Description
Part of the development of a scholar-practitioner is learning how to critique scholarly research. A good article critique is not just a summary of the findings–that’s what the abstract does—it is a critical analysis of various elements of a research study, including the research problem, study aims, theoretical foundation, research questions, research design, study population, sampling procedures, data collection procedures, analytical approach, and interpretation of findings. As you examine a peer-reviewed journal article, you should question whether the researchers made logical and appropriate choices, evaluate the writing style, note biases and contradictions, and consider potential strengths and weaknesses of the study.
In this Assignment, you will critically analyze a peer-reviewed journal article that demonstrates multilevel modeling. The article provided by the Instructor should provide a good example of multilevel modeling, but sometimes researchers miss important aspects of this complex and advanced analytic technique in their write-up. Pay close attention to the variables involved and types of statistical tests used. Ask yourself whether the authors have provided enough detail that you could repeat the statistical analysis in a similar manner? Were outliers tested? Were assumptions met? How were the models estimated? How many models were used? How was covariance considered? Be sure to provide evidence (i.e., quotes) from the journal article to support your critiques and opinions.
ASSIGNMENT
In two to three pages, write a critique of the assigned article that includes responses to the following prompts:
Explain why the authors used multilevel modeling in their analysis.
Describe the multiple levels addressed in the study.
Describe how the authors shared their results.
If the authors used a graphic to share their results, analyze the effectiveness of using a graphic to interpret the study.
Explain the impact multilevel modeling has on the interpretation of the results for public health practice.
Soriano, F. I. (2013). Chapter five: Quantitative assessment methods. In Conducting needs assessment: A multidisciplinary approach (2nd ed., pp. 75–108). SAGE Publications. https://doi.org/10.4135/9781506335780.n5
Soriano, F. I. (2013). Chapter six: Quantitative data preparation and statistical analyses. In Conducting needs assessment: A multidisciplinary approach (2nd ed., pp. 109–120). SAGE Publications. https://doi.org/10.4135/9781506335780.n6
Subramanian, S. V. (n.d.). e-Source: Behavioral & social sciences research: Multilevel modeling, Office of Behavioral and Social Sciences Research. https://obssr.od.nih.gov/sites/obssr/files/Multile…
Trochim, W. M. K. (n.d.). Survey researchConjoint.ly. https://conjointly.com/kb/survey-research/
Walden University Writing Center. (n.d.). Common assignments: Critique/analysis,
https://academicguides.waldenu.edu/writingcenter/a…
Warner, R. M. (2021). Moderation: Interaction in multiple regression. In Applied statistics II: Multivariable and multivariate techniques (3rd ed., pp. 215–253). SAGE Publications.
Warner, R. M. (2021). Mediation. In Applied statistics II: Multivariable and multivariate techniques (3rd ed., pp. 289–308). SAGE Publications.
SPSS
Institute for Digital Research & Education. Statistical Consulting. (n.d.). Choosing the correct statistical test in SAS, STATA, SPSS, and the University of California, Los Angeles. https://stats.idre.ucla.edu/other/mult-pkg/whatsta…
Institute for Digital Research & Education. Statistical Consulting. (n.d.). SPSS University of California, Los Angeles. https://stats.idre.ucla.edu/spss/
PUBH_8248_Module3_Assignment1 _Rubric
CriteriaRatingsPtsThis criterion is linked to a Learning OutcomeExplain why the authors used multilevel modeling in their analysis.
20 to >17.0 ptsOutstanding
Fully developed and supported, insightful, credible, and scholarly explanation of why the authors used multilevel modeling in their analysis.
17 to >15.0 ptsVery Good
Thorough, well-organized, and supported explanation of why the authors used multilevel modeling in their analysis.
15 to >13.0 ptsMeets Expectations
Adequate explanation of why the authors used multilevel modeling in their analysis.
13 to >0 ptsDoes Not Meet Expectations
Missing, unoriginal, or does not adequately explain why the authors used multilevel modeling in their analysis.
20 pts
This criterion is linked to a Learning OutcomeDescribe the multiple levels addressed in the study.
20 to >17.0 ptsOutstanding
Fully developed and supported, insightful, credible, and scholarly description of the multiple levels addressed in the study.
17 to >15.0 ptsVery Good
Thorough, well-organized, and supported description of the multiple levels addressed in the study.
15 to >13.0 ptsMeets Expectations
Adequate description of the multiple levels addressed in the study.
13 to >0 ptsDoes Not Meet Expectations
Missing, unoriginal, or does not adequately describe the multiple levels addressed in the study.
20 pts
This criterion is linked to a Learning OutcomeDescribe how the authors shared their results.
20 to >17.0 ptsOutstanding
Fully developed and supported, insightful, credible, and scholarly description of how the authors shared their results.
