Assessing individual and neighborhood social factors in child oralhealth-related quality of life: a multilevel analysis

Renata Saraiva Guedes • Chaiana Piovesan •

Jose Leopoldo Ferreira Antunes • Fausto Medeiros Mendes •

Thiago Machado Ardenghi

Accepted: 8 April 2014

� Springer International Publishing Switzerland 2014

Abstract

Purpose It has been suggested that subjective measures

of oral health are influenced by both individual and con-

textual characteristics. This study assessed the relationship

between neighborhood and individual social networks and

child oral health-related quality of life (COHRQoL).

Methods This study followed a cross-sectional design

using a multistage sample of 478 children aged 1–5 years

old. Caregivers completed the Brazilian version of the

Early Childhood Oral Health Impact Scale (ECOHIS) and

answered questions related to socioeconomic status and

social network. The dental examination provided infor-

mation on the prevalence of dental caries, dental trauma,

and occlusion. Contextual social determinants included the

presence of cultural community centers and of workers’

association in the neighborhood. Data analysis considered

the total ECOHIS scores as the outcome and neighborhood/

individual social networks as the independent variables. A

multilevel Poisson regression model was used to investi-

gate the association among individual and contextual

characteristics and COHRQoL.

Results Total ECOHIS scores ranged from 0 to 41 (possi-

ble range 0–52). The mean ECOHIS score was low

(mean = 1.8, SD = 3.9), and the functional domain pre-

sented the highest mean impact on COHRQoL (mean = 0.5,

SD = 1.4). Following adjusted analysis, low household

income, visiting a neighbor less than once a month, the

presence of anterior open bite, dental trauma, and dental

caries were identified as individual determinants of negative

impact on a child’s quality of life. These variables remained

associated with the outcome even after adding the contex-

tual-level variables in the model. At the contextual level, the

presence of community cultural centers was associated with

overall mean ECOHIS score; higher impacts on COHRQoL

were observed in those living in neighborhoods without

cultural community centers.

Conclusion There is a significant association between

individual and contextual social determinants and CO-

HRQoL; unfavorable social conditions and poor socio-

economic status have a negative impact on caregiver

reports of children’s oral health-related quality of life.

Keywords Multilevel � Children � Quality of life � Social

support � Socioeconomic status � Oral health

Abbreviations

OHRQoL Oral health-related quality of life

COHRQoL Child oral health-related quality of life

ECOHIS Child Perceptions Questionnaire

WHO World Health Organization

R. S. Guedes

Universidade Federal de Santa Maria (UFSM), Santa Maria,

RS, Brazil

C. Piovesan

Centro Universitario Franciscano (UNIFRA), Santa Maria,

RS, Brazil

J. L. F. Antunes

Faculdade de Saude Publica, Universidade de Sao Paulo,

Sao Paulo, Brazil

F. M. Mendes

Faculdade de Odontologia, Universidade de Sao Paulo,

Sao Paulo, Brazil

T. M. Ardenghi (&)

Universidade Federal de Santa Maria (UFSM), Rua

Cel.Niederauer 917/208, Santa Maria, RS 97.015-121, Brazil

e-mail: thiardenghi@hotmail.com

123

Qual Life Res

DOI 10.1007/s11136-014-0690-z

ICDAS International Caries Detection and

Assessment System

BMW Brazilian minimum wage

RR Rate ratio

CI Confidence interval

dmf-t Decayed/missing/filled teeth

ICC Intraclass correlation coefficient

Introduction

Oral health has been described as an important feature of

well-being. A poor state of oral health can negatively affect

children’s daily lives [1–3]. Therefore, the use of oral

health-related quality of life (OHRQoL) instruments has

been widely advocated as an adjunct to clinical examina-

tions in capturing the full impact of oral disorders [4, 5].

A set of COHRQoL instruments has been used in oral

health surveys [6–8]. The Early Childhood Oral Health

Impact Scale (ECOHIS) is an effective instrument for

assessing COHRQoL in children aged 2–5 years [8–12].

The questionnaire was developed in the USA [8], and

further studies have confirmed its validity and reliability in

France [13], China [14], Turkey [15], Iran [16], and Brazil

[9, 10].

Previous studies reported the role of oral health abnor-

malities and adverse socioeconomic conditions as indi-

vidual determinants of poor perception of oral health and

quality of life [17–20]. However, increasing evidence

suggests that a broad range of contextual social determi-

nants influences self-perception of oral health and quality

of life [21]. Some authors suggest that, independent of

socioeconomic level, more equal societies have better

health because they are more cohesive and supportive, and

individuals have a mutual understanding. These societies

have lower rates of mortality, morbidity, and violence and

higher levels of social support [22, 23].

The impact of the contextual network on oral health

emphasizes the multilevel nature of health outcomes.

Hence, attention has been paid to the complex interaction

between individual and contextual determinants of oral

health [21]. Recent studies have used statistical approaches

that integrate individual characteristics and contextual

variables into a single explanatory model using multilevel

analysis [24–28]. This approach has been carried out to

adjust for individual associations in different oral health

outcomes, such as dental caries [23, 26, 27, 29], use of

dental services [26, 27], fluorosis [28], periodontal disease

[30, 31], and dental pain [32]. A few studies have assessed

the effect of contextual social networks on OHRQoL;

however, these studies were conducted in a population of

adults and the elderly [33, 34]. There is a lack of evidence

about these associations in children. Therefore, a cross-

sectional study was carried out with a sample of 1–5-year-

old Brazilian children to assess the relationship between

neighborhood and individual social networks and CO-

HRQoL. This is the first study to investigate the interaction

between individual and neighborhood determinants and

COHRQoL in preschool children. Understanding the effect

of the neighborhood network on individuals’ perceived

quality of life may contribute to the planning of socially

appropriate oral health programs. For instance, if there is a

significant contextual variability in individuals’ percep-

tions of oral health, upstream public oral health strategies

focusing on the reduction in broad social inequalities

related to a geographic area would increase COHRQoL.

Materials and methods

Ethical considerations

This study was approved by the Committee for Ethics in

Research at the Federal University of Santa Maria, Brazil.

All participants and their parents/legal guardians signed a

free and informed consent form.

