Social inequalities in children’s oral health-related quality of life: the Generation R Study
Qual Life Res
Social inequalities in children's oral health-related quality of life: the Generation R Study
Lea Kragt 0 1 2 3 4 5 6 7
Eppo B. Wolvius 0 1 2 3 4 5 6 7
Hein Raat 0 1 2 3 4 5 6 7
Vincent W. V. Jaddoe 0 1 2 3 4 5 6 7
Edwin M. Ongkosuwito 0 1 2 3 4 5 6 7
Social 0 1 2 3 4 5 6 7
0 Eppo B. Wolvius
1 The Generation R Study Group, Erasmus University Medical Centre , Rotterdam , The Netherlands
2 Department of Epidemiology, Erasmus University Medical Centre , Rotterdam , The Netherlands
3 Department of Pediatrics, Erasmus University Medical Centre , Rotterdam , The Netherlands
4 Department of Public Health, Erasmus University Medical Centre , Rotterdam , The Netherlands
5 Department of Oral and Maxillofacial Surgery, Special Dental Care and Orthodontics, Erasmus University Medical Centre , P.O Box 2040, 3000CA Rotterdam , The Netherlands
6 Edwin M. Ongkosuwito
7 Vincent W. V. Jaddoe
Purpose Oral health-related quality of life (OHRQoL) is the most important patient-reported outcome measure in oral health research. The purpose of the present research was to study the association of family socioeconomic position (SEP) with children's OHRQoL. Methods This cross-sectional study was embedded in the Generation R Study, a population-based cohort study conducted in Rotterdam, The Netherlands. For the present study, OHRQoL was assessed of 3871 ten-year old children. Family SEP was assessed with the following indicators: maternal/paternal education level, maternal/paternal employment status, household income, benefit dependency, and family composition. Linear regression analyses were performed to evaluate the (independent) associations of family SEP indicators with OHRQoL. Results The median (90% range) OHRQoL score of the participating children was relatively high [50.0 (43.0-53.0)]; however, OHRQoL was consistently lower in children with low family SEP. Positive associations were found for all SEP indicators (p-values 0.05) except maternal employment status and family composition. Benefit dependency, paternal employment, and household income were the most strongly associated with OHRQoL. No family SEP indicator was significantly associated with OHRQoL independent of the other indicators. Conclusions Based on the present findings, interventions and policies promoting good oral health and oral well-being should target children from low socioeconomic position. More research is needed, however, to understand the pathways of social inequalities in children's OHRQoL especially for the effects of material resources on subjective oral health measures.
Quality of life inequalities; Children
Oral health-related quality of life (OHRQoL) is a
commonly used patient-reported outcome measures in dental
research. This measure is designed to assess the impact of
oral diseases from the patient perspective and is thus
subjective and multidimensional. It is particularly suited
to assess oral health of individuals, because it
encompasses their physical function, psychological state, social
interaction, and somatic sensation [
]. Quality of life
measures in medical and dental research become
increasingly important because of the patients’ more
active participation in their health treatment, because of
the need for new evidence in oral health practices, and
because more and more diseases cannot be cured by the
treatment although they improve the patient’s condition
Research on OHRQoL is directed by the Wilson and
Cleary model, which shows the possible link between
biological and clinical variables, characteristics of the
individual and the environment and other nonmedical
factors on OHRQoL [
]. Many studies suggest that
socioeconomically deprived persons tend to have worse
oral health and unmet dental treatment needs [
]. In line
with the Wilson and Cleary model, these inequalities have
also been shown in subjective oral health measures among
adolescents, adults, and the elderly [
]. Also several
studies have been conducted on the relation between
socioeconomic status and children’s OHRQoL, but these
make use of various study populations, various
socioeconomic indicators, various methods, and statistical analyses
]. Yet, the evidence for the relation between family
socioeconomic position (SEP) and OHRQoL in the
preschool-aged and the school-aged children is inconclusive
OHRQoL is important and it is necessary to identify risk
groups at an early stage, because poor oral health will track
through childhood into adolescence and adulthood [
Therefore, the aim of the present study was to investigate
the associations of family SEP with children’s OHRQoL.
For this research, we used the data from the Generation R
Study, which is a large multiethnic birth cohort in
Rotterdam, The Netherlands.
Material and methods
This cross-sectional study is performed within the
Generation R Study, which is an ongoing multiethnic
populationbased prospective cohort study. This study has been
described in detail elsewhere [
]. The Generation R Study
has been conducted following the World Medical
Association Declaration of Helsinki and was approved by the
Medical Ethics Committee at Erasmus Medical Centre,
Rotterdam, The Netherlands (MEC-2012-165). Written
consent was obtained from all participants before data
collection was started.