17 to >15.0 ptsVery Good
Thorough, well-organized, and supported description of how the authors shared their results.
15 to >13.0 ptsMeets Expectations
Adequate description of how the authors shared their results.
13 to >0 ptsDoes Not Meet Expectations
Missing, unoriginal, or does not adequately describe how the authors shared their results.
20 pts
This criterion is linked to a Learning OutcomeIf the authors used a graphic to share their results, analyze the effectiveness of using a graphic to interpret the study.
10 to >8.0 ptsOutstanding
If applicable: fully developed and supported, insightful, credible, and scholarly analysis of the effectiveness of using a graphic to interpret the study.
8 to >7.0 ptsVery Good
If applicable: thorough, well-organized, and supported analysis of the effectiveness of using a graphic to interpret the study.
7 to >6.0 ptsMeets Expectations
If applicable: adequate analysis of the effectiveness of using a graphic to interpret the study.
6 to >0 ptsDoes Not Meet Expectations
If applicable: missing, unoriginal, or does not adequately analyze the effectiveness of using a graphic to interpret the study.
10 pts
This criterion is linked to a Learning OutcomeExplain the impact multilevel modeling has on the interpretation of the results for public health practice.
10 to >8.0 ptsOutstanding
Fully developed and supported, insightful, credible, and scholarly explanation of the impact multilevel modeling has on the interpretation of the results for public health practice.
8 to >7.0 ptsVery Good
Thorough, well-organized, and supported explanation of the impact multilevel modeling has on the interpretation of the results for public health practice.
7 to >6.0 ptsMeets Expectations
Adequate explanation of the impact multilevel modeling has on the interpretation of the results for public health practice.
6 to >0 ptsDoes Not Meet Expectations
Missing, unoriginal, or does not adequately explain the impact multilevel modeling has on the interpretation of the results for public health practice.
10 pts
This criterion is linked to a Learning OutcomeWritten Communication: Extent to which writing is professional, appropriate, clear, properly formatted, grammatically and structurally correct, synthesized, supported, and scholarly AND the correct template is used.
20 to >17.0 ptsOutstanding
Writing is fully developed, exceptionally well organized, synthesized, supported, scholarly, and free of writing errors. Concepts are connected throughout paper with appropriate transitions and multiple appropriate resources and examples AND the correct template is used.
17 to >15.0 ptsVery Good
Writing is generally thorough and grammatically correct, with proper formatting and minimal concerns. Synthesis is demonstrated and ideas are supported without reliance on quoting AND the correct template is used.
15 to >13.0 ptsMeets Expectations
Writing adequately meets expectations for writing and synthesis but with infrequent and minor issues AND the correct template is used.
13 to >0 ptsDoes Not Meet Expectations
Writing does not meet basic expectations (e.g., clarity, tone, organization, grammar, spelling, punctuation, source citation, references, title page, synthesis of source material, insufficient originality, etc.) OR the correct template is not used.
20 pts
Total Points: 100
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742
European Journal of Public Health
……………………………………………………………………………………………………………………………………………….
The European Journal of Public Health, Vol. 31, No. 4, 742–748
ß The Author(s) 2021. Published by Oxford University Press on behalf of the European Public Health Association.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
doi:10.1093/eurpub/ckab013 Advance Access published on 24 February 2021
……………………………………………………………………………………………
Neighbourhood characteristics and children’s oral
health: a multilevel population-based cohort study
Agatha W. van Meijeren-van Lunteren
Lea Kragt1,2
1,2
, Joost Oude Groeniger3,4, Eppo B. Wolvius1,2,
1 The Generation R Study Group, Erasmus University Medical Centre, Rotterdam, The Netherlands
2 Department of Oral & Maxillofacial Surgery, Special Dental Care and Orthodontics, Erasmus University Medical Centre,
Rotterdam, The Netherlands
3 Department of Public Health, Erasmus University Medical Centre, Rotterdam, The Netherlands
4 Department of Public Administration and Sociology, Erasmus University, Rotterdam, The Netherlands
Correspondence: Agatha W. van Meijeren-van Lunteren, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands, Tel: þ31 10
7037733, Fax: þ31 10 7044645, e-mail: a.vanlunteren@erasmusmc.nl
Background: To understand determinants of oral health inequalities, multilevel modelling is a useful manner to
study contextual factors in relation to individual oral health. Several studies outside Europe have been performed
so far, however, contextual variables used are diverse and results conflicting. Therefore, this study investigated
whether neighbourhood level differences in oral health exist, and whether any of the neighbourhood characteristics used were associated with oral health. Methods: This study is embedded in The Generation R Study, a
prospective cohort study conducted in The Netherlands. In total, 5 960 6-year-old children, representing 158
neighbourhoods in the area of Rotterdam, were included. Data on individual and neighbourhood characteristics
were derived from questionnaires, and via open data resources. Caries was assessed via intraoral photographs,
and defined as decayed, missing and filled teeth (dmft). Results: Differences between neighbourhoods explained
13.3% of the risk of getting severe caries, and 2% of the chance of visiting the dentist yearly. After adjustments
for neighbourhood and individual characteristics, neighbourhood deprivation was significantly associated with
severe dental caries (OR: 1.48, 95% CI: 1.02–2.15), and suggestive of a low odds of visiting the dentist yearly (OR:
0.81, 95% CI: 0.56–1.18). Conclusions: Childhood caries and use of dental services differs between neighbourhoods
and living in a deprived neighbourhood is associated with increased dental caries and decreased yearly use of
dental services. This highlights the importance of neighbourhoods for understanding differences in children’s oral
health, and for targeted policies and interventions to improve the oral health of children living in deprived
neighbourhoods.
