the association of individual characteristics and neighborhood poverty on the dental care of...

The association of individual characteristics andneighborhood poverty on the dental care of Americanadolescentsjphd_340 1..7

Robert Atkins, PhD1; Michael J. Sulik, MA2; Daniel Hart, EdD3

1 Rutgers, The State University of New Jersey, Nursing and Childhood Studies, Camden, NJ2 Arizona State University, Psychology, Tempe, Arizona, AZ3 Rutgers, The State University of New Jersey, Center for Children and Childhood Studies, Camden, NJ

Abstract

Objective: The purpose of this study was to explore the extent to which neighbor-hood poverty was associated with the utilization of dental care by Americanadolescents.Methods: To accomplish the study goals we conducted multilevel modelinganalyses of two nationally representative data sets: National Longitudinal Studyof Adolescent Health (Add Health) and the Early Childhood Longitudinal Study,Kindergarten Class of 1998-1999 (ECLS-K).Results: As hypothesized, neighborhood poverty predicted frequency of dental carein both studies (t = 6.06; P < 0.001; t = 2.44; P < 0.05). Even after accounting forindividual level predictors such as household income, health insurance, and parentaleducation, adolescents living in poor neighborhoods are less likely than their coun-terparts in non-poor neighborhoods to utilize dental care.Conclusions: The findings from this study indicate that neighborhoods influencedental care utilization patterns in adolescents.

The best evidence converges on the finding that, in developedcountries, individuals living in high-poverty neighborhoodshave higher rates of morbidity and mortality than their coun-terparts in lower-poverty neighborhoods (1,2). Despite thisevidence, it can not be assumed that neighborhoods play a rolein health disparities. Factors such as individual disadvantage(e.g., houshold income) and neighborhood disadvantage(e.g., proportion of impoverished households in a neighbor-hood) have to be considered because there is the possibilitythat neighborhood variations in health are due to a greaterconcentration of individual disadvantage in disadvantagedneighborhoods (3). Individuals at the highest risk for poorhealthoutcomesmayalsobethemost likely to live indisadvan-taged neighborhoods. For example, there is evidence to showthat adolescents living in high-poverty neighborhoods havehigher rates of untreated tooth decay and greater tooth lossthan their counterparts in more affluent neighborhoods (4).

Without partitioning individual disadvantage andneighborhood disadvantage through multilevel analyticalmethods, it is not possible to resolve the question of whether

the dental health outcomes were related to disadvantages atthe level of the individual or disadvantages at the level of theneighborhood. A growing number of researchers have usedmultilevel modeling to investigate neighborhood effects onhealth. Multilevel analysis of data collected from adults sug-gests that there are neighborhood variations in oral health(3).The purpose of this study is to investigate through multi-level modeling whether neigbhorhood variations are associ-ated with adolescents’ use of dental care.

Understanding the factors that influence the utilizationpatterns of dental care and other forms of primary care isimportant because research has consistently shown that ado-lescents are low utilizers of primary care (5-7). Adolescentsare especially unlikely to utilize dental care (6,8). Forexample, findings from studies conducted in the UnitedStates and Chile show that between 20% and 32% of adoles-cents were not utilizing dental care services on an annualbasis (5,8,9). While there is evidence of an associationbetween the demographic and socio-economic backgroundof adolescents and their likelihood of utilizing dental care

Keywordsadolescents; neighborhood poverty; lowincome population; multilevel analysis; primarycare; dental care.

CorrespondenceDr. Robert Atkins, Rutgers, The State Universityof New Jersey, Nursing and Childhood Studies,Center for Children, 325 Cooper Street,Camden, NJ 08102. Tel.: 8562256483; Fax:856-225-6250; e-mail:[email protected]. Michael J. Sulik is adoctoral student in the Department ofPsychology at Arizona State University,Psychology. Daniel Hart is with Rutgers, TheState University of New Jersey, Center forChildren and Childhood Studies.

Received: 8/17/2011; accepted: 2/17/2012.

doi: 10.1111/j.1752-7325.2012.00340.x

Journal of Public Health Dentistry . ISSN 0022-4006

1Journal of Public Health Dentistry •• (2012) ••–•• © 2012 American Association of Public Health Dentistry

services, many adolescents who have access to dental care ser-vices – because the services are free or they are eligible basedon their health insurance – are not utilizing dental care (5,9).There is some evidence that populations living in poor neigh-borhoods are less likely than their counterparts living in moreaffluent neighborhoods to utilize primary care even afteraccounting for individual backgrounds; however, no studyhas focused specifically on how neighborhoods influence thedental care practices of adolescents (6,8,10).