Invitations to participate in the study were given to all
pregnant women with an expected delivery date between
April 2002 and January 2006 living in the study area
(Rotterdam, The Netherlands). From the original 9749
liveborn children included in the Generation R cohort, 7393
children still participated in the follow-up period from the
children’s age of 9 years onward. From these, we selected
the children with available data on OHRQoL, which was
assessed at the median (90% range) age of 9.79
(9.48–10.47). In total, 3871 children were included in this
OHRQoL of the children was assessed by parental
questionnaires. For this, a Dutch 11-item version of the
Children’s Oral Health Impact Profile (COHIP) was used,
which has previously been validated in a comparable
]. The questions cover the five subdomains
of children’s oral health: oral symptoms, functional
wellbeing, emotional well-being, school, and peer interaction.
The questions inquire about the frequency of oral health
impacts on daily life and are answered on a 5-point Likert
scale: never (5 points), almost never (4 points), sometimes
(3 points), often (2 points), and always (1 point). All
answers were added up to a final OHRQoL score (range
11–55 points), with the highest score indicating the best
quality of life. Missing values in the responses to the
questionnaire were replaced by the personal mean score of
the remaining answers to the questions, as it is proposed by
other researchers who used the original version of the
]. If there were more than 30% of the answers
missing, the participant was excluded from the analysis.
Family social position
Following socioeconomic indicators of family SEP were
considered in the present study: maternal and paternal
education level, maternal and paternal employment status,
and net household income, which are all traditional family
SEP indicators [
]. We also used receiving benefits and
single parenting as additional family SEP indicators,
because these were associated with oral health in previous
]. Parental education was assessed at the
children’s age of six by questionnaires and defined as low
(no education, primary school, lower or intermediate
vocational training, general school, or first year of higher
vocational training) or high (higher vocational training,
university, or PhD degree). Also, information on paternal
and maternal employment status was assessed by
questionnaires at the children’s age of six and categorized into
no paid job (unemployed, disabled, welfare recipient,
housewife or student, or other nonpaid work) or paid job
(paid or self-employed). In addition, information on net
household income (B2000€ vs.[2000€), receiving benefits
(no vs. social security, unemployment benefits, disability
allowances, or other), and single parenting was assessed in
parental questionnaires around children’s age of ten, which
were the same as for the assessment of OHRQoL.
Based on the literature and prior experience in clinical
practice, child’s sex, age, and ethnic background were
considered confounders in the association between family
SEP and OHRQoL. In addition, the following oral health
variables were considered as potential confounders for the
relationship between family SEP and OHRQoL: caries
experience, orthodontic treatment need based on either the
Dental health component (IOTN-DHC) or the aesthetic
component (IOTN-AC) of the Index of Orthodontic
treatment need and self-perceived orthodontic treatment need
]. Because not all the considered confounders were
assessed in the same follow-up period as OHRQoL, i.e.,
around the children’s age of 10, we used some
measurements from previous time points.
Child’s ethnicity was defined following the guidelines
for classification by Statistics Netherland [
ethnic background, assessed at enrollment in the
Generation R Study, was based on the country of birth of the
parents. Children of parents, with both parents being born
in the Netherlands, were classified as native Dutch. If at
least one of the parents was not born in the Netherlands, the
child was classified as nonnative Dutch.
Caries experience was assessed at the children’s age of
six with the decayed missing and filled teeth index (dmft)
which ranges from 0 to 20. The dmft-score (decayed
missing and filled teeth-score) of each child was obtained
from intraoral photographs. Before taking photographs, the
children brushed their teeth, and the teeth were dried with a
cotton roll. The images were taken with one of the two
intraoral cameras: the Poscam USB intraoral (Digital
Leader PointNix) and Sopro 717 (Acteon) autofocus
camera. Both cameras had a resolution of 640 9 480 pixels
and a minimal scene illumination of f 1.4 and 30 lx. The
whole dentition was captured with 10 photographs. The
photographs were judged by one pediatric dentist
(intrarater reliability J = 0.95), and a second calibrated
pediatric dentist judged 10% of the photographs (interrater
reliability of J = 0.62). Scoring dental caries per tooth on
intraoral photographs has been described elsewhere with a
high sensitivity (85.5%) and specificity (83.6%) compared
with ordinary oral examination [
]. If one or more
primary teeth were not able to be judged on the photographs,
no dmft-score was given. Children were categorized into
no caries experience (dmft = 0) vs. caries experience
(dmft [ 0).
The IOTN-DHC and IOTN-AC were assessed from
photographic and radiographic records of the children
taken around the age of ten. Assessment of the IOTN on a
combination of photographic and radiographic records has
been validated previously [
]. After 6 months, 10% of the
photographs were reassessed first by the same examiner
(LK) and second by another examiner (EO). With these
measurements, intrarater reliability (linear-weighted
J = 0.84) and interrater reliability were calculated
(linearweighted J = 0.68).
