Objectives. We measured racial/ethnic inequalities in US children’s dental health and quantified the contribution of conceptually relevant factors.
Methods. Using data from the 2007 National Survey of Children’s Health, we investigated racial/ethnic disparities in selected child dental health and preventive care outcomes. We employed a decomposition model to quantify demographic, socioeconomic, maternal health, health insurance, neighborhood, and geographic effects.
Results. Hispanic children had the poorest dental health and lowest preventive dental care utilization, followed by Black then White children. The model explanatory variables accounted for 58% to 77% of the disparities in dental health and 89% to 100% of the disparities in preventive dental care. Socioeconomic status accounted for 71% of the gap in preventive dental care between Black children and White children and 55% of that between Hispanic children and White children. Maternal health, age, and marital status; neighborhood safety and social capital; and state of residence were relevant factors.
Conclusions. Reducing US children’s racial/ethnic dental health disparities—which are mostly socioeconomically driven—requires policies that recognize the multilevel pathways underlying them and the need for household- and neighborhood-level interventions.
Race/ethnicity is a significant determinant of dental health in different countries, and racial/ethnic minority status is a well-known risk factor for poor dental health.1,2 Racial/ethnic differences in children’s dental health are common in the United States. Among children aged 2 to 11 years, Black children and Hispanic children are more likely to have decayed teeth and untreated dental problems than are White children.3,4 The rate of primary dentition caries in 1999 through 2004 was 55.0% for Hispanic children, 43.0% for Black children, and 39.0% for White children.3 About 6.5% of non-Hispanic White children have fair or poor oral health, compared with 12.0% of Black children and 23.4% of Hispanic children, with large racial/ethnic differences remaining after adjusting for age, gender, education, poverty level, dental insurance, and parental preventive care attitude.5
Racial/ethnic disparities also exist in children’s access to dental care in the United States. Larger unmet dental care needs are observed in non-White children. Moreover, Hispanic children have the highest likelihood of never having seen a dentist.6,7 In addition, among children who are publicly insured through Medicaid and the State Children’s Health Insurance Program, Hispanics and Blacks have longer intervals between dental visits and higher tooth decay rates.8
Although documenting racial/ethnic disparities in child dental health is important, of greater relevance is identifying the pathways that explain these inequalities to inform policies that can effectively reduce them. Although racial/ethnic disparities in child dental health have been well documented, few studies have explored their underlying pathways, and none has formally quantified the contributions of socioeconomic, demographic, and neighborhood characteristics to these disparities. Previous studies highlight some factors as relevant for racial/ethnic disparities, including socioeconomic condition, health literacy, educational attainment, dental insurance, language barriers, and cultural characteristics.2,9–11 However, these studies did not adequately characterize the individual contributions of these and other theoretically relevant factors to disparities. To fill this research gap, we measured racial/ethnic inequalities in child dental health in a nationally representative sample, and, with a decomposition analysis, we quantified the extent to which the observed disparities are attributable to conceptually relevant factors. In doing so, we have highlighted important pathways contributing to child dental health disparities.
We used data from the 2007 National Survey of Children’s Health (NSCH), a cross-sectional national survey conducted by the National Center for Health Statistics at the Centers for Disease Control and Prevention to assess different aspects of children’s health and access and use of health care services. Ours is the first study, to our knowledge, to use the 2007 NSCH data for studying child dental health disparities. The NSCH’s sampling design provides a nationally representative sample of children and adolescents (≤ 18 years) in the United States. After identifying a household, the NSCH randomly selected 1 index child and collected data from parents or caregivers on several demographic, economic, health and health care, and neighborhood characteristics.12
To avoid errors in maternal and child data from other respondents, in our sample we initially included 57 027 children aged 3 years or older whose mother completed the NSCH interview. We excluded children younger than 3 years because they had a very low frequency of dental problems (< 0.5%) and because the differences in this outcome between the study racial/ethnic groups were not significant (at P = .05).
The final sample for our analyses ranged from 43 972 to 45 237 children who had complete information on 1 or more of the dental health outcomes and all explanatory variables.