Overview

In this paper, we explore how neighborhood poverty influ-ences the dental care utilization of adolescents in the UnitedStates after controlling for individual level demographic andsocioeconomic factors. We present methods and findingsfrom two data sets. We hypothesized that neighborhoodpoverty would be negatively related to the utilization ofdental care, even after controlling for demographic, social,and economic variables known to be associated with healthand health outcomes. In Study 1, we use data from theNational Longitudinal Study of Adolescent Health (AddHealth). In Study 2, we use restricted data from the EarlyChildhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K) eighth-grade data files – which has variablessimilar to the Add Health – to replicate the findings in asecond sample of adolescents.

Study 1

Methods

Study design

The data used come from Wave I of the Restricted Use DataAdd Health, a school-based study of youth originally ingrades 7 to 12 (see (11)). All high schools in the United Statesthat included an 11th grade and at least 30 students were eli-gible for inclusion in the Add Health study.A sample of 80 eli-gible high schools was selected. The sample was stratified byregion, urbanicity (urban/suburban/rural), school type(public/private/parochial), ethnic mix, and size; schools wereselected with probability proportional to size (12). Wave Idata was collected between September 1994 and December1995. Participants ranged in age from 12 to 21 years.

Our research question focuses on neighborhood effects ondental care utilization. On average, adolescents from the sameneighborhood are more similar to each other than expectedby chance. Failing to account for this similarity producesnegatively biased standard errors and statistical tests (13). Toaddress this concern, we used multilevel modeling (SAS 9.2PROC MIXED). This technique accounts for similaritiesbetween individuals in the same neighborhood by partition-

ing variance separately at the individual level (level-1) and theneighborhood level (i.e., US census tract; level-2).

We hypothesized that neighborhood level poverty wouldpredict the adolescents’ frequency of dental care above andbeyond the level-1 variables used to control for individual dif-ferences. To test this hypothesis, we used likelihood ratio teststo compare the nested models that included and did notincludeneighborhoodpovertyasapredictor.Thedifference inthe -2 log likelihood (also known as deviance) follows a c2 dis-tribution with d.f. equal to the number of parameters thatdiffer between nested models (Singer & Willett (13)). To esti-matetheamountof variance ineachoutcomeaccountedforbythe set of predictors, we squared the correlation between thescores predicted by our model and the scores that were actuallyobserved in the data.This pseudo r2 statistic is analogous to ther2 statistic in multiple regression, and can be interpreted simi-larly (14).We also report the reduction in the variance compo-nents relative to a model without predictors [i.e., a null model;(15)] as an alternative measure of variance explained.

Participants

All Add Health respondents (n = 20,745) with complete datawere eligible for inclusion in the analysis. For families withmore than one child, the oldest child was selected for inclu-sion to avoid a second clustering variable, or, if the oldestchild was a twin, one twin was selected at random(n = 17,909). The analytic sample consisted of participantswith complete data on all study variables (n = 11,844).Descriptive statistics for all study variables are presented inTable 1.

Measures

Demographics

Parents reported the participant’s age, race/ethnicity, andgender. Race was dummy coded into the following categories:Caucasian (reference category), African American, Asian,Native American, and Other/Multiracial. Sex was alsodummy coded, with girls as the reference category.

Parent education

The highest level of education reported by the parent wasused to measure educational level. Educational attainmentranged from 1 (8th grade or less) to 9 (professional trainingbeyond a 4-year college or university).

Parent marital status

The current marital status reported by the parent was used tocategorize parental relationship status as married (1) or notmarried (0).

Adolescents, neighborhoods, and dental care R. Atkins et al.

2 Journal of Public Health Dentistry •• (2012) ••–•• © 2012 American Association of Public Health Dentistry

Health insurance

If the parent responded that the child had no health insuranceor that there had been a time over the past 12 months thattheir child had been without health insurance the child wascategorized as uninsured (0).Otherwise, children were classi-fied as insured (1).

Household income

The total net annual family income reported by a parent wasused to assess the participants’ economic status. This variablewas log-transformed prior to analysis.

Neighborhood poverty

The proportion of households in a census tract with a 1989income below that year’s federal poverty level, as collected inthe 1990 census, was used as a measure of neighborhoodpoverty. The proportion of impoverished households in aneighborhood ranged from 0% to 86%.