Self-perceived orthodontic treatment need was
measured in the parental questionnaires around the children’s
age of ten with the question: ‘‘Do you want your child to
get braces?’’ The question was answered on a five-point
Likert scale, with the answer possibilities ranging from
‘strongly disagree’ to ‘strongly agree.’ Answers were
categorized into ‘self-perceived need’ (strongly/somewhat
agree), ‘borderline self-perceived need’ (do not agree or
disagree), and ‘no need’ (strongly/somewhat disagree).
First, we used descriptive statistics to characterize the study
The associations of family SEP indicators with
OHRQoL were analyzed with series of weighted least squares
linear regression models. Different regressions models
were run, having each of the SEP variables as an
independent variable. Finally for all indicators, three different
models were created. First, we created the crude model
adjusted for child’s age, gender, and ethnic background
only. Second, we created model 1 adjusted for
confounders. Potential confounders were included in model 1:
when they changed the estimate by approx. 10%, or when
they were significant when entered into the crude model, or
when the R2 of the model improved. Finally, we created
model 2 adjusted for confounding variables and all other
family SEP indicators simultaneously. Model 2 was created
to evaluate the independent effects of each of the SEP
indicators. To assess the explanatory effects of the oral
health variables on the association of a particular SEP
indicator with OHRQoL, the differences between the crude
model and model 1 were compared:
b model 1Þ=bcrude modelÞ
This approach allows one also to evaluate the influence of
SEP indicators on oral health from the patient perspective.
Likewise, the explanatory effects of the other family SEP
indicators on the association between a particular SEP
indicator with OHRQoL were assessed. Significance of the
Total n (%)
Missing n (%)
Total n (%)
Missing n (%)
The table is based on the nonimputed dataset. Data are presented as
absolute numbers with percentages for categorical data and as median
with 90% range for continuous data
a Educational level: low = no education, primary school, vocational
training, general secondary school, and first-year higher vocational
training; high = higher vocational training, university, or PhD degree
b Benefits: social security, unemployment benefits, disability
allowances, and other
c Assessed at children’s age of 6
d Assessed at children’s age of 10
difference was assessed with a test for heterogeneity. In
addition, we categorized OHRQoL based on the median
value and conducted logistic regression models analogous
to the linear regression models.
Finally, we tested for multicollinearity in model 2 by
obtaining the tolerance and VIF values for each
determinant and covariate. Tolerance values above 0.10 and VIF
values below 10 were considered acceptable to rule out
We conducted a nonresponse analysis by comparing
children with data available on OHRQoL with the children
that had no data on OHRQoL on all family SEP indicators
and confounders using Mann–Whitney-U tests or
ChiSquare tests. Missing data in the determinants and
covariates were multiple imputed based on the other
determinants, covariates, and OHRQoL. Ten imputed
datasets were created using a fully specified model for
which we present the pooled regression coefficients with
95% confidence intervals (ab, 95% CI).
All analyses were performed using IBM SPSS Statistics
version 21.0 for Windows (SPSS INC., Chicago, IL, USA).
A significance level of p \ 0.05 was used for all analysis.
The nonresponse analysis showed that children without
information on OHRQoL had more often parents from a
lower socioeconomic status (supplemental Table S1). In
Table 1, the characteristics of the study population are
presented. Most of the children were native Dutch (67.8%).
Approximately one-third of all the children had a mother or
father with a low education level (32.7, 31.5% resp.).
Almost one-fifth of the children lived in a household with
an income below 2000€ per month. Prevalence of oral
health variables were relatively high, with approximately
18.4% of the children having caries experiences, 29.6% of
the children having objective, and 46.5% having subjective
orthodontic treatment need. The median (90% range)
OHRQoL score of the children was 50.00 (43.00–53.00).
Association of family SEP indicators with OHRQoL
The correlation between all family SEP indicators varied
between 0.08 and 0.54 (supplemental Table S2). The VIF
values for determinants and confounders in the models
used to describe the associations between family SEP
indicators and OHRQoL were all less than 1.5, and the
tolerance values were all above 0.70 (supplemental
Table S3). The results of the regression analysis having
each of the SEP variables individually as an independent
variable are presented in the supplement (supplemental
Table S4). In Table 2, the associations between family SEP
and OHRQoL are presented. Children of fathers and
mothers with low educational level had lower OHRQoL
than children of fathers with a high education level [crude
model father: ab: -0.45 (95% CI: -0.68 to -0.22); crude
model mother: ab: -0.34 (95% CI: -0.56 to -0.11)].