Sample and study design

A survey was performed to assess the oral health status of

1- to 5-year-old preschool children in the city of Santa

Maria, RS, located in the south of Brazil. The city has

263,403 inhabitants with 27,520 children under 6 years

old. This survey was conducted on National Children’s

Vaccination Day. According to the Ministry of Health, the

vaccination program has had a consistent uptake rate of

greater than 97 %.

Sample size was calculated with consideration of the

following parameters: sampling error of 5 %, 95 % confi-

dence level (CI), average ECOHIS score of 2.1 (SD = 3.8)

in the unexposed group (without caries) and 5.1 (SD = 6.9)

in the exposed group (with caries) [19]. The ratio of exposed

to unexposed was 1:1, and the correction factor of 1.6 (design

effect) was applied because of a change in the precision of

estimates generated by the process of clusters in two stages.

Considering possible losses, 20 % was added to the sample

size, resulting in a required minimum of 215 children. As the

present study was part of a survey in which other outcomes

were considered, the final sample size was greater than the

minimum required to verify differences in ECOHIS scores.

A sample group was selected from all children attending

health centers in the municipality. Health centers were used

as sampling points because the city is administratively

divided into five regions, each with health centers

Qual Life Res

123

responsible for vaccinating children living in that area. For

this study, multistage sampling considered all health cen-

ters with a dental office as primary survey units, and 15 out

of 28 health centers were randomly selected. The selected

centers accounted for nearly 85 % of the children attending

the vaccination program. The sample was inversely

weighted by the sampling fraction at each center. During

the survey, every fifth child in the queue for vaccination

was invited to participate. If parents did not agree to par-

ticipate, then the parents of the next child were invited

[35]. The sampling process was the same for all health

centers.

Data collection

Data were collected through clinical oral examinations and

structured interviews. Fifteen examiners participated in the

study, all graduate students with previous experience in oral

health surveys and who had received a 36-h training session

on standardized data collection. The training and calibration

process included theoretical explanation and in vitro and

clinical examinations. First, the examiners evaluated pho-

tographic images. Afterward, they independently examined

60 exfoliated primary teeth set in arch models aided by a

dental operating light, compressed air, plane dental mirror,

and WHO periodontal probe. After the individual examina-

tions, doubts and disagreements were discussed as a group.

One week later, the in vitro examinations were repeated for

training purposes. For clinical training and calibration, 10

children were examined twice by the same examiner with

1 week between each examination. More details about this

process were published previously [36].

Children were examined using a plane dental mirror,

WHO periodontal probes, wet gauze pads, toothbrush, and

dental floss while seated in a dental chair. The clinical

examination recorded the prevalence of dental caries, dental

trauma, and anterior open bite. Prevalence and severity of

dental caries were measured according to the ICDAS criteria

[37, 38]. The number of decayed/missing/filled teeth

(DMFT) was calculated considering the cutoff point 3 of the

ICDAS (0–2 sound, 3–6 carious). The index presented

comparability with standard criteria (WHO) in an epidemi-

ological survey of preschool children [39]. The prevalence of

dental caries considered children with DMFT C 1.

The examination for dental trauma included only pri-

mary maxillary and mandibular incisors. The criteria for

traumatic dental injuries followed those of the Children’s

Dental Health Survey in the UK [40].

Socioeconomic and demographic variables

To obtain socioeconomic data, each caregiver was inter-

viewed using a structured questionnaire. The questionnaire

elicited information on age, gender, race, household

income, and social support. Household income was mea-

sured in terms of the Brazilian minimum wage (BMW), a

standard for this type of assessment, which corresponded to

approximately 280 US dollars/month at the time of data

collection. The survey adopted ethnic group classification

according to the criteria established by the agency for

demographic analysis, the Brazilian Institute of Geography

and Statistics [41]. According to these criteria, children

were classified as ‘‘black’’ (black children of African and

mixed descent) and ‘‘white’’ (children of European

descendent).

Parents’ perception of social capital was assessed using

the question, ‘‘In the past 12 months, have you visited a

neighbor or had a neighbor visit you?’’ and the following

possible responses: 0 = no or less than once a month;

1 = yes, at least once a month; 2 = yes, at least twice a

month; 3 = yes, more than three times a month (This was

later dichotomized into ‘‘at least once a month’’ [codes

1–3] and ‘‘less than once a month’’ [code 0]). This is a

commonly used question in the social capital literature and

in the Brazilian population [42, 43]. In this context, pre-

vious studies have demonstrated that neighborhood con-

nection, such as time spent visiting friends or neighbors,

can be considered as a proxy of social capital; it is a

mechanism for the provision of public goods and for the

transmission of new ideas and shared values, contributing

to social support [44]. This measure has also been used in

previous data sets of the United States Bureau of Census

and in other oral health studies [45]. It also has been shown

that the frequency of contact with friends or neighbors may

reduce social isolation, which plays an important role in

maintaining oral health. The main effect of social partici-

pation is obtained from social relationships; individuals in

a social network are subject to social controls and peer

pressure that influence normative dental health behaviors.

The usability of the socioeconomic questionnaire was

previously assessed in a sample of 15 parents during the

calibration process. These parents were not part of the final

sample.

In order to assess the contextual-level influences on CO-

HRQoL, two community-related variables were obtained:

the presence of cultural community centers and the presence

of a workers’ association. These covariates have previously

been used as proxies for a community social network [21].

Context variables were defined by geographic area through

the neighborhood in which the child was living. This infor-

mation was obtained from the local government (municipal

official publication). The total number of cultural commu-

nity centers and workers’ associations was defined by the

geographic area through the neighborhood in which the child

was living. We used these variables as dichotomous indi-

cators of present/not present for analysis. The classification

Qual Life Res

123

of workers’ associations and community cultural centers was

provided by the local authorities and has been used in official

city publications.