We focused on non-Hispanic White, non-Hispanic Black, and Hispanic children. We measured and decomposed racial/ethnic disparities in 3 child dental health and care outcomes. The first dental health outcome measured whether the child had any of the following dental problems over the past 6 months: toothache, decayed teeth or cavities, broken teeth, and bleeding gums. We derived the second dental health outcome from the maternal rating of the child’s dental health status as excellent, very good, good, fair, or poor. We created a binary indicator combining excellent, very good, or good versus fair or poor. The third outcome was the number of preventive dental care visits during the past 12 months. Because the American Academy of Pediatric Dentistry recommends 2 preventive dental visits per year,13 we used a dichotomous indicator for 2 or more preventive dental visits.
We evaluated the contribution of several conceptually relevant categories of household and neighborhood or area characteristics to the racial/ethnic disparities in the dental outcomes. It is important to recognize the complexity and multiplicity of the underlying mechanisms influencing health and health behaviors when studying health disparities. Therefore, our choice of the explanatory variables was motivated by general microeconomic theory for health and health care demand and for health production supplemented with psychosocial and neighborhood effects, given their importance for health.
Given that theory motivates our model specification, we retained explanatory variables even if they had statistically insignificant effects on the outcomes, as their omission may result in bias in effects of other explanatory variables. In addition, we included only explanatory variables showing significant differences of Black or Hispanic children compared with White children because of their potential to explain the observed disparities in study outcomes; variables with a similar distribution by race/ethnicity cannot explain these disparities.
Demographics included maternal age and marital status and whether the child was born in the United States. Maternal age reflects skills, knowledge, and experience in child health management. Maternal marital status may influence the availability of childcare and economic resources. Birth in the United States may be associated with socioeconomic and cultural differences between American and immigrant children that are relevant for dental health. We did not include child gender and age because these were not significantly different between the racial/ethnic groups and therefore could not explain the observed disparities.
We measured maternal health from self-reported general health status (excellent, very good, or good vs fair or poor). Maternal health might reflect the presence of common risk factors for the child’s general and dental health. It might also affect maternal ability to care for the child, as poor maternal health can reduce the availability of time and economic resources needed for enhancing child health.
The third category, socioeconomics, included household income level, household employment status, and highest maternal educational attainment. Socioeconomic status can affect child dental health in several ways, such as by affecting parental knowledge and ability to enforce optimal dental health–related behaviors and to access dental care. Socioeconomic status also affects parental and child psychosocial status (e.g., stress and anxiety), which may affect dental health behaviors. Furthermore, education can directly affect maternal efficiency and ability to identify child dental problems and access needed dental care.
Indicators for public or private insurance relative to being uninsured at the time of the interview represented child insurance status, which can directly affect access to dental care. Household demographics included the total numbers of children and adults in the household, which may affect dental health through allocation of resources in the household and child care.
Neighborhood conditions may affect dental health and care in various ways, including physical safety, social networking, information about health care and dental services, and supply of dental care providers. Because neighborhood characteristics may vary by race/ethnicity and contribute to racial/ethnic disparities in dental health, we evaluated 9 neighborhood characteristics grouped into 3 categories. Neighborhood condition included the presence of litter or garbage, poorly kept or rundown housing, and the availability of a library or bookmobile. We measured neighborhood safety by the presence of vandalism and the maternal perception of child safety. Neighborhood social capital included indicators for people in the neighborhood helping each other, watching out for each other’s children, and counting on each other, and adults helping children in case they are hurt or scared. Finally, given the geographic variation in race/ethnicity distributions and in dental health and care (e.g., the distributions of dental professionals), we also evaluated the extent to which the state of residence explained the racial/ethnic disparities in dental health. Table 1 presents the study variables and their distributions by race/ethnicity.