Similarly, significantly lower OHRQoL was seen in
children of unemployed fathers [ab: -0.81 (95% CI: -1.39 to
-0.22)], children with a low household income [ab: -0.67
(95% CI: -0.99 to -0.36)], children living in a household
that receives any kind of benefits [ab: -0.68 (95%
CI:-1.07 to -0.30)], or a single-parent family [ab: -0.54
(95% CI: -0.87 to -0.22)]. Thus, all family SEP
indicators, except maternal educational level, were negatively
associated with OHRQoL.
All of these associations remained significant after
adjustment for oral health variables (model 1, Table 2).
The oral health variables explained between 1.9 and 47.6%
of the relationship between the different SEP indicators and
OHRQoL. After adjustment for the other family SEP
indicators, benefit dependency and paternal employment
status were the most strongly associated with OHRQoL
[ab: -0.33 (95% CI: -0.70 to -0.04), resp. ab: -0.29
(95% CI: -0.84 to -0.26)]. However, there were no
significant independent family SEP associations with
OHRQoL found (model 2, Table 2). The associations between
family SEP indicators and OHRQoL were explained by the
other family SEP indicators between 16.7 and 75.0%. The
results based on the logistic regression models are
comparable with the linear regression analysis and presented in
the supplement (supplemental Table S5).
Discussion and conclusion
Family SEP was consistently positively associated with
OHRQoL. Moreover, children with lower family SEP
perceived lower OHRQoL independent of their objective
oral health status.
Our results suggest that not only clinical variables, such
as caries and malocclusions, are associated with lower
OHRQoL, but also different socioeconomic and
environmental variables interfere significantly in children’s
conditions of daily life. This is in line with other research that
showed how socioenvironmental factors are related to
lower OHRQoL in the 12-year old children [
], as well as
with studies that show socioeconomic inequalities in
objective oral health [
Many studies suggest that, but have not found
conclusive evidence, the association of family SEP with
children’s OHRQoL may be related to oral health behavior,
like tooth brushing frequency, sugar intake, and regular
dental visits [
]. However, in different studies, low
socioeconomic status was associated with less oral
hygiene, higher added sugar intake, and less dental service
]. We did not specifically adjust our models for
oral health behavior. Remarkably, our results stay
significant after adjustment for oral health variables like caries
experience and orthodontic treatment need. Thus, children
with a low family SEP do perceive lower oral health,
although it might not necessarily be true. This indicates
that the effect of family SEP on OHRQoL is attributed to
several additional factors, the so-called mediators, rather
than simply to oral hygiene and oral health.
One mediator that might contribute to the association
between family SEP with OHRQoL is related to aspects of
self-esteem and self-perception about oral health and body
image. One study showed that socioeconomic disparities in
self-perceived oral health might partly be mediated by
psychosocial factors like self-esteem [
]. Other literature
studies about the influence of self-esteem and
(self-perceived) oral health are mainly focused on the orthodontic
field and remains inconclusive [
studies on the relationship between family SEP and
selfesteem are scarce. Whereas self-esteem has been shown to
be significantly associated with quality of life, the
associations between family SEP indicators and self-esteem
appear inconsistent [
]. Because we did not include
self-esteem in our analysis, we cannot conclude about its
role in the association between family SEP and OHRQoL.
However, considering the Wilson and Cleary model of
(oral health-related) quality of life [
], we highly
recommend further research to understand the role of self-esteem
in relationship with environmental factors and OHRQoL.
b [95% CI]
b [95% CI]
The data are presented as linear regression coefficients (b) with 95%-confidence intervals (95% CI). The
crude model is adjusted for gender, age, and ethnicity only. Model 1 is additionally adjusted for
confounders: caries experiences, orthodontic treatment need, aesthetic treatment need, and self-perceived
orthodontic treatment need. Model 2 is additionally adjusted for confounders and the other socioeconomic
Significant associations are printed bold
a Educational level: low = no education, primary school, vocational training, general secondary school,
and first-year higher vocational training; high = higher vocational training, university, or PhD degree
b Benefits: social security, unemployment benefits, disability allowances, and other
c Assessed at children’s age of 6
d Assessed at children’s age of 10
The strength of the associations between family SEP
indicators and OHRQoL slightly varied. In general, family
SEP indicators are associated with each other [
lack of independence among these variables makes it
difficult to conclude which factor is most important. Maternal
education and employment status were less related to
children’s OHRQoL. One reason for this might be that the
father is still most often the principal earner of the family,
which would make maternal variables less appropriate as
SEP indicators. Indeed, the correlation between maternal
and paternal education and employment status was fairly
low (supplemental Table S2). These considerations suggest
that maternal employment status might not be used as
favorable socioeconomic indicator in oral health research
among populations comparable to the Generation R
Cohort. The family SEP indicators directly related to
material resources of the household (benefit dependency,
paternal employment status, and household income) were
the most strongly related to OHRQoL (see also
supplemental Table S4). Dental treatment and care often involves
high costs. As this might indicate that oral health care is
less accessible to children with lower family
socioeconomic position, this finding should alert oral health care
providers and policy makers.