Oral health-related quality of life

COHRQoL was assessed using the Brazilian version of the

ECOHIS. The scale consists of 13 items, including a child

impact section (child symptoms, function, psychological,

and self-image/social interaction domains) and a family

impact section (parental distress and family function

domains) (Table 2). Answers were recorded using a Likert

scale with response options coded 0–5 (0 = never,

1 = hardly ever, 2 = occasionally, 3 = often, 4 = very

often, and 5 = do not know). Mean ECOHIS scores were

calculated for each domain and for the whole scale as a

simple sum of the response codes after recoding all ‘‘do not

know’’ responses as missing. Consistent with prior

research, for those with up to 2 missing responses in the

child section or 1 in the family section, a score for the

missing items was imputed as an average of the remaining

items for that section [8]. We excluded from the analysis

parents with missing responses to more than 2 child items

and 1 family item. Total scores ranged from 0 to 52. The

higher the score, the greater the negative impact of oral

health problems and related treatment experiences on

OHRQoL of preschool children and their families.

Data analysis

The STATA 12.0 software (Stata Corporation, College

Station, TX, USA) was used for data analysis. Unadjusted

and adjusted multilevel Poisson regression models were

used to describe the association between outcome and

predictor variables. The study considered the ECOHIS

scores as count variables and performed a parametric

assessment of scores associated with answers, which has

been proposed by previous studies [17, 19].

In our data set, children (first level) were nested in

neighborhoods (i.e., 1 of the 5 administrative regions of the

city) (second level). Multilevel models are appropriate for

analyzing data with a hierarchical cluster, because they

determine the relative size of the variance at each level [21,

46]. Multilevel Poisson regression analysis used a random-

effect model with random intercepts and fixed slopes to

evaluate the associations between ECOHIS overall mean

(primary outcome) and individual and contextual covari-

ates. This strategy allowed for the estimation of rate ratios

(RR) among comparison groups and their respective 95 %

confidence intervals (CI). It corresponds to the ratio of the

arithmetic mean of ECOHIS scores between exposed and

unexposed groups. The model can be viewed as a regres-

sion model with an added level 2 residual or a

neighborhood-specific intercept. In the model, the inter-

cepts or level 2 residual is a random variable representing

random differences between neighborhoods, assuming that

the slopes are fixed (the same slopes across different con-

texts). As the intercept is allowed to vary, the scores on the

dependent variable for each individual observation are

predicted by the intercept that varies across groups. The

full model specification for a count of ECOHIS for person i

in neighborhood j (including individual- and contextual-

level covariates) can be viewed in the following equation:

log lij

� �¼ b0 þ b1x1j þ � � � þ bnZij þ U0j þ eij

where b0 is the intercept, b1 is the coefficient for the

individual variable X, while the bn is the coefficient for the

group variable Z. The value of U0j is the neighborhood-

specific (level 2) random intercept, and the eij is the

residual.

In the first stage, an unconditional model (‘‘null’’ model)

estimated the basic partition of data variability between the

two levels before the inclusion of individual and contextual

characteristics was taken into account [46]. The second

model (Model 2) added covariates at the individual level;

the ‘‘full’’ final model (Model 3) included individual fac-

tors and contextual covariates.

We considered variables that presented a P value B0.25

in the unadjusted analyses for entry into the adjusted

models. They were retained and considered statistically

significant into the final models only if they had a P value

B0.05 after adjustment. In all models, the intraclass cor-

relation coefficient was calculated to demonstrate the

fraction of variability due to the covariates at the contex-

tual level.

Results

A total of 520 mother–infant pairs were invited to participate

in the survey; 478 (91.9 %) agreed to participate. The dis-

tribution according to a child’s age was as follows:

12–23 months, 97 (20.3 %); 24–35 months, 89 (18.6 %);

36–47 months, 119 (24.9 %); 48–59 months, 173 (36.2 %).

Non-participation was mainly due to a child’s refusal to

undergo the clinical examination. Inter- and intraexaminer

agreement (weighted kappa) for the ICDAS scores ranged

from 0.86 to 0.92 and from 0.77 to 0.94, respectively. For

trauma and oral disorders, examiners achieved a kappa

higher than 0.8.

Participants’ sociodemographic characteristics and

mean overall ECOHIS scores are presented in Table 1.

Among the 478 children studied, 232 (48.5 %) were boys

and 246 (51.5 %) were girls. Children were predominately

white; their parents mostly reported a low household

income, and 64.7 % of them reported visiting their

Qual Life Res

123

neighbors at least once a month. The prevalence of dental

caries, dental trauma, and anterior open bite was 147

(30.7 %), 66 (14.1 %), and 123 (26.8 %), respectively.

Further analysis demonstrated that the sample group did

not differ from the distribution of the city’s population of

preschoolers in terms of race, sex, and household income

(chi-square test; data provided by the Demographic

Council of the City).

Table 2 shows the descriptive distribution of total

ECOHIS and domain scores. A total of 33 children (7 % of

the sample) required data imputation, and only one (0.2 %

of the sample) was excluded after exceeding the allowable

missing data thresholds. ECOHIS scores ranged from 0 to

41 with an average of 1.8 (SD = 3.9). Domain-specific

scores had a large variation. All domains ranged from

‘‘never’’ (minimum) to ‘‘very often’’ (maximum) except for

the functional limitation domain (0–15); the functional

limitation domain presented the highest mean (0.5)

(Table 2).

The unadjusted assessment of the associations of overall

ECOHIS scores with individual- and contextual-level

variables observed household income (RR 1.7; 95 % CI

1.5–2.1), frequency of visits to a neighbor (RR 1.4; 95 %

CI 1.2–1.6), dental trauma (RR 1.6;95 % CI 1.4–1.9),

anterior open bite (RR 1.5; 95 % CI 1.3–1.7), dental caries

(RR 3.2; 95 % CI 2.8–3.7), and the presence of community

cultural centers in the neighborhood (RR 1.3; 95 % CI

1.1–1.4) as the main covariates of the total ECOHIS scores

(Table 3). Poisson regression analysis fits a multilevel

model by adjusting individual- and contextual-level

covariates for overall ECOHIS scores (Table 4). The

‘‘null’’ model shows that 17 % of the total variance was

due to neighborhood characteristics. After adjusting for the

individual covariates (Model 1), low household income

(\2 BMW) (RR 1.38; 95 % CI 1.1–1.6), having visited a

neighbor less than once a month (RR 1.28, 95 % CI

1.1–1.5), the presence of anterior open bite (RR 1.32; 95

CI % 1.1–1.5), dental trauma (RR 1.50; 95 CI % 1.3–1.8),

and dental caries (DMFT [ 0) (RR 2.67; 95 CI % 2.3–3.1)

were identified as individual determinants of negative

impact on children’s quality of life. These variables

remained associated with the outcome even after adding

the contextual-level variables in the model (Model 2—full

model). In this model, the influence of neighborhood social

network covariates could be noted at the contextual level,

as those who lived in areas with cultural community cen-

ters reported better COHRQoL.