……………………………………………………………………………………………
Introduction
espite improvements in recent years, low-socioeconomic house-
Dholds are still affected by poor oral health and its negative con-
sequences over the life-course.1 In The Netherlands, large inequalities
in oral health and dental care use among children exist, despite the fact
that dental care for children is fully covered by basic health insurance.2
Extant studies have mainly focussed on investigating the relationship between individual level determinants and oral health.3,4
However, these individual characteristics could not fully explain
disparities in dental caries and dental care utilization, and the success of individual behaviour interventions to reduce oral health
inequalities is limited so far.5,6 As a result, the interest to research
contextual determinants of oral health, has increased in the last
years.4,5,7 Especially the physical and social environment consist of
important determinants that may contribute to inequalities in oral
health.7,8 For example, it is widely known that an unhealthy diet,
including frequent consumption of sugars, increases the risk of dental caries, and that the food choices individuals make is dependent
on the availability of healthy foods in the area where they live.9 Also,
early preventive care by dentists is important to reduce the risk of
childhood dental caries. However, receiving dental care may be dependent on access to and availability of dental health services in the
area where individuals live.10 Lastly, socioeconomic characteristics
of the neighbourhood (e.g. the level of neighbourhood deprivation)
may also be an important determinants for oral health outcomes.4,8
While there have been several studies that found an association between contextual socioeconomic circumstances and oral health, it is
not always clear whether this reflects features of the area or individual characteristics of the residents within the area.8,11 Therefore, it is
of importance to consider the socioeconomic circumstances of the
individuals itself, as well as those of their neighbourhood, when
studying the relationship between contextual factors and individual
oral health outcomes.
An appropriate manner to study contextual factors in relation to
individual health outcomes is multilevel modelling. Multilevel modelling enables researchers to simultaneously analyse the effects of
both neighbourhood and individual level, and accounts for the dependency of individuals living in the same area.12 Few studies have
used multilevel analyses to investigate the relation between contextual determinants and oral health among children.13–17 Two studies
investigated whether the number of dentists in a neighbourhood was
associated with dental care use and dental caries, but no associations
were observed.15,17 A study in Japan showed that the number of
grocery stores per resident was positively associated with dental caries.17 Antunes et al. observed that in Brazil an increased level of the
human development index (a composite measure summarizing
neighbourhood income, instructional attainment and longevity)
was associated with a lower number of untreated carious lesions13,
although this finding was not observed in another Brazilian study.16
Neighbourhood characteristics and children’s oral health
Lastly, whereas two studies in Brazil and Japan found that a higher
average income per neighbourhood was associated with decreased
dental caries in children14,17, this association was not observed in
another Brazilian study.16
Because results of previous studies examining contextual
determinants of oral health are inconclusive, and studies in
Europe have not yet adopted multilevel analyses, this research combines neighbourhood data with individually collected data from
The Netherlands in a multilevel framework to study: (i) whether
neighbourhood level differences in caries and dental service use
exist, and (ii) whether supermarket availability, snack bar availability, dentist availability, and neighbourhood deprivation level are
associated with dental caries and dental services use.
Methods
This study is embedded in The Generation R Study, a populationbased prospective cohort study from foetal life onwards conducted
in Rotterdam, The Netherlands.18 All pregnant mothers living in
Rotterdam expecting to deliver between April 2002 and January
2006 were invited to participate. Data collection started during pregnancy, was continued prenatally, and is still ongoing at various time
points through several data collection methods.18 For the current
study, all data were collected when the children were 6 years. In this
phase 8,305 (85% of original cohort (n ¼ 9 749) children participated
in the study, of which 5 960 children were eligible for this study
(Figure 1). The study was approved by the Medical Ethical
Committee of Erasmus Medical Centre, Rotterdam, The Netherlands
(MEC 198.782/2001/31) and conducted according to the World
Medical Association Declaration of Helsinki. Written informed
consent was obtained from all participants. Water supplies were not
fluoridated during the study period in Rotterdam.