There are several important theoretical reasons why it is
important to study family SEP with children’s OHRQoL.
Family SEP refers to the social and economic factors
influencing which position individuals have within the
]. A low SEP has a negative influence on adult’s
oral health, and parental oral health in turn is a strong
predictor for child oral health [
]. In addition, the
influences of SEP act over the life course and are therefore
important to study as early as possible. Reduced child oral
health is a strong predictor for impaired oral health in later
]. Finally, all the different influences on OHRQoL
help to understand why relationships between clinical
status and OHRQoL are sometimes weak and inconsistent.
The social inequalities in children’s OHRQoL found in
the present study indicate that policies and interventions
aimed to promote oral health behaviors and prevent oral
disease as well as discomfort among socially deprived are
highly warranted. Based on our study, these strategies
should take social disadvantage into account along with the
other mediating factors such as oral health behaviors,
cultural differences, or self-esteem and could involve
education, social benefits for dental treatments, or
introducing insurance covering various kinds of dental
Certainly, some limitations of the present study need to
be discussed. First, as in every observational study, our
results might be affected by residual confounding, although
we have constructed the fully adjusted models to assess the
independent effects of different family SEP indicators. Yet,
family SEP is a complex concept, and we did not include
all kinds of family SEP indicators, as, for example, wealth
or neighborhood SEP indices [
]. Second, in this
study, children’s’ OHRQoL was assessed by parental
questionnaires which might have introduced information
bias. We used parental reports because of practical reasons
and because several studies found parents to be good
proxies for children’s OHRQoL [
]. Third, another
potential source of information bias in the present study
might be due to the assessment of certain SEP indicators at
earlier time points. Certain SEP indicators are dynamic and
change over time. However, educational level, for
example, is known to be relatively stable . Moreover, we
found consistent associations for almost all SEP indicators
with OHRQOL, which suggest that the earlier assessed
data are good proxies for the current SEP. Fourth, with
regard to the original sample size of the Generation R
study, this study had a great number of losses to follow-up.
This could have resulted in a selection bias, if the
association between family SEP and OHRQoL would be different
between the excluded and included study population.
However, this seems unlikely. Fifth, because the
population of the Generation R Study has a generally high SEP,
the generalizability of our results are potentially limited.
Sixth, because we analyzed many different socioeconomic
indicators in this study, multiple testing might be seen as a
threat to this study. However, because in our opinion
testing all these different indicators fit into one single
hypothesis, i.e., a consistent relationship between family
SEP and OHRQoL, we did not adjust for a multiple testing
problem. Last, the various family SEP indicators could be
seen as mediators for the association between
socioeconomic indicators and OHRQoL. Therefore, a few of the
effects of these indicators are not confounded but caused
by other socioeconomic indicators. This is most easily
explained for family composition, as single households, for
example, are linked to lower household income or benefit
21, 22, 46
]. We saw in our analysis that other
socioeconomic indicators explain 75.0% of the association
between family composition and OHRQoL. As a
consequence, relationships between the various socioeconomic
indicators with OHRQoL may have been underestimated in
model 2. Still, model 2 was not affected by
multicollinearity, as tolerance and VIF values for all covariates
were within the accepted range.
The advantages of this study include the large and
ethnically diverse study population and the availability of
multiple indicators of family SEP. A post hoc statistical
power calculation indicated that a sample of 910 children
would have been sufficient to show the present findings
with 80% statistical power (based on number of predictors
=14, a = 0.05, lowest R2 tested = 0.02) [
]. This study,
however, includes 3871 children. To our knowledge, this is
the first study that investigates the association between
family SEP and OHRQoL in such a large multiethnic
In conclusion, family SEP was consistently associated
with OHRQoL, as indicated by children from low family
SEP having lower OHRQoL. These associations were
independent of their clinical oral health status, reinforcing
the importance of OHRQoL as outcome measure in oral
health research. Given these disparities, interventions and
policies promoting good oral health and oral wellbeing
should target children from low socioeconomic position.
Nevertheless, more research is needed to understand the
pathways of social inequalities in children’s OHRQoL.