Dental care utilization

Participants were asked “When did you last have a dentalexamination by a dentist or hygienist?” Respondents ratedtheir most recent exam on a scale of 1 (less than a year ago); 2(1-2 years ago); 3 (2 years ago); or 4 (never). This measurewas treated as a continuous variable.

Results

Descriptive statistics

Table 1 reports descriptive statistics for the variables consid-ered in this study. Approximately 32% of adolescents had notutilized dental care services in the previous year.

Primary analysis

Table 2 depicts the findings from the models tested in Study 1and Study 2. In the null model, the level-2 variance was 0.053and the level-1 residual variance was 0.531, resulting in anintraclass correlation 0.09. This statistic represents theexpected correlation between two individuals from differentneighborhoods. As expected, a number of level-1 variableswere related to dental health care utilization. African Ameri-can, Asian, Native American, and other/multiracial adoles-cents had less lower dental care relative to whites, whereasfemales had more dental care relative to males. Being insured,family income, and parental education were all positivelyrelated to receiving dental care. Based on the squared correla-tion between the observed and predicted scores, the modelwith level-1 predictors explained 9.98% of the variance in fre-quency of dental care. While controlling for level-1 predic-tors, neighborhood poverty was a significant predictor ofdental care, t = 6.06, P < 0.001. Relative to the null model, themodel with level-1 predictors reduced the level-2 variance by72.4%. The model containing neighborhood level povertyshowed better prediction of dental outcomes relative to the

Table 1 Descriptive Statistics for Add Health and ECLS Study Samples

Add Health ECLS

Mean SD Mean SD

Age (years) 16.15 1.69 14.32 0.37Family income* ($) 47,328.47 52,091.16 75,779.08 65,774.96Parent education 5.59 2.34 5.19 1.92Neighborhood poverty 0.14 0.12 0.11 0.09

% %

Female 49.89 48.50Latino 15.64 10.53Not insured† 12.75 2.58Married 71.16 76.04Black 19.19 9.32Asian 1.02 4.45American Indian 4.84 2.55Other/multiracial 12.72 2.10

* The Add Health data was collected in 1995. The ECLS data was collected in 2007. Inflation andGDP increases over 12 years contribute to large changes in wages. Accounting for the average wageincrease of 64% between 1995 and 2007, closes the difference in mean family income between theAdd Health and ECLS.† 0 = uninsured; 1 = insured.

R. Atkins et al. Adolescents, neighborhoods, and dental care


model with level-1 predictors alone, c2(36.2, 1), P < 0.001.This model accounted for 10.31% of the total variance indental outcomes, a modest 0.33% increase over the modelwith level-1 predictors alone. Relative to the null model, themodel with level-1 and level-2 predictors reduced the level-2variance by 76.6%. This represents a 4.1% reduction in thelevel-2 variance over the model with level-1 predictors alone.

Study 2

Method

Participants

The ECLS-K is a longitudinal study of children in the UnitedStates. The same children were followed from kindergartenthrough the 8th grade (16). Beginning in kindergarten, datawere collected from these children, their families, teachers,and schools on the participants’ cognitive, social, emotional,and physical development. Information for the 8th gradesample of approximately 9,725 children was collected in thespring of 2007. Participants ranged in age from 12 to 16 years.One difference between the Add Health and ECLS-K data is inhow neighborhoods were defined. The Add Health used UScensus tracts that are defined by local participants, whereasthe ECLS used US postal (zip) codes, which are defined by USMail. To maintain consistency across studies, we only ana-lyzed data from zip codes that were comparable in size to theAdd Health census tracts (<20,000 residents), as 99% of thecensus tracts in the Add Health study contained 15,442 orfewer residents. All participants with complete data measures

from these zip codes (n = 8,430) were included in the analyses(sample sizes rounded to nearest 10 in order to adhere toInstitute of Education Sciences restricted data requirements).Descriptive statistics for all study variables are presented inTable 1.

Measures

Demographics

Parents reported the participant’s age, race/ethnicity, andgender.

Socioeconomic status

Parental education, parental occupation, and householdincome in the fall of 2006 were used to compute a compositesocioeconomic status.

Married

The current marital status reported by the parent was used tocategorize parental relationship status as married or notmarried.

Insurance

If the parent responded that the child had no health insurancethe child was categorized as uninsured.

Neighborhood poverty

The proportion of households in a zip code with a 1999income below that year’s federal poverty level, as collected inthe 2000 census, was used as a measure of neighborhoodpoverty. The proportion of impoverished households in aneighborhood ranged from 0% to 68%.