Neighbourhood characteristics
The following contextual factors on neighbourhood level were
studied: supermarket availability, snack bar availability, dentist
availability, and deprivation level. In The Netherlands, communities
consist of districts, and districts are subdivided into
Figure 1. Flowchart showing the selection of the study population
743
neighbourhoods, which is determined by Statistics Netherlands.
Moreover, the postal company in The Netherlands has subdivided
each community in a set of postal codes, which almost correspond
with the neighbourhood division.
The mean number of supermarkets and snack-bars within 1 km
distance for all inhabitants living per neighbourhood in the
year 2010, were available as open source data by Statistics
Netherlands.19 For a postal code area that corresponded with
more than one neighbourhood, the mean of the neighbourhood
variables of areas with similar postal code was calculated.
Dental clinic availability was retrieved via a registry, managed by
Vektis, that contains all health care providers and their working
locations in The Netherlands per postal code for the year 2010.20
For the analyses, the number of dental clinics per 10 000 inhabitants
was calculated and used.
Neighbourhood deprivation was determined by neighbourhood
status scores (NSSs) of the year 2010 derived from The Netherlands
Institute for Social Research.21 These scores are calculated for all
postal codes in The Netherlands on the basis of four characteristics:
average income, unemployed residents, residents with low education
and households with low income. Analyses were performed using a
categorical scale of the NSS: low NSS (< 1), middle NSS (1–1),
high NSS (>1), which is based on the standard deviation of the NSS
in The Netherlands.21
Oral health outcomes
For this study two outcomes were analysed: dental caries and dental
care use.
From October 2008–January 2012, 5 578 children visited the
research centre for hands-on measurements. After tooth brushing,
10 photographs of clean teeth were taken using an intra-oral camera
(Poscam USB intra-oral autofocus camera, Digital Leader PointNix,
640 480 pixels). All photographs were scored by one single calibrated dentist, and 10% of the photographs were scored by a second
dentist using the same method. Intrarater-reliability (Cohen’s
kappa ¼ 0.80) and inter-observer reliability (Cohen’s kappa ¼ 0.76)
were evaluated and both showed good agreement.22 Dental caries
was assessed in the primary dentition using the decayed, missing,
744
European Journal of Public Health
and filled teeth (dmft) index.23 Decayed teeth were assessed as
lesions extended into dentin, enamel caries was not taken into account. Missing teeth were only assessed when teeth were extracted
due to caries, which was individually judged based on the dental
development and caries pattern of the child. Filled teeth were scored
if teeth were restored due to caries. The use of intra oral photographs for scoring dmft in epidemiological studies showed high
sensitivity and specificity (85.5% and 83.6%, respectively) compared
to the clinical visual tactile inspection.22
Dental visits were assessed by means of parental questionnaires, in
which parents answered the question whether their child had visited
the dentist in the past year (yes/no).
Covariates
Socioeconomic status (SES) was retrieved via parental questionnaires and measured using: maternal education level, net household
income, maternal employment status, and marital status.
Educational level was defined as: low (no education, primary education, 4 years general secondary school or lower vocational training), middle (>4 years general secondary school or intermediate
vocational school), and high (bachelor’s degree, higher vocational
school or a university degree finished).24 Monthly net household
income was categorized as ‘ e2400’ and ‘> e 2400’, based on
the average monthly general labour income in The Netherlands in
2010.25 Employment status of the mother was dichotomized as ‘paid
job’ or ‘no paid job’. Marital status of the mother was dichotomized
as married (married or registered partnership) or not. Children’s
ethnic background was defined according to the Dutch classification
of ethnic background and classified as ‘Dutch’ if both parents of the
child were born in The Netherlands and ‘non-Dutch’ if one of the
parents was born in another country than The Netherlands.26 Sugar
intake during childhood was assessed in questionnaires with questions about the frequency of consuming high caloric snacks and
sugar containing beverages. For the analyses, sugar intake was
dichotomized as ‘low’ (2 sugar containing products a day) and
‘high’ (3 sugar containing products a day). Tooth brushing
frequency was assessed by means of questionnaires and dichotomized as ‘1 per day’, or ‘2 per day’.
Data analyses
Multilevel logistic regression models were used to estimate Odds
Ratios (ORs) of having mild (dmft 1–3) or severe caries (dmft > 3)
compared to children with no caries (dmft ¼ 0). Multilevel models
are useful to study clustered data, as in this study where children
(level-1) are clustered within neighbourhoods (level-2). We used
random intercept multilevel models for all analysis. In these models,
the intercept is allowed to vary across neighbourhoods thereby
accounting for the clustering of children within neighbourhoods.