Acknowledgements The authors gratefully acknowledge the
contribution of the participants, general practitioners, hospitals, midwives,
and pharmacies in Rotterdam, the Netherlands. The Generation R
Study was conducted by the Erasmus Medical Centre, Rotterdam, the
Netherlands, in close collaboration with the School of Law and
Faculty of Social Sciences of Erasmus University, Rotterdam; the
Municipal Health Service, Rotterdam area; the Rotterdam Homecare
Foundation; and the Stichting Trombosedienst & Artsenlaboratorium
Rijnmond, Rotterdam. The Erasmus Medical Centre, Rotterdam; the
Erasmus University, Rotterdam; and the Netherlands Organization for
Health Research and Development made the first phase of the
Generation R Study financially possible. V.W.V.J. received an additional
grant from the Netherlands Organization for Health Research and
Development (VIDI 016.136.361) and a Consolidator Grant from the
European Research Council (ERC-2014-CoG-64916). The funders
had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript. The authors declare no
potential conflicts of interest with respect to the authorship and/or
publication of this article.
Funding This work was supported by the Department of Oral &
Maxillofacial Surgery, Special Dental Care and Orthodontics of
Erasmus University Medical Centre in Rotterdam, the Netherlands.
The Erasmus University Medical Centre, Rotterdam; the Erasmus
University, Rotterdam; and the Netherlands Organization for Health
Research and Development made the first phase of the Generation R
Study financially possible. An additional grant from the Netherlands
Organization for Health Research and Development (VIDI
016.136.361 to V.W.V.J.) and a Consolidator Grant from the
European Research Council (ERC-2014-CoG-64916 to V.W.V.J.) were
received. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Compliance with ethical standards
Conflict of interests The authors declare that they have no conflicts
of interest with respect to the authorship and/or publication of this
Ethical approval All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the institutional research committee (MEC-2012-165) and with the
1964 Helsinki declaration and its later amendments or comparable
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
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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
1. Sischo , L. , & Broder , H. L. ( 2011 ). Oral health-related quality of life: What, why, how, and future implications . Journal of Dental Research , 90 ( 11 ), 1264 - 1270 .
2. Najman , J. M. , & Levine , S. ( 1981 ). Evaluating the impact of medical care and technologies on the quality of life: A review and critique . Social Science and Medicine Part F , 15 ( 2-3 ), 107 - 115 .
3. Wilson, I. B. , & Cleary , P. D. ( 1995 ). Linking clinical variables with health-related quality of life: A conceptual model of patient outcomes . JAMA , 273 ( 1 ), 59 - 65 .
4. Verrips , G. H. W. ( 2011 ). Een beter leven door een gezonde mond? Nederlands Tijdschrift voor Tandheelkunde , 123 ( 118 ), 123 - 125 .
5. Boyce , W. T., Den Besten , P. K. , Stamperdahl , J. , Zhan , L. , Jiang , Y. , Adler , N. E. , et al. ( 2010 ). Social inequalities in childhood dental caries: the convergent roles of stress, bacteria and disadvantage . Social Science and Medicine , 71 ( 9 ), 1644 - 1652 .
6. Fisher-Owens , S. A. , Gansky , S. A. , Platt , L. J. , Weintraub , J. A. , Soobader , M.-J., Bramlett , M. D. , et al. ( 2007 ). Influences on children's oral health: A conceptual model . Pediatrics , 120 ( 3 ), e510 - e520 .
7. Sabbah , W. , Tsakos , G. , Chandola , T. , Sheiham , A. , & Watt , R. G. ( 2007 ). Social gradients in oral and general health . Journal of Dental Research , 86 ( 10 ), 992 - 996 .
8. Geyer , S. , Schneller , T. , & Micheelis , W. ( 2010 ). Social gradients and cumulative effects of income and education on dental health in the Fourth German Oral Health Study . Community Dentistry and Oral Epidemiology , 38 ( 2 ), 120 - 128 .
9. Willems , S. , Vanobbergen , J. , Martens , L. , & De Maeseneer , J. ( 2005 ). The independent impact of household- and neighborhoodbased social determinants on early childhood caries: A crosssectional study of inner-city children . Fam Community Health , 28 ( 2 ), 168 - 175 .
10. Peres , K. G. , Peres , M. A. , Araujo , C. L. , Menezes , A. M. , & Hallal , P. C. ( 2009 ). Social and dental status along the life course and oral health impacts in adolescents: A population-based birth cohort . Health and Quality of Life Outcomes , 7 , 95 .
11. Vettore , M. V. , & Aqeeli , A. ( 2016 ). The roles of contextual and individual social determinants of oral health-related quality of life in Brazilian adults . Quality of Life Research , 25 ( 4 ), 1029 - 1042 .
12. Aida , J. , Kondo , K. , Kondo , N. , Watt , R. G. , Sheiham , A. , & Tsakos , G. ( 2011 ). Income inequality, social capital and self-rated health and dental status in older Japanese . Social Science and Medicine , 73 ( 10 ), 1561 - 1568 .