|Variables||White, Proportion or Mean (SD)||Black, Proportion or Mean (SD)||Hispanic, Proportion or Mean (SD)|
|Any dental problem (toothache, decay or cavities, broken teeth, bleeding gums)||24.35||33.98**||40.15**|
|Self-rated dental health (fair or poor)||4.85||10.62**||22.20**|
|Preventive dental visits (≥ 2 visits in the last y)||61.15||51.28**||46.81**|
|Mother's age, y|
|Mother's marital status (married)||82.04||40.68**||65.60**|
|Children born in the United States||98.71||96.06**||86.34**|
|Mother's general health (fair or poor)||7.74||18.79**||21.33**|
|Poverty level (income)a||6.06 (2.26)||3.99 (2.66)**||3.60 (2.65)**|
|< high school||5.12||11.32**||36.16**|
|≥ high school||71.94||55.95**||35.29**|
|Child insurance status|
|Public health insurance||16.32||50.81**||44.49**|
|Private health insurance||77.55||40.32**||35.47**|
|Number of children in the household||2.27 (0.91)||2.29 (1.00)||2.49 (0.91)**|
|Number of adults in the household||2.07 (0.50)||1.85 (0.70)**||2.15 (0.60)**|
|Neighborhood amenities and condition|
|Library or bookmobile||88.85||85.07**||83.06**|
|Litter or garbage on the street or sidewalk||13.53||26.86**||17.26*|
|Poorly kept or rundown housing||13.41||20.07**||14.26|
|Neighborhood perceived safety|
|Vandalism such as broken windows or graffiti||7.38||13.55**||16.18**|
|Feeling safe in the neighborhood||93.47||72.43**||77.49**|
|Neighborhood social capital|
|People helping each other out||93.80||79.35**||81.08**|
|Watching out for each other's children||92.97||82.68**||84.43**|
|Counting on people||93.88||79.12**||83.70**|
|Adults help if child gets hurt or scared||93.88||87.01**||86.80**|
aPoverty level: (1) < 100% of federal poverty level, (2) 100%–133%, (3) 133%–150%, (4) 150%–185%, (5) 185%–200%, (6) 200%–300%, (7) 300%–400%, (8) > 400%.
bAnyone in the household employed ≥ 50 of the past 52 weeks.
*P < .05; **P < .01 vs White children.
The use of the Oaxaca–Blinder type decomposition models14,15 has contributed significantly to understanding some of the underlying pathways for inequalities in health status and health care.16–19 The basic premise for these models is to quantify the extent to which differences in the distributions of explanatory variables between 2 groups (e.g., minority vs majority) account for their differences on a certain outcome. This decomposition approach, essentially, estimates a multivariate model for the outcome that includes all the explanatory variables of interest; substitutes the means of the explanatory variables for 1 group, 1 at a time, by means of the explanatory variables for the other group; and recalculates the difference in the conditional outcome means between the 2 groups after each explanatory variable mean substitution. The change in the outcome mean difference between the 2 groups with the mean substitution for a certain explanatory variable represents the contribution of that variable to the total outcome gap between these groups.
The Oaxaca–Blinder decomposition model is designed for linear outcomes. Because we studied binary outcomes, we employed the Fairlie decomposition model for nonlinear binary outcome models,20 which has been used previously to decompose health and health care disparities.17,21,22 Our goal was to quantify the contribution of each category of explanatory variables to racial/ethnic disparities in child dental health outcomes. We decomposed the disparities separately for Black children and Hispanic children compared with non-Hispanic White children. For each outcome comparison (e.g., any dental health problem between Black children and White children), we estimated a logistic regression including race/ethnicity (e.g., Black compared with White) and all explanatory variables. Next, we predicted conditional probabilities of the outcome (e.g., probability of any dental health problem) for each observation. Then, we randomly selected a subsample of an equal number of observations to the minority group (Blacks or Hispanics) from the majority group (Whites). Within this selected majority subsample and the minority group, we rank-ordered each observation by the predicted outcome probability. Next, we matched each observation in the minority group with the observation from the majority subsample with an equal rank. Then, 1 variable at a time, we replaced the values of each explanatory variable in the minority group by the values of the matched observations for the same variable from the majority subsample and estimated the difference in the dental health outcome probability between the minority and majority groups. This difference represents the disparity explained by a particular variable. The total outcome difference explained by all variables is the sum of the differences explained by the individual variables (which may also be obtained by switching all variables at the same time from the minority to the majority values). Similar to the individual variable analysis, categories of variables may be evaluated for their combined contribution to the racial/ethnic disparity. Appendix 1 (available as a supplement to the online version of this article at http://www.ajph.org) includes a further illustration of the statistical model.
