for economic Co-operation and development (OECD) equivalence poverty scale
Table 3 Bivariate correlations
between family factors and child
levels of behavior problems (r = 0.17, p = 0.002), meaning
that children who were exposed to cumulative risk in their
families had, on average, higher levels of behavior problems
Children were the unit of analysis in this study. Social
gradients in the outcomes of PMTO, namely, whether
cumulative risk moderates treatment effects, were examined
with autoregressive SEM analysis using Mplus 7 (Muthén
and Muthén 2012). We ran the models as intent-to-treat
analyses to examine intervention effects across treatment
conditions using the three time-points: T1, T2, and T3. The
outcomes in our models showed changes in the children’s
behavior problems from baseline levels. The SEM models
are displayed in Fig. 1 and Fig. 2. The treatment variable
shows the treatment effects for families that scored 0 on the
cumulative risk variable, the cumulative risk variable
displays the effects of exposure to cumulative risk for the
regular care group, and the interaction term cumulative risk
* treatment shows the treatment effects for the families who
were exposed to cumulative risk in the PMTO groups.
The measurement model, displayed in Figs. 1 and 2,
shows the standardized factor loadings and correlated error
terms for the three parcels constituting our latent outcome
variable (ECBI). The error terms in the three parcels were
correlated across the time points. These unanalyzed
associations represent shared sources of variability over and
above the latent factors. To investigate whether there were
any sample-specific differences due to differences in
cumulative risk and the intensity of treatment (i.e., dosage
and comprehensiveness), we ran auto-regressive
multigroup SEM models analyzing the BPT and the PMTO
interventions separately. We used several goodness-of-fit
indexes to evaluate our theoretical model fit: chi-square
statistics, the comparative fit index (CFI), the Tucker-Lewis
index (TLI), and the root-mean-square error of
0.14*** (0.04) -0.16** (0.05)
Fig. 1 Cumulative risk and child behavior change in PMTO
interventions vs. regular care. Autoregressive SEM analysis, posttreatment
(T2) regressed on pretreatment behavior (T1). Note: Eyberg Child
Behavior Inventory—intensity scale (ECBI). Interaction variable
Cumulative Risk multiplied by Treatment Condition (CR * Treat).
Coefficients were standardized on Y (equals Cohen’s d), standard
error is displayed in parentheses. Model fit information: X2(df) = 27.0
(20), CFI = 0.99 TLI = 0.99 RMSEA = 0.06. ***p < 0.001; **p <
0.01; *p < 0.05
approximation (RMSEA; see table and figure notes).
Moreover, we checked our data for potential outlier
observations through a visual inspection of residual plots and
the estimation of a five percent trimmed mean in the
outcome variable. Neither of these procedures indicated that
the effects of outliers biased our results.
Additionally, we performed several sensitivity tests to
inspect functional forms in our data and to address potential
rival conclusions. We also included child age and gender
as covariates in our analyses and tested non-linear patterns
in our data. Moreover, we evaluated the family risk factors
in the cumulative risk index using independent-additive
models to investigate the unique effects of each cumulative
risk indicator. Finally, we partialled out the control
group families that received no treatment to determine
whether the cumulative risk comparison group estimates
0.10** (0.04) -0.11† (0.06)
Problems Inattention Opposition
Fig. 2 Cumulative risk and child behavior change in PMTO
interventions vs. regular care. Autoregressive SEM analysis, follow-up
(T3) regressed on pretreatment behavior (T1). Note: Eyberg Child
Behavior Inventory—intensity scale (ECBI). Interaction variable
Cumulative Risk multiplied by Treatment Condition (CR * Treat).
Coefficients were standardized on Y (equals Cohen’s d); standard
errors are displayed in parentheses. Model fit information: X2(df) =
36.3 (20), CFI = 0.98 TLI = 0.97 RMSEA = 0.05. ***p < 0.001; ** p
< 0.01; *p < 0.05; † = 0.059
were influenced by receiving active treatment or no
There was little attrition in our sample. Of the 353
participating families, 301 (85%) completed T2, and 275
(78%) completed T3. When comparing the attrition group
with the completers in each trial, few differences in intake
characteristics emerged (for more details see Kjøbli et al.