13. Kumar , S. , Kroon , J. , & Lalloo , R. ( 2014 ). A systematic review of the impact of parental socio-economic status and home environment characteristics on children's oral health related quality of life . Health and Quality of Life Outcomes , 12 , 41 .
14. de Paula, J. S. , Leite , I. C. , de Almeida , A. B. , Ambrosano , G. M. , & Mialhe , F. L. ( 2013 ). The impact of socioenvironmental characteristics on domains of oral health-related quality of life in Brazilian schoolchildren . BMC Oral Health , 13 , 10 .
15. Isaksson , H. , Alm , A. , Koch , G. , Birkhed , D. , & Wendt , L. K. ( 2013 ). Caries prevalence in Swedish 20-year-olds in relation to their previous caries experience . Caries Research , 47 ( 3 ), 234 - 242 .
16. Skeie , M. S. , Raadal , M. , Strand , G. V. , & Espelid , I. ( 2006 ). The relationship between caries in the primary dentition at 5 years of age and permanent dentition at 10 years of age-a longitudinal study . International Journal of Paediatric Dentistry , 16 ( 3 ), 152 - 160 .
17. Kragt , L., van der Tas , J. T., Moll , H. A. , Elfrink , M. E. C. , Jaddoe , V. W. V. , Wolvius , E. B. , et al. ( 2016 ). Early caries predicts low oral health-related quality of life at a later age . Caries Research , 50 , 471 - 479 .
18. Kooijman , M. N. , Kruithof , C. J., van Duijn, C. M. , et al. ( 2016 ). The Generation R Study: design and cohort update 2017 . European Journal of Epidemiology , 31 ( 12 ), 1243 - 1264 .
19. Kragt , L. , Tiemeier , H. , Wolvius , E. B. , & Ongkosuwito , E. M. ( 2016 ). Measuring oral health-related quality of life in orthodontic patients with a short version of the child oral health impact profile (COHIP) . Journal of Public Health Dentistry , 76 ( 2 ), 105 - 112 .
20. Geels , L. M. , Hoogstraten , J. , & Prahl-Andersen , B. ( 2008 ). Confirmative factor analysis of the dimensions of the child oral health impact profile (Dutch version) . European Journal of Oral Sciences , 116 ( 2 ), 148 - 152 .
21. Galobardes , B. , Shaw , M. , Lawlor , D. A. , Lynch , J. W. , & Davey Smith , G. ( 2006 ). Indicators of socioeconomic position (part 1) . Journal of Epidemiology and Community Health , 60 ( 1 ), 7 - 12 .
22. Galobardes , B. , Shaw , M. , Lawlor , D. A. , Lynch , J. W. , & Davey Smith , G. ( 2006 ). Indicators of socioeconomic position (part 2) . Journal of Epidemiology and Community Health , 60 ( 2 ), 95 - 101 .
23. Brook , P. H. , & Shaw , W. C. ( 1989 ). The development of an index of orthodontic treatment priority . European Journal of Orthodontics , 11 , 309 - 320 .
24. Swertz , O. , Duimelaar , P. , & Thijssen , J. ( 2004 ). Migrants in the Netherlands . Heerlen: Statisitcs Netherlands.
25. Elfrink , M. E. , Veerkamp , J. S. , Aartman , I. H. , Moll , H. A. , & Ten Cate , J. M. ( 2009 ). Validity of scoring caries and primary molar hypomineralization (DMH) on intraoral photographs . Deciduous Molar Hypomineralisation, its Nature and Nurture , 10 ( Suppl 1 ), 5 - 10 .
26. Kragt , L. , Hermus , A. M. , Wolvius , E. B. , & Ongkosuwito , E. M. ( 2016 ). Three-dimensional photographs for determining the index of orthodontic treatment need in scientific studies . American Journal of Orthodontics and Dentofacial Orthopedics , 150 ( 1 ), 64 - 70 .
27. Hair , J. F. , Jr. , Anderson , R. E. , Tatham , R. L. , & Black , W. C. ( 1995 ). Multivariate data analysis (3rd ed .). New York: Macmillan.
28. Tabachnick , B. G. , & Fidell , L. S. ( 2001 ). Using multivariate statistics (4th ed.) . Boston: Allyn and Bacon.
29. Elani , H. W. , Harper , S. , Allison , P. J. , Bedos , C. , & Kaufman , J. S. ( 2012 ). Socio-economic inequalities and oral health in Canada and the United States . Journal of Dental Research , 91 ( 9 ), 865 - 870 .
30. Pieper , K. , Dressler , S. , Heinzel-Gutenbrunner , M. , Neuhauser , A. , Krecker , M. , Wunderlich , K. , et al. ( 2012 ). The influence of social status on pre-school children's eating habits, caries experience and caries prevention behavior . International Journal of Public Health , 57 ( 1 ), 207 - 215 .