Given that the results depend on the particular randomly selected majority subsample, we obtained 2000 randomly selected majority subsamples and averaged the decomposition results across these selected subsamples.20 This repeated majority subsample selection approximates the estimates that would be obtained if the total majority sample was matched to the minority sample. In addition, because the decomposition results for each variable category can be affected by the order in which its variable values are switched from the minority to the majority relative to the other categories, we randomly selected the category order at each replication for randomly selecting the majority subsamples. We expected the 2000 replications to approximate the average result from all possible variable–category orderings. We estimated all analyses using the NSCH sampling probability weights to obtain population-based results. We performed analyses with Stata 10.0 (StataCorp LP, College Station, TX).
We used publicly available data online that can be downloaded from the following Centers for Disease Control and Prevention Web site (http://www.cdc.gov/nchs/slaits/nsch.htm#2007nsch).
There were large differences in the prevalence of dental problems by race/ethnicity, particularly between Hispanic children and White children (Table 1). About 40% of Hispanic children had at least 1 dental problem during the past year compared with 34% of Blacks and 24% of Whites. More than one fifth of Hispanic children had their teeth reported in fair or poor condition compared with 10.6% of Blacks and about 5% of Whites. About 47% of Hispanic children received the recommended preventive dental care in the past year compared with 51% of Black children and 61% of White children.
There were also important differences in the explanatory variables by race/ethnicity. The rate of married mothers was highest among White children (82%) and lowest among Black children (41%). In addition, fair or poor maternal health was more than twice as common among Hispanics (21%) and Blacks (19%) as among Whites (8%). There were considerable differences in all socioeconomic and human capital indicators by race/ethnicity, with Hispanics having the highest poverty level and lowest employment rate and maternal educational attainment. Black children had the highest rate of public insurance, and Hispanic children were significantly more likely to be uninsured than were Black children and White children.
Compared with White children and Hispanic children, Black children were more likely to live in poor, rundown, and less safe neighborhoods. White children and Black children generally lived in neighborhoods with the highest and lowest social capital, respectively.
Table 2 presents the percentages of the racial/ethnic disparities in the dental health and care outcomes explained by the decomposition model. The model explanatory variables accounted for a substantial part of the racial/ethnic gaps in dental health and preventive dental visits. The model explained the entire gap in preventive dental visits between Hispanic children and White children and 89% of that gap between Black children and White children. Also, the model explained 77% of the disparities in dental health problems and self-rated dental health between Black children and White children as well as 65% and 58% of these disparities, respectively, between Hispanic children and White children.
Decomposition of Racial/Ethnic Disparities in Children’s Dental Health: National Survey of Children’s Health, 2007
|Any Dental Problem||Self-Rated Poor Dental Health||Preventive Dental Visits|
|White Compared With Black||White Compared With Hispanic||White Compared With Black||White Compared With Hispanic||White Compared With Black||White Compared With Hispanic|
aProportion of racial/ethnic disparities in the dental health and care outcomes explained and unexplained by the decomposition model.
Table 3 presents the racial/ethnic differences in the dental outcomes accounted for by each explanatory variable category. Figures 1 and 2 depict the proportions of the total gaps explained by these variable categories for Black children and for Hispanic children respectively. Appendix 2 (available as a supplement to the online version of this article at http://www.ajph.org) reports the contribution of each variable toward explaining these disparities. In general, household socioeconomic characteristics were the single most relevant category for explaining these gaps.