We verified that the relationship between each continuous predictor
and the outcome was linear on the logit scale, and that multicollinearity between predictor variables was absent. We constructed
three models for each dental outcome:
(1) Null model: this is an empty model which enabled to observe the
proportion of the total variance that is due to neighbourhood
differences. The variance partitioning coefficient (VPC) was calculated using a method where the individual level variance is
fixed at 3.29 (p2/3) for dichotomous outcome variables.27 The
percentage neighbourhood variance was calculated by dividing
the random intercept variance (neighbourhood level variance
component) by the sum of the individual and neighbourhood
level variances. The VPC can vary between 0 and 100%, the
higher this percentage the larger the role of neighbourhoods in
the existing difference of caries experience between individuals.
The VPC was calculated per imputed dataset and consequently
averaged to present one summary VPC per model.
(2) Model 1: this model includes one of the four neighbourhood
variables separately
(3) Model 2: this model includes all neighbourhood variables
simultaneously
(4) Model 3: Model 2þthe individual variables that were considered
as confounders
Multiple imputation was performed to account for information
bias associated with missing data in the covariates. Missing values
were multiple imputed by generating 10 independent datasets with
the use of chained equations, and effect estimates for each imputed
dataset were pooled and presented in this study. Imputations were
based on all variables in the models, but the main determinants and
the outcomes were not imputed. Statistical analyses were generated
using R 3.6.1 (R Core Team, Vienna, Austria) (packages: mice and
Lme4). P-values 0.05 indicated statistical significance.
Supplemental analyses
For the association between neighbourhood characteristics and
dental caries, sugar consumption and brushing frequency were
considered as mediators rather than confounding factors. To
observe the influence of these variables on the effect estimates, we
performed sensitivity analyses to additionally adjust our models
(Supplementary tables S1 and S2). The same applies for dental caries
as a potential mediator in the association between neighbourhood
characteristics and dental visits (Supplementary table S3). A nonresponse analysis was conducted to evaluate potential selection bias
by comparing the sample characteristics of children with (included)
and without (excluded) available information on postal code and
oral health outcomes (Supplementary table S4).
Results
Population characteristics
The prevalence of mild and severe caries in our study population
was 19.6 and 13.4%, respectively. In the total study population
92.4% visited the dentist yearly. Children with severe caries lived
in neighbourhoods with an average of 3.5 (6SD 2.1) supermarkets
and 15.1 (6SD 14.0) snack bars which is higher than children without caries (mean 6 SD 2.6 6 2.0; and 10.0 6 12.3, respectively). In
addition, 54.5% of children with severe caries and 41.6% of children
with mild caries lived in deprived neighbourhoods, compared with
30.7% of children without caries (Table 1).
Association between neighbourhood characteristics
and dental caries
Differences between neighbourhoods explained 2.7% and 13.3% of
the variance in mild and severe dental caries of 6-year-old children,
respectively (Table 2, null model). Of the neighbourhood
characteristics added in model 1, the VPC reduced the most for
severe caries when NSS was added to the model (VPC: 5.0%). After
controlling for individual characteristics (model 3), the VPC was
(almost) 0% for both mild and severe caries. A statistically
significant association was observed between neighbourhoods
with middle NSS and low NSS and severe caries compared to
neighbourhoods with high NSS (Table 2, model 2).The associations remained after adjustments for individual characteristics,
although not significantly for middle NSS with severe caries
(Model 3: middle NSS: OR: 1.32, 95% CI: 0.96–1.81; low NSS:
OR: 1.48, 95% CI: 1.02–2.15, Table 2).
Association between neighbourhood characteristics
and dental visit
Differences between neighbourhoods explained 2% of the variance
in yearly dental visits of 6-year-old children (Table 3, null model).