Discussion

This study demonstrated that contextual variables and

individual factors are associated with OHRQoL in pre-

school children. Our findings showed a great impact on

OHRQoL in children with lower socioeconomic status,

higher levels of oral disease, and living in areas with low

social networks. Although previous studies have already

described the impact of socioeconomic factors and adverse

oral clinical conditions on COHRQoL [3, 17, 19], this is

the first study to use multilevel analysis to describe the

association of COHRQoL with individual and community

determinants in preschoolers.

We found that ECOHIS scores ranged from 0 to 41

(possible range 0–52). Similar results have already been

reported [19]. The average ECOHIS score in our study was

1.8. Recent Brazilian studies found that average ECOHIS

scores ranged from 0.9 to 16.25 [9, 17, 19, 47–49]. How-

ever, it is important to highlight that previous studies have

shown a higher prevalence of dental caries, dental trauma,

Table 1 Mean ECOHIS overall scores according to clinical and

demographic characteristics of the sample (n = 478) (Santa Maria/

Brazil)

Variables N (%) ECOHIS scores

Mean (SD)

Individual-level variables

Gender

Male 232 48.5 1.9 (4.3)

Female 246 51.5 1.7 (3.5)

Skin color

White 379 79.3 1.8 (3.9)

Black 99 20.7 1.9 (3.7)

Household income

C2x Brazilian minimum wage 174 38.6 1.2 (2.6)

\2x Brazilian minimum wage 277 61.4 2.1 (4.5)

Have visited a neighbor

At least once a month 308 64.7 1.5 (2.9)

Less than once a month 168 35.8 2.2 (4.9)

Dental trauma

Without 401 85.9 1.5 (3.7)

With 66 14.1 2.2 (4.1)

Anterior open bite

Absent 336 73.2 1.5 (3.6)

Present 123 26.8 2.5 (4.5)

Dental caries

dmf = 0 331 69.3 1.0 (2.8)

dmf [ 0 147 30.7 3.4 (5.9)

Contextual-level variables

Cultural community centers (children living in the neighborhood)

Present 305 63.8 1.6 (3.4)

Absent 173 36.2 2.0 (4.5)

Workers association (children living in the neighborhood)

Present 282 59.0 1.8 (4.4)

Absent 196 41.0 1.7 (3.0)

Qual Life Res

123

and anterior open bite than our study [17, 19]. Therefore,

the lower prevalence of these conditions in our study could

have influenced our results. Similar to previous studies, the

functional limitation domain showed the highest mean in

child section of the ECOHIS [17, 19, 49]. Questions

included in this domain address issues related to chewing

difficulties and school performance. Our results are in

accordance with earlier studies indicating the impact of

those issues on children’s lives and oral health [49, 50].

Regarding clinical variables, our findings showed that

dental caries, dental trauma, and anterior open bite were

significant. The most studies performed in Brazil found

dental caries associated with COHRQoL [17, 19, 49, 51,

52]. However, dental trauma and anterior open bite pre-

sented mixed results in the literature [17, 47, 49, 51, 53].

According to our findings, in general, previous reports

demonstrated that socioeconomic conditions influence

COHRQoL [8, 17, 19].

The main results showed that the association between

individual-level characteristics and quality of life persists

even after adjusting for characteristics of the neighborhood

in which the child lives. Thus, it becomes difficult to

analyze the effect of individual variables without also

considering the effect of hierarchical contextual factors in

determining health-related behaviors [21, 54]. The multi-

level model is appropriate for analyzing such hierarchical

data because it takes into account the variance associated

with each level of nesting. By modeling neighborhood

variance simultaneously with children’s variance and

including contextual-level covariates with subject-level

characteristics in the analysis, multilevel models identify

the extent to which the individual outcome is accounted for

by group and subject-level variables. Moreover, it allows

for separate analysis of the variance at individual and

community levels [21, 55, 56].

In this study, the number of cultural community centers

located in the neighborhood was used as a proxy for the

degree of social networks. This type of support is theo-

retically related to social capital and social cohesion,

resulting in a positive or negative impact within a collec-

tive environment for the benefit of residents [57]. Fur-

thermore, social support within the contextual level has

been described as a key determinant for OHRQoL, being

more important than the availability of health services [58].

Moreover, social capital is the implicit value and is present

in the connections of a social network. The concept

Table 2 Descriptive distribution of total ECOHIS and domains scores

ECOHIS domains Mean (SD) Possible range Range

Child section

1. How often has your child had pain in the teeth, mouth, or jaws? (Symptoms) 0.3 (0.8) 0–4 0–4

How often has your child…because of dental problems or dental treatments? (Function) 0.5 (1.4) 0–16 0–15

2. Had difficulty drinking hot or cold beverages

3. Had difficulty eating some foods

4. Had difficulty pronouncing any words

5. Missed preschool, daycare, or school

How often has your child….because of dental problems or dental treatments? (Psychological) 0.4 (1.0) 0–8 0–8

6. Had trouble sleeping

7. Been irritable or frustrated

How often has your child…because of dental problems or dental treatments?

(Self-image/social interaction)

0.1 (0.6) 0–8 0–8

8. Avoided smiling or laughing when around other children

9. Avoided talking with other children

Family section

How often have you or another family member … because of

your child’s dental problems or dental treatments? (Parent distress)

0.4 (1.2) 0–8 0–8

10. Been upset

11. Felt guilty

How often … (Family function) 0.1 (0.5) 0–4 0–4

12. Have you or another family member taken time off from work because of your

child’s dental problems or dental treatments?

13. Has your child had dental problems or dental treatments that had a financial impact on your family?

Total ECOHIS 1.8 (3.9) 0–52 0–41

Qual Life Res

123

involves a number of elements that can influence people’s

lives, such as interaction between individuals (social net-

work) and social cohesion, and can also include social

support. The social network into which the individual is

inserted provides social support. Individuals who have

social networks with greater social support may have better

information and, therefore, make healthier choices [48].