Variable Contributions to Racial/Ethnic Disparities in Children's Dental Health: National Survey of Children's Health, 2007
|Any Dental Problem, Contribution (SE)||Self-Rated Poor Dental Health, Contribution (SE)||Preventive Dental Visits, Contribution (SE)|
|Variables||White Compared With Black||White Compared With Hispanic||White Compared With Black||White Compared With Hispanic||White Compared With Black||White Compared With Hispanic|
|Demographics||−0.020** (0.006)||−0.008 (0.005)||−0.003 (0.003)||−0.005 (0.003)||0.024** (0.007)||0.025** (0.005)|
|Maternal health||−0.007** (0.002)||−0.008** (0.003)||−0.012** (0.002)||−0.015** (0.002)||0.002 (0.002)||0.001 (0.003)|
|Socioeconomic variables||−0.017** (0.006)||−0.044** (0.010)||−0.018** (0.004)||−0.038** (0.007)||0.065** (0.006)||0.072** (0.011)|
|Child health insurance||−0.008 (0.006)||−0.008 (0.006)||−0.008* (0.004)||−0.014** (0.005)||−0.011 (0.006)||0.012 (0.007)|
|Household demographics||−0.001 (0.002)||−0.005* (0.002)||0.004* (0.002)||−0.004 (0.002)||−0.007** (0.002)||−0.003 (0.002)|
|Neighborhood condition||−0.000 (0.002)||−0.001 (0.001)||0.001 (0.002)||−0.001 (0.001)||0.005* (0.003)||0.002* (0.001)|
|Neighborhood perceived safety||−0.015** (0.004)||−0.017** (0.004)||−0.004 (0.003)||−0.003 (0.003)||−0.002 (0.004)||−0.006 (0.004)|
|Neighborhood social capital||−0.009* (0.004)||−0.005 (0.004)||−0.008** (0.003)||−0.002 (0.003)||0.004 (0.004)||0.003 (0.004)|
|Area fixed effects||−0.002 (0.003)||−0.001 (0.009)||0.004 (0.003)||−0.010 (0.007)||0.001 (0.003)||0.029** (0.009)|
|Observations||43 972||45 157||44 035||45 237||44 009||45 211|
*P < .05, **P < .01.
Among all model categories, demographic differences (mainly higher rates of unmarried mothers for Black children) explained the largest percentage (19.5%) of the gap in dental health problems between Black children and White children. Lower socioeconomic status (lower education and higher poverty level) and neighborhood safety among Black children were the next most relevant factors, accounting for 16.4% and 14.2% of this gap, respectively. Similarly, lower socioeconomic status accounted for 30.9% of the gap in prevalence of fair or poor rated dental health between White children and Black children, followed by maternal health, child’s insurance status, and neighborhood social capital, which explained 21.2%, 14.3%, and 13.8% of this gap, respectively. Lower socioeconomic status (lower education and higher poverty level) was also the most relevant for explaining the lower use of preventive dental care among Black children (70.7% of this difference), followed by demographic differences (mostly younger maternal age), which accounted for 26.0% of the gap in preventive dental care use.
Almost 30% of the higher prevalence of dental problems among Hispanic children was explained by lower socioeconomic status (lower education and higher poverty level), followed by lower neighborhood safety, which explained 11.3% of this gap. Similarly, lower socioeconomic status (also lower education and higher poverty level) was most relevant for explaining the higher rates of fair or poor dental health rating (24.3% of the gap), followed by the higher rates of poor maternal health, which explained 9.3% of that gap, and differences in insurance status, which explained 9.1% of this gap. Finally, lower socioeconomic status (mostly higher poverty level) explained 54.6% of the lower use of preventive dental care among Hispanic children, followed by the state of residence, which explained 22% of this gap.
We found significant differences in children’s dental health by race/ethnicity. Compared with White children, Hispanic children had the poorest dental health and lowest preventive dental care use, followed by Black children. More importantly, we were able to explain most of these disparities, especially for preventive dental care use, with lower household socioeconomic status—mainly lower maternal education and higher household poverty level among Hispanic children and Black children—generally being the single most important factor for explaining these disparities. Other relevant factors for explaining disparities in dental health included maternal health, age, and marital status, although the effects of these variables are less consistent than are socioeconomic status.