Dental care utilization

Parents were asked, “How long has it been since your child’slast visit to a dentist or dental hygienist for dental care?”Parents rated these visits on a scale of 1 (less than a year ago)to 4 (never).

Results

Using the same analytic strategy as Study 1, we predicted thefrequency of dental exams using multilevel models with indi-viduals (level-1) nested within neighborhoods (level-2). Inthe null model, the level-2 variance was 0.009 and the level-1residual variance was 0.146, resulting in an intraclass correla-tion 0.06. The model with level-1 predictors accounted for

Table 2 Multilevel Models Predicting Length of Time Since Last DentalVisit

Fixed effects

Add Health ECLS

b t b t

Intercept 1.39 1.25Female -0.04 -2.80** -0.02 -1.23Age 0.02 4.34*** 0.00 2.92**Latino 0.03 1.47 0.07 3.16**African American 0.19 9.47*** 0.01 0.54Asian 0.14 2.12* 0.05 1.61Native American 0.07 2.00* -0.06 -1.23Other race or Multiracial 0.10 4.23*** 0.04 0.44Married -0.03 -1.69 0.03 1.63No insurance 0.37 17.49*** 0.18 4.17***Family income -0.06 -5.84*** -0.04 -3.90***Parental education -0.03 -9.74*** -0.02 -4.85***Neighborhood poverty 0.42 6.06*** 0.21 2.44*

Random effects s z s zLevel 2 variance 0.012 3.84*** 0.002 1.02Residual variance 0.509 71.48*** 0.141 36.54***

Note. * P < 0.05, ** P < 0.01, *** P < 0.001.



5.82% of the variance in frequency of dental visits. In thisstudy, Latinos were less likely to receive dental care relative tonon-Latino youth; however, no racial variables were related tothe outcome. Older adolescents were less likely to receivedental care than younger adolescents. Being insured, familyincome, and parental education were all positively related toreceiving dental care. After accounting for level-1 predictors,neighborhood poverty was associated with frequency ofdental care, t = 2.44, P < 0.05. Relative to the null model, themodel with level-1 predictors reduced the level-2 variance by76.1%. As in Study 1, the model containing neighborhoodlevel poverty fit better than the model with level-1 predictorsalone, c2(4.2, 1), P < 0.05, and accounted for 6.01% of thetotal variance in dental outcomes, a 0.19% increase over themode with level-1 predictors alone. Relative to the nullmodel, the model with level-1 and level-2 predictors reducedthe level-2 variance by 81.1%. This represents a 4.9% reduc-tion in the level-2 variance over the model with level-1 pre-dictors alone.

Discussion

Our goal in this paper was to understand whether neighbor-hood poverty was associated with patterns of adolescentdental care utilization. As with previous studies, the resultsfrom this study suggest that, even after controlling for healthinsurance and other socioeconomic factors, one-third ofadolescents are not utilizing preventive dental care services(5,7-9,17,18).Consonant with the findings from other studiesin which the dental care utilization practices of adolescentshave been investigated factors, beyond access to care, influ-ence the dental care utilization practices of adolescents(5,7,9,11). As with previous studies of adolescent dental careutilization we controlled for socioeconomic factors such asparental education and health insurance. In contrast to previ-ous studies, we investigated whether neighborhood povertycontributed to differences in the dental care utilization ofadolescents. We found that even after controlling for demo-graphic and socioeconomic factors, adolescents living in poorneighborhoods received less dental care than their counter-parts living in more affluent neighborhoods.

Why might neighborhood poverty influence the utiliza-tion of dental care? According to the neighborhood institu-tional resource model proposed by Jencks and Mayer (19) thehealth and well-being of the youth is promoted through thepresence of community services and resources such as parks,after-school programs and wellness centers that contribute tohealth and well-being of community residents (19). Based onthis model, adolescents living in high-poverty neighbor-hoods, in which dentists are a scarce resource, have feweropportunities to visit the dentist. Although researchers havefound that there are fewer dentists in high-poverty, urbanneighborhoods, the scarcity of dentists in these communities

does not fully explain why neighborhoods effect patterns ofdental care (7).