Previous studies have shown that the presence of com-

munity centers in the district allows people to engage in

social activities, which increase social cohesion and

neighborhood trust [21]. Social support is directly related

to self-reported health status, since health behavior is

associated with good social networks [21, 59]. Therefore,

the probability of adopting a certain behavior depends in

part on the degree to which this behavior has already been

adopted in the community [60]. Furthermore, communities

with a high degree of cohesion are hypothesized to expe-

rience lower levels of psychosocial stress, which is

explained in part by psychosocial theory [61, 62].

According to Aida et al. [59], there is a significant

association between the network aspect of neighborhood

social capital and individual dental status regardless of

individual social networks and social support. However,

that study was performed with the elderly. Although one

study with Japanese children showed a correlation between

social cohesion, social network, and caries in primary teeth

[21], no study has assessed the relationship of self-per-

ception of oral health with neighborhood social networks in

Table 3 Unadjusted assessment of the association of overall ECO-

HIS scores with individual- and contextual-level variables

Variable ECOHIS

scores

mean (SD)

RR

(95 %CI)

p value

First level: individual

Gender 0.25

Male 1.9 (4.3) 1

Female 1.7 (3.5) 0.9 (0.8–1.1)

Skin color 0.25

White 1.8 (3.9) 1

Black 1.9 (3.7) 1.1 (0.9–1.3)

Household income 0.000

C2BMW 1.2 (2.6) 1

\2BMW 2.1 (4.5) 1.7 (1.5–2.1)

Have visited a neighbor 0.000

At least once a month 1.5 (3.7) 1

Fewer than once a month 2.2 (4.1) 1.4 (1.2–1.6)

Dental trauma 0.000

Without 1.6 (3.3) 1

With 2.7 (6.4) 1.6 (1.4–1.9)

Anterior open bite 0.000

Absent 1.5 (3.6) 1

Present 2.5 (4.5) 1.5 (1.3–1.7)

Dental caries 0.000

dmf = 0 1.0 (2.8) 1

dmf [ 0 3.4 (5.9) 3.2 (2.8–3.7)

Second level: neighborhood

Cultural community centers 0.001

Present 1.6 (3.4) 1

Absent 2.0 (4.5) 1.3 (1.1–1.4)

Workers associations 0.82

Present 1.8 (4.4) 1

Absent 1.7 (3.0) 0.9 (0.6–1.5)

RR ratio of arithmetic means

Table 4 Association of overall ECOHIS scores with individual- and

contextual-level variables determined by multilevel poisson

regression

Fixed effects Model 1

(‘null’)

Model 1 Model 2

(‘full’)

RR

(95 %CI)

RR

(95 %CI)

RR

(95 %CI)

Intercept 1.88

(1.5–2.3)

0.78

(0.6–1.0)

0.53

(0.4–0.8)

First level: individual

Gender (reference: female) 0.92

(0.8–1.0)

0.93

(0.8–1.0)

Skin color (reference:

blacks)

0.97

(0.8–1.1)

0.97

(0.8–1.1)

Household Income

(reference: \2x BMW)

1.38

(1.1–1.6)

1.36

(1.1–1.6)

Visited a neighbor

(reference: \once a

month)

1.28

(1.1–1.5)

1.27

(1.1–1.5)

Anterior open bite

(reference: present)

1.32

(1.1–1.5)

1.33

(1.1–1.5)

Dental trauma (reference:

present)

1.50

(1.3–1.8)

1.49

(1.3–1.8)

Dental caries (reference:

dmft [ 0)

2.67

(2.3–3.1)

2.66

(2.3–3.1)

Second level: neighborhood

Cultural community centers

(reference: absent)

1.62

(1.1–2.3)

Workers association

(reference: absent)

1.04

(0.8–1.4)

Random effects

Deviance (-2 log

likelihood)

2,824.67 2,346.91 2,308.84

ICCa 0.17 0.10 0.06

Model 1 (‘null’) represents the unconditional model; Model 2 rep-

resents individual covariates; Model 3 (‘full’) represents subject and

contextual-level covariates

RR ratio of arithmetic means (references categories are described in

the brackets)a Intraclass correlation coefficient: fraction of the total variance that

is due to the contextual level

Qual Life Res

123

preschool children. Our results show that individuals

residing in neighborhoods with higher numbers of com-

munity centers have fewer reports of negative OHRQoL.

This result could be related to the cohesion of residents

through social activities provided by the community cul-

tural centers [21]. Nevertheless, further research is required

to elucidate mechanisms by which social contexts affect

COHRQoL.

Socioeconomic data were used as predictors of

COHRQoL in this study. Our findings confirm previous

studies that related OHRQoL with socioeconomic factors

such as caregivers’ level of education [8, 19], household

income [8, 17], access to services [19], and employment [8].

It has been shown that family income may mirror the accu-

mulation of knowledge that influences the adoption of healthy

habits and improves social conditions [63]. In contrast,

socioeconomic disadvantage may limit people’s opportunities

for choice and decision making, which could lead to a more

severe negative impact on their quality of life [64].

Our findings confirmed the negative impact of dental

abnormalities on COHRQoL. All clinical variables were

significantly associated with the outcome. The negative

influences of dental caries on children’s quality of life

include dental pain, chewing, and sleeping difficulties,

changes in behavior, and decrease in school performance

[8, 29, 35, 64]. Children with dental trauma and anterior

open bite were more likely to have higher overall ECOHIS

scores. These relationships with children’s quality of life

have been explained by self-image dissatisfaction related to

dento-facial esthetics [65–67], confirming the important

role of dento-facial esthetics in social interactions and

psychosocial well-being.

We considered ECOHIS scores as count variables and

performed a parametric assessment of scores associated

with answers. Poisson regression is an appropriate analyt-

ical resource to assess factors associating with the varia-

tions of observed scores [68]. The model expresses the rate

ratios along with their 95 % confidence interval (RR,

95 %CI), which seems to represent a better alternative for

obtaining measures that are both more appropriate and

easier to interpret. In addition being better suited to the data

when the outcome variable is skewed (the situation posed

in this study), this approach has the additional advantages

of being able to accommodate differential exposure and

nonlinear effects [69]. Alternatively, some studies consid-

ered dichotomous classification of quality of life outcomes,

but this methodological option may also entail information

loss and reduced statistical power to assess covariates [70].