Neighbourhood characteristics and children’s oral health
745
Table 1 Individual and neighbourhood characteristics of the study population
Individual characteristics
Child’s gender
Boys
Girls
Child’s age at dental assessment (mean 6 SD)
Child’s age filling out questionnaire (mean 6 SD)
Maternal educational level
Low
Middle
High
Missings
Net income per month
Low (< e2400)
High (> e2400)
Missings
Employment status mother
Paid job
No paid job
Missings
Marital status
Married/registered partnership
Unmarried/no registered partnership
Missings
Ethnic background
Dutch
Non-Dutch
Missings
Sugar intake
Low (2 per day)
High (>2 per day)
Missings
Tooth brushing per day
Once
Twice
Missings
Dental visit in past year
No
Yes
Missings
Neighbourhood characteristics
Mean number of supermarkets within 1 km distance 6 SD
Mean number of snack bars within1 km distance 6 SD
Mean number of dental practices 6 SD
Mean dental practice density per 10.000 inhabitants 6 SD
Level of deprivation (mean NSS 6 SD)
Low NSS (most deprived)
Middle NSS
High NSS (least deprived)
Total population (n ¼ 5960) No caries (n ¼ 3105) Mild caries (n ¼ 909) Severe caries (n ¼ 620)
3 003 (50.4%)
2 957 (49.6%)
6.2 6 0.5
6.1 6 0.5
1 548 (49.4%)
1 557 (50.1%)
6.1 6 0.4
6.0 6 0.4
438 (48.2%)
471 (51.8%)
6.3 6 0.6
6.2 6 0.6
331 (53.4%)
289 (46.6%)
6.3 6 0.6
6.2 6 0.6
745 (14.4%)
1 699 (32.8%)
2 735 (52.8%)
781 (13.1%)
259 (9.5%)
844 (30.9%)
1 627 (59.6%)
375 (12.1%)
132 (18.4%)
271 (37.7%)
315 (43.9%)
191 (21.0%)
139 (32.1%)
180 (41.6%)
114 (26.3%)
187 (30.2%)
1 618 (33.2%)
3 262 (66.8%)
1 080 (18.1%)
722 (27.9%)
1 864 (72.1%)
519 (16.7%)
279 (41.3%)
397 (58.7%)
233 (25.6%)
244 (60.1%)
162 (39.9%)
214 (34.5%)
3 673 (74.9%)
1 231 (25.1%)
1 056 (17.7%)
2 073 (79.9%)
521 (20.1%)
511 (16.5%)
465 (68.3%)
216 (31.7%)
228 (25.1%)
211 (52.9%)
188 (47.1%)
221 (35.6%)
3 478 (67.0%)
1 714 (33.0%)
768 (12.9%)
1 790 (65.9%)
925 (34.1%)
390 (12.6%)
497 (68.6%)
228 (31.4%)
184 (20.2%)
313 (70.8%)
129 (29.2%)
178 (28.7%)
3 257 (55.8%)
2 581 (44.2%)
122 (2.0%)
1 859 (61.0%)
1 188 (39%)
58 (1.9%)
406 (46.2%)
473 (53.8%)
30 (3.3%)
170 (29.0%)
416 (71.0%)
34 (5.5%)
1 637 (32.5%)
3 394 (67.5%)
929 (15.6%)
886 (33.5%)
1 756 (66.5%)
463 (14.9%)
224 (32.4%)
467 (67.6%)
218 (24.0%)
123 (29.0%)
301 (71.0%)
196 (31.6%)
1 056 (20.8%)
4 018 (79.2%)
886 (14.9%)
501 (19.0%)
2 136 (81.0%)
468 (15.1%)
147 (21.4%)
541 (78.6%)
221 (24.3%)
111 (25.8%)
320 (74.2%)
189 (30.5%)
386 (7.6%)
4 716 (92.4%)
858 (14.4%)
210 (7.9%)
2 435 (92.1%)
460 (14.8%)
48 (6.9%)
649 (93.1%)
212 (23.3%)
27 (6.2%)
407 (93.8%)
186 (30.0%)
2.8 6 2.0
10.6 6 12.5
3.2 6 2.4
3.3 6 2.8
0.5 6 1.6
2 090 (35.1%)
2 322 (39.0%)
1 548 (26.0%)
2.6 6 2.0
10.0 6 12.3
3.3 6 2.5
3.43 6 2.9
0.3 6 1.6
954 (30.7%)
1 247 (40.2%)
904 (29.1%)
2.9 6 2.1
11.5 6 12.5
3.1 6 2.4
3.1 6 2.7
0.7 6 1.6
378 (41.6%)
334 (36.7%)
197 (21.7%)
3.5 6 2.1
15.1 6 14.0
2.9 6 2.2
2.9 6 2.4
1.2 6 1.5
338 (54.5%)
212 (34.2%)
70 (11.3%)
Numbers are presented as absolute numbers for categorical variables or as mean (SD) for continuous variables. NSS, neighbourhood status
score. Missing values are presented in italic type as absolute numbers and percentages.
The neighbourhood variance was 0% after including neighbourhood
characteristics (Table 3, model 2). Compared to neighbourhoods
with a high NSS, living in a neighbourhood with a low NSS
decreased the likelihood of visiting the dentist (Table 3, model 2).
This association remained after additional adjustment for individual
characteristics, but was no longer statistically significant (Model 3:
OR: 0.81, 95% CI: 0.56–1.18, Table 3).
Discussion
The results of this study show that neighbourhood level differences
in caries and dental health service use exist, but that these neighbourhood differences disappear after controlling for neighbourhood
level and individual level characteristics. Living in a deprived neighbourhood is positively associated with dental caries and suggestive
of decreased dental visits, even after adjusting for several individual
socioeconomic characteristics.