To our knowledge, we are the first to formally decompose and quantify the extent to which several conceptually relevant social, economic, demographic, and neighborhood characteristics explain racial/ethnic gaps in children’s dental health and preventive care use, especially with a nationally representative sample. Our findings are important, as they reveal that most of these disparities are socioeconomically driven. They also suggest that reducing racial/ethnic gaps in child dental health requires broad and comprehensive population-based interventions, beginning with improving household socioeconomic status, which may be the most effective pathway to reduce these disparities, and enhancing neighborhood quality. Our results are consistent with previous studies highlighting the importance of socioeconomic factors, such as income and education for racial/ethnic disparities in dental health.5,11,23,24 The important role of socioeconomic status, mainly household poverty level and maternal education, is strongly supported theoretically, which reinforces the validity of the results. Enabling characteristics such as income can substantially enhance access to preventive dental care through increasing the ability both to pay for dental care and to have better insurance, which is relevant for explaining racial/ethnic disparities on its own. In addition, socioeconomic status and maternal education can strongly influence maternal and household knowledge and enforcement of optimal dental hygiene practices and dietary patterns. Higher unemployment, which explained part of the gap in a fair or poor dental health rating between Black children and White children, may affect dental health beyond its effects on income and insurance, for example by affecting maternal psychosocial status and information-gathering ability.
Demographics, maternal health, neighborhood characteristics, and geographic location each contribute on their own to racial/ethnic disparities in children’s dental health. This highlights the complexity of the pathways leading to disparities and the importance of recognizing these when considering policies and interventions to reduce health disparities. Of particular importance are the effects of maternal health, marital status, and age, which vary by outcome. Maternal health and marital status are relevant for explaining disparities in dental health, whereas maternal age is relevant for explaining disparities in preventive dental visits. These effects are consistent for both Black children and Hispanic children. Previous studies support a positive association between maternal age and child’s dental health through knowledge about child health and parenting skills that are relevant to dental health.25
The observed effect of state of residence in explaining part of the disparity in preventive dental visits between Hispanic children and White children may reflect differences between states in policies and the distribution of dental care providers and their participation in public insurance programs (i.e., Medicaid and the State Children’s Health Insurance Program). Furthermore, reduced neighborhood safety and social capital, which are the most relevant neighborhood characteristics for the observed disparities, may affect dental health by restricting visits to dental providers or reducing the availability of nearby dental providers, who are more likely to locate in safer neighborhoods. Previous research supports the role of neighborhood characteristics in health and health behaviors, in part through sharing relevant information for health.26 In addition, previous studies have found an association of neighborhood characteristics with dental health through neighborhood socioeconomic conditions, social capital, and availability of and access to healthy foods.27–31
Understanding racial/ethnic disparities in child dental health is highly relevant because poor dental health affects children’s physical and social functions and lifetime outcomes related to general health, human capital, and socioeconomic status. Dental health problems during childhood have been found to affect behavioral and cognitive functioning and to have potential long-term effects on language, nutrition, systemic health, and quality of life.4,32–35 We have highlighted important pathways leading to racial/ethnic disparities and provided information for public health and population-wide interventions to improve child dental health and reduce disparities.
We have provided a framework for future studies to further characterize such disparities because further work is needed to fully characterize the underlying pathways and develop specific interventions. For example, the study had no information about household dental health-related behaviors, which may in part explain the observed effects of household socioeconomic status or the unexplained gaps. Similarly, we had no data on maternal attitudes and behaviors, which may explain the observed effects of maternal age and marital status. Future studies incorporating household dental health behaviors and knowledge are needed to explain the role of these factors for dental health disparities. Finally, we had no direct measures of preferences for dental health and prevention practices that may be related to cultural factors. Although we were able to explain most of the disparities, cultural factors may still be relevant for the unexplained gaps, and they deserve further research.
We have found that racial/ethnic disparities in child dental health and preventive care are explained largely by economic and social factors but that they are complex, as they involve household and neighborhood contributors. Therefore, there is no single intervention or policy that can substantially reduce these disparities on its own. However, most of the pathways underlying these disparities are amenable to policy interventions. Policies aimed at reducing racial/ethnic disparities in child dental health should recognize the need for household- and neighborhood-level interventions.
The National Institutes of Health and the National Institute of Dental and Craniofacial Research in part supported data analysis (grant 1 R01 DE020895).
Note. The authors adhered to the American Journal of Public Health’s policy on ethical principles and declare that they have no conflicting interests.
Human Participation Protection
The University of Iowa’s institutional review board exempted this research. No protocol approval was necessary because we obtained data from secondary sources.