This study followed a cross-sectional design, preventing

the establishment of causality between independent vari-

ables and outcome. However, we believe that cross-sectional

studies are important tools for identifying risk indicators to

be included in further longitudinal assessments. In our data

set, the large unexplained variability in the full model was

due to subject-level covariates. It is possible that the lack of

information on non-adjusted factors, such as mothers’ psy-

chological characteristics, could have influenced the low

level of explained variance in the individual-level model.

These factors should be assessed in a further study. Another

limitation is the lack of information on the level of rela-

tionship of the caregiver who accompanied the child during

the examination. However, the ECOHIS scores reported

were similar to those in studies on representative samples

and with similar methodologies [9, 19].

There is a significant effect of neighborhood social

context on COHRQoL. Unfavorable social conditions and

poor socioeconomic status have a negative impact on the

way mothers perceived their children’s oral health-related

quality of life. The observed variability related to the social

context in which the children live highlights the importance

of providing public health programs that also consider the

effect of contextual variables as determinants of individual

outcomes [5, 21]. For instance, if community contextual

influences on individual OHRQoL are substantial, a geo-

graphically targeted population approach based on soci-

odemographic determinants of oral health rather than

individual risk factors might be needed to reduce regional

inequality of self-perceived oral health [21]. We believe

that the current study provides information that supports a

redirection of resources allocated to public health. Not-

withstanding, COHQoL assessments reflect patients’ per-

ceptions about their oral health and thereby can improve

communication between patients, parents, and the dental

team. It provides a greater understanding of the conse-

quences and salience of oral health states in children’s lives

and the lives of their families. Finally, for researchers,

COHQoL assessments offer an adjunct measurement to

assess the outcomes of treatments and initiatives that may

help in the development of guidelines for an evidence-

based practice [5, 21].

Acknowledgments The authors would like to thank all the children

for their cooperation and the Health Authorities in Santa Maria for all

information and authorization and Fundacao de Amparo a Pesquisa do

Estado de Sao Paulo (FAPESP), process n. 2011/17068-1.

References

1. Locker, D. (1989). An introduction to behavioural science &

dentistry. London: Tavistock.

2. Wilson, I. B., & Cleary, P. D. (1995). Linking clinical variables

with health-related quality of life. A conceptual model of patient

outcomes. JAMA, 273, 59–65.

3. Piovesan, C., Antunes, J. L., Guedes, R. S., & Ardenghi, T. M.

(2010). Impact of socioeconomic and clinical factors on child oral

health-related quality of life (COHRQoL). Quality of Life

Research, 19, 1359–1366.

Qual Life Res

123

4. Slade, G. D. (1998). Assessing change in quality of life using the

Oral Health Impact Profile. Community Dentistry and Oral Epi-

demiology, 26, 52–61.

5. McGrath, C., Broder, H., & Wilson-Genderson, M. (2004).

Assessing the impact of oral health on the life quality of children:

Implications for research and practice. Community Dentistry and

Oral Epidemiology, 32, 81–85.

6. Broder, H. L., McGrath, C., & Cisneros, G. J. (2007). Ques-

tionnaire development: Face validity and item impact testing of

the Child Oral Health Impact Profile. Community Dentistry and

Oral Epidemiology, 35(Suppl 1), 8–19.

7. Jokovic, A., Locker, D., Stephens, M., Kenny, D., Tompson, B.,

& Guyatt, G. (2002). Validity and reliability of a questionnaire

for measuring child oral-health-related quality of life. Journal of

Dental Research, 81, 459–463.

8. Pahel, B. T., Rozier, R. G., & Slade, G. D. (2007). Parental

perceptions of children’s oral health: The Early Childhood Oral

Health Impact Scale (ECOHIS). Health Qual Life Outcomes, 5, 6.

9. Scarpelli, A. C., Oliveira, B. H., Tesch, F. C., Leao, A. T., Por-

deus, I. A., & Paiva, S. M. (2011). Psychometric properties of the

Brazilian version of the Early Childhood Oral Health Impact

Scale (B-ECOHIS). BMC Oral Health, 11, 19.

10. Tesch, F. C., Oliveira, B. H., & Leao, A. (2008). Semantic

equivalence of the Brazilian version of the Early Childhood Oral

Health Impact Scale. Cad Saude Publica, 24, 1897–1909.

11. Feldens, C. A., Kramer, P. F., Feldens, E. G., Pacheco, L. M., &

Vitolo, M. R. (2014). Socioeconomic, behavioral, and anthropo-

metric risk factors for traumatic dental injuries in childhood: A

cohort study. International Journal of Paediatric Dentistry, 24,

234–243.

12. Scarpelli, A. C., Paiva, S. M., Viegas, C. M., Carvalho, A. C.,

Ferreira, F. M., & Pordeus, I. A. (2013). Oral health-related

quality of life among Brazilian preschool children. Community

Dentistry and Oral Epidemiology, 41, 336–344.

13. Li, S., Veronneau, J., & Allison, P. J. (2008). Validation of a

French language version of the Early Childhood Oral Health

Impact Scale (ECOHIS). Health Qual Life Outcomes, 6, 9.

14. Lee, G. H., McGrath, C., Yiu, C. K., & King, N. M. (2009).

Translation and validation of a Chinese language version of the

Early Childhood Oral Health Impact Scale (ECOHIS). Interna-

tional Journal of Paediatric Dentistry, 19, 399–405.

15. Peker, K., Uysal, O., & Bermek, G. (2011). Cross-cultural

adaptation and preliminary validation of the Turkish version of

the early childhood oral health impact scale among 5–6-year-old

children. Health Qual Life Outcomes, 9, 118.

16. Jabarifar, S. E., Golkari, A., Ijadi, M. H., Jafarzadeh, M., &

Khadem, P. (2010). Validation of a Farsi version of the early

childhood oral health impact scale (F-ECOHIS). BMC Oral

Health, 10, 4.