Several studies have investigated the relationship between neighbourhood deprivation and oral health. In line with our results, three
studies found a relationship between deprived areas and caries13,
while two others did not16,31,32. However, only three studies used
multilevel analyses similar to our study.13,16,28 Moreover, merely one
of these studies controlled for individual socioeconomic indicators,
which makes it difficult to conclude whether the poor oral health
outcomes found in deprived areas reflect the individual SES or the
physical and social environment individuals live in.16,33 In our study
we used NSS as a measure of neighbourhood deprivation which is
based on four sociodemographic characteristics. However, many
other measures exist and using these may lead to different results.
For example, a multilevel-study in the UK using area deprivation
scores based on overcrowding in households, male unemployment,
proportion of low SES, and proportion of persons without a car, did
not find an association between area deprivation and the number of
sound teeth among adults.31 Similarly, in an Italian multilevel study
no association was observed between a city deprivation index and
DMFT in 12-year-old children.32 However, whereas the latter study
used the deprivation level of an entire city, we were able to assess
neighbourhood deprivation levels within a city and villages, which
d
d
c
c
NSS, neighbourhood status score; VPC, variance partitioning coefficient (representing the proportion of variance due to neighbourhood level differences).
a: Having no caries was reference category for all models. All analyses were performed using multilevel logistic binomial regression models, and results are presented as odds ratios (OR) with
corresponding 95% confidence interval (CI).
b: high NSS (least deprived) was reference category.
c: The neighbourhood variance and VPC per model, respectively: Supermarkets: 0.07, 2.0%; Snack bars: 0.07, 2.1%; Dental practice density: 0.07, 2.1%; NSS: 0.02, 0.5%.
d: The neighbourhood variance and VPC per model, respectively: Supermarkets: 0.36, 9.7%; Snack bars: 0.37, 10.1%; Dental practice density: 0.47, 12.4%; NSS: 0.17, 5.0%.
Null model: empty model with random intercept only.
Model 1: random intercept model per neighbourhood characteristic separately.
Model 2: model 1 þ all neighbourhood characteristics (number of supermarkets, number of snack bars within 1 km distance. dental practice density, NSS).
Model 3: model 2 þ individual characteristics (gender, age, maternal educational level, family household income, maternal employment status, maternal marital status, and ethnic background).
1.48 (1.02–2.15)
1.32 (0.96–1.81)
0.01
0.2%
1.10 (0.85–1.44)
1.01 (0.82–1.25)
0.00
0%
3.42 (2.22–5.27)
2.00 (1.36–2.93)
0.13
3.7%
1.73 (1.32–2.26)
1.23 (0.98–1.55)
0.01
0.4%
4.58 (3.09–6.78)
2.25 (1.51–3.35)
NA
NA
0.51
13.3%
NA
NA
0.09
2.7%
1.86 (1.49–2.33)
1.27 (1.01–1.59)
1.05 (0.96–1.14)
0.99 (0.98–1.01)
1.00 (0.97–1.04)
0.98 (0.86–1.12)
1.02 (1.00–1.04)
0.95 (0.90–1.00)
1.02 (0.93–1.12)
1.00 (0.93–1.12)
0.98 (0.95–1.01)
1.21 (1.12–1.31)
1.03 (1.02–1.04)
0.96 (0.90–1.02)
1.08 (1.03–1.13)
1.01 (1.00–1.02)
0.97 (0.93–1.00)
NA
NA
NA
NA
NA
NA
Neighbourhood variables
Number of supermarkets within 1 km distance
Number of snack bars within 1 km distance
Dental practice density per 10.000 inhabitants
NSSb (deprivation score)
Low NSS (most deprived)
Middle NSS
Neighbourhood variance
VPC
Mild caries
Mild caries
Severe caries (dmft >3)
Mild caries (dmft 1-3)
Severe caries
Mild caries
Severe caries
Model 3
Model 2
Model 1
Null model
Table 2 Association between neighbourhood and dental cariesa
1.00 (0.90–1.12)
1.01 (0.99–1.02)
0.99 (0.95–1.03)
European Journal of Public Health
Severe caries
746
gives a better understanding on how small local areas can influence
oral health of children. Studies investigating area characteristics and
dental service use are scarce, one previous study in England observed
an association between neighbourhood deprivation and the use of
dental services in elderly, which is comparable with the nonsignificant trend we observed in children.34
Our results indicate that the proportion of variance in dental
caries and dental visits due to neighbourhood level differences is
mostly accounted for by the included individual and neighbourhood
characteristics. Still, the same models also showed that compared to
non-deprived neighbourhoods, living in a deprived neighbourhood
is associated with severe dental caries and a lower likelihood of
visiting the dentist. In fact, the relevance of neighbourhood deprivation for oral health among children could indicate that contextual
factors do matter, but that the administrative boundaries used in
our study to differentiate between neighbourhoods might not be the
most relevant for explaining variation in oral health. Alternatively,
the relevance of neighbourhood deprivation may also suggest a residual effect of SES on oral health. Although we were able to adjust
for several individual socioeconomic indicators, it is possible that
the association between neighbourhood deprivation and poor oral
health reflects the individual socioeconomic circumstances of the
population living in deprived neighbourhoods.33 In absence of
detailed information at various (lower) aggregate levels, we are
not able to favour one explanation over the other and therefore
we elaborate on the potential mechanism behind deprived neighbourhoods and oral health in the following paragraph.