Of course, neighborhood poverty as measured by the per-centage of poor families in an administrative unit (e.g.,census tract or zip code) is only one aspect of neighborhooddisadvantage (20). Other neighborhood characteristics havealso been found to influence health outcomes and healthbehaviors. In addition, the census tracts and zip codes used tooperationalize neighborhoods are political–administrativedivisions and may not adequately define neighborhoodboundaries. For example, we found that in the ECLS-K dataanalysis, there was a significant interaction between povertyin a zip code and zip code size, such that poverty was morestrongly related to dental care in smaller zip codes; due to het-erogeneity among neighborhoods in poverty rates, povertyrates in large zip codes may not accurately reflect localneighborhood conditions. Future studies using other neigh-borhood characteristics and other methods to defineneighborhoods are important.

A limitation of the Add Health data set was that the major-ity of participants excluded from the study were due tomissing household income information, parental education,and health insurance. To explore whether this affected ourresults, we ran analyses that did not include these variables.Our primary finding – that neighborhood poverty predicteddental care utilization – remained robust. Another limitationof this study is that the sampling designs of the Add Healthand ECLS databases were school-based and the samplingweights provided to make nationally representative estimatesare based on this design. The research questions that wepursued, however, centered on census tracts and, conse-quently, the models were not weighted. Although this is astudy limitation, Carle (21) found that the differencesbetween weighted and unweighted multilevel model analysesare minimal and do not lead to different inferential conclu-sions (21). Moreover, the principal study finding – neighbor-hood poverty predicted how dental care services were utilized– was replicated in two large data sets. Consequently, con-cerns that the association between neighborhood povertyand dental care utilization is spurious are undermined andour theoretical argument that the association reflects theinfluence of neighborhoods is strengthened.

An important strength of this paper is that we controlledfor the socioeconomic factors found to influence dental careutilization (e.g., age, gender, family resources, health insur-ance) and focused on the extent to which neighborhoodpoverty level influenced dental care utilization patterns. Con-sistent with previous research, the neighborhood effects weremuch smaller than the individual and family level factors onprimary care utilization patterns. So as not to minimize theneighborhood effect, it is important to keep in mind that aportion of the variance explained by individual level predic-tors may actually be due to neighborhoods (due to covariance



between individual level variables and the choice of neigh-borhood). For example, some of the effect of race/ethnicityon dental care utilization may be due to geographical lack ofaccess to care. Unfortunately, many variables that might beassociated with dental care utilization such as structural (e.g.,availability of dental services) and psychosocial (e.g., dentalanxiety) factors were not available in the data sets used in thisstudy. It may be possible to investigate how these factors influ-ence dental care utilization in future studies.

There is a growing awareness among researchers andpractitioners that a better understanding of the healthactions of children and adolescents is necessary to diminishhealth disparities (11,22). As discussed in the study entitled:Adolescent Health Services: Missing Opportunities conductedby the Institute of Medicine (IOM), the importance ofunderstanding the health behaviors of adolescents – espe-cially those from poor and minority backgrounds – is para-mount. Unfortunately, most of the studies of adolescenthealth have focused on the risky behaviors that start or peakduring adolescence such as unprotected sex or substanceabuse (23). While it is important to understand the healthdamaging behaviors of adolescents, there is evidence that thehealth promoting behaviors of adolescents – such as preven-tive dental care – also influence health disparities. As high-lighted in the IOM report on adolescent health, adolescentsmake up a significant proportion of the US population andthe habits and skills that create a foundation for healthy lif-estyles and behaviors are developed during adolescence.Unfortunately, health services for adolescents tend to focuson the treatment of acute health problems – injuries andinfections – and the prevention of health problems such aspremature parenthood and substance abuse (23). However,addressing health problems and potential health problems istoo narrow a focus. The range of health services provided toadolescents should be broadened to include the health pro-moting services are going to require to be prepared forhealthy and constructive adulthoods. Finally, a shift in focusis also an important step in reducing health disparities in theUnited States. This shift will become even more important ifreforms of the health care system increase the number ofAmericans with health care coverage. Understanding howadolescents develop the attitudes and knowledge that moti-vate their utilization of primary care resources will increaseunderstanding of how consumer side decisions influencehealth and health outcomes.

Conclusions

The aim of this study was to explore how individual and neigh-borhood poverty influence dental care in the United States.Aswe hypothesized, even after accounting for individual levelpredictors such as household income, health insurance, andparental education, adolescents living in poor neighborhoods

were less likely than their counterparts in non-poor neighbor-hoods to utilize dental care. Future studies that include vari-ables not accounted for in this study (e.g.,availability of dentalcare providers) may improve our understanding of howneighborhood poverty influences access to dental care.

Acknowledgments

This study was supported in part through the Robert WoodJohnson Foundation Nurse Faculty Scholars Award program.

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