17. Abanto, J., Carvalho, T. S., Mendes, F. M., Wanderley, M. T.,

Bonecker, M., & Raggio, D. P. (2011). Impact of oral diseases

and disorders on oral health-related quality of life of preschool

children. Community Dentistry and Oral Epidemiology, 39,

105–114.

18. Wong, H. M., McGrath, C. P., & King, N. M. (2011). Rasch

validation of the early childhood oral health impact scale. Com-

munity Dentistry and Oral Epidemiology, 39, 449–457.

19. Goettems, M. L., Ardenghi, T. M., Romano, A. R., Demarco, F.

F., & Torriani, D. D. (2011). Influence of maternal dental anxiety

on oral health-related quality of life of preschool children.

Quality of Life Research, 20, 951–959.

20. Gradella, C. M., Bernabe, E., Bonecker, M., & Oliveira, L. B.

(2011). Caries prevalence and severity, and quality of life in

Brazilian 2- to 4-year-old children. Community Dentistry and

Oral Epidemiology, 39, 498–504.

21. Aida, J., Ando, Y., Oosaka, M., Niimi, K., & Morita, M. (2008).

Contributions of social context to inequality in dental caries: A

multilevel analysis of Japanese 3-year-old children. Community

Dentistry and Oral Epidemiology, 36, 149–156.

22. Pattussi, M. P., Marcenes, W., Croucher, R., & Sheiham, A.

(2001). Social deprivation, income inequality, social cohesion

and dental caries in Brazilian school children. Social Science and

Medicine, 53, 915–925.

23. Pattussi, M. P., Hardy, R., & Sheiham, A. (2006). The potential

impact of neighborhood empowerment on dental caries among ado-

lescents. Community Dentistry and Oral Epidemiology, 34, 344–350.

24. Bramlett, M. D., Soobader, M. J., Fisher-Owens, S. A., et al.

(2010). Assessing a multilevel model of young children’s oral

health with national survey data. Community Dentistry and Oral

Epidemiology, 38, 287–298.

25. Antunes, J. L., Peres, M. A., de Campos Mello, T. R., & Wald-

man, E. A. (2006). Multilevel assessment of determinants of

dental caries experience in Brazil. Community Dentistry and Oral

Epidemiology, 34, 146–152.

26. Celeste, R. K., Nadanovsky, P., & De Leon, A. P. (2007). Asso-

ciation between preventive care provided in public dental services

and caries prevalence. Revista de Saude Publica, 41, 830–838.

27. Barriuso, S., & Sanz, B. (2011). Multilevel analysis of the use of

oral health services by the pediatric population. Gaceta Sanitaria/

SESPAS, 25, 391–396.

28. Levin, K. A., Jones, C. M., Wight, C., Valentine, C., Topping, G.

V., & Naysmith, R. (2009). Fluoride rinsing and dental health

inequalities in 11-year-old children: An evaluation of a super-

vised school-based fluoride rinsing programme in Edinburgh.

Community Dentistry and Oral Epidemiology, 37, 19–26.

29. Oliveira, L. B., Sheiham, A., & Bonecker, M. (2008). Exploring

the association of dental caries with social factors and nutritional

status in Brazilian preschool children. European Journal of Oral

Sciences, 116, 37–43.

30. Lopez, R., Frydenberg, M., & Baelum, V. (2009). Contextual

effects in the occurrence of periodontal attachment loss and

necrotizing gingival lesions among adolescents. European Jour-

nal of Oral Sciences, 117, 547–554.

31. Ravald, N., & Johansson, C. S. (2012). Tooth loss in periodon-

tally treated patients: A long-term study of periodontal disease

and root caries. Journal of Clinical Periodontology, 39, 73–79.

32. Peres, M. A., Peres, K. G., Frias, A. C., & Antunes, J. L. (2010).

Contextual and individual assessment of dental pain period

prevalence in adolescents: A multilevel approach. BMC Oral

Health, 10, 20.

33. Brennan, D. S., & Spencer, A. J. (2012). Social support and

optimism in relation to the oral health of young adults. Interna-

tional Journal of Behavioral Medicine, 19, 56–64.

34. Turrell, G., Sanders, A. E., Slade, G. D., Spencer, A. J., &

Marcenes, W. (2007). The independent contribution of neigh-

borhood disadvantage and individual-level socioeconomic posi-

tion to self-reported oral health: A multilevel analysis.

Community Dentistry and Oral Epidemiology, 35, 195–206.

35. Bonecker, M., Marcenes, W., & Sheiham, A. (2002). Caries reduc-

tions between 1995, 1997 and 1999 in preschool children in Diadema,

Brazil. International Journal of Paediatric Dentistry, 12, 183–188.

36. Piovesan, C., Ardenghi, T. M., Guedes, R. S., Ekstrand, K. R.,

Braga, M. M., & Mendes, F. M. (2013). Activity assessment has

little impact on caries parameters reduction in epidemiological

surveys with preschool children. Community Dentistry and Oral

Epidemiology, 41, 204–211.

37. Ismail, A. I., Sohn, W., Tellez, M., et al. (2007). The International

Caries Detection and Assessment System (ICDAS): An inte-

grated system for measuring dental caries. Community Dentistry

and Oral Epidemiology, 35, 170–178.

Qual Life Res

123

38. Pitts, N. (2004). ‘‘ICDAS’’—an international system for caries

detection and assessment being developed to facilitate caries

epidemiology, research and appropriate clinical management.

Community Dental Health, 21, 193–198.

39. Braga, M. M., Oliveira, L. B., Bonini, G. A., Bonecker, M., &

Mendes, F. M. (2009). Feasibility of the International Caries

Detection and Assessment System (ICDAS-II) in epidemiological

surveys and comparability with standard World Health Organi-

zation criteria. Caries Research, 43, 245–249.

40. O’Brien, M. (1994). Children’s dental health in the United

Kingdom 1993. London: Her Majesty’s Stationery Office.

41. IBGE. Instituto Brasileiro de Geografia e Estatıstica. Pesquisa

Nacional por Amostra de Domicılios: Sıntese de Indicadores—

2003 [acessado em 29 novembro 2009]. Disponıvel em: http://

www.ibge.gov.br/home/estatistica/populacao/trabalhoerendimento/

pnad2003/sintesepnad2003.pdf.

42. Pattussi, M. P., Hardy, R., & Sheiham, A. (2006). Neighborhood

social capital and dental injuries in Brazilian adolescents.