2013; Kjøbli and Ogden 2012). A missing data analysis,
Little’s MCAR test, indicated that the missing data were
missing completely at random. Thus, we modeled the data
using full-information maximum likelihood, which uses all
the available information from the observed data to handle
missing data (Wothke 2000).
The post treatment effects (T2) for the pooled sample of
PMTO interventions are displayed in Fig. 1. The results
showed that the children in the PMTO group from families
with one additional cumulative risk generally experienced
more benefit from treatment; in T2, behavior problems were
reduced by an average of 16% of a standard deviation for
each accumulated risk (ß = −0.16, p < 0.01; results were
standardized on Y only). Conversely, for the regular care
group, scoring higher on cumulative risk was significantly
associated with lower treatment benefits; this group
displayed increased levels of problem behavior in T2 (ß =
0.14, p = < 0.001). This implies that one additional
cumulative risk entailed an increase of 14% of a standard
deviation in children’s behavior problems in T2. In Fig. 2,
the pooled PMTO group results were not significantly
replicated at T3 (ß = −0.11, p = 0.06); however, the
coefficient had a considerable size in a similar direction as in T2.
For the regular care group, the T2 results were replicated at
T3, (ß = 0.104, p < 0.01). Overall, the model fit was within
an acceptable range for the fit indexes in all the estimated
models (Hu and Bentler 1999); see table notes. The factor
loadings from the parcels in all the SEM measurement
models were >0.50; see Figs. 1 and 2. Moreover, for the
Fig. 1 results, we computed the simple slopes and calculated
the regions of significance; see Fig. 3 (Preacher et al. 2006).
Differences between the groups were significant for
cumulative risk scores above 0.9, meaning that group
differences between those who received parent training and
those who received regular care were significant for families
with one or more risks.
Next, we examined whether there were sample-specific
differences in the associations between cumulative risk and
changes in behavior problems and whether the treatment
effects differed for the families with cumulative risks
according to treatment intensity. The path coefficients
revealed such differences; see Table 4. The PMTO
intervention seemed to be particularly effective for children from
families with cumulative risks at both T2 (ß = −0.33, p =
< 0.001) and T3 (ß = −0.30, p = < 0.001). The
lowintensity BPT intervention results did not reveal any
significant changes in the treatment effects for the families
with cumulative risk. In both samples, the children who
received regular care experienced significant increases in
behavior problems at all time points; see Table 4. We
computed the region of significance for the path T1 → T2 in
the PMTO sample. We found that group difference between
the PMTO group and comparison group was significant for
cumulative risk scores of 1.7 and higher (Fig. 4; Preacher
et al. 2006).
Additionally, we performed several sensitivity tests to
gauge the robustness of our conclusions. First, we examined
PMTO interventions vs. Regular care
Region of significant group difference > 0.9*
Fig. 3 Simple slopes and region of significance for the interaction
between treatment conditions and cumulative risk, child behavior
change at T2
Table 4 Autoregressive multi-group SEM analysis displaying
separate path coefficients for the BPT and the PMTO samples
Eyberg Child Behavior Inventory—intensity scale (ECBI). Interaction
variable Cumulative Risk multiplied by Treatment Condition (CR *
Treat). a Model 1 ECBI T2 regressed on T1, model fit information:
X2(df) = 55.9 (48), CFI = 0.99 TLI = 0.99 RMSEA = 0.05
b Model 2 ECBI T3 regressed on T1, model fit information: X2(df) =
59.0 (48), CFI = 0.99 TLI = 0.98 RMSEA = 0.04
Coefficients were standardized on Y (equals Cohen’s d)
***p < 0.001; **p < 0.01; *p < 0.05
whether there were non-linear patterns in the cumulative
risk associations with changes in behavior problems. We
tested both a cubic parameterization of cumulative risk and
threshold effects for families with between 2 and 5
cumulative risks. No threshold or non-linear patterns were
significantly different from 0 (results available upon request).