There are several pathways via which living in a deprived
neighbourhood could affect oral health. First, neighbourhoods can
influence health via their physical characteristics.7, 9 In our study,
univariate models indicated that the number of supermarkets was
associated with both oral health outcomes, but these associations
attenuated after adjustments for other neighbourhood variables
(Tables 1 and 2). Thus, the relationship between number of
supermarkets and oral health is attributable to a higher number
of supermarkets and snack bars in more deprived neighbourhoods,
similarly shown before in other studies.35,36 Second, several theories
exist through which the social environment in neighbourhoods
could affect individual health.7 For example, Diez Roux and Mair
suggest that neighbourhood safety, social connectedness and local
institutions may affect health and corresponding behaviours.7 Other
theories note that individuals living in the same neighbourhoods
adapt their behaviours according to how others in the same
geographical and social area behave.37 Overtime, predominant
behaviours in an area can become a collective habitude, making
the relation between neighbourhoods and health status bi-directional.7,38 This implies that the unfavourable oral health outcomes found
in deprived neighbourhoods could reflect oral health-related behaviours of their inhabitants. However, individual behaviours such as
sugar intake and brushing frequency did not influence our results
(Supplementary tables S1 and S2). Also, dental caries experience was
not related to dental visits (Table 1, and Supplementary table S3).
The results of this study have to be seen in the light of some
limitations. Neighbourhood characteristics in this study were based
on aggregated data, and it is important to acknowledge that this
could have led to imprecise neighbourhood level data, causing
non-differential misclassification. Also, our study relies on the
assumptions of strict area borders, however, inhabitants might reside on the border of two areas and live closer to another neighbourhood. Although this would apply to a small number of children in
our study population it could have led to slightly biased results.
Furthermore, we have a large underestimation of the number of
children not visiting the dentist on a yearly basis when observing
regional statistics. In Rotterdam the percentage of children that did
not visit the dentist in 2010 is 37.5% (range: 28.9%–45.5%), whereas
in our study we only found that 7.6% of the children in our study
population did not visit the dentist in the past year.39 This might be
caused by misreporting of the caregivers, but it could also represent
Neighbourhood characteristics and children’s oral health
747
Table 3 Association between neighbourhood and dental visita
Null model
Neighbourhood variables
Number of supermarkets
within 1 km distance
Number of snack bars
within 1 km distance
Dental practice density per
10.000 inhabitants
NSSb (deprivation score)
Low NSS (most deprived)
Middle NSS
(Range) Neighbourhood
variance
(Range) VPC
Model 1
Model 2
Model 3
NA
0.87 (0.83–0.91)
0.90 (0.80–1.00)
0.90 (0.80–1.01)
NA
0.98 (0.98–0.99)
1.00 (0.98–1.02)
1.00 (0.99–1.02)
NA
1.02 (0.97–1.06)
1.01 (0.97–1.05)
1.00 (0.96–1.04)
NA
NA
NA
0.07
0.49 (0.37–0.65)
0.69 (0.52–0.91)
0.68 (0.48–0.97)
0.76 (0.57–1.03)
0.00
0.81 (0.56–1.18)
0.81 (0.60–1.10)
0.00
2%
c
c
0%
0%
NSS, neighbourhood status score; VPC, variance partitioning coefficient (representing the proportion of variance due to neighbourhood
level differences).
a: Children that did not visited the dentist in the past year were the reference category for all models.
All analyses were performed using multilevel logistic binomial regression models, and results are presented as odds ratios (OR) with
corresponding 95% confidence interval (CI).
b: high NSS (least deprived) was reference category.
c: The neighbourhood variance and VPC per model, respectively: Supermarkets: 0.00, 0%; Snack bars: 0.00, 0%; Dental practice density:
0.07, 2.1%; NSS: 0.00, 0%. Null model: empty model with random intercept only.
Model 1: random intercept model including neighbourhood characteristics.
Model 2: model 1þ all neighbourhood characteristics (number of supermarkets, number of snack bars within 1 km distance. dental practice
density, NSS).
Model 3: model 2 þ individual characteristics (gender, age, maternal educational level, family household income, maternal employment
status, maternal marital status, and ethnic background).
the selection bias due to differential participation in our study. The
non-response analysis showed that the majority of excluded participants had missing postal codes and missing caries data
(Supplementary table S4). However, excluded participants with
a