American Journal of Public Health, 96, 1462–1468.

43. Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neigh-

borhoods and violent crime: A multilevel study of collective

efficacy. Science, 277, 918–924.

44. Costa, D. L., & Matthew, E. K. (2003). Understanding the Amer-

ican decline in social capital, 1952–1998. Kyklos, 56, 17–46.

45. Merchant, A. T., Pitiphat, W., Ahmed, B., Kawachi, I., &

Joshipura, K. (2003). A prospective study of social support, anger

expression and risk of periodontitis in men. Journal of the

American Dental Association, 134, 1591–1596.

46. Snijders, T. A. B., & Bosker, R. J. (2003). Multilevel analysis: An

introduction to basic and advanced multilevel modeling. London:

Sage.

47. Aldrigui, J. M., Abanto, J., Carvalho, T. S., et al. (2011). Impact

of traumatic dental injuries and malocclusions on quality of life

of young children. Health Qual Life Outcomes, 9, 78.

48. Martins-Junior, P. A., Ramos-Jorge, J., Paiva, S. M., Marques, L.

S., & Ramos-Jorge, M. L. (2012). Validations of the Brazilian

version of the Early Childhood Oral Health Impact Scale

(ECOHIS). Cad Saude Publica, 28, 367–374.

49. Kramer, P. F., Feldens, C. A., Ferreira, S. H., Bervian, J., Ro-

drigues, P. H., & Peres, M. A. (2013). Exploring the impact of

oral diseases and disorders on quality of life of preschool chil-

dren. Community Dentistry and Oral Epidemiology, 41, 327–335.

50. Piovesan, C., Antunes, J. L., Mendes, F. M., Guedes, R. S., &

Ardenghi, T. M. (2012). Influence of children’s oral health-rela-

ted quality of life on school performance and school absenteeism.

Journal of Public Health Dentistry, 72, 156–163.

51. Scarpelli, A. C., Paiva, S. M., Viegas, C. M., Carvalho, A. C.,

Ferreira, F. M., & Pordeus, I. A. (2013). Oral health-related

quality of life among Brazilian preschool children. Community

Dentistry and Oral Epidemiology, 41, 336–344.

52. Leal, S. C., Bronkhorst, E. M., Fan, M., & Frencken, J. E. (2012).

Untreated cavitated dentine lesions: Impact on children’s quality

of life. Caries Research, 46, 102–106.

53. Carvalho, A. C., Paiva, S. M., Viegas, C. M., Scarpelli, A. C.,

Ferreira, F. M., & Pordeus, I. A. (2013). Impact of malocclusion

on oral health-related quality of life among Brazilian preschool

children: A population-based study. Brazilian Dental Journal, 24,

655–661.

54. Brennan, D. S., & Spencer, A. J. (2010). Social support and

optimism in relation to the oral health of young adults. Interna-

tional Journal of Behavioral Medicine. http://www.springerlink.

com/content/v2703r6483271427. Accessed 10 December 2011.

55. Diez Roux, A. V. (2002). A glossary for multilevel analysis.

Journal of Epidemiology and Community Health, 56, 588–594.

56. Hox, J. J. (2002). Multilevel analysis, techniques and applications

(pp. 1–36). Mahwah: Lawrence Erlbaum.

57. Santos, S. M., Chor, D., Werneck, G. L., & Coutinho, E. S.

(2007). Association between contextual factors and self-rated

health: A systematic review of multilevel studies. Cad Saude

Publica, 23, 2533–2554.

58. Wen, M., Browning, C. R., & Cagney, K. A. (2003). Poverty,

affluence, and income inequality: Neighborhood economic

structure and its implications for health. Social Science and

Medicine, 57, 843–860.

59. Aida, J., Kuriyama, S., Ohmori-Matsuda, K., Hozawa, A., Osaka,

K., & Tsuji, I. (2011). The association between neighborhood

social capital and self-reported dentate status in elderly Japa-

nese—The Ohsaki cohort 2006 study. Community Dentistry and

Oral Epidemiology, 39, 239–249.

60. Diez-Roux, A. V. (2000). Multilevel analysis in public health

research. Annual Review of Public Health Review, 21, 171–192.

61. Elstad, J. (1998). The psycho-social perspective on social

inequalities in health. Sociology of Health & Illness, 20, 598–618.

62. Sisson, K. L. (2007). Theoretical explanations for social

inequalities in oral health. Community Dentistry and Oral Epi-

demiology, 35, 81–88.

63. Petersen, P. E., Bourgeois, D., Ogawa, H., Estupinan-Day, S., &

Ndiaye, C. (2005). The global burden of oral diseases and risks to

oral health. Bulletin of the World Health Organization, 83,

661–669.

64. Davey-Smith, G., Blane, D., & Bartley, M. (1994). Explanations

for socioeconomic differentials in mortality: Evidence from

Britain and elsewhere. European Journal of Public Health, 4,

131–144.

65. Ramos-Jorge, M. L., Bosco, V. L., Peres, M. A., & Nunes, A. C.

(2007). The impact of treatment of dental trauma on the quality of

life of adolescents—A case–control study in southern Brazil.

Dental Traumatology, 23, 114–119.

66. Johal, A., Cheung, M. Y., & Marcenes, W. (2007). The impact of

two different malocclusion traits on quality of life. British Dental

Journal, 202, E2.

67. Cortes, M. I., Marcenes, W., & Sheiham, A. (2002). Impact of

traumatic injuries to the permanent teeth on the oral health-

related quality of life in 12–14-year-old children. Community

Dentistry and Oral Epidemiology, 30, 193–198.

68. Barros, A. J., & Hirakata, V. N. (2003). Alternatives for logistic

regression in cross-sectional studies: An empirical comparison of

models that directly estimate the prevalence ratio. BMC Medical

Research Methodology, 3, 21.

69. Hutchinson, M. K., & Holtman, M. C. (2005). Analysis of count

data using poisson regression. Research in Nursing & Health, 28,

408–418.

70. Guyatt, G., & Schunemann, H. (2007). How can quality of life

researchers make their work more useful to health workers and

their patients? Quality of Life Research, 16, 1097–1105.

Qual Life Res

123