Moreover, we tested whether cumulative risk had unequal
effects according to the child’s gender and age. Both
variables were entered into the analysis as covariates and into a
three-way interaction term with treatment and cumulative
risk (results available upon request). No significant effects
of gender or age emerged. Furthermore, we tested the nine
cumulative risk factors singularly in independent-additive
models to test for unique predictive validity. We found that
no significant results emerged from these analyses. Finally,
35% of our control group cases received no treatment. We
suspected that these children and families biased our
estimates, and we ran additional analyses without these 63
families. The results were similar to those of the original
models in terms of both the coefficient sizes and
significance levels, and the full sample was thus included in
our final analysis.
In this study, we extended the literature on health care
inequality in behavior problem interventions by examining
the relationships between family cumulative risk and
High-intensity PMTO vs. Regular care
Region of significant group difference > 1.7*
Fig. 4 Simple slopes and region of significance for the interaction
between treatment condition and cumulative risk for the high-intensity
PMTO sample, child behavior change at T2
treatment outcomes in evidence-based parent training and
regular care. We also examined cumulative risk associations
in a case comparison between low-intensity and
highintensity PMTO. First, we found that exposure to
cumulative risk differentially moderated the treatment effects of
PMTO interventions and regular care, as reflected by the
opposite social gradients of the changes in the children’s
behavior problems. The children who received PMTO
interventions and were exposed to one or more cumulative
risks experienced compensatory effects, meaning that the
children from families with low amounts of resources
experienced greater reductions in their behavior problems
than the children from high-resource families. Conversely,
the regular care group exposed to equal levels of risk
experienced more behavior problems over time, indicating
that the children from low-resource families had poorer
treatment outcomes with regular care than the children from
high-resource families. Second, we found that the families
with cumulative risk benefitted differently according to the
intensity of the PMTO treatments; the children who were
exposed to cumulative risks experienced vast improvement
with high-intensity PMTO. Thus, cumulative risk produced
social gradients in treatment effects according to both the
treatment conditions and the treatment intensity.
The effects of cumulative risk seem to be linear, and we
conclude that it is the sheer number of risk factors that
changes the treatment effects rather than differences in
treatment effects below and above a certain threshold.
Previous research regarding cumulative risk and the effects
of parent training is both limited and inconsistent (Liaw and
Brooks-Gunn 1994; Stolk et al. 2008). In this sample of
atrisk children, our results revealed opposite social gradients,
indicating that children from families with cumulative risk
were highly receptive to the type of care provided by the
Norwegian health care system.
The finding of opposite social gradients for the treatment
effects, related to the PMTO interventions and regular care,
implies that type of treatment may either create or reduce
health care inequality among low-resource families
receiving help for their child’s behavior problem. Hence, there
seem to be different mechanisms related to changes in
health care outcomes for those who receive parent training
and those who receive regular care. Unfortunately, we have
limited knowledge about the contents of regular care.
However, we know that the differences between PMTO
interventions and regular care are rooted in the differences
between structured, curriculum-grounded, evidence-based
parent training and the more unstructured parent counseling
provided in regular care. Hence, the mechanisms related to
inequality in regular mental health care and parent training
interventions, such as network mechanisms, sociocultural
mechanisms and practical mechanisms (Alegría et al. 2011;
Spencer and Grace 2016; Zilberstein 2016), applied more to
the regular care group in our study. It might be that these
mechanisms come into play more when the mode of
treatment is less structured and does not explicitly target
parenting style. Moreover, this could indicate that there was
more room for high-resource parents to influence treatment
content—and thus their children’s outcomes—under the less
structured health care conditions.
Conversely, the compensatory effects of PMTO
interventions for low-resource families indicate that other
beneficial mechanisms were operating within these structured
treatment conditions. It might be that the children from
lowresource families were more exposed to disrupted parenting
practices and that systematic parent training was more
adapted to their pre-intervention skills and family climate.
Moreover, separate analysis of our preventive BPT and
high-risk PMTO samples revealed that the compensatory
effects were more prominent under the latter treatment
condition. In the BPT sample, the families with both low
and high cumulative risk experienced positive changes in
their children, whereas in the more intensive PMTO
treatment group, the families with high cumulative risk
experienced a vast improvement. The reduction in their children’s
behavior problem levels had the effect size of
approximately 30% of a standard deviation change per level
increase in cumulative risk, which underpins this argument.