31. Sohn , W. , Burt , B. A. , & Sowers , M. R. ( 2006 ). Carbonated soft drinks and dental caries in the primary dentition . Journal of Dental Research , 85 ( 3 ), 262 - 266 .
32. Eisen , C. H. , Bowie , J. V. , Gaskin , D. J. , LaVeist, T. A. , & Thorpe , R. J. , Jr . ( 2015 ). The contribution of social and environmental factors to race differences in dental services use . Journal of Urban Health , 92 ( 3 ), 415 - 421 .
33. Bast , L. S. , Nordahl , H. , Christensen , L. B. , & Holstein , B. E. ( 2015 ). Tooth brushing among 11-15-year-olds in Denmark: Combined effect of social class and migration status . Community Dental Health , 32 ( 1 ), 51 - 55 .
34. Cruz , G. D. , Chen , Y. , Salazar , C. R. , Karloopia , R. , & LeGeros , R. Z. ( 2010 ). Determinants of oral health care utilization among diverse groups of immigrants in New York City . Journal of the American Dental Association , 141 ( 7 ), 871 - 878 .
35. Thompson , F. E. , McNeel , T. S. , Dowling , E. C. , Midthune , D. , Morrissette , M. , & Zeruto , C. A. ( 2009 ). Interrelationships of added sugars intake, socioeconomic status, and race/ethnicity in adults in the United States: National Health Interview Survey , 2005 . Journal of the American Dietetic Association , 109 ( 8 ), 1376 - 1383 .
36. Locker , D. ( 2009 ). Self-esteem and socioeconomic disparities in self-perceived oral health . Journal of Public Health Dentistry , 69 ( 1 ), 1 - 8 .
37. Fagg , J. H. , Curtis , S. E. , Cummins , S. , Stansfeld , S. A. , & Quesnel-Vallee , A. ( 2013 ). Neighbourhood deprivation and adolescent self-esteem: Exploration of the 'socio-economic equalisation in youth' hypothesis in Britain and Canada . Social Science and Medicine , 91 , 168 - 177 .
38. De Baets , E. , Lambrechts , H. , Lemiere , J. , Diya , L. , & Willems , G. ( 2012 ). Impact of self-esteem on the relationship between orthodontic treatment need and oral health-related quality of life in 11- to 16-year-old children . European Journal of Orthodontics , 34 ( 6 ), 731 - 737 .
39. Agou , S. , Locker , D. , Streiner , D. L. , & Tompson , B. ( 2008 ). Impact of self-esteem on the oral-health-related quality of life of children with malocclusion . American Journal of Orthodontics and Dentofacial Orthopedics , 134 ( 4 ), 484 - 489 .
40. Baker , S. R. , Mat , A. , & Robinson , P. G. ( 2010 ). What psychosocial factors influence adolescents' oral health ? Journal of Dental Research , 89 ( 11 ), 1230 - 1235 .
41. Mattila , M. L. , Rautava , P. , Sillanpa¨a¨, M. , & Paunio , P. ( 2000 ). Caries in 5-year-old children and associations with family-related factors . Journal of Dental Research , 79 , 875 - 881 .
42. Peres , K. G. , Peres , M. A. , Araujo , C. L. , Menezes , A. M. , & Hallal , P. C. ( 2009 ). Social and dental status along the life course and oral health impacts in adolescents: A population-based birth cohort . Health and Quality of Life Outcomes , 7 , 95 - 105 .
43. Bos , A. , Hoogstraten , J. , & Zentner , A. ( 2010 ). Perceptions of Dutch orthodontic patients and their parents on oral health-related quality of life . Angle Orthodontist , 80 ( 2 ), 367 - 372 .
44. Jokovic , A. , Locker , D. , Stephens , M. , Kenny , D. , Tompson , B. , & Guyatt , G. ( 2003 ). Measuring parental perceptions of child oral health-related quality of life . Journal of Public Health Dentistry , 63 ( 2 ), 67 - 72 .
45. Wilson-Genderson, M. , Broder , H. L. , & Phillips , C. ( 2007 ). Concordance between caregiver and child reports of children's oral health-related quality of life . Community Dentistry and Oral Epidemiology , 35 ( Suppl 1 ), 32 - 40 .
46. Galobardes , B. , Lynch , J. , & Smith , G. D. ( 2007 ). Measuring socioeconomic position in health research . British Medical Bulletin , 81 - 82 , 21 - 37 .
47. Soper , D.S. ( 2017 ). Post-hoc Statistical power calculator for multiple regression [Software] . http://www.danielsoper.com/ statcalc.