It seems that providing more intensive treatment to the more
troubled families exaggerates the compensatory effect
mechanisms. Thus, there is probably interplay between
compensatory mechanisms, such as parent’s
preintervention parenting skills, the etiology of child
behavior problems, and readiness for change, that produces
favorable outcomes for the families with cumulative risk in
the PMTO treatment. However, more research is needed to
reveal the mediational relationships behind these
Behavior problems in childhood contribute to social
gradients in child well-being, but behavior problems are
also partly the products of social disparities. Low-resource
backgrounds have been found to increase the risk that a
child will experience behavior problems (Piotrowska et al.
2015), and behavior problems themselves have negative
long-term developmental impacts, as children are exposed
to multiple threats to their well-being later in life (Moffitt
et al. 2002). From a mental health care perspective, this
underscores the need to provide effective care for this
vulnerable group of children and their families. If we fail to do
this, mental health care interventions aimed at behavior
problems will certainly produce health care inequality and
exacerbate existing health inequality (and ultimately social
inequality) among low-resource populations. The opposing
social gradients we found in care for behavior problems
support this argument; the type of treatment provided can
either produce or reduce health care inequality.
The steady negative development displayed by children
from low-resource backgrounds in the regular care group is
thus consistent with the theory of family resources as a
“fundamental cause” structuring flexible assets when coping
with children with behavior problems (Link and Phelan
1995; Muntaner et al. 2013). This negative development is
also consistent with behavior problems risk theory, which
postulates that social risk, in the form of family resources,
intensifies the development of behavior problems (Mazza
et al. 2016). When helping children at risk for such negative
development, evidence-based parent training interventions
seem to be an efficient strategy for counteracting health care
inequality and the lack of family resources as a
“fundamental cause” and thus for effectively preventing and
altering negative developmental trajectories for children
from low-resource backgrounds. It has been postulated that
evidence-based treatments can promote equality in care
(Cochrane 2004; Kristiansen and Mooney 2004). Regarding
the outcomes of treatment, our results support this
In this study, we had the advantage of using experimental
data gathered in the Norwegian regular care system for
children with behavior problems. However, several
limitations should be considered. Admittedly, combining risk
factors into a cumulative risk index will, to some extent,
obscure the etiology of social risk. We also applied an equal
weights assumption when we pooled the dichotomized risk
factors, and we combined risk factors from different
domains, such as family demographics and parental health.
However, we can offer insights into how different amounts
of risk exposure affect health care outcomes for children
with behavior problems. The predictive validity of our
cumulative risk index supports this notion. Moreover,
although we adhered to previously established cut-offs in
cumulative risk research, one may still argue that the
process of dichotomization inflicts arbitrary limits on risk
factors. Nevertheless, evidence is very limited regarding
cumulative risks and the outcomes of care for behavior
problems. Thus, the effects of cumulative risk must be
replicated in other samples and contexts. Moreover, the
RCT design evaluating treatment intervention packages,
such as PMTO, did not allow us to address which treatment
components that produced the compensatory effects of
PMTO (Collins 2014). Other approaches, such as factorial
designs, might be more appropriate for elaborating further
on the change mechanisms related to cumulative risk that is
at work in parent training.
Acknowledgements Asgeir R. Olseth, Bjørn Arild Kristiansen,
Elisabeth Askeland, Henrik Dae Zachrisson, John Kjøbli, Kristian
Heggebø, Silje Hukkelberg, Terje Ogden, Terje Christiansen, Torkild
Hovde Lyngstad, and all you in the Department of research and the
Department of child program development at the Norwegian center for
child behavior development.
Authors’ Contributions T.T.: wrote the manuscript and performed
the data analyses. J.K.: designed the study, administered the data
collection, and commented on the manuscript.
Compliance with Ethical Standards
Ethical Approval All procedures performed in this study were in
accordance with the ethical standards of an ethical review board; The
Norwegian National Committee for Research Ethics, Region South;
The Norwegian Social Science Data Services; and with the 1964
Helsinki declaration and its later amendments.
Informed Consent Informed consent was obtained from all
individual participants included in the study.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://crea
tivecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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